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thesis entitled

Strategies for Increasing Food Production
and Income on Smali Upland Farms in Lampung,

Indonesia -- A Linear Programming Analysis
presented by

Uben Parhusip

has been accepted towards fulfillment
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Ph.D. degree in Agrig. EQQII.

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STRATEGIES FOR INCREASING FOOD PRODUCTION AND
INCOME ON SMALL UPLAND FARMS IN LAMPUNG,

INDONESIA--A LINEAR PROGRAMMING ANALYSIS

BY

Uben Parhusip

A DISSERTATION

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

DOCTOR OF PHILOSOPHY
Department of Agricultural Economics

1980

C§//éw7¢)

ABSTRACT
STRATEGIES FOR INCREASING FOOD PRODUCTION AND

INCOME ON SMALL UPLAND FARMS IN LAMPUNG,
INDONESIA--A LINEAR PROGRAMMING ANALYSIS

BY

Uben Parhusip

The major purpose of this study was to analyze
empirically the effects of the adoption of the new tech-
nology on the traditional cropping system on the small
upland farms in the transmigration areas of Lampung, Indo-
nesia. The analysis emphasized the changes in key farm
variables of farm income, crop enterprise combinations,
resource use and productivity. Also estimates were made
of the price elasticities of the normative supply functions
of the major food crops of rice, corn, and cassava.

The survey method was used to collect empirical
data from a number of sample farms in the transmigration
areas of Lampung where upland farming for food crOp pro-
duction is the main agricultural activity. For the selec-
tion of the sample farms a two stage sampling method was
used. The first stage identified four villages to repre-
sent Lampung province. The second stage involved random
sampling of farms in each village. Tatakarya Village,

Adiluih and Bulusari Villages, and Pugung Raharja Village

Uben Parhusip

were selected to represent the North, Central, and South
of Lampung respectively.

A static linear programming model, designed to maxi-
mize the gross margin subject to meeting minimum food crop
consumption requirements of the farming household, was the
computational tool in this farm planning analysis. Variable
price programming was used to generate data for normative
supply functions of the three major food crops. Then regres-
sion analysis was used to estimate the price elasticities
of the normative supply of these major food crops.

The study suggests that in 1979 only one of the
three regions has much possibility to increase food pro-
duction and farm income. This is in North Lampung. The
results of the analysis show that there is little potential
to increase farm income and food production through the
adoption of currently available new technology for the
representative farms in Central and South Lampung. Most of
the currently available new technology apparently has been
adopted. In North Lampung some potential appears to remain
for further adoption of currently available new technology,
in this relatively newly settled transmigration area.

For Central and South Lampung the major efforts to
increase food production and farm income could be the devel-
Opment of research to produce new highly profitable inputs
or import them from the other areas with testing at the
farm level. Examples of such new inputs include new seeds,

fertilizers, insecticides, new cultural practices, and

Uben Parhusip

mixed cropping for the small farmers. Also needed is the
training of the farmers for the use of these new inputs, and
farmers' organizations for the distribution of these new
inputs and for marketing the products. The efforts such

as of Tani Makmur Project in this regard need to be con-
tinued and expanded.

The analysis also shows that there is a compara-
tive advantage in producing specific crops with specific
cropping patterns in each region. North Lampung has a com-
parative advantage in producing cassava and upland rice with
the cropping pattern: corn + upland rice { cassava. Central
Lampung has a comparative advantage in producing rice and
cassava with cropping patterns consisting of crop mixtures:
corn - soybeans + cassava, and corn + upland rice f cassava.
South Lampung has a comparative advantage in producing soy-
beans with cropping patterns: upland rice - soybeans, upland
rice + corn - soybeans, and triple crops of soybeans.

Labor is particularly limiting during land prepara-
tion and weeding seasons in North Lampung. The increase in
the availability of labor in these peak seasons could signi-
ficantly increase farm income and output. Hired labor is
very small during these peak seasons which may be due to the
absence of landless farmers in the study area. In general
operating capital was not a limiting factor for the repre-
sentative farmers, most of whom practiced mixed cropping.
But medium to long term credit may be needed for investment

in the additional sources of power (e.g., cows and other

Uben Parhusip

sources of power) to reduce the labor constraints in the
peak seasons.

The products of the variable price programming anal-
ysis were the step normative supply functions for rice,
corn, and cassava under traditional and new technologies.
These step supply functions were then transformed into the
continuous supply functions by means of regression analysis.
From these continuous supply functions then, price elas-
ticities were calculated for these three major food crops.
For specific use for policy makers these elasticity coef-
ficients were converted into absolute changes in output
response to a 1 percent change in prices.

All the supply functions of the three major crops
were inelastic in the price range from existing levels to
somewhat higher levels. Under the new technology the sup-
ply curves of the three crops shift upward to the right
indicating that the new technology would increase produc-
tion of rice, corn, and cassava at all price levels in the
study area.

The price elasticities for cassava and corn are
higher under the new technology than under the traditional
technology. In all cases the price elasticity of cassava
is the highest and price increases for cassava are the
most effective means to increase both cassava production

and farm income compared with the other two cr0ps.

Dedicated to my parents Saul br. Lumbanraja and
Jabidu Parhusip for all sacrifices they have made

for my education in Indonesia and the United States

ii

AC KNOWLED GMENTS

The author wishes to thank Professor John N. Ferris,
chairman of both my guidance and thesis committees. To him
I am indebted for his guidance and encouragement during the
preparation of this dissertation. Thanks to Professors
Stephen Harsh, Robert D. Stevens, and Carl Liedholm, for
their contributions and assistance. I benefitted a great
deal from Robert D. Stevens' comments on the earlier draft
of this dissertation.

Much appreciation is extended to Dr. Clive Gray in
the earlier preparation of my study program during his stay
in Indonesia; the United States Agency for International
Development for financial support through my graduate
studies; Ir. Sardjono Reksodimulyo, Director of Program
Development for Food Crops; Ir. Achmad Affandi, Director
General of Food Crops in the Ministry of Agriculture in
Jakarta for giving me a chance to study on leave; the Ford
Foundation Jakarta for supporting my family and partly fund-
ing my research project; Professor Yoshi Tsurumi, Director
of the Pacific Basin Business and Economic Study Center,
Graduate School of Management, University of California

(UCLA) Los Angeles, California, who has provided major

iii

funding for my research project in Lampung, Indonesia; Ir.
Kusnadi Affandi, Inspector of Lampung Agricultural Exten—
sion Agency and his staff members, Ir. M. Pohan, Ir.
Kamaluddin Sipayung for their help; Ir. Masdulhaq, Ir.
Saibun Sirait, Ir. Djamaan, Ir. Sulaeman Nasution of the
Lampung Extension Specialists for providing their help in
collecting the data; the farmers in Tatakarya, Bulusari,
Adiluih, and Pugung Raharja Villages in Lampung for their
invaluable cooperation and patience in providing information
upon which this study relied; Chris Wolf and Ing Ting for
their assistance in computer work.

A sincere debt of gratitude is owed to my brother
and his wife, my sister and her husband who have sacrificed
a lot in all ways to see that I made it this far. Last,
but not least to my wife Purnama, my daughters Lola, Betty,
Nimbella, and my son Bernard (Ucok) for their patience,
understanding, and encouragement during the preparation of
this dissertation.

Finally, I am grateful to God for guiding me and

my family and keeping us alive.

iv

TABLE OF CONTENTS
Page
LIST OF TABLES . . . . . . . . . . . . . . ix
LIST OF FIGURES. . . . . . . . . . . . . . xiv
Chapter
I. INTRODUCTION . . . . . . . . . . . . l
1.1 The Problem and Its Setting . . . . . 1

1.2 Theory of Agricultural Development. . . 8
1.3 The Research Approach . . . . .

o
H
U1

1.3.1 The Analytical Framework. . . . 17

1.3.2 Some Advantages and Limitations

of Linear Programming Approach . 20

1.3.3 Sources of Data. . . . 21
1.3.4 Sampling Method and the Sample

Size. . . . . . . . . . 22

1.3.5 Data Collection. . . . . . . 26
1.3.6 Construction of the Representa-

tive Farms. . . . . . . . 27

1.4 Organization of the Study. . . . . . 30

II. DESCRIPTION OF THE FARMING SYSTEM IN THE
STUDY AREA 0 C O O O O O O O O O O 3 1

2.1 Physical Characteristics of the Study
Area 0 O O O O O O O I O O O 31

2.1.1 Soil and Climate of the Research
Area. . . . . . . . . . 32

Land Use . . . . . . . . . . . 34
Cr0p Production and CrOpping Patterns. . 35
Technology of Agricultural Production. . 39
Some Demographic Characteristics of the
Representative Family Farms in the
Study Area . . . . . . . . . 41
2.6 Educational Level of Representative
Farm Household's Head . . . . . . 44

NNNN
O O I O
01wa

Chapter

2.7
2.8

Farm Labor Force . . . . . . . .
Farm Capital . . . . . . . . .

III. THE STRUCTURE OF THE LINEAR PROGRAMMING MODELS
FOR THE STUDY AREA 0 O C O O O O C O

3.1
3.2

3.3

3.5

Introduction . . . . . . . . .
The Objective Function . . . . . .

Activities in the Model. . . . . .

a. Crop Production Activities . . .
b. Labor and Animal Hiring Activities.
c. Capital Borrowing Activities. . .
d. Fertilizers, Insecticides, and Food
Buying Activities. . . .
e. Food Crop Consumption Activities .
f. Crops Selling Activities and Seed
Production Activities . . . .
9. Transfer Activities. . . . . .

Restrictions in the Model . . . . .
a. Land Restriction. . . . . . .
b. Labor Restrictions . . . . .
c. Operating Capital Restrictions . .
d. Consumption Requirements . . . .

Some Limitations of the Model. . . .

IV. ANALYSIS OF THE RESULTS FROM THE LINEAR
PROGRAMMING MODELS APPLICATIONS. . . . .

4.1

The Optimal Organization of the Repre-
sentative Farm Enterprise Under
Traditional with Existing Resource
Conditions in North Lampung. . .

The Effects of the New Technology with
Existing Resources in North Lampung .

Comparison of Optimal Plan Under the
New Technology and Existing Resource
Levels Between the Three Subregions
of Lampung . . . . . . .

The Effects of Releasing Family Labor
Constraints on the Optimal Plan Under
the New Technology. . . . . . .

vi

Page
44
54
59

59
62

65
65
70
74

76
76

78
79

79
79
80
81
82

82

84

86
96

111

115

Chapter

4.5 Comparison of Actual and Optimal Plan
of the Representative Farms Under
the New Technology with Existing
_Resources . . . . . . . . . .
4.6 Summary of the Results from Linear
Programming Analysis . . . . . .

V. NORMATIVE SUPPLY FUNCTIONS FOR MAJOR FOOD
CROPS UNDER TRADITIONAL AND NEW TECHNOLOGIES.
5.1 Introduction. . . . . . . . .
5.2 Normative Supply Function of Rice,
Corn, and Cassava . . . . . . .
5.2.1 Rice Supply Functions . . . .
5.2.2 Corn or Maize Supply Functions .
5.2.3 Cassava Supply Functions . . .
5.3 The Effects of the New Technology on
Price Elasticities of the Supply of
the Major Food Crops . . . . . .
VI. SUMMARY, POLICY IMPLICATIONS, LIMITATIONS OF
THE STUDY AND SUGGESTIONS FOR FURTHER
RESEARCH O O O O O O C O O O O O O
6.1 smary O O O O O O O O O O O
6.2 Policy Implications . . . . . .
6. 3 Limitations and Suggestion for Further
Study 0 O O O O O O O O O O
APPENDICES
Appendix
A. Maps of Indonesia and Lampung Province . . .
B. Major Food Crop Production by Subregions of
Laxnpung O O O O O O O O O O O O O
C. Explanation of the Abbreviations Used in
Cr0pping Patterns of the Representative
Farms in Lampung . . . . . . . . . .
D. Explanation of Abbreviations Used in the Linear

Programming Table of the Representative Farms
in Lampung . . . . . . . . . . . .

vii

Page

124

132

135
135
144
144

149
154

158

175

175
184

187

191

193

197

200

Chapter Page
E. Monthly Major Food CrOps Prices at the Nearest
Market to the Representative Farms in
Lampung O O O O I O O O O I O O O 2 0 8

BIBLIOGRAPHY .' . . . . . . . . . . . . . 210

viii

Table

2.7

LIST OF TABLES

Daily Wage Rates and Total Labor Inputs by Month
on the Representative Family Farms (Bulusari
Village) o .2 o o o o o o o o o o 0

Daily Wage Rates and Total Labor Inputs by
Month in Lampung on the Representative
Family Farms (Pugung Raharja Village) in
Lampung . . . . . . . . . . . .

Monthly Labor Inputs and Daily Wage Rates on

the Representative Farms (Tatakarya Village)
in Lampung . . . . . . . . . . .

ix

Page
Areas and Population of Indonesia by Province
for 1976 O O O O 0 O O O O O O O 3
Acreage Expansion of Lowland and Upland in
Lampung for the Period of 1969-1976 . . . 7
Number and Percentage of Farmers by Cropping
Patterns of the Representative Farms in
Lampung I O O O O O O I O O O O 36
Average Area Devoted to Each Crapping Pattern
by the Representative Farms in Lampung . . 38
Some Demographic Characteristics of the Repre-
sentative Farm Households in Lampung. . . 42
Average Representative Farm Household's Head
Educational Level . . . . . . . . . 43
Representative Farm Family Composition by Age
Groups in Lampung . . . . . . . . . 46
Total Labor Inputs by Month and Wage Rates on
the Representative Farms (Adiluih Village)
in Lampung . . . . . . . . . . . 47

48

49

50

2.12

3.1

3.2

4.3

4.4

Page

Man Equivalents Used in the RERU Farm Manage-
ment Studies . . . . . . . . . . . 54

Table of Conversion of Various Working Age and
Different Sex for Settlement Areas. . . . 55

Cash Expenses by the Representative Farms by
Month in Lampung. . . . . . . . . . 58

CrOp Production Activities in Tatakarya Village
(North Lampung) . . . . . . . . . . 66

Labor Requirement per Hectare of Selected Cr0p
Production Activities Under Traditional and
New Technologies. . . . . . . . . . 70

Hiring Labor and Animal Activities in
Tatakarya Village . . . . . . . . . 72

Borrowing and Transfer Activities in Tatakarya
Village. 0 O O O O O O C C O O O 75

Fertilizers, Food Crop Buying, Food Cr0p Con-
sumption and Selling Activities in Tatakarya
Village. 0 O O O O O O O O O O C 75

Optimal Plan, Gross Margin, Resource Use and
Average Returns Under Traditional Technology
and New Technology with Existing Resources of
the Representative Farm in North Lampung,
Indonesia . . . . . . . . . . . . 87

Marginal Value Products of Land, Family Labor,
Operating Capital (MVPs) on the Optimal Plan
Under Traditional Technology with Existing
Resources in North Lampung . . . . . . 92

A Comparison of the Optimum Farm Organization
of the Representative Farm Under Traditional
and New Technologies with Existing Resources
in North Lampung. . . . . . . . . . 97

Cost of Forcing in NonOptimal Activities (Results
of the Linear Programming Analysis) Under the
New Technology with Existing Resources in
North Lampung. . . . . . . . . . . 100

Table Page

4.5 A Comparison of Marginal Value Products (MVPs)
of Land, Family Labor, and Operating Capital
on the Optimal Plan Under the Traditional
and New Technologies with Existing Resources
of the Representative Farm in North Lampung . 103

4.6 Optimum Organization of the Representative
Farm Under the New Technology with Existing
Resource Levels in Central Lampung . . . . 107

4.7 Optimum Organization of the Representative
Farm After the Introduction of the New Tech-
nology with Existing Resource Levels in
South Lampung . . . . . . . . . . . 108

4.8 Comparison of the Level of Technology on
Optimal Plans for the Representative Farms
in the Three Subregions of Lampung . . . . 110

4.9 Comparison of the CrOpping Patterns of the
Optimal Plans Under the New Technology and
Existing Resources for the Representative
Farms of the Three Subregions of Lampung . . 112

