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This is to certify that the

dissertation entitled

Lessons from Ethiopia's High-input
Technology Promotion Program: How the
Organization of the Fertilizer Subsector
Affects Maize Productivity

presented by
Julia C. Stepanek

has been accepted towards fulfillment
of the requirements for

 

 

 

Ph.D. degree in Agricultural Economics
310] professor

Date December 14. 1999

MSU is an Affirmative Action/Equal Opportunity Institution 0-12771

 

 

 

LBSOXS YR
PROMOTION l
FllRTlUZIR

 

LESSONS FROM ETHIOPIA’S HIGH-INPUT TECHNOLOGY
PROMOTION PROGRAM: HOW THE ORGANIZATION OF THE
FERTILIZER SUBSECT OR AFFECTS MAIZE PRODUCTIVITY

By

Julia Caley Stepanek

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Agricultural Economics

1 999

LESSONS rm
lROSlOTlON P
TERTIUZER S

 

ABSTRACT

LESSONS FROM ETHIOPIA’S HIGH-INPUT TECHNOLOGY
PROMOTION PROGRAM: How THE ORGANIZATION OF THE
FERTILIZER SUBSECTOR AFFECTS MAIZE PRODUCTIVITY
By

Julia Caley Stepanek

Given high population growth rates, subsistence, low-input agriculture, and
consequent low and stagnant incomes, a challenge for economists and development
practitioners in Sub-Saharan Africa is to identify sustainable models of input intensification
to promote agricultural productivity. Ethiopia is used as a case study of how one county’s
experience with the challenge of introducing smallholders to high-input technologies and
simultaneously making steps to liberalize the agricultural output and input markets.

Ethiopia is one country where fertilizer use has increased dramatically since the
early 19905. This success has been largely attributed to aggressive promotion of a high-
input farm technology package first promoted through the Sasakawa/Global 2000 (86)
Program and later incorporated on a much larger scale into the govemment’s New

Extension Program (NEP) (accounting for roughly 76 percent of imports and 30 percent

of rural households in 1998).

Qualitative and quantitative survey work in 1998 revealed that it is questionable
whether extension, credit, and input markets were sufficiently developed to sustain long-
term productivity gains in maize production. The NEP acts as a surrogate for the credit
and input markets—ofien compromising the development of the improved seed and
fertilizer open markets, and private initiatives to extend smallholder credit.

There were signs in 1998 that there was an unmet demand for credit and
administrative delays in issuing credit led to delayed fertilizer deliveries. The quality of
extension was also suspect. A household fixed effects yield model revealed that quality
extension (timing and appropriate interaction between inputs) is an important component
of using the high-input technology efiiciently. However, in 1998 the number of farmers
per extension agent in the NEP far exceeded the ratio under the 86 program, thus likely
hindering the quality of extension.

Another concern regarding long-term production of high-input technology is
whether the input market in 1998 forced many farmers to pay artificially high prices for
fertilizer. Institutional change by introducing more competition in the retail market is one
avenue for developing a lower-cost fertilizer market. A hedonic fertilizer price
determinates model revealed that fertilizer prices are significantly higher in areas of the
country where governments appointed markets relative to regions where a fertilizer
auction was implemented. It is also possible to reduce costs through changes in the
organization of importing and wholesaling by taking advantage of the seasonal price
trends for fertilizer on world markets, as well as in domestic transport rates. Simulated

farm budgets revealed that the proposed cost-reducing changes can reduce the risk and

improve the profitability of adopting the high-input technologies.

 

 

Covyfight by
JULIA CALEY STEPANEK
1999

DEDICATION

Dedicated to Mommy, Daddy, Ahlia and Vanessa.

lam indebted to

 

 

 

 

Eu 9 been by mm;
"196933: Dr John !

hm Mk YOU to D

ACKNOWLEDGMENTS

I am indebted to a great number of people for helping to develop this research. I’d
like to begin by thanking my dissertation adviser, Dr. Thom Jayne, and my committee: Dr.
Valerie Kelly, Dr. John Strauss, Dr. Eric Crawford, and Dr. Michael Weber, and a
heartfelt thank you to Dr. Mulat Demeke of the Addis Ababa University in Ethiopia. I
would also like extend a warm thank you to Dr. Julie Howard for her continued support
and encouragement.

Thank you to Dr. Straussnfor his patience and keen insight into my econometric
models. Dr. Weber, thank you and the US Agency for International Development/MSU
Food Security 11 Cooperative Agreement for continued financial support for without
which this research would not be possible. Val, I thoroughly enjoyed working with you,
and appreciate your confidence in me. Most importantly, Thom, thanks for giving me this
research opportunity, for your continued words of support: “don’t lose faith”, and for
overall making my years at MSU an invaluable learning experience.

A particular warm thank you goes to my colleagues at the Grain Market Research
Project-Asfaw Negessa, Daniel Molla, and Ali Saidufor continued patience in fielding my
questions about how things really work in Ethiopia. An appreciative thank you to the
Ethiopian enuminators responsible for the Input Subsector Survey, the cornerstone of this

research: Mesfin Tadesse, Shirega Minuye, Taye Yadefa, Demeke Abate, Lelissa

arm Term A5...
Bel} KM: llmi' y:
is: It D! Kasa‘ur. Abe

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“mid m“ 10 ex

Chalchissa, Techane Adugna, Arkiso Masebo, Belaineh Taye, Beyene Taddesse, and
Belay Kebede. Thank you for your diligent work and infectious enthusiasm. Thank you
also to Dr. Kassahun Aberu for his diligent work on helping us to understand the process
of importing fertilizer. Dr. Mulat and Val, I am particularly grateful to your commitment
and assistance in conducting the Input Subsector Survey. Thank you.

There are also a number of faculty and stafi‘ in the Department of Agricultural
Economics at MSU that were valuable contributors to my research. Thank you to Dr.
Frank Lupi, Dr. Ray Black, Dr. Kelly Raper, and Dr. Dan Clay for their time and
invaluable advice. Dr. Mywish Maredia was also very helpful-in helping me to understand
the SG farm surveys. I would also like to thank Josie Kelly for coordinating my travel,
fielding editing questions, and always providing a smile on cloudy days.

I would like to extend a my deepest appreciation to Karin Stefi‘ens, Erin Boydston,
and Anwar Naseem for their unwavering warmth, support, and for being my sounding
board. Karin, I can’t say enough how gratefirl I am for your invaluable guidance and
continued support (and for always agreeing to get coffee with me). Erin, thanks for your
immense friendship and generosity. Anwar, thanks for your unwavering encouragement
and confidence in me. Thank you also to my officemates--Dr. Bocar Diagana, Dave
Mather, and Bishwa Adhikari--for great patience and for always providing timely comic
relief.

Lastly, a big hug for my family, my mom, dad, and sistersuAhlia and Vanessa—who

always believed I could do it!

vii

(3th [\TROD'.

 

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14 Methods
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4.1.1

CHAPTER]
1.]
1.2
1.3
1.4
1.5

CHAPTER 2

2.1
2.2
2.3
2.4
2.5
2.6
2.7

CHAPTER 3
3.1
3.2

3.3
3.4
3.5

CHAPTER4
4.1

TABLE OF CONTENTS

INTRODUCTION ......................................... l
The Ethiopian SituationnMoving Toward a Solution? .............. 4
The Challenge of Promoting Sustainable Agricultural Growth m SSA . . 6
Research Objective ........................................ 11
Methods ................................................ l4
Outline ................................................ l9
TRANSFORMING A LOW PRODUCTIVITY
AGRICULTURAL SECTOR ................................ 20
Cereal Yields ............................................ 21
Status of Traditional Practice of Maintaining Soil Fertility .......... 23
History of Agricultural Policy in Ethiopia ....................... 26
Level and Distribution of Fertilizer Use ......................... 32
Intensity of Fertilizer Use ................................... 36
Profitability of Fertilizer .................................... 39
Conclusion .............................................. 45
STRUCTURE ........................................... 46
Basic Conditions .......................................... 47
Regulatory Environment .................................... 48
3.2.1 Regulatory Environment of Fertilizer Imports .............. 50
Allocation of Foreign Exchange ................... 50
3.2.2 Regulatory Environment of Fertilizer Distribution ........... 55
Credit ...................................... 55
3.2.2.1 Organization of Fertilizer Distribution in 1998 . . 58
Tigray Region .................... 62
Amhara Region ................... 62
Ororniya Region ................... 65
Southern Region .................. 68
Barriers to Entry .......................................... 70
Vertical Integration ....................................... 75
Conclusion .............................................. 76
CONDUCT ............................................. 78
Pricing Behavior .......................................... 79
4.1.1 Pricing Behavior—Background ......................... 80

viii

42

CH: ltR6

6.4

 

 

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6.33

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6.4 l
6 4 2

4.2

4.3

CHAPTER 5
5.1
5.2
5.3
5.4
5.5

CHAPTER 6
6.1

6.2
6.3

6.4

6.5

CHAPTER 7

7.1
7.2

4.1.2 Price Determination in 1998 ........................... 81

4.1.3 Pricing Behavior by Vertically Integrated Retailers .......... 85
Monopoly Rents? ............................. 86
Cross Subsidization ............................ 89
Dumping .................................... 89
Collusion .................................... 90
4.1.4 Pricing Behavior by Independent Retailers ................ 91
4.1.5 Pricing Behavior by Service Cooperatives (SCs) ............ 92
Product Strategy ......................................... 94
4.2.1 Product Strategy by Vertically Integrated Retailers .......... 95
Distribution Network ........................... 95
Product Promotion ............................ 96
4.2.2 Product Strategy by Independent Retailers ................ 98
Conclusion .............................................. 99
FERTILIZER PRICE HEDONIC MODEL .................... 101
Hedonic Price Technique .................................. 101
Model Specification ...................................... 104
DAP Model ............................................ 112
Urea Model ............................................ l 18
Conclusion ............................................. 122

FARM-LEVEL DETERMINANTS OF MAIZE

PRODUCTIVITY GROWTH .............................. 124
Success of the SG ........................................ 126
Organizational Factors That Contributed to the 86 Success ........ 129
Expanding High-Input Technology to the Broader Population ...... 133
6.3.1 Complementary Factors in Using High-input

Technology Efficiently .............................. 134
6.3.2 Determinants of Fertilizer Adoption and Intensity of Use ..... 137
6.3.3 Key Difi‘erences Between the SG Technology Package Users and

the Broader Population .............................. 144
field Model ............................................ 147
6.4.1 Theory .......................................... 148
6.4.2 Data ............................................ 151
6.4.3 Model Specification ................................ 152
6.4.4 FE Model Results .................................. 162
Conclusion ............................................. 170

PERFORMANCE OF THE INPUT MARKETS:

A FOCUS ON FERTILIZER ............................... 173
Defining Performance ..................................... 176
Fertilizer Financial Import Parity Price ........................ 178

ix

7.3 Validating the Financial Import Parity Price .................... 185

7.4 Reducing Costs in Importing ............................... 191
Competitiveness in International Markets? ............... 191
Poor Coordination Between Aggregate Supply and Demand . . 194
High C.I.F. Price from Inflexible Timing of Imports ........ 195
Port Inefiiciencies .................................. 198
Offshore Bagging .................................. 198
7.5 Reducing Costs in Wholesaling and Retailing ................... 199
Coordination at the Farm and Regional Level ............. 203
Competition in Distribution ..................... 205
Under-weight Fertilizer Bags ......................... 207
Delayed Fertilizer Deliveries .......................... 207
High Transport Costs ............................... 210
Policy Uncertainty .................................. 216
7.5.1 Open Market (Cash Market) .......................... 220
7.6 Extension .............................................. 223
7.7 Conclusion ............................................ 225
CHAPTER 8 IMPROVING THE PROFITABILITY OF HIGH-INPUT MAIZE
TECHNOLOGY IN ETHIOPIA ............................ 227
8.1 Measuring Profitability .................................... 228
8.2 Financial Analyses ....................................... 229
8.2.1 Method .......................................... 230
8.2.2 Results .......................................... 236
8.2.2.1 Dominance and Marginal Analysis .......... 239
8.3 Simulated Profitability from Cost-Reducing Subsector Changes ..... 242
8.4 Conclusion ............................................. 246
CHAPTER 9 CONCLUSIONS AND POLICY IMPLICATIONS .............. 248
9.1 Conclusions ............................................ 249
9.2 Policy Implications ....................................... 254
APPENDICIES ..................................................... 260
APPENDIX 1
GMRP INPUT SUBSECTOR SURVEYS AND COVERAGE ..... 261
APPENDIX 2
FINANCIAL IMPORT PARITY PRICE CALCULATION NOTES . 288
APPENDIX 3
PROFIT ABILITY SIMULATIONS FROM CHANGES IN FERTILIZER
COST ................................................. 291
APPENDIX 4
REFERENCES ......................................... 296

Inn
1:51:22

1:51:23
I151: 24

Table 2.5

1:55:26
125:2?
121:1
1:731:29
hilt: 10
115;: 2.11

18:31
Table 31
TOE: 3.3
122123,
19m
7:51:42

la: 4. 3
7&1: 44

Table 5]
I212: 52
little 53

III: 5.5
I“ 5.6
1‘51: 57
I35: 6,]

 

 

Grim \'.:I
Mean Le.
1995 96

Mean Per
Proporua

Quaran-
19969“
Fertilizer.
Mean It
Rtsults r
VCR Cc
Maize .\
II“ Inc:
:1 Janna
DAP r.
10in} Q
Putts:
PCTCen:
GO't‘cf-r
Simple
1993 .

Dismb
Across

Table 2.1
Table 2.2

Table 2.3
Table 2.4

Table 2.5

Table 2.6
Table 2.7
Table 2.8
Table 2.9

Table 2.10

Table 2.11

Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 4.1
Table 4.2

Table 4.3

Table 4.4

Table 5.1
Table 5.2
Table 5.3

Table 5.4
Table 5.5
Table 5.6
Table 5.7
Table 6.1

LIST OF TABLES

Grain Yields, 19805-1998, MT/Hectare ........................ 22
Mean Level and Distribution of Household Cultivated Hectares by Region,
1995/96 ................................................ 24

Mean Percentage of Households Using Fertilizer by Region, 1995/96 . . 34
Proportion of Total Fertilizer Use By Crop, 1994/95 and 1996/97,

‘000 Quintals (QT) ............................ 35
Quantity and Proportion of Total Fertilizer Use By Cereals, 1994/95 and
1996/97, ‘000 Quintals (QT) ................................ 36
Fertilizer Recommendations for Maize, N-P20,-K20, kg/ha .......... 37
Mean Level of Fertilizer Use by Region, 1995/96 ................. 38
Results of ADD/NFIU Maize Trials ........................... 41
VCR Comparisons for Maize and Tef, 1992 and 1997 ............. 42
Maize MOA/SG Yields, MT lha .............................. 43
Net Income for Maize Farmers in Timma, Birr/ha
at January 1998 Prices ..................................... 45
DAP and Urea Imports by Importer in 1998, MT ................. 54
Total Quantity of Fertilizer Imported by Company, 1995-1998 ....... 55
Percent of Regional Fertilizer Sales by Distributor, 1997 ............ 61
Percent of Distributor Fertilizer Sales by Region, 1997 ............. 62
Government Fertilizer Retail Prices, Birr/Quintal ................. 80
Sample Prices Between Arnbassel and the AISE, Bin/Quinta], June-July
1998 ................................................... 87
Distribution of Open Market and Credit Service Cooperative Margins
Across Regions, Birr/Quintal ................................ 93
Percent of Integrated Retailers Engaged in Product Promotion Activities in
Amhara, Oromiya, and Southern Regions in 1998 ................. 98
DAP and Urea Model Specification .......................... 108

Descriptive Statistics of Continuous Variables in DAP model, n=3 86 . 113
Frequency Distribution of DAP Prices by Region,

Number of Observations ................................... 114
DAP Price Dependent Model Estimation Results, Bin/Quintal ...... 115
Frequency Distribution of Urea Prices ........................ 119
Urea Descriptive Statistics of Continuous Variables, n=291 ........ 120
Urea Price Dependent Model Estimation Results, Birr/Quintal ...... 121
Yields by Program Type for Maize in Jimrna, Oromiya Region ...... 127

1151: 6.2

11731: 6.3
iii: 6 4

3151:65
li‘icfiti
1:51:67

    
  
  

Aims:
Techno} '
Key Dif-
Number
201-2 Pr;
Merge
Merge
Mean Y2;

radius):

Selected (
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Model 5;
Dcscnptl‘
PiOI‘ch 1
Yield RC!
Seeding l
Calculati.
Calcuxau
Quantity
“d Sour
x32min j

Table 6.2

Table 6.3
Table 6.4

Table 6.5
Table 6.6
Table 6.7
Table 6.8

Table 6.9
Table 6.10

Table 6.11
Table 6.12
Table 6.13
Table 6.14
Table 7.1

Table 7.2 .
Table 7.3

Table 7.4
Table 7.5

Table 7.6
Table 7.7
Table 7.8
Table 7.9
Table 7.10
Table 7.11
Table 7.12
Table 7.13

Table 7.14
Table 7.15

Average Yields by Technology Type, With and “Without the High-input

Technology, kg/ha ....................................... 128
Key Difi‘erences Between the SG and NEP ..................... 130
Number of Participants in the NEP and Sasakawa/Global

2000 Programs .......................................... 13 1
Average Level of Labor Use by S6 and Traditional Technology ..... 135

Average Level of Animal Traction by Program Type and By Yield . . . 136
Mean Yield Comparisons Across Management Practices, SG and
Traditional Plots, Jimma and West Shewa Zones, Oromiya Region . . . 137
Comparison of Mean Household Indicators Between Fertilizer Users and

Non-Users ............................................. 139
Average Level of Household Characteristic Per Fertilizer Decile ..... 141
Selected Characteristics of SG Participants Households Versus the Broader
Population of Agricultural Households ........................ 146
Model Specification, n=80 (2 plots for each of the 40 households) . . . 153
Descriptive Statistics of SG and Traditional Plots ............... 156
Plot-Level Maize Yield Model Results, kg/ha ................... 163
Yield Response fi'om Mean Level of DAP (98 kg/ha) and Varied Levels of
Seeding Rate, kg/ha ...................................... 168
Calculation of 1998 Financial Import Parity Price for DAP ......... 180
Calculation of 1998 Financial Import Parity Price for Urea ......... 182
Quantity of Fertilizer Imports and C.I.F. Djibouti/Assabl Price by Importer
and Source of Funds, 1998 ................................. 184
Nazreth Financial Import Parity Price, 1998 .................... 185
1997 and 1998 DAP Retail Prices For Three Zones in Oromiya,

Birr/Quintal ............................................ 187
1997 and 1998 Urea Retail Prices For Three Zones in Oromiya,

_ Bin/Quinta] ............................................ 188
1998 International (Select Markets) F.O.B. DAP and Urea Prices and
C.I.F. Assab/Djibouti Prices, USS/MT ........................ 192
Nazreth Financial Import Parity Price for DAP Under Estimated FOB.
Price Reductions ......................................... 197
Comparison of Bulk and Bagged F.O.B. DAP Weekly Prices,

January 1993 - February 1999, USS/MT ....................... 199

Fertilizer Credit in the Three SG Study Zones, Oromiya Region,
‘000 Birr .............................................. 201
DAP and Urea Distributed on Credit in Kersa, Dedo, and Seka Cherkosa,

. Jimma Zone, Quintals ..................................... 202
Jimma Financial Import Parity Retail Price for DAP Under Increased . 207
Assab-Addis Ababa Freight Rates, 1994-1997 .................. 212
Addis-Jimma Freight Rates, 1993-1997 ....................... 214
Tunma Import Parity Retail Price for DAP Under Estimated Transport Rate
Reductions ............................................. 215

xii

 

  
   

fascia
Fir “isla'
Domini
Dorm“.
Compafi;
Bit: Qrzr.
Simiatei
Chi'iges :

Table 7.16
Table 8.1

Table 8.2
Table 8.3
Table 8.4
Table 8.5
Table 8.6

Table 8.7

Jimma Import Parity Retail Price for DAP Under Reduced Policy

Uncertainty (Direct Delivery fi'om Port to Retail Markets) ......... 219
Summary of Potential Cost Reductions in the SG—participating Wereda
Retail Prices of DAP and Urea, Birr/quintal, F.O.B. Djibouti . . 231
Financial Analysis by Technology Type and Yield ................ 237
Financial Analysis by Technology Type and Labor Use ............ 238
Dominance and Marginal Analysis by Yield Stratification .......... 240
Dominance and Marginal Analysis by Labor Stratification .......... 241
Comparison of Jimma Input Prices in 1997 and 1998 Across Programs,
Birr/Quintal ............................................ 243
Simulated Changes in Profitability fiom Cost-Reducing Institutional
Changes in the Fertilizer Subsector, Birr/ha .................... 244
xiii

 

Figure 1.1
Figure 1.2
Figure 2.1
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 4.1
Figure 5.1
Figure 6.1
Figure 6.2
Figure 7.1

Figure 7.2

Figure 7.3
Figure 8.1

LIST OF FIGURES

Map of Ethiopian Survey Areas, 1998 ......................... 16
Schematic Diagram for Simulated Maize Budgets ................. 18
Fertilizer Consumption in Ethiopia, 1970-1997 ................... 32
Fertilizer Credit and Consumption, 1983-1997 ................... 57
Structure of the Fertilizer Subsector in Amhara in 1998 ............ 64
Structure of the Fertilizer Subsector in Oromiya in 1998 ............ 66
Structure of the Fertilizer Subsector in Southern in 1998 ........... 69
Economies of Scale and Market Demand ....................... 73
Pricing Behavior Options Under Different Market Structures ........ 82
Pricing Mechanism and Distribution Framework ................. 106
Technology Transfer Flow Chart ............................ 126
Relationship Between Yields on SG and Traditional Plots .......... 154
Difference in Observed 1998 DAP Prices and Financial

Import Parity Prices ...................................... 189
Seasonal Index in Select World DAP Prices, USS/MT

(Constant 1998) ......................................... 196
Seasonal Freight Rate Index, Assab-Addis Ababa, 1994-972 ........ 213
Difference in 1998 Urea Market Prices and Cost Build-up Prices . . . . 245

xiv

ADD
AISCO
AISE
CERES
CSA
DAP
EAL
EGTE
FA
FAO

SC
SNNPR
TGE
USAID

KEY TO SYMBOLS AND ABBREVIATIONS

Agricultural Development Department
Agricultural Input Supply and Coordination Organization
Agricultural Input Supply Enterprise
Crop-Environment Resource Syntheses
Central Statistical Authority

Diammonium Phosphate

Ethiopia Amalgamated, Ltd.

Ethiopia Grain Trade Enterprise

Farmer Association

Food and Agriculture Organization

Federal Democratic Republic of Ethiopia
Grain Market Research Project

hectare

kilometer

kilogram

Michigan State University

metric ton

National Extension Program

National Fertilizer Industry Agency

Service Cooperative

Southern Nationalities and Nations Peoples’ Republic
Transitional Government of Ethiopia

US. Agency for International Development

quintal = 100 kilogram

CHAPTER 1

INTRODUCTION

Approximately ninety percent of Sub-Saharan Afiica’s (SSA) rural population is
currently considered poor (Pinstrup-Andersen and Delgado 1994). The sources of this
poverty include low agricultural productivity due to declining soil fertility, rising
population density, and low levels of commercial input use. Continued low agricultural
productivity and rapid population growth together imply that food security in the region
will remain threatened.‘ SSA food production per capita declined at a rate of more than 2
percent per year fiom the late 1970s to the early 19905 (Badiane and Delgado 1995). The
projections of future productivity are equally as bleak. Africa’s population is projected to
almost double between 1995 and 2020. By 2010, 70 percent of the world’s food-insecure
people will be in SSA; every third person in the region is likely to be food-insecure,
compared with every eighth person in South Asia and every twentieth person in East Asia
(Pinstrup-Andersen et a1. 1997).

Development theorists concur that agricultural intensification (the use of yield-
enhancing technologies to increase production) can help increase rural incomes and in

general, lead to an agricultural structural transformation-the transition from a low-

 

“‘Food security exists when all people at all times have physical and economic access to
suficient food to meet their dietary needs for a productive and healthy life” (USDA
1996z2).

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income, low-productivity, subsistence-oriented economy to one characterized by
specialized, high-input agriculture and a rise in rural incomes (Timmer 1990). An
agricultural transformation is usually necessitated by increasing population density and
increased land scarcity, which both put pressure on agricultural sectors to adopt intensive,
high-input technologies (Boserup 1961, 1981). The rising incomes that generally
accompany the use of high-input technologies, in turn, stimulate consumer demand for
market goods and thus facilitate a structural transformation of the economy (Mellor
1990).

The development of the Asian economy provides evidence of the potential for
technology to pave the way for economic development in SSA: The main lesson of the
Green Revolution in Asia (and in Latin America) is that the “adoption of yield-increasing
technologies is a ‘plus-plus’ solution, since it can increase food production and farmer
incomes, while reducing the cost of food to consumers and improving diets, i.e., it can
result in economic growth and poverty reduction simultaneously” (Borlaug and Dowswell
1995).

For over a decade, the Sasakawa-Global 2000 (SG) program, in partnership with
African governments,2 has promoted the Green Revolution approach to development by
introducing African farmers to high-input technologies such as improved seed and
fertilizer. Through a collaborative effort, the Sasakawa Foundation (recently renamed the

Nippon Foundation), The Carter Foundation’s Global 2000 program, and Norman

 

2SG programs have operated in Ghana, Benin, Togo, Nigeria, Guinea, Mali, and Burkina
Faso in West Africa, and Ethiopia, Eritrea, Tanzania, Mozambique, Sudan, Uganda, and
Zambia in Central, East and Southern Africa.

2

 
 
    
  

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:31 commtmen

11

Borlaug, the Nobel Peace Prize laureate, have demonstrated the potential for high-input
technology to raise foodcrop productivity across SSA. Pilot programs that introduce
high-input technologies through half-hectare farm-managed demonstration plots have had
significant success in raising yields, net incomes, and returns to labor for a variety of food
crops.

Unfortunately, SSA has had limited success in achieving long-term adoption of
improved agricultural technologies by farmers. SSA governments have a history of
supporting agriculture through direct subsidies for fertilizer and input credit, and also
sometimes through grain purchases at above-market prices. They also have a history of
introducing farmers to new technologies through various pilot programs in which
government agents or parastatals provided extension education and delivered inputs.
However, the high costs of maintaining these programs and expanding them to the general
population often resulted in severe budget deficits. In many cases, governments were
forced to withdraw support; and “disadoption” of the new technologies frequently
followed since input markets were not sufliciently developed to encourage sustained use
(Jayne and Jones 1997; Eicher 1985; Howard and Mungoma 1997).

Agricultural productivity growth in SSA in the 21" century will come from a
political commitment to creating open markets and strengthening the institutions7 that
reduce the cost of transactions (North 1989). Developing the necessary African political

commitment to agriculture is a key challenge: “Africa has never been given a chance to

 

“Institutions are rules, enforcement characteristics of rules, and norms of behavior that
structure repeated human interaction” (North 1989:1321).

3

 

   
  

mowed Coloma‘ cc

'mimdcnt Mann 5

fixuahiry gains

A: the damn of

 

Ram} pl'Odoctixit
need for: solution is 3
Ohm! 00¢ counts ‘5
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m Emmi: prgj
Ll The Ethiopia
I:00d 5mm}.

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t

succeed. Colonial control protected special-interest agriculture, as have newly
independent African governments. Only today is Africa opening to market-based national,
regional, and global opportunities” (Stepanek 19992108). African governments are
challenged to enter a partnership with the private sector to encourage long-term
productivity gains.

At the dawn of the 21" century, the challenge to SSA countries to improve
agricultural productivity, increase food-security, and reduce rural poverty remains. The
need for a solution is increasingly urgent. This research used Ethiopia as a case study to
observe one country’s experience with introducing high-input technologies to farmers
while simultaneously trying to liberalize input and output markets in order to ensure long-

term, sustainable productivity gains and an increase in rural incomes.

1.1 The Ethiopian Situation—Moving Toward a Solution?

Food security in Ethiopia over the last few decades has been threatened by two
periods of famine' severe enough to warrant international attention. In the mid-19905, an
estimated 52 percent of Ethiopia’s population was food insecure (i.e., below the poverty
line) as defined by the Federal Democratic Republic of Ethiopia (FDRE)’ (1996). There is

some evidence in Ethiopia of a transition from an agricultural system based on traditional

 

'Famines occurred in 1973 and 1984/85.

’The Transitional Government of Ethiopia (TGE) changed its name on August 23, 1995 to
the FDRE.

 

 
 
  
   

[analogies t0 00‘ 'r
m hcwofl. 9*:

m 'ma'cx 01' I
1993 and 1996. or: it
0ka food CID?” me?
M sorghum by 4
m (CSA 19% 91
growth in production
pawn pr: mum), f.
torsion Good ch:
05313 improvement.1
”Orr 29,568 tons to

film‘s tom ir

CEMEG to the use
3W to fewer tha:~
M had a 20% of 3 .

Ia

technologies to one based on improved technologies. This transition has been quite
recent, however, and it is not yet clear that it will be complete.

The index of per capita food production in Ethiopia grew from 98 to 106 between
1993 and 1996, but then dipped to 103 in 1997 (FAO 1997).10 The aggregate production
of key food crops increased dramatically during the last decade: wheat increased by 111
percent, sorghum by 44 percent, barley by 35 percent, maize by 30 percent, and tef by 14
percent (CSA 1990/91-199‘7/98). Increases in cropped area contributed somewhat to this
growth in production, but given the already high population density" and growth rate (3
percent per annum), future increases in production are unlikely to come from area
expansion. Good climatic conditions in the mid-19905 may also have accounted for some
of this improvement,12 but there was also significant growth in fertilizer consumption
(from 29,668 tons to 168,23 tons between 1981 and 1997 (NFIA 1998)). Much of
Ethiopia’s increase in fertilizer use and some of the grth in production has been
attributed to the use of the Sasakawa Global 2000 (SG) model, first as a pilot program
limited to fewer than 3,200 farmers and later as a major national extension program (NEP)
that had a goal of 3.6 million participating farmers for the 1998 cropping season (out of a

total of about 10 million rural households).

 

10The decline in 1997 may be due to the fall in production by 26 percent Ram 1996 to
1997 (FAO/WFP 1997).

"5.5 persons per hectare of arable and permanent crop land per capita versus 3.6 persons
per hectare on average in SSA in 1997 (FAO 1999).

"There were incidences of drought in 1988, 1991, 1992, and 1994, but 1996 and 1997
were relatively more favorable harvests (World Bank 1997).

5

 

 

mmum

ms in agriculture

 

decomizaoon b} if?
sector access to cred'a'.

molded by the nails

 

moved technologies

homologies m nor yr

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technolog to smalIh-o

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The FDRE has been thus far commended for its role in encouraging productivity
gains in agriculture: The NEP in Ethiopia is considered one of SG’s most successful
national programs. On paper the FDRE has made a move toward agricultural sector
decentralization by liberalizing input and output prices. However, by restricting private
sector access to credit and retail markets, the input sector, in particular, remains heavily
controlled by the national and regional governments. Despite the progress in introducing
improved technologies to farmers, many preconditions for the sustainable use of these

technologies are not yet fully in place.

1.2 The Challenge of Promoting Sustainable Agricultural Growth in SSA

Many SSA counties face the challenge of simultaneously introducing high-input
technology to smallholders and reducing direct government support of agricultural input
and output markets. Evidence from other countries can serve as a harbinger for Ethiopia.
Government efl‘orts across SSA to introduce improved technologies to farmers through
explicit or implicit agricultural subsidies and/or extension programs often succeeded in
raising the level of input use in the short-run. With their expansion, however, agricultural
programs quickly ran into budgetary pressures and became unsustainable.

Experience from Tanzania and Ghana reveal the problems of moving from a pilot
SG program to expanded and sustained increases in input use. The SG program provided
credit, a high level of extension supervision, and facilitated input delivery-bypassing the
cumbersome administrative channels of national extension programs. The challenge is to

scale-up the SG pilot extension programs into national programs and to sustain the level

of inputs once the SG draws to a close. Some countries (e. g., Ghana and Ethiopia)
developed national extension programs based upon SG principles, but it has been difficult
to duplicate the success because part of the SG success was contingent upon the program
being small-scale (it thus provided close supervision and partially subsidized inputs and
credit). Although the SG program has demonstrated that the technology exists on the
“shelf’ to significantly increase yields in some areas of SSA, the difficulties in maintaining
the increased yields once the pilot extension programs withdraw highlights the challenge
of forging sustainable national systems of research, extension, input supply, and credit.
Addressing the potential adoption of high-input technologies, Farrington states:

What is less clear is whether ‘parallel’ systems set up to test,

advise on and provide inputs for these technologies completely

independently of existing research and extension services can

achieve anything more than a very temporary alleviation of

chronic, deep-rooted problems” (Farrington 19952131).

Sasakawa-Global 2000 began operating in Tanzania in 1989. In 1991/92, program
participants in the Arusha Region of Northern Tanzania averaged maize yields of 4.9 tons
per hectare, up from the national average of 1.4 tons per hectare (Putterrnan 1995).
However, this success was ephemeral-~the SG program bypassed the dysfunctional input
and credit markets by delivering the recommended package and credit to program
participants on its own accord. It is unlikely that program participants would have seen

such yields without SG’s delivery of inputs and credit. Institutional credit and input

delivery systems had collapsed under earlier “reform” measuresufirnctions previously

mdied by Won“ C“
mess to credit ”*5 if

mm the 56 ‘v
‘disadoption' from th:
IahnoSmtt (3 L's-bi

marine to gta'antee -

moon Mountain 1 "i

 

 

 

m; iswes of dexeio
Bitch adoption of log:
hm being the Spearhez
It became yet another

Similar to the c
From in Ghana ma}

W Woductixity m,-

handled by regional cooperative unions. Thus when the SG program withdrew, farmers’
access to credit and inputs declined.

When the SG withdrew from Arusha’s Arumeru district in Tanzania, SG feared
“disadoption” from the high-input technology. The SG therefore arranged for
TechnoServe (a US-based NGO) to organize farmers’ associations, enabling the SG to
continue to guarantee loans to these farmers’ associations and even deliver inputs on
occasion (Putterman 1995). The SG program neglected to address the necessary and
critical issues of developing private sector input and credit delivery mechanisms, without
which adoption of high-input technology is not sustainable. Putterrnan states, “Rather
than being the spearhead of a Tanzanian Green Revolution, SG 2000 seemed all too likely
to become yet another dimly remembered foreign-handed project” (Putterman 1995:320).

Similar to the case in Tanzania, there is evidence that the SG pilot extension
program in Ghana may have compromised its objective of promoting long-run increased
food productivity (Y udelman et al. 1991). At the program’s inception in 1986 it was
expected that the delivery of extension services would work in harmony with public and
private credit and input supply institutions. However, it was quickly realized that if
farmers were to adopt the technology package, SG would have to extend credit (from its
own resources and national banks) and arrange for input delivery. Instead of
strengthening existing institutions, the program thus created its own delivery channels,
thereby avoiding the problem of input market development.

Farmer participation in the SG program in Ghana escalated in five years from 40

farmers in 1986 to an estimated 17,000 in 1990 (Yudelman et al. 1991). As the program

 

I
mended. the 56 n:

prnttded II tht pft‘g'
merely strained N:
was imequazely mar
1991) Over the C037
ten-fold from an at-ctal

Watch-ran et al 1991

 

recognizes that stalmg
(lineman et a1 1991 I

Ghana‘s expert"
arm such a the 5

Ethiopia Stmggies “‘1' L

expanded, the 86 no longer had the resources to sustain the level of input delivery it
provided at the program’s inception. Its ability to maintain it effectiveness thus became
severely strained. Not only was the provision of inputs problematic, but loan recovery
was inadequately managed and the quality of extension services declined (Y udelman et al.
1991). Over the course of the program, the number of farmers per extension agent rose
ten-fold from an average of 15 farmers per extension officer at the start of the program
(Y udelrnan et al. 1991). In an evaluation of the SG program in Ghana, it was belatedly
recognized that scaling up the program would require simultaneous institutional changes
(Yudelman et al. 1991).

Ghana’s experience warns Ethiopia of the danger of scaling-up pilot extension
programs such as the SG without also upgrading supporting institutions. However,
Ethiopia struggles with not only scaling-up the successful technological transfer seen in
the SG program, but also with the simultaneously withdraw of government support to
agriculture. The experience of Zimbabwe and Zambia offer insights into this process.

Zimbabwe is an example of a country in which dismantling govemment-supported
agriculture raised unforseen challenges and triggered a regression from the maize
production gains achieved in the 19805. The Government of Zimbabwe is now challenged
to accelerate agricultural productivity growth through a government/private sector
partnership.

Zimbabwe achieved unparalleled success in difliising hybrid maize seed both before
and after independence. However, as Eicher stated, “Zimbabwe’s smallholder-led Green

Revolution represented a ‘qualified’ success story because of its lack of fiscal

animal}. (1953':

 

Mathew-”l?“

madame and deli:

    
 

In 1991 Ztm':
Home. liberalize:
father use by the s.

199:»: and fell age.

sustainability” (19952831). Part of the success was attributed to an increase in smallholder
credit in the early 1980s Credit was often perceived as an entitlement, supervision was
inadequate and delinquency rates were high.

In 1991 Zimbabwe launched economic reforms to reduce maize subsidies.
However, liberalization met with the “disadoption” of fertilizer. For the period 1985-89,
fertilizer use by the smallholder sector averaged 119,000 tons, but fell to 98,000 tons in
1990-92, and fell again to 86,600 tons in 1993-94 (Jayne and Jones 1997).

Neighboring Zambia faced similar difficulties in maintaining the level of fertilizer
use after decontrol of the input and output markets and credit delivery. Fertilizer use
quadrupled between the 1960s and late 19805 due in part to a subsidy of 30 to 60 percent
of the landed cost of fertilizer in the 1970s and 19805 (Jansen 1988 in Howard and
Mungoma 1997), and also due to the extensive smallholder credit program in place during
that time. Until the early 1990s, roughly one-quarter of rural households received loans
each year (GRZ 1991 in Howard and Mungoma 1997). Subsidized credit was available to
smallholders through a system of cooperative depots. Input subsidies, coupled with a
controlled output market that guaranteed producer prices, resulted in a favorable incentive
structure for agricultural intensification. However, budgetary pressures in the mid-19805
threatened the sustainability of institutional support.

By 1992 President Chiluba in Zambia began to dismantle the parastatal system.
Large-scale farmers benefitted from the resulting increased trade (due to the liberalization
of the foreign exchange market), but Zambian smallholders suffered (Howard and

Mungoma 1997). Fertilizer use fell by 25 percent between its peak in 1986-87 and 1994-

10

 

95 (cs0 31%? 19;

pariah expiaioei bf.

 

105.4 in 1990-94 (H:

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eta-staining high-in;
W WHY}; gove'r:
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95 (CSO/MAFF 1995 in Howard and Mungoma 1997). This “disadoption” can be
partially explained by the increase in the nitrogen-maize price ratio from 3.3 in 1971-1989
to 5.4 in 1990-94 (Heisey and Mwangi 1997).

The experiences of Tanzania, Ghana, Zimbabwe, and Zambia reveal the difiiculties
of sustaining high-input use after the government withdraws its agriculture programs. In
each country, governments tried to subsidize their way to higher input use rather than
developing sustainable private sector systems. Private markets were thus insuficiently
developed to promote the sustained use of the new technologies when governments
withdrew support. The experience of these countries highlights the need to develop a role
for government in agriculture that complements, rather than supersedes, the private sector.
These lessons are timely, as Ethiopia is currently in the position to scale up its own SG

program and simultaneously liberalize input and output markets.

1.3 Research Objective

The objective of this research is to determine the degree to which Ethiopia is on
the path to sustaining agricultural productivity gains by examining government extension
services to introduce farmers to high-input technologies, and the extent to which private
input markets are developing that will ensure long-term adoption of the new technologies.
The research will seek to determine whether there is an important causal relationship
between input market development, quality extension services, access to credit, and
productivity grth of maize. The thesis of this research is that productivity gains for

maize can be achieved through organizational changes in the structure of the import and

11

tim’oution stages c
that ra‘sing rura‘ in:
housing ag'icuftur.
historically, .'
the relazite share to r
E5: Ind Southern A:
watts of new maize

(Byeriee and Eicher l‘,

 

in main is 313an a:
fair; - .
0 es p“ “PM In 1

ll)

distribution stages of the fertilizer subsector. The underlying assumption of the research is
that raising rural incomes and improving food security in Ethiopia can be achieved by
increasing agricultural productivity.

Historically, most fertilizer in Ethiopia has been applied to tef; however, recently,
the relative share to maize has increased—due in part to the relative profitability. Across
East and Southern Afiica maize is the most important food staple due in part to the
success of new maize seed and associated technologies in raising smallholder production
(Byerlee and Eicher 1997). In Ethiopia, tef has historically been the dominant foodcrop,
but maize is gaining an increasing share of daily diets (up from 21 percent of the daily
calories per capita in 1993 to 30 percent in 1998 (FAO 1998)) and is generally the most
productive food grain relative to tef, wheat, barley and sorghum.

The specific research questions and sub-questions asked in this dissertation are as
follows:

(1) How is the expected return to fertilizer use on maize influenced by
the organization of the inputs markets?
- In the 1990s did the expected return to fertilizer use

encourage its adoption?

- For whom does the expected return to fertilizer encourage
use?

- What are the determinants of fertilizer adoption and
intensity of use?

12

(3)

(I)

 

\‘x

(2) To what extent is the system of extension and inputs (i.e., credit,
fertilizer, and seed) sustainable in the long-run?

0 What were the returns to fertilizer use by participants,
graduates, and traditional farmers in the SG survey areas in
1997?

0 What factors contributed to high maize yields in the SG
program?

- Relative to the SG farmers, are farmers in the broader
population that are not as well endowed with land, labor,
livestock, and education, and farmers in less favorable agro-
ecological areas able to use the program’s recommendations
of high-input technologies when the program withdraws?

- What was the performance of the fertilizer subsector
in 1998?

- How can costs be reduced in the fertilizer subsector?

- How can net income and returns to labor for the
broader population be expected to change with cost-
reducing changes in the structure and conduct of
fertilizer import and distribution?

(3) What is the potential to develop a lower-cost fertilizer subsector?

What policy changes would be required?

13

  
 

.mfllg .1;
moms M be“

mum 1. produal“

1,4 Methods

This r5683) e
mail questions on?
Montana (SC? -’ p;
hedonic fertilizer Pfic'
aeiois autumnal ‘-
tgv‘tcrmm producflt

"pflafifiv fertilize:

sedation Exercise i'

Answering these questions will allow policy makers to make more informed
decisions about how to create an environment that enables Ethiopian farmers to boost

agricultural productivity.

1.4 Methods

This research employed various qualitative and quantitative methods to answer the
research questions outlined above. The research coupled the Structure, Conduct,
Performance (SCP) paradigm with a subsector analysis and two econometric models (a
hedonic fertilizer price determinants model and a SG fixed effects yield model). These
methods determined both the factors that are statistically significant in influencing
agricultural productivity as well as how the structure and conduct of the inputs subsector
(particularly fertilizer) influenced performance in 1998. The research also developed a
simulation exercise involving a fertilizer financial import parity price and farm budgets.
This method was used to evaluate the robustness of the profitability of the SG technology,
and thus its potential to promote long-term productivity growth.

A subsector is a vertical slice of an economynan “independent array of
organizations, resources, laws, and institutions involved in producing, processing, and
distributing an agricultural commodity” (Marion 1986). The subsector approach used in
this research examined the transformation and transactions that occur as the commodity
(fertilizer) moved through the stages of the vertical system fiom import to farmgate.

What happens at one stage of the subsector may affect what is happening at another stage.

Within each stage of the subsector, the SCP paradigm can be used to examine the

14

horizontal interactions between the structure, conduct, and performance of each stage. In
combination, the subsector analysis and SCP paradigm provided guidelines for examining
the overall performance of the fertilizer subsector.

The SCP paradigm is used to evaluate the long-term sustainability of technology
introduced by the SG and ultimately, to identify areas of policy intervention that could
improve the performance of the import and distribution stages of the fertilizer subsector.
The theory of industrial organization posits that the structure (8) of a market strongly
influences the competitive conduct (C) of firms within a market, which in turn strongly
influences market performance (P) (Marion and Mueller 1983, Scherer 1980). Market
structure refers to the sources of discretionary economic power that firms in the industry
can exercise (Staatz 1996), and market conduct refers to how firms behave in response to
market structure. Evaluating performance is sometimes less straight-forward: ‘Good’
market performance is subjective and characterized by numerous ill-defined, often
unquantifiable measures. In general, market performance is how well the market performs
the tasks that society reasonably expects it to perform.

The SCP analysis is based upon a survey of the Ethiopian input subsector
conducted in July and August 1998 by the Grain Market Research Project (GMRP).l3 The
structured surveys targeted the three primary fertilizer consuming regions-~Amhara,

Oromiya, and Southern“ Regions (Figure 1.1) (see Appendixl for survey coverage and

 

13A joint collaboration between the Ministry of Economic Development and Cooperation
and the Department of Agricultural Economics at Michigan State University.

"Formerly called the Southern Nation, Nationalities and Peoples’ Region (SNNPR).

15

me) irszramerr
at: the Nazism I
tl'rh'm each regio

farmer Senice C t

main roads out of

 

survey instruments). Importers and government ofiicials in the Ministry of Agriculture
and the National Fertilizer Industry Agency (NF IA) were surveyed in Addis Ababa.
Within each region, wholesalers, retailers, Ministry of Agriculture officials, members of
farmer Service Cooperatives, and transporters in select weredas (districts)ls along the

main roads out of Addis Ababa were also surveyed.

Figure 1.1 Map of Ethiopian Survey Areas, 1998
Source: UNDP-CUC 1996

 

 

   
  
 

Admlnlstratlve Reglons and
Zones of Ethiopia

, ’ru‘V‘V/

r ‘\

. ’ \
, i Somali \
\

 
    

.\
r-f

  

~-- .‘\_.__.”
\‘\‘

   

\

\

 

 

 

 

 

 

 

"A wereda (or district) is a geographic subset of a zone. There are 11 regions in Ethiopia,
21 domains, 55 zones, and 371 weredas.

l6

 

Qualitative .'
1998 were complex
technique was used '.
characteristic; spea,
fdiw price data ir
1998 GMRP Input S.

QUCSUORS reg

 

 

 

 

prom (adeption of
noticing descriptne

dcm'te sherbet the
Woes in terms of l
Dominion fixing in the

zed"WEE:

Qualitative responses from the survey regarding the performance of the market in
1998 were complemented by a hedonic fertilizer price econometric model. The hedonic
technique was used to determine the marginal implicit prices of different market
characteristics, specifically market institutions. The model used cross-sectional retail
fertilizer price data in the Amhara, Oromiya, and Southern Regions collected during the
1998 GMRP Input Subsector Survey.

Questions regarding the sustainability of the gains achieved through the SG pilot
program (adoption of high-input technology) are addressed through econometric
modeling, descriptive statistics, and a subsector analysis. Descriptive statistics are used to
determine whether the farmers chosen to participate in the SG program have more
resources in terms of land, labor, livestock and are better educated than the broader
population living in the same vicinity, thus rendering them better suited to adopt the new
technologies.

A production function approach to an econometric yield equation using household
fixed efl‘ects is utilized to determine the significance of soil characteristics, physical inputs.
managerial practices, and omitted factors specific to one farmer (such as farmer’s ability,
soil, and weather characteristics) in explaining variation in yields on SG and non-SG maize
plots in Jimma Zone, Oronriya Region (Figure 1.1). The level of yields achieved is a
critical component to determining the profitability of the high-input technology (Figure
1.2).

Agricultural productivity gains from the improved technology are represented by

estimates of increased net margin, returns to labor, and marginal rates of return (based

l7

upon a farm-level

mothers in fields
the new technolog
to farmers that me
Given the mpothe
motions of stres
meiibflih of the

it, t "'
m‘&‘ermtraa

Figur

upon a farm-level financial analysis reported in Howard et al. 1999). The extent to which
variations in yields and labor input affect profitability will determine whether expansion of
the new technologies under stress conditions--to less favorable agro-ecological areas and
to farmers that may not have sufficient resources to achieve optimum yields--is successful.
Given the hypothesis that profitability of the high-input technology may decrease under
conditions of stress, farm budget simulations are used to determine the extent to which the
profitability of the improved technology can be improved through organizational changes

in the fertilizer market that would reduce fertilizer prices (Figure 1.2).

Figure 1.2 Schematic Diagram for Simulated Maize Budgets

 

Haw-Rearm-Toulcw

 

 

 

 

l l

 

 

 

 

 

 

[ Mn-Ykld‘l’ficc ] TotalCosta-
A (a.)totalpackagecost
; (nprmredaeerLDAP.Uua)-+
(b.)labor
' A

- , and
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-Plowingoxen Methodmrrveyofmporten

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' econometricmodel

A financial import parity price will be calculated to show the cost build-up from
import to retail. Simulations in the financial import parity price will help show the extent
to which feasible reductions in the retail price of fertilizer affected the farmgate price of

fertilizer. The estimated reduced fertilizer price will be entered into the SG crop budgets

18

[9 determine the 0:

further pore

   
   
 

This resea'c
name fertilizer p"
technologies beyon;
projtm.

L5 Outline

This reward
Mattel for hi 5:154;-

oath '
JCS [ht pr0fitc

to determine the extent to which profitability will change fi'om a given reduction in
fertilizer price.

This research will thus use a variety of qualitative and empirical methods to
examine fertilizer profitability for typical Ethiopian smallholders as the FDRE extends SG

technologies beyond the better endowed farmers who participated in the SG 2000

program.

1.5 Outline

This research is organized in 8 additional chapters. Chapter 2 demonstrates the
potential for high-input technology to raise the productivity of maize. Chapter 2 also
outlines the profile of current fertilizer users and delineates the determinants of fertilizer
use to understand whether fertilizer use will be profitable to a diverse population. Chapter
3 presents the structure of the fertilizer market, focusing on the effect of the regulatory
environment established by the FDRE. Chapter 4 describes the conduct of the fertilizer
subsector. Chapter 5 presents the cross-section hedonic fertilizer price detemrinants
model that will be used to simulate changes in fertilizer profitability in chapter 8. Chapter
6 outlines farm-level determinants of maize productivity growth. Chapter 7 evaluates the
performance of the fertilizer subsector. Chapter 8 demonstrates how fertilizer profitability
can be improved through simulated cost reductions in the fertilizer subsector. Lastly,

chapter 9 presents the conclusions and policy implications.

19

 

TRANSFC

The on
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development tr
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dfi'eiOpme-nt.
kgqnl 10 30 kg
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Ethio;
but” in Et.‘
minced“.

'1 ”Optima;

 

CHAPTER 2

TRANSFORMING A LOW PRODUCTIVITY AGRICULTURAL SECTOR

The overall economic problem across SSA is that traditional agricultural practices
do not produce the level of agricultural growth required to stimulate incomes and
development in densely populated, land constrained economies. Intensified agricultural
production is one avenue for increased rural incomes, stimulated demand, and economic
development. In general, it is estimated that SSA needs to increase fertilizer use from 9
kg/ha to 30 kg/ha in the next decade if agricultural productivity is to meet food demand
(Weight and Kelly 1998).

Ethiopia is no exception. Given the current low level of use of improved seed and
fertilizer in Ethiopia there is great potential for agricultural intensification to raise crop
productivity. In 1995/96 only a third of Ethiopian farmers used inorganic fertilizer, most
at sub-optimal rates (CSA 1995/96). In addition, roughly less than 4 percent of farmers
have used improved seed (CSA 1995/96).

Ethiopia has the technology to promote real productivity gains in agriculture. The
joint effort of the National Field Trials Program (NFI'P), Sasakawa-Global 2000 pilot
extension program, and the expanded National Extension Program (NEP) have
demonstrated that efficient use of high-input technologies can double and even triple

yields.

20

 
  
  

This chapter_
'suiable option for
serum problem arl
of the productixit)‘ ;

(M01121) and Le

nm'ents to the soil i

 

(secoon 2.2). A‘thot
Home agricultural
‘9 l N the mtcnszn
‘0“ (section 2.5). A

“599" (Settion 2 6)

This chapter will examine the extent to which introducing high-input technologies
is a viable option for Ethiopia to increase agricultural productivity, solve Ethiopia’s food
security problem, and promote an agricultural transformation. In Ethiopia, the magnitude
of the productivity problem is illustrated by evidence that yield growth remains low
(section 2.1) and the ability of farmers to practice traditional methods of restoring
nutrients to the soil is mitigated by reduced farm size and competing usage of crop forage
(section 2.2). Although the FDRE has a history of promoting policies and programs to
promote agricultural development (section 2.3), the adoption of new technologies (section
2.4) and the intensity of use (kilograms/hectare) relative to recommended levels remains
low (section 2.5). A review of the profitability of fertilizer use in Ethiopia sheds light on
the feasibility and conditions of expanded fertilizer use (numbers of farmers and doses

used) (section 2.6).

2.1 Cereal Yields

Agricultural productivity is not increasing at a rate to feed a population that is
growing at 3 percent per annum. Demeke et al. (1998) provides a striking calculation to
the severity of the problem. Given an average farm size of 1 ha for a family of 5, cereal
yields (800-2000 kg/ha for maize) are barely adequate to feed household members (given
a recommended 156 kg/person/year). Therefore, 60 percent of households that cultivate
less than one hectare of land cannot be expected to generate sufficient cash income fi'om

farming after meeting their own consumption requirements (Demeke et al. 1998).

21

Froml9o’l-

sorghum by 1.5 per;

 

mam changer 't
positéw significant 1
positite nor negatit
corsimpoon begar
8139i from 1983
promotion has no
917M 52 pew

98 (chained rm

Table 2.1 C
Ya:

“tug:

»

From 1961-1998 maize yields grew at 1.6 percent/year, wheat by 2 percent,
sorghum by 1.5 percent, and barley by 1 percent.“ For maize, there is a significant
structural change" between 1961-1977 and 1978-1998: fi'om 1961-1977 there was
positive significant growth of 2.4 percent/year, but from 1978-1998 there was neither a
positive nor negative trend—yields remained constant. In the late 19805 fertilizer
consumption began to increase steadily, however, maize yields remained constant, with no
growth, fi'om 1988-98. Part of the reason for lack of productivity growth in maize is that
production has not outpaced area expansion over the last two decades: maize area
increased 52 percent and maize production increased 42 percent fiom the 19805 to 1990-

98 (calculated fi'om FAO 1999).

Table 2.1 Grain Yields, 19805-1998, MT/Hectare

 

 

Year Maize Tef Wheat Sorghum Barley
19805 average 1.62 0.84 1.11 1.27 1.13
1990 1.61 1.42 1.32 1.32 1.16
1991 1.22 0.87 1.41 1.34 1.13
1992 1.52 1.04 1.34 1.22 1.06
1993 1.74 0.90 1.55 1.40 1.36
1994 1.23 0.70 1.07 0.93 0.94
1995 1.36 0.83 1.31 1.24 0.86
1996 1.68 0.93 1.21 1.35 1.06
1997 1.74 NA 1.29 1.42 1.06
1998 1.62 NA 1.37 1.10 1.09

 

Source: FAO 1999; tef data from Dejene, A. in Demeke 1995.

 

“Author’s computation from FAO 1999 data. Demeke et al. (1998) also conducted a
similar analysis with data fi'om 1980 to 1995.

1"Determined by the Chow test (1960) for structural change at 99.9 percent confidence.

22

  
   
 

more popular beta-
keep up with Ethioj
I980 yields of mi:
”16.111115 forcing {a
walk“ product
thong farmer hous.
”UP PTOdUCtixity d
mils Cllbd by fa‘
1995796, declining
“Palm aim fe
blaming
agrimgmm int ens
Ethiopian farms a
mean Silt Of

(82 Pficem of E1
followed b3, Oror
mm“? (T a':

mm“ (75 p.