4.10 Comparison of Resource Use and Productivity on
the Optimum Plans Under the New Technology
with Existing Resources of the Representative
Farms in the Three Subregions of Lampung . . 114

4.11 Comparison of CrOp Productivities in Various
CrOpping Patterns Under the New Technology
Between the Three Subregions of Lampung . . 116

4.12 Optimal Plan of the Representative Farm Under
New Technology with Existing Resource Levels
and Increased Family Labor in North Lampung . 118

4.13 Optimal Plan of the Representative Farm Under
New Technology with Increased Family Labor
in Central Lampung . . . . . . . . . 121

4.14 Optimal Plan of the Representative Farm Under
New Technology with Increased Family Labor
in South Lampung . . . . . . . . . . 123

4.15 A Comparison of Actual and Optimal Plan of the

Representative Farm Under New Technology and
Existing Resource Levels in North Lampung. . 125

xi

 

 

Table

4.16

4.17

5.5

5.6

5.7

A

Page

Comparison of Actual and Optimal Plan of the
Representative Farm Under New Technology and
Existing Resource Levels in Central Lampung . 128

Comparison of Actual and Optimal Plan of the
Representative Farm Under New Technology and
Existing Resource Levels in South Lampung. . 130

Comparison of Normative Supply Functions for

Rice Under Traditional and New Technologies

with Existing Resources of the Representative
Farm in North Lampung . . . . . . . . 146

Comparison of Farm Income Under Traditional
and New Technologies with Rice Price Varied
on the Representative Farm in North Lampung . 148

Comparison of Normative Supply Functions for

Corn Under Traditional and New Technologies

with Existing Resources and Labor Increased

for the Representative Farm in North

Lampung . . . . . . . . . . . . . 150

Comparison of Farm Income Under Traditional

and New Technologies with Corn Price Varied

for the Representative Farm in North

Lampung . . . . . . . . . . . . . 153

Comparison of Normative Supply Functions for
Cassava Under Traditional and New Technologies
with Existing Resource and Labor Increased for

the Representative Farm in North Lampung . . 155

Comparison of Farm Income Under Traditional

and New Technologies with Cassava Price Varied

for the Representative Farm in North

Lampung . . . . . . . . . . . . . 157

Estimated Supply Functions for Rice Under Tra-

ditional and New Technologies for the Repre-
sentative Farm in North Lampung . . . . . 161

Estimated Supply Functions for Corn Under Tra-

ditional and New Technologies for the Repre-
sentative Farm in North Lampung . . . . . 162

xii

Table

5.9

5.10

5.11

5.13

Estimated Supply Functions for Cassava Under
Traditional and New Technologies for the
Representative Farm in North Lampung . . .

Estimated Price Elasticities of Rice Supply
Functions Under Traditional and New Tech—
nologies for the Representative Farm in
North Lampung. . . . . . . . . . .

Estimated Price Elasticities of Corn Supply
Functions Under Traditional and New Techno-
logies for the Representative Farm in North
Lampung. . . . . . . . . . . . .

Estimated Price Elasticities of Cassava Supply
Functions Under Traditional and New Tech-
nologies for the Representative Farm in
North Lampung. . . . . . . . . . .

Absolute Increases in Production of Rice in
Response to a 1 Percent Increase in Its Price
Under Traditional and New Technologies for
the Representative in North Lampung . . .

Absolute Increases in Corn Production in
Response to a 1 Percent Increase in Its Price
Under Traditional and New Technologies for
the Representative Farm in North Lampung. .

Absolute Increases in Cassava Production in
Response to a 1 Percent Increase in Its
Price Under Traditional and New Technologies
for the Representative Farm in North
Lampung. . . . . . . . . . . . .

xiii

Page

163

165

167

169

171

172

173

LI ST OF FIGURES

Figure Page

2.1 Monthly Rainfall Pattern in Bandarjaya, Central
Lampung, Indonesia, 1970-76 . . . . . . 33

5.1 Farm-Firm Step Supply Function. . . . . . 142

5.2 Normative Supply Functions for Rice Under Tra-
ditional and New Technologies for the Repre-
sentative Farm in North Lampung. . . . . 147

5.3 Normative Supply Functions for Corn Under Tra-
ditional and New Technologies of the Repre-
sentative Farm in North Lampung. . . . . 152

5.4 Normative Supply Functions for Cassava Under

Traditional and New Technologies for the
Representative Farm in North Lampung . . . 156

xiv

CHAPTER I

INTRODUCTION

1.1 The Problem and Its Setting

Indonesia has an area of about 2,027,000 square
kilometers covering about 13,000 islands, of which about
2,000 are inhabited. The major islands are Sumatra, Java,
Sulawesi, Kalimantan and Irian Jaya. Lampung is located
in the south part of Sumatra. See map in Appendix A. For
the purpose of agricultural statistics, the country is
divided into (a) Java and Madura and (b) Outer Java Islands
(FAO/UNDP, 1976). Indonesia to a great extent relies on
the agricultural sector. For 1978 the agricultural sector's
contribution to the Gross Domestic Product accounted for
31.1 percent compared with 17.8 percent for mining, manu-
facturing 9.3 percent, trade and financial intermediaries
and other services 31.4 percent (Central Bureau of Statis—
tics, Indonesia, 1979). It is also estimated that about
75 percent of the total population is directly dependent
on the agricultural sector for their livelihood. About
61.5 percent of the total labor force engages in agricul-

tural activities. This sector is expected to supply food,

raw materials, labor, and capital to the other sectors of
the economy.

The performance of agricultural sector has not kept
pace with the growing population of Indonesia, even though
the program for increasing food production, especially rice
was initiated a long time ago. For example, in 1977/78
Indonesia imported about 3.3 million tons of food grain
consisting of 2.3 million tons of rice and 1.0 million tons
of wheat (Glassburner, 1978; World Bank, 1979). Even assum-
ing an expanded government investment in the food crop sub-
sector, improved technology, efficiently operating services
and marketing management, and a growth rate of production
at an average 3.5 percent per annum, with demand increasing
at a rate of 5.0 percent per annum (Timmer, 1974; Parhusip,
1976), it is likely that Indonesia will continue to face a
basic food deficit, i.e., rice. It is projected that the
rice deficit could be between 2.0 and 3.0 million tons in
1990 (World Bank, 1979).

The problem of increasing food production in Indo-
nesia is also aggravated by the concentration of population
on the island of Java. Table 1.1 shows the detail of popu-
lation distribution by provinces. As can be seen from the
table, for 1976, the population of Indonesia consisted of
130.7 million people of which 82 million people or 65.3
percent live in Java, an island with only 7 percent of the

total area of Indonesia.

 

Table 1.1.--Areas and Population of Indonesia by Province
for 1976.

 

 

 

Total Area .

12:53:22,121?

Sq. Km. Percent

1. D.K.I. Jakarta 590 0.03 4925
2. West Java 46300 2.43 23849
3. Central Java 34206 1.80 23675
4. D.I. Jogyakarta 3169 0.17 2637
5. East Java 47922 2.52 27079
JAVA & MADURA 132187 6.95 82166
6. D.I. Aceh 55932 2.91 2299
7. N. Sumatra 70787 3.72 7347
8. W. Sumatra 49778 2.61 3077
9. Riau 94652 4.96 3070
10. Jambi 44924 2.36 1746
11. S. Sumatra 103688 5.44 3847
12. Bengkulu 21168 1.11 625
13. Lampung 33307 1.75 3452
SUMATRA 473606 24.86 22480
14. W. Kalimantan 146760 7.70 2136
15. C. Kalimantan 152600 8.01 834
16. S. Kalimantan 37660 1.98 1872
17. E. Kalimantan 202440 10.63 929
KALIMANTAN 539460 28.32 5773
18. N. Sulawesi 19023 1.00 1931
19. C. Sulawesi 69726 3.66 1047
20. S. Sulawesi 72781 3.82 5729
21. SE. Sulawesi 27686 1.45 798
SULAWESI 189216 9.93 9057

Table 1.1.--Continued.

 

Total Area

 

 

. Population
PrOV1nce

Sq. Km. Percent (thousand)
22. Bali 5561 _ 0.30 2333
23. W. Nusatenggara 20177 1.06 2474
24. E. Nusatenggara 47876 2.51 2496
25. Maluku 74505 3.91 1309
26. Irian Jaya 421981 22.16 1042
INDONESIA 1904569 100.00 130766

 

Source: Central Bureau of Statistics, Jakarta,
Indonesia.

Lampung province, the south part of Sumatra island
has a population of 3.5 million people or 2.5 percent of
the total population of Indonesia. Of the land area culti-
vated in Java (46.7 percent of the total area) more than 70
percent of the farms had less than one hectare. The average
family size on all farms was five. The land area cultivated
in Lampung is only about 24.7 percent of the total area
(Central Bureau of Statistics, 1979). On the outer islands
there are vast areas of land potentially available for
increasing food production. In Lampung itself there are
2.0 million hectares of such land (Bogor Soil Research
Institute, 1971).

The problem of population distribution has been
partly tackled by the transmigration program from Java to
outer islands. Thus these programs have the dual strate-
gies of easing Java's population pressure and also develop-
ing the outer islands. The intended impact is to increase
food production, increase income per capita, create employ—
ment opportunities and create new economic growth centers
outside Java which are intended to attract new migrants.

Lampung province has great potential both for
increasing food production and as a transmigration program
recipient. Lampung has been the major recipient of the
transmigration in terms of the number of families for the
period of 1969/70 through 1974/75. This is primarily due
to its strategic location which is very close to Java, only

one half hour by air or two hours by bus plus four hours

by ferry. It also has relatively good transportation and
communication facilities compared to the other regions of
the outer islands.

Another significant constraint on the development
of agriculture is the low level of farm investment. The
result is that land and labor continue to be the main
inputs in food production, especially for upland crops in
Lampung. The use of purchased inputs such as fertilizers
and pesticides/insecticides are extremely low, especially
in upland farming. The tools used in farming are mostly
hoes and ploughs. The low level of production technology
has been cited as one of the causes of poor performance of
agricultural sector. Low productivity in Lampung province
can be also attributed to the lack of the price incentive,
insufficient resource base including lack of capital or
credit, managerial know-how, shortage of labor and low
land fertility.

In Lampung the increase in food production is mostly
due to increases in areas of lowland and upland cultivated.
The following table shows the area expansion of upland and
lowland for food crop production during the period of 1969-
1976. From the table we can see that, during that period,
the total acreage increase was 30,000 ha and 90,000 ha or
the rate of 5.6 percent and 7.5 percent per annum for low-
land and upland respectively.

In terms of acreage, upland contributed about 72

percent of the total area cultivated for food production.

Table l.2.--Acreage Expansion of Lowland and Upland in
Lampung for the Period of 1969-1976.

 

Annual Change Annual Change

 

 

 

“81:?“ ”3.1.2?

ha % ha %
1969 65,793 - — 150,000 - -
1970 71,282 5,489 8.34 168,188 18,188 12.13
1971 77,836 6,554 9.19 198,877 30,689 18.25
1972 83,431 5,595 7.19 220,955 22,070 11.10
1973 90,791 7.360 8.82 227,014 6,059 2.74
1974 89,843 -3,703 -4.08 233,044 6,030 2.66
1975 89,843 2,755 3.16 227,103 -5,941 —2.55
1976 95,792 5,949 6.62 240,900 13,797 6.08
Average
Annual 4,300 5.5 12,965 5.5
Change

 

Source: Dinas Pertanian Rakyat Lampung: Repelita III
Subsector Pertanian Tanaman Pangan Propinsi Lampung 1979/80-
1983/84.

This is due to the well distributed rainfall during the
year, and a very short dry season which is suitable for a
year around upland cropping system (Kongres Agronomi,

Jakarta, 1977).

1.2 Theory of Agricultural Development

A background framework of theory of agricultural
development will help place the results of this study in
perspective. Schultz (1964) viewed that peasants, in tra-
ditional agricultural systems, were rational, efficient
resource allocators. They remain poor, because they had
limited technical and economic opportunities to which they
could respond. In this theory the agricultural transforma-
tion requires investment in producing and introducing the
new higher productivity agricultural inputs, such as high
yielding seed varieties, and chemical fertilizers. This
theory is incomplete since it treats technical change as
exogenous to the economic system. Ruttan and Hayami (1971)
augment agricultural development theory with an "Induced
Development Model" which treats technical change as endo-
genous to the development process in which both the private
and public sectors are responsive to the relative price of
factors. For example the mechanical technology is induced
to be developed where there is labor scarcity (high cost
labor) and biological technology is induced to be developed

where land is a scarce resource (has high cost).

Competitive conditions are assumed with profit maximizing

behavior by farmers.

To achieve a sizeable increase in agricultural pro-
duction and productivity through the introduction of the
new technology, "development services" or "unconventional
inputs" such as agricultural research, education, and
extension are required that broaden the range of alternative
production possibilities available to the farm operators.
New information which strengthens their capacity to make and
execute decisions on the basis of more adequate knowledge
of productive agricultural technology is of particular
importance (Johnson, B. F. and Mellor, J. W., 1961).

In Lampung currently in the dryland farming, the
status of agricultural transformation is uncertain. Two
hypotheses are proposed here that may reflect the status of
agricultural transformation in Lampung as follows:
Hypothesis 1: That the traditional agricultural technology

brought over from Java has reached its
equilibrium.

Hypothesis 2: That a certain amount of new technology has
been adopted by the farmers. Subhypotheses
here are: (a) that whatever new technology
has become available it has already been
incorporated and new equilibrium has been
reached; and (b) that there remains signifi-

cant opportunity for greater use of the

10

currently available new technology for increas-
ing food production and farm income.

To explore these hypotheses the study was conducted
in Various regions of Lampung where transmigration is con-
centrated and the upland farming system for food crop pro-
duction is the main agricultural activity. Lampung is
divided into three Regional Development Areas (North,
Central, and South) based on population density and
administrative regions, and perhaps agroclimatic condi-
tions (BAPEPPDA Lampung, 1979). See the map of Lampung
province in Appendix A.

The Third Indonesian Five Year Plan states that
large scale mechanized public or private farms should also
be encouraged outside Java to increase food production
through the opening of new land. These large scale farms
make extensive use of machinery and other modern inputs such
as improved seeds, fertilizers, insecticides, herbicides,
and better management. Examples of such farms are P.T.
Mitsugoro, P.T. Daya Itoh, and P.T. Pago in Lampung pro—
vince. These three farms have demonstrated the possibil-
ities of turning alangZ land (Imperata cylindrica, which
due to this tenacious grass before was considered as waste
land) into productive ground. Food crops grown include
corn, upland rice, and cassava. The yields were more than
double that obtained on local traditional farms (Tsurumi,
1977). But in this study, these large scale highly mech-

anized farms are excluded and attention was given only to

11

to the traditional and transitional small scale (subsis-
tence) farms. This is not to say that the large scale
mechanized farm is unimportant, but our emphasis here is
to study those small scale subsistence farms. We assumed
that the development of small scale farms in Indonesia
will be the right strategy for increasing food production
and income of millions of small farmers. This approach is
supported by R. D. Stevens (1977) whose arguments can be
summarized as follows:

1. Increasing food production on small farms is a
primary route for improving national income dis-
tribution.

2. In rural areas where unemployment is endemic and
sufficient off-farm employment opportunities are
unlikely available in the coming decade, due to a
combination of rapid population growth and low rates
of growth in employment in the service and particu-
larly in the industrial sectors of developing
nations.

3. The rising of food prices and global food scarcities
has drawn attention to role of small farmers might
play in food production.

4. Increasing energy costs for agricultural produc-
tion, contributed sharply by recent sharp rises in
petro prices and subsequent increases in the cost

of nitrogen fertilizers.

12

Robert S. McNamara (1972), President of the World
Bank, also registered concern about progress on small farms
when he states "without rapid progress in small holder
agriculture throughout the developing world, there is little
hope of achieving long term stable economic growth or sig-
nificantly reducing the level of absolute poverty."
Fred Winch (1976) conducted research in Northern Ghana on
costs and returns of six alternative rice production systems
based on farm size and source of power for land preparation,
and concluded that the small farm (3.9 acres) labor inten-
sive approach produced substantial output and income to
farmers and generated economic profits. This implied that,
in a strategy to increase rice production and farm income,
emphasis should be given to the development of small farms.

These authorities have indicated that the solution
to the national problems of food supply, income growth, and
employment depends on the increasing productivity and
creating new job opportunities among the vast number of
small farmers throughout the developing nations.

Korea is the example of a success in developing its
agriculture through the develOpment of small farms under a
noncommunist system as a strategy to increase food produc—

tion and improve the welfare of the rural population.1

 

1See Workshop on the Development Strategies for
Small Farmers, National Agricultural Economics Research
Institutes, Ministry of Agriculture and Fisheries, Republic
of Korea, 1974.

13

The development of small scale farms is also con-
sistent with the food policy in Indonesia, i.e., increasing
per capita food consumption to improve nutritional standards,
especially among the poorest groups of the society, and
improvement of productivity of small farms in order to
increase their incomes. At present, the new technology for
increasing food production is chiefly offered through
"BIMAS" (Mass guidance for increasing food production)
for lowland rice. Less attention is given to the develop-
ment of upland crops. Programs aimed at investigating
upland cropping patterns are rarely applied in the outer
islands. But in Lampung this cropping system research has
been conducted by the two separate agencies: The "Tani
Makmur Project" and the Central Research Institute for
Agriculture (CRIA), which are operating independently. The
Central Research Institute for Agriculture received tech-
nical assistance from the International Rice Research Insti-
tute (IRRI) and the Tank Makmur Project received technical
assistance through Japanese bilateral foreign assistance
and guidance from the Directorate General of Food Crops in
the Ministry of Agriculture. In general, CRIA cropping
system experiments feature higher level of cropping inten-
sity than the Tani Makmur Project system, but the CRIA sys-
tem has not yet been extensively tested in the farmer's
fields, as has the Tani Makmur system. The Tani Makmur
Project also enhances the farm organizations' ability to

cope with the introduction of the new technology,

14

especially by farmer's training, distribution of agricul-
tural inputs, and introduction of revolving funds. For

the purpose of this study the Tani Makmur Cropping system
experiment output is more relevant than the CRIA as a source
of data, since we are focusing on an analysis of the

farmers' cropping activities.

1.3 Objectives
The primary aim of this study is to analyze the
effects of the new technology on food production, resource
use, productivity, and income of small farmers on the dry
upland farming systems in Lampung. The specific objectives
are:

1. To develop a comprehensive description of the crop-
ping systems presently used on upland farms in the
study area.

2. To identify and analyze the technological poten-
tials presently available to increase food produc-
tion and farm income, by studying the effects of new
technology on cropping patterns, resource use and
productivity, the use of new inputs, and on farm
income.

3. To identify the major constraints facing small
farmers in increasing food production and their
income.

4. To derive the normative supply functions of major

food crops, i.e., rice, corn, and cassava; and to

15

estimate their price elasticities of supply of

these major crOps.

5. To derive from the result of the study some possible
implications for development policies and strategies
for increasing food production and income of small
upland farmers in Lampung.

To achieve the above objectives various analytical
tools are available: (1) linear programming in its multi-
faceted forms; (2) budgeting; (3) aggregate time series data
analysis; (4) marginal analysis; and (5) simulation. These
techniques are not necessarily exclusive, but each can be
combined with one or more of the others. In general, the

choice of analytical technique depends, most importantly,

upon the availability of data and the purpose of the study.

1.4 The Research Approach

Previous studies in Indonesia were conducted by
McCarl (1978) who used a combination of linear programming
and budgeting in studying the prospects of food production
in Indonesia employing secondary data. Dibyo Prabowo (1977)
conducted research in the Solo river basin (Central Java)
using linear programming approach to determine the agri-
cultural production activities to which farmers should
allocate their scarce resources of land, labor, capital,
and water so as to maximize net returns to the limited
resources. He also determined how resource use and pro-

duction are affected when certain constraints are relaxed.

16

The study shows that the irrigated farm has the highest
potential for increasing farmers' income and unirrigated
farm has the lowest potential for increasing small farm
income. -

These linear programming studies have been carried
out in Lampung. Sultoni Arifin (1978) examined the effect
of village labour resource on the feasibility of new crop-
ping system in Central Lampung, by the use of linear pro-
gramming approach. Rusdian Lubis (1978) examined the impact
of cropping system on production, employment and income in
resource management of upland areas in Central Lampung.
Nurzaman Bachtiar (1978), examined the cash liquidity in
adoption of the new cropping system in Central Lampung by
using linear programming approach. All of these studies
that have been conducted in Central Lampung used data from
multiple cropping experimental plots of 1,000 square meters
in one village (Bandarjaya) conducted by Central Research
Institute for Agriculture in 1976/77. There is no clear
way of how to adjust the data example of yields from the
experimental plots to the real farm conditions (physically,
economically, institutionally, etc.) on the small farm in
Lampung. Wardhani (1976) used linear programming approach
to examine various constraints for food production in the
settlement areas of Lampung with the use of secondary data
from various government agencies which most frequently are
unreliable in develOping countries, so that the results

of the analysis may be questionable.

17

Linear programming approach as an analytical tool
has proved to be useful in the analysis of the small
farmer's behavior in Indonesia as indicated by these
studies. Other approaches could have been followed.
Linear programming was selected because standardized tech-
nique have been developed and it is to somewhat easier to
apply than certain other methods such as simulation. The
major problem has been data sources and quality. In this
study, primary data were collected through survey methods
in the areas where new technology has been introduced by
"BIMAS" and/or "TANI MAKMUR PROJECT" to the small upland
farms in Lampung province. The data provided estimates
of relevant parameters under new and traditional technolo-

gies on the representative operating farms.

1.3.1 The Analytical Framework

To achieve the above objectives, the linear program-
ming tool was chosen. Linear programming is highly suitable
for analyzing farm adjustment problems and estimating supply
functions in the approach of previous environment where no
time series data exist. A static linear programming model
of the "representative farm" in the study area will be used
to obtain the optimum farm plan under traditional tech-
nology and existing resource constraints as a base for com-
parison. And then new activities, constraints, production
coefficients reflecting the use of the new technology with

modern inputs under farmers' conditions were introduced

18

into the model to find the optimal plan with new tech-
nology. A comparative analysis of these farm plans was
used to indicate the direct change in farm income, resource
productivity, and cropping patterns that could result from
full introduction of the new technology into the existing
farming system.

Static, normative supply curves of the major food
crops under both technologies were derived by means of
variable price (parametric) programming. Regression analy-
sis was used to estimate the price elasticities of supply
of the major food crops.

It has been recognized that production and con—
sumption considerations are both important for small farm-
firms, so an attempt has to be made at integrating the two
decisions into a single methodological framework. Endo-
genous determination of consumption activities in linear
programming allows the staple food to be grown for home
consumption or for sale in the market. For example, the
minimum level of rice consumption must be supplied from
production or purchases. The use of linear programming as
a computational tool in farm planning exercises is based on
the hypothesis that peasant farmers tend to behave in ways
which optimize their objective(s) given the constraints
within which they operate.

Risk factors are an important consideration in
smallholder decision making. Therefore some method of

incorporating risk factors into the linear programming

 

19

framework is needed. A number of approaches have been
developed to take into account risk factors in linear pro-
gramming models (McCarl, 1978; Andrew, 1976; Kennedy and
Francisco, 1974), but there is yet no clear guidance for
choosing the best method. Also, data such as time series
on yield, prices, and‘production costs, needed to measure
income variability may not be available. These data are
required for the application of Quadratic Programming tech-
niques to peasant farmer behavior under uncertainty. It is
likely that peasant farmers are concerned about achieving

a minimum level of production with certainty rather than
minimizing income variance (Sow, 1974). In this study risk
factors will only be incorporated in the analysis as con-
sumption constraints for the major food crops.

The general procedure to achieve the objectives
involves (Low, 1974): (1) survey the specific areas in the
three major development regions of Lampung using sample
farm data to define representative farm resource situations
and various alternative production possibilities in each
region; (11) construct and structure the framework for the
linear programming model by determining the technical coef-
ficients, the operational constraints, activities or pro-
cesses based upon the sample data, (iii) program the repre-
sentative farm in order to carry out objectives 2, 3; and
(iv) derive policy implications from the results of the
analysis for development policies and strategies to increase

farm income and food production.

 

20

1.3.2 Some Advantages and
Limitations of Linear
Programming Approach

The main advantages of the linear programming tech-
nique is that it can encompass in the analysis several com-
modities as farm activities, seasonal labor, land constraints,
more than one production technique, land-capital substitu-
tion, and choice among several farm activities which are
subject to different economic, resource and behavioral con-
straints (Mudahar, 1974). Thus linear programming can be
used to provide a more adequate analytical description of
the whole-farm situation than other commonly used compu-
tational techniques for farm planning. Another advantage
of the linear programming technique is that it allows the
determination of certain important economic measures of
optimal plan. For example, it is possible to say how stable
the optimal solution is, measured in terms of the changes
in the net revenue of the enterprise needed to bring about
a change in the level of activities in the optimal solution.
The productivity of each farm resource can be assessed and
the importance of the various planning constraints can be
evaluated.

A limitation of linear programming is that some of
its assumptions may be unrealistic. For example, tradi-
tionally in linear programming we assume that there is no
farmer's enterprise preferences, they have perfect knowledge
of various alternatives, there is no scale of economies in

the processes, and risk and uncertainty do not enter

 

21

decision making. We also assume that farmers adjust to the
optimal solution instantaneously, i.e., without a lag.
Upton has discussed and provided an excellent summary of
some of the methodological problems that comprise the most
important limitations to the application of the linear pro-

gramming method to subsistence farms (Upton, 1974).

1.3.3 Sources of Data

For developing a farm plan by linear programming,
data are typically needed on the production alternatives on
the farm, the technical coefficients of production, prices
of inputs and outputs at the farm level, and resources that
are available on the farm. Data can be obtained from
secondary or primary sources. Consideration of the rele-
vance and reliability of the data on one hand, and time and
funds/resources available on the other hand will determine
the balance between the two sources. The use of secondary
data sources are cheap, but the data available may not pro-
vide reliable estimates of the parameters of the population
being studied. In developing countries it is common that
data from secondary sources are frequently unreliable.
Official sources may not have the data needed or the avail-
able data may be inaccurate. So the collection of the
primary data from the field is the common need for social
science research in developing countries. The empirical

analysis in this study will draw mostly from primary data

 

22

sources, by interviewing a number of sample farmers in the
study area.
1.3.4 Sampling Method and
‘ the Sample Size
Sampling Method

Lampung province is administratively divided into
three Kabupatens (equivalent to the county level), i.e.,
South, Central, and North Lampung. Each Kabupaten is then
subdivided into many Kecamatans (equivalent to the township
level). South Lampung consists of twenty-four Kecamatans,
Central Lampung with twenty-three Kecamatans, and North
Lampung with twenty-four Kecamatans. Each Kecamatan again
is divided into many villages.

In terms of population density South Lampung is the
most populated area (150-200 persons per square kilometer),
next is Central Lampung (100-150 persons per square kilom-
eter), and the least populated is North Lampung with less
than fifty persons per square kilometer. The administrative
regions are overlapped with the Development Regions as
mentioned earlier. We believe that variation in population
density is closely related to the length of time the area
has been settled, and may be due to the variation in natural
conditions, and infrastructure facilities, and consequently
to the variation in cropping system and the kinds and the
number of crops grown in each region. The Japanese Survey

Mission for Agricultural Development in Lampung (1972)

 

 

 

23

concluded that each of the three Kabupatens has its own
characteristics. In terms of the cropping ratio of food
crops and cash crops (perennial), South Lampung tends to
cash crops rather than food crops, whereas Central Lampung
jpoints mostly to food crop production and North Lampung
standing midway between the two areas. Production of the
rnajor food crops by subregion (Kecamatan) in each region
(Kabupaten) can be seen in Appendix B.

These heterogenous conditions of the areas in
llampung province in terms of population densities, length
(Df settlement, cropping patterns and number of crops grown
Sled us to the use of a two stage sampling method. The first
fiatage involves purposive sampling up to the village level
Eind.the second stage involves simple random sampling at the
\Iillage level (Friedrich, 1977). By using stratification
13y region then every region (Kabupaten) was represented
.in.this study.

After two days of observations with extension per-
Esonnel and local official of the three Kabupatens and after
.in-depth discussion we came to the following conclusion.
VVe should select village by the purposive method to be
irepresentative of Lampung province as follows: South region
(of Lampung represented by Pugung Raharja village; Central
region of Lampung represented by Adiluih and Bulusari vil-

lages; North region of Lampung represented by Tatakarya
village. Since Central Lampung is more diversified in terms

of crop and cropping pattern, two villages were selected

 

 

 

 

24

to represent this region. The selection of these four vil-
lages was made after discussion with each village head and
with "Mantri Pertanian Kecamatan" (extension worker at the
township level). The villages that have been selected were
judged to be typical of the area they represented in
important attributes which influence the cropping patterns.
From each Village selected, random sampling was used to

select the representative farmers.

Sample Size

The sampling frame was drawn from the list of all
:Earm cultivators in each village which is available in the
trillage office.

Statistical theory can help us determine the sample
Esize for a particular survey in a universe provided we
Specify the variance of the variable and the degree of
éaccuracy of the estimates we want to derive (Yang, 1965).
fPhis system will work well for problems where only one
\Jariable is handled. However in this study we dealt with
aseveral important variables such as labor utilization for
(different enterprises, prices received, wage rates of
laired labor, etc. These make it impossible to apply formal
statistical procedure and to achieve statistical represen-
‘tativeness of the sample.

A statistical formula was not used in this study
to arrive at the number of farmers to be selected for

interview. Instead, budget and time, and the availability

 

 

25

of the interviewer candidates, and experience of the
researcher determined the size of our sample. The primary
sampling unit was the family farm household representing
Iboth the production unit as well as a consumption unit.

'The sampling frame was the whole family farm cultivators in
'the selected village, which can be found in the village
:record. From the list of cultivators in each village a
:random quota sample of twenty-three farm households were
(drawn. Yang (1965) concluded that roughly twenty farms
sflnould be included in each class (stratum) in order to make
a: reliable comparison. Friedrich (1977) suggested twenty
tx: twenty-five observations being necessary to produce reli-
Eflole estimates for each group. The reliability of esti-
Inates however, very much depends on the actual variability
<>f the population. This then made up of the total sample
(Bf ninety-two farm households from the four villages that
Vvere selected. For a random selection in each village,
tihe researcher asked for the help of the village head after
linstructions were given by the researcher. This was done
1:0 hopefully reduce the suspicions from the village com-
hnunity during the survey period. Then the village head
:introduced the interviewers to the selected respondent farm

Ilouseholds and briefly explained the purpose of the survey.

1Definition of household or farm family, those
people eating from one pot (Norman, 1973). They also work
together as a production unit.

26

1.3.5 Data Collection

The survey schedules (questionnaires) were designed
and developed for the purpose of collecting the needed data.
[Data for only one year cropping cycle of 1978/1979 were
<:ollected. Four agricultural university graduates helped
‘to conduct the interviews during the period of the survey
:from August 1979 through December 1979. They are Extension
Sipecialists (government employees) who are stationed in the
aareas of study and are familiar with the local farm con—
ditions.1 But on the other hand, there is a problem of
ciual allegiance (dual loyalty one as extension worker/
snovernment employee and the other as an interviewer), which
nuakes supervision or control difficult, or if not is impos-
Stible (Friedrich, 1977). During the first month data were
Cxollected on the previous years cropping cycle farm opera-
tLion (September 1978-August 1979). Again with the same
iJnterview procedure, the interview was repeated in September
Elind October after the good "rapport"2 between the inter-

\?iewer and the respondent farmer had been established. To

iJmprove data reliability, then every month from September

\

1For detailed procedures in selection of the inter-
‘7iewer candidates see: Frank Lynch, S. J. (1976), Field Data
(Zollection in Developing Countries: Experiences in Asia,
E3eminar Report No. 4, June 1976, Agricultural Development
(Bouncil, Inc., New York.