05: literal-es. Ir

2.2 Status of Traditional Practice of Maintaining Soil Fertility

Agricultural productivity growth through use of commercial fertilizer is becoming
more popular because traditional methods of maintaining soil fertility are no longer able to
keep up with Ethiopia's mounting population pressure and arable land constraint. Since
1980 yields of maize, tef, sorghum, and wheat are not increasing at a sustained, positive
rate, thus forcing farmers and policy makers to think of alternative means of increasing
agricultural productivity. Declining soil productivity is an increasing farmer concern:
among farmer households surveyed in 1995/96, 73 percent stated that they had seen their
crop productivity decline between 1992 and 1995 (GMRP 1995/96). Similarly, among the
reasons cited by farmers for increasing the amount of fertilizer used between 1992 and
1995/96, declining soil productivity was ranked number one followed by successful farmer
experience with fertilizer (Demeke et al. 1998).

Increasing population pressure on the land translates into the demand for an
agricultural intensive development strategy, one that is land-saving and labor-using.
Ethiopian farms are small, reducing the option for fallow to restore nutrients to the soil.
The mean size of cultivated household area in Amhara, Oromiya, and Southern Regions
(82 percent of Ethiopia’s population) in 1995/96 was 0.5 hectares in the Southern Region,
followed by Oromiya and Amhara with an average of 1.3 hectares and 1.2 hectares,
respectively (Table 2.2). In the Southern Region, the lower three quartiles of the
population (75 percent of the population with the least hectares cultivated) had less than

0.52 hectares. In both Oromiya and Amhara, the lower three quartiles (75 percent of the

23

households) had less than 1.24 hectares and 50 percent of the households had less than 0.7

hectares (Table 2.2).

Table 2.2 Mean Level and Distribution of Household Cultivated Hectares by
Region, 1995/96

 

 

 

Region Percent of Mean 1“ Quartile' 2"“ Quartile 3" Quartile 4“ Quartile
Total Hectares (mean (mean (mean (mean
Population Cultivated hectare) hectare) hectare) hectare)
Tigray 5.4 0.89 0.21 0.49 0.84 1.97
Afar 1.8 1.03 0.27 0.76 1.10 1.91
Amhara 24.9 1.19 0.25 0.69 1.24 2.55
Oromiya 32.9 1.25 0.27 0.70 1.24 2.76
Somalie 2.4 0.70 0.12 0.37 0.72 1.59
Benishangul and Gumez 2.5 1.18 0.31 0.81 1.24 2.37
South 23.8 0.53 0.11 0.28 0.52 1.21
Gambela 2.3 0.58 0.06 0.26 0.57 1.38
Harar 1.3 0.58 0.22 0.41 0.64 1.03
Addis Ababa 1.5 2.32 0.61 1.49 2.65 4.56
Dire Dawa 1.2 0.70 0.15 0.41 0.63 1.63
National 100 1.02 0.19 0.52 0.99 2.34

 

Source: CSA 1995/96.

Note: 'Quartiles are the division of households at the national level into 4 groups by hectares cultivated.
The first quartile represents 25% of the national sampled survey that have the lowest level of cultivated
hectares, the 4" quartile has the highest level of cultivated hectares.

Long fallows in SSA have been traditionally relied upon to recapitalize soils and
restore yields; however, increasing population densities have thwarted this option--
cropped areas are not left to fallow for as long. The shorter fallow does little to restore
soil organic matter. Fallows of 15 to 30 years are required to recapitalize soils (Weight
and Kelly 1998). A soil’s natural organic matter will decline over time without deliberate
recapitalization. The little organic matter that is returned to the soil competes with other
activities for use. Increasing deforestation forces farmers to use manure as firel and
organic matter is often fed to livestock. In addition, land scarcity requires households to

24

 

  

swim ”‘5‘ t '

m1 ; matirnurr

Al’hough these fit

, .. |
ternltzer to haxe pc-

 

patticular, is much 3
may: and Keily 1
Practice: stitch can.
@9383: is low-i:
reams to hbor an:

theme;

pra
ror

supplement their livestock's feed with crop forage. In the highlands, animals may only
receive a maximum of 50 percent of their feed requirements through grazing (IFDC 1993).
There are concerns that rapid increases in use of conunercial fertilizer will

exacerbate environmental problems of increased soil acidification and water pollution.
Although these are real concerns, the alternative is worse. In SSA “the potential for
fertilizer to have positive impacts on the environment in general, and soil quality in
particular, is much greater than the potential for it to cause environmental damage”
(Weight and Kelly 199824). Agricultural intensification reduces extensive agricultural
practices which can accelerate deforestation and soil erosion. An alternative to extensive
agriculture is low-input sustainable technologies. However, the net benefit in terms of
returns to labor and in sustaining soil fertility is less than the high-input technology
alternative:

However much they must respect traditional farming

practices, agricultural scientists must resist the temptation to

romanticize them. They must not succumb to the illusion that,

confronted with explosive population growth, Afiica’s food

needs can be met through the improved ‘low-input

sustainable’ systems that are based largely on traditional

practices but require much more from farmers in terms of

labor, knowledge, and skill (Borlaug and Dowswell

1995: 123).

In sum, Sub-Saharan Africa faces a food crises and organic matter does not

provide sufi'rcient nutrients to replenish those lost with crop production. Further, organic

matter is increasingly scarce due to competing uses such as fuel and construction materials

25

 

(Weight and Keli} .I

to m product

2.3 History of .-
Ethiopia has
markets by state co:

oortrolhng trade as:

 

 

hput prices. but it re
input market in part.
that mcourages sus:

The imperia
Ethic SUCCCSS 'm DIOI
Fwy“! Develop:
fans and Export of
Yul DCVCiOpmem

331101111 sch-'Sufiitli

r135 - .
hq‘fiy mflUen

thong dODOrs at t‘r

gh‘mpm technOlr

(Weight and Kelly 1998). Chemical fertilizers, together with organic matter, must be used

to increase productivity and sustain yields.

2.3 History of Agricultural Policy in Ethiopia

Ethiopia has a history of heavy direct government involvement in agricultural
markets by state controlled firms purchasing grain, distributing agricultural inputs, and
controlling trade and prices. During the 19905 the government liberalized output and
input prices, but it remains to be seen whether the remaining government presence in the
input market, in particular, will foster the development of a private sector input market
that encourages sustainable use of high-input technologies.

The Imperial Government of Ethiopia developed four Five-Year Plans, but with
little success in promoting agricultural growth (Molla et a1. 1995). The First and Second
Five-Year Development Plans (1957-1967) focused on developing large-scale commercial
farms and export of cofl'ee to the neglect of subsistence farming methods. The Third Five-
Year Development Plan (1968-1973) was shaped by the surprising shift in the 19605 fiom
national self-sufficiency in crop production to a net importer for the first time. The Plan
was heavily influenced by the “Integrated Rural Development” programs fashionable
among donors at the time. It emphasized building transport infrastructure, disseminating

high-input technology, credit, and extension, and developing cooperative societies.

26

    
 

extension. ln effec:
implemented (Wort
tnti industrial Devel
Agrioilture distribut
The Fourth l
'5 {it P110111) CTDps
the Plan “as never

””3 r956111111811 o

Three comprehensive extension programs emerged;" however, they were costly
and the benefits were heavily concentrated in select high-potential areas of the country.
These factors prOmpted a geographic expansion of the program, but provision of fewer
services: Minimum Package Program (MAPS) which provided fertilizer, credit, and
extension. In efl’ect, the MAPS were only located along major road ways and were poorly
implemented (Workneh 1994 fiom Molla et al. 1995). During this time the Agricultural
and Industrial Development Bank (AIDB) procured fertilizer and the Ministry of
Agriculture distributed fertilizer to farmers.

The Fourth Five-Year Development Plan (1974-1978) identified cereals and pulses
as the priority crops and was designed to continue with the package approach. However,
the Plan was never implemented: the combination of the severe famine of 1973/74 and
rising resentment of the Imperial Govemment’s feudal land tenure system prompted the
overthrow of the monarchy by the military in February 1974 (Molla et al. 1995). The
Derg (military regime) instituted a radical land reform in 1975 titled Proclamation 31
whereby peasants were no longer indentured to landlords and land holdings in excess of
10 hectares were confiscated by the state and distributed to landless peasants or organized
into state farms or cooperatives. However, although farmers continued to have usufi'uct
rights over their holdings, the sale of land remained prohibited. When the reform
measures were implemented, little land was actually redistributed and many landless

farmers remained, making the new law prohibiting use of hired labor appear contradictory.

 

"The Chilalo Agricultural Development Unit (CADU) in 1967, the Wolamo Agricultural
Unit (WADU) in 1970, and the Ada District Development Project (ADDP) in 1972.

27

During the

   

the extetsion prog'
prompted the trans:
the Agriwlniral M.
1976 with the mar:
ItSF‘OYISFDility for in,
Me 1995)

The AV C er
the :0th imported r
Womble for min
mmgh WEN): 1 8,

Continuing 1

310A the Derg 1m;

During the first few years of the Derg, the volume of fertilizer distributed through
the extension program continued to expand and the demands on transport and storage
prompted the transfer of farm input marketing fi'om the Ministry of Agriculture (MOA) to
the Agricultural Marketing Corporation (AMC) in 1978. The AMC was established in
1976 with the mandate to control the marketing and distribution of grains, adding the
responsibility for input markets “optimized use of storage and transport facilities”
(Demeke 1995).

The AMC entered a partnership with the Ministry of Agriculture (MOA) whereby
the AMC imported and distributed fertilizer to MOA marketing centers. The MOA was
responsible for estimating farmer demand and distributing fertilizer to farmers on credit
through roughly 18,000 peasant associations (NFIA/MOA 1987 from Demeke 1995).

Continuing with the monarchy’s Minimum Package Program (MPP) under the
MOA, the Derg implemented the MPP II from 1981-1987. MPP II expanded planned
coverage three-fold, but the necessary financial support was lacking and therefore the
actual coverage of the MPP II was limited in its ability to provide inputs and extension
(Molla et al. 1995).

Both the MOA and AMC became increasingly inefficient: overstocking resulted
from poor demand estimates and deliveries were delayed due to a lack of institutional
coordination (Demeke 1995). In 1984 the AMC folded and the Agricultural Input Supply
Corporation (AISCO) emerged under the Ministry of Agriculture. AISCO was
responsible for importing, distribution to marketing centers, and retailing of agricultural

inputs. In the same year, the MPP H was replaced by the Peasant Agricultural

28

 

Development Prog'

emerged as fire ins:
I

Aimugh input and
rise had is problcrr. :

.. .lcr
lug. |
fair;
5101?.
orga:

0'7'
510

 

Development Program (PADEP) in which roughly 2,900 farmer service cooperates (SC)
emerged as the institutional vehicle to extend inputs, credit, and extension to smallholders.
Although input and output marketing responsibilities were now under one roof, AISCO
also had is problems:

...lengthy bureaucratic process of securing foreign exchange,

high import price, high freight costs, lack of proper port

facilities at Assab, high cost of inland transport, problems of

storage and transport, inaccurate demand estimates, and

organizational inefiiciency hampered the operation of AISCO

(Demeke 1995).

A civil war in 1990/91 led to the overthrow of the military regime by the
Transitional Government of Ethiopia (TGE). In 1996 the TGE announced its objective of
doubling per capita incomes over 15 years and of narrowing the “food gap” (FDRE 1996).
This daunting task presented the FDRE with the challenge of redefining its role in
promoting agricultural intensification.

Grain markets were partially liberalized in the late 19805 and then completely
liberalized in March 1990. The AMC was restructured into the Ethiopian Grain Trade
Enterprise (EGTE) with the mandate to purchase grains for an emergency grain reserve.
Since liberalization, the performance of the grain marketing system has largely improved.
Econometric results revealed that ceteris paribus cereal price spreads significantly
declined in most markets suggesting increased competition (Negessa and Jayne 1997).

Wholesale prices in major surplus-producing areas increased while prices in consumer

markets declined (N egessa and Jayne 1997). Overall, output market liberalization brought

29

bereftts to both pr:

 

by reducing the tra"
FDRE :52.

the gain market. I

feilizer, but its cur

promote open, com;

 

extension Seniccs
m senor. Hop. 6.
“dim-1’ In add
We: Wm lCtu
“5 Nth imports. w}
Companjes, 11 Was 1
317k in cm." 1998 1
mm result ir.

mains and fan,

benefits to both producers (in higher prices received) and consumers (lower prices paid)
by reducing the transaction costs between the farmgate and the point of retail.

FDRE efforts to liberalize the input market have lagged behind similar moves in
the grain market. The Government of Ethiopia has a history of implicitly subsidizing
fertilizer, but its currently stated policy (although not yet necessarily implemented) is to
promote open, competitive input markets supported indirectly by government-funded
extension services. In 1991 the TGE announced its plans to liberalize the agricultural
input sector. However, during the mid-19905 fertilizer prices remained pan-territorial and
subsidized.” In addition, although private importers entered the market, 3 out of 5 .
importers were actually companies owned by national or regional government interests.
As with imports, wholesale and retail markets remained largely'in the hands of government
companies. It was not until February 1997 that fertilizer retail prices were liberalized and
only in early 1998 that wholesale prices were liberalized. However, liberal prices does not
necessarily result in a competitive market due to the govemment’s continued control over
marketing and farmer input credit. Overall, the research presented in this study is in the
context of a dynamic, evolving market.

The Federal Republic of Ethiopia’s (FDRE) agricultural policy has been two
pronged: making steps toward‘liberalizing the output and input markets (more so for the
output market), but also in promoting agricultural productivity growth thru improved

extension services. In 1993 Sasakawa—Global 2000 (SG) began a pilot program in

 

"Fertilizer subsidies ranged from 20 percent to 39 percent across the country until January
1997. The subsidy was a reaction to the fertilizer price rise that followed the devaluation
of the Birr in the early 1990s

30

partnership with the Ministry of Agriculture’s (MOA) Department of Extension and
Cooperatives. President Meles Zenawi commended the SG focus on improving
agricultural productivity as the “best entry point for addressing the issue of food security
in Ethiopia” (Zenawi 199825). The program objective was to introduce farmers to new
technologies for a couple of years in hopes that after the program withdraws farmers
would be convinced of the benefits of high-input technologies and would continue their
use. The MOA/SG program was targeted to high-potential cereal growing areas and
characterized by farmer-managed plots of 0.5 hectares, provision of fertilizer, seed, credit,
and close supervision by the extension agent. The SG program was successfirl in raising
net income and returns to labor for maize and tef (Howard et al. 1999). Based upon the
same high-input technologies and practices, in 1995 the FDRE began to rapidly scale up
the MOA/SG into a National Extension Program (NEP) with govemment-administered
and guaranteed credit and government organized input distribution.

Overriding all agricultural programs is the importance of providing farmer access
to credit. Input credit was historically channeled through service cooperatives in an
arrangement between SCs and the banks. However, in the late 19805 and early to mid
1990s default rates began to rise and the .govemment intervened in 1997 by guaranteeing
input credit in order to encourage banks to continue lending to the agricultural sector (see

chapter 3 for more details).

31

14 Level and

In spite of e

 

 

Mam grow?
subsistence agricul:
pecan ofan cstirna
rm (CSA in Deme}
and inctmsed by "'41-.
had reached 169,03.

Fertilue use
Darcie 1995). This

PM butthcnrc

 

Figure

2.4 Level and Distribution of Fertilizer Use

In spite of efl‘orts by the FDRE to develop the agricultural sector, agricultural
productivity growth remains low and the majority of farmers practice low-input, even
subsistence agriculture. Inorganic fertilizers in Ethiopia are used by approximately 30
percent of an estimated six million farm households and cover 37 percent of the cultivated
area (CSA in Demeke et al. 1998). Fertilizer use in Ethiopia started fi'om a very low base,
and increased by 700 percent from 1981-1996 (Figure 2.1). By 1997, DAP consumption
had reached 169,000 MT and urea use reached 52,000 MT.

Fertilizer use increased 41 percent annually between 1971 and 1979 (IFDC in
Demeke 1995). This increase tapered off in the early 19803 as the price of fertilizer

increased, but then rose again by the late 19805 (Figure 2.1). In the early 1990s fertilizer

Figure 2.1 Fertilizer Consumption in Ethiopia, 1981-1997

 

 

 

 

 

 

 

 

 

 

 

 

 

300,000
——DAP
250,000 r 4‘
L—j ....... Um [\
200,000 *4 A
_Toml Y
2 150,000 A
100,000
50,000 W ,o ..
0 . r-T'%-:':T.mm L22... . .

 

 

%\%s.g\ we
@@$\°PP€P\$

Year

 

 

 

Source: FAO l981-l997.

32

 

use Masad- she:
960C111 meease' in

DA? consumption _

rttrhrted to the 56'

 

“pension had yet t;-
16 million demons:

MS ofDAP, urea

 

Much of the recent '

dimtied 100 kg'ha !

use increased sharply primarily due to donor support, but declined in 1993 due to a 40
percent increase in the price of DAP (Demeke 1995). However, the market recovered and
DAP consumption reached a peak in 1996 at 209,883 MT. The decline in 1997 may be
attributed to the denial of additional credit to defaulting SCs and the fact that the NEP
expansion had yet to reach a point of significant impact (as it did in 1998 with a planned
2.6 million demonstration plots). Urea consumption has historically been lower than the
levels of DAP, urea consumption reached a peak in 1997 at 51,808 MT (Figure 2.1).
Much of the recent increases in fertilizer use is attributed to the NEP. If in 1998 the NEP
delivered 100 kg/ha of fertilizer for each of the 2.9 million planned farmers, then it
consumed 290,000 MT of fertilizer, 76 percent of the total quantity imported in 1998.
Examination of the distribution of fertilizer users across regions revealed that
fertilizer use is concentrated in regions of the country that are high-potential agro-
ecological areas and that are close to Addis Ababa and therefore have relatively higher
levels of infiastructure. Overall, in 1995/96 49 percent of the households the Oromiya
Region used fertilizer; 39 percent in Amhara, and 36 percent in Southern Region (Table
2.3). The highest density of fertilizer users lay in the two regions of Addis Ababa, where
97 percent of households used fertilizer and Harar with 81 percent. However, these
regions are largely peri-urban and do not reflect characteristics (i.e., underdeveloped

markets) facing typical rural smallholders.

33

Table 2.3 Mean Percentage of Households Ural; Fertilizer by Region, 1995/96

 

 

Region Percent of National Population Percent of Households Using Fertilizer
Tigray - 23 45
Afar l 13
Amhara 22 39
Oromiya 38 49
Somalie 1 6
Benishangul and Gurnez ' 12 23
South 26 36
Gambela 0 0
Harari 65 81
Addis Ababa 95 97
Dire Dawn 2 34

 

Source: CSA 1995/96.

Cereals receive the largest share of fertilizer in Ethiopia. In 1994/95, 99 percent of
fertilizer used nationally went to cereals and 95 percent in 1996/97 (CSA 1995/96).
Pulses and oilseeds received roughly 2 percent of total fertilizer applied. “Permanent”20
crops such as chat, cofl‘ee, enset, tobacco, and cotton received roughly less than 3 percent

of total fertilizer consumed in 1994/95 and 1996/97. However, the share of urea to

“permanent” crops increased in 1996/97 to 6.6 percent of all urea used from 0.8 percent in

1995/96.

”The listed crops (expect coffee) are not necessarily permanent, but it is how they are
classified by the Central Statistical Authority.

34

Itble 2.4

 

10*“

“3%),

urealo 1e;-

keel-n \r

Table 2.4 Proportion of Total Fertilizer Use By Crop, 1994/95 and 1996/97,

 

 

 

 

 

 

‘000 Quintals (QT)
DAP Urea
1994/95 1996/97 1994/95 1996/97

“000 QT' Percent ‘000 QT Percent '000 QT Percent ‘000 QT Percent
Cereals 1,076.62 90.9 1,123.89 90.5 197.41 91.00 ' 236.09 87.0
Pulses -53.14 4.48 42.66 3.4 : 6.52 3.00 9.74 3.6
Oilseeds 9.40 0.80 E 28.71 2.3 ‘ 1.73 0.80 1.37 0.5
Others’ 42.15 3.56 5 36.66 3.0 9.47 4.40 5.5 2.1
Permanent’ 3.14 0.29 ‘ 10.14 0.8 . 1.73 0.80 - 18.64 6.8
AllCrops 1,184.72 100 1,242.06 100 216.86 100 271.34 100

 

 

Source: CSA 1994/95 and 1996/97.

Notes: 'Thcre is a discrepancy between these fertilizer use figures and the figures in Figure 2.1 due to
difierent data collection methods of the sources.

’Fenugreek, spices, potatoes, other vegetables.

’Chat, coffee, enset, cotton, tobacco, fruits, other permanent. (Some of these crops are not necessarily
“permanent” but it is how they are classified by the CSA.)

The shift in the share of fertilizer to individual cereals revealed changes in
profitability. As government programs are designed to expand high-input use, it must be
remembered that part of the equation for profitability, along with the input-output price
ratio, is the net increase in yield achieved for a particular crop. From 1994/95-1996/97
the share of DAP to maize remained relatively constant, at 12 percent of total DAP
allocated to all cereals (Table 2.5). However, an increasing share of urea to maize relative
to tef during this period was observed, perhaps an indicator of the rising yield
responsiveness of maize to urea (a top dressing-applied after planting, critical for cob
nutrients), in combination with a favorable the input/output price ratiorelative to tef (in
part due to government input programs). From 1994/95 to 1996/97 the share of total

ureato tef fell from 45 percent to 37 percent and the share to maize increased fi'om 10

percent to 29 percent (Table.2.5).

35

IrbkLS

Table 2.5 Quantity and Proportion of Total Fertilizer Use By Cereals, 1994/95

and 1996/97, ‘000 Quintals (QT)

 

 

 

 

 

DAP Urea
1994/95 : 1996/97 : 1994/95 . 1996/97

‘000 QT Percent *000 QT Percent ‘000 QT Percent ‘000 QT Percent
Tef 457.33 42.5 538.80 48.3 89.27 45.2 86.10 36.6
Barley 176.24 16.4 130.94 11.7 1 20.59 10.4 20.78 8.8
Wheat 250.71 23.3 238.80 21.4 55.40 28.1 35.18 14.9
Maize 137.23 12.8 135.76 12.1 190.71 9.9 68.99 29.3
Sorghum 6.88 0.6 14.59 1.3 6.78 3.4 17.51 7.4
Millet 43.36 4.0 56.52 5 5.1 4.09 2.1 6.47 2.7
Oats 4.87 0.5 . w u 1.57 0.8 u 99
Total 1,076.62' 100 1,115.41 100 197.00 100 ' 235.03 100

 

 

 

Source: CSA 1996/97 and 1994/95.

Note: 'There is a discrepancy between these fertilizer use figures and the figures in Figure 2.1 due to
difierent data collection methods of the sources.
1“ denotes data not significant.

2.5 Intensity of Fertilizer Use

As with fertilizer adoption, the level (kg/ha) of fertilizer use in Ethiopia is low.
Fertilizer was introduced to Ethiopia following demonstrations from 1967 to 1969. In the
early 1970s, the Ministry of Agriculture (MOA) recommended 100 kg/ha DAP and 50
kyha of urea per hectare for all crops and all areas. Using the economic optimum rate“
recommendations were revised alter the Agricultural Development Department/National
Fertilizer and Inputs Unit (ADD/NFIU”) conducted over 1,200 fertilizer trials fiom 1988

to 1991. The recommended rate of N-PZO,-I(20 (nitrogen-phosphatepotash) for maize on

Nitosols (one of the most common soil types for maize production) is 75-80-0 kg/ha.

 

21The economic optimum rate is the point on the yield response curve where the last Birr
invested in an input will provide the farmer with a net return of one Birr (ADD/NFIU
1992).

22The NFIU was later renamed the National Fertilizer Industry Agency (NFIA).

36

Ihe rezomr

enan;

lek 2.6
Soil T111

 

Carrrbtsols
2%?9”
Bud 5011s
Gm Solis
Rat‘s Solis-
Brmm Sci‘:
5m AD:
511:: "Nltos

.501]; eg-

The recommended rate on Andosols, the other common soil type for maize, is 50-55-0

leg/rte (Table 2.6).23

Table 2.6 Fertilizer Recommendations for Maize, N-PZOi-Kzo, kg/ha

 

 

 

Soil Type Shewa/Gojjam Across the Country
Nitosols' 75-80-0 75-80-0
Cambisols 50-50-20 50-50-20
Andosols 50-55-0 50-55-0
Black soils 80-80-0 65-55-0
Grey Soils 50-55-0 55-55-0
Red Soilsz 80-80-20 65-75-20
Brown Soils 55-50-0 55-50-0

 

Source: ADD/NHU 1992.
Notes: 'Nitosols and Andosols are the most important types of soils on which maize is grown.
2Red soils cover Nitosols, most Luvisols, and some Cambisols.

Increased fertilizer consumption translated into expanded number of fertilizer users
as well as increased intensity of use by individual farmers. Between 1970-74 and 1991-95
fertilizer consumption in Ethiopia increased more than 10-fold. Nationally, the average
dose of fertilizer was 35 leg/ha for all farmers (users and non-users) and 95 kg/ha for users
in 1995/96 (Table 2.7)- surpassing the average for SSA of 8 kg/ha (IFDC 1998).
Ethiopia’s intensity of use is still below that of Kenya and Zimbabwe, representative
countries for Ethiopia due to their relative success in adapting high-input technologies. In

Zimbabwe fertilizer use was at a high of 70 kg/ha in 1980, but fell to 53 kg/ha in 1995

 

23Soils in which maize and tef are grown contain the highest available potassium (K), with
more than 60 percent of the soils containing over 300 ppm of available K (ADD/NFIU
1992). At 30 kg/ha, a negative maize yield response to potash (K20) was observed in
Gojjam. In addition, the maize yield response to potash (K20) was low, about 3.5 kg/ha of
grain per kg of fertilizer in Shewa and Sidamo (ADD/NFIU 1992).

37

(IFDCIM), In:
rllgnnms l.

leselofusein199:

tsllthtnlsn

 

 

(IFDC 1998). In neighboring Kenya fertilizer use also declined fi'om 29 kg/ha in 1990 to
19 kyha in 1995 (IFDC 1998). Although the intensity of use in Ethiopia is growing, the
level of use in 1995 remained below the average level of use for all of Africa of 21 kg/ha;

159 reg/rut in Asia; and 98 reg/ha in North America in 1995 (IFDC 1998).

Table 2.7 Mean Level of Fertilizer Use bLRegion, 1995/96

 

 

 

Region Dose (kilogram/hectare)

Across All Farms Users Only
Tigray ll 51
Amhara 22 75
Oromiya 47 100
Southern 47 126
Natiorull ' 35 95

 

source: CSA 1995/96 in Demeke et al. 1998.

Overall, there is a potential to both increase the numbers of farmers using fertilizer
as well as increase the intensity of use. Among the farmers that do use fertilizer, many use
sub-optimal doses. Application rates are well below the recommended levels of 100 leg/ha
DAP (46 kg/ha P20, and 18 kg/ha N) and 100 kg/ha urea (46 kg/ha N). However, there
was significant regional variation in fertilizer intensity. The regions of Harar and Addis
Ababa used the highest average level of fertilizer, but as mentioned earlier these regions
are not characteristic of rural Ethiopia due to the high degree of urbanization. Excluding
these regions, the Southern Region used fertilizer most intensely at 126 kg/ha, followed by

Oromiya at 100 kg/ha, and Amhara at 75 kg/ha (Table 2.7).

'38

16 Profit
Intro:
intensive scai
pa'tlcula'h ft
iteration of
Common me
mm. and a
Uffdtilizcr.
Wile usin
550“ “heme
0035 of the f
buth the PhFE
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Significant ad
0011mm“), ac
VCR “luau c

How

2.6 Profitability of Fertilizer

Introduction of high-input technology to Ethiopian farmers on a broader and more
intensive scale is only a viable option to increasing agricultural productivity if input use,
particularly fertilizer, is profitable. Profitability of fertilizer in Ethiopia is defined by the
interaction of the agronomic response rate of fertilizer and the input-output price ratio.
Common measures of fertilizer profitability are value cost ratios, the marginal rate of
return, and a financial analysis of net income and returns to land and labor from adoption
of fertilizer. This section reviews several studies that have estimated the profitability of
fertilizer using different methods. A value/cost ratio" (VCR) or a marginal rate of return25
show whether the “financial returns for the yield maximizing dose of fertilizer exceeds the
costs of the fertilizer treatment” (Yanggenjet al. 1998:31). VCRs take into consideration
both the physical response and output and input prices. “Many observers contend that the
marginal agronomic response must be at least twice the nutrient-to-grain price ratio for
significant adoption to occur” (Heisey and Mwangi 1997: 199). This translates into
commonly accepted guidelines that provide an incentive for fertilizer use by farmers: a
VCR equal or greater than 2 and a marginal rate of return of 100.

However, in theory, if the incremental value of production exceeds the cost of

fertilizer, then fertilizer use will be profitable. “The rules-of-thumb requiring that the value

 

2‘Value-cost ratios are defined as the net revenue fiom fertilizer use (output price'the
fertilizer response rate) divided by the price of fertilizer.

2’Marginal rate of return is defined as the ratio of the additional net benefit to the
additional costs resulting fi'om the adoption of increasing levels of inputs (Crawford and
Kamuanga 1988).

39

ofthe yield i
ionizer liter
ash quantil
yields. or in;
Probe
retreated by‘
711$ng from
Wile trials
ShCWGOfi an
(I able 2.8) '1
We‘Ileii'Kefa
Accorc
3mm“, Cau‘.
Those eSIlrhate
control PM I'll
3°“ cm: or
"333° field 0

7'

0&5th i5 pr.

of the yield increase be at least two times the fertilizer cost is a convention used in the
fertilizer literature to allow for factors which affect farmers’ input decisions «but are not
easily quantifiednfarmers’ risk attitudes, on-farrn yields that are lower than agronomic trial
yields, or high yield variability due to climate, for example” (Yanggen et al. 1998231).

Probably the most favorable estimates of profitability come from trial data
estimated by the ADD/NFIU. Estimated VCRs for maize across Ethiopia are all above 2,
ranging fi'om 2.8 to 5.3 (Table 2.8). The estimated net yield increase for maize from the
fertilizer trials under the ADD/NFIU recommended dose ranged from 1,268 in
Shewa/Gojjam with black soils to 1,855 kg/ha in Wellega/Kefa/Illubabor with any soil type
(Table 2.8). The incremental profit rates were also high—up to 892 Birr/ha in
Wellega/Kefa/Illubabor.

According to these estimates, fertilizer use on maize is profitable across Ethiopia.
However, caution must be extended when making policy recommendations fi'om trial data.
These estimates may be overestimated for many reasons, one of which is that the average
control plot yields were higher than the national average. The average yield of control
plots of 262 maize trials was 2,516 kg/ha which was 46 percent higher than the national
average yield of 1,694 kg/ha for the period 1988-1991 (ADD/NFIU 1992). This

difi‘erence is probably attributed to the use of improved seed varieties and row planting.

40

 

Table 2.8 Results of ADD/NFIU Maize Trials

 

 

Zone Soil Control Recommended Dose Yield Profit VCR
Type Yield N P20, Increase' (Birr/ha)
(ks/ha) (kg/ha) (kg/ha) (ks/ha)

Shewa/Gojjarn Black 3,333 85 81 1,268 534 2.8
Shewa/Gojjam Gray 2,734 57 58 1,490 766 4.8
Shewa/Gojjam Red2 2,576 85 90 1,734 822 3.7
Shewa/Gojjam Brown 2,278 58 55 1,578 832 5.3
Across the Country Black 2,603 64 56 1,142 532 3.5
Across the Country Gray 2.734 57 58 1,490 766 4.8
Across the Country Red 2,454 66 79 1,493 719 3.9
Across the Country Brown 2,273 58 53 . 1,560 824 5.3
Wellega/Kefa/Illubabor all colors 2,641 90 90 1,855 892 3.8
Gamo Goth/Sidamo all colors 2,179 46 64 1,212 597 4.1

 

Source: ADD/NFlU 1992.
Notes: 'The increase in yield due to nutrient application.
2Red soils cover Nitosols, most Luvisols, and some Cambisols. Most maize is grown on Nitosols.

Relative to the trial estimates, Demeke et al. (1998) presented discouraging results
of the profitability of fertilizer use on maize following liberalization of the input and output
markets in the 19908. VCRs were calculated given the 1992 (before input market
liberalization) and 1997 (after liberalization) input and output prices,.but holding the level
of fertilizer use constant at the ADD/NFIU recommended rates across the two years.
During this period the input/output price ratio declined as a result of the increase in input
prices outweighing the rise in grain prices."6 Across the country, fertilizer prices increased
from 21 to 39 percent as a result of the removal of the subsidies and pan-territorial pricing

(Negessa and Jayne 1997).

 

“These VCR calculations used grain prices immediately following harvest in which prices
are at their seasonal low.

41

Resolts s
from rates it
nlnnliang felt
doses in 1992 (
stage from 3
from 44-: to l
MOW the out:
Bailey was the

lUliOWCd b\ w

l
lbw

Doha

 

Results showed that fertilizer profitability at the ADD/NFIU recommended
fertilizer rates fell sharply between 1992 and 1997. These results indicated that the profit
maximizing fertilizer doses in 1997 were considerably lower than the profit maximizing
doses in 1992 (Demeke et al. 1998). Between 1992 and 1997 VCRs for maize fell on
average from 3.97 to 1.32. For example, the highest VCR listed for maize, in Shewa, fell
fi'om 4.44 to 1.48, a decline of nearly 50 percent (Table 2.9). Overall, in 1997 VCRs fell
below the cutoff of 2 for the majority of the site/crop combinations (Demeke et al. .1998).
Barley was the most robust crOp with respect to changes in input and output price,

followed by wheat, maize, sorghum, and tef.

Table 2.9 VCR Comparisons for Maize and Tel, 1992 and 1997

 

 

 

1992 1997
CM“ Fert Cost Output VCR Felt Cost Incremental Output vca
(Birr/ price (Birr/ yield with price
quintal) (Bin/kg) quintal) fertilizer (Bin/kg)
(kg/ha)

Is;

Shewa 211.67 1.22 3.69 515.86 641 ' 1.35 1.67
Gojam 197.26 1.22 3.66 480.48 592 1.35 1.66
Arsi/Balc 160.39 1.22 3.60 390.54 473 1.35 1.63
Other 91.98 1.22 2.59 222.60 195 1.35 1.18
Across the Country 192.25 1.22 3.74 468.42 590 1.35 1.69
M

Shewa 193.77 0.65 4.44 471.98 1,325 0.53 1.48
Gojjam 295.90 0.65 4.24 720.00 1,932 0.53 1.41
Welega/Kefa/ 314.10 0.65 3.84 765.00 1,855 0.53 1.28
Illubabor

GamuGofa/Sidamo 190.60 0.65 4.13 463.36 1,212 0.53 1.38
Other 131.83 0.65 2.93 322.78 594 0.53 0.97
Across the Country 216.08 0.65 4.24 526.08 1,410 0.53 1.41

 

Source: Demeke et a1. 1998.

Note: The fertilizer cost in 1992 and 1997 was calculated by multiplying the recommended fertilizer doses
for each crop and zone (recommendations will vary) by the going fertilizer price in that year and zone.
Thus across years, the fertilizer price changes but the reconunended dose is held constant

42

 

 

   
   

use. and therefore
irritation of the \'
W Weeding
wits-floors that
5316 In addition
llStn arbitrary g
Plfitthility is a la
htnh labor),
ESllmates
moment ove-
Pllfilabilny Ol fer
W tOChnol0gjeg
Mt‘lpams in the
med“ the natio

mmlwhflc 49‘

The value cost ratio is a useful tool to use as an indicator of the profitability of
fertilizer, but it is by no means an inclusive measure of all the costs included in fertilizer
use, and therefore a poor guideline for policy recommendations. The most important
limitation of the VCRs is that it does not take into account the additional indirect costs of
increased weeding or harvesting labor from increased fertilizer use, or increased marketing
costs-factors that raise the price paid for inputs and reduce the price received for grain
sales. In addition, the rule of thumb of 2 for a “profitable” VCR is not very useful because
it is an arbitrary guideline, set to cover unidentified costs. An improved measure of
profitability is a farm-level financial analysis of net margin (which includes the value of
family labor).

Estimates of the profitability of fertilizer fiom SG farmer-managed plots is an
improvement over the government trials; however, they too may overestimate the
profitability of fertilizer use. Maize yields ranged from 2.8-3 .8 MT/ha for relatively low-
input technologies (local seed varieties, some DAP, no urea) on plots managed by the 86
participants in the two zones of Jimma and Woliso (Table 2.10). This dramatically
exceeded the national average of 1.9 mm and the Oromiya Regional average of 2.1

MT/ha (where 49 percent of households use fertilizer).

Table 2.10 Maize MOA/SG Yields, MT/ha

 

 

Year MOA/SG MOA/SG MONSG National Oromiya Potential
participants graduates using participants average regional ‘ yields on
high-input using low- input average trials
technolo technolo
1998 5.5 5.8-6.8 £838 1.9 $1 9-10

 

Source: Howard et al. 1999.

43

 

moons and rm.

 

I
nifcrtflizcr) in:

(Renard ct ti 1?
ndéitiotttl costs 0;
WE (median
d1?) for all $6 am
following hag-veg)
toll BM). 0,, 1
MW“ (Howe
Mum plots (ma

”WY double thy

 

Rome for the p10:

Wanton)

The Sasakawa-Global 2000 (SG) program had resounding success in raising net
incomes and returns to labor for its maize and tef technology packages (improved seed
and fertilizer) in the Jimma, West Shewa, and East Shewa Zones of the Oromiya Region
(Howard et al. 1999). The additional yields from the SG input package outweighed the
additional costs of the high-input technologies of improved seed and fertilizer. Returns to
family (mediate and extended) labor far exceeded average daily labor rates (3-6 birr per
day) for all SG survey areas (Howard et al. 1999). At January 1998 prices (immediately
following harvest) the returns to labor day were 15 Birr/day on program plots compared
to 11 Birr/day on traditional plots (with local seed, DAP) managed by the‘same SG
participants (Howard et al. 1999). The mean net income for maize for the SG current and
graduate plots (many of which continued to use the SG technology under the NEP) was
roughly double that of a “traditional” plot (Table 2.11). Even at all yield terciles, the net
income for the plots with $6 technologies far exceeded net income with the “traditional”
inputs (Table 2.11).

The SG package thus far reported included improved seed and fertilizer; however,
fertilizer alone, without the improved seed, can also raise net income and returns to labor.
Net income for the low-input, local seed, no fertilizer package was 597 Birr/ha (not shown
in table 2.1 l) compared to 1,030 Birr/ha for the local seed, 103 kg/ha DAP application
(“Traditional” plot) (Howard et al. 1999). Returns to labor also increased, from 8 to 11

Bin/labor day with the introduction of fertilizer (Howard et al. 1999).

44

ltblt 2.11 l

but

 

Who:

CumSGprog ‘
SGmthatrplot ‘
'lndmoml‘ alo .
Scream-order l

WYIHHRYIJ

 

17 Condo: ,
This chat
Ifttsiblc avenuc
Wm can com
W PYOfiIability i
Olinpm and Outpt

{mm adopt the

 

oriim'zation of in

Molla”, fertiliz

 

 

Table 2.11 Net Income for Maize Farmers in Jimma, Birr/ha at January 1998

 

 

 

Prices
Yield Terciles
Mam Type 1 2 3 Mean
Current SG program plot 1,385 1,963 2,757 2,044
86 graduate plot 1,740 2,627 3,257 2,543
“Traditional” plot 459 933 1,667 $030

 

Source: Howard et al. 1999. , ,
Note: Yields are ranked in ascending order and split into three equal groups, a third of farmers in each
81'0“?-

2.7 Conclusion

This chapter demonstrated that introducing fertilizer to smallholders in Ethiopia is
a feasible avenue to increased agricultural productivity. On-farm trials demonstrated that
fertilizer can contribute to significantly higher yields for maize, but it must be remembered
that profitability is a function not only of the fertilizer response rate but the current levels
of input and output prices. The combination of three factors will determine whether
farmers adopt the high-input technologies. The following chapters will show how the
organization of inputs markets determines the shadow cost of purchasing inputs,

particularly fertilizer, and ultimately, determines the pace of agricultural intensification.

4S

The strucr
when and buyers
"Maker SUUCIure
damninc “11m it

hnnon 1986 210

 

than“, Ofmarkt
m“ “Brenna
WW3 W1 other It
6‘?! of con 810m
Whom“, in wh

Mom, 060m

CHAPTER 3

STRUCTURE

The structure of a market is expected to influence the competitive conduct of
sellers and buyers in the market, which in turn influences how well the market performs.
“Market structure refers to the organizational characteristics of a market that largely
determine where it falls in the competitive spectrum between monopoly and competition”
(Marion 1986:210). Market structure is the framework within which firms operate. Key
influences of market structure include concentration (the number of buyer and sellers),
product difi’erentiation (the extent to which a seller has some degree of independence in
pricing and other marketing decisions (Staatz 1996)), barriers to entry and exit, and
degree of conglomerateness. These elements of structure are outcomes of the regulatory
environment in which firms operate. In sum, “structure refers to the sources of
discretionary economic power that firms in the industry can exercise” (Staatz 1996). This
research examines the key factors underlying the structure, conduct, and hence

performance of the fertilizer market.

The structure of any market is shaped by two key factors: consumer preferences
and market regulation (“rules of the game”) (Rubey 1995). In Ethiopia, as will be shown,

consumer’s demand for credit and government policies played an important role in shaping

46

the stmcrur
inertricablj
Ethiopian
influence 1

process of

3.1 Ba
in:

mat to l
conditlom ar
all: help to s}
As in

‘33515 are high.
filming in he
Due to these 1:
food Pm“) of
”330mm,.-
fétthle rm” f
M'W’ZFMM

M We on

  

the structure of the market. This chapter focuses on the role of government which is
inextricably tied to defining the opportunity sets available to all participants in the
Ethiopian fertilizer subsector. The following sections outline the basic conditions that
influence market structure and then describe the structure of fertilizer imports and the

process of distribution.

3.1 Basic Conditions

In order to understand market structure, it is important to step backward for a
moment to review the influences of structure, the basic conditions of a market. Basic
conditions are the “underlying characteristics of the product and the environmental setting

that help to shape the type of market that evolves for the product (Staatz 1996).

As in other SSA countries, fertilizer is a low margin, high-risk product: transport
costs are high because fertilizer is bulky and distances are great, and risks are high in
investing in fertilizer due to seasonality of demand, storage costs, and interest incurred.
Due to these factors and due to the political importance (ultimately for low, stable urban
food prices) of the provision of low-cost and stable fertilizer prices has usually meant
heavy government intervention in setting prices and organizing distribution. Another
plausible reason for government intervention in the fertilizer sector is the premise that
rent-seeking behavior of middlemen would cause prices to rise if the government pulled

out. However, this has not yet been proven to be the case under open markets.

47

SU‘JCIUIC
to New
replied t

(the ort'n

could be c
finports v.1
AM

{We sub.
M therefore
bCfore a sale
through tnl st I

”Film or la:

thmw

 

The seasonality of demand is basic condition that plays out in shaping market
structure. Fertilizer is consumed primarily during the larger, meher season (roughly July
to November), but also during the earlier beIg (April to July). Most fertilizer is actually
applied between March and July. To exacerbate the issue of seasonality, the port of Assab
(the only port of import before the conflict with Eritrea in May 1998 forced imports
through Djibouti) is limited in capacity. Thus not all fertilizer consumed in a given season
could be ofllloaded in the months immediately prior to distribution. Thus, coordination of

imports was required in order to ensure suflicient fertilizer supplies.

Another basic condition that can influence the structure and performance of the
fertilizer subsector is that fertilizer is easily adulterated. Fertilizer is sold in bags by weight
and therefore it is possible for added impurities such as sand or dirt to go undetected
before a sale. Thus, the nature of the transaction between farmer and supplier must be
through trust and this may create a barrier to entry to potential entrants. Thus, established
suppliers or larger companies backed by the government (assuming trust in government)

may have an advantage in a market in which they have a presence.

3.2 Regulatory Environment

In Ethiopia in 1998 there was a heavy presence of government at every stage of
the fertilizer subsector. All commercial fertilizer was imported and channeled primarily
into government-controlled credit (and fertilizer retail/distribution) programs, very little
was sold on the open market (on cash). The government controlled access to forex for

imports as well as the timing of access to forex. The government (primarily regional

48

government
credit progr
Entersi on F
merit, to it

audit is def

governments) also controlled which distributors could distribute through its fertilizer
credit programs.’ Government provided credit to farmers primarily for the National
Extension Program (NEP) through the district Bureaus of Agriculture, and to a lesser
extent, to farmer Service Cooperatives (SC) for the regular credit program. Regular
credit is defined as credit that had been historically channeled through SCs for fertilizer
purchases by farmers prior to the expansion of the NEP. NEP credit was extended to
participating farmers for the extension package of fertilizer, improved seeds and
sometimes pesticides.

The fertilizer market is highly vertically integrated: 5 of the 7 integrated retailers
are also importers. Wholesale transactions were made either from importer/integrated
retailers to the 2 wholesalers or to independent retailers (who sell only in the open market
and do not have access to the. government input/credit programs). Retail transactions
occur primarily from the integrated retailers, through the NEP and SC credit programs to

farmers or by independent retailers to farmers.

Farmers thus primarily received inputs as a result of wholesale transactions
between the government and large-scale suppliers and not through individual cash
purchases. Consequently, the cash market was underdeveloped and in some areas
suppressed by the government. In 1998 cash sales were directly and indirectly influenced
by the regulatory environment, particularly the organization of credit. The volume of
open market (cash) sales was highest in areas where the government credit programs were

not meeting the needs of farmers or where the credit programs were absent (GMRP

49

1998). T.

shaping ti

1998). The next two sections provide detail of how the government was instrumental in

shaping the fertilizer market at all stages of the subsector in 1998.

3.2.1 Regulatory Environment of Fertilizer Imports

Before the Transitional Government of Ethiopia (TGE) came to power in 1991
fertilizer import was the sole responsibility of the government parastatal, the Agricultural
Inputs Supply Company (AISCO) (later renamed the Agricultural Inputs Supply
Enterprise (AISE)). Private trade in fertilizer was illegal. Since then, the Federal
Democratic Republic of Ethiopia (FDREY’ opened imports to private companies, but the
process of obtaining foreign exchange is still controlled by the government.

. Allocation of Foreign Exchange. The current organization of fertilizer imports is
largely shaped by the World Bank National Fertilizer Sector Project which began in
February 16, 1996. The project involves provision of hard currency for imports in
exchange for fertilizer sector policy reforms. The FDRE complied with the requirements:
subsidies were removed, prices were deregulated, and private sector importers were
granted “equal“ access to hard currency. Foreign exchange for fertilizer importers is
thought to be allocated among the five “private” fertilizer importers (there were zero

importers in 1991) and the parastatal on a competitive basis and in a very transparent way

 

2"The government changed names in August 1995.

2”One importer reported in 1998 that it was required to provide a higher percentage
collateral for hard currency for fertilizer imports than other importers (GMRP 1998).

50

(Sodhi 1999). All fertilizer importers use their own credit facilities to provide the local
currency equivalent to the import value before they are allocated foreign exchange.

The FDRE primarily relies upon multilateral and bilateral donors for foreign
exchange for fertilizer imports although it also annually allocates a portion of its own
foreign exchange for imports. Supporting donors in 1998 included Japan, EU, Italy,
FDRE, Netherlands, World Bank (IDA),” and Germany. From the inception of the World
Bank project to mid-1999 the government financed 31 percent of the total value of
imports, the World Bank financed about 15 percent, and other donors financed about 54
percent (Sodhi 1999). By early 1999 The World Bank total disbursements summed to
US$42 million (Sodhi 1999). The closing date for the World Bank project was scheduled
for December 31, 2000, however, and as of mid-1999 there were plans to extend this date.
When the project draws to a close, the FDRE will have to find an alternative source of

foreign exchange resources to continue importing at its current level (Sodhi 1999).

Dependency on donor fimds influences the organization of imports in several
respects. Fertilizer imported through donor funds is often conditioned: the quantity,
source of supply, type of fertilizer, and port of entry may be specified. The timing of
imports is also influenced by donors. Foreign exchange tenders are only ofl‘ered when

funds are made available, often at unspecified times of the year.

 

2’Starndard IDA credit terms include repayment of the principal in 40 years, with a grace
period of ten years and an annual service charge of 3/4 of 1 percent, plus an annual
commitment charge (to be determined annually by IDA) not to exceed ‘/2 of one percent

(Sodhi 1999).
51

 

 

organiz
niacin

fdt'ids ll'll

The low lot size of fertilizer tenders is another key characteristic of the
organization of fertilizer imports. Lot size has been constrained by two factors: port
capacity’0 and the value of foreign exchange provided by donors. The donors provide
funds independent of one another and are constrained by their own budgets. Thus tenders
are ofl‘ered based upon the amount of funds available at a particular time by one donor.
Overall, the administrative costs per metric ton imported could potentially be reduced with

larger shipments (see chapter 7 on performance).

The established importers (they must have proven access to capital) are alerted to
when a fertilizer tender is open and all companies are expected to submit a bid. The
requirement for winning a tender for fertilizer imports is the lowest price (no quota is in
efi‘ect). A bid bond worth 2 percent of the value of the minimum lot of 25,000-30,000
MT is required. In addition, after winning a tender, a company must open a Letter of
Credit with an EthiOpian bank in order to receive the hard currency equivalence of the

value of imports to pay the international supplier.

This system of foreign exchange allocation for fertilizer imports contrasts with the
fi’eedom of fertilizer import found in most other countries. In Ethiopia foreign exchange
allocation for fertilizer is tightly controlled by the government, however, all other
imported commodities in Ethiopia use foreign exchange that is purchased in established
weekly auctions. This in contrast to Kenya where fertilizer is treated no differently fi'om

other commodities. Another difference from Kenya is that in Ethiopia the FDRE does not

 

”The port capacity at Assab is limited to 30,000 tons (prior to the conflict with Eritrea all
fertilizer arrived through Assab).

52.

permit Ethiopian companies to obtain credit from foreign countries, constraining foreign
currency assess to the periodic government auctions.

Due to heavy involvement of the state and the dependence upon donors, the
process of fertilizer importation is lengthy. In 1998 the time between entering a bid for
foreign exchange and off-loading fertilizer may be 3 months. A fertilizer tender is floated
for one month in which all importers participate, it is another 2 weeks until the winner is
known, an additional 2-3 weeks to open a Letter of Credit, and an additional 1-2 months
before the fertilizer arrives. It is stated that, on average, a ship is at port for 20 days, but it
can take up to 45 days to get fertilizer off-loaded. One importer was able to get its
supplies out of the port within 3 weeks which was considered a “fast operation” (GMRP
1998). Overall, fertilizer importers are required to estimate world prices up to roughly
two months in advance. During this time, world prices may change, but also the cost of
transport in Ethiopia can vary considerably due to the poor road conditions during the
rainy season. In addition, importers carry the burden of interest on their loan (see chapter
7).

Under the FDRE’s import “liberalization” policy, the number of importers
increased fi'om one to five. The first company to enter the import business was Ethiopia
Amalgamated Ltd. (EAL), a private company. In 1995 it imported for the first time since
the beginning of the military regime in 1974. A year later in 1996, Ambassel Trading
House Private Limited entered the market. It is labeled a private company by the FDRE,
but is owned by the Amhara Regional Government (and up until 1999, sold only in the

Amhara Region). In 1998 two more companies began to import: Fertiline, a private

53

company, and the other, Guna, owned by the Tigray Regional Government and
operational only in Tigray.

Since the liberalization of fertilizer imports, AISE (the former input/output
parastatal) has remained a prominent importer. In 1998 AISE was the largest importer,
importing 50 percent of DAP imports in 1998 and 38 percent of urea imports (Table 3.1).
Ethiopia Amalgamated Ltd. (EAL) is the second largest importer of DAP with a share of
19 percent. EAL did not import any urea in 1998 due to large carry-over stocks. In 1998

Ambassel, Guna, and Fertiline gained a small share of imports at 16, 13, and 9 percent,

respectively.

Table 3.1 DAP and Urea Imports by Importer in 1998, MT

 

 

 

 

DAP Urea Total

Importer MT percent share MT percent share MT percent share
AISE 144,371 50.2 35,437 37.5 179,808 47.1
Ethiopia Amalgamated 56,000 19.4 0 0 56,000 14.6
Ltd

Ambassel Trading House 42,000 14.6 19,000 20.1 61,000 16.0
Guna 25,000 8.7 25,000 26.5 50,000 13.1
Fertiline 20,000 6.9 15,000 15.8 35,000 9.2
Total 287,371 100.0 94,437 100.0 381,808 100.0

 

Source: NFIA 1998.

With the exception of 1997, the share of imports to AISE in 1998 was declining.
In 1995 AISE imported 81 percent of fertilizer imports, 65 percent in 1996, 100 percent in
1997 and down to 47 percent in 1998 (Table 3.2). There was a large carry-over of

fertilizer supplies fi'om the 1996 season, so fertilizer imports in 1997 were unusually low.

54

 

lrbl

Ya

h litl
Earle:
Dina:
dimibl
Ethio;

min

Table 3.2 Total Quantity of Fertilizer Imported by Company, 1995-1998
Agricultural Inputs Supply Ethiopia Fertiline Ambassel Guna

 

 

Year Enterprise (AISE) Amalgamated Ltd. Trading House ‘ TM
Percent of Total for Each Year MT
1995 80.7 19.3 0 0 0 287,619
1996 64.8 27.9 0 7.2 0 338,780
1997 100.0 0 0 0 0 160,000
1998 47.1 14.6 9.2 15.9 13.0 381,808

 

Source: NFIA 1998.

3.2.2 Regulatory Environment of Fertilizer Distribution

As with imports, prior to 1992 the state—owned parastatal, AISCO controlled the fertilizer
market. In 1992 the government developed the New Marketing System (NMS) with the
primary objective of liberalizing the fertilizer market and creating a multi-channel
distribution system (Demeke 1995). By 1998, the structure of fertilizer distribution in
Ethiopia was shaped by two government programs, the National Extension Program(NEP)
and the Regular credit program. ‘

Credit. In 1997 it was estimated that roughly 80 percent of fertilizer consumed in
Ethiopia was advanced to farmers on credit by government institutions responsible for
distribution of both fertilizer and credit (Demeke et al. 1997). Therefore, how credit is
organized is critical to understanding market performance—namely, whether credit
availability meets demand, the timeliness of fertilizer delivery, and the profitability of
investing in fertilizer at the farm, wholesale, and retail levels. Private fertilizer distributors
were bound to government policy if they wanted to sell on formal credit to farmers

because many'farmers could not afford fertilizer without credit and in some areas the cash

55

 

mater W15
chapter 4),
In th
tilinllnlral r
Del'eloprnerl
Cooperative:
forthe loan a
helm, 1
Sim a loan
‘Pltmlum Or
&‘ “When
his were or
BWUS of
Wlhutim
Po
late 198 35
h 1990;,

in the lm

311nm) g(

market was actively discouraged by the government (discussed further in section 3.2.3 and
chapter 4).

In the early 19905, the FDRE held primary responsibility for allocation of
agricultural credit in a partnership between AISCO and the Agicultural Industrial
Development Bank (AIDB)."’l Credit was channeled from the banks to the 2,900 Service
Cooperatives (SCs) and member peasant associations (PAs). The SCs were responsible
for the loan and therefore, also for screening member farmers. The loan was approved by
the Wereda Agricultural Bureau and then signed between the AIDB and the SC. The SC
signed a loan agreement between the bank and the SC. The SCs then charged the farmers
a premium over the loan rate provided to the SC by the banks. However, in 1998, with
the exponential expansion of the NEP and the low repayment record of the SCs, many
SCs were no longer permitted to receive input credit for their members. By 1998, the
Bureaus of Agiculture were responsible for the administration of credit and fertilizer
distribution.

Poor loan recovery became an increasing problem in the early 19903. During the
late 1980s the recovery ratio was roughly 80 percent; however, the rate fell to 54 percent
in 1990; 37 percent in 1991; and 15 percent in 1992 (Demeke 1995). Part of the reason
for the low recovery rate was the collapse of SCs after 1991 with the overthrow of the

military government. The institutional vehicle for collecting loans had collapsed and the

 

31The Commercial Bank of Ethiopia (CBE) extended some loans in 1986 and 1987 as a
temporary measure but did not become firlly involved until 1993.