2"Rapport" could be described as genuine respect
and understanding of both interviewer and the farmer, which
to a large extent determines the quality of communication
and the accuracy of response.

27

through December 1979, data were collected on family farm
activities, off-farm activities, and consumption of the
major food crops. This allowed for relatively short recall
on the part of the respondent. More emphasis was given to
the collection of data on labor use and family labor allo-
cation during the peak demand for labor (September-
December). The "periodic-visit survey" method was used,
which represent the compromise between "single- and multi-
visit" surveys, in collecting information on a few well-
timed rounds organized around the completion of crucial
phases, but also require a time period of a complete pro-
duction cycle, usually one year (Friedrich, 1977). So the
interview was based on monthly recall by the representative
farmers. Each interview lasted from one hour to one-and-a-
half hours for each respondent. Each interviewer was
assigned one village in each region with which he is
familiar.

The problems encountered during the data collection
throughout the survey period are similar to those discussed
by Spencer (1972) and Norman (1973).

1.3.6 Construction of the
Representative Farms

The ideal approach is to program every farm unit,
but cost of programming may be prohibitive, and it is not
a practical approach. This led to the use of the repre-
sentative farm, the representative of farm situation as the

unit of linear programming analysis. In areas where there

28

is a reasonable homogeneity with respect to major resources
particularly natural resources, such as soil type, topog-
raphy and climate, linear programming can be used to obtain
the solution toja "representative farm" situation in order
to guide planning for individual farms.

The usefulness of the representative farm approach
is limited by the manner by which the representative farm
is constructed. Collinson (1974) has discussed three
alternative techniques for deriving representative farms.
These are:

(a) the identification of a particular farm as the
typical farm,

(b) the use of an "average farm" (derived from an
average resource, input-output, and net price coef-
ficients of a sample farm) as a representative
farm

(c) a "hypothetical" or synthesis of composite farm
from different components of the population.

It is not easy to find a single farm that could be
validly considered typical in all respects. It requires
consideration of a wide range of criteria. The selection
and the construction of the criteria are difficult tasks
and data for this purpose may not be available nor easy to
collect.

The use of "average farm" as the representative
farm, brings with it the aggregation bias. Miller (1966),

Buckwell (1972) and Carter (1963) have discussed the

29

aggregation bias inherent in the average farm approach.
Aggregation bias exists when the sum of the solution from
the individual farms in the set does not equal the estimate
obtained by the optimum solution to the entire set directly.
While the synthesis of composite farms reduces the aggrega-
tion bias, it has the practical weakness in that it is diffi-
cult to identify several institutional variables and human
factors and their distribution within the population.

These nontypical variables involve institutional constraints,
motivations, preferences, managerial ability, etc. which
have an important impact on farm organization, production
efficiency, and earnings (Plaxico and Tweeten, 1962). The
choice of the method for construction of the representative
farm depends on the purpose for which the result of the
study is to be used. In this study the objective is to
identify the constraints and farm adjustments and to esti-
mate the degree of farmers response to changes in prices of
major food crops in a given area, so the use of the "average
farm" as representative farm can be justified. The repre-
sentative farm is based on data obtained from the survey.
The farms in the sample were considered sufficiently similar
with respect to the key variables that affect adjustment.

In the study area all the sample farmers were trans-
migrants from Java, and they were allocated land of about
two hectares per household. Although there is a tendency
toward land ownership concentration in the area of longer

settlement, no one was found without land. Thus the level

30

of initial land resources for each individual farmer was

rather homogeneous.

1.5 Organization of the Study

This chapter was devoted to the problem definition
and its setting, the statement of the objectives and methodo-
logical approach for achieving these objectives. Chapter II
will present the description of the farming system in the
study area as revealed from the data obtained from the
survey. The programming model containing activities,
constraints, technical coefficients and prices will be
discussed in Chapter III. In Chapter IV the results of
the various applications of the models will be reported.
The derived impact of the new technology on farm income,
cropping patterns, resource use and its productivity will
be discussed. In Chapter V normative supply schedules for
major food crops (rice, corn, and cassava) both under tra-
ditional and new technology will be presented. The response
of the major food crops to changes in their prices will be
examined. The effect of the new technology on the price
elasticity of supply for major food crops will be dis-
cussed. Chapter VI contains the summary along with a dis-
cussion of the policy implications for development strategy
from the result of the analysis, limitations and suggestions

for further study.

CHAPTER II

DESCRIPTION OF THE FARMING SYSTEM IN

THE STUDY AREA

A good knowledge of the structure of the farming
system in the area under study is required for proper repre-
sentation of the agricultural conditions in a linear pro-
gramming framework. This chapter will try to describe some
important attributes of the farming system in the study
area as revealed mostly by the study conducted by the author
from July through December 1979. The description is pre-
sented in terms of the characteristics of the representa-
tive farms. The description of the farming system in Central
Lampung will be supplemented with the results of the Base
Line Survey conducted by Central Research Institutes for
Agriculture (Djauhari, Aman, 1977).

2.1 Physical Characteristics of the
Study Area

There are about 46 million of hectares of red yellow
podsolic soil out of a total of 200 million hectares of
land in Indonesia. About 1.5 million hectares of this red-
yellow podzolic soil is in Lampung. It is estimated that
about 500,000 hectares of this land is covered with

31

32

alang-alang grass (Imperata cylindrica) as the result of
the shifting cultivation in the past.
2.1.1 Soil and Climate of the

Research Area

The soil in the research area is a podzolic plain
and generally flat, varying from red-yellow through brown
in color. In general, this $011 is very porous and very
low in fertility. Under these conditions and due to cutting
of the forest and lack of agricultural land conservation,
alang-alang grass (Imperata cylindrica) is the dominant
vegetation. The soil has a low pH, is low in nitrogen,
phosphorus and potassium content (McIntosh and Suryatna,
1976). The productive capacity of these soils has been
seriously questioned by many agricultural scientists. In
fact these areas have been described as alang-alang infested
waste lands due to production constraints such as low
inherent soil fertility, excessive drainage and low pH.
But on the positive side there are several assets. The
rainfall and its distribution are very good for a year
around upland crop production. The average monthly rain-
fall during the period of 1969-1975 is shown in Figure 2.1.
The driest month is in July and wettest month is in March.
The rainfall is well distributed and based on this data,
the Lampung region falls within Type DZ in the agro-
climate map. Rainfall exceeds 200 mm (80 inches) for six

months and 100 mm for three months. The remaining three

33

Rainfall (mm Mm;
360

 

 

200

I00

 

 

Fig.’2,1. Monthly rainfall pattern in Bandarjaya, Central Lampung,
Indonesia, 1970-76.

Source: CRIA, Bogor, Indonesia, 1977.

34

months are drier but average rainfall is only a little less
than 100 mm. Since the soils are well drained, the heavy
rains during the rainy season do not inhibit upland crops
production, and a year around cropping is possible. Run
off problems are minimized by the rapid infiltration of

the rain water. Consequently, the soils are acid and
leached. But fortunately the soils do not contain exces-
sive levels of aluminum. There is sufficient clay and
organic matter to hold applied nutrients but fixation of
phosphorus is not a problem. So, these soils can be highly
productive if managed properly (Central Research Institute

for Agriculture, 1977).

2.2 Land Use
Most of the transmigrants received two hectares of
land. But they could only cultivate less than a hectare by
depending on the family as a source of labor. Most of the
farmers have only subsistence farming in the upland areas
using multiple cropping with the major crops of upland rice,
corn, and cassava or sometimes with legumes.
The research area can be divided into two cate-
gories based on the length of settlement. These are:
Category I - Old alang-alang areas (Pugung Raharja,
Adiluih, and Bulusari Villages)
Category II - Newly Opened areas (Tatakarya Village

which was settled about six years ago).

35

Usually the farmers start land preparation in the beginning
of the rainy season (in September) and planting in October
and November each year with rice, corn, and cassava in a

mixed cropping System.

2.3 Crop Productions and Cropping Patterns

There were nine crops grown in the study area
during the survey period. These crops included cereals,
root crops, and legumes, and perennial crops. They are
grown either as a sole or as a mixture, but are mostly grown
as a mixture (multiple cropping or intercropping). Tech-
nical reasons for mixed cropping are: complementary relation-
ships derived by the crops, soil protection and reduction
in the incidence of disease and pest attacks. The socio-
economic reasons included the maximization of returns by
more efficient uses of resources and higher total output
and the need for security by more frequent harvest. The
crop combinations and the number of farmers growing each
combination are presented in Table 2.1.

The table shows that the number of farmers that
practiced the mixed cropping system accounted for 60 per-
cent, 70 percent, 98 percent, and 48 percent for Adiluih,
Bulusari, Pugung Raharja, and Tatakarya villages respec-
tively. The newly Opened areas, Tatakarya had the lowest
number of farmers with multiple cropping system in their
farming operation. Perhaps they are not yet fully adjusted

to the local farming conditions.

36

Table 2.l.--Number and Percentage of Farmers by Cropping
Patterns of the Representative Farms in Lampung.

 

 

 

 

Areas/Villages
Cropping Patterns* Adiluih Bulusari Eggggga Tatakarya
No. % No. % No. % No. %
l. C + ULR / CV 4 20 8 38 - - 16 69
2. ULR / CV 2 10 10 47 - - 6 26
3. ULR 4 20 2 9 - - - -
4. CV 5 25 10 5 - - 20 87
5. LLR 8 40 1 - - - l 4
6. ULR - SB - - - - 13 65 l 4
7. C + ULR - SB 5 25 - - 2 10 - -
8. C + SB / CV - - - - 8 40 - -
9. C + ULR/CV - - - - 5 25 - -
10. SB-SB-SB/CV - - - - 6 30 - -
11. C/COC 4 20 - - - - - -
12. GNDT - - - - - - 4 l7
l3. PEP - - - - l 5 2 8
14. C / CV - - - - - - 2 8
15. ULR + SB/COC 1 5 - - - - - -
16. COF + COC l 5 2 9 - - - -
l7. CLO + COC 3 15 - — - - - -
18. COC/FC 2 10 l 5 5 25 - —
19. COF/FC 3 25 3 l3 - - - -
20. PEP + CLO - - - - 5 25 - -
21. CLO/FC - - - - 3 15 - -

 

Source: Field Survey.

*+ Two crops planted together at the same time or
the second crop planted within a week after the first crOp.

/ The second crop planted a month later after the
first crop.

- The second crop planted after the first crop
harvested.

/ The last crop planted between border or fencing
or between the existing perennial crop.

For explanation of abbreviations, see Table Cl,
Appendix C.

37

Table 2.2 shows the number of hectares devoted to
different crop enterprises by the representative farmers in
the study area during the survey period. Mixed cropping
accOunted for 61 percent, 70 percent, 77 percent, and 68
percent for Adiluih, Bulusari, Pugung Raharja, and
Tatakarya villages respectively. Both in terms of the
number of farmers and the total area, multiple cropping or
mixed cropping is dominant cropping pattern in the study
area. It can be also expected that the longer the area
is settled, the more land will be devoted to perennial
crOps such as clove, coconut, coffee, and pepper. Such
crops occupy very little cultivated land on farms in
Tatakarya, the newly settled area, and average 0.2 ha or 13
percent for Bulusari, 0.21 ha or 14 percent for Adiluih,
0.35 ha or 24 percent for Pugung Raharja villages. Area
devoted to food production decreases as the longer the area
has been settled. This can be explained as follows: First,
the deterioration of soil fertility, which more and more
suitable for perennial crops rather than for food crop pro-
duction. Second, land ownership concentration increases
as the longer the area settled, so perennial crops again is
the answer to cope with labor shortage for these larger
farms, since perennial crops require less labor both in the
total labor per hectare and seasonality in labor use. Third,
that relative price between food crops and perennial crops
deteriorated, may be due to the effect of government policy

and external effect of the increasing demand for coffee,

38

Table 2.2.--Average Area Devoted to Each Cropping Pattern
by the Representative Farms in Lampung.

 

 

 

 

Areas/Villages

Cropping Patterns* Adiluih Bulusari Eggggga Tatakarya
(ha) (ha) (ha) (ha)
1. C + ULR f CV 0.28 0.40 - 0.62
2. ULR / CV 0.18 0.31 - 0.19
3. ULR 0.17 0.10 - -
4. CV 0.16 0.27 - 0.37
5. LLR 0.25 0.23 - 0.02
6. ULR - SB - - 0.36 0.03
7. C + ULR - SB - - 0.28 -
8. C + SB f CV 0.11 - - -
9. ULR + C/CV - - 0.20 -
10. SB-SB-SB/CV - - 0.24 -
ll. CV/CO 0.10 - 0.02 —
12. GNDT - - - 0.06
13. PEP - — - -
14. C + CV - - - 0.09
15. ULR + SB/CO 0.02 - - -
l6. COF + CLO 0.02 - — -
l7. CLO + CO - - - -
18. COF/FC 0.10 0.05 - -
l9. CO/FC 0.09 0.04 - -
20. CLO + PEP - - 0.12 -
21. CLO - - 0.23 -
Average Size of Farm 1.48 1.44 1.45 1.38
Average Food Crops 1.27 1.25 1.10 1.38
Average Perennlal 0.21 0.19 0.35 0.00

Crops

 

Source: Field Survey.

*For explanation of the abbreviations, see Table C1,
Appendix C.

39

clove, by international market. Government policies with
respect to food and price stabilization in a low level
(consumer oriented policies) for the price of food crops,
e.g., rice, corn. There is no such effort for perennial
crops which is promoted for export commodities. The

likely impact to the farmer's decision in the longer run is
to devote more land for perennial crop production andlless
for food crops. This trend is unfavorable or contradicted
with the self-sufficiency food program at the national level.
The transmigration area is expected at least to become self-
sufficiency in food, and some food surplus to be supplied

to the city, e.g., Jakarta. This hope may not be ful-
filled if this trend continued. It seems there is a
conflict between food production self-sufficiency program

at the national level and in the micro level or farm level

in the transmigration area in Lampung.

2.4 Technology of Agricultural Production
Two kinds of agricultural production technology

found in the study area, the traditional and the new tech-
nologies. The traditional technology are two types, one

is the indigenous traditional technology with shifting cul-
tivation or slash and burn is still exists mostly practiced
by the indigeneous population (Lampungnese) and the second
type is locally develOped by the transmigrants in their
settlement areas, e.g., mixed cropping and/or sole cropping

without use of any new modern inputs. The practice of

40

multiple cropping may be influenced by multiple cropping
system in Java, the origin of the transmigrants. In this
study the traditional technology considered is the one that
locally developed by the transmigrants only, not included
the shifting cultivation. New technology is use of new
inputs and/or new techniques such as the use of fertilizers,
insecticides, improved seeds, tractor for land preparation
to the traditional farming. The traditional food crop pro-
duction technology currently being replaced partly by the
new technology through the extension activities of BIMAS

and TANI MAKMUR PROJECT, which introduce the use of improved
seeds, fertilizers, insecticides/pesticides to the existing
locally traditional cropping systems. Seeds use are mostly
local varieties for rice, cassava, soybean. Corn seeds used
were improved varieties (such as Metro, DMR). Fertilizers
input is considered to be the main modern input of the new
technology. On the average about forty kilograms of urea
and twenty kilograms of phosphate fertilizers and two liters
of insecticides were used per hectare by the representative
farm in the study area, much less than recommended level at
200 kilograms of urea and phosphate fertilizers. Land prep-
aration starting in August/September up to November]
December. Land preparation by manual labor and some with
the use of animal power both for traditional and new tech-
nologies are existing in the study area. Use of tractors
and other farm machineries are absent in the small upland

farm operations.

41

The large scale highly mechanized farms do make
extensive use of machinery and other modern inputs such as
improved seeds, fertilizers, insecticides, herbicides, and
better management including better products, processing and
marketing. Examples of such farms are P.T. Mitsugoro, P.T.
Daya Itoh, P.T. Pago and other similar estate farms in
Lampung, both foreign and domestic enterprises. Food crops
grown including upland rice, corn or maize, and cassava as
a single crops or pure stands. The yields were more than
double that obtained on local traditional farm. However
these large scale highly mechanized farms were not included
in this study because of the particular objectives as has
been mentioned before was emphasized on small upland farms.