56

 

Figure 3.1 Fertilizer Credit and
Consumption, 1983-1997
Source: NFIA, 1983-1997

 

 

300,000 400,000
250000 .. r 350-000 .5
' .1» 300,000 I
i- 2°°-°°° " it 250,000 3
2 150,000 -r~ .. 200.000 3-
100'000 m. . l- 150,000 a;

.. 100,000
50,000 .. 4 50.000 g

o . - o

 

 

 

 

 

 

«965 «956 «959 991' «996

 

 

 

1:1 Fertilizer Consunption + Fertilizr Credit

 

 

 

 

government was not prepared to take its place. In the early 1990s the government
stepped in to reverse this trend and promote expanded rural input credit. In 1992 the
AIDB was absolved of its massive defaults and given a clean slate. It was renamed the
Development Bank of Ethiopia (DBE) which, together with the Commercial Bank of
Ethiopia (CBE), extended smallholder fertilizer credit. Between 1993 and 1994 the
amount of credit extended to farmers increased 10-fold (Figure 3.1). This probably
contributed to the sharp increase in fertilizer sales in 1994 but also helps explain the poor
recovery rate: farmers purchased more fertilizer on credit because they thought it was a

gifl fiom the government (Demeke 1995).

57

By 1996/97 the FDRE agreed to guarantee credit: the responsibility for credit
disbursement and collection was transferred fiom the banks to regional governments.32
The CBE and DBE extended credit as in the past, but in 1998 the government ageed to
pay the banks the full amount of the loan in case of default. The risk of the loans was
shifted to the regional governments. By the mid-19905 loan recovery improved
considerably relative to the early 19905. CBE reported a recovery rate of 92 percent in
1995/96 and 83 percent in 1996/97 (Demeke et al. 1997). DBE also reported a recovery
of 95 percent and 87 percent during the same period (Demeke et al. 1997). When the
regional governments took responsibility for credit, they took measures to enforce

repayment through the threat of fines and imprisonment.

3.2.2.1 Organization of Fertilizer Distribution in 1998

The organization of the fertilizer subsector in 1998 changed markedly in some
regions between 1997 and 1998 but key influences remained--the government controlled
credit and “partystatals” (government-favored distributors) dominated some markets.
Although there is some evidence of moving toward a more transparent, competitive
fertilizer market in some regions in 1998, other regions remained monopolistic. In
contrast, the fertilizer sector in Q regions in 1997 was characterized by a high degree of

market concentration where market shares of the six fertilizer distributors were directed by

 

32The margin of 4.5 percent, the difference between the 10.5 percent loan rate and the 6
percent savings rate was shared equally between the bank and the regional governments.
Prior to 1998, the bank received the entire margin.

58

 

errant of a “e.

illCllOtls “it
lil‘mtms
thaw“
50mm R,
MW 01hr
W'll‘Slata
“Mafia

gvl’t’nm

SCSZI g

Wilt
mhhc

imam

£7qu “'8.

the regional governments without any pretense of competition. In any given zone the
regional governments nominated one primary supplier.

In 1998, the market remained vertically integrated, importers were involved in
retailing, but the degree of competition in the market increased marginally due to the
entrant of a new distributor and the introduction of fertilizer auctions. Although fertilizer
auctions were planned in several regions, policies were reversed in some areas and
government-nominated companies were awarded the market. The responsibility of
fertilizer distribution in Tigray was given to one distributor. Similarly, in the Amhara and
Southern Regions one distributor held the responsibility to distribute fertilizer, but they
invited other distributors to enter “their” markets as their stocks declined. These
“partystatals” can supply the government credit/fertilizer programs (as do other large
wholesalers), but in addition, they have preferential access to the services of local
governments such as transport and access to warehouses owned by the government or

SCs at subsidized or no cost (Demeke 1999).

Fertilizer auctions were instrumental in improving competition, particularly in the
Oromiya Region. Participating retailers ofi‘ered competing bid prices to distribute a
specified type and quantity of fertilizer to a wereda. The auction is a departure from 1997
in which the fertilizer distributor for each wereda was assigned by the regional
governments.

Another change in 1998 was that the fertilizer subsector saw a dramatic increase in
the number of farmers participating in the National Extension Progam (NEP). Fertilizer

credit was extended through the NEP, and was available, but to a lesser extent, outside the

59

ram

325 Ilia

the reg:
lt'porte.’
compare

the ai’iere
offered in g
In 15

“353150 high
[“998 nation
“535591, Dinst
mil/113 1713}: [I

 

extension program, in what was once the SC regular program credit. In 1998 there was a
dramatic decline in the amount of credit extended to farmers outside the extension
program. In fact, the regular program credit was dissolved in many weredas as the NEP

expanded (and SCs defaulted on loans).

In 1998, the two input credit programs, the NEP and regular program credit, were
administered separately. The primary difference, in general, between the two programs
was that the NEP was administered (at least in Oromiya) by the Bureau of Agriculture and
the regular credit program was administered by the local government administration. It is
reported that, in general, the Bureaus of Agiculture were relatively transparent and fair
compared to the local administration (Demeke 1999). As will be seen in the next chapter,
the differences in administration may have an impact on the resulting efficiency and prices
offered in the two programs.

In 1998, the degree of seller concentration in retail distribution at the national level
was also highly concentrated and increasingly concentrated at the regional and zonal level.
In 1998 nationally, there were 7 integrated retailers (AISE, EAL, Fertiline, Guna,
Ambassel, Dinsho, and Guns-5 of which also imported); however, only one to three

companies may have operated in any region (Table 3.3).

60

three com;
want of
toncentrate
(Table 3 .3).
distn'hutors:
liable 3.3).
In 199
M °P¢T3tior
ill/Tiling mos:

mammal [ill

nle 3.3 Percent of Regional Fertilizer Sales by Distributor, 1997

 

 

 

Tigray Amhara Oronniya South Others‘
ftfibIItOf DAP Urea DAP Urea DAP Urea DAP Urea DAP Urea
SE 36.2 29.4 0 0 44.9 49.6 83.0 94.2 37.7 49.7
L 34.6 36.0 1.2 0.1 30.4 17.6 17.0 5.8 45.2 41.8
nbassel 0 o 98.8 99.9 o 0 0 o 17.1 8.5
tsho 0 o 0 o 24.7 32.8 0 0 0 0
na . 29.2 34.6 0 0 0 o 0 0 0 ' 0
cent 100 [100 7100 ' 100 ‘ 100 "100 7100 100"?" 100 100

 

cc: NFIA 1998.
:: 'Includes Benishangul and Gumez, Somalie, Harar, Addis Ababa, Dire Dawa, and Gambela

Primarily one company operated in Amhara; two companies in-the South; and
e companies in Oromiya and Tigay. In 1997 in Amhara, Ambassel supplied 99
:ent of both the DAP and urea markets (Table 3.3). The South was also heavily
centrated, with AISE supplying 83 percent of DAP sales and 94 percent of urea sales
ble 3.3). The regions of Tigray and Oronniya were roughly split among three
ributors: AISE, EAL, and Guna in Tigray, and AISE, EAL, and Dinsho in Oromiya

ble 3.3).

In 1997 the integrated retailers varied in the degree to which they concentrated
r operations in particularly regions. EAL and AISE had broad distribution networks--
plying most of the primary fertilizer consuming regions. Whereas Ambassel and Guna

centrated their sales in Amhara and Tigray, respectively (Table 3.4).

61

 

1'

its more
immen
mm the
1‘helm they
0min Reg
(lithutors, e;

1993.311. art

firm! legion

 

Table 3.4 Percent of Distributor Fertilizer Sales by Region, 1997

 

 

 

 

AISE EAL Ambassel Dinsho Guna
flu DAP urea DAP urea DAP urea DAP urea DAP urea
Tigay , 3.9 7.3 6.8 18.7 - - - - 100.0 100.0
Amhara - - 1.5 0.1 97.5 94.5 - - . .
Oromiya 57.0 57.5 70.1 42.6 - - 100.0 100.0 - -
South 35.0 14.2 13.0 1.8 - - . - - -
Others' 4.1 21.0 8.6 36.8 2.5 5.5 - - - -
Tutalfgrgy 100 ‘100 .100 100 I 100 ' 100 100 '100‘ *5 210077100?
— :NFIA1998. -

Note: 'Includes Benishangul and Gurnez, Somalie, Harar, Addis Ababa, Dire Dawa, and Gambela.

Tigray Region. The organization of the system of fertilizer distribution in Tigray
was more monopolistic than any of the other regions. In 1998 Guna, the regional
government afiiliated company, did not distribute fertilizer to Tigay as it had in the past.
In 1998 the Tigay Regional Government asked three importers, AISE, EAL, and Guna,
whether they should divide the region amongst themselves or issue tenders as in the
Oromiya Region. The year earlier, 1997, Tigay divided the market between these three
distributors, each supplying different weredas. When faced with an invitation to supply in
1998, EAL and Guna declined. Therefore, by default AISE was responsible for supplying
the entire region, distributing 220,000 MT by August, 1997.

Amhara Region. Prior to 1998 the responsibility of fertilizer distribution in the
Amhara Region was in the hands of several distributors, each with a roughly equal
designated share of the market. However, by 1998 Ambassel, the regional government

afiliated company, was the sole distributor in the region. In that year the Amhara

62

Reg

um

I

Were til th
for non.\l
5C5 um u
ferrite ll
Motion ca
Million 1})
In the
Mil lhe prr
inlet in mm) a
W"? ll'arch0u
MW the {err

mm the!

Legional Government dismissed the central government proposal to hold fertilizer

uctions and gave Ambassel exclusive distribution rights.

Two private companies, Ethiopia Amalgamated (EAL) and Fertiline, were told

hey were not permitted to participate in fertilizer distribution in the Amhara Region:

In principle, the distributor selection should be made in
bidding method. This was also confirmed by guidelines which
were disseminated by the regional government to every
Wereda Bureau. This was in vain. Reversely, the Regional
Government was standing in favor of Ambassel, the only
seller, rejecting other suppliers (GMRP 1998).

In Amhara, in 1998 the NEP was extensive: it was reported that most farmers
vere in the NEP (GMRP 1998). However, there were areas when the SC regular credit
'or non-NEP farmers was still available. Where the SC credit was not available, often the
le were used by the NEP to facilitate the organization of credit and distribution of
ertilizer. When the regular credit was available, the SC obtained credit from a lending
astitution called the SC Follow-up Bureau. The NEP obtained its credit fi'om a separate

nstitution, the Amhara Rural Credit Association.

In the NEP in general, whether a fertilizer auction or government-appointed
market, the program required that fertilizer be distributed through the SOS or a retail
lutlet in rural areas. However, often the appointed distributor distributed fertilizer fiom
:s own warehouses (in the wereda capital—usually far fiom the rural SC) or else it
elivered the fertilizer to the wereda Bureau of Agriculture whereby the Bureau often

:ansported the fertilizer to the farmers (without compensation from the responsible

63

got

[heapen
n

distributor). In Amhara, in particular, Ambassel was given the right by the regional
government to use SC warehouses to stock its fertilizer.

For the SC regular credit program the SC received a 2 percent per annum interest
rate, the remaining 10.5 percent was split between the banks and the regional
government(interest rates were 10.5 percent/annum in the Oromiya and Southern
Regions). The SCs take the down payment, along with a voucher from the Bureau of
Agiculture directly to the designated suppler who then takes the voucher to the bank,

receives payment in full, and then proceeds to distribute.

The open market in Amhara, as in all regions, was thin. The volume of fertilizer in

the open market was highest in areas where the NEP was not meeting farmer demand for

 

Figure 3.2 Structure of the Fertilizer Subsector
In Amhara in 1998

’ E‘E am“...
i

'[WholoaalcllntagratodRctailcr] gm

a...  _ \

l Independent

| Retailera

Open Market
(Cash salon)

/

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

has '9
prices
first tr'rr
trailer.

the aucti-
li'ge rmpo
ASE, the o.
the Oromiya
Pitts were fir
“P”? Suppll c

(Hill (33 pert

it lurker) (De

Wet; thus rer

 

inputs or where the credit programs were absent. Independent retailers reported that
government omcials often threatened farmers to not purchase fertilizer fi'om independent
retailers, perhaps to encourage farmers to participate in the NEP (GMRP 1998).

Oromiya Region. The organization of the fertilizer market in the Oromiya Region
has been in flux. Within a decade fertilizer supply shifted from a system of controlled
prices and monopoly supply to relatively increased competition in 1998. In 1998 for the
first time fertilizer auctions were held in which retailers competed on the basis of price for
a given bid. Although more competitive than a govemment-appointed market, access to
the auctions was not completely open, but controlled by the regional government. Four
large importers were invited to participate: two private comparnies, EAL and Fertiline;
AISE, the old government agricultural grain and input parastatal, and Dinsho, owned by
the Oromiya Regional government. In 1997, a year before retail and wholesale fertilizer
prices were fully “liberalized”, three companies supplied Oromiya farmers: Agicultural
Input Supply Company (AISE) (37 percent of the market), Ethiopia Amalgamated Ltd.
(EAL) (33 percent of the market), and Dinsho Private Limited Company (31 percent of
the market) (Demeke et al. 1998). These three companies had geographically separate
markets, thus removing competition at the retail level. In 1997 credit was allocated to the
designated distributor who was responsible for supplying a particular area. Therefore, if a
farrnerwanted to purchase fertilizer on credit, it was not possible for him or her to choose
their source of supply. In 1997 farmers received credit through their SC which obtained a

loan from a local bank.

65

 

Figure 3.3 Structure of the Fertilizer Subsector
1n Oromiya in 1998

'VEEGW

l

. .. 7W.
_ .ylvwmanmmmnmlw lamknportara

\

 

 

 

 

 

 

 

 

Independent
Retailers
Open Muskrat
(Cash sales)

 

 

 

 

 

 

t..- m.
/

 

 

 

Legend: Iphyalealtlowaofbrtlllzcr
I Z-mm I

 

 

 

 

In contrast to the Amhara Region, the regular SC credit program was relatively
active in the Oromiya Region. As in the Amhara Region, the SC regular credit and NEP
credit were organized by separate institutions. In 1998 the SCs received approval for
credit fi'om the regional government, but the administration of credit lay with the SC and
banks, independent from the regional government. In contrast, in the Amhara Region
farmer groups would not interact directly with the banksuthe Wereda Bureau of

Agiculture coordinated this stage of the process.

An illustration from the Oromiya Region provides a glimpse of the detailed
procedures involved in administering the regular credit/fertilizer distribution program.

Often administration of credit under the regular program was performed at difl‘erent

66

levels-

rcsporr

0553:;

levels—by the peasant associations (PA) and farmer associations (F A)-but ultimately
responsibility lay with the larger SCs.33 Members of a PA, PA, or SC, usually elected
oficials, collected last year’s loan from farmers, screened farmer fertilizer requests for the
upcoming season based upon ability to pay, and then collected the down payment for the
new loan. The FA, PA, or SC assessed the amount of down payment the farmer could
afi‘ord. A down payment of 25 percent was desired, but particularly poor farmers received
100 percent credit and many others received 90 percent credit. Once the demand estimate
was gathered, it was forwarded to the Cooperative Promotion office (CPO) in the Wereda
Bureau of Agriculture. The CPO and the Wereda Bureau of Agriculture Input
Coordination Unit (ICU)" reassess the fertilizer demand requirements. The wereda ICU
revises the fertilizer demand based upon the previous year’s repayment record and the
amount of credit allocated in the current year. The ICU then sends the demand estimates
to the zonal level. Again the demand estimates may be revised before being passed to the
regional level. It is at the regional level that the amount of credit to the region was
confirmed and divided among the weredas and also divided between the extension and
regular credit programs.

Once the quantity demanded was approved, the PA/FA/SC collected the down

payment from farmers, delivered the down payment to the bank, and received a bank order

 

”In some areas it is common that between four to six F As or Peasant Associations (PA)
make up one Service Cooperative. The PA or PA has roughly 200-300 members and is
better suited to screen farmers than the SC which can have over 1,000 members.

3‘A committee of representatives fi'om the Bureau of Agriculture, the Cooperative
Promotion Oflice (CPO), local administration, and banks.

67

from the bank. This extra responsibility of the SC is not insignificant because often a trip
to the bank requires a day of travel and involves transporting the cash down payment
which risks robbery. Additionally, once the members of the SC (three SC officers were
required) reached the bank they each started the paperwork as if for a personal loan,
which may be excessive. Once the bank order is delivered to the distributor, the ‘
distributor may begin releasing its stock. After delivery the distributor receives a delivery
order upon which presentation at the bank permitted the bank to pay the distributor in full.
For regular program credit, the FA/SC was told by the government that it must
have 100 percent repayment in order for it to receive fertilizer on credit in the subsequent
year. However, this percentage was relaxed in many cases. In effect, there was no
uniform guideline of what percent default rate was permissible. Some SCs received credit
with a 65 percent repayment rate whereas others with the same repayment rate did not.
“Unsatisfactory" repayment rates meant the SC could not obtain credit the following

season.

Southern Region. Similar to the Amhara Region, in the Southern Region one
supplier dominated the fertilizer market in 1998. Fertilizer distribution for the early, belg
crop was carried out by one distributor, Wendo, owned by the Southern Regional
Government, but AISE was invited to distribute for the later, meher crop. When Wendo’s
supplies began to dwindle toward the end of July, after most of the fertilizer had been
distributed for the meher season, the Southern Region issued tenders. AISE and Wendo
were the only bidders and AISE won 80 percent of the tenders. There was speculation

that AISE was able to bid because it had stock in the region, ready for distribution, but

68

EAL i
expired
fcr’dlize

WEN US

author
prohibit

Souther

EAL, who wanted to participate, was not permitted because their stock in the region had

expired and thus was no longer suitable for use (GMRP 1998).

In the South the Service Cooperatives were removed from the role of distributing
fertilizer credit to farmers and remained active in the sector only because their warehouses

were used by the government-appointed distributors for the NEP (often uncompensated).

The cash market in the South was very thin. There were reports that the
warehouses of some independent retailers were locked by government oficials in order to
prohibit cash sales (GMRP 1998). Independent retailers in other regions called the
Southern open market a “black market” (GMRP 1998). Many NEP participants in the
South sold unwanted NEP fertilizer on the open market. Independent retailers trucked

fertilizer out of the region, primarily to the East Shewa Zone in Oromiya Region where

 

Fig-34 MattheFertllheISubeeeterhtDeSeItIhlm

. 7 '@ Sirnportetu
7 iv

_ 1 Whom.
Bunk.

5 41 —"-"|\Mtoteuientttegmtedneuner ”'35":
..... / e l \ 7""
Mai \lme W

\ 1 // mac:
7

 

 

 

 

 

 

 

 

 

,i

 

[ Farmers

 

 

 

 

Legend: 3 Motion“!
[ -8mflm er

 

 

 

69

33

policies"
bam'cr t
trims
entry to
abSOluIe
bchaVior'
demise
fitted
”Met.

JIcre is a

beam.

there was an unmet demand for fertilizer (GMRP 1998). These open market cash sales

were reported to undercut the sales of independent retailers in Oromiya (GMRP 1998).

3.3 Barriers to Entry

In Ethiopia real and perceived barriers (whether or not a manifest of government
policies) to entry into the fertilizer market will contribute to the structure of a market. A
barrier to entry provides incumbent firms in an industry an advantage over potential
entrants. Four key barriers to entry are discussed here and shown how they may afi'ect
entry to the import and distribution market in Ethiopia. These entry barriers include an
absolute-cost advantage, economies of scale, capital cost requirement, and strategic
behavior barriersuall of which are influenced to some extent by government regulation
described in the last section. Strategic behavior by incumbent firms (often govemment-
afiiliated firms) is a barrier to entry when it deters potential newcomers from entering the
market. As will be seen, evidence in chapter 7 on input market performance reveals that
there is a relationship between reduced barriers to entry, improved competition, and retail

prices in the sector.

A capital cost barrier refers to the size of investment required for efficient entry.
Fertilizer import tenders are issued in lot sizes ranging anywhere from 15,000-25,000 MT.
Importers, therefore, must have access to a minimum amount of capital. Upon entering a
tender an importer was required to provide a 2 percent bid bond and then, after the
importer wins a bid, he or she is required to provide the full Ethiopian Birr value of the
shipment (on credit) in exchange for US dollars.

7O

bid for i
than \‘C’d
cost disa
mambe
entrant.

transacti
Credit hi:

iI“porter

mailers
0381' a Cl
Same inn

mag-m

mmmu
Lie mjm'n

egg“

If a potential new entrant does not already have the capital available to submit a
bid for import, he or she will face a search cost to find credit and may face higher rates
than veteran borrowers. Thus, new entrants at the import stage may also face an absolute-
cost disadvantage. An absolute-cost advantage barrier occurs when the unit cost of the
incumbent firm for all levels of output is lower than that which can be achieved by a new
entrant. No matter the volume of imports the incumbent firm will still face the fixed
transaction cost of finding capital. The importers operating in Ethiopia in 1998 all had a
credit history with the Ethiopian banks (because the companies were well established
importers and obtaining foreign credit is illegal)”

Other evidence of an absolute-cost advantage is that, even if the independent
retailers were permitted to participate in the fertilizer auctions, they may not be able to
offer a competitive bid because the independent retailers purchased their fertilizer from the
same integrated retailers that they would have to compete against in a bid. The integrated

retailers have an absolute-cost advantage, they have access to lower cost inputs.

Economies of scale can be an important factor contributing to barriers to entry
both at the import and distributions stages of the fertilizer subsector. Scale barriers exist if
the minimum efficient scale of a plant or firm is large relative to the industry size and thus
smaller sized firms (lower production capacity) are subject to significantly higher costs.

Economies of scale may be a factor contributing to increased concentration. That is, the

 

3’Fertiline, who was a new importer in 1998, is a subsidiary of the Noble House and
Trustworthy Private House Ltd. who has been importing consumer goods since 1991.
Ethiopia Amalgamated Ltd. used to import fertilizer under the Imperial Government, but
has imported a range of agricultural inputs in the interim.

71

lT‘rEL

3V6

the 1

mart

aces.

map

OLIpr
fat a:
Park:

tech r

market is only large enough to support a few suppliers, each operating at their minimum

average cost.

In Ethiopia, in 1998 several large firms were able to command a large portion of
the market, not because they represented a natural monopoly in their regions and the
market had determined the efficient (each operating at minimum average cost (AC))
number of firms, but rather, the government had determined which companies had market
access. Thus, the number of firms was a function of government policy, not a function of

the presence of economies of scale.

Figure 3.5 shows that average unit costs (AC) can be reduced with expanded
output. Contrary to the typical U-shaped cost curves, under economies of scale costs first
fall and then are constant over a large range of outputs. If several large firms exist in the
market (as is the case in Ethiopia) and account for a large portion of the total market they
each may be able to realize economics of scale and charge P2 (where D2 intersects the
average cost (AC) curve). However, when a new firm enters the market, it will face a
smaller market at D, and therefore, its output will be constrained and it will charge a
higher, uncompetitive price, P1. Thus, the structure of costs relative to market demand
can serve as a barrier to entry. If a few large firms existing in a market can fulfill
Ethiopia’s fertilizer market at a lower cost than if there were a greater number of firms,
each with a lower volume, then the incumbents will have a cost advantage, consumers will
:‘ace lower prices, and the market will have a high level of concentration. At the
listribution stage of the subsector economies of scale may be realized due to the high

ixed costs of trucks, storage and market search costs.

72

Strait

Figure 3.5 Economies of Scale and Market Demand

 

 

 

The presence of economies of scale can also explain why the level of demand is
critical to providing low-cost fertilizer. For example, if demand was at Dl then that
quantity demanded would be insufficient to produce at a firm’s average minimum unit cost
(where D2 intersects the AC curve). However, if demand shifis out with an increase in
population (or due to the expanding NEP), for example, to D2 then the price could fall to

P2 because the unit costs of the firm declined as output increased (Figure 3.5).

In Ethiopia the government also practiced strategic behavior in order to influence
the market structure according to its interests. A firm’s (or govemment’s) strategic
behavior can create a barrier to entry that deters new entrants at the retail level. Fertilizer
distributors that are tightly connected to regional governments, such as Ambassel in the
Amhara Region, can use aggressive tactics to deter new entrants into the business of

fertilizer distribution, but also deter new competitors in a geographic area.

In 1998, the national and regional governments were very successfial in their

strategic behavior to influence targeted distributors and to shape markets according to

73

 

their in
in the r
the regi
AISE tl
fertilize
and flna
AISE a
safegua

Supplies

Mich ti

their interests. For example, AISE was invited by the Southern zones to position supplies
in the region (GMRP 1998). AISE acted fast, hoping to distribute the stock it imported to
the region. However, when the fertilizer season arrived the regional government told
AISE that there was a change in policy and Wondo was given the responsibility for
fertilizer distribution. AISE waited roughly 8 months for permission to distribute its stock
and finally did in August 1998, after most farmers had planted their crops. The cost to
AISE was tremendous. The regional government may have used AISE’s stock as a
safeguard against the possibility that Wondo—its favored company-had insufficient
supplies.

In 1998 in many areas the regional governments actively discouraged cash sales
which threatened businesses of independent retailers who sold solely in the open market
and were not permitted to participate in the government input credit programs. A factor
that may have motivated the regional government’s decision to restrict market
participation of the independent retailers (those that are engaged only in cash sales at the
retail level) may have been a fear that the “small” traders did not have the capacity to
deliver the contracted amount in the credit programs, and therefore would have a higher
probability of default than the larger companies. However, obtaining the necessary
quantity of fertilizer to fill a bid was not a problem for some traders. The independent
retailers surveyed across five zones in Oromiya sold from 403,000 quintals of DAP and
fiom 20-205 quintals of urea, more than an average tender in the credit programs (GMRP

1998).

74

 

3.4

Vera-ca
OWDCI l

itrtica’

Etmopi

retail er:

3.4 Vertical Integration

Vertical integration may be a manifest of government input and credit policies.
Vertical integration is an organizational market structure in which a firm remains the
owner of a product over multiple stages of the subsector. The higher the degree of
vertical control, the more degrees of freedom the firm has with respect to conduct (Staatz
1996). As a consequence of the national and regional government policies, in 1998 the
Ethiopian fertilizer subsector was highly vertically integrated with all of the fertilizer

retailers also acting as importers with the exception of Wondo and Dinsho.

The presence of vertical integration can reduce retail prices through cost-savings
from reduced transaction costs and improved coordination at different stages of
production. However, there also may be drawbacks to vertical integration. Vertical
integration can remove the potential efficiency gain from specialization and gains fi'om
trade. One importer in Addis noted in 1998 that he entered the business of fertilizer
importing with the intent to sell his stock at the port. To his surprise he was unable to sell
his stock at the port and was instead strongly encouraged by the national government to
enter fertilizer distribution at both the wholesale and retail levels. The unexpected plan to
distribute may have meant the importer lost money in the market if he or she did not cover
the start up costs involved in searching for a market, transportation, and storage, and also
remain competitive.

The presence of integrated firms in a market may lead to reduced market
competition. Independent retailers can not compete at the same level with the integrated

fimIS because they purchase their inputs fi'om the integrated firms. Thus the small retailers

75

 

85‘ sq:
market
farmers

COHCCIII

urinate

get squeezed out of the market and the integrated firms gain a greater portion of the
market. A few large integrated firms in the market can mean lower-priced fertilizer to
farmers (through reduced transaction costs); however, due to their increased market
concentration, the m of these firms to raise prices above marginal cost may lead

ultimately to higher prices for farmers.

3.5 Conclusion

This chapter demonstrated that the structure of the fertilizer subsector in Ethiopia
in 1998 was characterized by a high degree of regional and national government
intervention at every stage of the market, from import to retailing, as well as in both credit
and open market cash sales. Government controlled which distributors could sell on credit
and credit sales dominated the subsector. Thus how credit was organized shaped the
profile of distributors--the number of distributors and their market share. Distributor
access to credit for retail sales was tied to distribution through the NEP and regular credit
programs. The small independent retailers that sold only in the open market did not have
access to government-guaranteed credit.

The organization of credit influenced property rights, namely, which retailers had
access to which markets. Thus, the organization of credit served as a barrier to entry to
the market, if the trader was an independent retailer. However, larger, private integrated
retailers (e. g., EAL, Fertiline) were also squeezed out of some credit markets by the

govemment-favored “partystatals.”

76

 

The
pricing beta
extent to wt

pcn’onnance

The following chapter on market conduct will describe the chosen product and
pricing behavior of firms in this environment. This research will then proceed to show the
extent to which market structure influences conduct which, in turn, affects market

performance.

77

 

If
adaptingi
“Consists.
mils in [if
midlife:

In 1
boundaries
claims. In
Minded q
manugCIUn
memo” is 1

commits.

CHAPTER 4

CONDUCT

Market conduct refers to the patterns of behavior that enterprises follow in
adapting to the markets in which they sell (Bain 1968). More specifically, market conduct
“consists of a firm’s policies toward its product market and toward the moves made by its
rivals in that market” (Caves 1982:48). A finn’s conduct is a reaction to the market

structure and also a pro-active attempt to change market structure.

In theory, a benevolent market-oriented government would define the legal
boundaries of market conduct in cases of price fixing, product labeling, and advertising
claims. In this case, the government’s primary objective is to support an environment that
provided consumers with a low-cost, quality product, as well as provided input suppliers,
manufacturers, and distributors with an “even” playing field. In Ethiopia, an important
question is to what extent does the government protect and promote selected favored
companies.

This chapter examines to what extent government’s policy affects market conduct,
as well as market structure. In the extreme, the govemment-controlled access to credit
restricted access to regional retail markets. This chapter will outline the product and

pricing strategies practiced by firms in the fertilizer subsector and how the government

78

influence
conduct 1
to deterr.

to the im

Strategy
they are

markets

influenced these practices. The following chapter will use the observations of market
conduct presented in this chapter to estimate an econometric hedonic price model in order
to determine the determinants of spatial fertilizer price variation, with particular attention

to the impact of different pricing mechanisms and distribution channels.

Two key issues of conduct are examined here: pricing behavior and product
strategy (Caves 1982). Practices of coercion and entry deterrence are also examinedufor
they are the means to defining a finn’s product and pricing options and ultimately its

market share.

4.1 Pricing Behavior

Until 1998 fertilizer wholesalers in Ethiopia were more or less passive when it
came to marketing strategies. They had little option but to accept the government’s
selling price and in addition, their market share was also often defined by the government.
In 1998 for the first time, with the liberalization of prices, fertilizer distributors found that
they had a range of potential marketing options. However, in 1998 this freedom in market
_ conduct was unexpectedly constrained by government policies that either forced firms out
of the market entirely or thwarted plans to secure a market. Hence, decontrol of fertilizer
prices coincided with greatly increased government control over the conduct of private

firms in the fertilizer market.

79

513

to d:
no,
Com
not, C

a dist

4.1.1 Pricing Behavior—Background

In 1998 for the first time in Ethiopia’s history the government was not involved in
setting fertilizer prices. During the Imperial Government and throughout the military
regime of the Derg, the Ethiopian Government set pan-territorial and generally subsidized
fertilizer prices (Table 4.1). The objective of the fertilizer policy was to ensure low,
stable, uniform prices. Prices were calculated by government, based upon a c.i.f. value on

the international market, handling charges, and internal transport costs.

Table 4.1 Government Fertilizer Retail Prices, Birr/Quintal

 

 

Year DAP Urea
1982-91 average 85.22 68.52
1992 107.10 95.30
1993 (subsidized price) 149.80 132.80
1994 (subsidized price) 143.35 131.15
1995 178.00 168.00
1996 200.00 190.00
1997 govt. set prices for 150 wholesale/retail markets
1998 decontrolled decontrolled

 

Source: NFIA 1998.

In 1991 after the overthrow of the military regime, the FDRE made a declaration
to decontrol the input market. However, liberalization in effect was slow to emerge.
From 1992 to 1996, retail and wholesale prices remained subsidized and pan-territorial.
Competition increased at the retail level but the distribution margin set by the FDRE did
not cover more than 20 kilometers from primary marketing centers and therefore served as

a disincentive for private traders to serve the more remote areas.

80

RSI?

Ci'Opi

or dis}

In February 1997 retail prices were liberalized, but wholesale prices remained
administered. Wholesale prices were calculated by the government for 150 wholesale
centers in the regions of Tigray, Amhara, Oromiya, and SNNPR based upon a weighted
average of carryover stock and new imports in 1997. As a result of decontrol, fertilizer
retail prices increased by more than 25 percent in 1997 due to the removal of implicit
subsidies (Demeke et al. 1998). Consequently, it is reported that in 1997 fertilizer use fell
by 20 percent due to the removal of the implicit subsidy as well as due to credit
restrictions on defaulting farmers (F AO/GIEWS 1998). It was not until the 1998/99

cropping season that both retail and wholesale prices were liberalized.

4.1.2 Price Determination in 1998

Following the liberalization of retail and wholesale prices in early 1998, a variety
of distinctly different fertilizer market pricing mechanisms emerged in the three regions of
Amhara, Oromiya, and Southern-varying from monopoly markets and fertilizer auctions
for credit sales, to open market arrangements through cash sales. Although the
government did not directly intervene to set prices, most areas of the country were still
heavily influenced by government regulation. Most fertilizer was sold on credit and
channeled through institutions heavily influenced by the FDRE, namely the NEP and

regular credit programs (through Service Cooperatives).

In many areas of the country the government nominated a fertilizer supplier and

privately negotiated a retail price for the NEP and regular credit programs for a known

81

 
 

quaint

rights

was .
COW:

ma'i

978d

quantity of demand. In other areas, 3-5 wholesalers placed bids in a fertilizer auction for

rights to supply a known quantity of demand in the NEP and regular credit programs.

Overall, prices were considered to be competitively determined in the residual
open (i.e., cash) market, but in some areas of the country the cash market was actively
suppressed by regional government influences. Both the large wholesalers and the
independent retailers competed in the open market. Therefore, if the independent retailer
was purchasing his/her supplies from the wholesaler then he/she may not have been able to
compete in the retail market if the wholesaler decided to sell directly to farmers in the cash

market as well.

Due to the difi‘erent pricing mechanisms present in the country, retail prices were
predicted to diverge by varying degrees from the competitive equilibrium. Economic
theory indicates that a monopolist will set output at Q, (where marginal cost=marginal
revenue) and price at P, (the demand for Q,), and under perfect competition, prices will be
lower, at P, (where marginal cost=demand at the firrn’s minimum average cost in the long-
run) and output will be higher, at Q, (Figure 4.1). Under oligopoly, prices are
indeterminate, but it is known they will either equal P, (under the Bertrand equilibrium) or

lie between P, and P,.

82

Figure 4.1 Pricing Behavior Options Under Different Market Structures

 

QMR?
Qt Q2

 

 

The fertilizer hedonic price model presented in the following chapter will
determine the magnitude of the price differences in the alternative price setting
arrangements employed by regional governments. Understanding how alternative
arrangements afi‘ect the price level, other factors constant, can shed light on how to
develop a lower-cost fertilizer subsector.

The price setting mechanism in Amhara was primarily through private negotiation
between a govemment-nominated supplier (Ambassel) and the Amhara Regional
Government (GMRP 1998). Unlike in the Oromiya Region, there were no fertilizer
auctions held in Amhara. Ambassel was designated by the regional government as the
primary supplier for NEP and regular credit sales. AISE, EAL, and Fertiline also
distributed in Amhara, but were instructed to distribute only after Ambassel ran low of
stocks (particularly urea). All credit prices by these distributors were settled between the

integrated retailer and the regional government.

83

in i!
94F?
8’53
to h:
govt
tar?)

Wne

not;

Mai.

Three distinctly difl‘erent pricing mechanisms were present in the Oromiya Region
in 1998: fertilizer auctions, private negotiations between the regional government and
suppliers, and the competitive open market. Of the three regions, Oromiya had the
greatest proportion of fertilizer auctions. However, in many weredas the announcement
to hold an auction was retracted and Dinsho, owned by the regional government, was the
government-nominated supplier. In other weredas Dinsho distributed fertilizer for the
early maize crop, but fertilizer auctions were held for the later tef and wheat crops.
Whereas the largewholesalers were invited to submit bids for the auctions, the
independent retailers were not permitted to participate because they were told they were
not permitted to sell on credit. It is speculated that the government did not trust that these

retailers would be able to uphold their contract (GMRP 1998).

A characteristic of the pricing behavior in many govemment-designated markets
was that at the time that the down payment was collected from farmers (especially for the
earlier maize aw in Oromiya), the fertilizer price was not known. The DAs (extension
agents) or SC omcials estimated the down payment based upon last year’s fertilizer price.
They reported that the delay in announcing the price was because the price was still being

negotiated between the government and Dinsho, the nominated supplier (GMRP 1998).

In the Southern Region, most fertilizer prices were set in a private negotiation
between Wondo—the designated regional supplier—and the regional government. Toward
late July, when Wondo’s stocks were low, a nominal fertilizer auction was held in which
AISE was the only bidder. However, AISE’s bid offer was not accepted. The regional

government told AISE that it had to lower its price to an average of the earlier, belg price

84

itwr

pm

1ch

pried
inde;

inc:

and the previous year’s meher price if it was going to sell in the region. AISE conceded,
it was too late in the season to redirect its stock to another market.

In sum, this section demonstrated that the regional governments were instrumental
in determining the pricing arrangements and even predeterrnining the results of the pricing
process. The government determined who could submit bids for the particular regional
auctions, in efi‘ect the government partitioned the national fertilizer market into “regional
markets.” Now that the principle pricing mechanisms have been introduced by region, the
pricing mechanism characteristic of specific actors-vertically integrated fimts,
independent retailers (who sell only in the open market and purchase supplies fi'om the

integrated firms), and service cooperative-will be studied.

4.1.3 Pricing Behavior by Vertically Integrated Retailers

The majority of fertilizer retailers in Ethiopia are vertically integrated backward
into wholesaling and/or importing. AISE, Ambassel, EAL, Fertiline, and Guna retail
fertilizer to farmers that they themselves import. Dinsho and Wondo, “government”
retailers in the Oromiya and Southern Regions, respectively, purchase their stock fi'om
these importers/wholesalers. Without comprehensive cost data it is difficult to definitively
define the pricing strategies of the fertilizer retailers. However, an understanding of the
structure of the market combined with anecdotal evidence fiom the fertilizer subsector

survey provided evidence to support speculations.

85

19

Su:
ma
We
gm
“'81

”pt

A3,,

One key characteristic of oligopoly pricing is conjectural variation: the recognition
by members of an oligopoly that the outcomes of their strategies are critically dependent
on how their rivals react to their actions (i.e., perceived mutual interdependence) (Staatz
1996). However, what was distinctly different about the fertilizer sector in Ethiopia in
1998 from most oligopolies was that one of the competing players was the government
and its actions were not necessarily constrained by speculation to how other firms would
react. This section examines whether fertilizer distributors in Ethiopia followed specific
strategic options in determining prices in 1998 and whether such options were constrained

by government moves in the market.

Monopoly Rents? Provided a firm faces the entire market demand curve and it is
successfitl in mitigating the threat of entry, a firm may be able to gain monopoly rents in a
market. This was the case in Amhara, SNNPR, and in some areas of Oromiya: prices
were determined privately between the government-nominated supplier and the
government. In Amhara, the FDRE told all distributors, aside fiom Ambassel, that they
were not permitted to distribute to farmers in the region. One fertilizer importer/retailer
reported, “Ambassel gave us a hot warning not to enter the market” (GMRP 1998). Due
to the absence of competitors, the only potential constraint to achieving monopoly rents in

Amhara by Ambassel was a check by the regional government:

Of the 4 consecutive months in which the SCs were active in
distributing fertilizer to farmers, the first 2 months were the
time that no supplier except Ambassel participated in the
wereda. It was also the time to sell in which the farmers
seriously needed fertilizer for their maize plots. Ambassel
therefore, has got an opportunity to sell with whatever the
price it sets by itself (GMRP 1998).

86

10

Ta

Elf: 9

PW:
L0 01
80W:

mini:

However, Ambassel’s shortage of stocks eroded its ability to prevent new entrants
into the credit and open market. AISE and EAL were able to enter the market once it
became clear that Ambassel had insufficient stock, particularly urea. AISE entered the
market with prices roughly 3-5 Birr/quintal less than Ambassel’s prices (Table 4.2).
Ambassel declared that AISE would have to set its sale prices the same as Ambassel, but

to little avail—Ambassel dropped its credit price by 2-6 Bin/quintal.

Table 4.2 Sample Prices Between Ambassel and the AISE, Birr/Quintal, June-

 

 

July 1998
Ambgsgl, Before AISE’s Eng AISE n En
Credit Cash Credit Cash
DAP 245.0-249.5 249.5-253.0 2430-2450 2430-2450
Urea 206.0-206.5 na 1980-203.0 1980-203.0

 

Source: GMRP, Input Subsector Survey 1998.

Observations about the way Ambassel operated in the region contradicted how
private firms operate in a developed market: how could a supposedly private firm dictate
to other market participants what price to set? In a market economy the role of
government is to encourage private sector investment by reducing transaction costs,

minimizing risk, and reducing uncertainty-mot play favoritism.

Once AISE and EAL entered the market in Amhara, Ambassel practiced coercion
and entry deterrence to maintain its market share. The objective of exclusionary
(coercive) strategies is to gain advantage over, weaken, control, or eliminate competitors
(Staatz 1996). For example, Ambassel attacked rival product quality by announcing that

farmers should not purchase supplies from AISE because AISE’s fertilizer had expired.

87

W!

1h:

an

W!

In

31E
5.1 s
”11

TL“.

AISE denounced the claims, reporting that “distorted information was provided to the
farmers by Ambassel about AISE to win the market” (GMRP 1998). Ambassel’s tactics

were reported to be “blaming and undermining” (GMRP 1998).

Coercive tactics in the Southern Region were also not uncommon in both the open
and credit markets. EAL wanted to participate in the fertilizer auction for credit sales, but
the government did not accept its offer on the grounds that EAL did not currently have
any stock in the region, whereas AISE did. Perhaps the regional government was
concerned that EAL would not be able to deliver promptly. EAL felt this was an
unfounded excuse provided by the regional government to give the market to AISE

(GMRP 1998).

The regional government in the South also played a heavy hand in discouraging
open market sales. It was reported that the stores of the independent retail distributors
were locked by the regional authorities to prevent them from distributing (GMRP 1998).
In the southern zone of Kembata one independent retailer in the open market complained
bitterly that market entry was more difficult now than in previous years because the
government was prohibiting retailers from operating in order to encourage NEP
expansion. This retailer explained that his store was closed by Wondo who had “political
backing” because he offered a lower cash price than the credit price. He was informed
that he could operate as long as his price equaled that of Wondo, the government-favored
distributor. Independent retailers in other Southern zones stated that farmers were
intimidated by local government officials so they only bought on credit fiom Wondo’s

retail outlets.

88

1h

Gt

rt.

9cm

Cross Subsidization Another strategy to undercut competitor’s prices is cross
subsidization. Cross subsidization occurs when a firm subsidizes one product with
receipts fi'om another to gain a competitive advantage or to establish a market niche
(Staatz 1996). This may occur across geographic regions or across different business
activities within one firm.

Overall, it is difficult to decipher if this is occurring in the Ethiopian fertilizer
subsector because many of the costs (e.g., storage and transport) are joint costs across
more than one activity. Fertilizer trade is not the sole business activity of most large scale
and independent retailers; therefore, they have the ability to practice cross subsidization.
For example, EAL and Fertiline are companies in which the fertilizer business is only one
business among many. EAL has been involved in a variety of businesses since the late
1970s-including sales of other agricultural inputs and machinery. Fertiline is an ofi-shoot
of The Nobel and Trustworthy House Private Ltd. Enterprise which imported a wide
range of consumables (e.g., Gillette products and pharmaceuticals). Both companies had
the financial means to subsidize one product with receipts from another product to gain a
competitive advantage or establish a market niche. AISE did not have this advantage.
Government oflicials sit on the board of directors at AISE and it is prohibited by law from
selling below cost (GMRP 1998). Overall, the am of the large wholesalers to practice
this option in pricing fertilizer can create a potential barrier to entry to competition.

Dumping. Dumping may occur when a firm recognizes its carry—over stock is a
sunk cost and therefore its primary objective is to liquidate its stock which can mean

selling below cost. The 1998 fertilizer market has been referred to as a ‘slaughter’ due to

89

 

in Ar
relati

iOSC

ferti]

nor .
pnc
enti
rais‘
imp
c0n

int

“/

,3»

3:13

oversupply and the consequential decision by many firms to dramatically cut prices and

sell below cost. To some extent EAL was successful in liquidating its cany-over stocks it
had in the Amhara Region since 1995. EAL reports having lost at least 10-15 Birr/quintal
in Amhara in 1998 (GMRP 1998). Thus the price of EAL open market sales was very low
relative to AISE, a government body, who, as mentioned above, was by law not permitted
to sell below cost. Coordination of demand and supply was a common problem in the
fertilizer subsector and thus the perpetual risk of large carry-over stocks being liquidated

can create a disincentive for other potential entrants at the retail level.

Collusion. Ultimately firms prefer cooperation to competition. Iftwo firms do
not collude, a Bertrand equilibrium" will result whereby each firm sets the competitive
price as the consequence of the fear that the rival will undercut a firm’s price and steal the
‘ entire market.” However, if firms successfully collude then each can capture rents by
raising their prices above the competitive price. Prior to 1998 the large
importers/wholesalers were able to split the market fairly successfully so there was little
competition at the retail level. It is theorized that the integrated retailers that participated
in the auctions colluded in setting their offer bid prices such that they could divide the
market among themselves. The price model presented in the next chapter tests the
hypothesis that the bid prices where auctions were held were lower than prices in markets

where the regional governments awarded the market to a particular firm. In addition, due

 

3‘Also called a Nash equilibrium.

3"The equilibrium condition can potentially hold when two firms have the same cost
structure and their products are undifi‘erentiated.

90

 

10".

mo}

in;
cre
qui
mc
in
the

bu:

the

To

to collusion the bid prices in auction markets may not have been as low as prices in the

more competitive cash market offered by independent retailers.

4.1.4 Pricing Behavior by Independent Retailers

Independent retailers are defined as traders who purchase their supplies from the
larger, integrated retailers and are not permitted to participate in the government
credit/mput programs. In 1998, independent retailers traded between roughly ZOO-3,000
quintals in the meher season (GMRP 1998). Often the independent retailers that traded
more than 3,000 quintals were not truly independent but were sales agents for the
integrated retailers. Independent retailers were engaged in fertilizer trade primarily during
the four or five months of a year in which farmer demand is at its peak. The primary
business for these traders may be merchandise trade, transport, hotel, or grain or oil
milling.

Independent retailers primarily set prices through a private treaty with a buyer but
they may be price takers depending upon the proximity and volume of her/his competitors.
To the extent to which the integrated retailers were also in the market (from whom the
smaller retailers purchased there stock), profits margins of the small retailers were eroded
between 1997 and 1998 (GMRP 1998). The integrated retailers often set their cash
(retail) price equal their wholesale credit price. Survey evidence suggests that profits of
the independent retailers were higher in weredas where the credit program was deficient in
meeting the needs of farmers and where the larger wholesalers were absent fi'om the cash

market (GMRP 1998).

91

 

SCs'
{mm
and i

price

and ,
’esui.
Paint

Sc, ,

4.1.5 Pricing Behavior by Service Cooperatives (SCs)

Prior to 1998, much of the fertilizer supplied in Ethiopia was channeled through
SCs via the credit programs. Not only did the SCs provide the necessary structured
framework for organizing fertilizer distribution but they may have had their own agenda
and in many cases charged for services provided farmers by adding a margin to the credit

price set by wholesalers.

In 1998 there was a large degree of variation in the extent to which SCs were
engaged in fertilizer distribution activities. Under the Derg, SCs actively traded in
agricultural inputs and consumables; however, during the war in which the military regime
was overthrown, many of the fertilizer sales outlets, the SCs, were destroyed. The
number of SCs fell from 2,900 in 1989/90 to 550 in 1991/92 (IFDC 1993 from Demeke
1995). Rebuilding the SCs has been slow. For SCs to operate, the FDRE requires strict
guidelines for formal registration. By 1998 only a fraction of the previously existing SCs
had restructured. The primary activities among those that were active in 1998 were
buying and selling grain, grain milling, provision of animal drugs, sale of farm tools, and
sale of a variety of consumables (e.g., sugar, salt, soap, blankets, nails).

In 1998 in the Oromiya and Amhara Regions, many SCs had rebuilt themselves
and were once again active participants in the fertilizer sector. For credit sales under the
regular program, SC personnel often assisted in gathering the demand estimates and down
payment from their members. However, contrary to 1997, in 1998 the Southern Region
SCs were no longer engaged in fertilizer distribution due, in part, to inadequate repayment
rates of fertilizer loans by the SCs, but also due to the rapid expansion of the NEP and the

92

subst

facili

den

was

the

substitution of NEP credit for SC regular credit. Prior to 1998 the SCs in the Southern
Region negotiated a margin with the suppliers. The Ministry of Agriculture used the SC

facilities but not SC personnel to distribute NEP supplies.

The extent to which SCs were active in open market sales depended upon the
demand for open market sales in the wereda (the demand rose in weredas in which credit
was restricted) and the degree of organization of the SCs. SCs often reported that they
preferred credit sales because they were able to obtain a higher margin than with cash.
With cash sales, SCs were competing against the large distributors fi'om which they
purchased their supplies (similar to the independent retailers). However, a key
difi'erentiating feature was that SCs would distribute fertilizer from the SC, often closer to

the farmers than the sales outlets of the integrated retailers located in market towns.

On average the margins charged by SCs for credit and cash sales in Amhara were
5.2 Bin/quintal and 4.4 Bin/quintal in Oromiya (Table 4.3). In general, SCs in the South
were not engaged in fertilizer distribution. Among those that were, the average margin

was lower than in either of the other two regions, 1.2 Birr/quintal.

Table 4.3 Distribution of Open Market and Credit Service Cooperative Margins
Across Regions, Birr/Quintal

 

 

Region Minimum Maximum Average n
Amhara 0' 12 5.2 28
Oromiya 0 10 4.4 ‘70
South2 0 7 1.2 6

 

Source: GMRP, Input Subsector Survey 1998.
Note: 'Some SCs did not charge a margin.
’I'herewerenoSCeashsalesreportedinthesurveyintheSouth.

93

 

no
the
orc'

su;

pm:

011]}

fannt
fine

to, .

In 1998, as a consequence of the new policy of restricted access to fertilizer credit
only through the NEP in many areas, the demand for open market sales rose. On
occasion, a SC would act independently of the credit program to secure open market
supplies for its farmers when there was a shortage. In Amhara, for example, Ambassel did
not have sufficient supplies to fiilfill one SC’s order for open market supplies, and
therefore the SC went to AISE to purchase more supplies. However, a SC usually
ordered fertilizer for cash sales along with government credit sales, and received the

supplies when the nominated supplier delivered the supplies on credit,

Efi‘orts by the SCs to engage in open market sales were often thwarted by
government-influenced policies. As an example, in Amhara, SCs were discouraged from
purchasing fertilizer for open market sales because Ambassel told the SCs that they could
only raise their fertilizer price by 1 Bin/quintal above the wholesale price (GMRP 1998).

At that margin the SC decided to refrain from entering the open market.

4.2 Product Strategy

Aside from pricing policies firms can also influence their market share by providing
farmers with other attributes (Lancaster 1971, 1998) of fertilizer that may be demanded by
farmers such as timely delivery, white colored DAP, and accessible stock (both in distance

fiom the farm and in termsof timely sales hours).

94

4.2.1 Product Strategy by Vertically Integrated Retailers

Distribution Network. The large “retailers” were primarily wholesalers that sold in
retail outlets in major towns along the major roads leading out of Addis. They generally
provided fertilizer to SCs for their credit and cash programs but would not necessarily
deliver the fertilizer to the SCs. Either the Wereda Bureau of Agriculture transported
fertilizer to the SCs in case of the NEP; the SC officials collected it; or farmers were
required to travel to the wholesaler’s marketing center to collect it. This presented a
problem. In South Welo, Amhara, “farmers couldn’t afford to rent a mule or camel to
collect the fertilizer in the town” (GMRP 1998). An additional drawback, fi'om the
farmer’s point of view, was that often the retail outlet of the large retailers was only open

certain days due to a shortage of staff.

Ifa company was awarded a government contract then their market was defined
and much of the risk associated with marketing was removed. The presence of the
government credit programs can reduce the incentive for the large retailers to seek out
new markets. Ministry of Agriculture officials reported that the large companies “didn’t

deliver to remote areas even if accessible by truc ” (GMRP 1998).

Each of the large importers/wholesalers had its own network of retail dealers. In
the early 19905 the number of licensed retail and wholesale agents of these companies
increased. This move mistakenly was interpreted as a sign that the fertilizer market was
opening to private competition (KUAWAB 1995, World Bank 1995). AISE covered the
largest geographic area with distributors, wholesalers and retailers in almost all parts of

the country. In 1996 Ambassel was AISE’s primary distributor, but in addition, AISE had

95

1C

103 wholesalers, 901 retailers, and it served 860 service cooperatives (Demeke et al.
1998). However, in 1998 the number of AISE retail marketing centers was reduced,
particularly in the Amhara Region where Ambassel took over most AISE marketing
centers (again, an indication that Ambassel had greater authority (government-granted)
than that afi‘orded traders in a market economy). In Amhara, AISE “was not operating at
fiill potential. It is better to say its status as a little better than shutting down” (GMRP
1998). In 1996, EAL also had a wide-ranging network: 230 direct retail sales centers,
1,285 “private” retailers (commission agents), and 550 service cooperatives. In the
Oromiya Region, Dinsho had a similar network as EAL in the zones of East Shewa, West

Shewa, North Shewa and Arsi.

Product Promotion. As mentioned earlier, the integrated retailers distributed most
of their supplies through the NEP or regular credit programs. Thus, there was little return
to product advertising other to inform the government that the company had stock
available for delivery. It is rumored that the qualifying factor for winning a bid was not
only price, but also whether the supplier had stocks in the vicinity at the time of the bid
(GMRP 1998). This was reported to be the reason for the dismissal of EAL in the auction
in the South. Otherwise, the criteria for a government contract was related to being the
favored company or by submitting the low-priced bid in an auction. However, in addition
to government credit sales, most integrated retailers were also engaged in open market
sales where a return to advertising may have been realized. For example, Ambassel was
reported to have promoted its product by emphasizing that it had new stock, contrary to

the expired bags supplied by its rival, AISE.

96

18‘

no

Te

Efi‘orts to provide timely delivery of fertilizer to farmers were often thwarted by
the slow organization of government credit as well as retracted government policies (both
issues discussed further in chapter 7 ). There were several examples where integrated
retailers tried to position supplies early, in their retail marketing centers or in SC
warehouses. However, not all companies had this option. For example, in the Amhara
Region, Ambassel was the only company with permission by the regional government to
stock fertilizer in the SC warehouses prior to organization of the government credit
program. In another case, a large wholesaler tried to get an “edge” in a market but was
not rewarded for this tactic. AISE positioned stocks in December in the South but was
refused permission to distribute in both the government credit program and open market

sales until August when government-appointed Wondo ran short of supplies.

Open market sales appeared to be a secondary activity of the integrated retailers
with the exception of companies that already had stocks in an area when they lost bids or
when they were forced out of the government credit market when another company was
granted sole distribution rights by the FDRE. With regard to open market sales, many
integrated retailers reported that they provided delivery to farmers (61 percent), price
reductions (35 percent), and promotion of prearranged contracts with farmers (31
percent) (Table 4.4). It is hypothesized that the extent to which product promotion
occurred among the integrated retailers was overstated, because the subsector survey did
not necessarily find evidence that these activities were occurring (GMRP 1998). The

reported promotion of delivery to farmers conflicted with reports from Wereda Bureaus of

97

 

(WI

1hr
p11
iIlc

“1

kiic

Agriculture ofiicials and members of SCs that stated that the integrated retailers usually

only distributed as far as the wereda capital and not to farmers.

Table 4.4 Percent of Integrated Retailers' Engaged in Product Promotion
Activities in Amhara, Oromiya, and Southern Regions in 1998

 

 

Never Sometimes Often Total
Repacking fertilizer into smaller bags as 6 6 f ‘ 100
Using roving agents 81 0 19 100
Demo fields 100 0 0 100
Price reductions ~ 30 35 35 100
Credit flexibility 31 6 13 100 . ‘
Ofier technical advice on how to use the products 61 11 28 100 8‘
Deliver fertilizer to farmers 28 11 61 100 ‘
Offer prearranged farmer contract 44 25 31 ' 100
Promote eash sales 53 41 6 if 100

 

Source: GMRP, Input Subsector Survey 1998.
Note: 'Multiple responses (18 in total) were recorded for each of the 7 integrated retailers.

4.2.2 Product Strategy by Independent Retailers

The independent retailers primarily relied upon word of mouth to promote their
product. Retailers were also involved in the grain trade and flour milling which reduced
the search cost for their fertilizer market. Independent retailers did provide farmers with
price reductions and credit flexibility (GMRP 1998). In Jimma, a particular concern of the
independent retailers was to provide farmers with the preferred white-colored DAP.
White DAP can be used more efficiently because it is visible on the fields when
broadcasted. Another concern of the retailers was that they provided farmers with 25-50

kilogram bags, rather than with a quintal (100 kilograms) (GMRP 1998).

98

Most independent retailers in 1998 wanted to expand their businesses next season
(GMRP 1998). Among those that did not, the reasons cited were insufficient profitability
and that the government credit program left no market for open market sales (GMRP
1998). The primary constraint to expanding their business was credit, but if traders could
expand their business they reported that they would use roving agents more readily to sell
fertilizer at the farmgate and would purchase fertilizer directly fiom importers (GMRP

1998).

4.3 Conclusion

In sum, this chapter outlined the range of pricing strategies practiced by fertilizer
distributors as well as the non-pricing strategies to secure and expand a firm’s market
share. The chapter revealed that market conduct was important in detemtining market
shares of distributors as well as influential in determining retail prices. Overall, both the
government credit and open markets were less than competitive in many areas of the
regions studied in 1998. Coercive tactics conducted by the wholesalers (often with
government backing) were successful in allowing distributors to carve out market niches

and reduce competition.

The following chapter tests whether the market conduct practices observed in this
chapter were indeed significant in explaining the spatial variation in retail prices. Ifmarket
conduct was important in determining prices in 1998 then it presents itselfas a target for
policy directives to influence the performance of the fertilizer subsector.. Evidence fi'om

this chapter suggests that the organization of the market geographically and across the

99

government credit and open markets is very heterogeneous. Ambassel dominated
distribution of fertilizer for the NEP and regular credit sales in Amhara as did Wendo in
the Southern Region. The introduction of fertilizer auctions in the Oromiya Region led to
a relatively more even playing field among the integrated retailers. However, the “delay”
in announcing bids in the Oromiya Region often meant Dinsho received the government
credit fertilizer market for the earlier maize crop. Open market sales were relatively more
profitable for independent retailers if a retailer could position herself/himself closer to the
farmers, or in areas where the NEP or regular credit programs were absent or failed to

meet farmers’ demand.

100

CHAPTER 5

FERTILIZER PRICE HEDONIC MODEL

The previous chapters showed that national and regional governments play an
influential role in the fertilizer market by determining which fertilizer companies were
allowed to operate in the market, and the pricing arrangements that were used to
determine prices. These moves by the government severely restricted the possible pricing
strategies practiced by competitive firms. The fertilizer price model presented in this
chapter will test the effect of government policy on fertilizer price outcomes. For
example, do different market structures (i.e., distribution channels) and market conduct
practices (i.e., alternative pricing arrangements) affect retail prices and did these practices
have significantly different effects from one another? The valuation of characteristics of
the fertilizer market will help determine which characteristics of market structure and

conduct are relatively more favorable to building a lower-cost fertilizer market.

5.1 Hedonic Price Technique

A hedonic price technique is used to estimate retail prices of fertilizer in Ethiopia.
Commonly used in environmental economics, it is a method for estimating a price for

goods for which there is no market. Each non-market characteristic of the fertilizer

101

market is defined as a good and bundled together to define the broader good, fertilizer.
The hedonic price technique is related to, but preceded, Lancaster’s theory (1971, 1998)
that utility is derived from the characteristics of goods, not necessarily the good itself. On
the demand side, utility is derived from the physical properties of fertilizer, but also by a
package of attributes that are tied to purchasing fertilizer. Consumers demand a host of
characteristics that are tied to the physical product, fertilizer, such as the availability of
fertilizer credit and the timeliness of fertilizer delivery. For example, the price of fertilizer
in the NEP may be relatively high because farmers demand credit, improved seed, and
extension services that accompany fertilizer. On the supply side, producers distribute
fertilizer but, in addition, they supply attributes such as readily accessible, interest-flee,
cash sales, or fertilizer in outlying rural areas. The interplay of the supply and demand for
fertilizer with difl‘ering characteristics leads to a relationship between equilibrium prices

and the levels of characteristics that is referred to as the “hedonic price firnction.”

The hedonic price model represents equilibrium prices in a market--a locus of
equilibria between bids for and offers of characteristics of the good." The model specifies
that price is a function of the determinants of both the supply side and demand side of the
characteristics markets (Freeman 1993). The objective of the model is to determine the
marginal implicit price of a characteristic which can be found by differentiating the hedonic

price function with respect to that characteristic.

 

3'The underlying assumptions of the model are that preferences are weakly separable and
firms are heterogeneous and thus their cost functions differ.

102

Waugh (193 8) was one of the first agricultural economists to investigate the
efi‘ects of quality on prices. He used multiple correlation analysis to investigate whether
quality characteristics such as size, shape, maturity, and other factors afi‘ected prices of
vegetables in a Boston market. This inquiry was extended by Griliches (1971) who used
regression analysis to adjust price indexes for quality changes over time. Today hedonic
price models are commonly used by economists in the field of environmental economics to
measure the marginal value of non-market environmental qualities (e.g., air quality), but
the application of the technique has spanned a variety of applications. For example, the
hedonic price technique has been applied in labor economics to estirnate spatial wage
difl'erentials and is still used in its original design, to estimate the significance of product
heterogeneity such as quality, grades and standards (e.g., Jordan et al. 1995, estimation of
the quality characteristics of tomatoes). Overall, in all applications, the hedonic price
technique is used to determine the implicit prices of different quality characteristics of a

good.

The Texas Agricultural Market Research Center (1996) extended the hedonic price
technique to capture different pricing institutions in the US. cattle slaughter market by the
top steer and heifer packing plants. In addition to capturing differences in quality of
cattle, the model also estimated the effects of market structure and conduct. Market
concentration and different procurement and pricing methods that influence prices in an
oligopoly were used to explain the variation in observed prices. Difi‘erent pricing
mechanisms such as forward contracts and formula pricing were included as explanatory

variables. In addition, the market share of each firm in a region was included to capture

103

characteristics of market structure--that slaughter cattle procurement is a concentrated

market.

‘ The price model presented here for fertilizer in Ethiopia will also try to capture
whether difi‘erent characteristics of the market, namely the pricing arrangement and
distribution channels, influence prices. The model attempted to determine the underlying
transaction and institutional costs explaining price difi‘erences across the regions. If the
spatial variation in retail prices can be partially explained, then the hypothesis that the

1998 subsector was characterized solely by spatial market inefficiencies can be rejected.

5.2 Model Specification

The objectives of the model are to first determine whether there are regional
fertilizer price difi‘erences, and if so, estimate whether the regional price difl’erences can be
further explained by different market characteristics. Ultimately, the goal of the model is
to estimate the marginal values of these market characteristics and thus determine the

impact of difi‘erent market characteristics on fertilizer prices. The specific hypotheses are

delineated as follows:
- transport costs will positively influence fertilizer prices;

0 the quantity demanded by the NEP and regular credit programs will be

inversely related to prices;

- the marginal value of fertilizer distributed through the extension program

will be relatively higher than prices in the open market due to the presence

104

of non-competitive pricing arrangements in the NEP relative to the open

market; and

° the presence of a fertilizer auction will negatively afi‘ect retail prices relative
to areas in which the supplier was nominated by govemment—a

“mandated” market.

The hedonic price model is specified as follows:

(1)P=P(R, T.MD. Q)
where P is the retail price of fertilizer; R is a vector of regional dummy variables; and T is
a vector of characteristics of the good relating to transport costs. M, D, and Q are vectors
of market structure: M is a vector of pricing mechanisms by which the good is prOVided,
D. is a vector of distribution channel characteristics, and Q is the volume demanded by the

wereda for its credit programs.

Within each of the three regions of study, Amhara, Oromiya, and Southern, there
were three distinct distribution channels and for each distribution channel there was a
unique pricing arrangements by which prices were determined (Figure 5.1). The
distribution channels were (1) the NEP credit program, (2) the regular credit program, and
(3) the open market. The pricing arrangements were determined either through a fertilizer
auction or privately between the government and the nominated-supplier for the credit
programs, and competitive in the open market to the extent that there were competitors in
the market but otherwise monopoly rents may have been realized. In the open market,

sales were made either by the integrated retailers (who also participated in the credit

105

programs) or independent retailers (who did not participate in the credit programs, they

purchased their stock fiom the integrated retailers).

Figure 5.1 Pricing Mechanism and Distribution Framework

Region Distribution PM“!
Channel Arrangement

\ NEPcredit

——->
'11>< Government-
V 149.er / nomrntatedsupp
6
/V
__>

 

 

 

Auction

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Oromiya credr t
Integrated
retailers
retailers

 

 

 

 

 

 

Four models were estimated for DAP and urea each (Table 5.1). The data for the
price model was collected during the 1998 Input Subsector Survey by the Grain Market
Research Project (GMRP). The dependent variable is the observed retail price of fertilizer
measured in Bin/quintal. There are multiple observations of prices of DAP and urea in
each wereda sampled, but the exact location of the quoted price within a wereda is not
known. Retail prices used in the model are the prices farmers pay (excluding farmer
interest). In cases where a farmer service cooperative (SC) adds a margin to the credit
price paid by farmers, the margin is included in the retail price. There were no observed

open market prices for the SCs although some SCs did engage in open market sales.

106

Fertilizer retail prices are recorded for the 1998 belg and meher seasons, a range of
roughly 4 months fiom April through July. There are 376 prices for DAP and 291 prices
for urea. The retail prices are quoted by the retail distributors as well as by Wereda

Bureau Of Agriculture officials and officials of the Service Cooperatives.

The models specified are linear in variables and linear in the parameters. Log-log
and semi-linear models were estimated but the model results were not different fi'om the

linear model and therefore not reported.

Model 1 estimates prices against regional dummy variables to determine whether
regional (spatial) differences exist in prices. It is hypothesized that prices in the Oromiya
Region will be the lowest relative to prices in the Amhara and Southern Regions. The
markets in Oromiya Region were generally closest to the port so prices will generally be
lower than prices in the Southern Region and also lower than the south-western parts of
the Amhara Region: The choice of regional dummy variables as opposed to a smaller
political unit such as a zone is to test whether the FDRE’s efforts to decentralize and shift
responsibilities to the regions had been realized. The dummy variables would capture

regional-specific input sector policies.

107

Table 5.1 DAP and Urea Model Specification
Model Specification Definition
(1)P - p(R) Mg] 1: price regressed on regional dummy variables
(2) P = p(R, T, M, D, Q) Model 2' price regressed on regional dummy variables, transport costs,
pricing mechanisms dummies, distribution channel dummies, quantity of
fertilizer ordered for the credit programs
M Model 2 plus a quadratic road density variable

(3) P - p(R, T, M, D, Q) Mgel 4; Model 3 plus interaction eflects between pricing mechanisms and
distribution channels

 

 

 

Model 2 attempts to explain some of the significant regional variation hypothesized
in Model 1. It estimates the additional explanatory power of distance fi'om the port, road
density, quantity of fertilizer ordered by the wereda, pricing arrangement dummies (i.e.,
government credit auction sales (in the interceptusee Table 5.4), government-appointed
credit sales, independent retail open market sales, and integrated retailer open market
sales), and the distribution channel dummy (the case where both the NEP and regular
credit programs were available is in the intercept). It is hypothesized that prices will be

positively related to the distance from the port and negatively related to road density.

Distance fi'om the port of Djibouti to the midpoint of the wereda is used as a proxy
for transport costs. The exact location of the observed prices within a wereda is unknown
so the distance from the port to a rough estimate of the midpoint of the wereda is used as
a proxy. Admittedly, there is some degree of measurement error in capturing this
relationship. Most of the fertilizer shipped in 1998 arrived at the port of Djibouti;
however, prior to the conflict with Eritrea, most imports came through Assab. A portion

of the fertilizer consumed in 1998 was carry-over stock and originally arrived in Assab;

108

however, that stock is assumed to be a sunk cost and therefore, transport costs is no
longer a factor in determining the supply price.

Using distance fi'om the port as a proxy for transport costs assumes that transport
costs are constant over time and constant over space. Both assumptions are false, but
acceptable given the inability to identify the timing of delivery, nor the exact location of
the observed price. Transport costs will increase as fertilizer travels ofi‘ the main arteries
extending from Addis and into outlying areas. Costs will also increase during the rainy
season or during periods of peak demand for trucks (e.g., when food aid imports arrive).
A quadratic form of distance from the port was included in the model to determine
whether the relationship between the distance from the port and prices was increasingly or
decreasingly positive. The quadratic, distance, failed to pass the hypothesis test that its
coeficient was significantly different fiom zero and therefore was omitted from the model.

Road density was added to the model to explain some of the differences attributed
to transport costs within a zone. Road density is calculated by weighing the lengths of 5
difi‘erent road qualities (highest weight given to the highest quality road) observed in each
zone and then dividing by the area of the zone. The highest quality road is an all-weather
paved road and the lowest quality is a regularly traveled dirt path. In theory, higher levels
of road density will reduce transport costs due to the cost savings from being able to haul

fertilizer to its destination within the zone by truck rather than on bicycle or on foot.

In Model 3 (Model 2 will be explained shortly) the linear term of road density is
extended by including a quadratic term. It is hypothesized that prices were lower in

weredas with higher road densities and that the prices decreased at an increasing rate.

109

In model 2 it is hypothesized that there was a negative relationship between prices
and the quantity of fertilizer ordered by Wereda Bureaus of Agriculture for their NEP and
regular credit programs (a wereda-level variable). For each of the NEP and regular credit
programs, regardless of pricing mechanism (bid or mandated market), the Wereda Bureau
of Agriculture aggregated total demand for fertilizer, then found a contractor either
through the auctions or privately. In a competitive market equilibrium, the quantity of
fertilizer ordered by the wereda would be endogenous in a price-dependent model;
however, it is not endogenous in this model because the quantity ordered for the credit
programs occurred prior to and independently of the process of price determination.
Quadratic and cubic powers were estimated for the quantity of fertilizer ordered by the
wereda for its credit programs but they did not contribute to the explanatory power of the
model and their coefficients were not significantly different from zero so they were

dropped from the model.

As mentioned earlier, it is hypothesized that alternative pricing institutions will be a
significant factor in explaining price variation. The observed pricing institutions were:
auctions for credit sales, credit prices determined privately between the govemment-
nominated supplier and the government, and open market prices. Dummy variables are

used to capture these effects and are estimated at the price level.

It is hypothesized that an auction pricing arrangement will result in the lowest
relative price due to the competition resulting from multiple bid ofi‘ers for one tender. A
Wereda Bureau of Agriculture issued tenders for a fixed quantity of fertilizer for each of

the NEP and regular credit programs in its wereda. A separate tender was issued for DAP

110

and urea and for each point of delivery at a SC. The Wereda Bureau of Agriculture
invited integrated retailers to participate in the tender and the lowest-priced bid won.
Credit prices in government-appointed markets, where prices were determined
privately between the supplier and the government, are hypothesized to be the highest due
to the lack of competition. It is not known how the nominated supplier and the regional
government decided upon prices. It is hypothesized the government-appointed contract
prices are higher than the winning bid prices in the auctions. It is also hypothesized that
the winning auction prices in the regular and NEP credit programs will be lower than the
cash prices because independent retailers purchased their supplies from the integrated
retailers that supplied the credit programs. However, cash prices are predicted to be

lower than the credit price in mandated markets.

In many weredas the regular credit programswere replaced by the NEP credit
program as the NEP gained momentum and SCs defaulted on regular credit. The model
tested the effect of this administrative decision on prices. Did the effort to streamline the
process of credit organization under the NEP put a downward pressure on prices relative
to prices in which both SC and NEP credit programs remain? The model tested the
degree to which the process of credit organization and fertilizer distribution were
harmonized across credit programs. It is hypothesized that prices in weredas with both
credit programs will be higher than prices in which only the NEP is available due to the
additional administrative procedures required by any supplier who supplies both programs.
There were only a few integrated retailers therefore it is probable that one wholesaler will

supply fertilizer on credit for both programs where both programs are present. Additional

111

costs may be required to supply (or offer bids for) both programs because even though
both programs are administered by the Wereda Bureau of Agriculture, they had separate
budgets and thus remained administratively separate. Thus transaction costs may be
increased if the timing of the two programs is different and if separate paperwork is

required to submit bids and process the contracts.

Model 4 includes interaction variables between the pricing mechanisms and the
distribution channels. Within each wereda, prices were set separately in each distribution
channel and by one of the identified pricing institutions. The interaction revealed whether
the unique combination of choice of distribution channel and the choice of pricing
mechanism afi’ected prices significantly differently from their separate effects. For
example, if prices. were determined by an auction, does the fact that one wereda had only
the NEP present and the other had both credit programs affect prices differently? Did
integrated retailers offer higher bid prices when both programs were present relative to

when there was only the NEP available to cover additional transaction costs?

5.3 DAP Model

This section will present the results of the hedonic price model for DAP. The
mean DAP price was 239.5 Birr/quintal with a range fi'om 212.5-270 Birr/quintal (Table
5.2). The mean distance from Djibouti to the wereda in which the price was observed was
983 kilometers with the distance ranging from areas north-east of Addis in the Amhara
Region that are closest to the port to areas in the Southern Region and Western Amhara
that are most distant. The greatest variation among the continuous variables is the

112

quantity of DAP ordered for the credit programs in a wereda-ranging from 700-31,000

quintals.

Table 5.2 Descriptive Statistics of Continuous Variables in DAP model, n=386

 

 

Mean Std. Dev. Min Max
Price (Birrlqtl) 239.5 13.7 212.5 270.0
Distance from port (km) 982.6 164.6 523.0 1,294.0
Road density (100 krn/km’) 6.6 2.3 3.2 13.4
Quantity DAP ordered for credit 8,931.7 7,479.9 700.0 31,0280

prosmns (quintals)

Most of the observed fertilizer auctions were held in the Oromiya Region, with
zero fertilizer auctions held in the Amhara Region and only 1 in the Southern Region
(Table 5.3).” The distribution of the observed privately negotiated prices between the
government and its nominated supplier was less skewed, with 50 percent of the privately
negotiated prices observed in Amhara, 16 percent in Oromiya, and 33 percent in the
Southern Region (Table 5.3). Prices offered by the independent retailers were
predominately observed in the Oromiya Region (26/40 cases), followed by Amhara(11/40
cases) and the Southern Region (3/40 cases). The independent retailers were forcibly
prohibited from selling in some areas in the South due to the active promotion of the NEP.
As a portion of the sampled weredas, the Amhara Region had the greatest portion of

weredas in which only the NEP was present, followed by the Southern Region, and

 

”The simple correlation coefficient between the Oromiya Region dummy variable and the
fertilizer auction dummy is 0.54.

113

Oromiya where only 15 percent of the sampled wereda had only the NEP credit program
present. Conversely, 85 percent of the weredas sampled in the Oromiya Region had both

the NEP and regular credit programs available in 1998.

Table 5.3 Frequency Distribution of DAP Prices by Region, Number of

 

 

Observations
Amhara Oromiya Southern Total
Mam
Credit, auction price arrangement 0 120 1 121
Credit, private, govt-nominated 64 20 42 126
supplier price ‘
Independent retailer open market ' ll 26 3 , 40
pnce
Integrated retailer open market 40 36 13 89
pnee
. Total - 376
NEP only credit program in wereda 110 30 56 196
NEP and regular credit programs 5 172 3 180
present
7 g p _ Total =376
[Total Observed-Prices'bv Regen 115 202 59 Total = 376

 

The estimated hedonic DAP model revealed that the identified characteristics of
the DAP market, namely the pricing institutions and geographic market of fertilizer were
significant in explaining the observed variation in prices (Table 5.4). Overall, regional
dummy variables alone explained 45 percent of the spatial variation in prices and
confirmed that indeed considerable variation in prices across the three regions exists
(model 1). Prices in the Amhara Region were an estimated 21 Birr/quintal higher than
prices in the Oromiya Region (in the intercept). Prices in the Southern Region were

roughly 13 Bin/quintal higher than prices in the Oromiya Region. Thus, as expected,

114

 

 

 

 

 

 

 

Table 5.4 DAP Price Dependent Model Estimation Results, Bin/Quinta]
Model 1 Model 2 Model 3 Model 4

B t' B t B t B t
E . ! 1.3 . ,
Amhara 20.83 16.8‘I 14.15 8.66’ 12.32 7.41‘ 10.81 6.12‘
South 12.85 8.5‘ 5.63 2.92‘ 5.64 2.98‘ 2.89 1.42
W
Distance from port, 0.17 5.42"I 0.19 6.34" 0.24 7.44"I
(108 kilometers)
Road density (100 -1.21 -7.68 4.79. -7.15 452*
km/km’)
Road density squared 0.37 4.08" 0.35 3.88"
Quantity DAP (1,000 quintals) -0.30 -4.46"‘ -0.38 -5.54" -0.38 -5.58‘
Cash. independent 5.93 3.52" 5.92 3.59" 10.81 3.26“
retailer
Cash, integrated retailer 6.48 4.58‘ 5.77 4.13‘ 14.72 525"
Credit, government- 10.52 7.07‘ 9.90 6.76‘ 5.76 2.68‘I
appointed supplier
.12' 'l . g l E . .
NEP 1.70 1.23 1.37 0.93 -5.27 -2.35*
m’ ‘
Government-appointed 12.99 4.03"
supplier‘NEP
Cash, integrated -11.95 -3.59*
retafler‘NEP+reg
Cash. independent -4.16 -1.08
retailer‘NEPfleg
M 231.85 3313* 222.26 54.32‘ 245.59 3520* 239.99 34.05.
R2 0.61 0.62 0.64
Adjusted R’ 0.59 0.61 0.63

2The Oromiya Region is in the constant.
3The bid price from the fertilizer auction is in the constant.

‘The distribution channel that is either the NEP or the regular credit program is in the constant.
’The bid price in weredas with the NEP and regular credit programs is in the constant.

376

115

376
N—T'Lotes: 1 statistics, H,:a=o; 11mm, * indicates 5% significance, n indicates 10% significance.

376

prices are higher the farther the retail market is from the port (most fertilizer consumed in

Amhara is in the western part of the region).

Model 2 adds in the effects of transport costs, pricing mechanisms and distribution
channels which will shed light on whether the market is efficient. Overall, the magnitude
of the efl‘ects of the regional dummies was reduced as expected—the model is now
accounting for some of the physical and institutional factors that explain regional
difl‘erences in model 1. The regional effect of Amhara dropped from 21 to 14 Bin/quintal
above the intercept (the Oromiya effect, bid effect, and NEP plus regular credit program
price effect). The distance from the port was significant at the 5 percent level as was the
road density of a zone. The marginal effect of an additional 10 kilometers fi'om the port
was 0.17 Bin/quintal. The marginal effect of road density was -1.21 Bin/quintal. Thus
fertilizer retail prices in zones that have higher road densities Will be lower due to the

lower transport costs of distribution within the zone.

Overall, distance from the port and road density were significant at the 5 percent
level but the magnitude of their effect on prices was relatively small. The magnitude of the
effect of pricing institutions was significant and relatively higher. The coeflicients on three
pricing arrangement variables were significantly different from the intercept-auction
pricing arrangement in weredas with both the NEP and regular credit programs. Prices in
“mandated” markets (i.e., one supplier identified by the government to supply all fertilizer)
were the relatively highest, 10 Bin/quintal higher than bid prices. Thus a tentative
conclusion can be drawn that relatively more competitive pricing institutions exerted a

downward pressure on prices.

116

The open market prices by the independent retailers and cash market prices by
integrated retailers were also higher than the auction process, by about 6 Bin/quintal in
each case. Perhaps integrated retailers wanted to recoup losses in the cash market that
they lost in ofi‘ering below-cost bid prices in auction pricing arrangements. One
distributor reported that he had to raise his cash price to cover the loss experienced by
ofi‘ering a lower-than-cost price in the auction (GMRP 1998). Alternatively, perhaps the
cash market was not as competitive as first perceived. Perhaps high transport costs results

in markets that were segmented and not well integrated.

Alast finding from model 3 is that prices in the weredas in which only the NEP
was present were not significantly higher than prices in which both programs were present.
It was hypothesized that since the two programs were administered separately that .
distributors would raise their price to account for the duplication in paper work and added
transport costs of separate delivery destinations for the two programs. Perhaps the
distributors only supplied one or the other program. Also, perhaps the delivery point was

the same for both programs, thus reducing the cost of supplying both programs.

Model 4 adds the interaction terms of pricing mechanism and distribution channel.
The model runs into high collinearity problems and therefore some variables were dropped
fi'om the estimation (credit, auction‘NEP; credit, government-appointed
supplier‘NEPi-reg; cash, integrated retailer’NEP; cash, independent retailer‘NEP). One
observation fi'om the model is that when open market sales by integrated retailers were
interacted with weredas with both SC and NEP credit programs, the net efi‘ect of prices

was that they were 2.77 Bin/quintal higher than the Oromiya winning bid prices in

117

weredas with both credit programs. This reinforces the earlier finding that cash prices of
integrated retailers were higher than the corresponding integrated retailer’s bid prices.
Due to the additional transaction costs of providing a sales clerk to handle individual
transactions, cash prices were higher than the pricing outcome of the auction process,

other factors held constant.

The net 'efi‘ect of the independent retailer’s price is 6.20 Birr/quintal higher than the
Oromiya winning bid price with both credit programs present. As hypothesized, the
independent retailer price is higher than the integrated retailer from which they purchase

their stock.

5.4 Urea Model

A hedonic price model for urea is specified the same as the models for DAP,
however, with 291 observations instead of 376 observations. The reduced numberiof
observations for urea mirrors the distribution of fertilizer in the market: less urea than
DAP is used in the three regions. There were no fertilizer tenders held in the Amhara
Region, one tender held in the Southern Region, and 98 prices observed fi'om auction
arrangements in the Oromiya Region (Table 5.5). The observations for privately
negotiated prices between the government and the government-nominated supplier were
more equitably distributed between the three regions, with 64 observations in Amhara, 16
in Oromiya, and 35 in Southern. The region with the highest proportion of weredas with

only the NEP present was the southern Region (47 out of 142 observations). Similar to

118

DAP, Oromiya had the highest proportion of observations with both the NEP and regular
credit programs available (131 out of 139 observations).

The mean urea retail price was 189 Bur/quintal, 21 percent lower than the average
price of DAP observed (Table 5.6). The mean level of urea ordered for the NEP and
regular credit programs was 3,237 quintals-«Jess than halfthe quantity of DAP ordered.
East Shewa, Oromiya, had the highest level of urea consumption across credit programs
among the surveyed weredas. In fact, it was found that there was excess demand for urea
in East Shewa; the excess supply of NEP urea in the South was resold in East Shewa

(GMRP 1998).

Table 5.5 Frequency Distribution of Urea Prices

 

 

Amhara Oromiya Southern Total
Prig'ng Mgmfi m
Credit, auction arrangement 0 98' 1 99
Credit, private, govt-nominated 64 16 35 115
Independent retailer cash price 3 l4 1 l8
Wholesaler cash price 21 26 13 59
Total = 291
D' 'o l
NEP only credit program in 82 23 47 152
NEP and regular credit programs 6 131 2 139
. Total = 291
TetalObservedPriwsbyRegion as 154 49 . 291‘

 

Note: ' The simple correlation coefficient between these two binary variables is 0.59.

119

Table 5.6 Urea Descriptive Statistics of Continuous Variables, n-29l

 

 

. Mean Std. Dev. Min Max
Price (Birrlqtl) 189.1 25.5 143.4 249.0
Distance from port (km) 954.1 169.5 523 1,294.0
Road density (km/km’) 6.7 2.4 3.2 13.4
Quantity urea ordered for 3,237.5 2,643.5 44 15,7315

credit programs (quintaIS)

The urea model estimation results are presented in Table 5.7. Similar to the DAP
model, the regional dummy variables revealed that there were significant regional
difi‘erences in prices (Table 5.7). Prices in the Oromiya Region were 39 and 27
Bin/quintal lower than prices in the Amhara and Southern Regions, respectively. The
regional dummies alone accounted for up to 46 percent of the variation observed in urea
prices.

Similarly to the DAP model, model 4 estimation with the addition of interactive
effects had high collinearity problems, therefore some effects were dropped fi'om the
estimation (credit, government-appointed supplier*NEP+reg; cash, integrated
retailer‘NEP; cash, independent retailer‘NEP; cash, independent retailer‘NEP+reg).

When other market characteristics were added to the model, the effects of the
regions diminished as expected but were still strong. As with the DAP model, the winning
bid price fiom auctions for urea was the lowest observed pricing mechanism relative to the
credit price privately negotiated between the government and supplier and lower than the
cash prices. The privately negotiated price was 21 Bin/quintal higher than the winning bid

price (in Oromiya in weredas with both the NEP and regular credit available) and was

120

 

 

 

Table 5.7 Urea Price Dependent Model Estimation Results, Bin/Quinta]
Model 1 Model 2 Model 3 Model 4
B t‘ B t B t B t
E . lg . 1
Amhara 38.77 14.72‘ 22.55 6.18" 18.22 4.67" 18.03 4.42"I
South 26.87 8.76‘ 14.90 3.57‘ 14.59 3.54‘I 13.85 3.09‘
W
Distance from port, 0.08 1.19 0.13 1.92" 0.16 229*
(10s kilometers)
Road density -1.54 -2.87‘ -10.63 -3.31" -11.49 -3.49‘
(100 km/km’)
Road density squared 0.51 2.87“ 0.56 3.08‘
Warsaw.
Quantity urea (1,000 quintals) -0.66 -l.57 -0.50 -1.18 -0.46 -1.08
Prig'ng Mgghm‘sm Dumuries3 -
Cash, independent 11.69 264* 13.61 3.08" 17.82 3.63“
retailer .
Cash, integrated retailer 6.72 2.10‘ 5.17 1.62 24.50 2.49“
Credit, government- 21.12 650* 20.44 6.36‘l ' 15.60 3.46“
appointed supplier
Disg'bution Chmel Dummies‘
NEP 4.31 1.38 4.34 1.41 -14.63 -l.45
mm’
Credit, Auction‘NEP 14.71 1.28
Govemment-appointed 24.47 2.33‘
supplicr‘NEP
Cash, integrated -21.44 -2.05"'
retailer‘NEPfleg
@nstant 174.68 119.90’ 173.10 21.64“ 204.21 15.23“ 204.66 14.55'
R2 0.46 0.59 0.60 0.61
Adjusted R2 0.45 0.58 0.59 0.59
n 291 291

 

Notes; ‘ T statistics, II,:B,=0: H,:B,¢0, * indicates 5% significance, " indicates 10% significance.

2The Oromiya Region is in the constant.
3The bid price from the fertilizer auction is in the constant.

‘The distribution channel that is either the NEP or the regular credit program is in the constant.
’The bid price in weredas with the NEP and regular credit programs is in the constant.

121

significant at 5 percent. One noticeable difference in the urea model from the DAP model
was the efi‘ect of open market sales by the independent retailers. In the DAP model the
cash prices ofi‘ered by the independent retailers and the wholesalers were roughly equal
relative to the winning bid price. However, in the urea model the prices offered by the
independent retailers were 14 Birr/quintal higher than the winning bid price and the prices
ofi‘ered by the wholesalers are 5 Bin/quintal higher than the winning bid price from the
auction process. This can be explained by the observation that in some of the Southern
zones (i.e., Kembata) farmers would sign up for the NEP because it was the only available
source of fertilizer credit but then sell their unused urea on the market for cash.
Independent retailers would transport the urea north into Oromiya and sell it in areas

where an excess demand existed.

As with the DAP model the quadratic on road density is positive and significant at
5 percent. The total effect of the government-appointed supplier in a wereda with only
the NEP present is that it is 40 Birr/quintal higher than the intercept—the winning bid price
in weredas with the NEP and/or SC credit. This sum is not unimportant-it represents 21

percent of the mean urea price.

5.5 Conclusion

The hedonic price model for DAP and urea revealed that significant price
differentials observed in the three regions can be partially explained by transport costs
(distance from the port and road densities in a zone), pricing mechanisms, and distribution
channels. Overall, credit prices determined privately between the government and its

122

nominated-supplier were significantly higher than prices determined in a fertilizer auction.
This is a signal that one way to reduce retail prices is to introduce more competition into

the retail market.

Another finding is that for both the DAP and urea models, the effect of restricting
availability of fertilizer to the NEP program (using the auction process) did not result in
prices significantly different fi'om winning bid prices in weredas with both the NEP and
regular credit programs available. This contradicted the hypothesis that the separate
administration of the two programs meant higher transaction costs, and thus, higher
prices. Another finding is that credit prices, when determined through a fertilizer auction,
were significantly lower than cash prices. Overall, policy changes to encourage more
competition at the retail and wholesale levels is recommended, perhaps first by reducing

government favoritism in the market and thereby develop an open and transparent market.

123

CHAPTER 6

FARM-LEVEL DETERMINANTS OF MAIZE PRODUCTIVITY GROWTH

Understanding the farm-level determinants of maize productivity growth is an
important factor in expanding adoption and long-term sustainability of high-input
technology. The 86“ program in Ethiopia proved that improved maize technologies exist
which can double yields and dramatically raise net incomes and returns to labor (Howard
et al. 1999). However, past 86 programs in other SSA countries have shown that
sustained use of improved farm technologies can be problematic if the overall system of
technology diffusion and distribution is not well coordinated. Sustainable farm—level usage
of new technology requires that problems of extension, credit, and input delivery be
worked out in a cost effective and sustainable manner. Another concern is whether the
SG program’s success in expanding adoption in high-potential areas of Ethiopia can be
replicated in less favorable agro-ecological areas and to farmers that may not be as well-
suited (in terms of land, labor, oxen, and education) to adopt the new technologies and use

them emciently.

This chapter will investigate the productivity factors that contributed to the

successfirl uptake of the SG technology by farmers. The following chapter on input

 

”Formally the joint program of the Ministry of Agriculture, Ethiopia and Sasakawa-Global
2000.

124

market performance will determine whether the NEP can maintain the SG quality of
extension to introduce farmers to the new technologies, and equally important, whether
input and credit markets are sufficiently developed to allow farmers to continue
purchasing the inputs once they graduate from the NEP (farmers are expected to graduate
after two years in the program). Evaluating whether Ethiopia can expand the SG success
may depend on whether the NEP is able to duplicate the factors that contributed to the

success of the 86 on a larger scale.

There are three levels of analysis that are important for evaluating whether the
stage is set for rapid agricultural productivity growth in Ethiopia. First, how did the
organization of the SG enable it to efficiently (i.e., on time input delivery, adequate farmer
supervision) provide its technological package-fertilizer, seed, and management practices-
-and are these factors present in the NEP (point A in figure 6.1)? Second, what is the
relative importance of each component of the technology package (e. g., improved seed,
fertilizer, management practices) in increasing yields over the traditional practices (point
3.). Additionally, to what extent was this success attributed to targeting well-equipped I

farmers in high—potential areas (point C.)?

125

Figure 6.1 Technology Transfer Flow Chart

adennon _, Input;_A._> Farmer -—‘—>Yie1ds+

A
Appropriate 5 C.
W61? W00 Characteristics
on level and - Available labor
timing of - Available oxen
application - Education
- Favorable agro-
ecological envimoment

6.1 Success of the SC

The SG was phenomenally successfirl in raising net income and returns to labor for
maize production under high-input technologies (Howard et al. 1999). This success is, in
part, due to a doubling and tripling in yields over traditional technologies of little to no
fertilizer and local varieties of seed. The 86 technology package consisted of specified
level of inputs and timing of activities. For half-hectare farmer-managed plots, 12.5 kg
(kilogram) BH-660 hybrid maize seed was recommended, 50 kg urea, 50 kg DAP, and
180 kg marshal (pesticide treatment for seed). The mean yield of the farmer-managed SG
maize plots in 1997 was 5.4 MT/ha compared to 2.8 MT/ha on the traditional plots (Table
6.1) (Howard et al. 1999). However, in 1997 mean yields of 6.7 MT/ha on graduate plots
(previous SG participants that were under the NEP in 1997) surpassed the 86 yields,
probably due to an emciency gain from experience from improved management practices
and modified levels of inputs in accordance with the deficiencies in the soil (Howard et al.

1999).

126

Although mean SG yields were high, there was considerable variance in yields
across farmer plots within the same program. The observed variance in yields within
programs raises the question: Do yields vary within the 86 program due to‘difi‘erences in
levels of inputs, differences in managerial practices, or soil or weather-related variables?
In 1997 the mean yield in the lowest tercile under the SG plot, 4.1 MT /ha, was lower than
the mean yield in the highest tercile using traditional methods (4.2 MT /ha) (local seed,
DAP, no urea). The standard deviation of yields was 1.2 MT/kg for both SG plots and
traditional plots, and 1.5 MT/kg for the SG graduates (Table 6.1). The higher variance
among SG graduates is not surprising because some graduates continued to use the SG

package under the NEP and others scaled back the SG-recommended level of fertilizer.

Table 6.1 Yields by Program Type for Maize in Jimma, Oromiya Region

 

 

 

Program ' Yield (ton/hectare)‘ Standard DAP urea % use 11 used in
Type 12 2 3 Mam Devratron km kg/ha “aged calculations
86 4.1 5.4 6.9 5.4 1.2 102.4 102.4 100 68
Graduate 4.9 6.8 8.2 6.7 1.5 96.1 96.1 100 38
Traditional 1.6 2.7 4.2 2.8 1.2 91.8 0 0 47

 

Source: Compiled from data provided by Howard et al. 1999.

Notes: 'Yield differences between 86 and traditional plots (plots belonging to the same 86 households);
between 86 and graduate plots: and between traditional and graduate plots were significant at the 95%
level.

1 The first yield tercile includes the third offarmers that received the lowest yields, the 3"l tercile is for a
third of farmers that received the highest yields.

Due to the large degree of variation in yields within each technology type, four

technology types were specified to obtain an improved picture of the yield response fi'om a

127

given level of inputs (Howard et al. 1999). The specified technology types are: (1) local
seed with no DAP; (2) local seed plus DAP; (3) improved seed and lower-than-SG
recommended levels‘1 of fertilizer; and (4) improved seed and recommended or higher
levels of fertilizer (Howard et al. 1999). In general, technology types 1 and 2 refer to the
traditional or “control” plot, technology type 3 is split between predominately graduates
and the current 86 participants (48 percent of SO participants and 61 percent of .

graduates). Type 4 is predominately 86 current participants.

Table 6.2 Average Yields by Technology Type, With and Without the High-

 

 

 

input Technology, kg/ha
Tech- Description Yield, Standard Net Net Nutrients, kglha
nology MT/ha Deviation, Income, Income!
131:: mm Birr/ha 131!» DAP Urea
ay
(1) Local seed with no DAP 1.8 0.8 597 7.6 0 0
(2) Local seed plus DAP 2.9 1.2 1,053 11.3 100 0
(3) Improved seed and lower 6.0 1.7 2,261 19.6 87 87
than recommended level of
fertilizer
(4) Improved seed and 5.9 1.4 2,107 13.0 116, 116
recommended or higher
levels of fertilizer

 

Sources: Howard et al. 1999; Standard deviation calculated by author.

, The standard deviation in yields across all technology types is not necessarily lower
than the standard deviation across program types; however, it is now possible to conduct a
comparative analysis of net income and returns across more narrowly defined technology

packages (seed and fertilizer). Mean yields were 5.4 MT/ha for the SG program, but

 

“100 kg/ha DAP and 100 kg/ha urea.

128

 

increased to 5.9-6.0 MT/ha for the high-input users (technology types 3 and 4) (Table
6.2). Another insight fiom the technology categorization is the contribution of DAP (not
controlling for other factors): mean yields with DAP and local seed were 2.9 MT/ha, were

higher than with local seed and no DAP, 1.8 MT lha (Table 6.2).

Differences in yields translates into different levels of profitability of the
technologies. Net income doubled fiom technology type 2 (local seed and DAP) to
technology types 3 and 4 (improved seed, DAP and urea). Returns to labor also increased
fiom 11.3 Birr/labor day (with DAP, no seed) to 19.6 Birr/labor day for (DAP, urea, and
seed, technology package 3). Thus, a tentative conclusion (that will be tested later) is that
the SG technology package increases yields. The finding that farmers in technology type 4.
(who used at least the recommended level of fertilizer) received lower net incomes than
farmers in technology type 3 (who used less than the recommended fertilizer dose)

suggests that perhaps the fertilizer recommendation was too high.

6.2 Organizational Factors That Contributed to the SG Success

Once it is recognized that, in general, the high-input technologies (improved seed
and fertilizer) contributed to raising net incomes and returns to labor, the factors
contributing to this success must be understood for successful expansion of the high-input
technology. These factors may be categorized into three influences. The first is the direct,
technical relationship between inputs and output, and the second is the indirect influence

of the organization of the 86 program that permitted timely delivery of credit and inputs

129

and encouraged eficient use of the improved inputs to farmers. The third point of

influence is how well-suited a farmer is to maximize yields given the new technology.

There are several characteristics of the 86 program that set it apart fi'om the NEP
(Table 6.3). Among the factors contributing to the success of the 86 were: (1) the small-
scale nature of the program, permitting close supervision (e. g., an average of 11 extension
visits per farmer per season); (2) input delivery largely through the private sector, but
facilitated by SG (e. g., there are instances where--at the expense of the SGufertilizer was
transported to ensure timeliness); (3) credit (without interest) administered and guaranteed

. directly by SG and its agents; and (4) a focus on better-ofl‘ farmers in high potential areas.

Table 6.3 Key Differences Between the SG and NEP

 

 

Sasakawa-Global 2000 National Extension Program
pilot extension program national production campaign
credit provided from own resources ' credit provided by banks
program facilitated input delivery primarily relied upon existing input delivery system
high extension agent-to-farmer ratio lower extension agent-to-farmer ratio

 

, The S6 program began in 1993 with 153 plots, expanded to its maximum of 3,185
demonstration plots in 1995, and declined by 2,003 plots in 1997 when it withdrew fiom
demonstration programs and shified into post-harvest demonstration programs (Table
6.4). By contrast, the NEP began with 32,046 participants in 1995, increased dramatically
in 1998 to 2.9 million demonstration plots (roughly 7 percent of the rural population), and

planned to target 3.6 million in 1999 (Table 6.4).

130

Table 6.4 Number of Participants in the NEP and Sasakawa/Global 2000

 

 

 

Programs

NEP Sasakawa/Global 2000 Total
1993 0 153 153
1994 0 1,322 1,322
1995 32,046 3,185 35,231
1996 341,244 2,127 343,371
1997 600,634 - 2,003 602,637
1998 2,900,000 0 2,900,000
1999 ' 3,600,000" 0 3,600,000

Source: MOA 1999.

Note: "' Planned.

Although the NEP tried to replicate the design of the SG, its large-scale operation
restricted its ability. The NEP is based upon the SG concept of closely supervised farmer-
managed demonstration plots, with the same levels of recommended fertilizer and
improved seed. In practice, many NEP participants were either “model” or “copy”
farmers. The “model” farmers (who have been in the NEP at least a year) were used in
lieu of extension agents to demonstrate to “copy” farmers how to use the new technology.
The 86 was designed as a pilot program whereby farmers would be exposed to the
benefits of the high-input technology and would be encouraged to use the improved inputs
after a couple years of close technical supervision. By contrast, the NEP is thought to
promote the FDRE’s goal of food self-sufficiency. Some even considered it a production
campaign (GMRP 1998). This means that in effect NEP participants that should graduate
fi'om the program, do not. It is known that they will not be able to sustain the NEP

practices without the facilitative role of the NEP (discussed fiirther in the next chapter) in

131

providing credit and inputs, thus the NEP will continue to enroll farmers past the

recommended two years.

Many aspects of NEP administration differ substantially from the earlier 86
program due to its reliance on a rapidly changing input markets. The rapid NEP
expansion has taken place at a time of major changes in markets, policies, and institutions
afi‘ecting the agricultural sector: a new credit system in 1994, gradual liberalization and
privatization of the fertilizer market from 1991 to 1997 (when the last subsidies were
removed), and government decentralization (administrative and fiscal responsibilities being
shifted from the national to the regional level). The NEP credit system is more complex:
(1) there are multiple actors (banks provide credit, regional governments guarantee credit,
and development agents (DAs) approve participants and collect payments); (2) interest is

charged; and (3) local policeare used for enforcement.

In addition, the NEP needs to deal with a fertilizer sector characterized by supply
ineficiencies ofien aggravated by policy uncertainties and government-protected markets.
Whereas the SG had the resources to facilitate timely delivery of inputs, the NEP must
rely upon the existing market mechanisms for delivery of inputs (govemment-guaranteed
bank credit and input distribution system described in chapters 4 and 5) which often led to
tardy deliveries (GMRP 1998). When asked about the difi‘erences between the SG and
NEP, one NEP official replied, “The only difference is budgetinguthe allocated budget by

the S6 was more than enough, unlike the extension program” (GMRP 1998).

132

Overall, the key administrative difi‘erences in the two programs may afi‘ect the
NEP’s ability to deliver a quality extension package and thus have an impact on

productivity growth (discussed firrther in chapter 7).

6.3 Expanding High-Input Technology to the Broader Population

Fertilizer use is profitable for a portion of rural households that are currently using
high-input technology at some level. Profitability of the new technology is a firnction, in
part, of the yield received and thus also a function of whether the farmer is technically
eflicient (recognizing that the input/output price ratio is also a fimction). Highlighting the
key differences in household profiles between fertilizer users and non-users can provide an

insight of what factors may increase adoption and levels of fertilizer use.

Thus far it is shown that the organization of the SG may have allowed it to provide
timely delivery of inputs, as well as a quality extension service, relative to the large-scale
NEP. However, the successfill use of the high-input technology on farms may only be
partially attributed to such factors. Farmers’ ability to use the package efficiently may
complement a high quality extension service in order to realize the potential of the new
technology. Can the SG technology be introduced to a broader set of farmers under
similar agro-ecological conditions and face the similar impressive yield growth?
Specifically, were the SG program participants better suited in terms of resources (such as
animal traction, labor, education, timely inputs and credit) to use the new technologies

more emciently than the broader population? In addition, as the SG program is scaled-up,

133

will the quality of the NEP—in providing low-cost, timely inputs and quality extension—be

an important factor for expanded productivity gains?

6.3.1 Complementary Factors in Using High-input Technology Efficiently

The $6 high-input technology is labor intensive relative to traditional technologies.
The increased labor requirements of the SG high-input technology results in higher levels
of labor used, but also an increased use of mutual (extended family) and hired labor. For
both the SG and traditional plots, family labor is an important component of total labor,
representing 61 percent of total labor days for the 86 participants and 73 percent for
traditional technology users. Mutual labor accounted for 25 percent of the 86’s total
labor days and hired labor accounted for 13 percent. For traditional technology users,
mutual labor accounted for 15 percent of total labor hired labor accounted for 12 percent.

The numbers of family days under the SG plots in ploughing, planting, and
particularly in weeding and harvesting activities was higher than the number of family days
used on the traditional plot. It is the comparisons across mutual‘2 and hired labor that is
more striking. The number of mutual labor days in all activities for high-input users was
27.5 compared to 8.2 under the traditional technology (Table 6.5). Disaggregation of the
sample into the lower and upper halves by yield reveals a rough positive relationship

between the amount of mutual labor used in weeding and yields. The lowest yield half

 

”Mutual labor refers to labor provided by close fiiends or extended family member with
neither inkind nor monetary payment.

134‘

under the SG technology used 8.4 weeding days per hectare of mutual labor compared to

13.7 weeding days in the top half (Table 6.5).

Table 6.5 Average Level of Labor Use by SC and Traditional Technology

 

 

 

 

SG Plot Traditional Plot
Bottom half Upper half All Plots Bottom half Upper half All Plots
yields yields yields yields
Yield Range (kg/ha) 2,420-5,460 5,461-8,117 1,018-2,783 2,784-6,343
. mle size n=28 n=29 n=57 n=23 » n=23 n=46
yield (kg/ha) 4,441.5 6,444.1 5,460.4 1,918.8 3759.5 2839.1
DAP (kg/ha) 96.3 109.0 102.8 88.9 98.9 93.9
urea (kg/ha) 96.3 109.0 ' 102.8 0.0 0.0 0.0
seed(kg/ha) 23.9 27-6 25-8 ....... .322. ........... 33.0.. w 36.1 .e
Lawson 103.9 um 10,, 53.5 ‘ m sss
family 66.7 64.8 65.8 41.6 40.5 41.0
hired 11.4 16.7 14.1 4.0 9.3 6.6
_ mutual2 25.8 29.1 27.5 .. 7.9 . _ _ _ V 8.4 8.2
imam-shins ,' 31.8 32.1 31.9 184 184 184
family ploughing 28.1 28.2 28.2 18.2 16.8 17.5
mutual ploughing 3.6 3.2 3.4 0.2 1.5 0.8
hired ploughing 0.08 0.7 0.4 g .010... ‘ ._ ._ 0.0 0.0
Totalplantrng ’ , 15.1, 13.9 ' 14.5 fi§§giji§8i4§§ftx¥j; 7.8 8.1
family planting 5.7 6.8 6.3 5.9 5.2 5.5
mutual planting 9.4 7.0 8.2 2.5 2.6 2.6
hired planting“ ‘ 0.0 0.05 .03 y 0.0 7 0.0 0.0
‘ Total weeding . I ,.3.8?."3 1 44.5 ' ' 41.5 i 7 16.4 18.9 17.6
family weeding 22.9 22.4 22.6 11.7 11.4 11.6
mutual weeding 8.4 13.7 11.1 2.9 3.9 3.5
hired weeding 6.9 8.5 7.7 L, 1.7 3.5 2.6
Totalharvest ‘ 5 18.7 20.2 ' . 19.5 E]: {-10.4 » ' 13.1 11.7
family harvesting 10.0 7.4 8.7 5.8 7.0 6.4
mutual harvesting 4.4 5.2 4.8 2.2 0.3 1.3
hired harvesting 4.4 7.5 5.9 2.3 5.7 4.0

 

 

Source: Compiled from Howard et al. 1999.
Notes: ILabor day/ha, day=8 hrs. All columns are averages and thus line items may not sum to subtotals.

2Mutual labor refers to labor provided by close friends and relatives where neither an in-kind nor cash
payment is made.

135

The SG package not only required additional labor, but required farmers follow
specific guidelines on timing of planting and weeding and plant spacing. For example, the
86 program recommended 3-5 plowings before planting and that the planting date occur
during the rainy period, when the soil is moist. The S6 recommended that the seeds are
covered by hand; however, due to farmer reports of a labor shortages during planting
time, oxen were often used to cover seeds (Buta 1997). The number of oxen days used on
the SG plots is 23 days versus 21 on traditional plots (Table 6.6). Although few
households own their own oxen, most farmers borrow (rather than rent) oxen through

exchange of their labor services.

Table 6.6 Average Level of Animal Traction by Program Type and By Yield

 

 

 

SG Plot Traditional Plot
Bottom half Upper half All Plots Bottom half Upper half All Plots
yields yields yields yields

yield range (kg/ha) 2,420-5,460 5,461-8,117 1,018-2,783 2,784-6,343
sample size n=28 n=29 n=57 n=23 n=23 n=46
ArumalTr-actton . A _ ' 1 . V , f   , 4 ‘ ' i; 7 .
total ox days 67.9 66.7 67.3 41.7 47.2 44.4

own or borrowed 65.5 66.0 65.7 41.4 46.9 44.1

rented 2.4 0.7 1.6 0.3 0.3 0.3

 

Source: Compiled from Howard et al. 1999.
Note: All columns are averages and thus line items may not sum to subtotals.

A comparison of means revealed that the planting date and row spacing are two
management variables in which deviations from the SG recommended practice negatively
afl'ected yields. Yields for farmers that followed the SG recommendation regarding

planting date were significantly higher (5.55 MT/ha) relative to yields for farmers that did

136

not follow the recommended planting date (5.03 MT/ha) (Table 6.7). Divergence from
SG recommendations were not necessarily due to farmer negligence. Ifthe delivery of
improved seed or fertilizer is late, as was often the case for NEP participants in 1998, then
planting, and consequently, the first weeding date will be delayed. Yields were not
significantly different for the timing of the first weeding date or for plant spacing;

however, they were significantly difi‘erent for row spacing (Table 6.7).

Table 6.7 Mean Yield Comparisons Across Management Practices, SG and
. Traditional Plots, Jimma and West Shewa Zones, Oromiya Region

 

 

 

Mean Yield (MT/ha)
Farmer practice equals 86 Farmer practice does not equal 2-tailed
recommendation SG recommendation significance
(p-value)
Planting date 5.55 5.03 .032
First weeding date 5.44 5.13 .286
Row spacing 5.86 , 5.15 .028'
Plant spacirg 4.38 5.21 .229

 

Source: Compiled from Howard et al. 1999. .
Note: 'Null hypothesis of equal variances was rejected, test assumes unequal variances.

6.3.2 Deterruinants of Fertilizer Adoption and Intensity of Use

Given that efficient use of the high-input technology may require additional labor
and management skills, the households that are relatively wealthier and better educated
may be in a favorable position to achieve maximum yields fi'om the new technology. Not
only were there significant difl‘erences in key characteristics of farmers between the SG
and the broader population in the Oromiya Region and nationally, but there were key

differences between fertilizer users and non-users across the country. Regardless of agro-

137

ecological area, there may be significant household factors across Ethiopia that determine
whether a household will adopt fertilizer and use it efficiently. Identifying these factors
can improve an evaluation of inputs market performance-whether the inputs markets are

conducive to expansion of the high-input technology.

The probability of fertilizer adoption (whether or not a household uses fertilizer)
and the intensity (level) of use is explained primarily by variables relating to access and
profitability. Access can be endogenous to the household, household access, in terms of
liquidity (e. g., crop income) and wealth (e.g., ownership of land and oxen), but there is
also an issue of market access which is exogenous to the household. Examples of market
access include household proximity to all-weather roads, access to information regarding
prices and location of fertilizer supplies in the market. Both factors come into play for
credit. The ability or inability to utilize credit may be due to a constraint internal to a
household (lack of collateral or poor repayment record, for example), or exogenous
because credit is not available for any households in the vicinity due to lack of banks in the

area or government policy that restricts access.

Farm income, the net cash position and stock of wealth of a household, positively
influences its ability to purchase fertilizer (Croppenstedt and Demeke 1996). In 1998,
although most farmers received fertilizer on credit, a down payment (usually 25 percent of
the principle) was required at the beginning of a season. In 1995/96 the gross income of

households using fertilizer was 28 percent higher (3,241 Bin/season") than for non-using

 

“Data is for the larger, meher seasonna significant portion of most household’s crop.

138

households (2,534 Bin/season) (Table 6.8). There was also a significant difference in the
value of food crops between users and non-users with users receiving 2,260 Bin/season
compared to 1,096 Birr/season (Table 6.8). Thus, crop income may be a constraint to

fertilizer adoption, given that off-farm income was lowuroughly 3 percent of total income

(Table 6.8).