2.5 Some Demographic Characteristics of the

Representative Family Farms in the
Study Area

In the following table shows the main demographic
characteristics of the representative family farms popula-
tion in the study area. Sex ratios in all villages are above
one, that is the proportion of males is more than 50 percent.
The sex ratios of the active populations are below 60 per-
cent and vary between 53.8 percent to 59.4 percent. The
dependence ratio1 is the highest in the newly settled area,

i.e., Tatakarya Village with a 0.87 ratio. This can be

 

1Dependent ratio is the ratio between the number of
dependent children plus the number of dependent aged and the
number of active population. See Table 2.3.

42

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43

understandable since most of the family heads are rela-
tively young with more dependent children. This ratio is
50 percent for the Tatakarya Village. The number of
dependent aged (over 65 years old) is low in all the vil-
lages. The average family size in the study area is 5.7
persons. The highest size of the family is in the newly
settled area, i.e., six persons for Tatakarya, 5.9 persons
for Bulusari, 5.6 persons for Pugung Raharja, and 5.3
persons for Adiluih.
2.6 Educational Level of Representative
Farm Household's Head

The following table shows the educational level of
the representative farm household's head for each village.
On the average it is very low at 3.3 years varying from 0
to 6 years. This is near to the illiterate level. They
can hardly keep records on their farming operations.

Table 2.4.--Average Representative Farm Household's Head
Educational Level.

 

 

Village Aviéige Efiucati°n $231136
(years) years) Size
1. Tatakarya 41.6 3.2 6.0
2. Bulusari 43.4 3.6 5.9
3. Pugung Raharja 45.3 3.0 5.6
4. Adiluih 48.0 3.4 5.3
Overall Average 46.1 3.3 5.7

 

Source: Field Survey

44

A. Raji Ahmed (1972) in his research on the size
of cattle feeding operations has concluded that the suc-
cess of the optimum size unit is a function of quality of
the managerial inputs, the managerial behavior of which is
characterized by willingness to accept a higher degree of
risk, appreciation for and application of scientific criteria
in decision making, and higher levels of formal education.

The low level of agricultural productivity in this
transmigration area may be related also to the high risk
factors in adapting the new profitable technology. So, it
is not just enough to provide them with more profitable
new production techniques, but also measures to reduce risk
to an acceptable level are required. For more detailed dis-
cussion of these problems, see Ruttan and Hayami (1971)
and comment by George L. Beckford. Supply of goods and
services and provision of better institutions and better
Opportunities may not be sufficient to achieve the farm
income goals although they are necessary conditions for the
development of the small farmers. The managerial skills of
the small farmers should be improved for small farmer devel—

opment.

2.7 Farm Labor Force
The family is the major source of the farm labor
force. The representative farm in the study area on the
average consists of 5.7 persons. A detailed composition of

the labor force of the representative family farms can be

45

seen in Table 2.5 which is recorded at the beginning of

the survey. The available labor on the representative
family farm is 2.75 adult male equivalents. The avail-
ability of family labor is determined by many factors such
as the family structure (age and sex), health, nutrition,
major occupation of each member of the family, customs, and
job opportunities outside the farm. It is estimated that
about 240-260 days of farm work per year for adult male
workers and only about 125 days of farm work for women

are available for the representative family.

As one would anticipate in a society where landless
does not exist, most work is undertaken on the farm by the
family itself. In Pugung Raharja Village the total labor
inputs by the representative farm is 315 mandays equivalent
per year, of which about 92 percent come from the family
sources and only about 8 percent from hired daily labor.
This labor hiring activity is small, but it may be important
due to their use in the critical season such as for land
preparation, weeding, and harvesting. Monthly labor inputs
and wage rates are presented in Tables 2.6 through 2.9.
Exchange labor is common in the study area ranging from
7 to 20 percent of the total labor input on the farm. The
highest percentage of exchange labor is found in the rela—
tively new settled area, i.e., Tatakarya and becomes less
and less as the area becomes longer settled. Exchange
labor tends to switch to hired labor system, especially

when cash crops and perennial crops become more important

46

Table 2.5.--Representative Farm Family Composition by Age
Groups in Lampung.

 

Average Number of

Persons in Age

 

 

Groups (years) Average Work
Villa e Family Force
9 Size Available
Less Over (person) (man—day)*
Than 8-14 15-64
64
8
l. Tatakarya 1.70 1.20 3.10 - 6 0 2.50
2. Bulusari 1.50 1.10 3.30 - 5 9 2.80
3' Pugung 1.00 1.56 3.05 0.09 5.7 2.90
Raharja
4. Adiluih 1.00 1.10 3.15 0.05 5.3 2.78
Average 1.30 1.24 3.15 0.03 5.7 2.75

 

Source: Field Survey

*Marwoto (1975) has calculated the available labor
force for a family in Lampung area with unit labor as

follows:

Adult Male (Father)

Adult Female (Mother)

Children less than 8 years :

Children 8-14 years old

Children over 14 years Old

Other Adult

DJO

O

labor unit

(if all children under
12 years old)

(some children over 5
years old)

(if all children over
12 years old)

(if not attending
school)

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47

 

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48

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49

 

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50

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51

in the areas. Exchange labor is mostly evident during the
planting and weeding seasons. There is no clear social
arrangement on how to use and repay exchange (gotong royong)
labOr, but everybody understands and agrees that the farmer
or members of his family can work as repayment. There is
no standard time within which to repay borrowed labor, but
on the average the time span for repayment is within two
weeks (Sultoni Arifin, 1978). In this study exchange labor
will be treated as family labor and should be repaid in
labor within a month.

Hired labor generally is obtained simply on the
daily hiring basis. The average wage rates for hired labor
in the study area ranged from Rp500 to Rp600 per day, which
included two meals, breakfast and lunch. These meals
accounted for Rp 50 and Rp 150 respectively. There is not
much variation of wage rates between seasons, except for
rice harvesting with the "bawon system."

The bawon or "harvest share" is common in the study
area for rice harvesting. In the traditional bawon system,
rice harvesting takes the form of a community activity in
which all or most of the community members can participate
and receive a certain share of output. The owner normally
gives a harvest share to the harvesters of one-sixth to one-
fifth of what they harvested in Lampung area. This is
called "bawon." So, the amount of bawon received depended
upon the amount harvested, which in turn related to age and

sex. The value of bawon in money terms was between Rp 1000

52

and Rp 1300 per day in 1979 in Lampung for adult males,
which was more than double the average daily wage rates at
Rp 500. That is why workers prefer joining the rice har—
vest rather than other farm activities. Hiring labor with
bawon system for rice harvesting is also as a social device
to redistribute output or income to the less beneficial
groups in the village society. For more detailed discus-
sion on the bawon system and recent change in this system
see Collier gt El- (1973) and Sturgess and Wijaya Hesti
(1979).

The family labor is the most important in the rela-
tively new settled area, where cash flow is also the lowest,
i.e., Tatakarya. The higher percentage of hired labor is in
the older settled area with 18 percent for Adiluih, 14 per-
cent for Bulusari, and only 9 percent for Tatakarya Village.
In Pugung Raharja Village, the oldest settlement, hired
labor is only 8.6 percent. This low percentage of hiring
labor may be due to differences in crop production emphasis
and cropping system. In Pugung Raharja soybeans and corn
are the major crops rather than rice as in the other vil-
lages. So the labor distribution is more even in Pugung
Raharja Village during the year, because weeding and har-
vesting is less critical seasons for labor demand for both
crops. It is also true that land devoted to food crop is
the smallest at 1.10 ha in Pugung Raharja from 1.45 hectare

area under cultivation (see Table 2.2). But there is no

53

landless in this area. Cropping intensity may also be
higher in this area.

In this study it is sufficient to measure labor
use in mandays.; The assumption in measuring manday units
is that the length of the working day is in part dependent
on the cultural pattern of the society. It is about eight
hours per day in Lampung area, starting early in the morning
at 7 o'clock up to 12 o'clock, then resting for two hours,
and then starting again at 2 o'clock up to 5 or 6 o'clock
in the afternoon.

Another problem in measuring labor inputs is what
weight to use in aggregating man, woman, and children days
of work. Work may be affected by the age and sex of the
worker as well as by the task being performed and the cul-
ture in the society. Norman (1973) has estimated for North
Nigeria the weights to a common denominator for converting
to labor units. His assumption is that initially there is
a positive correlation with age and then a negative corre-
lation with age. He also assumes that the physical produc-
tivity of woman is lower than man. The following table
shows the Norman equivalent to adult male worker.

Marwoto (1975) has calculated the available number
of labor units for settlement areas in Indonesia as is

shown in Table 2.11.

54

Table 2.lO.--Man Equivalents Used in the RERU Farm Manage-
ment Studies.

 

 

Labor Class ; Age gZiEV:?:::S
Small child Less than 7 0.00
Large child 7-14 0.50
Female adult 15-64 0.75
Male adult 15-64 1.00
Female adult 65 or more 0.50
Male adult 65 or more 0.50

 

2.8 Farm Capital

The two main inputs of traditional agriculture are
labor and land. The amount of capital and the proportion
of income invested are very low. Mellor (1967) has, how-
ever, emphasized that saving and investment are functions
of two main factors: (a) the attitude toward saving, invest-
ment, and consumption; (b) the low level of capital forma-
tion in traditional agriculture is not necessarily because
the capacity of saving is low but because of the low rates
of return on investment. Most of the capital is actually
simple tools or improvements produced mainly by labor.
At this low level of technilogy the productivity of addi-
tional capital of this type is very low.

Capital in farming includes items such as machines,
tools, buildings, roads, land improvements, tree crops,

livestock, seeds, fertilizers, etc. These assets are

55

Table 2.11.--Table of Conversion of Various Working Age
and Different Sex for Settlement Areas.

 

 

Labor Class Male Adult Equivalents
Adult male (father) : 1.00 labor unit
Adult female (mother) : 0.30 (if all children under
5 years)

: 0.50 (if some children over
5 years)

1.00 (if all children over

12 years)
Children less than 8 years : 0.00 labor unit
Children 8-14 years : 0.20 labor unit

00
O
0

U"
C

labor unit (if attend-
ing school full time)

Children over 14 years

Children over 14 years : 1.00 labor unit (if not
attending school)

Other adults : 1.00 labor unit

 

Source: P. T. Agrindo/Harrison Fleming.

56

usually classified into fixed capital (such as machines,
tools, buildings, land improvements, tree crops) with a
productive life more than a production cycle;1 and working
capital or Operating capital (such as seeds, fertilizers,
insecticides/pesticides, hired labor) which are used up in
a single production cycle.

The level of fixed capital in the study area is very
low compared to labor. Use of capital equipment to substi-
tute for labor is very small. Each family farm owns three
hoes, three sickles, one chopping knife, and three "arit"
which is used for weeding and cutting grasses. The total
value is about Rp 7,800 ($12). In fact most of other
capital costs such as in tree crops, land improvement,
livestock are the embodiment of labor provided by the
farmers. Livestock activity is not important in the study
area, restricted only to the backyard poultry.

Operating capital is the value of fertilizer, seed,
insecticides/pesticides, and hired labor used in the pro-
duction process. Use of hired labor and seed do not neces-
sarily mean cash expenditures since most of the labor hired
is paid in kind (bawon system) and seed used is mainly saved
from the farmer's previous harvest. Fertilizer is Rp 80 per
kilogram supplied by the government at subsidized and con-

trolled proces.

 

lProduction cycle in this study can be grouped into:

(1) clearing and preparation of land; (2) sowing, planting,
and fertilizing; (3) cultivation--weeding, and (4) harvest-
ing.

57

Personal saving is the main source of cash for pur-
chase of operating capital. Institutional credit is not
available except for the farmers who joined the "BIMAS"
program. Table 2.12 shows the cash expenditures of the
representative farm on purchased inputs by month. These
are the amounts estimated that have been spent on hired
labor, fertilizers, seeds, insecticides/pesticides, and for
food and drinks for hired and exchange labor during the
1978-1979 production season. The total cash expenditure
for one year production cycle amounted to Rp 30,000, Rp
54,250, and Rp 18,000 for Tatakarya, Buluwari and Adiluih,

and Pugung and Raharja respectively.

58

Table 2.12.-—Cash Expenses by the Representative Farms by
Month in Lampung.

 

Cash Expenses (Rupiah)

 

 

 

Month
Tatakarya Adiluih & Bulusari Pugung Raharja
August 6000 4500 -
September 1000 5000 1500
October 9600 19750 1500
November 325 5000 4500
December 500 4000 2000
January 1000 5000 1500
February 500 2000 3000
March 500 4500 1000
April 7000 500 1000
May — 500 1000
June - 2000 1000
July - 1500 1000
Total 29250 54250 18000
Source: Field Survey

CHAPTER III

THE STRUCTURE OF LINEAR PROGRAMMING MODEL
OF THE REPRESENTATIVE FARMS FOR

THE STUDY AREA

3.1 Introduction

A typical decision faced by management is the
optimum allocation of scarce resources. Management's task
is to achieve the best possible outcome with the given
resources. Linear programming is very useful technique in
aiding management for making these decisions. This new
technique has been accepted by farm management workers in
many countries to solve management problems of individual
farms (Yang, 1965).

Linear programming problems must meet the following
requirements (Lee, 1976).

a. The objective function: Linear programming problems
must have an explicit objective criterion to opti-
mize. The objective function may be one of either
maximization or minimization of the criterion.

b. Limited resources: In order to apply linear pro—
gramming, a decision problem must involve activ-
ities that require consumption of limited resources.

59

60

The amount of limited resources is usually
expressed as constraints for the problem.
c. Linearity and additivity: The primary requirement
of linear programming is the linearity in the
objective function and in the constraints. The
word "linear" implies that relationships among
decision variables must be directly proportional.
d. Divisibility: Linear programming requires complete
divisibility of resources utilized and the units
of the decision variables. In other words, frac-
tional values of decision variables and resources
must be permissible to obtain optimal solution.
e. Deterministic: In linear programming all model
coefficients (e.g., unit profit contribution of
each product and the amount of available resources)
are assumed to be known with certainty. In other
words, linear programming implicitly assumes a
decision problem in a static time period.
Linear programming is usually applied to the complex prob-
lems which involve many interacting variables that con-
tribute to objective criterion functions.

In linear programming, if a problem involves n-
decision variables and m-constraints, the typical linear
programming model can be constructed mathematically, as

either a maximization or a minimization formulation.

where:

61
The maximization problem,

n
Maximize Z = Z Cj Xj

subject to

aij Xj i bi (i = l, 2, . ., m)
Xj 3 0 (j = l, 2, , n)
The minimization problem
n
Minimize A = Z Cj Xj
i=1
subject to
aij Xj 3 bi (i = l, 2, . ., m)
Xj 3 0 (j = l, 2, ., n)

only nonnegative level of each decision variables

will be considered.

Cj = unit contribution rate, the marginal contribu-
tion of each decision variable.

Xj = decision (or activity) variables

aij = technological coefficients, how much a

resource is required for each activity unit

bi = given resources or the amount of resources
available (right hand side value)

62

For a mathematical formulation of linear programming
models in matrix notation, see Heady and Candler (1973).
The assumptions of linear programming include addivity of
resources and activities, linearity of objective function,
nonnegativity of decision variables, divisibilities of
activities and resources, finiteness of the activities and
resource restrictions, proportionality of the activity level
to resources and single value expectation, also discussed
by Agrawal and Heady (1972).

In the following section, the three essential ele-
ments of linear programming, the objective function, the

activities and system constraints, will be discussed.

3.2 The Objective Function

It is likely that small farmers entertain a number
of objectives (e.g., profit or income maximization, output
maximization, cost minimization, security) although they
are not necessarily exclusive, Schultz (1964) and Hopper
(1965) believe that peasant farmers are profit maximizers.
Miller and Charles (1964) argued that, "profit maximization
is not always the primary goal of farm management. Others
demand sure and sometimes substantial returns before they
risk additional operating capital for problems that are
compounded by great variability in production, and a high
level of risk and uncertainty. Under such circumstances
it is difficult to follow rigorous decision-making cri-

teria. As a result, many of these farmers do not follow

63

a management strategy that would maximize their profits

but instead follow a strategy that minimizes costs."