Table 6.8 Comparison of Mean Household Indicators Between Fertilizer Users
and Non-Users

Income' Value of Value of Value of Value of Hectares Household

 

 

(Birr) food crops food crop ofl-farm livestock size
(Birr) per hectare income (Bit!) (Number of
. (Biff) (Biff) mtg)
Non-users’ 2,534 1,096 1,373 30 2,140 0.88 4.9
Users 3,241 2,260 1,563 97 3,233 ' 1.52 5.6
i>>|tl3 0.050 0.000 0.00 0.079 0.000 0.00 0.000

 

Sources: Income variables provided by Yamano, T. and Central Statistical Authority 1995/96. Table
computation by author.

Notes: ‘Income=valueoffoodcrops+valueofcashcrops+valueofofl'-farmincome-+0.2"(valueof
stock of livestock). The assumption is that 20 percent of the stock on livestock is income (e.g., sale of
milk and eggs).

2 69 percent (1,947 households) of the sample are households that do not use any fertilizer. n=2, 824.
3 Two-sample t test with unequal variances.

In addition to a cash income, fertilizer users also have a larger stock of wealth,
measured by land and livestock. The households using fertilizer had a higher value of
livestock relative to non-users (Table 6.8). Livestock reduces the risk of using fertilizer
and thus, its real cost because a household is “insured” against crop failure—they have the

option of selling their livestock to purchase food. In addition, livestock--particularly

139

oxen—are employed in ploughing activities (to prepare the seed bed) and are even used as

a substitute for labor in planting (to cover seeds) and weeding activities.

Whereas farm size usually has a positive influence on fertilizer adoption, the efi’ect
on the level of fertilizer use may be positive or negative (Demeke et al. 1998). Much
evidence supports the view that the incidence (as opposed to intensity) of adoption of
high-yielding fertilizer varieties is positively related to farm size (Feder et al. 1985).“
Although fertilizer appears to be a scale-neutral input, there may be a fixed “set up” cost
in terms of locating markets and learning how to apply fertilizer. Farmers with larger land
holdings may have greater access to extension, market information, and credit due to
higher levels of collateral (the land) and/or higher levels of social capital in the area."
Overall, in Ethiopia land size is a strong predictor of fertilizer adoption (Demeke et al.

1998; Mekuria 1995;1(ebede et al. 1990; Itana 1985).

Another reason larger land holdings may be an indicator for increased high-input
adoption stems from the “safety-first” models (Smale et al. 1995). Farmers that are able
to secure their food needs on a portion of their land may be more willing to take the risk
of adopting a new technology on the remaining area. In Ethiopia, nearly 40 percent of
rural households had less than 0.5 hectares of land and about 60 percent have less than

one hectare (CSA 1995/96). Fertilizer users had, on average, 1.52 hectares of cropped

 

“An assumption here is that the effect of high-yielding varieties and fertilizer is the same.
"See Feder et al. 1985 for a discussion of Weil’s 1970 land size/credit relationship.

140

area which was significantly higher than the 0.88 hectares for non-users (p=0.000) (Table
6.8).

Although a good predictor of fertilizer adoption, land size is not a good indicator
of the level of use. The first five deciles of fertilizer users, using fertilizer least intensively,
had an average of 1.62 hectares of cropped area compared to the last 5 deciles who had an
average of 1.43 hectares of cropped area (Table 6.9). Households with relatively smaller
farms may be inclined to intensify (use higher levels of fertilizer per hectare) because they

do not have the option to practice fallow to maintain soil fertility.

Table 6.9 Average Level of Household Characteristic Per Fertilizer Decile

 

 

 

Fertilizer docile by fertilizer use per Household total Value of Number of
hectare cropped area, household household adult
hectares livestock, Birr equivalents
Households that do not use fertilizer 0.83 2,489.22 4.07
F '1' r '1 m '
1 s 9.8 1.71 3,048.07 4.92
2 9.9 - 18.6 1.69 3,563.91 4.69
3 18.7 - 28.1 1.59 3,919.33 4.61
4 28.2 - 37.5 1.57 4,023.33 4.76
5 37.6 - 49.0 1.55 3,699.47 4.81
6 49.1 - 60.9 1.46 3,463.38 4.54
7 70.0 - 78.5 1.44 3,409.55 4.74
8 78.6 - 102.4 1.39 3,238.61 4.45
9 102.5 - 135.7 1.35 3,113.36 4.63
10 2135.8 1.51 3,274.62 4.86

 

Source: CSA, Rural Household Survey 1995/96.

In general, factors such as access to credit and access to fertilizer are relatively

more important than the profitability of fertilizer in determining whether a household uses

141

fertilizer (adoption). However, the perceived profitability“ of fertilizer is unquestionably
significant in a farmer’s decision of how much fertilizer to purchase (Crooppenstedt and
Demeke 1996; Itana 1985; Demeke et al. 1998)." An indicator that profitability is
importantto the decision to use fertilizer is that most fertilizer is applied to tef, the most
lucrative grain in Ethiopia. Tef is produced on 34 percent of cropped area for non-users
compared to an average of 41 percent for fertilizer users (CSA 1995/96 and 1996/97).
Additionally, among fertilizer users, there is a positive relationship between the amount of
area to tef and the intensity of use. Fertilizer users in the lowest fertilizer decile allocated
31 percent of their area to tef compared to 45 percent in the highest decile (CSA

1995/96).

Profitability of alternative farm investments is also a consideration in the decision
to use fertilizer. A household that is a net grain buyer may purchase fertilizer because it is
cheaper to produce the additional grain than to purchase it on the market. In rural areas
75 percent of household expenditures are on food (World Bank 1998). Of the food
consumed by households, 53 percent is obtained by cash purchases from the local market
and roughly” 45 percent of food consumed comes from subsistence production (World

Bank 1998).

A separate issue to whether a household has the liquidity to afi‘ord fertilizer

(household access) is whether the market provides the households the opportunity to use

 

“Represented by the incremental output due to fertilizer, and input and output prices.

"Because fertilizer prices were pan-territorial (do not vary across space) for much of
Ethiopia’s recent history, they can not be entered into econometric models.

142

fertilizer in an emcient manner (i.e., availability of information and access to inputs).

High transport costs and lack of transport remain primary reasons for low productivity,
lack of trade, and low incomes. “Supply-side constraints are often more important than
demand factors in limiting growth in consumption” (Heisey and Mwangi 19972195). The
market price facing the farmer may not necessarily be a good indicator of profitability
because it does not factor in the full cost to a farmer of using fertilizer which may include
transaction costs such as transport or search costs. These “non-price” factors increase the

shadow cost (financial plus opportunity cost) of fertilizer.

Increased intensity of infiastructure increases the probability that a household will
adopt fertilizer (Demeke et al. 1998; Croopenstedt and Demeke 1996). The number of
fertilizer distribution centers, number of commercial banks, and distance from markets in
the wereda, and access to an all-weather roads were significant factors in predicting
adoption. The World Bank calculated a normalized road index of 55 for Ethiopia, which
is much lower than the index of 115 for Kenya and 144 for Zimbabwe (World Bank
1998).“

Another factor related to transaction costs is credit. Access to fertilizer credit can
facilitate the use of fertilizer (by eliminating the search costs for fertilizer as well as easing

the liquidity constraint). Prior to 1998, when SCs were the primary provider of input

 

“The normalized road index is calculated from the total length of roads where the
expectation is conditioned on population, population density, per capita income,
urbanization, and regional-specific dummies. The “normal” value id 100. Ifthe index is
greater than 100 then the stock of roads in a country exceeds the calculated expectation.

143

credit, farmer access to credit and therefore, inputs was improved with membership to
service c00peratives (Demeke et al. 1998).

An important factor determining the level of fertilizer use (intensity) is knowledge
of the recommended level of fertilizer use and farm practices that improve the profitability
of use. In neoclassical economic theory profit maximizing households will increase the
level of fertilizer until the incremental net revenue equals the cost of fertilizer. In practice,
there are often liquidity, but also knowledge constraints to realizing this optimum.
Variables that represent interaction with extension agents and farmer distance to extension
ofices (proxies for farmer knowledge) are important in determining variations in fertilizer

use (Y 1rga et al. 1996).

6.3.3 Key Differences Between the SG Technology Package Users and the
Broader Population

Similar to the comparison of fertilizer users and non-users, a household profile
(i.e., selected characteristics) of 86 program participants looks very different from the
same household profile of typical agricultural households located in the same vicinity and
covered by the National Central Statistical Authority Agricultural Sample Surveys" (Table
6.10). The SG participants were selected based upon the following criteria: farmers own
their own farm land, were members of a farmer association, were clear of debt, had

suitable land for the extension package, and were likely to repay their debt (Buta 1997).

 

"The SG survey was conducted in 1997 and the CSA households were surveyed in
1995/96; however, a comparison is still valid on the premise that household characteristics
such as farm size will not change significantly over 2 years.

144

The comparisons revealed that participants in the SG program cultivated more
land (both absolutely and per capita), had larger household sizes (i.e., more available
labor), appeared to have more capital (more livestock and traction animals), and had better
educated household heads than the typical households described by the CSA data
(Howard et a1. 1998). For example, the mean hectares cultivated per capita in East Shewa
was 0.62 hectares, 0.34 hectares in West Shewa, and 0.31 hectares in Jimma, compared to

a mean of 0.21 hectares for all of Ethiopia (Table 6.10).

145

Table 6.10 Selected Characteristics of SC Participants Households Versus the
. Broader Population of Agricultural Households

 

East Shewa (te1)‘ West Shewa Jimma (maize)y Ethiopia
(maize)’ average

Program CSA Program CSA Program CSA
partici- sample partici- sample partici- sample
pants farmers pants farmers pants farmers

 

Mean area cultivated 3.0 2.0 2.6 1.5 2.1 1.0 1.0
(ha/household)

Mcan population 7.1 5.7 8.7 5.5 7.4 5.0 5.2
(persons/household) ,
Mean hectares cultivated 0.6 0.4 0.3 0.3 0.3 0.2 0.2
per capita‘

Percent of literate 95 22 85 36 95 19 22
household heads

Mean Livestock Units 5.1 4.7 5.4 4.0 4.7 3.1 3.5
per household’ ,

Mean number ofdraft 2.7 1.9 2.3 1.7 2.3 1.5 1.1

animals Er household
Sources: Howard, J. et al. 1999; Central Statistical Authority, Rural Household Survey, meher crops,
1995/96.
Notes: l205 households from the Central Statistical Authority survey were used in the East Shewa
analysis which covered Boset, Lome, Ada, Dugda, Arsi Negele, Shashemene, Seraro, and Akaki weredas.
2221 households from the Central Statistical Authority survey were used for the West Shewa analysis
which covered Woliso, Becho, Ambo Zuria, Dano, Wonchi, and Dendi weredas.
’478 households from the Central Statistical Authority survey were used in the Jimma analysis which
covered Limu Seka, Limu Kosa, Sokoru, Tiro Afeta, Kersa Mana, Goma, Gera, Seka Cherkorsa, Dedo,
and Omanada weredas.
‘Calculated at the household level first, then averaged across households to give each household equal
weight in the calculation; note that the same result will not be obtained when dividing sample mean area
by sample mcan population.
5 Calculated using following weights: cattle= 1, sheep/goats =0.5, horses/mules =0.7.

The observed differences in level of household assets between $6 participants and
the broader population and between fertilizer users and non-users is a particular concern
because efficient use of the improved technologies may be predicated upon a household’s
ability to draw on other resources (e.g., labor, oxen, and farmer skill in understanding,
implementing, and fine-tuning management practices). The yield model presented in the
following section will attempt to empirically test whether these complementary inputs are

significant. explanatory factors in the variation in yields. Overall, successful expansion of

146

high-input technology across farmers will require attention to providing low-cost inputs as
well as greater access to credit, extension, and fertilizer markets-factors that reduce the

real cost to the farmer of using fertilizer and thus improve its profitability.

6.4 Yield Model

Thus far it has been shown that the organizational differences in the SG and NEP
are likely to have an effect on the efiiciency of inputs delivery in the NEP. In addition, the
difi‘erences in household characteristics between the SG and broader population are also
likely to have an impact on the efiiciency of use of the improved technologies. An
empirical test of whether the technology package (seed, fertilizer, and management
practices) provided by the 86 was significant in explaining yield differences across farmer
plots can validate concerns about the NEP’s organizational structure and its ability to
deliver inputs efficiently.

To determine the marginal contribution to yield growth of the different
components of the SG technology package it will be required to respecify a model
estimated by Howard et al. (1999) which estimated yields as a firnction of technology type
groupings (defined in section 6.1). By entering input and management practices into the
yield model separately it will be possible to determine the importance of individual inputs
as well as the quality of extension in adopting the new technology.

A secondary objective is to determine whether management practices (level and
timing of input application) are more important to yield optimization on the SG plots

relative to the traditional plots. In addition, the model will determine whether household-

147

constant omitted variables are significant in explaining the variation in yields across
households, after controlling for other inputs such as technology, soil characteristics, and
management practices. To what extent is successfirl maize production a function of
farmer’s ability (among other factors)?

As was shown in section 6.2 the broader p0pulation was not as well endowed in
terms of available family labor, thus it is hypothesized that they may not be able to use the
new technologies as efficiently as the 86 households. For example, fertilizer dramatically
increases the population of weeds which, if not controlled, can reduce the potential yield
from the new technology. It was also shown earlier that the broader population is not as
educated as the farmers that participated in the SG program, thus it is questioned whether
efficient use of the high-input technologies is dependent upon the strict management

practices regarding planting and weeding dates and row and plant spacing.

6.4.1 Theory

Yield equations are derived from production fiinctions to estimate the determinants
of yield variability across farmers or tracts of land; however, they often do not adequately
control for omitted factors such as farmer management (ability) which can bias the
estimated coefficients. The “disregard of quality differences in our measures of labor leads
to an upward bias in the estimates of the elasticity of capital inputs and a downward bias in
the estimate of the elasticity of labor inputs” (Griliches 1957:8).

A typical yield firnction is specified without controlling for omitted factors: Y =

F(X,, C3, E.) where Y is yield, X, are management practices, C,- are variables describing the

148

crop condition, and E, are environmental variables (Byerlee et al. 1991). The X, variables
represent the level of inputs as well as the timing and method of their use. The Cj
variables describe the condition of the crop, e.g., infestation by pests, and the E, variables
measure weather as well as soil and site characteristics.

The omitted variables that are often not controlled, such as farmer’s management
skills, characteristics of the soil, and weather, will influence explanatory variables such as
the level of inputs, their timing and method of use, as well as the incidence of disease. If
the unobserved farmer effect is uncorrelated with the explanatory variables then the
unobserved efi‘ect is just another unobserved factor afi'ecting yields which is not
systematically related to the observable explanatory variables whose effects are of interest.
However, if the unobserved effect and the regressors are correlated then serious
estimation problems can occur, the estimated coefficients can not be consistently
estimated. Ifthe correlation between the omitted variable and a regressor is positive, then
the coeflicient on the regressor will be overestimated.

Estimation techniques have been developed, using information on agronomic
practices on two different plots of land under the same farmer, to mitigate the omitted
variable problem under certain assumptions. Given that there is some unobserved a, that is
invariant across plots of land for a single farmer, it is possible to remove the unobserved
efl'ect and consistently and unbiasedly estimate yield. The general model with an

unobserved efi‘ect is specified as:

(6.1) (ylxenr) = Bo + 31.13 + a.

149

where y is yield, xi, is a vector of input levels, agronomic practices, soil characteristics,
and weather; and a, is the unobservable effect for each farmer i and constant across p plots
of land. An assumption is that the unobserved effect represents, among other things, the
farmer’s innate management skills that do not change across plots of land the farmer is
cultivating. Ifit is assumed that the unobserved effect is allowed to be arbitrarily
correlated with the regressors, a fixed effects (FE)’° model can be used to estimate yields
for 40 households that cultivate both a plot under the SG package and a plot under
traditional methods.

A FE model is estimated by specifying a pooled OLS regression with the inclusion
of dummy variables representing each 0,. In fact, the fixed effects estimator is sometimes
called a dummy variable estimator. With the inclusion of n dummy variables, one for each
farmer (with the omission of the one in the intercept), each a, is estimated along with B:
(6-2) Yip = a0 + “1hr '1' achzmanhn + Bxip '*' Us.»
where p=1,2 for plot one and two; i=1,2...n; h,...h,, are household dummy variables and
the estimated a,...u,, are the individual household effects. Specifying the model with n
dummy variables can be computationally cumbersome given models with thousands of
individual effects (09 and limited computer storage space. Partition regression methods
can be used in specifying an equivalent, and simpler model (Greene 1993). Given the

typical linear unobserved effects model

(6.3) y, = xi, B + a, + 11,, for p=1... P,

 

”Ifthe unobserved efi‘ect is uncorrelated with xi, then random effects (RE) is the
appropriate estimation method.

150

a fixed effects transformation can be obtained by taking the average of (6.3) over p=1...P

to get.

(6.4) 5: Strum;

where y: =

— 1
yr». .=—
1 Pp

II M’e
ll M76

1
-— xtp,and ui=— 2111p,
PI) 1

Subtracting (6.4) fi'om (6.3) for each p reveals (6.5) which is the commonly used

specification of a FE model:
(6.5) yaw-3;: (le-Efl-i-Ulp-l-li.

The plot-demeaning process on the original equation removed the individual specific effect
01,.

It must be recognized that the FE model specified will control for the omitted
household variables, but will not control for the potentially omitted plot-level
characteristics. It is conceivable that the farmer chose his or her most productive plot of
land for the 86 package (due to the higher cost of investment) and left the less productive
land for the traditional methods. Ifthe model does not adequately capture the factors

influencing this decision, then biased estimates may exist.

6.4.2 Data
The data used in the model come from a Ministry of Agriculture/Michigan State
University survey conducted in October and November 1997 of 80 farmer plots in Kersa

and Seka Cherkosa Weredas in Jimma Zone, Oromiya Region. The model estimates yields

151

for 40 households that cultivated two 0. 5 hectare plots simultaneously: a plot under the
SG package of improved seed, DAP, urea, and close extension supervision, and a 1
traditional plot of only DAPand no direct extension supervision. Thus, because there are
two plots of land for each farmer, it is possible to use FE as a method to control for
household-constant factors.

The survey collected data on farmer input use and practices during the farmer’s
most recent meher season. Plot-level and household-level data was collected through
farmer interviews. Thus, the recall period was roughly 8 months-fiom the time of
planting in April/March thru harvest in October-December. Additional plot-level data,

area of plot and yields, were measured directly by the enumerators.

6.4.3 Model Specification

Four difl’erent model specifications will be estimated, each estimated with and
without FE (Table 6.11). The model is estimated in levels. This choice does not
necessarily assume a linear relationship between inputs and outputs. The use of quadratic
terms will be incorporated to capture diminishing returns. Log-log and semi-log
firnctional forms were tested to determine whether they improved the fit of the model but
they did not. Household dummy variables are used to estimate the omitted efi‘ects.

Model 1. The first model will estimate the effect of the SG package in its entirety
(fertilizer, seed, and recommended management practices) on variation in yields across the
plots under both the SG and traditional practices. It is hypothesized that the use of the

package significantly increased yields over the traditional practices.

152

Table 6.11 Model Specification, n=80 (2 plots for each of the 40 households)

 

Model Specification Model Defined

Mgkl 1 Regress yield y,, (kg/ha) on a dummy variable

y,, = a. + (1,11,. +...a.,h,, + B,z-, + 11,, indicating whether the plot was under the 86
technology, represented by the dummy variable
for improved seed and urea, 1%.

M 12 SameasModellbutwiththeadditionofa

ya = 00 + nth: hath. + Bil. + Bsxe + Ban. + 3th + up vector of physical inputs xi, (DAP (kg/h).
‘ seed (kg/ha»; a vector of soil and site
characteristics, v,,, (red soil dummy, dummy
for presence of plant discase); and a
management practice, w,,, (difference in weeks

between planting and weeding date).
M 13 SameasModeleutwiththeadditionof
y,, = 00 + tr,h2 +...a,,h,, + 8.1,, + 8,8,, + 8,17,, + B,w,, + 11,, quadratic terms for DAP, seeding rate, and
weed timing. '
M 14 SameasModel3butwiththeadditionof

y,, = a0 + a,h, +...o.,h,, + B,z,, + [321, + 83v, + B,w,, + u, interaction terms between inputs: seeding rate,
DAP, improved seed dummy, and weed timing.

There a few key differences in inputs across the SG and traditional plots. First,
only the SG plots used improved maize seed (a hybrid called BH-660); the traditional
plots used indigenous varieties. The SG plots used equivalent levels of DAP and urea,
103 kg/ha each, higher than the 100 kg/ha recommended of each. The traditional plots
used an average of 93 kg/ha of DAP but no urea (Table 6.12). Due to the high
collinearity between urea and improved seed (F096), seed type also represents the efi‘ect
of urea. Seeding was conducted in rows on both the SG and traditional plots, however,
the SG had specific recommendations with regard to row and plant spacing and seeding
rate.

All models will first be specified without controlling for the household-constant

effects and then respecified using household fixed effects. It is hypothesized that the

153

household-constant factors may include farmer management skills as well as other omitted
variables such as rainfall and soil characteristics (to the extent that these variables are not
specified in the model). It is also hypothesized that the inclusion of household FE will
influence the estimates of the regressors, to the extent that the regressors are correlated
with the omitted factors. A graph of traditional yields against 86 yields reveals that there
may be an underlying structural relationship between the two farmer plotsua farmer’s skill
may positively influence yields on both the SG and traditional plots (Figure 6.2).” That is,
if yields on a farmer’s SG plot are above average, then it is likely that the farmer’s yield on

the traditional plot will also be above average.

Figure 6.2 Relationship Between Yields on SG and Traditional Plots

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

9,0“)

3.000 . 6’

7,00) ‘ q 3 0
En 6000 t , «4’1 °
5%: 5000 :3. as. 6
§‘ 4,000 -——0—‘—oi ° L

2,0“)

1,0“)

0 1 r r
0 2,0» 4,0“) 6,0“) 8,”
Traditional plot yield, kg/ha

 

 

 

 

"The correlation coefficient between the SG and traditional plot yields was 0.35, at a 0.05
2-tailed significance level.

154

Model 2. The S6 package will be disaggregated into its separate
components-fertilizer, seed, and management practices. Model 2 will test the hypothesis
that the separate elements of the SG technology package will have significantly difl’erent
efi‘ects on yields, and thereby reduce the magnitude of the effect of the improved seed,
binary variable in model 1. Descriptive statistics revealed that although the SG technology
package is standardized, the SG participants did not necessarily follow the
recommendations with respect to level and timing of input application and management
practices. Model 2 will attempt to reveal which inputs or management practices are
especially critical for yield optimization.

Specifically, model 2 will build onto model 1 by adding a vector of physical inputs,
a, (quantity of DAP and seeding rate), a vector of soil and site characteristics, v,,,
(presence of red soil dummy and plant disease), and a management practice, w,,
(difference in weeks between planting and weeding date).

Agra-ecological Variables. Given that Ethiopian maize is predominately rainfed,
the variations” and level of rainfall are key to a good harvest. Rainfall and other omitted
soil and site characteristics, as well as household management skills, will be captured in
the household dummy variables. However, to the extent possible, factors such as soil and
site characteristics and plant disease will be controlled. Trial data revealed that red, gray
and brown soils are particularly responsive to nitrogen and phosphate compared to black

soil (ADD/NFIA 1992). A binary variable to represent red soil will be entered in the

 

”Moisture stress immediately following seeding has relatively little impact on yield
compared to moisture stress later in plant growth, at and following flowering (Amede
1992).

155

model: 75 out of 80 plots had red soil and the remaining 5 had black soil. The slope of the

plot (level or steep slope) was entered in the model, but it was not reported because its

hypothesis test for significance failed. Plant disease, partly controlled by the application of

herbicides,” is thought to negatively affect plant density, and hence yields. The incidence

of plant disease, farmer’s assessment of the magnitude of the problem, will be entered as a

dummy variable.

Table 6.12 Descriptive Statistics of SC and Traditional Plots

 

All Plots so Plot Traditional Plot
n=80 n=40 n=40
Mean ' Standard Mean. _ ' Sta “a. m ' Meansranda Kira ;;:.:¥
. . . _ e . ‘ . ' ' Deviation ‘ ' . Devratlon -:f[f;,;.§f§5.__.7',.”_',on"
WM
Yield (kg/ha) 4227.0 1846.1 5570.1 1208.8 2,884.9 1320.9
ntin R rs
Seed (kg/ha) 30.2 10.1 26.3 6.3 34.1 11.6
Urea (kg/ha)‘ 51.5 53.8 103.0 20.6 0 0
DAP (kg/ha) 97.9 28.2 103.0 20.6 92.8 33.6
Number of weeksbetween
planting and first weeding 5.2 2.6 5.2 2.3 5.3 2.8
~ . f v ‘ ' Number creases Number ofCases Number creases
Mares—son
Seedtype 1=improved seed 40 40 O
0=loeal seed 40 0 40
Soil type 1=red soil 75 37 38
0=blac1t soil 5 3 2
Plant l=damaging 12 6 6
disease 04% 68 34 34

 

 

 

53Only one farmer in the sample applied herbicides after planting. The improved seed was

treated with a pesticide treatment, marshal, before planting.

156

Seed Quantity and type of seed, their interaction, and plant density" are
hypothesized to be important factors in explaining yield variations. Both .86 and
traditional farmers exceeded the recommended seeding rate (25 kg/ha), at 26.3 kg/ha and
34.1 kg/ha, respectively. It is hypothesized that this overdose negatively afl‘ected yields.

There is a trend of increasing yield with increasing plant density (provided
suficient moisture) (Amede 1992). Thus, there may be a misconception by farmers that
more seeds means higher plant density. In general, recommended plant density is 44,444
plants/ha for full season varieties (as modeled here) (Amede 1992). However, plant
density for both the SG and traditional plots was much lower: 27,375 plants/ha for the
irnproved variety and 22,812 kg/ha for the local variety.

Farmers using the improved seed may have relatively more control over plant
density for a variety of reasons, high-quality seed being one. Seeding density (kg/ha) for
the improved seed was positively correlated with plant density (r=0.19), but there was a
negligible relationship between seeding density and plant density (r=-0.03) with the local
seed. Quality seeds contain suflicient nutrients to emerge, but animal and insect attacks
and soil crusting immediately following planting can reduce plant density (Schulthess and
Ward 1999). The 86 recommendation regarding plant spacing (80 cm between rows, 50
cm between plants, and 2 seeds planted per whole) is one method to encourage farmers to
increase their plant density. However, the recommendation did not produce the desired
plant density—one reason for the less-than-recommended average plant density may be

that 6 out of 40 farmers reported incidence of plant disease on their improved variety plot.

 

s‘Measured as the number of plants per hectare, recorded roughly a month before harvest.

157

Num‘ents. Ethiopian soils are predominately deficient in phosphate and nitrogen”
and therefore, their application is hypothesized to significantly contribute to explaining
yield variation. Tropical soils, in general, and specifically in Ethiopia, are either deficient
in phosphate or have a high phosphate fixing capacity (thus rendering the phosphate
unavailable for plant growth). Soils in J imma Zone have historically been deficient in
phosphate (Kena et al. 1992). Phosphate (P) is combined with nitrogen (N) in DAP
(diammonium phosphate) which is 46 percent P and 18 percent N. DAP is primarily
applied as basal, during planting. Phosphate is particularly valuable for stalk growth as
well as increasing the effectiveness of nitrogen. The efi‘ect of P in I 1mma can last for three
years irrespective of the rate, but the efl‘ect decreases with time (Kena et al. 1992).
Nitrogen, the one nutrient most needed by maize, is primarily applied as a top dressing,
usually about a month afier planting.

It is hypothesized that the level of optimum fertilizer use is a firnction of whether
local maize varieties or hybrid maize are used as well as plant density. Plant density can be
increased significantly if fertilizer is used in combination with hybrid seed. It is
hypothesized that there exits a positive relationship between fertilizer and plant
density-thus an interaction will also be added to the model.

It will also be tested whether the SG recommendations of 100 kg/ha DAP and 100
kyha are optimal to maximize yields, given the SG recommendations hold with regard to
seeding rate and plant spacing and timing of weeding. It is observed (mentioned earlier in

the chapter) that some $0 graduates used lower-than-recommended levels of fertilizer and

 

”Ethiopian soils are not deficient in potassium (ADD/NFIU 1992).

158

received higher yields than 86 participants that followed the recommendations. As
mentioned earlier, the SG plot received higher-than-recommended levels of fertilizer. The
yield model will attempt to determine whether the recommended fertilizer level was too
high.

Labor. As noted earlier in this chapter, the labor requirements of the SG
technology were much higher than that demanded of the traditional technology. Labor
may be entered in the yield model either in levels or in terms of timing of activities. Some
activities such as planting (particular specifications on row and plant spacing were
required) and weeding will require a substantial number of labor. hours; however, in both
tasks the quality of labor may also be important (perhaps more important than the quantity
of labor).

Labor data (total labor days) as well as labor days by activity (ploughing, planting,
weeding) were entered in the model, but the test hypotheses failed, they were neither
jointly nor individually significant. Perhaps labor, thus specified, is unimportant in
explaining yield variation because the quality of labor is not captured. Thus family versus
extended family and hired labor were entered separately-the hypothesis being that the
quality of family labor would exceed the quality of extended family members and hired
labor. However, the hypothesis tests of significance of labor, when broken down by type
of labor, also failed. One reason that labor was not significant may be that it is correlated
with difi‘erent inputs. For example, the simple correlation coeficient between use of

improved seed, an indicator for the 86 technology, and total weeding labor days is 0.69.

159

Management Practices. One method in which labor can be captured in the model
is through the timing of application of different cropping activities. The 86 program
provided close supervision of the timing and method of input application on farmer-
managed plots. The model will attempt to test whether specified extension messages (e.g.,
quantity of input application, plant and row spacing, and timing of input application) were
significant in explaining yield variations. The quantity of seeds applied to the SG plot, in
part, is an indicator of whether the farmer followed SG recommendations regarding plant
and row spacing. .

Timing of the first weeding is another important 86 specification. Maize is
susceptible to damage by the parasitic witchweed (Striga spp.) which caused a yield loss in
Jimma as high as 36 percent when maize was left unweeded compared to when it was
weeded three times (Fessehaie et al. 1992). In addition, it is reported the most Ethiopian
smallholders practice a late first weeding (Ransom et al. 1992). In Jimma the 86
recommended that the first weeding occur 4 weeks afier planting.

In I ma the majority of farmers weed by shilshan—the practice of weeding using
a pair of oxen—at 30 days, 40 days, and then again at 49 days after planting (Fessehaie et
al. 1992). Oxen are commonly used in Irmma, particularly to prepare the seed bed, cover
seeds, and for shilshalo. Oxen pull a maresha, a wooden plough that has a single metal
chisel, to cultivate the soil. As shown earlier, SG participants owned more oxen than the
broader population and yet oxen were still rented and borrowed. Most Ethiopian

smallholders exchange oxen for labor services (Gordon et al. 1995). Thus, it is possible

160

that weeding activities may be delayed due to the inability to obtain oxen, particularly if all
farmers demand oxen services at the same time.

Another important variable in explaining yield variations is the timing of planting.
Late planting can lead to greater weed problems, nitrogen deficiency, and water deficiency
which can reduce yields (Ransom et al. 1992). The planting date was entered in the model
as a dummy variable for whether the farmer planted during the recommended planting
week, and two continuous variables, one for the number of weeks that the number of
weeks exceeded the recommended planting date and another for the number of weeks that
planting preceded the recommended week. All variables were dropped from the reported
model because their slope coefficients were not significantly difi‘erent fi'om zero and they
did not contribute to the fit of the model (measured by adjusted R2 ).

Model 3. Model 3 keeps the variables as specified in model 2 but adds a quadratic
term on DAP, seeding rate, and weeding timing. The number of weeks between planting
and first weeding is expected to positively contribute to yields (allowing sufficient weeds
to grow before weeding), however, after some date, late weeding (perhaps due to a labor
or an oxen constraint) may reduce its impact on yields. Another variable that may face
marginal diminishing returns is application of DAP. It is hypothesized that an increased
application of DAP will have a positive net effect on yields, but the net effect will decline
as the level of application increases.

Model 4. Model 4 attempts to improve the explanatory power of the regressors
with the addition of interaction terms between the various inputs and timing of weeding.

It is hypothesized that the net effect of any one input is sensitive to the levels of other

161

inputs and practices and thus this will highlight the importance of farmer management in
optimizing yields. In addition, it is hypothesized that the effect of inputs and practices
may difi‘er when combined with improved seed compared to local maize varieties. The
combination of seeding rate and DAP is one interaction. Agronomic CERES“ maize
simulation modeling revealed that when improved maize seed is used, increasing plant
density can significantly increase yields, but only when accompanied by fertilizer
(Shulthess and Ward 1999). Increased plant density makes little difference in yields
without fertilizer. It is hypothesized that seeding rate will have a negative slope .
coefficient, but when combined with DAP, it will be positive-seeding rate can be increased
when combined with fertilizer because the added plant density is complemented by
additional soil nutrients. It is hypothesized that the net effect of seeding rate is positive. It
will be additionally tested whether the use of improved seed is important in determining

the levels of seed and DAP.

6.4.4 FE Model Results

Model 1. The binary variable, improved seed/urea, is highly significant in
explaining yield variations. On average, the 86 yields were 2,685 kg/ha higher than
traditional plot yields. Even when household-constant omitted factors were included in

the model the magnitude of the difference remained roughly the same (that is, the

 

“CERES (Crop-Environment Resource Synthesis) simulates crop growth for cereal crops,
including maize, wheat, sorghum and rice. CERES and CROPGRO (for legume crops)
models are specialized models within the larger, Decision Support System for
Agrotechnology Transfer (DS SAT 3.0) model (Jones and Kiniry 1986).

162

Table 6.13

Plot-Level Maize Yield Model Results, kg/ha

 

 

 

 

Model 1 Model 2 Model 3 Model 4
w/o FE’ w/FE w/o FE' w/FE w/o FE' WIFE w/o FE' w/FE
Seed Fertil' ‘
Improved seed/urea 2685.11 2685.11 2451.80 2674.73 2361.21 2569.60 -618.55 -113.02
1=improved (9.48)‘“ (11.79)‘“ (7.99)‘” (9.39)‘“ (7.63)‘” (8.85)'” (-0.43) (-0.07)
seed/urea, 0=10cal
variety/no urea
DAP (ks/ha) 11.80 1.96 21.71 -0.78 .2104 -62.57
' (2.34)” (.03) (0.94) (-0.03) (-0.65) (-1.61)
Seed rate (kg/ha) -18.76 -3.61 -132.77 -181.59 -304.15 -443.46
(-1.l8) (-0.20) (-l.35) (-1.48) (-2.84)“‘ (-3.19)‘”
Mement
Weed timing - 71.83 14.52 378.08 160.04 682.80 559.67
number ofweeks (1.33) (0.14) (1.91)‘ (0.55) (3.15)‘ (1.91)‘
between planting and
first weeding
Soil and Site Characteristics
Red soil 1124.69 1470.19 1343.94 1566.05 1904.30 972.77
1=red, 0=black (2.00)” (1.64) (2.35)” (1.75)‘ (3.40) (1.13)
Presence of plant 073.06 1631.90 -983.07 1188.89 -755.67 -97.92
disease damage, (-1.91)‘ (1.33) (-2.36)“ (0.96) (-1.83)‘ (0.08)
l=present
Interactions
DAP‘DAP «0.03 0.06 -0.08 0.07
(-0.21) (0.42) (-0.55) (0.45)
Seed rate‘Seed rate 1.61 2.39 3.57 5.45
(1.17) (1.38) (2.47)” (2.92)”‘
Weed timing‘Weed -22. 14 -7.12 -17.00 4.97
timing (-1.62) (-0.35) (-1.18) (0.25)
Seed rate‘ DAP 1.17 1.51
(2.80)‘ (2.73)”
Improved seed‘DAP 28.31 29.63
(1.51) (1.30)
Seed rate' Improved 9.46 -7. 15
seed (0.18) (0.12)
Weed timing‘Seed -10.87 -13.68
rate (4.86)" (4.20)”
Constant 2884.99 2069.87 1 100.93 485.01 1225.22 2631.66 4986.48 9477.26
(14.41)‘” Q84?” (1.21) (0.33) (0.68) (1.12) (2.20)“ (2.91 1‘”
R2 0.54 0.85 0.60 0.86 0.63 0.87 0.69 0.91
Adjusted R’ 0.53 0.69 0.57 0.69 . 0.58 0.69 0.64 0.75
Joint F-test:
for hh dummies NA [0.0118] NA [0.0583] NA [0.0783] NA [0.0676]
for DAP [0.0375] [0.4347] [0.0402] [0.0376]
for seed rate [0.3152] [0.3185] [0.0050] [0.0192]
for improved seed NA NA [0.0000] [0.0000]
for weed timing [0.1220] [0.7895] [0.0195] [0.0859]
for quadratic terms [0.2213] [0.3689] [0.0349] [0.0233]
for interaction terms NA NA [0.0071] [0.0444]
n

 

NOE: T statistics reported in parentheses (11.2830: H.:B,:0); p-value reported in brackets;
‘ denotes 10% significance; ” denotes 5% significance; “‘ denotes 1% significance.
‘ Models are specified with and without household fixed effects; NA = not applicable.

163

household-constant omitted factors afi‘ected both plots equally). The household dummy
variables were jointly significant in explaining variations in yields, thus validating the
hypothesis that some household-constant factor (likely farmer skill or rainfall) is correlated
with yields. The mean yield on traditional plots fell fi'om 2,885 kg/ha to 2,070 kg/ha with
the inclusion of household FE, thus it may be possible that the improved seed/urea dummy
in model 1, without FE, was picking up some of the omitted efi‘ects, thereby raising its
impact on yields.

Model 2, Without FE. Model 1 demonstrated that the so package explained 54
percent of the variation in yields; however, model 2 revealed that yield variation can be
further explained by controlling for red soil, the incidence of disease, as well as by
separating out the elements of the SG package. The magnitude of the coeflicient on seed
type/urea fell from 2,685 kg/ha to 2,452 kg/ha. Improved seed/urea, DAP, red soil and
the presence of plant disease were significant. Each additional kg/ha of DAP yielded an
additional 11.80 kg/ha of maize. Recall that the dummy for red soil represented 75 out of
80 plots, thus, although it contributes to the fit of the model, its explanatory power is
questionable. In addition, as with other variables, red soil may be correlated with omitted
factors (e. g., rainfall, management), and thus may not exclusively explain the relationship
between soil and yield.

Model 2, FE. When household FE was estimated many regressors lost their
significance, but especially noticeable was the effect on DAP. DAP lost all of its
explanatory power which suggests two explanations: one, that the omitted efl'ects were

positively correlated with DAP and therefore, DAP was picking up the efl'ect of these

164

other variables before FE was estimated; or two, that DAP is so highly correlated with the
omitted efl‘ects that it is not possible to separate out the separate effects. There may be a
time lag present in which the farmer must discover the optimum level and mix of fertilizer
on his or her plot as well as the appropriate timing and level of complementary inputs and
practices. Accessibility to extension services can reduce this learning curve to some
extent.

The number of weeks between planting and first weeding is not significant,
although it contributed to the fit of the model. Different specifications of weed timing
were entered in the model, but none of the slope coefficients were significantly different
fi'om zero. A dummy variable to represent the recommended 4 weeks between planting
and weeding was specified. The number of weeks that a farmer exceeded the
recommended weed week, if a farmer was late in weeding, was also entered. On average,
weeding was about a week late on both 86 and traditional plots. A similar variable for
early weeding was also entered, but it also failed the hypothesis test that the slope .
coefficient was significantly different from zero.

Model 3, Without FE. Model 3 adds quadratic terms to the specification in model
2; however, among the three variables-~seed density, timing, and DAPuthe net effect of
DAP (linear plus quadratic term) was the only variable that was significant.

Model 3, FE. With the addition of fixed effects, none of the three termsuseed
density, timing, and DAP—were significant. 86 participation remained significant, as did
red soil and presence of disease damage. When the model was estimated with FE, the

presence of plant disease and weed timing were no longer significant. Overall, the ,

165

addition of quadratic terms were not jointly significant and therefore added very little
explanatory power to model 2.

Model 4, Without FE. The interaction terrnsuseed rate and DAP, improved seed
and DAP, seed rate and improved seed, and weed timing and seed. rate—were jointly
significant as were the quadratic terms. DAP, seed rate, weed timing, and use of
’ improved seed were all jointly significant when combined with their respective quadratic

and interaction terms. However, this model may sufi‘er fi'om biased estimates because it
does not control for the omitted household fixed effect.

The total efi‘ect’7 of DAP on yield is a function of the interactions of DAP
combined with seeding rate, and improved seed, as well as a function of the level of DAP
(given a nonlinear relationship between DAP and yields exists). The total effect of weed
timing (how an additional week later than the planting date affects yields) is 180 kg/ha. It
was tested whether the effect of weed timing had a difi‘erent impact on yield depending on
whether improved or local seed was used; however, a hypothesis test of the slope
coeficient of the interaction of weed timing and improved seed was not significantly
different from zero. The total effect of seeding rate (one additional kg/ha of seed) was
significant and negative: -31 kg/ha for local seed and -21 kg/ha for improved seed. The

total effect on yield of one additional kg/ha of DAP was -1 kg/ha for local seed and 27

 

5"The total efi‘ect of any variable (by how yields will change with an additional unit of that
variable) is calculated by adding up the partial derivatives of the separate interactive
efi‘ects which means the coeflicient of an interactive variable is multiplied by the partial
derivative of the variable in question. For example, for DAP, the coefficient of DAP I"DAP
is 0.07 so 0.07 is multiplied by the mean of DAP (the result of the partial derivative of
DAP‘DAP with respect to DAP). The partial derivative of DA'P‘seed rate would also be
added to the sum of partial derivatives, 1.5 1"‘(the mean of seed rate).

166

kg/ha for improved seed. The total effect of improved seed was 2,440 kg/ha higher than
the predicted yield achieved with local seed.

Model 4, With FE Model 4 with fixed efi‘ects is the preferred model. The FE
specification with the inclusion of additional interaction terms improved model 3's
explanatory power (the joint F test of the interaction terms was significant). In contrast to
model 3, the joint efl'ect of the quadratic terms was also significant in model 4. Aside
from weed timing, all other total efi‘ectsuDAP, seed rate, and improved seed-were jointly
significant.

The total effect of DAP (at the mean level of DAP and seeding rate) was 3.25
kg/ha less yield per kilogram of DAP when local seed was used, and 26.35 kg/ha
additional yield per kilogram of DAP when improved seed was used. Thus, at the means
of seeding rate and DAP and with the use of local seed, yields decrease with an additional
kilogram of DAP. Iffarmers using the local maize variety increased their DAP application
to 121 kg/ha, fi'om the mean of 98 kg/ha, ceteris paribus, then the total DAP efi‘ect on
yield would be positive (the response rate fi'om an additional kilogram of DAP on yield
would be positive).

Model 4 reveals that if local seed is used, then maximizing yields given the use of
DAP is dependent, not only upon the level of DAP, but also upon the seeding rate. At the
means of DAP and seeding rate, the total effect of DAP on yields is negative if local seed
is used; however, if seeding rate is increased (and DAP is kept at its mean), the DAP
efi‘ect on yields is positive. If, for the local seed users, the level of DAP was held at its

mean (97 .9 kg/ha) and the intensity of seeding rate increased to its 7 5" percentile, 36.60

167

kyha (the mean is 30.20 kg/ha), then the total effect of DAP is positive: an additional
kilogram of DAP results in an additional 6.40 kg/ha of maize (Table 6.14)."

This result is corroborated by the fact that plant density can be significantly
increased with the use of DAP, but not if no fertilizer is used. Although the seeding rate
of local and hybrid seed users exceeded the recommended rate (25 kg/ha), the resulting
plant density on the SG and traditional plot was much lower than the recommended
density (roughly 25,000 plants/ha compared to the recommended 44,0000 plants/ha).
Perhaps both the local and hybrid seed were not the high quality expected by the extension
service and therefore never germinated. Thus, given low seed quality, the seeding rate

should be increased, as recommended by model 4.

Table 6.14 Yield Response from Mean Level of DAP (98 kg/ha) and Varied
Levels of Seeding Rate, leg/ha

 

Seed Type Mean (30.20 kg/ha) Mean of 25" percentile Mean of 75‘ percentile
(22.40 kg/ha) (36.60 kg/ba)

Local -3.26 kg/ha -15.06 kg/ha 6.40 kg/ha

Hybrid 26.34 kg/ha 26.34 kg/ha 36.00 kg/ha

 

Source: Calculated from coefl'lcients obtained from table 6.13 and means obtained from table 6.12.
Note: The 25‘II percentile represents the lowest quarter of seeding rates observed, the 75" percentile
represents the highest quarter.

Iffarmers are using hybrid seed and urea and 98 kg/ha DAP is applied, then the
total DAP effect at the 25" percentile of seeding rate is 14.5 kg/ha and 36 kyha at the 75"I

percentile. Thus, if a farmer shifts from using local seed to a hybrid seed/urea technology

 

”If farmers are using local seed, fertilizer at its mean, 98 kg/ha, and the seeding rate is
reduced to the 25" percentile, 22.40 kg/ha, then the total efl‘ect of an additional kilogram
of DAP would result in reducing yields by 15 kg/ha.

168

package, holding seeding rate at its 75" percentile (37 kg/ha) then maize yield will increase
by roughly 30 Me (the difference between 36 and 6.4 in table 6.14).

The fertilizer response ratio fi'om table 6.14 can be used to estimate VCRs for
adopting DAP (maintaining use of local seed). Ifthe fertilizer response ratio for local seed
users is 6.40 kg of maize for a kilogram of DAP (with seeding density at its 75"
percentile) the VCR at 1997 prices is 1.34, below the recommended threshold for
profitability of 2. Fertilizer prices would have to drop by 33 percent for a VCR of 2.
Chapter 8 will delve more into the profitability of adopting the high-input technologies,
but this exercise hints that real profitability gains will be had primarily fi'om adopting
hybrid seed and also by repeated use of the technology for a few years in order to achieve
the optimal combination of improved seed, seeding rate, and level of DAP.

The partial effect of the interaction of seeding rate and DAP on yields is the only
DAP interaction that is significant (at 1 percent significance). The partial efl‘ect of seeding
rate interacted with DAP at their means is 45.60 kg/ha additional yield, ceteris paribus.

At the 25" percentile of seeding rate (22.40 kg/ha), the partial efi‘ect is 33.80 kg/ha
additional yield ceteris paribus. At the 75" percentile of seeding rate (36.60 kg/ha), the
partial effect is 55.26 kg/ha of additional yield, ceteris paribus.

The total efl‘ect of seeding rate was significant and negative: yields fell by 45 kg/ha
with an additional kg/ha of seed for improved seed users, and fell by 38 kg/ha with an '
additional kg/ha when local seed was used. The seeding rate should increase (holding
other inputs at their means) to at least 35 kg/ha for both types of seed if the total effect of

mding rate is to make a positive contribution to yields (average seed rate was 26 kg/ha

169

and 34 kg/ha for SO and traditional plots, respectively). In every model specification thus
far, seeding rate has not been significant, but the joint efi‘ect of seeding rate with DAP, .
weed timing, and improved seed is significant. Ifthe level of seed is held at its mean (30.2
kg/ha) then DAP use would have to increase to 125 kg/ha for the local maize seed and
130 kg/l'la for the hybrid maize seed before a positive total efl‘ect on yield is possible.

The total effect of improved seed was 2,572 kg/ha higher than the mean achieved
using local seed. An interaction between red soil and improved seed was entered, but it
failed the hypothesis test that its slope coefficient was significantly different fi'omzero.

In sum, the yield model demonstrated that interactions between inputs and
management practices can be important factors in explaining yield variations. Model 4
suggests that the level of application of seed, fertilizer, and type of seed may require some
adjustment in order to achieve a complementary balance between inputs that will maximize
yields. In addition, the interactions between various inputs and improved seed,
specifically, were also important in demonstrating that the use of improved seed may
require unique management practices, practices that differ fi'om those used in producing
maize using local seed. It is thus concluded that it is imperative that a high-quality
extension service accompany the development of input markets for fertilizer, hybrid seed,

and credit if real productivity gains are to be attained in the near filture.

6.5 Conclusion
The objective of this chapter was to determine to what extent the high-input

technologies were significant in raising yields for the 86 participants. Ifso, then research

170

emphasis on the organization of credit and input markets is warranted (see chapter 7)-to
ensure that use of the new technologies is increased and sustained.

A fixed efl‘ects econometric yield equation was used to determine the relative
importance of the SG-recommended combination of inputs and respective management
practices in explaining yield variations across SG plots. The yield model revealed that the
use of improved seed and urea significantly contributes to substantial yield increases above
the local maize variety. In addition, interaction effects are important to maximizing yield.
Given that weed timing is a significant determinant of yields, it is also important that
farmers are able to plant during the optimal time such that the timing of flowering
coincides with the timing of maximum rainfall. Abede (1992) reported that late planting
often compromises the quality and timing of weeding, thus indirectly the sequencing of
events, even back to delivery of inputs, is crucial to maximizing yield growth fiom the new
inputs. As shown earlier, the NEP relies on various institutions (banks for credit, multiple
companies for fertilizer, the National Seed Enterprise for seed) to coordinate input
delivery which can mean delays. In addition, the NEP may not necessarily have the
financial means to step in to provide transport and collect fertilizer if it looks like its
distribution network is going to be delayed.

A second finding in this chapter was that it appears that the SG participants were
better suited to use the high-input technologies more efficiently than the broader
population and thus this may effect the efiicient use and expansion of the high-input
technologies. It was revealed that 86 participants and fertilizer users, in general, have

significantly higher levels of assets such as land, labor, livestock, and education. Statistical

171

and econometric analysis demonstrated that yields are sensitive to management practices
such as weed timing, planting date, row spacing, seeding rate, and level of inputs. Given
that the broader population is not as well educated (22 percent) as the SG farmers (95
percent), and that the NEP extension agents may not be able to provide the same level of
supervision it is questionable whether the new technologies will see the same impressive
yield increases as observed on the SG plots.

Given that the high-input technologies can significantly increase yields, a
comparative analysis was used to determine what characteristics of the organizational
structure of the SG program may have led to the efficient delivery of inputs and
information that could have complemented farmer knowledge. It was revealed that the
nature of being small-scale and reducing the reliance on other institutions (for credit, for
example) may have facilitated timely delivery of input and a high-quality extension
message.

The following chapter will delve more deeply into the ability of the input market,
and the NEP specifically, to provide quality extension, accessible credit, and timely and

afl‘ordable inputs to smallholders across Ethiopia.

172

CHAPTER 7

PERFORMANCE OF THE INPUT MARKETS: A FOCUS ON FERTILIZER

The SG technologies were tremendously successful in increasing yields and raising
net incomes and returns to labor for SG program participants in Ethiopia. Ethiopia is
currently trying to scale-up the SG success, through its national extension program.
However, as demonstrated in chapter 1, many SSA countries have had limited success in
achieving long-term productivity gains through high-input use, because as governments
expand their agricultural programs they often overextend their financial means and
eventually are forced to withdraw support. Farmers are then unableto rely on the market
to sustain their high-input packages because the input, credit, and output markets are
insufliciently developed and “disadoption” follows. Sustained and expanded use of new
technologies is predicated upon how well the three functions of extension, credit, and
input delivery, as well as output markets meet the needs of farmers. This chapter will
detemline whether these functions-but particularly fertilizer deliveryuof the input market
met the needs of Ethiopian farmers in 1998, one year alter the SG withdrew from
production demonstration programs.

Fertilizer (nitrogen and phosphate) is an important ingredient to increased
productivity. growth in Ethiopia. Research trials have proved the success of fertilizer in

raising yields (ADD/NFIU 1992), and evidence fi'om farmer experience with fertilizer

173

strengthens this conclusion. As shown in the last chapter, eficient fertilizer use on farms
in Jimma was dependent upon appropriate complementary practices, namely seeding rate
and weeding tinting.”

There are limits to expanding yields with fertilizer use when local maize varieties
are used, however. As shown in the yield model in the last chapter, the use of improved
seed and urea (holding all else constant) significantly increases yields. When improved
seed is combined with fertilizer and appropriate (high) plant densities, yields can be further
increased (Schulthess and Ward 1999). Although not directly addressed in this research, it
is generally agreed that the improved seed market needs to be improved to complement
the fertilizer and input credit markets for sustained productivity growth to take hold in
Ethiopia. In 1998 there was ample anecdotal evidence that the hybrid maize seed was
poor quality and also that there was a severe supply constraint (GMRP 1998). Improved
seed distribution is dominated by the parastatal, the Ethiopian Seed Enterprise which
supplied an estimated 47 percent of total demand in 1999 (Howard 1999). The only other

distributer is Pioneer Hy—bred International Inc.“0 who supplied an estimated 5 percent of

 

”In another example, Gordan et al. (1995) found that in Awassa, Oromiya Region, maize
yields increased fi'om 20 to 50 quintal/ha as fertilizer increased from 50 to 200 kg/ha,
using local seed (and holding all else constant). Additionally, the net yield increase fi'om
fertilizer can raise incomes: cash income increased 31 percent with the use of 100 kg/ha
DAP and 50 kg/ha urea in Ada district, Oromiya Region, keeping prices constant (Belete
et al. 1992).

“In 1991 Pioneer and the Ethiopian Government established a joint venture. In 1998
Pioneer sold roughly 1,000 MT to the Seed Enterprise, but most of its sales went to
Kenya. Pioneer stated in 1998 that it had the capacity to fill market demand in Ethiopia,
but perhaps the constraint was that its retail price was higher than that charged by the
National Seed Enterprise. In 1998 Pioneer was experimenting with selling seed on
consignment (Woreda. 1998. Director of Operations, Pioneer, Ethiopia, personal

174

the market in 1999 (Howard 1999). The government distributes the seed through the
NEP, leaving little to no seed supply for the open market. Thus, farmers enter or stay in
the NEP if they desire to use the high-input technologies.

The cost of fertilizer represented roughly 30 percent of the total cost of adopting
the SG high-input technology and improved seed represented 8 percent (including the
value of labor) (calculated from data in Howard et al. 1999). Reducing the cost of
fertilizer ceteris paribus will improve the profitability of fertilizer, thus, improving the
accessibility of the high-input technologies to a broader population-4o farmers in less
favorable agro-ecological zones, and with lower levels of assets. However, fertilizer
consumption is a function not only of cost, but of a host of attributes relating to access to
fertilizer markets-tirnely delivery of inputs, quality extension, and credit. This chapter
will address all issues, but will concentrate on evaluating whether costs can be reduced in
the fertilizer subsector.

This chapter will follow a step-by-step analytical method to identify“ and quantify
potential cost reductions in the fertilizer subsector. Important market attributes are

identified through qualitative responses‘52 fi'om survey respondents in the 1998 GMRP

 

communication).

“Identification of important performance dimensions is in part based upon past research:
international donors (World Bank, 1992; International Fertilizer Development Center,
1995; Economics Department of the University of Addis Ababa 1994-1998; Grain Market
Research Project 1995-1998) have reported on the potential for cost-reduction in the
Ethiopian fertilizer subsector since the rise of the TGE in 1991.

“Respondents vary, including importers, wholesalers, independent retailers, transporters,
as well as officials of Service Cooperatives, and government officials in the National
Fertilizer and Inputs Agency and the Ministry of Agriculture.

175

Input Subsector Survey. A fertilizer cost-build up, reinforced with secondary data on
f.o.b. (fi'ee on board)“3 international fertilizer prices and Ethiopian transport costs, will be
used as a guide to determine how much costs could be reduced in the system at both the
import and distribution stages.

In the next chapter the identified quantified cost reductions throughout the
subsector will be inserted into farm budgets and the resulting net income will be simulated.
The town'of Jimma, 450 kilometers south-west of Addis Ababa in Jimma Zone, Oromiya
Region, is the selected maize area used to simulate changes in profitability because it is
one location where the SG pilot program operated in 1997 and for which farm budgets
were collected by Howard et al. (1999) for farmers that managed both a plot under the SG

technology and a plot managed under traditional technologies and practices.

7.1 Defining Performance
‘Good’ market performance is subjective and characterized by numerous ill-

defined, often unquantifiable measures. Evaluating the performance of a subsector is not
an easy task for there are a wide range of performance measures and dimensions.(Bain
1968; Sosnick 1964; and Brandow 1977). Brandow (1977 :81) illustrates the quagmire
economists face: ‘

Economists asked to appraise the economic performance of an

industry have a difficult task. Ifthey confine themselves to the

elegant abstractions of rigorous general theory, they find few
handles by which to grasp the inelegant real world and are

 

“The price of an export loaded in the ship that will carry it to foreign buyers (Gittinger
1982)

176

wholly unprepared for some of the institutional and dynamic
characteristics of the industry. Ifthey adopt the approach of
industrial organization economics, they find standards
imprecise, measurement both conceptually and empirically
dificult, and judgement usually necessary to reach
conclusions.

In accordance with Brandow’s concerns, the theoretical method used in this
analysis will not attempt to define a quantified index of performance measures or norms,
but will use “judgement and relative comparisons” (Jesse 1978) to evaluate the
performance of the fertilizer subsector. The methodology assumed here is that a degree of
causality exists between market structure, conduct, and performance. That is, underlying
institutions (rules of the game) and property rights, both choices of government, can efl‘ect
the outcome of a market.

When performance of a subsector is evaluated it is commonly understood that first
an established set of performance criteria need to be agreed upon by society. In general,
the marketing functions that are useful to society should be performed at the lowest
possible cost. Although a wide variety of performance dimensions exist, there are
common concerns. Some of the key performance criteria of the fertilizer subsector are:
reduced costs in the sector, price transparency, timely delivery, adequate supply, product
suitability, and adequate level of profit rates. More broadly: (1) Production decisions
should be responsive qualitatively and quantitatively to consumer demands (aggregate

demand equals aggregate supply, and the sector is low-cost); (2) Operations of producers

should be progressive; that is, there should be increased output per unit of input over time;

177

and (3) Operations of producers should facilitate stable, full employment of resources

(Scherer 1980).

7.2 Fertilizer Financial Import Parity Price

A financial import parity price“ is computed fiom a cost build-up: an accounting
technique designed to identify the cost components in one subsector of the economy. In
the case of the fertilizer sector in Ethiopia, it adds all costs and margins fiom the port of
export to retail sale, including the f.o.b. price at the international port of export, shipping,
port fees, stevedoring, transport, storage, handling, as well as margins and interest.

One benefit of the financial import parity price framework is that it allows for a
detailed inspection of the costs and the magnitude of the costs within a subsector. The
financial import parity price can be used to “challenge maddening myths about agricultural
marketing systems, which are widely believed in certain groups” (Holtzrnan 1989). For
example, a common misconception is that marketing middlemen serve no' productive
purpose and receive excessive returns (Holtzrnan 1989). The financial import parity price
identifies the productive, value—added activities that occupy marketing agents. Marketing
agents provide time, place, and form utilities (i.e., assembly, transport, and storage). The
financial import parity price thus validates marketing agents by identifying the real cost of

engaging in marketing activities.

 

“Financial import parity price is the term used to refer to-a calculated domestic market
price under competitive market conditions and in the absence of taxes and subsidies. It is
thus assumed that “competitive market” domestic prices are not necessarily available.

178

The financial import parity price answers the question: Does the cost of importing
fertilizer and delivering it to farmers reflect the cost of doing business in Ethiopia? It is
easy to test the validity of the technique-if the financial import parity price diverges
significantly fi'om observed fertilizer retail prices then some costs in the system have not
been correctly identified (e.g., perhaps excessive returns exist).

There are benefits and limitations of a financial import parity price approach to
examining market performance. One limitation is that it is a static picture of the “typical”
costs associated with a market for a specified unit of production. It is not designed to
calculate costs over time or for different units of production. Therefore, it may not reveal
the real cost associated with doing business in an environment subject to uncertain and
changing government policy and demand that is subject to fluctuations in the weather.
However, one benefit of the financial import parity price is that it enables researchers to
identify the major cost components in the marketing chain. Once identified, filrther
investigation can determine whether it is possible to target these areas through investment
or institutional change to reduce costs in the subsector.

Tables 7 .1 and 7.2 outline the price structure of delivering DAP and urea to Jimma
Zone in the Oromiya Region in 1998 (detailed notes provided in Appendix 3). The
financial import parity price is based upon a “typical” timing of imports and distribution of
imported fertilizer by an integrated retailer. If fertilizer changes hands fi'om importer to
wholesaler to retailer additional administration costs would be added to the budgets. An
international contract is signed any time fiom September to December and delivery occurs

fi'om February to May. Another specification is that the financial import parity price is

179

 

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182

characteristic of a “free out” contract whereby stevedoring and bagging are covered by the
cargo owner." Costs are specific to a certain point of time and thus may not capture the
seasonality observed in some prices. Hence, some of the variability in costs will be
specified in a sensitivity analysis. The £o.b. price is one line item that will be entered with
a high and low level to represent the variation observed in f.o.b. international export DAP
and urea prices throughout 1998. However, for the high-price scenarios for both Assab
and Djibouti the financial import parity price will begin with the c.i.f. price and not include
information on £o.b. prices, insurance, and fi'eight. It is assumed that freight and
insurance remain relatively constant compared to f.o.b. prices, so a change in c.i.f. prices
reflects a change primarily in f.o.b. prices.

. In 1998 the weighted“ c.i.f. (cost, insurance, fieight)“ Assab/Djibouti" price for
imports was Uss/MT 250 for DAP and US$IMT 147 for urea; however, there was great
variability around these means (Table 7 .3). The c.i.f. Assab/Djibouti DAP price ranged
from US$fMT 240-252. The low price for DAP imports used in the financial import

parity price represents the lowest c.i.f. price received in 1998 among all importers: EAL

 

"This contrasts to a “liner out” contract where these activities plus the cost of bags are
covered by the ship owner. One deviation from these definitions is that in the financial
import parity prices specified the cost of the bags is included in the f.o.b. international
prices.

“Weight based upon the quantity purchased that corresponds to the price. Prices quoted
for larger quantities receive a larger weight.

“The c.i.f. price is the landed cost of an import at a certain location, which includes
international freight, insurance, and handling costs (Gittinger 1982).

“1998 imports primarily arrived in Djibouti due to the conflict with Eritrea.

183

imported 25,000 MT with IDA fimds at a c.i.f. price of USS/MT 240 (Table 7.3). The
highest c.i.f. price used in the analysis for DAP was USS/MT 252, received by both AISE
and Guna for a total of 108,371 MT (38 percent of total DAP imported) in contracts

funded by the EU, Italy, and FDRE (NFIA 1993).

 

 

 

 

 

 

 

 

 

 

Table 7.3 Quantity of Fertilizer Imports and C.I.F. Djibouti/Assab1 Price by
Importer and Source of Funds, 1998
Company Shipment DAP Ureg
MT C.I.F. (QSSIMT) MT 91,17. (QSflMn
' MBCSector' : * g , . - . . ' , . , ~ 35.437: V‘
AISE 1 58,000 251.95 10,437 263
2 10,371 251.95
3 35,000 245.00
4 26,000 249.63
5 15,000 251.95 25,000 123.87
. f A Total 144,371 35,437
Private ' 14,3000 59,000 ‘
EAL 1 31,000 242.97
2 25,000 239.89
_ Total__ 56,000 0
Ambassel2 1 25,000 249.63
2 17,000 249.55 19,000 147.50
Total 42,009 .......... . 19.000
Guna 1 25,000 251.95 25,000 124.90
..... Total 25.000...-..-.. .. 25.000
Fertiline 1 20,000 246.23 15,000 144.42
Total 20,000 . 15,000 i

 

 

Notes: .‘ The specific port is not known, either Assab or Djibouti.
2'I'l1eFDREclassifiesAmbasseland Gunaasprivatecornpanies, buttheyareownedbytheregional
governments of Amhara and Tigray, respectively.

The c.i.f. urea prices in 1998 ranged fi'om US$/MT 124-263. The high price of
US$/MT 263 was for 10,437 MT (out of 94,437 MT) imported by AISE and firnded by

Japan. Japan required that fertilizer be bagged before it reached Assab or Djibouti. This

184

high c.i.f. price under Japanese funds is treated as an exception (explained later) and not
factored into the estimates of low and high urea c.i.f. prices used in the financial import
parity price. For the financial import parity price for urea, the lower boundary used in the
build-up, USS/MT 111 f.o.b. Egypt received by EAL which, with shipment and insurance,
sums to USS/MT 125 c.i.f. in Assab. The high c.i.f. price for urea represents the average
weighted urea c.i.f. price in 1998, USS/MT 147.

Using the financial import parity price just specified, a financial import parity price
for DAP and urea was calculated to the central storage area, Nazreth, as well as for a
retail market, Jimma (Table 7.4).1 On average, DAP and urea are marginally more
expensive from Djibouti than Assab, primarily due to difi‘erences in ocean freight and
inland transport rates. In addition, there is a spread of roughly 9-11 Bin/quintal for DAP
and 16-17 Bin/quintal for urea depending upon whether it is a low or high c.i.f. price

scenario, based upon difi‘erences in international f.o.b. prices.

Table 7.4 Nazreth Financial Import Parity Price, 1998

 

 

Port of Scenario DAP Urea
Import
USS/MT Bin/quintal USS/MT Bin/quintal
Assab low-price case 313.78 225.92 192.27 138.43
high-price ease 326.57 235.13 215.38 155.08
Djibouti . low-price case 326.94 226.04 206.16 142.54
high-price case 339.83 234.95 228.74 158.15

 

Source: Tables 7.1 and 7.2.

7.3 Validating the Financial Import Parity Price
The price of fertilizer is a common indicator of performance. However, it is only

usefirl if there is some reference point in which to judge: Does the retail price of fertilizer

185

render it profitable for use by farmers (given yields and output prices); Does the price
allow for returns to capital such that the market attracts investment; and Does the price
accurately “clear” the market (does it equilibrate supply and demand)? Although this
chapter will not address these issues in detail, the financial import parity price can reveal
by how much market prices diverge from what is a “reasonable” (competitive) pricrthus
highlighting potential inefficiencies in the sector.

The DAP financial import parity price for Jimma Zone ranged from 23 8-255
Bin/quintal which is roughly comparable to the actual 1998 observed DAP price, 242
Bin/quintal. The urea financial import parity price for Jimma Zone ranged from 160-179
Bin/quintal, compared to 184 Birr/quintal observed in the market in 1998.

A sample of DAP and urea prices in 1998 in three zones in which the 86 was
active in production demonstration plots reveals that 1998 market prices fell considerably
below the government prices facing the SG farmers in 1997. Thus, assuming constant
yield and output price, profitability of fertilizer use would increase between 1997 and 1998
in East and West Shewa and Jimma, Oromiya Region. In 1998, DAP prices for credit
ranged fi'om 160-220 Bin/quintal compared to the 1997 government price of 241
Bin/quintal (Table 7.5). This decline can be partly attributed to liberalization of retail
prices and the presence of competitive fertilizer auctions in Oromiya; however, it may also
be attributed to the fact that some companies may have been liquidating their stock in
1998 after a couple years of carry-over.

DAP open market cash prices were higher than credit prices in East Shewa and

West Shewa in 1998. This may be explained by the fact that cash prices are primarily

186

prices offered by independent retailers that purchase their stock from integrated retailers

who supply the credit programs.

Table 7.5 1997 and 1998 DAP Retail Prices For Three Zones in Oromiya,

 

 

 

Bin/(Ental
East Shewa West Shewa Jimma
1997 Govt 19933 1997 Govt 1998 1997 Govt. 1998
Price’ Price Price
Credit' 241.0 160-220 247 222-226 255 242-255
Cash 241.0 218-232 247 225-240 255 220-254

 

Source: NFIA 1998 and 1998 GMRP Input Subsector Survey.

Notes: 'Credit price is for NEP and regular credit.

2The 1997 price is calculated by the government. East Shewa government price was calculated for Debre
Zeit W. Shewa government price was calculated for Woliso. Jimma government price was calculated for
Jimma town.

’I'he 1998 price represents a non-random sample for Ada wereda in E. Shewa, Woliso and Wonchi
weredasinW. Shewa, andfortheJimmaZone.

Similarly for urea, the market prices in 1998 were considerably below the
government price in 1997 in the three sampled zones in Oromiya (Table 7.6). The 1998
cash and credit market prices were equivalent in both East Shewa and West Shewa but
were higher in Jimma. Credit prices in Innma ranged between 187-198 Birr/quintal and
cash prices ranged fi'om 196-254 Bin/quintal.

The decline of DAP and urea prices over the course of two years may be a rough
indicator that perhaps productivity gains were realized in the subsector relative to the
system of government-established prices or a reflection of a decline in f.o.b. prices of
fertilizer in 1998 relative to 1997 in key exporting countries; however, it provides no
guidelines to whether prices in 1998 were higher or lower than the prices that could be

reasonably be expected by society (under competitive market conditions). In order to

187

Table 7.6 1997 and 1998 Urea Retail Prices For Three Zones in Oromiya,

 

 

 

Bin/Quinta]
Bast Shewa West Shewa Jimma
1997 Govt 1998 Market 1997 Govt 1998 Market 1997 Govt. 1998 Market
Price’ Price’ Price2 Price’ Price’ Price’
Credit' 226 155 232 161-163 241 187-198

Cash 226 155-173 232 162-180 241 196-254

Sources: NFIA 1998 and 1998 GMRP Input Subsector Survey.

Notes: 'Credit price is for NEP and regular credit.

2The 1997 price is calculated by the Govemmnt East Shewa government price was calculated for Debre
Zeit W. Shewa government price was calculated for Woliso. Jimma government price was calculated for
Jimma town.

’The 1998 price represents a non-random sample for Ada wereda in E. Shewa, Woliso and Wonchi
weredas in W. Shewa, and for the Jimma zone.

gauge the relative efliciency of fertilizer prices in 1998, the financial import parity price of
DAP" is subtracted from the observed retail prices from the 1998 GMRP Input
Subsector Survey and the difi’erence is averaged across zones. In figure 7.1 the bars
above zero represent the amount that the actual market price observed in 1998 exceeded
the calculated financial import parity price and the bars below zero represent the opposite.
Overall, 1998 observed prices lay within 25 Birr/quintal of the financial import
parity prices. Among the 17 zones surveyed, prices in nine zones lay above the financial
import parity price and in eight zones prices lay below. Among the six zones where the
observed price exceeded the financial import parity price by over 10 Birr/quintal, five were

in the Amhara Region. This is not surprising because fertilizer distribution was not

 

69Transport costs plus unloading fees are added on to an average Nazreth wholesale price
(average of the low-price and high-price scenarios) presented in Table 7.4 for DAP. The
transport cost used was 0.04 cents/quintal/km, a rough average of fi'eight rates fi'om
Nazreth to outlying retail markets. Unloading fees are 0.50 Birr/quintal.

188

competitive in the Amhara Region: the sole distribution rights of Ambassel potentially

permitted it to charge monopoly prices.

Figure 7.1 Difference in Observed 1998 DAP Prices and
Financial Import Parity Price in Select Zones
Note: Bars represent the financial import parity

price subtracted fiom the market price.

 

 

201

 

10'

.20:

 

 

 

 

 

 

 

Bin/quintal
‘3

 

 

—

 

 

 

' i 1 v - - - v - f - t v t - i '

w». #st %%%%%%%% “ob:
“6‘34, 343%,45

Region, Zone
There are also many zones that have prices that are below the calculated financial
import parity price. One plausible explanation is that the fertilizer auctions held primarily
in Oromiya were indeed very competitive and some companies may have offered below-
cost prices in their bid. Another reason is that some companies had carry-over stock, and
in an efi‘ort to liquidate their stock, charged below-cost prices. One importer/wholesaler

called the 1998 retail market a “slaughter” (GMRP 1998). One reason that prices

189

observed in some zones in the Southern Region may be lower than the financial import
parity price is that farmers often dumped the unused portion of their NEP package on the
market.

Another reason the financial import parity price may be less than the observed
prices is because the financial import parity price calculation did not accurately capture
transport costs within weredas. The calculated financial import parity prices represent the
m costs and timing of importing DAP and urea in 1998 to the major market town in
each zone (not to rural areas outside the zonal capital). In 1998 fertilizer was delivered
late in all surveyed weredas, thus the financial import parity price may not have accounted
for increased transport rates that occur when delivery coincides with the May-July rains
(GMRP 1998). In addition, the exact location of the observed market price within a
wereda was not known for the financial import parity price calculation, so transport costs
were calculated to the midpoint of the wereda and on paved roads. Ifthe price was
recorded in an outlying area, accessible only by a dirt road, then the resulting price will be
higher.

Overall, this exercise revealed that the financial import parity price is not capturing
all the costs in the system. Aside fi'om the uncertainty of transport costs, there also may
be large variances in margins across distributors. The difl‘erent market structures across
the country mean that larger returns may be captured by some distributors, whereas in

other areas, the specified margin in the financial import parity price may be squeezed.

190

7.4 Reducing Costs in Importing

Private investment in the sector, and in importing specifically, has increased as a
result of market liberalization. Several importers entered the business since 1993,
including a private company, Fertiline, in 1998. In addition, new wholesalers have
emerged on the market: Wondo, in the South, was a new entrant in 1998. Although,
progress toward a more open import process has been made, qualitative evidence from
1998 revealed that perhaps the organization of imports hinders the development of a
lower-cost import process. This section will provide evidence fiom the 1998 GMRP
Input Subsector Survey as well as supporting empirical analyses to determine whether
costs of importing can be reduced through reorganization of the process of imports.

Competitiveness in International Markets? It is typically assumed that small
countries, like Ethiopia, are price-takers on international markets,70 and that they therefore
face market rates for its imports. However, it appears that the c.i.f. Assab/Djibouti DAP
prices Ethiopia paid in 1998 were higher than market rates even after including insurance
and height."1 The average f.o.b. price Jordan in 1998 was about USS/MT 216 which
sums to an estimated c.i.f. price Djibouti of USS/MT 231 when fi'eight is included. This is

between 9-21 USS/MT less than the average c.i.f. Assab/Djibouti price Ethiopia actually

 

”Compared to large importers such as India and China that may have a strong bargaining
position in international markets.

7‘With the exception of the bagged shipment funded by Japan, the urea prices appeared to
be relatively more in line with market rates.

191

received for DAP in 1998 (Table 7.7)."2 Even if DAP comes from the US Gulf, Morocco
or Tunisia, the cost of freight still brings the c.i.f. price to less than the maximum c.i.f.
price Ethiopia paid.

One plausible explanation for the observed discrepancy is that the shipments
procured are small and thus a premium is added onto the offer price. However, this is
probably not the case because it is known that no price discounts are offered if a shipment
increases fiom 20,000-100,000 MT (8 out 11 of Ethiopia’s 1998 shipments were at least

20,000 MT) (Goss 1999).

 

 

 

Table 7.7 1998 International (Select Markets) F.O.B. DAP and Urea Prices and
C.I.F. Assab/Djibouti Prices, USS/MT
Source of Mean Standard Minimum Maximum C.I.F. Assab or Djibouti‘
Fertilizer F.O.B. Deviation F.O.B. F.O.B. Weighted Min Max
USS/MT USS/MT USS/MT average” USS/MT USS/MT
USS/MT
1234.2 249.90 239.89 251.95
US Gulf 201.60 5.15 192.00 212.00
Jordan3 215.70 4.81 207.50 227.50
Morocco 209.40 4.10 202.50 222.50
Tunisia 212.00 2.82 208.00 222.00
LEE 147.43 123.87 263.00
US Gulf 114.70 18.80 85.00 145.00
Middle East 107.80 10.71 90.00 130.00
Black Sea 83.10 9.55 65.00 100.00

 

Sources: FMB Consultants Ltd. 1998 and NFIA 1998.
Notes. 'Origin of fertilizer rs not known.
”Average computed based upon the quantity imported at the corresponding price.
3Ethiopia imports much of rts DAP from Jordan.

 

12This comparative analysis assumes that monthly f.o.b. prices do not vary more than 12

USS/MT.

192

Another explanation for the higher-than-market price is that international suppliers
know that entering a sale with Ethiopia is risky because of the uncertainty surrounding
when payment will be received, and hence, the delivery date. There have been occasions
in Ethiopia where there is a delay of nine months to a year between the time a contract is
signed with a foreign exporter and the fertilizer is shipped. The delay the supplier faces is
due to the importer’s tardiness in opening a Letter of Credit with an Ethiopian bank."3

The delay may be attributed to the hesitancy by the Ethiopian importer to import,
given the uncertain distribution policies that may favor government companies or it may
be due to administrative delays in receiving a loan, perhaps if a company is overextended
(GMRP 1998). In 1998 three forex announcements were made for fertilizer imports, late
September, late November, and late December (NFIA 1998). The Letter of Credit for
these imports was not opened until mid-December, mid-February, and late April,
reSpectively (NFIA 1998). This means there is a delay of two and a half to four months
between the time an international supplier offers an importer a quote (in order for the
importer to submit a bid) and the money for the contract is transferred. Once the money is
transferred, shipping can take from 4 to 6 weeks.

The exporter anticipates this delay and signs a price that is higher than the current
world prices to cover any potential price increase during the delay between when a price is
quoted and the delivery is made (Goss 1999). From the observed Ethiopian c.i.f. DAP
prices, descriptive statistics of the 1998 international f.o.b. prices, and a report from an

international supplier that exports to Ethiopia (Norsk Hydro), a conservative estimate is

 

73Ethiopian importers are required by law to source fertilizer credit domestically.

193

that international suppliers will add a premium of up to 5 percent of the international f.o.b.
price due to the uncertainty of doing business with Ethiopia.

Poor Coordination Between Agregate Supply and Demand Poor coordination
between supply and demand is a reflection of inefiiciencies in a sector. The quantity of
imports is a function of NEP targets and availability of hard currency (i.e., donor funds),
with little role for prices and effective demand to determine quantities imported.

Ethiopia has a history of national excess supply of fertilizer. In 1996 only 60
percent of the total amount of fertilizer available by all firms was sold (a carry-over of
roughly 164,932MT) and in 1997 companies continued to face problems of excess stock
(Demeke et al. 1997)." In 1998 it is estimated that up to 26 percent of the fertilizer
consumption was carry-over stock--16 percent for DAP and 58 percent for urea (author’s
computation based on NFIA data).

Excess supply mitigates the robustness of an economy because it is a signal that,
perhaps due to lack of competition, too many resources are employed in a sector that
could be fully employed elsewhere. Excess supply is costly in terms of the obvious
storage and interest charges incurred, but equally important is the message it sends to
potential entrants of the degree of uncertainty in the market. Uncertainty will keep

potential entrants out of the market thus potentially keeping the market concentrated.

 

7‘EAL was particularly hard hit, for it only sold 30 percent of its supply in 1996. AISE
and Ambassel fared better, selling 73 percent and 75 percent of their stock in 1996,
respectively. However, a year later positions were reversed; AISE sold only 46 percent of
its total supply and EAL sold a larger percent, 69 percent of its stocks. EAL dumped its
stock by selling fertilizer to other distributors such as Ambassel and Dinsho at below
retail-prices (Demeke et al. 1997). i

194

High C.I.F. Price from Inflexible Timing of Imports. It will be determined
whether the government’s import policy-controlling access to import credit, as well as the
timing of imports-«raises the cost of imports. The timing of the government-controlled
process is uncertain and often delayed. In 1997 it was reported that it took from 30-136
days for donors to approve firnds, an additional 30-90 days for the government to float
tenders, and another 75-109 days for a shipment to arrive (Demeke et al. 1998). In 1998
most importers agreed that the process had improved considerably, although there was
still room for improvement (GMRP 1998). As mentioned earlier, the Letter of Credit for
1998 tenders was not opened for roughly two to four months after Ethiopian importers
received their international quote (NF IA 1998) which can raise the ofl‘er price by
international suppliers.

The second issue is that the inflexible timing of imports reduces the possibility that
importers can take advantage of low-priced months on the international market. Not only
is the process rigid, but inevitably delays occur (recall that the FDRE determined the
timing of imports based primarily on the availability of donor funds). It is hypothesized
that Ethiopia would be able to import at a lower c.i.f. Assab/Djibouti price if importers
had the independence to determine the timing of their shipments. Over the 6—year period
from 1993-1999 DAP f.o.b. prices in Morocco, Jordan, Antwerp, and the US Gulf
exhibited a visible seasonal trend (Figure 7.2). Prices began to decline around March and
April, were lowest around June, and then began to increase steadily to a high in
December. For example, the f.o.b. bulk price in Jordan for DAP in May was roughly 13

USS/MT lower than the price in February. The spread is a little more pronounced for

195

f.o.b. DAP prices in Morocco, a difl‘erence of 18 USS/MT between June and December

(Figure 7.2).

Figure 7.2 Seasonal Index in Select World DAP Prices,
USS/MT (Constant 1998)

“:in /4
m? V /"

1 1

Jnréofiuakmyrurhxhsépognbvnh

 

 

 

   

 

 

 

+191me +Antwerp-I-USGulf

Source: FMB Consultants 1999.

As seen in the previous section, Ethiopia pays a premium for administrative delays
once a contract is signed with an international supplier. Before trying to organize how to
improve the f.o.b. price (e.g., fiom Jordan, Morocco, or the US Gulf) through improved
timing of imports, the possibility of improved bargaining with international suppliers
should be explored. IfEthiopia could ensure that once a contract was signed with an

international supplier, funds would be immediately transferred and delivery could proceed,

196

then the average f.o.b. price may be reduced by 5 percent” (Scenario 1: f.o.b. price of
DAP falls from USS/MT 225 to USS/MT 214) fi'om the low-price scenario estimated in
table 7.4 for DAP. In such a scenario, the financial import parity price calculated for
Nazreth, imported through Djibouti, could be reduced by about 12 percent, from 247
Birr/quintal to 217 Bin/quintal (Table 7.8).

Assuming that Ethiopia could both improve its bargaining position and improve
the timing of its imports, it is assumed that the contracted £o.b. price could be reduced by
10 percent (Scenario 2: f.o.b. price falls from USS/MT 225 to USS/MT 202). In this case,
the estimated wholesale price would be reduced by 15 percent, from 247 Bin/quintal to

209 Bin/quintal (Table 7 .8).

Table 7.8 Nazreth Financial Import Parity Price for DAP Under Estimated

 

 

 

 

F.O.B. Price Reductions
Assab Djibouti

”m0 USS/MT Bin/quintal USS/MT Bin/quintal
Base scenario, low- and high- Low: 344.44 Low: 237.67 Low: 357.30 Low: 246.54
price scenarios High: 357.24 High: 246.50 High: 370.20 High: 255.44
ganario 1 Reduced Shipment
Date Uncertainty (5% reduction 302.16 208.44 315.11 217.43
in low fob. price)
M; Reduced Shipment
Date Uncertainty and Improved 290.23 200.26 303.18 . 209.19
Timing (10% reduction in low

fob. price)

 

”5 percent is an average of the difference between the actual c.i.f. Djibouti prices received
in 1998 and the calculated financial import parity price c.i.f. Djibouti price, and a rough
figure provided by an international supplier (N orsk Hydro 1999).

197

Port Inefliciencies. Another issue relating to imports, port congestion, can raise
the cost of importing fertilizer in two ways: first, congestion at the port prompts shipments
on liner terms which can add a premium"6 to the f.o.b. price relative to charter terms; and
second, port charges, including bagging and transport charges fiom the port are higher
when the port is congested."

Demeke et al. (1998) and the World Bank (1995) reported that demurrage charges
in Assab may no longer be a problem because the port is increasingly eflicient in 0&-
loading cargo and thus contract terms should be switched fi'om liner terms to charter .
terms. By 1998 many importers confirmed that Assab was relatively more eflicient, but
they reported that port inefficiencies were an issue in Djibouti (where fertilizer imports
arrive since the beginning of the Eritrean-Ethiopian conflict in May 1998). As already
mentioned, transport charges inland were higher from Djibouti than Assab due to the
delays trucks faced in the port of Djibouti. Another import cost that is efi‘ected is that
demurrage charges can be high in a congested port. The demurrage charge for a 25,000
MT ship can be as much as USS 50,000 for a 5-day delay in unloading (2 USS/MT)
(Kassahun 1998).

Oflshore Buying. Most fertilizer imported into Ethiopia is bagged in either Assab

or Djibouti; however, on occasion, a donor will stipulate that fertilizer imports are bagged

 

"A premium is paid to insure against the chance that demurrage charges will be owed to
the ship owner by the cargo owner. Demurrage is the detention of a vessel or fi'eight car
beyond the time stipulated.

7’Liner terms are advantageous to Ethiopian importers when the port is congested because
the contract specifies that the ship owner pays the demurrage charges.

198

prior to arrival at these ports. Japan is one such donor. It is reported that a fertilizer
shipment fidnded by Japan left the US Gulf in bulk, was transported to Antwerp for
bagging,'and then delivered to Ethiopia (Goss 1999). From 1993-1999 bagged f.o.b.
prices were higher than bulk prices (Table 7.9). For example, if fertilizer comes fi'om
Morocco, bagged DAP is an average of almost 12 USS/MT higher than the bulk price
(FMB Consultants Ltd. 1999). Although a rare instance, and only applicable for 10,000
MT (3.6 percent of total 1998 DAP imports), it is an unnecessary cost and can be avoided

in the future.

Table 7.9 Comparison of Bulk and Bagged F.O.B. DAP Weekly Prices,
January 1993 - February 1999, USS/MT

 

 

 

Jordan Morocco Tunisia
Bulk Bajgged Bulk Bagged Bulk Bagged
Mean 207.0 220.2 198.7 210.6 199.8 211.1
Medium 208.9 222.4 200.1 211.1 202.5 212.4
Maximum 237.2 242.0 236.9 241.7 237.2 241.7
Minimum 175.1 190.2 156.7 164.9 - 156.2 156.2
Observations 261 ‘ 261 240 240 238 238

 

 

Source: Compiled from FMB Consultants Ltd. 1999.

7.5 Reducing Costs in Wholesaling and Retailing

The organization of credit distribution may be largely responsible for preventing
the development of a lower-cost fertilizer subsector, particularly at the wholesale and
retail stages of the sector. Access to credit is increasingly important as the NEP expands
to farmers who have fewer resources and lower incomes than 86 participants, yet there
were signs during the 1998 fertilizer season that the credit program was failing to meet

farmers needs. Fertilizer application occurs during planting time, the lean season in most

199

farmers’ cash flow, and thus access to credit may determine whether a farmer uses
fertilizer.

Credit problems are of two types: (1) the amount of the credit portfolio made
available to difl‘erent zones and credit programs (NEP and regular), and (2) problems
associated with the way the credit is administered. The administration problems are linked
to coordination problems between supply and demand, the time that agriculture personnel
devote to processing credit (perhaps negatively efi‘ecting the quality of the extension
message), and the regional government’s use of the credit system to establish regional
fertilizer monopolies that reduce competition and raise prices.

Survey evidence revealed that the total amount of credit is inadequate to meet both
NEP and regular demand (GMRP 1998). Since 1997 credit has been guaranteed and
administered by regional governments who determine allocations to zones who, in turn,
determine allocations by wereda. Approximately 30 percent of farmers used fertilizer in
1995 when the NEP began and most were purchasing it with credit. With the expansion
of the NEP the total credit available must now be divided between rapidly expanding NEP
needs and regular credit for non-participants (disbursed through SCs).

For many weredas the total credit allocation has declined. For many other
weredas the total amount of credit increased, but the amount allocated for regular credit
declined substantially or disappeared entirely. One agronomist with the Bureau of
Agriculture complained, “It is known that the land couldn’t grow crops without fertilizer;
however, the regular program peasants were not given a chance to access credit” (GMRP

1998). In the Oromiya Region one SC (non-NEP) reported that only 62 percent of its

200

fertilizer demand was delivered, limited credit being the reason for the shortage (GMRP
1998)

Among the three zones in which SG farmers were surveyed by the GMRP and the
Ministry of Agriculture in 1997, West Shewa was the only zone where total (NEP and
regular) fertilizer credit increased, by 22 percent, between 1996/97 and 1997/98 (Table
7 . 10). During the same period, total fertilizer credit fell by 6 and 23 percent, respectively,

in Jimma and East Shewa (Table 7 . 10).

Table 7.10 Fertilizer Credit in the Three SG Study Zones, Oromiya Region,

 

 

‘000 Birr
' Zone 1993/94 1994/95 1995/96 1996/97 1997/98
Jimma 6,753.0 17,432.4 16,4260 14,0805 13,2464
East Shewa 39,417.5 55,201.3 62,705.8 52,663.6 40,559.0
‘VWest Shewa f 27,861.3 44,633.1 57,002.9 42,159.4 . 51,2800
Oronuya 7138,2952   190.4792   211,1356 ’. 210,758.61 225,971.6   ’

' Reg 'on Total.
Source: Oromiya Regional Goverrunent 1998.
Note: All Oromiya zones are included under “Oromiya Region Total”, not just the 3 listed here.

 

Overall, the NEP is gaining a larger share of declining credit resources in Jimma
Zone. The amount of NEP fertilizer disbursed in the SG—participating weredas in Jimma
increased by 61 percent and the amount of fertilizer that was disbursed through the regular
program fell by 70 percent in the same time period. The amount of fertilizer sold on
regular credit in Seka Cherkosa Wereda, J imma Zone fell from over 2,000 quintals to zero

quintals--thus farmers that were not participating in the NEP could not obtain credit fi'om

201

any government source (Table 7.11). The declining amount of fertilizer purchased on
credit is not a concern except that most farmers do not purchase fertilizer except on credit
(cash sales as a percent of total sales in the Seka Cherkosa were less than 1 percent
(GMRP 1998)). Of the three SG-participating weredas in Jimma, Dedo is the only wereda
that continues to extend a significant amount of fertilizer credit to farmers outside of the

NEP.

Table 7.11 DAP and Urea Distributed on Credit in Kersa, Dedo, and Seka
Cherkosa, Jimma Zone, Quintals

 

 

 

 

Wereda Program 1995 1996 1997 1998'
Kersa NEP 200.0 944.0 1,244.0 6,927.0
Regular na na na 2,389.0

. ...... 1938.1 ............................ .290 0 0.! ........................ I! .8. ............... 2.2.1.69...
Seka Cherkosa NEP 168.0 1 149 3 1 271 0 1,996 0
Regular 5 072.0 3 346.5 2 285 5 0 0

. .................................. 193.8! ......................... 5.2.40.9 ................ 1.4.25.8 ................ 2.5565119969"
Dedo NEP 20 0 293.8 9 087 5 9 715 0
Regular 7,379.3 6,843 5 3,355.5 2,086.5

“ram 7.322.: 74W

 

 

 

Source: GMRP Input Subsector Survey 1998.
Note: 'Until August, 1998.

One reason for the declining credit volume to the regular credit program may be to
rapidly expand the NEP. It is hypothesized that in 1998 credit was used to facilitate and
even “bribe” farmers into joining the NEP (GMRP 1998). When the NEP becomes the
only credit option or regular credit is severely limited, SG and NEP graduates and non-
participants cannot obtain fertilizer credit unless they sign up for the NEP. Consequently,

the transfer of technology may not be spreading as rapidly as intended.

202

Another reason for lower levels of regular credit may be that the government has
toughen its credit requirements (e.g., use of police, confiscation of assets) and deny SCs
(who administer regular credit) access to credit that have not met minimum repayment
requirements. It was found that many farmers in West Shewa were denied credit for the
highly profitable early maize crop due to a poor credit history (GMRP 1998). This issue
leads into the second concern regarding the administration of credit: access to markets is
tied to government credit, and two, the process of credit administration delays the delivery
of fertilizer.

Coordination at the Farm and Regional Level. In an efficient market, prices
adjust to equilibrate demand and supply; however, the Ethiopian fertilizer market is more
representative of a command economy: regional governments directed the flow of
product through its control of credit. Inevitably, as in many planned sectors, there were
pockets of excess demand and excess supply across the regions of Amhara, Oromiya, and
Southern in 1998. One source of the coordination problem is that at no level (except in
the thin cash market) were prices allowed to equilibrate the market. Prices are decided
based upon a predetermined quantity of fertilizer demand (either through the fertilizer
auctions or privately negotiated with regional governments).

In the Amhara Region in 1998 there was sufficient fertilizer stock but the regional
government’s control of the market created an artificial shortage. Ambassel was the
government-appointed distributor and no other companies were permitted to distribute.
However, Ambassel faced a severe shortage of urea; and even though EAL claimed that it

had positioned ample supplies in the region, many farmers were left without urea because

203

additional supplies could not be arranged fast enough. Arranging for additional supplies is
costly in terms of additional transaction costs (additional search costs as well as the
potential premium paid for rapid delivery).

Given the limited ability of the NEP to accept all farmers into the NEP, due to the
withdrawal of regular credit, and due to weak cash markets (e. g., the case of areas in the
Jimma Zone), it is reported that many farmers who desired fertilizer and had the means to
purchase some amount could not (GMRP 1998). There is a cost to excess demand--it is
the foregone crop revenue that could be realized from increased fertilizer use.

As a consequence of the structure of credit, fertilizer prices played only a limited
role in. determining trade volumes. In fact, in some areas prices were not known at the
time the down payment was collected. A typical scenario is that farmers were enrolled in
the credit programs to the extent that credit was mobilized for the NEP, the down
payment was collected, the demand estimates were forwarded to government, the
government found a supplier (either through an auction or government-nomination), and
only then was a price set for the fertilizer deliveries. In West Gojjam, Amhara, a SC began
distributing fertilizer in April without knowing the price that the appointed supplier,
Ambassel, was going to charge (GMRP 1998). The SC collected the down payment fiom
farmers based upon the previous year’s prices and was permitted to begin distribution.
Ambassel set its prices in May (after distribution had begun) and instructed the SC to raise

the prices to farmers (GMRP 1998). The SC refiised and Ambassel threatened not to

deliver the fidll amount.

204

In addition to excess demand at the farm level, there was evidence of excess supply
at the farm level as Well. Due to the lack of price information and a strong push by the
government to expand the NEP, a standard package of inputs (100 kg/ha urea, 100 kg/ha
DAP, and improved seed) was delivered to NEP farmers—even though it was often more
fertilizer than desired. Farmers agreed to the package because in some areas there was no
other source of fertilizer credit (or improved seed), but it is also reported that the DAs
were coercive in enrolling farmers (GMRP 1998). Consequently, a thin secondary cash
market for fertilizer (especially urea in the South) developed in 1998 in which farmers sold
the unused portion of their fertilizer obtained on credit. Retailers outside of Addis
complained that the “black market” for fertilizer was undercutting their sales and the
illegal trade must be stopped (GMRP 1998). In North Shewa Zone, Amhara, farmers
were reported to sell part of their NEP fertilizer package to obtain money for recreational
purposes--primarily for local alcoholic drinks (GMRP 1998).

If effective demand and prices were permitted to play a larger role in guiding the
allocation of fertilizer within the sector, it is possible that coordination between demand
and 'supply could be improved and costs reduced.

Competition in Distribution. ”Obtaining access to government farm input credit
programs is key to marketing fertilizer in Ethiopia. Additionally, integrated retailers
preferred credit to cash sales because the government removes the risk of marketing for
credit sales. The government pays the suppliers the full amount of a contract upon

delivery, shifting the risk onto itself. However, access to credit is not open to all

205

distributors"--government’s efforts to restrict access to credit can result in a concentrated,
high-cost market. In the Amhara Region, for example, the government used access to
credit as a way to promote a monopoly of its favored company, Ambassel. Introduction
of competitive influences in the market, through reduced barriers to access to government
credit programs. Who distributes should not be a government decision.

The chapters on structure and conduct of the fertilizer market revealed that an
array of market structures and corresponding pricing mechanisms were present in 1998.
This raises the question: Is there a relationship between market structure, conduct, and
performance? The hedonic price model for DAP and urea presented in the previous
chapter demonstrated that markets that were relatively more competitive had lower prices.
Credit prices that were determined in a market held by a government-appointed supplier
rather than through a fertilizer auction were up to 10 Bin/quintal higher for DAP and up
to 21 Birr/quintal higher for urea relative to the winning bid prices (Table 7.12). This
indicates, and as will be determined in the next chapter, that profitability of fertilizer may
increase if more competitive influences are promoted in the market. The government has
a unique position to design policy that will encourage the development of a low-cost
fertilizer market because its policy is critical in shaping who and how many actors are in
the market, and hence the outcome of a market. In 1998, as one fertilizer importer/retailer
noted, “Fertilizer is a political strategy product--suppliers are not rewarded for being low-

cost” (GMRP 1998).

 

"Government can deny the large wholesalers access to credit, but also to independent
retailers—on the grounds that they are not permitted to sell on credit (GMRP 1998).

206

Table 7.12 Jimma Financial Import Parity Retail Price for DAP Under Increased
Competition
Assab Djibouti
Scenario USS/MT Bin/quintal USS/MT Bin/(mintal
Low? High? LowP High? LowP High? Low? PEEP

B scenario 344.44 357.24 237.67 246.50 357.30 370.20 246.54 255.44
low- and high-
price scenarios

cenario 3 227.67 234.50 236.54 245.44
Improved
competition

Under-weight Fertilizer Bags. The govemment-favored companies (Ambassel in
Amhara, Dinsho in Oromiya, and Wondo in the South) were accused of under-packing
fertilizer bags-at 35-46 kilograms instead of the “standard” 50 kilograms (GMRP 1998).
Due to lack of competition in some areas, there was no pressure for the regional
goverrunent companies to provide a quality product.

In Amhara, Ambassel’s careless packaging created a negative extemality. Due to
their experience with Ambassel, AISE reported that farmers were skeptical whether
AISE’s bags were the correct weight; therefore, all AISE transactions were weighed--
adding a cost to the sale. A quick calculation reveals the magnitude of the problem: at the
average retail DAP price of 239.5 Birr/quintal, farmers paid 2.39 Birr/kg. If bags actually
weighed 40 kilogram instead of the “standar ” 50 kilogram, farmers paid 2.99 Birr/kg,
0.60 Birr/kg more. Thus farmers paid nearly 25 percent more per kilogram due to under-
weight bags. This translates into an increase in the price per quintal from 239 Birr/quintal
to 299 Bin/quintal, a difference of 60 Birr/quintal.

Delayed Fertilizer Deliveries. Another concern relating to credit administration is

that fertilizer delivery was often delayed due to logjams in getting credit organized.

207

Fertilizer cannot be delivered until past loans are paid and a down payment is made.
However, the FDRE has a history of poor credit enforcement, thus in the late 1980s and
early 1990s farmers got into the habit of getting fertilizer credit without settling their
previous debt, thus also increasing bank debt. In the mid- to late-19908 the FDRE
absolved the banks of their debt and took over control of credit by guaranteeing bank
loans. In doing so, it also toughened its collection method. However, when in 1998 the
SOS were told by the Oromiya Government to put pressure on farmers to repay loans and
to screen out defaulters, the request of new fertilizer loans, and hence its arrival, was
delayed.

The SCs or the DAs are responsible for collecting the total fertilizer payment.
Ministry of Agriculture officials reported that fertilizer debts are to be repaid during
harvest (November-January) when farmers have cash; however, in practice loan collection
can occur anytime from April, during the belg season, to well into the planting time for the
second crop, the meher, in June and July. It is theorized that farmers in Oromiya are
known to delay payment of their previous loans in hopes that when planting time comes
for the next season the government will forgive past loans.79 In a sense, farmers attempted
to hold the regional government hostage, knowing they cultivate the most productive land
in the country. However, sometimes the plan backfired: in Wonchi, Oromiya Region,
farmers were still waiting for fertilizer in late July 1998, months after maize planting (the

most responsive crop to fertilizer), but in time for August tef planting (GMRP 1998).

 

7"There was anecdotal evidence that farmers would encourage each other to delay
payment-threatening that they would not attend the fimeral of those that paid (GMRP
1998)

208

Another source of delay is that the farmers are often reluctant to pay-mot only
past loans, but the required down payment. For example, in West Shewa, Oromiya
Region, the government is known to change its policy regarding the amount of down
payment required. One Service Cooperative oflicial explained, “Because such relaxation
is becoming a tradition in the government, farmers purposely wait up until June until the
down-payment is zero” (GMRP 1998). The regional government, knowing that planting
time was fast approaching, gave 100 percent credit in a last minute decision to get
fertilizer delivered close to the time of planting.

Once distribution is approved for credit sales there may be supplier inefficiencies
that also result in delay. One reason for the delay is that the supplier may not have the
stock available that was agreed in the government contract (e. g., Ambassel did not have
suflicient urea available). Another reason for the delay is that often by the time credit is
organized, the rainy season has descended, and sometimes distributors refuse to deliver
because their contract price did not account for the added transport cost (GMRP 1998).
Yet another reason is that the distributor may have insufficient sales clerks. ln Amhara,
Ambassel only had one sales person to open multiple warehouses in a zone (GMRP 1998).

Delayed deliveries can mean that transport coincides with the annual highs in
transport rates. High transport costs and lack of transport remain a primary reason for
low productivity, lack of trade, and meager incomes. Productivity gains can be achieved
by reducing transaction costs, allowing greater specialization and exchange (Sadoulet and
de Janvry 1995). In Ethiopia, an estimated 75 percent of all farms are more than 5 km

from the nearest all-weather road (KUAWAB 1995), thus farmers already face high

209

transport costs to the main markets, minimizing transport rates to maize producing areas
will help reduce the overall cost of distributing fertilizer to the farm.

High Transport Costs. Due to the rigidity of the timing of the credit programs,
fertilizer delivery is delayed and distributors are often forced to transport fertilizer into
rural areas during the higher-costs months of the year. Not only are roads scarce in
Ethiopia, but the combination of poor roads and a dated and small trucking fleet translates
into high transport rates across Ethiopia. The effect of a thin and deteriorated trucking
fleet actually accelerated road damage because trucks were heavily overloaded (IFDC
1993). Although axle load regulation was passed in September 1990, it has not been
enforced. In 1993, on the main routes to the port, over 60 percent of the trucks checked
were over the recommended 10 tons (IFDC 1993).

The transport cost of fertilizer from the ports to Nazreth can add up to an average
of 12.5 percent of the ex-Nazreth wholesale price. The additional costs of transporting
fertilizer from Nazreth to retail markets is equally significant and as variable. Transport
rates are sensitive to the month of delivery and quality of road. One importer/retailer
reported that transport rates can increase fiom around 0.039 Birr/quintal/km on the
Djibouti-Nazreth road (827 km) to 0068-0090 Birr/quintal/km in June and July for
distances up to 400 kilometers from Nazreth, depending upon the nature of the road
(GMRP 1998).

A reduction in transport costs will shift out the supply curve and reduce the cost of
delivering a given quantity of fertilizer. Empirical evidence abounds that roads have a

significantly positive effect on farm productivity--on input use, particularly fertilizer

210

(Ahmed and ”Donovan 1992). However, short of investing in building roads and in the
quality and quantity of the trucking fleet, it is hypothesized that transport costs can be
reduced by taking advantage of the seasonality in freight rates. Transport costs fiom the
ports of Assab and Djibouti to Nazreth account for up to an average of 12.5 percent of the
ex-Nazreth wholesale price.

When a shipment arrives at the port, the cargo owner will negotiate transport costs
with several transporters to handle a shipment of 25,000 MT. Freight rates, in general, are
a function of the distance traveled (the farther the distance, the lower the rate), type of
road, and the availability of back-haul. Due to the rigidity of the timing of allocation of
foreign exchange, fertilizer imports oflen coincide with food aid imports in May and June
which increases the demand for trucks, thereby increasing freight rates. An improved
timing schedule would require an importer transport fertilizer inland during March and
April to take advantage of the seasonal lows in freight rates (this assumes costs of all other
activities are held constant).

A calculation of a seasonal index'o assists in determining when freight rates are at
their seasonal low (Table 7.13). The seasonal index of fieight rates on the Assab-Addis
route exhibited a bimodal pattern that is easily explained by trade patterns in Ethiopia
(Figure 7.3)."1 For example, the high-priced months of May-August correspond to a

period of food aid imports. Freight rates peak again in December and January, but by a

 

”Ferris, J. 1998.

"The use of Djibouti as a port of import is a relatively new practice since the conflict with
Eritrea, therefore, there is no freight rate data available.

211

lower magnitude, due to the harvest season. Trucks are in demand to transport grain from
rural areas to Addis as well as between regions and thus there is a scarcity of trucks on the
Assab-Addis route. From the seasonal index computed for 1994-1997 it is observed that
freight rates of private transporters increased by 22 percent between March and August.
The range in the seasonal index was lower for the public transports, a 13 percent price

difference between the low in March and the high in July.

Table 7.13 Assab-Addis Ababa Freight Rates, 1994-1997

 

 

 

 

Private Truck-Trailer Public Truck-Trailer
Mam Real Price Mean Seasonal Mean Real Price. Mean Seasonal
Index Index
cents/ton/kml % cents/ton/km %

January 3.24 99.96 3.32 106.14
Febmary 3.25 102.18 3.13 99.31
March 2.97 92.12 2.96 93.10
April 2.98 92.64 2.98 94.22
May 3.06 96.16 3.14 99.34
June 3.32 104.04 3.20 101.92
July ‘ 3.32 104.35 3.28 ' 105.15
August 3.46 I 12.80 3.07 98.64
September 3.15 99.27 3 .04 98.98
October 3.00 94.33 3.11 100.15
November 3.21 102.41 3.12 102.85
December 3.21 103.77 3.04 99.89

 

Source: Compiled from MEDAC 1999.
Note: 'Multiply cents by 0.01 to get the Birr equivalent.

Capturing periods of low transport rates on the Nazreth-retail market routes is also
a concern. Delayed transport from Nazreth to retail markets (in part due to lengthy credit
processing) can mean fertilizer is transported during the higher-cost rainy season. For

example, during the rainy season of May thru July, transport costs can more than double

212

Figure 7.3 Seasonal Freight Rate Index, Assab-Addis Ababa, 1994-97

 

115

ari-

A

i: L \

X #1 A E

\ / “V 2:.
\4/

90 i 4 i % i i i i + ‘r i i
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month

 

J
F

 

 

 

1

 

 

 

 

 

 

Source: Compiled from MEDAC 1999.

(0.056 Birr/quintal/km to 0.13 Birr/quintal/km) on the Addis Ababa-Woliso route (124
km, half-way to Jimma from Addis) (GMRP 1998). Due to the seasonality in rains, the
seasonal fi'eight rate index for the Addis-Jimma route reveals high-priced months in May-
July for public transporters (Table 7.15). Surprisingly, the same trend is not visible for the
private truckers, although the seasonal high in October can be explained by the beginning
of the meher harvest. Although anecdotal evidence revealed that the seasonality in
transport rates is larger than that recorded for 1993-1997, the conservative estimate will
be used in simulating a reduced Jimma retail price. It is estimated that if fertilizer could be
delivered from Nazreth to Jimma in February-March, rather than April-May, the transport

rate could be reduced by 5 percent (0.16 cents/quintal/km--an average of the difference

213

 

between the two sets of months and for both public and private truckers (calculated from

Table 7.14)).

Table 7.14 Addis-Jimma Freight Rates, 1993-1997

 

 

 

 

Private Truck Public Truck
Mean Real Price Mean Seasonal Mean Real Price Mean Seasonal
Index Index
Ethiopian cents/ton/km' % Ethiopian cents/ton/km %
January 3.68 99.54 3.62 94.26
February 3.77 101.48 3.82 97.98
March 3.61 97.51 3.84 98.75
April 3.97 108.07 4.15 106.80
May 3.64 99.13 3.93 101.69
June 3.58 97.86 3.89 101.35
July 3.62 99.43 4.07 106.77
August 3.52 97.89 3.76 98.91
September 3.49 97.16 3.57 93.52
October 3.73 106.23 3.54 92.64
November 3.53 98.40 3.56 99.25
December 3.56 99.74 3.91 109.26

 

Source: Compiled by MEDAC 1998.
Note: 'Multiply cents by 0.01 to get the Birr equivalent.

Several scenarios are estimated to calculate the potential price reduction from
reductions in transport costs (Table 7.15). In scenario 4 transport rates from the port to
Nazreth are reduced by 12 percent (equivalent to the mean spread in prices between
March and July, 1994-97) and transport rates from Nazreth to Jimma are reduced by 5
percent.

In scenario 5 transport rates on the two segments of the trip to .T1mma will be
reduced (as in scenario 4), but an additional assumption is that 100 percent of the fertilizer

is delivered from the port inland (none stored in the port, which is only applicable in

214

Table 7.15 J imma Import Parity Retail Price for DAP Under Estimated
Transport Rate Reductions

 

 

 

 

Assab Djibouti
W0 USS/MT Bin/quintal USS/MT Bin/quintal
LowP High? LowP High? LowP High? LowP HighP
W low-and 344.44 357.24 237.67 246.50 357.30 370.20 246.54 255.44

high-price scenarios

Sgnarig 4 12% Reduction 335.85 348.65 231.74 240.57 347.62 360.52 239.86 248.76
in Port-Nazreth Transport

Rate and 5% Reduction in

Nazreth-Jimma Transport

Rate

mmmmmus 333.66 346.46 230.22 239.05 342.19 355.08 236.11 245.01
100%DirectDelivery’

Sanatio 6 Scenario 5 plus 321.72 346.46 221.99 239.05 330.26 355.08 227.88 245.01

10% Reduction in Low- '

priced 17.0.13. (Reduced

supplier

uncertainty/improved

timing)
Notes: ‘Assumes the Djibouti-Nazreth freight rates is 0.0456 USS/MT/km instead of 0.057 USS/MT/km
and the Assab-Nazreth rate is reduced to USS/MT/km 0.035 from US/MT/km 0.44.
”Savings from direct delivery only applicable in Djibouti.
Djibouti). It is feasible that reduced transport rates (i.e., ample supply of trucks) will
accompany reduced port storage in Djibouti because port storage occurs when there are
insufficient trucks to transport the fertilizer inland. In Djibouti roughly 25 percent of a
ship’s cargo will be stored at the port for lack of transport. Assuming a shipment of
25,000 MT of DAP is imported, a Transit Charge of USS 7.20/ton is paid on the portion
stored in addition to USS 1.50/ton charged on the entire shipment--thus summing to USS
3.30/ton. Ifzero fertilizer is stored at the port this charge drops to USS 1.50/MT. The
net savings on a 25,000 MT shipment is USS 45,000 (equal USS 1.80/MT savings).

By reducing transport rates alone, on both legs of the route to Jimma, the J'nnma

import parity price can be reduced by 7 Birr/quintal fi'om an average of 246 Birr/quintal

215

for DAP (calculated from Table 7.15). Calculating in the additional likelihood that port
storage could be avoided with increased supply of trucks, the Timma price is reduced by a
total of 9 Birr/quintal (4 percent of the base case). The additional assumption of a 10
percent reduction in the obtained f.o.b. price drops the Jimma retail price to 228
Birr/quintal, 7.6 percent of the base case.

. Policy Uncertainty. Due to the fact that farmers prefer to purchase inputs on
credit and that government credit removes the risk of marketing, fertilizer distributors
preferred to rely upon the organization of credit for their sales (GMRP 1998). In effect,
the organization of credit in 1998 was uncertain, and also tardy. For example, many more
zonal governments announced plans to hold fertilizer auctions than were actually realized
(GMRP 1998). In other cases, supposed tacit contracts with regional governments were
broken. Overall, a common problem in 1998 was that markets were unexpectedly closed
to integrated retailers and independent retailers due to unpredictable government policy.

Policy uncertainty can add considerable costs (in terms of delays and lost markets)
to doing business, thus serving as a disincentive for private retailers to enter the inputs
market. Because distances are great, there is seasonality in transport rates, and because
fertilizer demand is seasonal, securing a market early in the season is critical for
distributors to remain competitive. If a potential market is unexpectedly closed at the

beginning of a season, the probability of finding a new market is low.

216

In 1998 stated fertilizer polices to liberalize markets were often abandoned,‘I2 thus
markets were not as “free” as the fertilizer distributors originally perceived. Although
regional governments announced that fertilizer auctions were to be held in many areas,
there was uncertainty as to when and whether regions would actually hold the auctions.