Norman (1973) concluded that for small farmers in Zaira area
in Northern Nigeria, both security and profit maximization
were relevant goals. Heyer (1971) in her study on con-
straints on peasant farmers in Kenya had difficulties in
finding out what it is the subsistence farmers aimed for.
Subsistence farmer's objectives are ambiguous, but then

she suggested, "insuring an adequate food supply during a
drought years, producing preferred variety of diet, maxi-
mizing the number of people fed, maximizing the market value
of output" were possible alternatives to be considered.

The complexities of small farmer objectives make it diffi-
cult to operationalize their decision in a linear pro-
gramming framework.

Risk factors are an important consideration in small
holder decision making. Risk aversion is a rational and
almost universal characteristic of small farmers, particu-
larly with respect to the family food subsistence crops.

A number of recent studies have cited the importance of
risk on the decision making by peasant farmers (Norman,
1973; Dillon and Anderson, 1971; Woglin, 1975). As recent
empirical studies of peasant behavior indicate, "safety
first criteria” tend to be followed whenever the satisfac-
tion of the basic needs may be at stake.

In this study the safety first criterion will be

used in the formulation of the linear programming. Small

64

farmers in the study area are assumed seeking security and
risk aversion (such as provided by the practice of multiple
cropping) as well as maximization of net farm income. The
security objective will be incorporated in the matrix table
as constraints, so that necessary amounts of rice, corn,
cassava for meeting the minimum family consumption levels
will be obtained first. According to this rule, an impor-
tant motivating force for the decision maker in managing
the productive resources that he controls and, in particu-
lar, in choosing among technological Options is the security
of generating returns large enough to cover subsistence
needs (Moscardi and Janvry, 1977). The required amounts of
food crop consumption were estimated from the simple con-
sumption survey conducted by the researcher in the study
area. It was found that rice is the most preferred food
staple, then corn and the last one is cassava. These con-
sumption requirements can be either produced or bought.

Net farm income in this study will be expressed as
"gross margin." Gross margin is defined as the total value
of production less variable costs of production. In other
words, net return to resources such as land and labor which
are considered "fixed" in this study. Fixed cost other

than land and labor are negligible in the study area.

65

3.3 Activities in the Model
There are seven groups of activities which will be
included in the model for the representative farm of
Tatakarya Village:
(a) Crop production activities
(b) Labor hiring activities
(c) Fertilizers, insecticides, food crop buying
activities
(d) Food crop consumption activities
(e) Crop selling activities
(f) Capital borrowing activities

(g) Transfer activities

(a) Crop Production Activities

The crop production activities for the representa-
tive farm are outlined in Table 3.1, Column Al through A14.
They consist of four food crops: upland rice, corn, soy-
beans, and cassava in a single crop stand and in mixed
crops. They are comprised of four sole crops (upland rice,
corn, soybean, and cassava), three crop mixtures of two
crops (upland rice f cassava, corn f cassava, upland rice -
soybeans) and one three crop mixture (upland rice + corn +
cassava) enterprises. These identified crop production
possibilities are the most important production opportun-
ities with respect to the area devoted to each crop/crop
mixture and the number of farmers involved. They are sig-

nificant in terms of their contribution to family food

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68

requirements as well as their contribution to farmer's
income. Two levels of production technologies are iden-
tified for each cropping pattern. One represents the pro-
duction of crops under traditional technology with no use
of modern/new inputs and the new technology with the use

of new inputs such as fertilizers, insecticides/pesticides.
Only for upland rice + corn f cassava cropping pattern did
the data from the survey distinguish two levels of fertili-
zation by the representative farms in the study area.

When two or more crops are interplanted in a mixture,
then the production activities are considered as single
enterprise in terms of the mixture, rather than the indi-
vidual crops in the mixture as a separate enterprise. This
method saves us from the problem of allocation of join
resources such as labor and land use for each crop. There
are other problems associated with the use of mixed crop-
ping as production activities in linear programming models
as discussed by Crawford (1977).

The activity unit is one hectare. The objective
function coefficients (Cj) for the crop production activ-
ities represent the costs of fertilizers, insecticides, and
wages for hired labor for each unit of the activities.
Negative signs are assigned because costs reduce the income
of the farmers.

The input-output for traditional and new technol-
ogies are presented in Table 3.1. These coefficients are

the amounts of various resources required to produce one

69

unit of production activities (e.g., to produce one hectare
of mixed crop corn + upland rice f cassava). They are
assigned positive signs because each activity used up the
available resources or decreased the magnitude of restric-
tions in the model. Coefficients with negative signs indi-
cated the increase in the magnitude of the restrictions or
resources associated with them.

Both technological coefficients for the traditional
and the new technologies were generated from the results
of the survey in the study area. Some of the character-
istics of the study area and the representative farms were
presented in Chapter II. The elements of the study design
were presented in Chapter I. The average input-output
coefficients for each crop production activity in the model
were determined from the grouping of farmers assumed to be
similar in the planting time, rate of fertilizer applica-
tions, monthly labor use, yield per hectare, seeding rate,
plant population, and level of management. It was also
assumed that the soil conditions were similar from farm
to farm in the study area. Each coefficient in the model
is the mean of the small sample observation from relatively
similar farm situations.

The differences of coefficients between traditional
and the new technologies are mostly due to the differences
in the rate of fertilizations and use of insecticides. The
labor inputs are consequently higher per hectare for the new

technology due to more labor use for fertilization, spraying,

7O

weeding and harvesting due to a higher yield. In fact, as
the rate of fertilization increased, the land required more
intensive weeding by the family labor plus some exchange
labor. The following table represents the average annual
labor requirement under traditional and new technologies
for selected crop enterprises. The adoption of the new
technology increased the labor requirements in all cases.
Table 3.2.--Labor Requirement per Hectare of Selected Crop

Production Activities under Traditional and
New Technologies.

 

Labor Requirement per Hectare

 

 

(mandays)
Crop Enterprises*
Traditional New Percent of
Technology Technology Change
C + ULR f CV 193 266 38
ULR + CV 224 251 12
C + CV 224 259 13

 

Source: Field Survey
*For explanation of abbreviations, see Table Cl,
Appendix C.
(b) Labor and Animal Hiring
Activities
In the study area farmers needed to augment their

family labor with hired labor. For land preparation oxen

power is sometimes used to replace man labor. Labor and

71

animal hiring activities are presented in Column A15 to A30
in Table 3.3. Hired labor can be attained through various
hiring arrangements including exchange labor, contract basis
for specific farm operations or simply on a daily basis.
Work paid per day was the most common system. The activity
unit is one manday. Hiring animals for land preparation
usually consists of an oxen team with one man. The unit of
activity used here was the Animal Working Day (AWD) (a
shorter period of time than a manday) which consists of
about six hours per day for a team. The wage rates used
were the wages prevailing in the study area during the
period covered by the survey. Hired labor is renumerated
in cash and/or in kind. For convenience of analysis the

in kind payments were converted into money terms by multi-
plying the quantity received by its existing price.

For land preparation family labor, hired labor, and
animals hired, after appropriate conversions, are assumed
to be nearly perfect substitutes. For other types of activ-
ities hired and family labor are assumed to be perfect
substitutes. The hiring labor activities have negative
coefficients in the family labor rows, indicating that an
increase in one unit of hired labor relaxes the family
labor constraints by one unit. The animal hiring activ-
ities in the same way have negative coefficients of three,
indicating that for land preparation an additional unit of
animals hired will relax three units of family labor con-

straints. The use of hired labor will decrease capital by

72

 

 

 

 

 

 

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74

its wage/payment rate. The availability of operating
capital will determine the extent to which family labor
will be substituted by the hired labor and the availability
of the family labor itself.

Labor and animal hiring activities have negative
values on the Cj of the objective function. Each additional
unit of labor or animal hired will reduce the value of
objective function by the rate of wage/payment. Selling
labor activities is not included because the representative

farmers in the study area hire labor in the net.

(c) Capital Borrowing Activities

There are very limited formal loans in the study
area both by government and the "Tani Makmur Project."
The credit is a package deal consisting of fertilizers,
insecticides, seeds, and some living allowance. Very few
of the sample farmers joined this proqram. Borrowing
activities were included in the model to evaluate the con-
tribution (potentially) of credit facilities to farm income
and enterprise organization. The capital borrowing will
be specified on monthly terms with 18 percent interest
rate annually. The activity unit will be in Rupiah (Rp).
This activity is represented in the matrix Table 3.4 from

Columns A52 to A63.

75

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76

(d) Fertilizers, Insecticides, and
Food Crop Buying Activities

Fertilizers and insecticides are the major new
inputs in the new technology and have been used by some
farmers in the study area. Fertilizer buying activities
are included in the model to allow farmers to purchase
fertilizers. These buying activities are presented in the
matrix table from Column A31 to A40 (Table 3.5). The activ-
ity unit is one kilogram (kg).

Fertilizer prices are subsidized by the government
at Rp 80/kg at the farm level. Buying activities have
negative coefficients in the objective function since buy-
ing one unit of it will reduce the value of objective func-
tion. Fertilizer buying activities also have negative coef-
ficients on the fertilizer rows because they will increase
the stock of fertilizer. Food crop buying activities for
family consumption have positive coefficients in the food
consumption rows since food crop buying increases the con-
sumption constraints. Food crop buying activities have
negative coefficients in the objective function since they
will reduce the value of objective function by the price
of food crop bought per unit.

(e) Food Crop Consumption
Activities

The farm family consumption consists of grains

(rice, corn) and root crops (cassava). The food crop con-

sumption activities are shown in Table 3.5 from Column A41

77

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78

to A43. The activity unit is one kilogram (kg). These
activities have positive coefficients in the output rows of
rice, corn, and cassava because they reduce the stocks of
theSe crops. Soybeans produced are mostly for sale.
(f) Crops Selling Activities and
Seed Production Activities

The crop produced is either to be consumed or sold.
Minimum family consumption requirements will be satisfied
first before any selling activities are undertaken. Soy-
beans are sold without any constraints, since they are mostly
produced for sale purposes. It is also assumed that selling
activities are undertaken in the month of harvesting, so
there will be no storage activities included in the model.
Storage of food crops for the purpose of family consumption
is allowed and storage for cassava could take place in the
ground. Selling activities are represented in Table 3.5
from Column A44 to A47. The activity unit is one kilogram
(kg). The prices of the products are the prevailing prices
in the nearest market during the harvesting periods
(Appendix E). The objective function coefficients are
positive because selling added to the value of the objective
function. The row coefficients of the output of the crops
are also positive since selling activities reduce the stock

of that crop.

79

(9) Transfer Activities

These activities were shown in Table 3.4, Columns
A64 to A74 represent activities which are used to pass sur-
plus capital from one month to the next month during the
year. Column A75 represents activity which is included as
a device by which any capital surplus over requirements for
operating capital is accumulated at the end of the crop
year. This activity has a zero value in the objective
function, a positive sign for the last month (July) oper-
ating capital row and a negative sign on the amount of
transfer row, which means increasing the capital accumula-

tion at the end of the crop year.

3.4 Restrictions in the Model
In this study farming is carried out under a number
of constraints or restrictions, which include the avail-
ability of land for cultivation, family labor, hired labor,
operating capital, family consumption requirements, and the
assumption of nonnegativity of activity levels. These con-

straints are outlined in Table 3.1. Each type of these

restrictions will be described below.

(a) Land Restriction

The amount of land for cultivation by the represen-
tative farm is about 1.38 hectares, consisting of all dry-
land for the upland farming system. Each family in this
transmigration area received two hectares of unprepared land

from the transmigration program, so most of them have idle

80

land not yet cultivated, possibly due to scarcity of family
labor, other sources of labor or power, and capital.

There was little evidence of land selling or rent-
ing in the study area. So, no provision is made in the
model for such activities.

Only upland farming is considered in the model.
Lowland farming is omitted because it was nearly absent in
our sample farms, except in Central Lampung. Land is also
assumed to be homogeneous in quality and fertility, soil
structure and other conditions that affect the soil capa-

bilities. The row unit is in hectares.

(b) Labor Restrictions

Family labor restrictions are specified on a monthly
basis in the model. The focus is in the family labor
restrictions in the critical month of farming operations,
but noncritical months of labor requirements are also
included in the model to provide a complete accounting for
labor within the system. The row unit is mandays. The
amount of family labor in each month was assumed to be
equal to the number of days actually spent by the family
members during each month. These figures were obtained ,
from the survey by the researcher in the study area and
presented in Tables 2.6 through 2.9 in Chapter II.

Family labor could be augmented by hired labor and
animals hired especially for land preparation. The amount

of labor and animals hired depend on the amount of labor

81

required relative to the family labor available, the amount
of hired labor and animals available in relation to their
wage rates/payments, and the amount of the operating capital
available for hiring labor and animal power. The amount of
animal power available was estimated from the number of
adult cows and buffaloes usually used as sources of power
for land preparation on the representative farms in the
study area. The amount of hired labor available was
assumed to be equal to the number of actual hired labor
used in each month by the representative farm. The amount
of operating capital was estimated from interviews at the
beginning of the cropping year (August). The figure was
also estimated from the actual expenditures obtained by

the surveys conducted during the crop season.

(c) Operating Capital Restrictions

Difficulties were encountered in collecting the
relevant data for estimation of the amount of operating
capital available for farming activities. Consumption and
production activities are difficult to separate in the small
farmer's decision making process. In this study cash
expenses of individual farm families are used as a proxy for
the amount of the operating capital.

The amount of cash expenses for the representative
farm was set equal to the amount estimated to have been
spent on hired labor, fertilizers, insecticides/pesticides,

and animals hired during that crop year (1978/1979). These

82

were estimated from the survey conducted by the author as
presented in Table 2.12 in Chapter II. The row unit is
Rupiah (Rp). These operating capital constraints are also
presented on the monthly basis. Transfer activities were
used to pass surplus capital from one period to the next

during the year.

(d) Consumption Requirements

It is assumed that the representative farm is moti-
vated by the "first security rule," that is, the first
priority is to produce their family food consumption
requirements. Therefore, they need to produce the minimum
amount of rice, corn, and cassava for the family consumption.
For this purpose constraints were incorporated in the model
to ensure the production of that minimum amount. The food
consumption requirement for the family were derived from
the results of a simple consumption survey undertaken by the

author at the beginning of the crop year.

(e) Nonnegativity Restriction

One of the requirements for the use of linear pro-
gramming in analyzing the small farm behavior is the non-
negativity of the activities in the model. None of the

activities in the model can be operated at negative levels.

3.5 Some Limitations of the Model
Other activities and constraints could be included

in the model. For example, nonfarm activities of family

83

members are excluded as are various types of the activities
in the farming process. For example, some activities may
require different types of labor. Other activities which
might have been considered include additional level of
fertilizations, different planting space for each crop,
different planting times, and different harvesting methods.
The number of activities in the model depends on the avail-
ability of data and the objective of the study. Large and
complex models are costly in terms of time, money, and other
resources. It is not always certain that the benefits to be
derived from additional activities in terms of precision

for planning purposes are sufficient to justify the addi—
tional costs. Complex models may be difficult to interpret
in terms of tracing the logical causal relationship between
a change in one variable and the resulting changes in pro-
duction. The model developed in this study was kept as

simple as possible but complete enough to reflect the farm

situations in the study area.

In this chapter we have described the linear pro-
gramming model, its requirements, and the components to be
employed. The results of the various applications of the

models will be discussed in the following chapter.

CHAPTER IV

ANALYSIS OF THE RESULTS FROM THE LINEAR

PROGRAMMING MODELS APPLICATIONS

In Chapter III the structure of the linear program-
ming in this study was described. This Chapter presents
the analysis of the results of the applications of linear
programming models for the representative farms. The analy—
sis is focused on the changes in farm income, cropping patterns,
resource use and productivity. Some criteria for economic
efficiency measures in relation to the use of the new tech-
nology are also presented.

The base plan is described as the Optimal plan under

the traditional technology1

with existing resource situations.
Then more activities and coefficients reflecting the use of
the new technology were added into the model and then the
Optimal plan for the new technology with existing resources

was determined. This plan is compared to the base plan in

order to explain the likely effects of the ad0ption of the

 

1In the following, traditional technoloqy is inter-
changeable with before the introduction of the new tech-
nology, and new technology is interchangeable with after
the introduction of the new technology.