In the Oromiya Region, auctions were held, but they were late, leaving the distributors to
ponder whether to redirect their fertilizer stock. One company reported that they had sent
an employee, armed with the cash bid bond, to a wereda capital“3 to submit a bid for an
auction. However, when the employee arrived in town, he faced a delay of up to week,
raising the cost of participating in the government’s auction (GMRP 1998). In the
Southern Region, only nominal auctions were held, at the end of the fertilizer season in
August. When auctions were indeed held, it was not uncommon for the FDRE to
unexpectedly inform some integrated retailers, and all smaller independent retailers, that
they could not participate in the auctions.

Even in regions where fertilizer auctions were not as common, government policy
was uncertain. EAL incorrectly assumed it could distribute the stock it held in the Amhara
Region and was surprised to find it was denied distribution rights at the beginning of the
season. Only when it became evident that Ambassel (the government-nominated supplier)
was running short of urea, a contract was awarded to EAL for 30,000 quintals. However,

after the government paid for 50 percent of the contract and took delivery of the 'paid

 

"Fertilizer prices were liberalized, but government control of credit and favoritism in
distribution meant that competition was severely constrained.

”There was no choice but to deliver the cash bid bond in person in the designated .point of
registration, the wereda capital.

217

portion, it canceled the remaining order, leaving EAL feeling “tricked” (GMRP 1998).
EAL had the stock ready for delivery and found, after the contract was broken, that it was
too late to position stocks elsewhere.

If fertilizer distribution policies were more transparent then it may be possible to
reduce costs in the system. For example, if it was possible to increase the direct delivery
from the port to the point of retail (if a distributor was certain he/she could secure a
market), then storage and handling costs, as well as interest could be saved. Currently,
most fertilizer is stored in Nazreth for up to two months, waiting for government
distribution policies to be set, before it is sent on to retail markets.

The cost savings from bypassing unloading, storage,“ and interest for two months
in Nazreth is 4.10 Bin/quintal which is relatively small compared to the transport cost
savings that would be achieved in this scenario. If the fertilizer can be shipped directly
fiom the port to erma it is possible to obtain, at a minimum, the lower port-Nazreth rate
for the entire port-Jimma trip." The total cost of transport fi'om the ports to Jimma after
2 months in Nazreth is 44.22 Bin/quintal from Assab and 52.95 Bin/quintal from
Djibouti. Iffertilizer is transported directly from the port to Jimma at the freight rate

offered for the port-Nazreth route, and 100 percent of fertilizer was directly delivered

 

“Storage costs in most outlying areas (e.g., Jimma at 0.25 Birr/quintal/month) were lower
in 1998 than the cost of storage in Nazreth (0.35 Birr/quintal/month).

85The cost of transport fi'om Assab to Nazreth is 23.80 Birr/quintal and 32.53 Bin/quintal
from Djibouti to Nazreth and the cost from Nazreth to Jimma is 20.42 Birr/quintal. Given
that rates are reduced for longer distances, and the road from Nazreth to Jimma is
relatively good, it may even be possible to negotiate a lower rate than currently received
for the port-Nazreth route.

218

(none lefl in storage at the port), the total cost of the trip is 37.28 Birr/quintal fiom Assab
and 49.99 Birr/quintal from Djibouti (cost savings of 6.94 Birr/quintal and 2.96
Birr/quintal, respectively). Under these assumptions, J imma retail prices can be reduced

by up to 13 Birr/quintal (roughly 3.5 percent of the 1998 mean Jimma DAP retail price)

(Table 7.16).

Table 7.16 Jimma Import Parity Retail Price for DAP Under Reduced Policy
Uncertainty (Direct Delivery from Port to Retail Markets)
Assab Djibouti
Scenario USS/MT Bin/quintal USS/MT Bin/quintal
LowP High? LowP I—IighP LowP High? LowP HIE;
W low- and 344.44 357.24 237.67 246.50 357.30 370.20 246.54 255.44
high-price scenarios
§Qnario 7 Direct 327.71 340.29 226.13 234.81 344.54 357.22 237.74 246.49
delivery from port to
retail market
§9§nario 8 Scenario 7 + 304.23 340.29 209.93 234.81 321.07 357.22 221.55 246.49
low-priced f.o.b. reduced
by 10% (improved
timing/reduced
uncertainty)

If it is additionally assumed that fertilizer importers could obtain a 10 percent
lower price on the international markets (perhaps from increased certainty of delivery date
or from purchasing fertilizer during the low-priced months) then the Jimma retail price

could be reduced by 10 percent from the base case scenario (Table 7.16).

219

7.5.1 Open Market (Cash Market)

The administration of government credit has direct implications for the structure
and consequent underdevelopment of the cash market.“ In many areas of the country,
there was an unmet demand for fertilizer credit that was not being adequately met by the
cash market, either because cash sales were unavailable or the cash price of fertilizer
rendered fertilizer unprofitable (GMRP 1998).

The combination of the active promotion of the NEP, the high demand for credit,
and the high degree of government-encouraged vertical integration resulted in a
particularly thin cash market in 1998." Some local governments actively discouraged the
development of a retail network for cash sales because of the perception that potential
clients of cash sales would primarily be those having defaulted on past credit loans and
therefore should be punished (GMRP 1998). Another explanation is that if farmers had
alternate sources of fertilizer they would not sign up for the NEP and program targets
would not be met. In the Southern Zone of Kembata one independent retailer selling only
on cash complained bitterly that it was more difficult to enter the fertilizer business that
year because the government prohibited retailers from operating in order to ensure that the
NEP expanded (GMRP 1998). Independent retailers in other Southern zones stated that

farmers were intimidated by local government officials: they were told they could not

 

“As mentioned in chapter 3, the amount of fertilizer sold on cash varies considerably
across regions and even between neighboring zones within a region.

"This was also reported by Demeke et al. in 1997, a year earlier than this review.

220

 

purchase fertilizer fiom the independent retailers, so they only bought on credit fi'om
Wondo (GMRP 1998).

The government also discouraged the development of cash sales by placing
restrictions on pricing behavior of cash retailers. An independent retailer in the South
explained that his store was closed by other distributors that have “political backing”
because he offered a lower cash price than the credit price (GMRP 1998). He was
informed that he could operate as long as his price equaled that of Wondo, the
government-appointed distributor.

The high demand for credit is one key reason why cash sales were low in 1998;
however, it is not necessarily a signal that farmers could not purchase fertilizer without
credit. One independent retailer in West Shewa, Oromiya said, “Availability of credit for
farmers is the major problem, because farmers want to get fertilizer on a credit basis even
if they have the ability to purchase on cash basis. This hinders retailers to activate cash
sales” (GMRP 1998). Indeed there are many extremely poor farmers that require credit;
however, it is hypothesized that in 1998 there were pockets of excess demand for fertilizer
that could have been fulfilled by the cash market. An independent retailer in Sendefa,
North West Shewa, Oromiya, noted that, “Some farmers sell livestock in order to increase
their fertilizer cash purchasing power” (GMRP 1998).

Overall, in most areas of the country, the NEP and SC credit programs and the

cash market did not operate simultaneously. The cash market proved to be robust in cases

 

"Previous actions, such as denying rival companies access to credit, revealed that the
regional government supported this companies’ position in the market.

221

where the NEP was not present or was unable to firlfill the demand for fertilizer. There
were cases where the “inability of farmers to repay last year’s credit made independent
retailers to enter without competition because the large wholesalers/retailers were not
there” (GMRP 1998). As seen fi'om the estimates of the hedonic fertilizer price model
(chapter 5), cash prices were higher than credit prices when a fertilizer auction was held;
however, cash prices were lower than credit prices if a mandated market was present. It is
possible that the cash market can be a low-cost option for farmers that do not have access
to the NEP, and it is certainly a lower-cost option than the monopoly markets that
provided limited high-cost fertilizer to the select NEP farmers.

Although the NEP has been expanding rapidly (reaching a goal of nearly 7 percent
of the rural population in 1999), it can not serve all farmers, and therefore adoption of
new technologies will require that credit is available outside the NEP and that the cash
market is sufficiently developed (i.e., that the cash market exists and farmers find the price
profitable). Several independent retailers and also officials of Service Cooperatives
suggested that the government provide credit for poor farmers and encourage the rich
farmers to purchase on cash (GMRP 1998). One SC official reported, “Credit for
fertilizer is becoming a culture, any farmer, even those that can buy fertilizer on cash,
wants credit” (GMRP 1998). This independent retailer said he plans to “agitate the
government not to give credit to resource rich farmers” (GMRP 1998).

Overall, independent retailers were fully aware of the heavy involvement of the

government in the fertilizer sector to the detriment of their business. An independent

222

retailer in Hadiya, Souther Region noted, “Political organizations shouldn’t participate in

fertilizer trading. It should be left to individual businessmen” (GMRP 1998).

7.6 Extension

The NEP has exhibited phenomenal short-run success in rapidly expanding the
program to a broad base of farmers. However, the rapidly expanding program is already
experiencing some difficulties in delivering a timely and quality input/extension package
because input and credit markets are not evolving as rapidly and efficiently as needed and
extension services are being forced to fill in the gaps rather than focus exclusively on the
extension mission.

Analysis of the SG 1997 survey data confirms that farmers must learn appropriate
management practices if they are to get the full benefits from improved technologies.
Yield determinant models showed that for each week of deviation from ideal planting
dates, yields declined by 194 kg/ha (p=0.03) (Howard et al. 1999). In addition, the yield
model presented in the previous chapter revealed that there are important
complementarities between timing of weeding and seeding density. Thus, there are
concerns that the momentum of the NEP program may be suppressed by the (1) diluted
quality of extension advice due to increased demands on time of existing personnel, and
(2) inefficiencies in the administration of the credit program.

There is evidence that DAs (extension agents) may become overextended with the
rapid expansion of the NEP and thus compromise the quality of the extension service.

Given that the broader population served by the NEP will need more, rather than less

223

supervision due to lower literacy rates (25 percent literacy for household heads nationally
versus 90 percent for 86 participants), the substantial increase in plots per agent is
worrisome. SG agents in 1997 covered 60-70 plots; however, in 1998 NEP case loads
ranged fi'om 150-500 plots (the recommended level is 100 (Abate 1997)). Survey data for
1997 showed that SG agents made an average of 12 visits per farmer which is no longer
possible with greater than 150 plots per agent.

The second extension problem is that the NEP attempts to serve as a surrogate for
well-firnctioning fertilizer and seed markets89 by providing administrative support90 to
banks and fertilizer distributors (processing credit applications, collecting reimbursements,
and transporting fertilizer) without due compensation. The Ministry of Agriculture is
paying the salaries of the DAs, supervisors, as well as administrators in the Wereda
Bureaus of Agriculture to provide services that could be provided by the private sector.
District agricultural officers and extension agents interviewed in 1998 reported insufficient
time to perform their normal duties due to these additional tasks and noted that their role
in collecting credit repayments created an undesirable adversarial relationship between
farmers and themselves.

As shown in the last section, in some cases the credit workload is so heavy that it
leads to delays in fertilizer distribution (due both to delayed repayment of prior credit and

slow processing of current loans). In 1998 all Service Cooperatives and Agricultural

 

”Hybrid maize seed is not commonly available on the open market, only through the NEP.

S"The 1998 Input Subsector Survey revealed that instead of “support”, the NEP may be
imposing m on banks, regional government administration, and government extension
services.

224

Bureaus interviewed reported late deliveries due primarily to delays in credit
reimbursement and credit processing, with some cases of supplier inefficiencies which
required government personnel to provide substitute services. For example, government
personnel and vehicles were used when one supplier failed to fulfill their NEP contract due
to lack of transport and storage. The Agricultural Bureau was forced to incur the costs of
transport to the outlying distribution centers without due compensation. Thus, the NEP
may not be financially sustainable in the long-term because too many non-extension tasks
are being imposed on extension agents.

The overall impact of increased plots per agent and extra tasks is a lower quality
extension message with increased (non-extension) costs attributed to the extension
program. Thus, the NEP may have to cut back, rather than expand, its coverage.
Ensuring sustainable adoption of the improved technology is in part predicated upon the

sustainability of the NEP to deliver a high quality extension message.

7.7 Conclusion

This chapter revealed that there were wanting signs in the 1998 inputs markets that
farmers may not be receiving the necessary low-cost fertilizer, access to credit, and quality
extension required to ensure long-term adoption of high-input technologies. The FDRE’s
actions in the inputs market are not characteristic of a government striving to promote a
competitive, low-cost input market. It was observed that in 1998 the FDRE used access
to input credit to promote selected companies in the market, actively discouraged the

development of a cash market for fertilizer, crowded out other private fertilizer

225

distributors from the market, and may have overextended its extension program. This
chapter provided qualitative and quantitative evidence that the fertilizer subsector was not
providing a fertilizer product to farmers at least cost nor providing an attractive
environment for private sector investment.

It is shown that, with government support, Ethiopia can realize savings from the
reorganization and improved coordination of the process of import and distribution.
National and regional governments’ heavy hand in all stages of the subsector prevented a
lower-cost system by constraining entrepreneurial innovation which stems from
competition. Costs can be reduced, for example, if the private sector has the ability to
determine the timing of its activities (e.g., to take advantage of season lows in prices).
The point of this chapter was not to determine an optimal timing scenario that could
reduce fertilizer costs to the farmer, but to show that with increased private sector
autonomy and improved coordination among actors in the system costs could be reduced.
The identified cost reductions in this chapter will be summarized in the following chapter
and used in a simulation exercise to determine the extent to which fertilizer profitability

can be‘ increased.

226

CHAPTER 8
IMPROVING THE PROFITABILITY OF HIGH-INPUT MAIZE
TECHNOLOGY IN ETHIOPIA

’ In Jimma Zone in 1997 the cost of fertilizer was 34 percent of the total variable
cost of investing in the SG high-input technology--improved seed equaled 8.5 percent,
labor was 45 percent, animal traction, 8 percent, and hand tools and sacks was 5 percent
(compiled fi'om Howard et al. 1999). At this magnitude, the cost of fertilizer can
determine whether adopting high-input technology is a viable option for increased maize
productivity growth in Ethiopia, especially as the input package is eXpanded to farmers
that are less well-off or less educated than the SG participants or to farmers in less
favorable agro-ecological areas. This chapter addresses the questions: (1) How profitable
is fertilizer use and the SG package, specifically, given variations in yields within each
technology level—that is, what is the production risk associated with the new technology;
(2) Does the net benefit of the high-input technology exceed the cost, given the high-input
technology requires additional labor; and (3) To what extent could the proposed cost
reductions (from chapter 7) in the fertilizer subsector effect the profitability of the new

technology?

227

8.1 Measuring Profitability

Two techniques are used to capture the attractiveness of investing in new
technologies by farmers: a partial budget and a marginal analysis. The partial budget
approach is used to evaluate changes in profitability from changes in fertilizer prices by
calculating net income and returns to labor. Partial budgets indicate the net gain
attributable to switching from current practices to recommended practices. Partial
budgeting “was developed to calculate the total costs that vary and the net benefits for
each treatment of an experiment” (CIMMYT 19989). The term “partial” indicates that
only costs are included that are affected by alternative technology packages--costs that
change over and above the control practice: local seed and roughly 100 kg/ha DAP.

A marginal analysis captures whether the increase in net income exceeds the
incremental costs when moving from one technology combination to a more costly one.
Higher benefits will not attract investment if accompanied by even higher costs. A
marginal rate of return (MRR) was calculated--the marginal net benefit (i.e., the change in
net income) divided by the marginal cost (i.e., the change in variable costs)--to determine
the increased net income from a unit of investment (CWYT 1998). It is assumed that
the objective of the farmer is to invest in a new technology if and only if the. expected
return is equal or greater than the return in alternative investments; however, such data is
not usually available. A limitation to the partial and marginal analyses is that even when
fertilizer is absolutely profitable, there is evidence that farmers still do not use fertilizer

(Yanggen et al. 1998). A possible explanation is that absolute profitability is a necessary

228

but not a sufficient condition for fertilizer adoption (Yanggen et al. 1998). Fertilizer must
be profitable, but it also must be more profitable than competing investments.”1

The rule of thumb used in this analysis for determining whether a farmer should
invest in a new technology is that the new technology (e.g., improved seed or fertilizer)
must produce a minimum of a 100 percent marginal rate of return (CIMMYT 1998).
However, if the technology is an adjustment in a current practice (e.g., increasing the

fertilizer rate), then a 50 percent rate of return may be appropriate (CIMMYT 1998).92

8.2 Financial Analyses

This chapter examines the robustness of the profitability (i.e., net incomes, returns
to labor, and MRR) of improved maize technologies under different fertilizer prices.
Proposed areas for cost reductions in the fertilizer subsector, delineated in the last chapter,

were used to simulate changes in profitability of using the high-input technology.

 

91As an example, evidence exists that in Jimma Zone in 1992 the profitability of maize was
774 Birr/ha compared to 419 Birr/ha for coffee; and the return to maize per workday was
6.65 Birr and 2.35 Birr for coffee (Seyoum and Regassa 1992).

92An alternative approach to calculating the target MRR is to take the actual interest rate
on a formal lean (10.5 percent in Ethiopia in 1998) and add 20 percent for a risk premium
(Penin et al. 1979 in Crawford and Kamuanga 1988). In Ethiopia it was found that
farmers were using fertilizer at a return of 41 percent, higher than the 30.5 percent
recommended rate that this approach gives (Demeke et al. 1998).

229

8.2.1 Method

Farm budgets from a recommendation domain” of three weredas--Kersa, Seka-
Chekorsa, and Dedo-surrounding Jimma town in J imma Zone, Oromiya Region, were the
focus of this analysis. These areas share similar agro-ecological characteristics—all have
predominately red soil and similar rainfall pattems—and all considered high-potential maize
areas.”

The financial import parity price in Jimma town in the last chapter was recalculated
for the 86 participating weredas: Seka Chekorsa, Kersa, and Dedo. The average
additional distance of these three weredas from the zonal capital is 21 km. Thus transport,
loading and off-loading costs were added onto the Jimma financial import parity price
calculation presented in the last chapter.

Chapter 7 outlined possible areas for cost reduction in the fertilizer subsector from
liberalization of the rigid timing stnrcture imposed on fertilizer importers and distributors.
The base scenario for fertilizer prices was the financial import parity price for low and
high-priced f.o.b. scenarios, calculated from a detailed cost build-up presented in Tables
7.1 and 7.2. The maximum prices could reasonably be expected to decline with these

specified cost reductions is 10.9 percent for DAP and 9.4 percent for urea (Table 8.1).

 

”A group of farmers whose agro-ecological environment is sufliciently similar to warrant
the same technology recommendation.

S"Of the three weredas, Dedo receives the lowest average annual rainfall (900-1400 mm),
Kersa receives roughly 1410 mm, and Seka-Chekorsa receives between 1400-1600 mm
(Buta 1997). Average rainfall is lower than other maize producing regions, Ilubabor,
south-west of Jimma, and East Wolega, north-west of Addis Ababa.

230

Table 8.1 Summary of Potential Cost Reductions in the SG-participating‘
Wereda Retail Prices of DAP and Urea, Birr/quintal, F.O.B. Djibouti

 

 

Scenario Description Assumed Cost Reduction DAP” Urea
(percentage change from
base case in parentheses)

Base case Financial Import Parity 248.80 163.47
Price
1 Reduced shipment date 5% reduction in low-case (from 240.60 161.47
uncertainty tables 7.1 and 7.2) f.o.b. price (3.29) (1.23)
2 Scenario 1 plus Improved 10% reduction in f.o.b. price (various 232.37 157.55
Timing origin) (6.60) (3.62)
3 Improved competition DAP price falls by and urea price 238.80 142.47
(results from hedonic price falls by (4.02) (12.85)
model)

4 Reduced transport costs 12% reduction in port-Nazreth rate 239.86 156.80
and 5% reduction in Nazreth-Jimma (3.59) (4.08)
rate

5 _ Scenario 4 plus 100% Direct delivery reduces the Transit 236.11 155.56

direct delivery (zero Charge by 54.5% (5.10) (4.83)
fertilizer stored at the port)

6 Scenario 5 plus reduced 10% reduction in fob. price (various 227.88 147.38

shipment date uncertainty origins) (8.40) (9.84)
7 Reduced marketing Bypass unloading and storage in 237.74 156.07
uncertainty, direct delivery Nazreth. Direct delivery reduces the (4.44) (4.52)
from port to retail market Transit Charge by 54.5%

8 Scenario 7 plus reduced Low-priced f.o.b. reduced by 10% 221.55 148.03
shipment date uncertainty (10.95) (9.44)

 

Source: GMRP, Input Subsector Survey 1998.

Note: 'Rccorded for the same weredas in Jimma as the 86 survey: Dedo, Kersa, and Seka Chekorsa, n=20
for DAP and n=9 for urea.

”The low price scenario will be used in the budgets as representative of the maximum price reduction
possible under improved coordination in the fertilizer market.

Three scenarios will be used in a sensitivity analysis of farm profitability of the
high-input technology: scenarios 3, 6, and 8 (all scenarios are reviewed in table 8.1).
Scenario 3 represented results from the hedonic fertilizer price model in chapter 5. It
shows that increased competition whereby credit prices that were determined in a market
held by a government-appointed supplier rather than through a fertilizer auction were up

to 10 Birr/quintal higher for DAP and up to 21 Birr/quintal higher for urea relative to the

231'

 

winning bid prices. However, J imma Zone was a relatively competitive market, thus the
simulation analysis will show by how much profitability would decline if the fertilizer
market had been appointed by the regional government and fertilizer auctions were not
held.

Scenario 6 was a reduction in transport rates on both the port-Nazreth route as
well as on the Nazreth-Jimma route (12 percent reduction in port-Nazreth freight rate and
5 percent reduction on the route fi'om Nazreth to Jimma) as well as an assumption of 100
percent direct delivery (no fertilizer stored at the port). Scenario 6 also assumed that
international f.o.b. prices in Jordan for DAP could be reduced due to increased certainty
by international suppliers of the delivery date.

Scenario 8 assumed that due to a reduction in policy uncertainty (e.g., whether and
when fertilizer auctions were held) direct delivery from the port to a retail market such as
Jimma would be possible, bypassing 2 months of storage in Nazreth and reducing port
storage. Scenario 8 also assumed a 10 percent reduction in the f.o.b. prices obtained by
Ethiopia in 1998 from various international markets.

Four levels of technology were evaluated in the financial analysis (as defined in
chapter 6)--two under the SG package and two using traditional technologies.
Technology types 1 and 2 represented non-SG technologies: technology type 1 used
neither fertilizer nor improved seed, and type 2 used local seed and roughly 100 kg/ha

DAP. Technology types 3 and 4 represent farmers that were either current or past

232

 

participants of the SG program. Technology type 395 used improved seed, 86 kg/ha DAP,
and 86 kg/ha urea (less than the recommended 100 kg/ha of each), and technology type
(4) used improved seed, 116 kg/ha DAP, and 116 kg/ha urea. Many of the SG graduates
in 1997 participated in the NEP which followed basically the same recommendations as
the 86.

A return is calculated from the product of the adjusted yield and the farmgate price
of maize. Yield is adjusted downward to capture the loss in grain from storage from when
maize is harvested in November and crop sale in January (storage losses of 1.98 percent
per month). The farmgate price used in the analysis is recorded in January, immediately
following harvest, when a large portion of households sell a large portion of their
harvest.”S All prices are recorded as financial market prices (as opposed to shadow prices
in an economic analysis).

Two measures of return are calculated: net margin per hectare and gross margin
per hectare. Net margin is the return (value of output) minus variable costs--package
costs (improved seed, DAP, urea, herbicide, insecticide, and fungicide), labor costs, cost
of animal traction, and cost of hand tools and sacks. For net margin, the family and

mutual97 labor was valued at its opportunity costs-the daily wage rate (3 -6 Birr/ha,

 

”Technology 3 consists of 61 percent of the graduate sample and 49 percent of the 86
sample.

”In Ethiopia, on average 18 percent of crop sales are made in January, 17 percent in
February, 16 percent in March, 16 percent in April, 13 percent in May (CSA 1995/96).

S”Mutual labor refers to labor provided by extended family and friends free of charge (for
neither cash nor in-kind payment).

233

average 4.5 Birr/ha). For the second measure, gross margin, the cost of family and mutual
labor was not counted. The returns to family and mutual labor was calculated by dividing
net income per hectare by the number of family and mutual labor days.

The classic approach is to value labor by its opportunity cost--“the wage which
could be earned in off-farm employment, or the value of the time spent on another farm
enterprise, or the value which the worker places on leisure” (Penin et al. 1979 in
Crawford and Kamuanga 1998). There are limitations to this approach due to several
reasons as specified by Crawford and Kamuanga (1998): (1) it is difficult to value leisure,
(2) ofi‘-farm labor may not be a viable option, and (3) farmers often are willing to work on
their farm for less than the off-farm wage. Nonetheless, given that one objective of this
analysis is to compare the profitability (MRR) of different technology treatments and given
the importance of labor as a cost, the MRR calculations were based on net margin per
hectare, which takes into account all labor costs.

In Howard et al. (1999) the high-input technologies were highly profitable for each
technology type on average, however, it is questionable whether the profitability of the
new technology is robust at all yields levels, given that there is considerable variation in
yields within each technology grouping. The standard deviation (SD) of yields for
technology type 2 was 1,245 kg/ha (mean 2,905Dkg/ha); for technology type 3 the SD was
1,364 kg/ha (mean 5,922 kg/ha); and the SD for technology type 4 was 1,595 kg/ha (mean
5,910 kg/ha). Successful expansion of the high-input technologies is predicated upon the
robustness of the 1997 SG Jimma success in the face of less favorable soil and climatic

conditions and management practices, and thus, lower-than-average yields.

234

 

One method of estimating the risk associated with the high-input technology is to
observe the across-location and across-technology yield variability (CIMMYT 1988).

This is accomplished by dividing the broad technology groupings into two groups—the 50
percent of farmers with the lowest yields and the 50 percent of farmers with the highest
yields. Did farmers that received the lowest yields within each technology group achieve
profitability similar to the average profitability? How sensitive is profitability to changes in
yields? This issue is particularly important as the high-input technologies are expanded to
less favorable agro-ecological areas and to farmers with perhaps lower management skills
than the 86 participating farmers.

A second level of analysis is to divide the technology groups into halves, not by
yield, but by the level of labor use (family, mutual, and hired labor). Defining technology
treatments by the level and type of seed, and the level of fertilizer omits a significant cost
component of labor. As mentioned at the beginning of the chapter, labor is 45 percent of
the cost of the SG technology. On average, technology type 2 used 88 labor days,
compared to 102 labor days for technology type 3, and 144 labor days for technology type
4. Is the increase in costs associated with technology type 4 compensated by an increase
in net margin? Thus, technology treatments should not only concentrate on the level and
type of seed and fertilizer, but whether sufficient labor is available and whether the high-

input technology renders the additional labor input profitable.

235

 

8.2.2 Results

The purpose of a financial analysis is to assess the attractiveness of an enterprise to
farmers (Crawford and Kamuanga 1988). This section will present a partial budget of the
high-input SG technology and then calculate the net margin (valuing family/mutual labor
at its opportunity cost), gross margin (which does not deduct the cost of family/mutual
labor), and the MRR to using the high-input technology from a base case whereby farmers
used local seed and no fertilizer.

The total average cost of using the SG high-input technology (technology type 4)
increased 11 percent over technology type 2 (no seed, some DAP) (Table 8.2). The
combination of fertilizer and seed was 74 percent of the total variable cost of the SG
technology package. The cost of hired labor also increased—from 36 Birr/ha in technology
2 to 50 Birr/ha for the SG farmers (technology type 4).

The net margin per hectare almost doubled from the case of local seed/no fertilizer,
to partial adoption of the SG package (local seed, but some DAP)—from 246 Birr/ha to an
average of 667 Birr/ha. A benefit of the disaggregation by yields, rather than only
calculating averages for each technology type, reveals that farmers using local seed and
DAP (no urea) in the lower yield grouping (technology 2) received lower net margins and
gross margins than if they had continued with the local seed/no fertilizer practice. Thus
there is a production risk associated with the adoption of fertilizer.

If, however, a farmer adopted improved seed and used varying levels of both DAP
and urea, then gross and net margins for all yield levels far surpassed the returns calculated

for the local seed/no fertilizer treatment as well as the local seed/DAP treatment.

236

 

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Notes for Tables 8.2 and 8.3:

'Technology types were split into two: the 50% of farmers with the lowest yields and highest yields.
2Assumes no grain lost during shelling. Adjusted yield assumes maize harvested in November and storage
losses of 1.98% per month until crop sale in January.

3MOA/$62000 maize package consists of 25 kg/ha seed. 100 kg/ha DAP. 100 kg/ha urea.

480 participants pay no interest; MOA program participants pay 10% interest annually for 10 mo.
SMutual labor provided by extended family members. Family/mutual labor valued at an ave. 4.5 Birr/day.
°Valued at cash/in-kind payment rates provided by survey participants.

7Sum of rental costs reported by survey respondents; and for owned/borrowed oxen. maintenance

plus depreciated value of animals and animal traction. Equipment multiplied by percentage of total farm
represented by the MOA-8G. traditional or graduate plot.

8Depreciated value of 2 hoes. 2 axes. 2 cutting knives.

9Depreciated value of sacks needed to transport maize marketed in January. Since sacks are retained by
farmers and used for other purposes. cost is apportioned by multiplying depreciated sack value by percentage
of total farm represented by MOA-SG or graduate plot.

Table 8.3 Financial Analysis by Technology Type and Labor Use

 

 

 

 

 

Tech 1 Technology 2 Technology 3 Technology 4
Budget Item Local seed. Local seed plus DAP Improved seed. DAP. Improved seed. DAP
no fert. and urea -- recornm. and urea "= recomm.
T1.0 T2.l T22 13.] T32 T4.l T4.2
lowcr’ upper lower upper lower upper
11 4 21 22 28 28 25 25
1. Maize yield (kg/ha) 1835.00 2886.88 2921.77 4619.05 7060.26 5606.21 6237.74
1.a. Adjusted maize yield (kg/ha)2 1763.00 2829.72 2863.92 4527.59 6920.47 5495.21 61 14.23
2. Farmgatc price ofmaize (Bin/ha) 0.54 0.54 0.54 0.54 0.54 0.54 0.54
3. Return (3) 952.02 1528.05 1546.52 2444.90 3737.05 2967.41 3301.69
4. Total Variable Costs (5.+6.+7.+8.+9.) 705.90 682.85 1064.84 1235.16 1460.84 1500.77 1894.71
5. Total Package Costs (5.:1-‘-5.b.+...5.1‘.)3 49.00 249.59 349.44 534.20 562.48 710.50 730 49
5a. Seed (Bin/ha) 49.00 33.14 44 107.6 113.08 142.97 147.04
5b. DAP (Bin/ha) 0.00 216.45 303.74 219.35 230.23 291.85 299.41
DAP kg/ha 0.00 84.22 115.93 84.69 88.21 114.9 116.5
DAP Bin/kg 0.00 2.57 2.62 2.59 2.61 2.54 2.57
5c. Urea (Bin/11a) 0.00 0.00 0.00 206.64 217.00 274.61 281.93
Urea kg/ha 0.00 0.00 0.00 84.69 88.21 114.90 116.50
Urea Bin/kg 0.00 0.00 0.00 2.44 2.46 2.39 2.42
5d. Herbicide (Bin/ha) 0.00 0.00 0.00 0.00 0.00 1.07 0.00
5.e. Pesticide (Birr/ha) 0.00 0.00 0.00 0.61 0.00 0.00 0.00
5.1". Fungicide (Bin/ha) 0.00 0.00 1.70 0.00 2.18 0.00 2.11
6. Interest (Birr/ha)‘ 0.00 0.00 0.00 11.06 26.23 13.73 21.18
7. Labor (Bin/ha) (7.a.+7.b.) 387.00 240.50 532.58 448.11 468.95 458.22 830.31
7a. Family & mutual labor (Birrfna)s 351.00 217.22 484.43 396.18 371.43 418.55 770.49
7.a.i. Family & mutual labor days 78.00 48.27 107.65 88.04 82.54 93.01 171.22
7.b. Hired labor (Bin/1m)6 36.00 23.28 48.15 51.93 97.52 39.67 59.82
8. Animal traction (Bin/ha)7 261.00 103.13 92.33 100.3 186.65 146. 13 122.12
9. Hand tools & sacks (Birr/ha) (9.a.+9.b.) 8.90 89.64 90.45 141.49 216.53 172.20 190.62
9.a. Tools (Birr/ha)8 3.10 3.03 2.80 2.92 4.72 4.01 3.49
9h. Sacks (Bin/ha)" 5.80 86.61 87.65 138.57 211.81 168.19 187.13
Net Margim’Hectare (3.4.) 246.12 845.20 481.67 1209.74 2276.21 1466.64 1406.97
Gross Margin/Hectare’labor Daylo 7.66 22.01 8.97 18.24 32.08 20.27 12.72

(3.4.+7.a.)/7.a.i.

238

On average technology type 3 received the highest net margin/ha (1,806 Birr/ha),
compared to the net margin/ha of 1,439 Birr/ha for technology group 4 (primarily SG
participants). The next section will discuss this finding fiirther.

Given that the high-input technologies are considered relatively labor intensive
relative to traditional technologies, a more appropriate measure of each technology
treatment is to include the required labor days. In Table 8.3 the technology types are
divided into halves by the level of labor application (family, extended family, and hired
labor). Within each technology type, those farmers that applied the most labor also used
the most DAP and urea—in keeping that fertilizer produces more weeds as well as

additional grain for harvest.

8.2.2.1 Dominance and Marginal Analysis

A dominance analysis is conducted by listing the treatments in order of increasing
costs to compare the costs and benefits of each treatment. Any treatment that has net
benefits (i.e., net margin) that are less than or equal to those of a treatment with lower
costs that vary (variable costs) is dominated and should be eliminated fi'om consideration
(CIMMYT 1998). The dominance analysis illustrates that the value of the increase in
yield may not be sufficient to compensate a farmer for an increase in costs. In addition,
the dominance analysis helps to eliminate inferior treatments and thus helps farmers and
researchers move toward making a firm recommendation of which technological treatment

is superior among the alternatives in profitability.

239

 

If the technology groupings are split by yield then no farmer would choose the
dominated treatments T42", T41, or T2.1 (marked with a D in the net margin column in
Table 8.4). This is because these treatments have higher variable costs compared to the
previous treatment, but a lower net margin. All technology treatments listed are profitable
but, the dominance analysis indicates which treatments are profitable relative to the
alternative treatments.

The recommended treatment, among those listed, is T3.2 (improved seed, and less-
than-recommended fertilizer). Although the MRR of T2.2 is higher (MRR of 377 percent)
it is advisable that a farmer continue to increase the costs of inputs as long as the MRR is
above the 100 percent threshold, because the net margin continues to increase. The lower
yield half of technology type 3 (T3.1) is not profitable relative to the alternative

technologies (MRR at 42 percent is below the recommended 100 percent).

Table 8.4 Dominance and Marginal Analysis by Yield Stratification
Technology Net margin! Total variable Incremental net Incremental MRR

 

Type hectare (Birr/ha) cost (Bin/ha) margin/hectare variable cost (percent)
(Birr/ha) (Birr/ha)

T4.2l 1917.2 D 1767.3

T4.] 956.1 D 1628.5

13.2 2275.7 1461.3 1196.8 534.5 223.9 %
T3.l 1209.7 1235.2 130.8 308.4 42.4 %
T2.2 1078.9 926.8 832.82 220.9 377.0 %
T2.l 219.8 D 827.1

Tl.0 246.1 705.9

 

Notes: 'Corresponds to technology type 4 (T4), the upper yield half (2).
2The difl'erenoe between T2.2 and T1.0 because T2,! is dominated and would not be a preferred treatment
by a farmer.

 

9'Technology types were spilt into two yield groups—lower and upper. T42 refers to
upper yield half of technology type 4.

240

When the technology types are split according to labor use, the financial analysis
looks very similar to when split by yields. No farmer would choose the dominated
treatments technology 1, technology 2 (DAP, local seed), or technology 4 (marked with a
D in the gross margin column in Table 8.5) because it is possible to increase returns over
these scenarios (these treatments have higher variable costs than the previous treatment,

but lower net benefits than one of the previous treatments).

Table 8.5 Dominance and Marginal Analysis by Labor Stratification

 

 

 

Technology Net margin! Total variable Incremental net Incremental MRR
Type hectare cost (Birr/ha) margin/hectare variable cost (percent)
(Birr/ha) (Birr/ha) (Birr/ha)
T4.2 1406.2 D 1895.5
T4.l 1467.1 D 1500.3
13.2 2275.7 1461.3 1430.1' 778.8 183.6 %
T3.1 1209.7 1235.2 364.12 552.7 65.8 %
T2.2 481.6 D 1064.9
Tl.0 246.1 D 705.9
T2. 13 845.6 682.5
Notes: 'The difference between T32 and T21 because T2.2 is dominated and T3.1 is not profitable given
a threshold of a MRR of 100%.

2The difference between T3.1 and T2.1, the lower-cost treatment that is not dominated.
3Usually the base treatment, T10, is the first listed in a dominance analysis. That is an anomaly of the
data, explained by higher levels of expenditure on animal traction and labor by farmers in T10

Among the alternative treatments of local or improved seed and varying levels of
DAP and urea, the recommended treatment is technology type 3.2. The MRR of T3.1 is
66 percent, not a profitable investment relative to the alternatives, given the threshold for
investment is a MRR equal or greater than 100 percent.

Thus the recommended treatment is to use improved seed and levels of DAP and

urea lower than the SG—recommended levels (technology 3). By comparison, farmers do

241

not profit fiom using DAP (technology type 2) without also adopting improved seed and
urea. This is corroborated by evidence from the yield model in chapter 6 which showed
that the combination of improved seed and fertilizer significantly increased yields over the
local seed/fertilizer combination.

The 86 recommended technology is profitable, but returns could be increased with
lower levels of fertilizer. This result may be a result of poor farmer management practices
and does not necessarily mean that the levels of inputs are too high (resulting in
diminishing returns). Many of the farmers using the 86 technology had no experience
with the new technology, whereas many of the farmers in technology type 3 had at least
one year of experience with the high-input package. Experience with the inputs on farm,
and, as shown in the yield model in chapter 6, appropriate mixes of inputs and

management practices, can be the key to maximizing returns.

8.3 Simulated Profitability from Cost-Reducing Subsector Changes

Fertilizer prices in 1998 were lower than the SG and NEP prices offered in 1997;
however, the relevant question is whether prices could be reduced fiirther with cost-saving
institutional changes in the sector to improve the profitability of the high-input technology.
Given that the SG farmers appeared better suited to use the high-input technology
(chapter 6), their variable costs (e. g., greater access to family and mutual labor and animal
traction) fi'om investing in the high-input technology may be lower than the variable cost

of farmers in the broader population. Thus if the high-input technology is profitable to the

242

SG farmers it may not necessarily be profitable to the less well-off farmers and farmers in
lower agricultural potential areas.

In order to simulate 1997 farm budgets with cost-savings calculated from 1998
financial import parity price simulations, 1998 market fertilizer prices should be
comparable to the financial import parity price. The 1998 financial import parity prices for
Jimma Zone for DAP ranged from 249-258 Bin/quintal, compared to 244 Birr/quintal
observed in the market (Table 8.6). The 1998 market was considered a
“slaughter”—companies may have offered below-cost prices for the fertilizer auctions in
Tunma Zone (GMRP 1998). For urea, the financial import parity prices ranged from 163-
179 Birr/quintal, compared to 183 Bin/quintal in the market (GMRP 1998). In 1998,
urea was in short supply, particularly in Amhara and East Shewa, Oromiya, which

potentially could have increased prices (GMRP 1998).

Table 8.6 Comparison of Jimma Input Prices in 1997 and 1998 Across
Programs, Bin/Quinta!

 

 

DAP Urea

Bin/quintal Bin/quintal
Technology type 4 (primarily SG current participants) 255 240
Technology type 3 (86 current participants and SG graduates, who 262 247
Technology type 2 (traditional plots)2 261 no urea used
1997 Government price 255 241
Input subsector survey observed market price' . 242 182
Calculated financial immrt my price at SG-mrticigting 249-258 163-179

 

 

Source: Howard et al. 1999, NFIA 1998, and 1998 GMRP Input Subsector Survey.
Notes: 'NEP participants received fertilizer and seed on credit from the NEP.
2 Farmers using fertilizer on traditional plots obtained fertilizer either on credit through Service

Cooperatives or on cash.
3 Recorded for the same weredas in Jimma as the 86 survey: Dedo, Kersa, and Seka Chekorsa, n=20 for

DAP and n=9 for urea.

243

The changes in profitability from simulated cost savings from institutional changes
in the fertilizer subsector appear small (Table 8.7). However, the cost savings in the
expenditure on fertilizer can enable a farmer to use the savings to purchase more fertilizer

or labor—inputs that may be particularly financially constrained.

Table 8.7 Simulated Changes in Profitability from Cost-Reducing Institutional
Changes in the Fertilizer Subsector, Birr/ha
Tech 1 Technology 2 Technology 3 Technology 4

 

 

Scenario Profit Measure local local seed, irnprv seed, imprv seed,

seed, no DAP DAP/urea< DAP/urea>=

fert. recomm rate recomm rate

T1' T2.1 T2.2 T3.1 T12 T4.1 T4.2

Base case Net Margin/Ha 246 846 482 1210 2276 1467 1406

Gross Margin/IiafDay 7.6 22.02 8.9 18.2 32.07 20.3, 12.7

(3) Reduced competition- Net Margin/Ha 246 837 476 I 185 2254 1422 1368

-if market was appointed

:{fifii‘l‘g‘zflefig‘t’fimfl Gross Margin/Ha/Day 7.6 21.8 8.9 17.9 31.8 19.8 12.5
model) . . '

(6) Improved transport Net Margin/Ha 246 871 522 1319 2394 1603 1553

timing, avoid port

We. impmved import Gross Margin/Ha/Day 7.6 22.5 1 9.4 19.5 33.5 21.7 13.6
procurement . . '

(8) Reduced policy Net Margin/Ha 246 875 529 1323 2398 1609 1558
uncertainty (transport

directly from port to retail

market), avoid port .

storage, capture seasonal Gross Margtn/Ha/Day 7.6 22.6 9.4 19.5 33.5 21.8 13.6

lows in intl. f.o.b. prices

 

Note: 'Same coding as table 8.2, technology types 1-4 are broken into 2 by level of labor use, T2.!
represents the lower yield halfof technology type 2.

Due to the larger discrepancy between the observed market prices in 1998 and the
calculated financial import parity price of urea the cost savings in the price of DAP are
relatively small compared to the savings in urea. In addition, because Jimma Zone is
relatively competitive and close to the calculated financial import parity price calculations

under the assumptions of a competitive market, the observed changes in profitability are

244

relatively small in Jimma compared to what can be expected in less competitive regions
like Amhara.

As observed in figure 8.1, the observed 1998 urea prices, in general, were higher
than prices calculated in the financial import parity price analysis for urea. This difi‘erence
may be due to unaccounted transport rates or rents in the market. However, given
published transport rates, the unidentified transport rates would not account for the full
amount of the gap, over 80 Bin/quintal in some zones in Amhara. The difference was
largest in the Amhara Region where Ambassel received monopoly rights and was
relatively lower in Oromiya. However, the gap between actual prices and the financial

import parity prices was higher across all regions relative to DAP.

Figure 8.1 Difference in the 1998 Urea Market Prices and
Financial Import Parity Prices
Note: Bars represent the financial import parity
price subtracted from the market price.

100

 

 

 

 

 

 

 

 

 

 

Burr/quintal
8: '8’} *1
g I

 

 

 

 

' - ' - v v v ‘ v v - ' V - v '

186,423.38, 88, q%%%3%e%e%h%%%%%
&¢:6¢1:a§:%$4 825;.» “34:31: 83888.38,

Region, Zone
245

Ifthe SG weredas had faced a government-appointed market, as in Amhara, and
fertilizer auctions were not held, then the profitability of the SG technology would not be
as high (scenario 3 in Table 8.7). Profitability would fall from 1-3 percent under improved
seed, varying levels of DAP and urea.

Under the conditions of scenario 6, if the price of DAP and urea had been lower,
farmers in technology type 4 (primarily the SG program) could have saved 282.3 Birr/ha
on fertilizer. This sum is equivalent to 192 kilograms additional urea or 114.3 kilograms
additional DAP. In scenario 8, the cost savings in fertilizer is 293 Birr/ha relative to the
base case. Under the cost-savings fi'om scenario 8 relative to the base case, technology
type 2 could purchase an additional 14 kilograms of DAP and 21 kilograms of urea for all

levels of labor, thus easing the budget constraint.

8.4 Conclusion

This chapter revealed that adopting high-input technologies-local or improved
seed with DAP alone or with urea-is a profitable investment. However, when the
alternative technologies are compared to one another, the recommended technology
package is to use improved seed and levels of DAP and urea at levels lower than those
recommended by the SG. Technology type 4 (primarily the SG participants using DAP
and urea at rates greater than or equal to 100 kg/ha) was dominated, meaning costs
increased over technology type 3, with the lower levels of fertilizer, but the net margin

declined, and therefore, it would not be a treatment chosen by farmers.

246

Overall, the magnitude of changes in profitability from the proposed price
reductions in fertilizer prices are small. In 1998 J imma DAP prices were actually lower
than the calculated financial import parity price, but urea prices were almost 20
Birr/quintal higher than the financial import parity price. As the hedonic price model
showed in chapter 5, in less competitive markets, the price of DAP can be 10 Bin/quintal
higher and the price of urea can be up to 21 Birr/quintal higher than prices in a relatively

more competitive market such as Jimma.

247

CHAPTER 9

CONCLUSIONS AND POLICY IIVIPLICATIONS

Across Sub-Saharan Africa (SSA) governments are faced with the challenge of
finding alternatives to government subsidies and pilot extension programs to maintain and
increase high-input agricultural technology. Goverrunent programs are often
overextended and once they withdraw agricultural support, high-input use may decline
because the existing private sector input markets were not sufficiently developed to
encourage sustained adoption. Thus at the dawn of the 21" century, the challenge remains
of improving agricultural productivity, increasing food-security, and reducing rural
poverty in SSA.

Ethiopia provides evidence of one country’s experience with the challenge of
introducing improved, profitable agricultural technologies to farmers through an expanded
Sasakawa-Global 2000 high-input technology program (the NEP); and simultaneously
developing input and credit markets that will ensure widespread, sustainable adoption of
these technologies. Evidence suggests that although much progress has been made, the
preconditions for long-term productivity growth for maize in Ethiopia are not sufiiciently

developed.

248

9.1 Conclusions

The FDRE is experimenting with one approach to promoting rapid productivity
growth through an extensive extension program (the National Extension Program)
whereby farmers are provided extension, and improved seed and fertilizer on credit. The
program is far reaching and dominates the input market. In 1998 the NEP enrolled 2.9
million households and planned to target 3.6 million farmers in 1999 (42 percent of rural
households). In 1998 almost 80 percent of fertilizer imports were channeled into the
NEP. Participation in the NEP in 1998 was heavily encouraged by the national and
regional governments. Participation in the NEP was the only viable source of fertilizer
and improved seed on credit in areas where it operated, which served to facilitate the
NEP’s efforts to rapidly increase the numbers of participating farmers. Another factor
that helped extension agents meet NEP targets was that in some areas of the country the
open market for fertilizer was actively suppressed by regional government authorities
(e. g., governments locked warehouse doors of small retailers and threatened farmers not
to purchase fertilizer). The FDRE’s approach to encouraging productivity grth is
characterized by heavy government involvement in all stages of the input sector--from
quantities of import, to allocation of credit and fertilizer market shares--to even setting
retail prices.

Evidence from other countries (e.g., Ghana, Tanzania) suggests that although the
NEP may be able to increase the level of input use in the short-run, Ethiopia’s approach
may not be financially viable in the long-run and therefore, the country risks losing

potential productivity gains made under the NEP. This research demonstrated that the

249

 

NEP may not necessarily provide an efficient system of extension, credit, and input
delivery—key elements for long-term agricultural productivity growth.

Extension. There were warning signs in 1998 that the quality of the extension
service may have been compromised by the rapid expansion of the NEP. Quality
extension will be crucial to high-input adoption because it reduces the production risk of
the new technologies, thereby improving its profitability. An econometric yield model
(chapter 6) showed that management practices (interaction between timing of weeding and
level of input use) were key to optimizing yields under the higher-input technologies. It
was found that the level of DAP used by farmers that used local seed and DAP was sub-
optimal, given their seeding rate, perhaps due to a liquidity constraint, but also perhaps
because of lack of information.

In another example, it was found that there is a production risk associated with
fertilizer use (with local seed). Use could actually result in reduced net margins and
returns to labor compared to when a farmer uses local seed and no fertilizer (chapter 8).
This is a concern because Jimma Zone represents a high-potential maize area and the SG
participants were characterized as being relatively well-suited to use the high-input
technologies efficiently compared to the broader population. Ifthese farmers face reduced
benefits in adopting fertilizer then it is likely that the broader population will not find the
new technology profitable. Farmers that do not receive adequate extension advice or do
not own or have access to oxen in timely manner (assuming the net effect of oxen use

raises returns to labor) may be particularly susceptible to this risk.

250

Overall, as the high-input technology is expanded to a broader population,
maintenance of a quality extension service, as well as provision of area-specific
technological recommendations will become increasingly important. The CERES-maize
biological simulation model can be used to determine the success of the SG technologies
under conditions of moisture stress and sub-optimal management practices (Schulthess
and Ward 1999).

It is questionable whether the NEP can provide a high quality extension message
that would ultimately increase the profitability of the new technologies (through higher
yields, ceteris paribus). As the NEP expands, the ratio of extension agents to farmers
decreases, thus diluting the close supervision afforded farmers in the SG program. An
additional concern regarding the quality of extension is that the extension agents in 1998
were increasingly burdened with non-extension activities (e.g., organizing credit) that
distracted from the delivery of the extension message.

Credit. In 1998 the regional governments’ organization of NEP input credit led
to concerns regarding the performance of the input and credit markets. In 1998 there
remained an unmet demand for fertilizer and improved seed outside the NEP that was not
allowed to be met by neither an open market for credit nor an open market for fertilizer.
Credit resources were limited: many program “graduates” remained in the program due to
limited opportunities for purchasing inputs outside the NEP program, however, this policy
reduced the NEP’s ability to reach non-adopters. Additionally, as mentioned, the fertilizer
open market was thin and there was virtually no improved seed available in the open

market.

251

Another concern regarding credit was that the regional governments controlled
which fertilizer distributors could supply the NEP, the respective market shares of the
distributors, and to some extent, their retail prices. Thus, the choice of supplier for
farmers that purchased with NEP arranged credit was determined by the regional
governments. Due to the fact that government guaranteed input credit, most large
distributors preferred to channel their stock through the credit programs because it shifted
the risk of marketing from retailers to the government. However, because the government
relied upon multiple public institutions to process credit and input delivery, fertilizer
delivery was ofien delayed. Delays add a cost to distributors (e. g., storage and interest) as
well as to farmers. At the time that farmers realized delivery was going to be late, they
were already committed to accepting the input package, and therefore the profitability of
fertilizer is reduced because late fertilizer application can reduce yields.

Fertilizer and Seed Markets. As with extension and credit, evidence suggests that
costs in the fertilizer and seed markets could be reduced in 1998 to warrant sustained
high-input use. In some areas of the county fertilizer prices were high relative to
competitive market conditions, and fertilizer was both delayed and poor quality (thus
raising its price to farmers). A hedonic fertilizer price model demonstrated that fertilizer
prices were lower in regions of the country which were relatively more competitive than
government-appointed markets (10 Birr/quintal for DAP and 21 Birr/quintal for urea)
(chapter 5). The financial import parity price calculation (chapter 7) calculated the
magnitude of the gap between observed 1998 market prices and the price level that society

could reasonable expect under competitive market conditions at every stage of the

252

subsector. It was discovered that the gap was more pronounced for urea than DAP, and
in Amhara for both urea and DAP relative to other regions.

Overall, high-input technology use is a viable approach for achieving productivity
grth because the technology works. This issue is, however, how to deliver these
techniques to farmers in a cost-effective and sustainable way. This involves providing the
incentives for long-run private investment and involvement. Simulated farm budgets from
Jimma Zone (chapter 8) revealed that the profitability of the SG technologies was robust
at all yield levels and levels of labor use. However, it must be remembered that Jimma is a
relatively competitive, high-potential maize production area. It is recommended that a
profitability analysis be conducted in years with less favorable rainfall than 1997 (1997
was a relatively good rainfall year).

Profitability of fertilizer was less robust for the partial adopters (local seed and
DAP); however, still profitable relative to the local seed/no fertilizer option. The
robustness of the technology is increased further through proposed cost savings in the
fertilizer subsector. Costs can be reduced through institutional changes in how fertilizer is
distributed through the government credit programs, but it was also revealed through
qualitative survey evidence and statistical analyses that retail prices of fertilizer can be
reduced by reorganizing government policies regarding import and distribution such that
the private sector is able to take advantage of seasonal prices in international fertilizer
prices as well as in domestic transport rates. It was found that the retail prices in Jimma
could be reduced by up to 10.5 percent for DAP and 9.8 percent for urea relative from the

1998 financial import parity price calculation of a competitive market. The proposed cost

253

reductions in the fertilizer subsector may be relatively more critical to improving
profitability in areas such as Amhara rather than Oromiya because prices in Amhara are

relatively higher because distributors are appointed by the regional government.

9.2 Policy Implications

Alternative strategies exist to forging a sustainable system of input delivery, credit,
and extension grth in Ethiopia than that chosen by the FDRE. However, the
distributions of benefits and costs will differ considerably under alternative strategies. The
FDRE has adopted a command approach to introducing high-input technologies to
farmers. The govemment-favored companies will lose rents if the government shifis to an
approach whereby the government specializes in introducing high-input technologies
(through provision of extension, credit, and facilitated input delivery), but simultaneously
encourages increased private sector participation such that farmers are able to use the
high-input technologies with the government’s assistance on a sustained basis. The FDRE
will also incur a cost if it actively pursues to gain “ownership” over the donor import-
support program—by encouraging increased flexibility in import conditions as well as in
timing. However, there are benefits-~including a more equitable distribution of returns,
increased agricultural production, potentially reduced expenditures on domestic food
security programs, and even increased savings in hard currency (fi'om reduced grain
imports or increased receipts fiom grain exports).

An option for long-term productivity growth includes:

- Encouraged private sector input market investment;

254

. Improved access to credit by importers, distributors and farmers;

- Assurance that the NEP is financially sound; and

- Maintenance of a quality extension service.

Encourage Private Sector Investment. Overall, FDRE actions in the input markets
in 1998 were not characteristic of a government promoting the development of a low-cost,
open, transparent market. Govemment-favoritism prevailed, policy uncertainty was
commonplace, and access to credit and markets was tightly controlled. Such an
environment does not invite private sector investment. The degree of private sector
frustration and losses in the market in 1998 was extreme: one fertilizer importer and
distributor (one of the two truly private firms) announced its planned to withdraw fi'om
the market in 1999. The government may not realize that its predeterrnination of timing of
imports, quantities of fertilizer consumed (based upon NEP targets), and market shares of
distributors can increase costs in the market.

In Ethiopia the private sector has never been provided the opportunity to invest in
the input market (particularly for improved seed). This research revealed that increased
private sector participation and autonomy with regard to timing of operations and access
to credit and input markets can realize cost savings in the inputs market (chapter 7). The
NEP is characterized by high prices in some areas and late deliveries (because it relies
upon various institutions for organization of credit and input delivery). In contrast, the
private sector has shown that it is quick to respond to market opportunities. Survey -
evidence revealed that the cash market was vibrant in areas where the NEP was limited in

scope or non-existent (GMRP 1998). In other areas, independent retailers felt that their

255

 

 

business had been restricted by the expansion of the government credit through the NEP.

The private sector can be encouraged to invest in input distribution by allowing
open market retailers to sell inputs to farmers in the government credit programs and
permitting the open market retailers to decide their market strategies and selling prices.
The government may decide to step in where the private sector fails to operate-perhaps in
low-potential agricultural areas, for example.