84

85

new technology. The output of the linear programming analy-
sis in each situation provided information on the value of
objective function, the Optimal enterprise combination, the
resOurces use and their respective marginal value producti-
vities (MVPs), the nonoptimal activities with the cost
associated with forcing each of them into the Optimal
solution and the stability limits of the optimal plan
(Stephen B. Harsh and J. Roy Black, 1975).

On the first and second sections of this Chapter
(Chapter IV) discussion emphasis is on the comparison of
the optimal plan of the representative farm enterprises
under the traditional and new technologies. The comparisons
are based on the changes on farm income, resource use and
productivity, average returns to resources as indications
of the effects of the new technology in the three subregions
of Lampung. In the third section, comparisons of the Optimal
plan between the three subregions are presented to see the
different effects of the new technology on the cropping
patterns, resource use and crOp productivity, and average
returns to limiting resources. In section four the effects
of releasing family labor constraints are examined. In
section five comparisons are made of the actual plan and the
Optimal plan in each subregion to see the extent of adjust-
ments by the representative farms. In the last section of
this Chapter a summary of the results of the linear program-

ming analysis is presented.

86

4.1 The Optimal Organization of the Represen-
tative Farm Enterprise Under the Traditional
Technology with Existing Resources
in North Lampung

The validity of the Optimal solution depends on the
realism of the assumptions made concerning prices, techno-
logical coefficients and constraints. The Optimal linear
programming solution may differ from farmers' experiences
due to the limitations of the linear programming analysis
which includes unknown omissions of some important factors.
Thus some points of the farmer's behavior when making
decisions may not be captured.

The Optimal plan resulting from the present analysis
under traditional technology with existing resources is
presented in Table 4.1. The value of the objective function
or total gross margin is equal to 167,383 Rupiah. Fixed
cost does not include such items as depreciation of farm
tools because in general fixed cost items are neligible in
the study area. Thus total gross margin is the estimate of
the net farm income. Average return estimates with reSpect
to limiting resources such as land and labor are provided
as follows:

Gross margin = Total value of products - cash expenses
Returns per hectare = Total gross margins - unpaid
family labor - interest on owner's Operating capital
(cash expenses) divided by the total area under culti-
vation

Return per manday of labor = Total gross margin - inter-

est on owner's Operating capital (cash expenses) divided
by the total labor used.

87

Table 4.l.--Optimal Plan, Gross Margin, Resource Use and
Average Returns Under Traditional and New Tech—
nologies with Existing Resources of the Repre-
sentative Farm in North Lampung, Indonesia.

 

 

Item* Unit Activity Level
1. C + ULR f CV (NT) HA 0.246
2. ULR 1‘ CV (NT) HA 0.195
3. Ulr - SB (NT) HA 0.682
4. CV (TT) HA 0.136
Total Land HA 1.259
Total Gross Margin RP 167,383
Family Labor Manday 188
Hired Labor Manday 33
Total Labor Manday 221
Animal Hired AWD 2.5
Operating Capital RP 20,500
Return/Hectare RP 55,526
Return/Manday of Labor RP 741

 

Source: Computed

*For explanation of the abbreviations

Appendix C.

see Table C.l,

88

The most critical problem is the valuation of family labor,
since the family labor input is the largest portion. Using
the existing hired labor wage rates as an estimate of the
Opportunity cost of family labor may be too high, since
employment Opportunities outside farm is small or lacking.
Valuing the family labor at zero is unrealistic, since
family labor is limiting in the peak seasons. It is also
true there is a slack season for family labor use. Even in
the slack seasons the value of family labor may not zero,
at least the value of family labor in this season is equal
to the value of leisure time, which is believed to have
positive value for the farmers themselves. Based on the
labor situations in the study area where there are no land-
less farmers and by the researcher's judgment, it is likely
that the family labOr valued at the existing hired labor wage
rate is relevant. This is overvalued labor in a non peak
season months, but in fact, wage rates are very stable from
season to season and without Government regulation on the
minimum wage rates that affected the study area.

The second problem is to assign the correct value
to the cost of borrowing capital or the opportunity cost of
own capital. This problem is not as crucial as valuing
family labor since cash exPenses are only small portion of
total expenses and are usually formal and informal interest
rates. In the study area according to the results of the
interviews, the farmers are not familiar with borrowing from

moneylenders, family or friends, which is somewhat surprising.

89

A formal source of credit is from the ”BIMAS" program with
18 percent interest on loans in the form of fertilizers,
insecticides, and a small amount of cash for cost of living
if farmers join the prOgram. This interest rate has been,
in fact, subsidized by the Government, because inflation
rate was 25 percent in 1979. By assuming that the govern-
ment of Indonesia will continue this BIMAS program loan to
the small farmers, the 18 percent interest rate may be rele-
vant to be used as a cost of borrowing or lending for the
study area. This is not a realistic interest rate, since

in real terms it is -7 percent with the rate of inflation

at 25 percent. The representative farmers in the study area
are not familiar with institutional borrowing or lending.
But even if the level of interest rate is under valued, it
would not change the results of the analysis since cash
borrowing is negligible or completely absent and cash
Operating expenditure is only a small portion of the total
cost. Based on the above considerations, interest on bor-
rowing and lending capital will be valued at 18 percent
annually.

The optimal plan under the traditional technology
for Tatakarya village in North Lampung is presented in
Table 4.1. The Optimal plan included all crops (upland rice,
corn, soybeans, and cassava) and utilized almost all of the
available land for cultivation. In the optimal plan mixed
crOpping covered about 90 percent of the total cultivated

area and single or pure stands covered only 10 percent.

90

In these instances cassava was the only crop in a pure

stand in the optimal plan. Within the mixed cropping, three
crop mixture of corn, upland rice and cassava covered

26.5 percent, and the two crop mixtures of upland rice and
cassava covered 19.5 percent, and mixture of upland rice

and soybeans covered 54 percent (the largest share) of the
cultivated land area. Soybeans are grown in the study area
as a cash crOp and not for direct consumption by the family
farms.

Not all the crop mixtures grown by the representative
farm in the study area, were included in the optimal plan.
Examples are a mixture of corn, upland rice and soybeans,
and three crOps of soybeans in sequence (three crops of soy-
bean in one crOp year). They are not competitive enough to
be included in the optimal plan, e.g., three soybean crOps in
sequence is not included in the otpimal plan due to its very
intensive labor requirements.

The total labor use in the optimal plan is 221 mandays
which is made of 188 mandays of family labor and 33 mandays
of hired labor. This does not include 2.5 animal working
days which is equivalent to about 7.5 mandays of man labor
for land preparation. Very limited Operating capital was
used consisting of payments for hired labor and animal hired
cost at about Rp20,500. This Operating capital is supplied
from the initial funds available in the beginning of the
cr0p year and the sales of surplus product beyond family con-

sumption requirements.

91

The marginal value products (MVPs) are presented in
Table 4.2 which is the shadow prices of resources within
the constraints in the linear programming model. In this
case the marginal value product of disposal activities is
defined as the increase in the value of total output that
is obtained from the use of additional units of the resource
with all other inputs held constant. This condition may not
met in the linear programming framework, because technologi-
cal coefficients for activities are assumed to be at a fixed
ratio to one another. So, the increase in one unit of only
one input will require the increase in other inputs in order
to keep the ratio or coefficient fixed. Although with this
deficiency, marginal value product can give information on
the most likely resources to be expanded in order to increase
the value of the objective function or farm income. The
marginal value product of a resource is constant over the
Specific range and the solution holds until other resources
become limiting. At that point another enterprise organiza-
tion becomes Optimal and the marginal product of the
resources change.

The marginal value products indicate the productivity
of resources on the farm. They indicate the amount of total
gross margins of the farm products that could be increased
by the utilization of the additional one unit of that
resources. So they give information about the possible
gains or losses in income which are possible through the

acquisition of that scarce resource. The marginal value

92

Table 4.2.--Margina1 Value Products of Land, Family Labor,
Operating Capital (MVPs) on the Optimal Plan
Under Traditional Technology with Existing

Resources in North Lampung.

 

 

Resource Unit MVPs (RP)

1. Land 0
2. Family Labor in August HA 0

3. Family Labor in September Manday 377

4. Family Labor in October Manday 6,922

5. Family Labor in November Manday

6. Family Labor in December Manday

7. Family Labor in January Manday 0

8. Family Labor in February Manday 0

9. Family Labor in March Manday 0
10. Family Labor in April Manday 0
11. Family Labor in May Manday 0
12. Family Labor in June Manday 0
13. Family Labor in July Manday 0
14. Operating Capital in August RP 6.8
15. Operating Capital in September RP 0
16. Operating Capital in October RP 0
17. Operating Capital in November RP O
18. Operating Capital in December RP 0
19. Operating Capital in January RP 0
20. Operating Capital in February RP 0
21. Operating Capital in March RP 0
22. Operating Capital in April RP 0
23. Operating Capital in May RP O
24. Operating Capital in June RP 0
25. Operating Capital in July RP 0

 

Source: Computed.

93

products of slack resources are zero and positive for the
limiting factors or constraint resources. A higher a marginal
value product of a resource indicates scarcity of that
resOurce. The more limiting that resource the higher its
marginal value product. So to be meaningful it should be
compared to the cost of its acquisition or to its marginal
factor cost. It is profitable to acquire an additional unit
of resources if its acquisition cost is less than its mar-
ginal value product. So maximum income can be obtained only
if all marginal value products of all resources are equal to
their marginal factor costs.

Family labor was limiting for crOp production in
September and October. These months correspond to the peak
periods of land preparation and planting seasons. An addi—
tional unit of labor in these months will increase farm
income by the amount indicated by their respective marginal
value products. The marginal value product of family labor
is highest in October indicating that family labor was the
most constraining in this month. The marginal value product
of family labor in October is substantially higher than the
prevailing wage rate. Family income could be increased if
the family members are willing to work extra hours or days
during this month or had the funds available for hiring
labor (assuming hired labor is available). This high mar-
ginal value product of labor also indicates that the farmers
could afford to pay a much higher wage than the prevailing

wage rate in order to attract more hired labor in this peak

94

season. In the month of September the marginal value product
of labor is lower than the marginal factor cost of hired
labor. Hence it would be unprofitable to hire more labor
during this month.

The marginal value product of Operating capital in
August is 680 percent. Thus Operating capital in August is
also a constraint in crOp production. In this month the
marginal value product of Operating capital was much higher
than its marginal factor cost of 18 percent. Hence farm
income can be increased if more operating capital was made
available. This may imply the need for short term credit to
relax that Operating capital constraint which would permit
purchase of more fertilizers and insecticides. In the
following months the operating capital marginal value pro-
ducts have zero values, since there is a slack of Operating
capital during these months.

Operating capital can be supplied after August from
the sale of crOp products such as cassava in September,
soybeans in February, corn in March, and rice in May. This
source of funds for operating capital is more than enough to
00pe with the operational cash costs of cr0p production.

The only credit they need is at the beginning of the
farming operating which is used as cash expenses for land
preparation. The amount needed is also very limited at about
Rp9000 or equivalent to U.S. $15. This initial operating
capital is usually also made available by the farmers them-

selves, since there is no formal or informal credit available

95

for the farmers in the study area. This Operating capital
can be obtained by working outside their farm for wage or
from the previous product sales. So, it seems that credit
is not a limiting factor in the context of traditional tech-
nology for the representative farmer in the study area when
they practiced mixed crOpping. This traditional mixed
crOpping system is without additional new or modern inputs.

The output of the linear programming routine also
provided information on the activities excluded from the
Optimal plan. They are excluded from the Optimal plan
because they are the least profitable enterprises. The
cost of forcing those excluded enterprises into the Optimal
solution indicates how much the value of the objective
function would be decreased (income penalty) if one unit of
that enterprise was forced into the optimal plan. So it
reveals the competitive position of that enterprise. The
higher the cost or loss in income, the lower the competitive
position of that enterprise and vice versa. The lower the
cost of forcing that activity into the Optimal solution,
the greater the potential to enter the Optimal plan if one
or more of the contraints is relaxed.

Low productivity of traditional farming is con-
sidered the major constraint for increasing food crOp pro-
duction and the income of the farmer in the study area.
Without the introduction and application of the new inputs
or new methods of production it is hardly possible that

both production and income of small farmers can be improved.

96

The introduction of the new technology is believed to be
the basis for improving the small farmers' conditions in the
study area. But it will not guarantee this, unless supporting
services are available in the right place and at the right
time at the farm level, such as extension services, new
agricultural inputs which are profitable, and transportation
and marketing services. The following section of this
Chapter will examine the changes in the cropping patterns,
farm income, resource use and their productivities after the
introduction of the new technology on the representative
farm in the study area.

4.2. The Effects of the New Technology with

Existing Resources in North Lampung

The comparison between Optimum organization of the
representative farm before and after the introduction of the
new technology will provide information on the likely effects
of the new technology. This comparison is presented in the
following Table 4.3.

The total gross income of the representative farm is
Rp283,089 which represents an increase of about 69 percent
over the total gross income of the optimum farm plan under
the traditional technology. So, it indicates that farmers
who are income or profit maximizers could improve their
gross income by approximately 69 percent through the adoption
of the new technology. The Optimum plan after the introduc-
tion of the new technology with the existing resource levels

included 0.214 hectares crop mixture of corn + upland rice f

97

Table 4.3.--A Comparison of the Optimum Farm Organization of
the Representative Farm Under Traditional and
New.Technologies with Existing Resources in
North Lampung.

 

 

Before After

Introduc- Introduc—

tion of tion of

new Tech— New Tech- Change
Item* Unit nology nology Percent

1. c + ULR f cv (TT) HA 0.246 0.214 )

) 190

2. C + ULR f CV (NT) HA - 0.500 )

3. ULR + CV (TT) HA 0.195 - -100

4. ULR - SB (TT) HA 0.682 - )

-21

5. ULR - SB (NT) HA - 0.536 )

6. CV (TT) HA 0.136 - -100
Land HA 1.259 1.250 0
Total Gross Margin Rp 167,383 283,089 69
Family Labor Manday 188 232 23
Hired Labor Manday 33 33 0
Total Labor Manday 221 265 20
Animal Hired AWD 2.5 9.5 280
Operating Capital Rp 20,500 39,025 90

 

Source: Computed.

*For explanation of abbreviations see Table C.l,
Appendix C.

98

casava under the traditional technology and 0.500 hectares
of the same crop mixture under the new technology. It
should be noted that after the introduction of the new tech-
nolOgy into mixed cr0p systems of upland rice + corn f casava
in the Optimal farm plan, there still existed two levels of
technology. One was without use of new inputs (traditional)
and one was with use of new inputs such as fertilizers,
insecticide, and improved seeds (new technology), which
covered 0.214 hectares and 0.500 hectares respectively.
This means that this crOp mixture is competitive both under
traditional and under the new technologies in the conditions
in the study area. This cr0p mixture is the most pOpular
crOpping pattern practiced by the farmers both in terms of
acreage devoted to it and the number of farmers practicing
it (see Table 2.1 and Table 2.2 in Chapter II). The second
crOp mixture under the new technology in the optimal plan
is upland rice - soybeans with use of new inputs of fertili-
zers and insecticide. This crOp mixture is the most competi-
tive under the new technology which covered 0.536 hectates.
After the introduction of the new technology, the
cropping pattern is less diversified and all crOpping systems
are in mixtures. None of the sole crOps appeared in the
Optimal plan under the new technology. This means that the
mixed crOpping pattern is more competitive than the sole
crOpping pattern. The crOpping pattern of mixed crops of
upland rice + cassava is eliminated in the Optimal plan

'under the new technology. Sole crOp of cassava is also

99

eliminated in the Optimal plan after the introduction of
the new technology. The costs of forcing nonoptimal crop
production activities into the Optimal solution (Table 4.4)
revealed that mixed crop of upland rice + corn f cassava
with the use of animal power for land preparation (classified
as traditional technology) has the highest potential to
enter the Optimal plan. So, this cropping pattern with the
use of animal power for land preparation has the lowest
cost or income penalty when entered into the optimal plan
under the new technology if one or more restrictions were
relaxed.