The fertilizer auctions are relatively more competitive than the govemment-
appointed markets; however, the auctions are not a viable method of promoting increased
investment in the inputs markets. If a retailer loses a tender for an auction and his or her
stock is already in the area then there is a cost to redirecting that stock and a risk that
markets in other regions are already saturated. Ironically, a tacit requirement by regional
governments for participation in the auctions to have stocks in the region and to have an
established transport and warehouse network (GMRP 1998). Thus, independent retailers
are not permitted to participate in the auctions and larger integrated retailers are also shut
out of markets in which they have invested in fixed assets such as warehouses. This is a
concern because the fertilizer market outside the NEP is thin and underdeveloped.

Overall, underlying the development of any efficient market is established markets
rules and property rights. In 1998 there was ample evidence to suggest that neither factor
was suficiently developed in Ethiopia-markets were often unexpectedly closed to
distributors. As any society moves from subsistent agriculture to increased productivity

through specialization and hence, a greater reliance on purchased agricultural inputs, the

256

role of coordination across markets and between stages of a market becomes increasingly
important.

Given economies of scale, there may be limited scope for increased competition at
the import level. However, increased flexibility in terms of timing of imports--achieved
through legalized foreign credit, open access to forex auctions, and pooled donor funds--
can lower import costs. The primary constraint with regard to import is the availability of
hard currency, thus forex tenders are scheduled when any one donor provides firnds. If
donors want to continue to provide forex specifically for fertilizer (keeping it separate
fi'om the weekly forex auctions) then perhaps they could pool their resources in order to
provide a more timely schedule of forex auctions to capture the seasonal lows in
international prices.

Monitor the NEP ’5 Financial Sustainability. Howard et al. (1999) found that the
NEP is economically profitable given that domestic maize production substitutes for
commercial imports, however, less so if the policy objective is to export maize. Thus for
the NEP to continue to have a net benefit for society, it is critical that it is able to control
its costs, in addition to maintaining a high-quality extension service that ensures that yields
are maximized.

However, there were signs in 1998 that perhaps the “economic” analysis did not
cover all costs imposed on society by the NEP and therefore the NEP may not be
financially sustainable in the long-run. For example, in 1998 there was evidence that many
of the program costs were not being sufficiently covered (e. g., local governments were not

compensated for providing storage and transport facilities to the NEP). In addition,

257

improved seed was subsidized. The NEP sale price was considerably higher than Pioneer
Hi-Bred International Seed Company’s quoted price in 1998 (for importing basic seed and
duplicating it in Ethiopia) (GMRP 1998).

The degree to which the current fertilizer market is subsidized and hence whether
costs in the long-run will increase is an issue for sustainability of the inputs. It is
speculated that former government assets (e. g., trucks and warehouses) were transferred
to government-affiliated companies (on concessionary terms or as gift) and thus, the
sector may not reflect the hill cost of investing in the inputs market. As these assets
depreciate and investment in new assets occurs, costs in the inputs markets may rise.
Another concern is that, assuming repayment rates of input credit are less than 100
percent, the FDRE subsidizes credit. Over time, accumulated government losses may
restrict its ability to continue to extend credit, resulting in reduced input use because
alternative credit institutions are not well developed.

One method of keeping program costs down and improving the quality of
extension is to ensure that program “graduates” can continue the high-input practices after
two years in the program and are not “carried” in the program year after year, as was the
practice in 1998. There was evidence in 1998 that farmers can and will purchase inputs on
cash (chapter 7). Another idea is for the NEP to focus greater attention to providing
quality extension, thus reducing the production risk facing farmers, crop failure, and
ultimately, credit default. The NEP has been using “model” farmers and volunteers to

assist in extension, however, the quality of the message may be higher if trained extension

agents are able to visit all participants.

258

 

Overall, this research has implications for other SSA countries struggling to
simultaneously increase food productivity growth through increased high-input use and
liberalize credit, input, and output markets. The task is not easy, but facilitated by basic
tenets of development as learned by more developed countries: established property rights
and rules of the game, and competition are central.

Donors have an important role to play. In Ethiopia, although pan-territorial prices
were no longer set by the government in 1998, access to inputs and competition did not
necessarily increase. It was a superficial measure to “liberalize” markets. Donor oversight
or compliance with limited input market liberalization is to the detriment of the Ethiopian
farmer. Continued donor provision of import support for fertilizer will not necessarily
ensure long-term productivity gains.

The FDRE is commended for tackling an age-old problem: farm credit. The
government has provided inputs on credit and is thus far successful (through use of local
police) in maintaining high repayment rates (Howard 1999); however, credit is only one
component of the development of a complex web of interlinked markets that are required
for sustained productivity growth. The government is encouraged to give the private

sector a chance to participate in the development process.

259

 

APPENDICIES

260

 

APPENDIX 1

GMRP INPUT SUBSECT OR SURVEYS AND COVERAGE

261

Five separate survey instruments were used:

 

 

1. Bureaus of Agriculture .......................................... 263
Service Cooperatives (SO/Farmer Associations (FA) and Salaried
Retailers/Manager 267

3. Independent Retailers and Wholesalers .............................. 273

4. Transportation costs for fertilizer distributors other than SCs/F As and salaried
retailers/managers ............................................. 281

5. Transporters ................................................. 283

Table 1. Coverage of GMRP 1998 Input Subsector Survey, 208 Surveys in

Total-at least one Bureau of Agriculture Survey for each wereda,
followed by a survey of a Service Cooperative
Region Zone Wereda (Number of surveys in parentheses)
Amhara West Bure (3), Wonbera (4), Yilmana Densa (4), Dembecha (5)
Amhara East Machakal (5), Gozamin (4)
Amhara Awi Banja (2), Shikudad (4), Dangla (3)
Amhara South Kalu (1)
Welo

Amhara North Gidem (4), Efiratana (4), Ensara and Wayu (1), Moret and Jim (2), Basona
Shewa Worena (2), Ensaro and Wayu (1), Denaba (1)"

Oromiya North- Sululta Milo (2), Kimbibit (2), Girar Jarso (3), Wueale (2), Bure-Aleltu 0,
West . Sendefa, Wonchi, Woliso ()
Shewa

Oromiya East Ada (5), Shashemene (5), Lume (3), Arsi-Negele (2), Adam (5)

Oromiya West Ambo (6), Alemgena (4), Dandi (4), Ejere (2), [In (2), Becho (3), Chaliya (5)
Shewa

Oromiya Jimma Manna (3), Seka Cherkorsa (2), Orna Nada (3), Kersa (2), Dedo (1), Asandabo

Oromiya Arsi Tiyo (5), Gadab (3), Kofele (2), Dodola Sire (2), Digelu Tijo (2), Dhera (1),

Hitossa (3), Lemu Bilbido (4)

Oromiya Bale Dodola (3), Adaba (3), Sinana Dinsho (3)

Oromiya East Guto Wayu (2), Anno (1), Bulu Sayo (l), Guduru (l), Sire (5)
Wellega

Southern Hadiya Lemu (7), Badawacho (3)

(S.N.N.P.R)

Southern Gurarge Goro (3), Mekana Maroko (3), Checha (l)

(S.N.N.P.R.)

Southern Sidama Aleta Wondo (1), Awassa (2)

(S.N.N.P.R.)

Southern Kembata

(S.N.N.P.R) North

 

Alaba (4), Kachabira (3), Kedida Gemela (2)
Sodo Zuria (4), Bolosso Sore (3), Domot Gale (2)

 

"Only one survey, an independent retailer.

262

 

Zone Wereda Date

 

 

 

1998 Input Subsector Survey Questionnaire
Bureaus of Agriculture

The objective of this survey is to (I) understand how the organization and performance of
the input subsector has evolved in response to recent changes in macro-economic and
sectoral policies affecting the agricultural, transport, and fertilizer sectors and (2) to
evaluate the potential for cost reductions in the fertilizer sector that could ultimately lower
farmgate fertilizer prices, thereby increasing fertilizer demand. Our main role is to
understand the situation for designing a questionnaire for the forthcoming study. We
would also make recommendations to the government about what types of policies and
investments can be made to improve the efficiency of the fertilizer sector and increase
fertilizer demand.

 

1. Respondent ID and characteristics

1. Position of respondent:

2. How long has respondent been in this
position:

 

11. Overview of the input market in this area

1. What is the role of the bureaus of agriculture with respect to inputs in this area?
1. Fertilizer purchases and sales

2. The distribution and dissemination of improved seeds

3. The distribution and dissemination of of chemicals

4. The distribution and dissemination of farm implements

5. The distribution and dissemination of heifers and improved
livestock technologies

6. The distribution and dissemination of Soil conservation and forestry
technologies

7. The distribution and dissemination of other technologies (specify)

2. What are the main components/inputs of the extension package in the woreda with
respect to:

Belg crop production

Meher crop production

Horticultural crops

Modern storage

Livestock husbandry

Natural resource conservation and forestry

263

Zone Wereda Date

Introduction of new crops
Introduction of irrigation schemes

Others (specify)

3. What are the procedures for getting inputs to farmers participating in the
government extension program?

 

 

 

 

 

 

 

 

1. How are farmers selected?

2. How and when are quantities of inputs for the program determined?

3. Who collects the down payments and the final
payments ?

4. Who keeps the records of inputs received and
payments made ?

5. Who (what distributor) supplies the inputs?
fertilizer ;seed ;pesticides and
herbicides , other

6. How are the distributors selected? If it is a bidding process, who organizes
the bids and how many bidders were there this year?

7. How many distribution points for extension packages are there in the
wereda?

8. Were all the inputs for the packages delivered to the wereda on time this

year? If not, what were the problems?

4. Did the SG program provide farmers in your woreda with inputs in the past?
(yes/no)

 

If yes, how did that distribution system differ from the one for the government extension
program described above?

 

 

 

 

 

 

 

 

 

5. How much fertilizer (in quintals) was sold in this woreda? Note quantities in
NEP and Regular Credit program if ossible
1995 (1987) 1996 (1988) 1997 (1989) 1998 (1990) so far .....
DAP
urea
6. How is it determined how much fertilizer should be supplied to your area?
7. What are the main problems with respect to fertilizer distribution ? (For example,
timing, quantities, credit, etc.)
1.
2.

264

 

 

Zone Wereda Date

 

3.
III. Details of participants in the fertilizer market in this area

1. How many of the following operate in this woreda? importer/distributors

 

independent retailers/wholesalers
2. How many sales centers (places at which farmers collect their fertilizer purchase
or shiyach tabia) are there in the woreda? _
3. How many SCs are there in the woreda?
4. Where are the independent retailers/wholesalers located? (Get enough info so that
you can locate them for the survey)
Woreda
Market town
Rural area
Have you seen fertilizer trade increase or decrease in this area in the last 5 years?
If there has been a change, what is this change attributed to?
Is the SG-2000 program functioning in your area this year? (yes/no)
What are the principal activities of the SG-2000?

 

 

9°99.“

9. What is the range of distance farmers travel to pick up fertilizer fiom:
Min. kms. Max. kms.

the SCs:
the importer/wholesaler sales outlet:

 

 

the government extension program:

 

10. What is the different modes of transport used by farmers to transport fertilizer
fi’om the above locations
__ pick-up (1=common, 2=sometimes, 3=never)
_ car (1=common, 2=sometimes, 3=never)
_ ox-cart (1=common, 2=sometimes, 3=never)
_ pack animal (1=common, 2=sometimes, 3=never)
_ bicycle (1=common, 2=sometimes, 3=never)
_ on foot/human load (1=common, 2=sometimes, 3=never)
_ other (specify)
(1=common, 2=sometimes, 3=never)

 

l 1. We understand that fertilizer traders are required to have a license. Is the
agricultural bureau involved in:

issuing licenses (yes/no)

verifying licenses (yes/no)

 

 

265

Zone

Wereda Date

 

 

 

 

 

 

 

 

 

 

 

 

 

 

12. Ifthe agricultural bureau is involved in issuing licenses, what is the procedure?

13. Ifthe agricultural bureau is involved in verifying licenses, what is the procedure?

14. If this agricultural bureau does not participate in these activities, where do these
activities occur (at killil level?):

issuing licenses
verifying licenses

IV. Credit

1. What is the role of the bureau of agriculture with respect to fertilizer credit?

2. Describe the process of obtaining credit in this area (what does farmer do, SC, ag
bureau, zone bureau, finance bureau, others?)

3. Ifthe allocated amount of credit is less than the quantity demanded, then what
adjustments do you make in allocating fertilizer? '

4. How many bank branches are located in this woreda?

5. What was the average level (or percent) of down payment for fertilizer purchases
in each of the following years?

Program 1995 (EC. 1987) 1996 (1988) 1997 (1989) 1998 (1990)

Extension

Regular

6. During the last 4 years, has fertilizer credit received by the wereda ever been less
than what was necessary to cover fertilizer demanded by farmers?

7. Ifyes to 6., how did you deal with the situation?

8. What is the extent of cash sales for fertilizer in the woreda (estimate the % in the

total sales)?

266

Zone Wereda Date

 

 

1998 Ethiopian Fertilizer Subsector Survey Questionnaire, July 1998
Service Cooperatives (SQ/Farmer Associations (FA) and Salaried
Retailers/Manager

The definition of a salaried retailer/manager is one who works at the stores for an
importer/wholesaler for a fixed salary.
We would like to talk to a Member of the SC committee.

“Thank-you for agreeing to participate in this survey. I need to begin by getting some
general information about you and your firm.”

 

 

 

L Respondent ID and characteristics
1. Name of organization:
FOR SC/FA: Respondent’s position (circle one) 1. manager
2. sales clerk
3. For how long has the respondent been in this position? months and
years
4. Education of respondent: Formal schooling in number of years: primary
secondary technical university
Other types of training: type years
Did the respondent receive special training related to fertilizer use or sales?
(yes/no) Ifyes, explain. (magnitude)
5. What are the months in which you work in fertilizer activities? (Circle the

number(s) below corresponding to the month in the Ethiopian calendar)

Months 1 2 3 4 5 6 7 8 9 10 11 12 13 [circle]
6. Is this the same time as in previous years? (yes/no) If not, explain
changes.

7. FOR SCs/FAs: What other services do you ofi‘er to your members?

267

Zone

Wereda Date

 

10.

11.

12.

“Now I would like to get a picture of the fertilizer retail trade in your area of
operation.”

FOR RETAILER: Has the competition you face from other retailers or
distributors increased, decreased, or remained the same since you became involved
in fertilizer?

If there has been a change, to what do you attribute it?

From how many kilometers away do your clients/members come to get fertilizer?
(Give the range in kilometers from those that travel the farthest to those that are
closest)

FOR SCs/FAs: Do farmers in your area participate the NEP program? (yes/no)

Is it any more difiicult to get NEP inputs than regular inputs? Have there been any
problems? .

Who are the input distributor(s) for the NEP program in your wereda?

Do all retailers in this wereda sell to farmers at the same price? (yes/no)

Ifnot, explain the differences.

Quantitative questions about overall fertilizer activities

How much have you stocked this season, 1990? (1998?)

DAP quintals
urea quintals
What were your carryover stocks from 1989? (1997?) (last season?)
DAP quintals
urea quintals
How much have you sold so far in this season, 1990? (1998?)
DAP quintals
urea quintals
What are your anticipated carryover stocks this season, 1990? (1998?)
DAP quintals
urea quintals
What is the sale price for:

For credit sales (DO NOT INCLUDE INTEREST)
DAP minimum price , maximum price ,

always birr/qt
urea minimum price , maximum price ,
always birr/qt

268

Zone Wereda Date

 

For cash sales
DAP minimum price , maximum price ,

always birr/qt
urea minimum price , maximum price ,
always birr/qt
13. For SCs/FAs: What is your margin per quintal of fertilizer? birr

14. FOR SCs/F As: How many fertilizer-using members are there in your SC/F A?

 

15. FOR SCs/FAs: What was the tinting of the following activities for this year?
Month begin Month end

 

 

Report fertilizer needs to agricultural
bureau

 

Collect down payment for credit
Arrival of fertilizer at SC/F A

 

 

Sales to farmers

 

 

 

 

 

16. What are the terms of the credit provided to farmers?
18 there a down payment? (yes/no)

If yes, the most 1. lowest %

2. highest %

3. common %

 

 

 

Annual interest rate?
Duration of loan?
Other details

 

 

 

17. FOR SCs/F As: What is the repayment recovery rate of your members for last year?

How many paid , How many did not pay

 

 

18. FOR SCs/F As: What determines whether you can receive credit next season?
(what percent of loan is required for payback? 100%?)

19. FOR Scs/F As: What do you do to enforce repayment?

269

Zone Wereda Date

“Now I would like to understand the process that you must follow to obtain your
fertilizer supply, market it, and management of stocks.”

IV. Fertilizer procurement

1. FOR SCs/F As: Who was your supplier for DAP?
for urea?

2. FOR SCs/F As: Are the (1) fertilizer supplies delivered to you or (2) do you have
to collect the fertilizer supplies on behalf of the farmers?
(circle one)

3. FOR SCs/FAs: Ifyou do have to collect fertilizer supplies, how many km did you
have to go for DAP , for urea

4. FOR SCs/FAs: Did you hire transport? (yes/no)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5. FOR SCs/FAs: Ifyou did not hire transport, how do you collect the fertilizer?
6. FOR SCs/FAs: If transport was hired by you to collect fertilizer, could you give
us the details of different exam les of collection:
origi destination kilometers total transport off- month
n quantity cost loading loading
(Specify costs costs
units)
1.
2.
3.
7. What were the biggest problems you encountered in getting your fertilizer supplies
this year?
1.
2.
3.
4.
8. Is there anything that you can do differently next year to avoid these problems?
1.
2.
3.
4.
9. Is there anything that other actors in the fertilizer sector (importers, retailers,
government, extension agents) can do to reduce these problems?
1.

270

 

Zone

Wereda Date

 

 

2.
3.
4.

“We would now like to ask a few questions about your sales.”

FOR SALARIED RETAILER ONLY: Has your company done anything to
encourage farmers to use more fertilizer? (circle one for each)
Advertising
1 - sometimes, 2 - often, 3 - never
Repacking fertilizer into smaller bags than the standard
1 - sometimes, 2 - ofien, 3 - never
Using roving agents
1 - sometimes, 2 - ofien, 3 - never
Demo fields
I - sometimes, 2 - often, 3 - never
Price reductions
1 - sometimes, 2 - ofien, 3 - never
Credit flexibility
1 - sometimes, 2 - often, 3 - never
Ofiering advice on how to use products
1 - sometimes, 2 - ofien, 3 - never
Deliver fertilizer to farms
1 - sometimes, 2 - often, 3 - never
Offer prearranged farmer specific contracts to deliver a specified amount
on a certain date
1 - sometimes, 2 - often, 3 - never
Promote cash sales
1 - sometimes, 2 - ofien, 3 - never

Was there ever a period when farmers wanted fertilizer and you had none in stock?
(yes/no)

 

If yes to 2., what was the source of the problem?

Has the volume of sales expanded, contracted or remained the same over the last 5
years?

 

If there has been a change in the volume of sales, to what do you attribute it?

How much have you sold this year in:
cash quintals (both DAP and urea)

 

271

 

 

 

Zone

 

 

10.

 

 

Wereda Date
credit quintals (both DAP and urea)
in-kind quintals (both DAP and urea)

 

What were major problems that you encounter in your fertilizer sales this year?
Problems to mention for prompts: Unexpected costs, delays in delivery to clients, complaints
about fertilizer quality, dissatisfied clients, delays in payments by clients, other.

1.

2.

3.

4

Is there anything that you can do differently next year to avoid these problems?
1.

2.

3.

4.

Is there anything that other actors in the fertilizer sector (importers, retailers,
government, extension agents) can do to reduce these problems?

1.

2.

3.

4.

What has been your experience with cash sales? Do they pose different problems
than credit sales? If yes, explain.

272

Zone Wereda Date

 

Ethiopian Fertilizer Subsector Survey Questionnaire, July 1998
Independent Retailers and Wholesalers

Definitions: Independent operators are ones who purchase their own supplies of fertilizer
and resell them.

Wholesalers have annual turnover of 150 to 3000 tons; they get supplies fi'om distributors
and do not go to the port themselves.

Retailers have annual turnover from 10 to 150 tons; they get supplies from distributors (?)
or wholesalers.

This questionnaire is designed for the owner of the retail or wholesale operation. If you
encounter a manager or a sales agent, administer the questionnaire for salaried retailers

instead of this questionnaire.

III. Respondent ID and characteristics

 

 

 

 

 

 

 

1. Respondent’s name: Name and type (ret/whole)
of firm:
2. Respondent’s position in the firm A. owner
B. manager
C. other
3. What type of operation is involved: (circle applicable response)
1. Purchases supplies fi'om multiple distributors
11. Purchases supplies from only one distributor
4. How many sales outlets do you have?
I. In this wereda?
2. In other wereda? (specific names of other wereda).
5. Education of owner:
Formal schooling in number of years: primary_ secondary
technical university
Other types of training: type years
6. We would like a few details about how you got started in your fertilizer business.
a. What year did you begin trading fertilizer?
b When did you leave the fertilizer business?
c. First location of operation:
d How did you get the idea to become a fertilizer
dealer?
e. Time elapsed between the first active step to starting up business and first
sales
f. Paperwork involved? Specify amount of time in days/weeks/months

required for each activity?

273

Zone

Wereda Date

 

 

10.

11.

12.
13.

(1) Obtaining License

(2) Obtaining Other Necessary Perrrrits
(3) Obtaining Financial capital

(4) Training .
(5) Other (specify)

 

Ifsomeone wanted to start a retail fertilizer business in this area now would it be
easier, more difficult, or about the same level of difficulty as when you started in
your business?

What are the months in which you work in your fertilizer business? (Circle the
number(s) below corresponding to the month in the Ethiopian calendar)

Months 1 2 3 4 5 6 7 8 9 10 ll 12 13 [circle]
What other business do you operate? (yes or no)

1. grain trade 4. transport service

2. merchandise trade 5. farming (livestock)

3. flour mill 6. others

 

Which of the businesses in question 9. function at the same time as your fertilizer
business (list numbers from question 9)?

Why did you leave the fertilizer business? Please explain in detail. (Ali, Did he
lose money or is it not as profitable as other activities?)

Would you ever re-enter the fertilizer business? (yes/no)
Under what conditions would you re-enter the fertilizer business?

 

“Now I would like to get a picture of the fertilizer trade in your area of operation.”

14.

15.

16.

17.
18.

From how many km away do your clients come? (Give the range in kilometers
from those that travel the farthest to those that are closest)

How many other dealers sell directly to farmers or farmers associations in this
wereda?

1. Independent retailers or wholesalers (Purchase own stocks)

2. Wholesalers/distributors outlets (Salaried stafi’ sells stocks owned

by one of major importer/distributors)

3. Others (specify)
Has the competition you face from other dealers increased, decreased, or remained
the same since you began your business?
Ifthere has been a change, to what do you attribute it?
Do you sell at the same prices as other dealers in the wereda? If not, explain the
differences and any problems these differences create for you.

274

 

Zone

Wereda Date

 

 

 

Quantitative questions about overall fertilizer retail business

19.

20.

21.

22.

23.

24.

25.

26.

How much have you purchased this season, 1990? (1998?) DAP
urea
What were your way over stocks from 1989? last season? DAP
urea
How much have you sold so far in this season, 1990? (1998?)? DAP
urea

What are your anticipated carryover stocks
in this season, 1990? (1998?) DAP
‘ urea

What are the minimum and maximum prices you paid for fertilizer purchases since
last season, September 1989 in which you took delivery at the distributors?

 

DAP minimum price maximum price _
always at birr no such cases

urea minimum price maximum price __
always at birr no such cases

What are the minimum and maximum prices you paid for fertilizer purchases since
last season, September 1989 in which fertilizer was delivered to you?

DAP minimum price maximum price
always at birr no such cases

urea minimum price maximum price
always at birr no such cases

What are the minimum and maximum prices you charged for fertilizer sales which
farmers picked up at your store this year?

DAP minimum price maximum price
always at birr no such cases

urea minimum price maximum price
always at birr no such cases

What are the minimum and maximum prices you charged for fertilizer sales which
you delivered to farmers at their village this year?

DAP minimum price maximum price
always at birr no such cases

urea minimum price maximum price
always at birr no such cases

275

Zone Wereda Date

 

27. Has the geographic coverage of your fertilizer business expanded, contracted or
remained the same since you began operations?

28. If there has been a change, to what do you attribute it?

29. What is the minimum margin between purchase price and sales price that you
could accept and still make a reasonable profit.

30. Ifyour volume of sales doubled, could you accept a smaller margin?

 

 

“Now I would like to understand the process that you must follow to obtain your
fertilizer supply, market it, and take care of any unsold stocks. I’m particularly
interested in the timing of the key activities, the costs associated with each, and how
the process has changed during the last 5 years.”

Fertilizer procurement

 

31. What determines when you purchase your fertilizer and how many times you
purchase during the year

32. How do you determine from whom you will purchase the fertilizer?

33. How is your purchase price determined? (Circle the one that is most common)
A. Set by supplier
B. Negotiated between yourself and supplier privately
C. Other, explain

34. Could you get a quantity discount if the size of your purchases were larger? If so,
explain relationship between quantities and prices.

35. If quantity discounts are possible, what prevents you from increasing the size of
your purchases?

36. Does the month of the year in which you purchase fertilizer make a difference in
the price you pay? If so, explain how prices vary by month of purchase.

37. What type of financing arrangements do you use to purchases your fertilizer

 

stocks?
A. Personal resources specify source of cash
B. Bank credit

specify bank
specify interest rate (e. g., 12%/yr or 2%/mo, etc.)
specify period of loan (total months)
specify total credit of this type received
C. Supplier credit
specify interest rate (e. g., 12%/yr or 2°/o/mo., etc.)
specify period of loan (total months)
specify total credit of this type received
D. Other details

 

 

 

 

 

 

 

 

276

Zone

Wereda Date

 

38.

39.

40.

41.

42.

 

What were the biggest problems you encountered in getting your fertilizer supplies
this year?

A

B.

C.

D.

Is there anything that you can do differently next year to avoid these problems?
A.
B.
C.

Is there anything that other actors in the fertilizer sector (importers, distributors,
government) can do to reduce these problems?

A.

B.

C.

D.

Is there anything you could have done to reduce your purchase costs this year?
(Larger order, cash payment, earlier repayment of credit, etc.)

What prevented you from doing these things?

We would now like to ask a few questions about your sales

43.

44.

Who are your principal clients (rank by volume of sales per group)

A. Individual farmers purchasing for belg production
B. Individual farmers purchasing for meher production
C. Farmers associations or service cooperatives

D. Other (specify)

Have you done anything special to encourage farmers to use more fertilizer?
(circle one for each)
A. Advertising
1 - sometimes, 2 - often, 3 - never
B. Repacking fertilizer into smaller bags than the standard
1 - sometimes, 2 - often, 3 - never
C. Using roving agents who go to villages
1 - sometimes, 2 - ofien, 3 - never

277

Zone Wereda Date

 

 

D. Demonstration fields
1 - sometimes, 2 - often, 3 - never
E. Price reductions

1 - sometimes, 2 - often, 3 - never
F. Credit flexibility

l - sometimes, 2 - often, 3 - never
G. Offering advice on how to use products

1 - sometimes, 2 - often, 3 - never
H. Other (specify)

1 - sometimes, 2 - often, 3 - never

45. Has the volume of sales for your retail fertilizer business expanded, contracted or
remained the same since you began your business?
46. Ifthere has been a change, to what do you attribute it?
47. How is the sale price determined: (circle all that apply)
A. Set by yourself based on what other dealers are charging
B. Negotiated individually with each sale
C. Other (specify)
48. How much have you sold this year in (specify unit: quintals or tons):
A. cash
B. credit
C. in-kind payment at time of harvest
D. other (specify)
49. Was there ever a period when clients wanted fertilizer and you had none in stock?
50. If yes, when did this occur and what was the source of the problem?
51. What were major problems that you encounter in your sales operation this year?
(Don’t repeat problems already mentioned thus far.)
A.
B.
C.
D.

 

 

 

 

INTERVIEWER NOTE: For each type of problem encountered provide detailed explanation of what
happened and the impact on the business:

Possible problems: Unexpected costs, delays in delivery to clients, complaints about fertilizer quality,
dissatisfied clients, delays in payments by clients, other

Possible impacts: Lower profits, reputation negatively affected, other.

52. What could you have done to avoid these problems next year?

53. What could other actors in fertilizer subsector do to diminish these problems?

278

 

Zone Wereda Date

 

 

 

Operating costs

Now we are going to ask some more specific questions about general operating costs and
your ideas about how these could be reduced

Ifyou incur any of the following types of costs in running your fertilizer business please
help us understand the magnitude of these costs:

 

[Estimated cost in stimate ere this uld [Efrem anything you

 

 

 

 

 

 

 

 

birr (fill in at least time car’s costs ese costs do to reduce these
'COSt (3319801? escribe one of two columns spent by 955» same, reduced costs in the future? If
xact below): on for r more ' the yes, explain
ture of is than last olume of
cost . activity year’s costs? our
7°” P“ ., |(days/yr) urchases/
ye" ' es was
ter? If
98s.
explain
1Licenses
Taxes
Storage
1888
uilding
maintenance
hone, fax,
tc.

 

 

 

 

 

 

 

 

 

9’ Ifstorage space is rented, report total annual expenditure on rent during months in
which fertilizer is stored. If storage space is owned, ask respondent for an estimate of
how much income he could make by renting the storage space to others or how much it
would cost him to rent a similar amount of space from others during the months that he
stores fertilizer.

279

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Zone Wereda Date
[Estimated cost in stimate ere this [Is there anything you
birr (fill in at least time car’s costs ese costs can do to reduce these
C031 (13138011 ribe one of two columns nt by 655. same, costs in the future? If
xact below): ou for r more yes, explain
ture of 's last
cost 1r ctivity year’s costs? our
otal per ' (13
w . _ ( ys/yr)
Salaries and "“11"”
wages loyees
art time
oyees
[Mugging
Olher
(specify)
Annual loss during
(report in ltransport
qunital/ton) .
unng
Forage
Transport See separate
rt pages
er

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

NOTE: Go to transport cost questionnaires for (I) hired or (2) owned/operated

vehicles.

280

 

Zone Wereda Date

Ethiopian Fertilizer Subsector Survey Questionnaire, July 1998
Transportation costs for fertilizer distributors other than SCs/FAs and salaried
retailers/managers

 

This questionnaire is for all fertilizer market participants that are hiring transport for
fertilizer purchases and/or sales.

 

Many reports we have read about fertilizer prices in Afiica claim that one of the reasons
for very high prices in Afiica is that transportation costs are very high. For this reason, we
would like to better understand all the elements that go into the transport costs that you
pay for transporting fertilizer.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1. For all cases where you have paid for fertilizer transport please report the
following details:

Iorigin destina—tion kms Road condition total trans- load-ing off- month
1. poor quantity port costs loading
condition/dirt (quintals) cost costs
2. good (bin!
condition/dirt (BMW
3. poor km)
condition/gravel
4. good
condition/gravel
5. poor
condition/paved
6. good
condition/paved
7. other (specify)

1
2
3
4
5
6
2. Would the transport costs listed above have been lower if: (yes/no)

the fertilizer shipments were larger
the timing of the shipment was different
the roads were better

other (specify)

 

 

 

281

Zone Wereda Date

 

 

“We would now like to ask some details about what vehicles you own and costs of vehicle
operation.”

 

 

 

 

 

3. How many different types of transport do you own?
Type of transport Capacity (kg, quintals or tons)
pick-up
small truck
big truck
other

 

 

 

 

282

 

 

Zone Wereda Date

 

Ethiopian Fertilizer Subsector Survey Questionnaire, July 1998
Transporters

This questionnaire is for all fertilizer market participants (importers, wholesalers, private
retailers, etc.) and transporters who OWN their own vehicles.

I]. “Thank-you for agreeing to participate in this survey. I need to begin by getting
some general information about you and your firm.”

QUESTIONS FOR TRANSPORT COMPANIES ONLY, ALL OTHERS GO TO
PAGE 2.

 

 

 

 

 

 

 

 

1. Name of firm:

2. Respondent’s position in firm:

3. How long in that position:

4. How long with firm:

5. Prior work experience before joining the firm:

6. How long has firm been transporting fertilizer in Ethiopia:

7. What was required of your firm to enter the transport business (amount of financial
capital, contacts)?

8. Is the firm involved in transportation of other products? Ifso, what products:

9. Would you like to expand your fertilizer business? (transport more fertilizer?)

 

10. Could you take on more fertilizer transport contracts during the peak fertilizer

period?
11. What are your busiest months during the year for transport of any commodities?
(Ethiopian calender months)

 

QUESTIONS FOR ALL TRANSPORT OWNERS:

12. Do you consider your overall transport business (circle one)
1 . unprofitable
2. slightly profitable
3. very profitable

13. Has the profitat. .iity of your business increased, decreased, or remained the same
since the beginning of your operations?

14. If there has been a change, to what do you attributed it?

283

ll

Zone

15.

16.
17.

@3099.“

9°

10.

ll.

l2.

l3.

Wereda Date

 

Is transporting fertilizer (l) more (2) less (3) about the same level of profitability
as transporting other products such as grains?

If fertilizer transport is more or less profitable than grain transport, explain why?
What could you do to increase the profitability of your transport activities?

“We would now like to get some cost details of transporting fertilizer on your
most recent fertilizer shipment in which you transported fertilizer.”

Route:
origin:
destination:

 

 

Kilometers traveled

Quantity of shipment quintals
Month of travel

Total cost of transport on this route
Other charges associated with this shipment?

 

 

 

 

 

 

 

loading? birr/quintal
off-loading? birr/quintal
other? (specify units)
Truck capacity tonnes
What percent of the truck was filled with fertilizer? percent

 

Were other goods also transported at the same time as fertilizer? (yes/no)

What products?
Did you carry a return load? (yes/no) If yes, what
product(s)?

 

 

If the amount of the fertilizer shipment was to double in size, would you ofl‘er a
lower transport cost per quintal per kilometer? (yes/no) _

Ifyes to 11., what would that lower rate be? (If they don’t
know the unit rate, ask for the total cost.)

Condition of road (circle the one that best applies)
1. good condition dirt

poor condition dirt

good condition gravel

poor condition gravel

good condition paved

poor condition paved

other (specify) __

NQ‘MPP’N

284

 

Zone Wereda Date

We would now like to ask details of the cost you face in your transport business.
[The information required of transporters is to determine the largest component of their
costs. To accomplish this, we can follow the list of costs below as a rough outline of a

typical cost build-up for transporters]

285

ll

 

 

Zone Wereda Date

IV. Transport cost build-up

1. Variable costs
“Please specify the following costs for two types of different size trucks (pick-up, truck,
trailer) you may have”

[Item [Vehicle type tons [Vehicle type tons

 

 

 

 

't] last years costs encountered 't] 1997 costs ncountered
[less, same, [165. same,
more] re]

osts [specify mparison to lPrcblems Costs [specify mparison to leems

 

 

vehicle
cost

 

 

ge at
urchase

 

ted
ears of
service
r

urchase

 

 

uty
'd/taxes

t

urchase

 

vage
ue

 

ced
(% of
livered
st)

 

interest

 

 

rate

1‘?“
riod

(year)

 

 

reciati

on/year

 

 

tilization
ear

 

/o of
ehicle
pacity

 

 

 

 

 

 

 

 

286

Zone

Wereda

 

Date

 

Vehicle type

10115

 

Vehicle type

1005

 

 

Costs

Item .
unit]

[specify

Comparison
to last years
costs [less,

same, more]

Problems

1 encountered

COSts [Specify
unit]

Problems
encountered

Comparison
to 1989
costs [less,
same, more]

 

.-
111811131106

 

replacement
tire cost (all
tires)

 

average tire

life (km)

 

fuel
consumption
(litre per 100
km)

 

fuel cost per
litre

 

lubrication/o
il/km or year

 

estimated
annual
maintenance

 

 

 

 

 

 

 

 

2. Miscellaneous overhead costs

 

Item

Quantity

Cost

(Specify
unit)

Problems
encountered

Suggestions for
improvement in the
efficiency of this
operation

 

vehicle
registration/license!
taxes

 

office space/garage
(rent)

no. of offices
no. of garages

 

utilities

 

labor

 

no. of full-time
,_/ycar

 

no. of part-time
_/week or per

 

month

 

 

 

 

287

 

 

APPENDIX 2

FINANCIAL IMPORT PARITY PRICE CALCULATION NOTES

288

Appendix 2 serves to provide more detail of the notes explaining the financial
import parity price calculations for DAP and urea on page 180.

In 1998 shipments that were originally scheduled to arrive in Assab were rerouted
when the Ethiopian-Eritrea conflict began in May 1998. All port transactions in Assab
were paid in US dollars and calculated based upon a fixed exchange rate of USS 7.20/Birr
due to an agreement between the two governments at succession. In Djibouti the
exchange rate used was the prevailing market rate in Addis. Bank charges were a fixed
fee equal to 1.25 percent of the c.i.f. value for the service of transferring money from an
Ethiopian bank to a foreign bank. Insurance is purchased either from an Ethiopian
insurance company or a foreign insurer. Rates vary by insurers and also vary inversely
with the total quantity insured.

Interest charged on the loan of the Ethiopian importer was added to the cost build-
up. It was roughly 3 months between the time the loan was approved and the fertilizer
arrived, thus there was an interest charge of 10.5 percent per annum on 100 percent of the
c.i.f. value for 3 months. Additional interest payments occurred while the fertilizer is
stored in Nazreth for 2 months before it was shipped to retail markets.

Once the ship was at the quay, a transit company managed the clearing operations
of transferring the cargo from ship to truck and sending it on its way to Ethiopia. The
transit company received the bulk of the payment, the port authority only received the port
charges. In both Assab and Djibouti the port charge was USS l/MT. A transit charge
was paid to a transit company (independent of the port authority) for handling cargo ex-
quay until the cargo is loaded into a truck (typically a day later). A “transitor” traces
discharged cargo, performs payment for loading cargo into a truck (by hiring port
equipment like cranes, fork lifts and labor). Transit charges vary depending on how much
fertilizer is stored at the port. In the past, some importers stored up to 40 percent of their
shipment, but in 1998 typically 25 percent of a 25,000 MT shipment was left in storage
due to the unavailability of transport. The transit charge was US$ 1.50/MT charged on
the entire shipment and an additional US$ 7.20/MT charged on the portion left in storage
at the port. In Djibouti a grace period of 45 days was permitted for stored fertilizer and in
Assab, the limit was 30 days. Typically, fertilizer was removed from port storage within
these periods.

Stevedoring refers to any charge related to unloading cargo from a ship to dock
and is primarily a payment for labor services rendered. A minimum of 18 laborers are
required around each of the 4 ship hatches for unloading. An additional 18 laborers are
required for bagging and loading into trucks.

Crane charges are charged for use of cranes to off-load fertilizer. In Assab, a
crane charge is charged regardless of whether ship or shore cranes are used. Crane
charges were variable and exclusively charged (Kassahun 1998). In Djibouti ship cranes
were usually used to transfer fertilizer directly from the ship into the funnel of the bagging

289

machine that sits on the ground. Bagging was conducted immediately after fertilizer is
off-loaded.

Typically three bagging machines were used on a shipment of 25,000 MT. A
bagging machine usually runs 16 hours/day, a total of 18.75 bagging-days for a shipment
of 25,000 MT. The rent for one machine was USS 48/day, a total of USS 900 for 25,000
MT (USS 27.77/MT). Some companies such as EAL have their own bagging machines.
A fee of USS 4.25/MT was charged for use of own machines. Often before fertilizer is
bagged, rent of excavators was required to mix up caked DAP (but not urea) on the ship.

In Assab, many of the port fees were fixed for stevedoring, bagging, etc., but in
Djibouti many of the rates were more flexible and negotiated privately between two
parties. Other costs in the cost build-up were also less transparent and may be a reflection
of the relative efiiciency of one company over another. For example, losses,
administration and overhead, and margins must be estimated at an average although they
will vary between suppliers. An estimated 0.5 percent of the c.i.f. price is calculated as
losses for one company (Kassahun 1998). Administration and overhead also will vary
considerably from importer to importer (USS 0.15/MT is calculated for one relatively
efficient company). A procurement margin of 2 Birr/quintal was added into the cost-bqu
up. Fertilizer imports felt this margin was acceptable, a margin of 3-5 Birr/quintal was
considered quite high in 1998 (GMRP 1998). The fertilizer subsector in Ethiopia is highly
vertically integrated thus importers are also often engaged in wholesale and retail sales. A
wholesale/retail margin of 2 Birr/quintal is added in addition to the 2 Birr/quintal importer
margin.

Once fertilizer is bagged and loaded onto truck-trailers it is transported to the
central storage warehouses in Nazreth. Only truck-trailers are used on the Assab or
Djibouti routes, the capacity of which is between 22-40 tons, although 90 percent of the
time, the capacity of the truck-trailers is 30 tons. There are no economies of scale present
in inland transport, transport rates do not vary according to whether the truck-trailer is
filled to capacity. The Djibouti-Nazreth in-land transport rate (0.057 Birr/MT/km) was
higher than the Assab-Nazreth rate (0.044 Birr/MT/km) because Djibouti was a less
efficient than the port of Assab. The turnaround time in Djibouti was longer for truck-
trailers due to inefficient labor in coordinating and loading trucks (Kassahun 1998).

290

APPENDIX 3

PROFITABILITY SIMULATIONS FROM CHANGES IN FERTILIZER COST

291

 

Table 1. Financial Analysis by Technology Type and Labor Use

 

 

 

 

Tech 1 Technology 2 Technology 3 Technology 4
Budget Item Local seed. Local seed plus DAP Improved seed, DAP. Improved seed. DAP
no fen. and urea -; recomm. and urea .»= recorrun.
Tl.0 T2.] T2.2 T3.1 T33 T4.] T4.2
lower' upper lower upper lower upper
n 4 21 22 28 28 25 25
1. Maize yield (kg/ha) 1835.00 2886.88 2921.77 4619.05 7060.26 5606.21 6237.74
1.8. Adjusted maize yield (kg/ha)2 1763.00 2829.72 2863.92 4527.59 6920.47 5495.21 6114.23
2. Fanngate price ofmaize (Birr/ha)3 0.54 0.54 0.54 0.54 0.54 0.54 0.54
3. Return 952.02 1528.05 1546.52 2444.90 3737.05 2967.41 3301.69
4. Total Variable Costs 705.90 682.85 1064.84 1235.16 1460.84 1500.77 1894.71
5. Total Package Costs‘ 49.00 249.59 349.44 534.20 562.48 710.50 730.49
5a. Seed (Bin/ha) 49.00 33.14 44 107.6 113.08 142.97 147.04
5.b. DAP (Bin/ha) 0.00 216.45 303.74 219.35 230.23 291.85 299.41
DAP kg/ha 0.00 84.22 115.93 84.69 88.21 114.9 116.5
DAP Bin/kg 0.00 2.57 2.62 2.59 2.61 2.54 2.57
5.c. Urea (Bin/ha) 0.00 0.00 0.00 206.64 217.00 274.61 281.93
Urea kg/ha 0.00 0.00 0.00 84.69 88.21 114.90 116.50
Urea Bin/kg 0.00 0.00 0.00 2.44 2.46 2.39 2.42
5.d. Herbicide (Bin/ha) 0.00 0.00 0.00 0.00 0.00 1.07 0.00
5.e. Pesticide (Bur/ha) 0.00 0.00 0.00 0.61 0.00 0.00 0.00
ST. Fungicide (Bin/ha) 0.00 0.00 1.70 0.00 2.18 0.00 2.1 1
6. Interest (Bin/ha)5 0.00 0.00 0.00 11.06 26.23 13.73 21.18
7. Labor (Bir'r/ha)6 387.00 240.50 532.58 448.11 468.95 458.22 830.31
7.a. Family & mutual labor (Birrdraf’ 351.00 217.22 484.43 396.18 371.43 418.55 770.49
7.a.i. Family & mutual labor days 78.00 48.27 107.65 88.04 82.54 93.01 171.22
7.1). Hired labor (Bin/ha) 36.00 23.28 48.15 51.93 97.52 39.67 59.82
8. Animal traction (Bin/ha)” 261.00 103.13 92.38 100.3 186.65 146.13 122.12
9. Hand tools and sacks (Bin/ha) 8.90 89.64 90.45 141.49 216.53 172.20 190.62
9.a. Tools (Bur/ha)9 3.10 3.03 2.80 2.92 4.72 4.01 3.49
9.b. Sacks (Bin/ha)‘0 5.80 86.61 87.65 138.57 211.81 168.19 187.13
Net Margin/Hectare (3.-4.) 246.12 845.20 481.67 1209.74 2276.21 1466.64 1406.97
Gross Margin/Hectare/Iabor Day 7.66 22.01 8.97 18.24 32.08 20.27 12.72
(3.-4.+7.a.y”l.a.i

 

Notes for Tables 1-4:

'Technology types were split into two: the 50% of farmers with the lowest yields and highest yields.
2Assumes no grain lost during shelling. Adjusted yield assumes maize harvested in November and
storage losses of 1.98% per month until crop sale in January.

3Grain sold in January, immediately following harvest.

4MOA/SG2000 maize package consists of 25 kg/ha seed, 100 kg/ha DAP. 100 kg/ha urea.
SSG participants pay no interest: MOA program participants pay 10% interest annually for 10 mo.
6Valued at cash/in-kind payment rates provided by survey participants.

7Mutual labor = extended family members. Family/mutual labor valued at an ave. 4.5 Bin/day.

8Sum of rental costs reported by survey respondents: and for owned/borrowed oxen. maintenance

plus depreciated value of animals and animal traction. Equipment multiplied by % of total farm

represented by the MOA-SG. traditional or graduate plot.

9Depreciated value of 2 hoes, 2 axes. 2 cutting knives.

'ODepreciated value of sacks needed to transport maize marketed in January. Since sacks are retained
by farmers and used for other purposes. cost is apportioned by multiplying depreciated sack value by

percentage of total farm represented by MOA-SG or graduate plot.

292

Table 2. Budgets by Labor Use — Scenario 6, reduced transport, 100% direct,,
delivery 10% reduction in low1riced international f.o.b. price

 

 

 

Tech 1 Technology 2 Technology 3 Technology 4
Budget Item Local seed. Local seed plus DAP Improved seed. DAP, Improved seed. DAP
no fert. and urea recomm. and urea ‘>= recomm.
lower| upper lower upper lower upper
n 4 21 22 28 28 25 25
l. Maize yield (kg/11a) 1835.00 2886.88 2921.77 4619.05 7060.26 5606.21 6237.74
1.a. Adjusted maize yield (kg/ha)2 1763.00 2829.72 2863.92 4527.59 6920.47 5495.21 61 14.23
2. Farmgate price ofmaize (Birr/ha)3 0.54 0.54 0.54 0.54 0.54 0.54 0.54
3. Return3 952.02 1528.05 1546.52 2444.90 3737.05 2967.41 3301.69
4. Total Variable Costs 705.90 657.58 1024.27 1125.91 1343.53 1364.04 1749.09
5. Total Package Costs‘ 49.00 224.32 308.86 424.95 445.17 573.77 584.86
5.11. Seed (Bin/ha) 49.00 33.14 44 107.6 113.08 142.97 147.04
5.b. DAP (Bin/ha) 0.00 191.18 263.16 192.25 200.24 260.82 264.46
DAP kg/ha 0.00 84.22 115.93 84.69 88.21 114.9 116.5
DAP Bin/kg 0.00 2.27 2.27 2.27 2.27 2.27 2.27
5.c. Urea (Bin/ha) 0.00 0.00 0.00 124.49 129.67 168.90 171.26
Urea kg/ha 0.00 0.00 0.00 84.69 88.21 114.90 116.50
Urea Bin/kg 0.00 0.00 0.00 1.47 1.47 1.47 1.47
5.d. Herbicide (Bin/ha) 0.00 0.00 0.00 0.00 0.00 1.07 0.00
5.e. Pesticide (Bin/ha) 0.00 0.00 0.00 0.61 0.00 0.00 0.00
51’. Fungicide (Bin/ha) 0.00 0.00 1.70 0.00 2.18 0.00 2.11
6. Interest (Bin/ha)’ 0.00 0.00 0.00 11.06 26.23 13.73 21.18
7. Labor (Birr’ha)6 387.00 240.50 532.58 448.11 468.95 458.22 830.31
7.a. Family. mutual labor (Bin/ha)" 351.00 217.22 484.43 396. 18 371.43 418.55 770.49
7.a.i. Family & mutual labor days 78.00 48.27 107.65 88.04 82.54 93.01 171.22
7.11. Hired labor (Bin/ha) 36.00 23.28 48.15 51.93 97.52 39.67 59.82
8. Animal traction (Bin/ha)” 261.00 103.13 92.38 100.3 186.65 146.13 122.12
9. Hand tools and sacks (Bin/ha) 8.90 89.64 90.45 141.49 216.53 172.20 190.62
9.a. Tools (Bur/ha)9 3.10 3.03 2.80 2.92 4.72 4.01 3.49
9b. Sacks (Birnha '0 5.80 86.61 87.65 138.57 211.81 168.19 187.13
Net Margin/Hectare (3.4.) 246.12 870.46 522.25 1318.99 2393.53 1603.37 1552.60
Gross Margin/Hectare/Labor Day 7.66 22.53 9.35 19.48 33.50 21.74 13.57

(3.4.+7.a.)/7.a.i

293

Table 3. Budgets by Labor Use — Scenario 8, direct delivery from port to retail
mktg, 100% direct delivery, 10% reduction in low-price international f.o.b. price

 

 

 

Tech 1 Technology 2 Technology 3 Technology 4
Budget Item Local seed. Local seed plus DAP Improved seed. DAP. Improved seed. DAP
no fert. and urea ' recomm. and urea -'= recomm.
lower' upper lower upper lower upper
11 4 21 22 28 28 25 25
1. Maize yield (kg’ha) 1835.00 2886.88 2921.77 4619.05 7060.26 5606.21 6237.74
l.a. Adjusted maize yield (kg/ha)2 1763.00 2829.72 2863.92 4527.59 6920.47 5495.21 61 14.23
2. Farmgate price ofmaize (Birr‘ha)3 0.54 0.54 0.54 0.54 0.54 0.54 0.54
3. Return‘ 952.02 1528.05 1546.52 2444.90 3737.05 2967.41 3301.69
4. Total Variable Costs 705.90 652.95 1017.89 1122.10 1339.56 1358.87 1743.85
5. Total Package Costs“ 49.00 219.69 302.48 421.14 441.20 568.60 579.62
5.a. Seed (Bin/ha) 49.00 33.14 44 107.6 113.08 142.97 147.04
5.b. DAP (Bin/ha) 0.00 186.55 256.78 187.59 195.39 254.50 258.05
DAP kg/ha 0.00 84.22 115.93 84.69 88.21 114.9 116.5
DAP Bin/kg 0.00 2.215 2.215 2.215 2.215 2.215 2.215
5.c. Urea (BIITIh81 0.00 0.00 0.00 125.34 130.55 170.05 172.42
lirea kg'ha 0.00 0.00 0.00 84.69 88.21 114.90 116.50
I‘m Bin/kg 0.00 0.00 0.00 1.48 1.48 1.48 1.48
5.d. Herbicide (Birr/ha) 0.00 0.00 0.00 0.00 0.00 1.07 0.00
5.e. Pesticide (Bin/ha) 0.00 0.00 0.00 0.61 0.00 0.00 0.00
5.f. Fungicide (Bin/ha) 0.00 0.00 1.70 0.00 2.18 0.00 2.11
6. Interest (Birr/ha)’ 0.00 0.00 0.00 11.06 26.23 13.73 21.18
7. Labor (Birr/ha)‘S 387.00 240.50 532.58 448.11 468.95 458.22 830.31
7.1. Family. mutual labor (Bin/11a), 351.00 217.22 484.43 396.18 371.43 418.55 770.49
7.a.i. Family & mutual labor days 78.00 48.27 107.65 88.04 82.54 93.01 171.22
7b. Hired labor (Bin/ha) 36.00 23.28 48.15 51.93 97.52 39.67 59.82
8. Animal traction (Birrr'ha)8 261.00 103.13 92.38 100.3 186.65 146.13 122. 12
1). lland tools and sacks (Birr/ha) 8.90 89.64 90.45 141.49 216.53 172.20 190.62
9.a. Tools (Bur/ha)9 3.10 3.03 2.80 2.92 4.72 4.01 3.49
9h. Sacks (Birr»ha)'" 5.80 86.61 87.65 138.57 211.81 168.19 187.13
Net Margin/Hectare (3.4.) 246. 12 875.10 528.63 1322.80 2397.50 1608.54 1557.84
Gross Margin/Hectare/Iabor Day 7.66 22.63 9.41 19.52 33.55 21.79 13.60
(3.-4.+7.a.)/7.a.i

 

294

Table 4. Budgets by Labor Use - Scenario 3, If fertilizer market is appointed
by the regional govt. (hedonic model results)

 

 

 

Tech 1 Technology 2 Technology 3 Technology 4
Budget Item Local seed. Local seed plus DAP Improved seed, DAP, Improved seed. DAP
no fert. and urea '1 recomm. and urea r= recomm.
lowerl upper lower upper lower upper
11 4 21 22 28 28 25 25
1. Maize yield (kg/ha) 1835.00 2886.88 2921.77 4619.05 7060.26 5606.21 6237.74
1.3. Adjusted maize yield (kg/ha)2 1763.00 2829.72 2863.92 4527.59 6920.47 5495.21 6114.23
2. Farmgate price ofmaize (Bur/ha)" 0.54 0.54 0.54 0.54 0.54 0.54 0.54
3. Return3 952.02 1528.05 1546.52 2444.90 3737.05 2967.41 3301.69
4. Total Variable Costs 705.90 691.27 1070.64 1259.72 1482.90 1545.58 1933.16
5 Total Package Costs‘ 49.00 258.01 355.23 558.76 584.54 755.31 768.93
5.8. Seed (Bin/ha) 49.00 33.14 44 107.6 113.08 142.97 147.04
5.b. DAP (Bin/ha) 0.00 224.87 309.53 226.12 235.52 306.78 311.06
DAP kg/ha 0.00 84.22 115.93 84.69 . 88.21 114.9 116.5
DAP Birr/Irg 0.00 2.67 2.67 2.67 2.67 2.67 2.67
5.c. Urea (Bur/ha) 0.00 0.00 0.00 224.43 233.76 304.49 308.73
Urea kg/ha 0.00 0.00 0.00 84.69 88.21 114.90 116.50
Urea BIrr/kg 0.00 0.00 0.00 2.65 2.65 2.65 2.65
5.d. Herbicide (Birr/ha) 0.00 0.00 0.00 0.00 0.00 1.07 0.00
5.e. Pesticide (Biff/ha) 0.00 0.00 0.00 0.61 0.00 0.00 0 00
5.1. Fungicide (Bin/ha) 0.00 0.00 1.70 0.00 2.18 0.00 2.11
6. interest (Bin/1m)5 0.00 0.00 0.00 11.06. 26.23 13.73 21.18
7 Labor (Birr/ha)6 387.00 240.50 532.58 448.11 468.95 H 458.22 830.31
7.a. Family, mutual labor (Bin/11:1)~ 351.00 217.22 484.43 396.18 371.43 ' 418.55 . 770.49
7.a.i. Family & mutual labor days 78.00 48.27 107.65 88.04 82.54 93.01 171.22
7.b. Hired labor (Bin/ha) 36.00 23.28 48.15 51.93 97.52 ‘ 39.67 59.82
8. Animal traction (Bir'r/ha)8 261.00 ‘ 103.13 92.38 100.3 186.65 146.13 122.12
9. Hand tools and sacks (Bin/ha) 8.90 89.64 90.45 141.49 216.53 172.20 190.62
9.a. Tools (Bin/11a)9 3.10 3.03 2.80 2.92 4.72 4.01 3.49
9.b. Sacks (Birrxha)'” 5.80 86.61 87.65 138.57 211.81 168.19 187.13
Net Margin/Hectare (3.-4.) 246.12 836.78 475.88 1185.18 2254.15 1421.83 1368.53
Gross Margin/Hectare/Labor Day 7.66 21.84 8.92 17.96 31.81 19.79 12.49

(3.4. +7.a.)’7.a.i

295

 

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