The adoption of the new technology also changes the
allocation of resources among crops or among crOp mixtures.
For the mixed crOp of corn + upland rice f cassava in the
Optimal plan under new technology, land devoted is 0.714
hectares from 0.246 hectares before the introduction of the
new technology. There is an increase of 190 percent of land
devoted to this mixed cropping pattern. For mixed cr0p of
upland rice - soybeans there is a decrease in land devoted
to it after the introduction of the new technology from 0.682
hectares before the introduction of the new technology to
0.536 hectares, a decrease of 21 percent. Crop mixture of
upland rice + cassava was eliminated after the introduction
of the new technology. There is no significant change in
the total land devoted or under cultivation after the intro-
duction of the new technology compared with the optimal

plan before the introduction of the new technology.

100

Table 4.4.--Cost of Forcing in NonOptimal Activities (Result
of the Linear Programming Analysis) Under the
New Technology with Existing Resources of the
Representative Farm in North Lampung.

 

Cost of Forcing in Into

 

Nonoptimal Activities* Optimal Plan (RP)
l. C + ULR 1‘ CV (TT2) 8477**

2. C + ULR 1‘ CV (NTl) 59216

3. ULR f CV (TT) 101430

4. ULR f CV (NT) 21891

5. C + CV (TT2) 202163

6. C + CV (NT) 20216

7. ULR - SB (TT) 17934

8. CV (TTl) 74146

9. CV (TT2) 131800

 

Source: Computed.

* For explanation of abbreviations see Table C.l,
Appendix C.

**The lowest cost (income penalty) of forcing in
nonoptimal activities into the Optimal plan.

101

After the introduction of the new technology there
are increases in use of family labor, hired labor, and animal
draft power. Family labor use increases from 188 mandays to
232 mandays or an increase of about 23 percent. There is no
change in the use hired labor after the introduction the new
technology. Animal draft use increases from 2.5 animal
working days to 9.5 animal working days. Animal draft is
only used for land preparation in the beginning of the
cropping season. Hired labor is assumed as a substitute
for family labor. Hired labor is used for land preparation,
planting, weeding, and harvesting.

Operating capital increases from Rp20,500 before
the introduction of the new technology to Rp39,025 after
the introduction of the new technology. The increase in the
cash expenses is for payment of hired labor, animals hired
and for the purchases of the new inputs such as fertilizers
and insecticides. The average return to the factors of
production also increases after the introduction of the new
technology.

The average return per hectare of land before the
introduction of the new technology was Rp55,526. The return
increased to Rp132,639 after the introduction of the new
technology, an increase of about 139 percent. The average
return to family labor per manday after the introduction
of the new technology was Rp1,04l, an increase of about

40 percent from the traditional technology.

102

There is a remarkable increase in the use of Oper-
ating capital of about 90 percent after the introduction of
the new technology.

The marginal value products of the resources under
the new technology and under the traditional one are compared
in Table 4.5. The marginal value product of land under both
technologies is zero, i.e., land is idle. Family labor is
constraining in September and October before the introduction
of the new technology and family labor is limiting in Novem—
ber and January after the introduction of the new technology.
So, it seems that there is a shift in family labor as
limiting factor during land preparation under the traditional
technology into the weeding season under the new technology.
This may be due to more heavy weeds under the new technology.

The marginal value product of family labor is highest
in October under the traditional technology during the land
preparation and planting seasons. The marginal value product
of family labor is highest in November and January under the
new technology and is much higher than the prevailing wage
rates in the two months. Farmers can afford to hire more
labor at a much higher wage. These factors emphasize the
need for fund availability to hire more labor to break the
labor constraint during these two months with the assumption
that hired labor supply is not constraint. Operating capital
is the only limiting in August during the land preparation
and just a month before cassava harvesting. By limiting the

amount of capital to be borrowed in each month (for example

103

Table 4.5.--A Comparison of Marginal Value Products (MVPs) of Land,
Family Labor, and Operating Capital on the Optimal Plan
Under Traditional and New Technologies with Existing
Resources of the Representative Farms in North Lampung.

 

Marginal Value Product

 

 

Resource Unit
Traditional New
Technology Technology
1. Land HA 0 O
2. Family Labor in August Manday 0 O
3. Family Labor in September Manday 377 0
4. Family Labor in October Manday 6922 O
5. Family Labor in November Manday 0 100
6. Family Labor in December Manday 0 0
7. Family Labor in January Manday O 24
8. Family Labor in February Manday 0 0
9. Family Labor in March Manday 0 0
10. Family Labor in April Manday 0 0
11. Family Labor in May Manday 0 0
12. Family Labor in June Manday 0 0
13. Family Labor in July Manday 0 0
14. Operating Capital in August RP 6.8 6.8
15. Operating Capital in Sept. RP 0 0
16. Operating Capital in Oct. RP 0 0
17. Operating Capital in Nov. RP 0 0
18. Operating Capital in Dec. RP 0 0
19. Operating Capital in Jan. RP 0 0
20. Operating Capital in Feb. RP 0 O
21. Operating Capital in March RP 0 0
22. Operating Capital in April RP O 0
23. Operating Capital in May RP 0 O
24. Operating Capital in June RP 0 0
25. Operating Capital in July RP O 0

 

Source: Computed.

104

in August the limited amount can be borrowed is set at 6000
Rupiah (see Table 3.1 for other months limited borrowing)
the linear programming analysis shows that the marginal
value product of operating capital is zero except for August
which is 680 percent. It is very high and it is a very
crucial time for the farmers to have this fund available at
the beginning of the farming operation, i.e., for land
preparation.

By conducting sensitivity analysis, by changing the
limited borrowing in August from 6000 Rupiah to 9000 Rupiah,
the analysis shows that the marginal value product of cash
operating capital in August becomes zero. The critical fund-
ing required in August for land preparation is well known
by the farmers in the study area from their experience.
According to the result of the interview with the farmers
in August 1979 at the beginning of land preparation, the
representative farm has 9000 Rupiah cash already available
on hand. This amount is used as the limited amount of
borrowed capital in the sensitivity analysis and the results
shows that the marginal value product of cash Operating
capital in August becomes zero. This may imply that there
is enough cash available by the farmer without borrowing to
cover the cash expenses even under the new technology. The
cash for expenses can be obtained by the farmer himself by
working off of his farm for wages or from the previous sales

of his crOps.

105

The total cash expenditure increases dramatically
with the introduction of the new technology by 90 percent
from 20500 Rupiah to 39025 Rupiah. This increase in cash
expenses is for payment of additional hired labor, animals
hired and for purchases of the new inputs such as fertilizers
and insecticides. The increase of the cash expenses after
the ad0ption of the new technology is usually covered by
the farmer by selling his crop products after harvest such
as cassava in September, soybean in February, corn in March,
and rice in May. This procedure is possible since the
representative farmer has been familiar with the mixed
crOpping systems. In addition, farmers can work off of
their farm for wages before the land preparation. Operating
capital for the representative farmer is not critical in
most of the months.

There is a very significant increase in animal draft
use from 2.5 animal working days under the traditional tech-
nology to 9.5 animal working days under the new technology.
The supply of hired labor is also limited since there are
no landless farmers in the study area. The major source of
hired labor is the newly arrived transmigrants and their
family members. They have to sacrifice some of their labor
for wages in order to sustain living for family at least in
first season of their arrival. Seasonal labor from Java is
also limited due to relatively high transportation costs
compared to their expected income. Social and cultural

factors also limit the mobility of labor from Java to Lampung.

106

The study conducted by Gloria Davis (1979) in Lampung
show that the longer the transmigrants have settled in the
area, the larger the area of the available unprepared land
(two hectares fOr each family) that was brought into pro-
duction of food crOps as well as perennial crOps. This means
that the number of family labor hired out becomes less and
less, so that the supply of hired labor becomes a limiting
factor.

Other sources of power need to be considered to
release part of these labor constraints. Investment in these
sources of power such as cows and/or buffaloes, and tractors
is required. For this purpose medium to long term credit
may be needed by the representative farmers in the study
area since they do not have enough savings due to the low
income. According to the results of the interviews with
the representative farms in North Lampung, this need was
expressed.

The Optimal plans after the introduction of the new
technology for the representative farms in Central and South
Lampung are presented in Table 4.6 and Table 4.7 respectively.
Similar analysis could be done for each region as has been
done for North Lampung (represented by Tatakarya village).
The complete analysis will not be repeated here as for North
Lampung. Only some differences will be discussed.

For the representative farm of Central Lampung not
only upland cr0p production activities but also lowland rice

or irrigated rice production activities are included in the

107

Table 4.6.--Optimum Organization of the Representative Farm
Under the New Technology with Existing Resource
LeVels in Central Lampung.

 

 

Item* Unit Adiluih and Bulusari
1. C + ULR f CV (NT) HA 0.262
2. ULR f CV (TT) HA 0.240
3. C + SB f CV (NT) HA 0.518
4. LLR (NT) HA 0.240
Total Land HA 1.260
Total Gross Margin RP 182,238
Family Labor Manday 219
Hired Labor Manday 71
Total Labor Manday 290
Animal Hired AWD 12.8
Operating Capital RP 70,000
Return/Hectare RP 52,074
Return/Manday of Labor RP 585

 

Source: Computed.

*For explanation of the abbreviations see Table C.l,
Appendix C.

108

Table 4.7.--Optimum Organization of the Representative Farm
After the Introduction of the New Technology
with Existing Resource Levels in South Lampung.

 

 

Item* Unit Pugung Raharja
1. ULR - SB/CV HA 0.325
2. SB - SB - SB/CV HA 0.321
3. ULR + C - SB/CV HA 0.454
Total Land HA 1.100
Total Gross Margin RP 192,970
Family Labor Manday 257
Hired Labor Manday 5
Total Labor Manday 262
Operating Capital RP 7,500
Return/Hectare RP 63,223
Return/Manday of Labor RP 731

 

Source: Computed.

*For explanation of the abbreviations see Table C.1,
Appendix C.

109

linear programming model. Total irrigated rice fields
covered 0.24 hectares for the representative farm. For
this irrigated rice, use of fertilizers, insecticides, and
improved seeds has been far in advance of the dryland crop
farming. On the average the use of fertilizers is the
highest in the Central part of the Lampung subregion. Also
of hired labor is the highest in Central Lampung compared
to the other subregions of Lampung.

The optimum plan for Pugung Raharja (representing
South Lampung subregion) after the introduction of the new
technology is presented in Table 4.7. Fertilizer, the main
input for the new technology, is not yet familiar to the
representative farmer in this subregion. They did not use
fertilizer in their crOp cultivation. The only modern input
they have used is insecticides for soybean crop protection.
Soybeans are the major cash crOp for the representative
farmer in this area. For North and Central Lampung rice
and cassava are the major crOps for the representative
farm (Table 4.9).

Component and the level of the technology on the
optimal plans after the introduction of the new technology
in the three subregions of Lampung is presented in Table 4.8.
The comparison is in terms of the level of fertilizers,
insecticides, and use of improved or local seeds. Land
preparation by manual labor and/or by the use of animal
draft power is still considered to be traditional technology.

The table shows that the highest level of fertilizers on

110

Table 4.8.--Comparison of the Level of Technology on Optimal Plans for
the Representative Farms in the Three Subregions of Lampung.

 

 

 

Tatakarya Adiluih and Pugung
Bulusari Raharja
Item
(N.Lampung) (C.Lampung) (S.Lampung)
l. Fertilizers/Hectare 112.8 kg 140.5 kg —
2. Insecticides/Hectare 2 liters 2 liters 3.3 liters
3. Seed Variety Mixed Mixed Local
4. Land Preparation (M+A)** (M+A)** M
*Mixed = Use of both local and improved seeds.
**(M+A) = For land preparation by man labor and use of animal

draft power.

(M) = For land preparation use of man labor only.

the Optimal plan after the introduction of the new technology
is for Central Lampung and next to it is North Lampung, and
none for South Lampung. This is due to the fact that the
representative farm in South Lampung not yet used fertilizers.
So, there is no crOp production activities with use of
fertilizer in the linear programming model for South Lampung.
The levels of fertilizers in the optimal plan after the
introduction of the new technology are much lower than the
recommended levels of fertilizers by "BIMAS" program or by
l"TANI MAKMUR PROJECT." Both of these agencies recommend
fertilizer use of 200 KG/hectare. The level of insecticide
use is similar to the project recommendations of about 2

liters per hectare.

111

4.3. Comparison of Optimal Plan Under the
New Technology and Existing Resource Levels
Between the Three Subregions of Lampung

In the previous sections of this Chapter the results
of the applications of the Linear Programming model for the
representative farms in North Lampung has been presented.
The presentation included the analysis of the Optimal plan
under traditional technology and the analysis of the effects
of the new technology by comparing the base plan with the
optimal plan under the new technology. In analyzing the
effects of the new technology emphasis was given to the
changes in farm income, resource use and their productivities
and crOp enterprise combinations. In this section compari-
sons of the Optimal plan under new technology and existing
resource levels between the three subregions of Lampung will
be presented. The level of technology, crOpping patterns,
and the level of crOp productivities will be compared between
the three subregions in Table 4.9 and Table 4.10.

The dominant crOpping patterns in North Lampung are
mixed cropping of corn + upland rice f cassava and mixed
cropping of upland rice - soybeans. For Central Lampung the
dominant cropping pattern is the mixed crOpping of corn +
soybean i cassava. In South Lampung the dominant crOpping
pattern is mixed crOpping of corn + upland rice - soybeans.
From the two tables it can be seen that the major crops for
North and Central Lampung are rice and cassava in terms of
land devoted to this crops and in terms of total crOp pro-

duction. {For South Lampung the dominant crOp is soybeans

112

Table 4.9.--Comparis0n of CrOpping Patterns of the Optimal
Plans Under the New Technology and Existing
Resources for the Representative Farms of the
Three Subregions of Lampung.

 

 

 

 

. Sub Region
Item Unit
North Central South
CrOpping Pattern:*
l. ULR + C f CV (NT) HA 0.500 0.262 -
2. ULR + C { CV (TT) HA 0.214 — -
3. ULR + C - SB/CV (NT) HA - - 0.454
4. ULR f CV (TT) HA - 0.240 -
5. ULR - SB (NT) HA 0.536 - 0.325
6. C % CV - SB (NT) HA - 0.518 -
7. SB - SB - SB/CV (NT) HA - - 0.321
8. LLR (NT) HA - 0.240 -
Total Land HA 1.250 1,260 1.100
Total CrOp Production:
Rice KG 1,607 1,827 624
Corn KG 407 469 228
Soybean KG 322 260 771
Cassava KG 9,714 7,649 3,015
Source: Computed.
*Perennial crOpping pattern not included. For

explanation of abbreviations see Table C.1, Appendix C.

113

in terms of land devoted to this crop and in terms of the
value of the crop.

The differences in crops and crOpping patterns may
be attributed some to the differences in soil, rainfall,
prices of agricultural products and marketing services
available to the representative farms. For example, the
cassava processing industry recently has been concentrated
in Central Lampung and now is at over capacity (Nainy, 1979).
This will encourage cassava production if the price of
cassava is attractive to the farmers. Cassava not only is
a staple crOp but also is a cash crOp for the representative
farm in the study area. Lampung has been a major receiving
area for transmigrants and has a very active cassava based
industrial sector producing both starch for domestic con-
sumption and pellet for export. Production grew rapidly
from 300,000 tons of fresh roots in 1969/70 to 8000,000 tons
by 1977 (Dixon, 1979). The following table shows the level
of technology in the Optimal plan in terms of use of modern
inputs such as fertilizers, insecticides, crOp productivities
in the three subregions of Lampung.

The highest level of fertilizer use is in Central
Lampung and next to it is North Lampung. N0 fertilizer is
used in South Lampung. The level of insecticide use is near
the recommended level of 2 liters per hectare.

From the comparisons made in this analysis there
appears to be a comparative advantage in type of crOp pro-

duced and the type of crOpping patterns to be used in

114

Table 4.10.--Comparison of Resource Use and Productivity on the Optimal
Plan Under New Technology with Existing Resources of the
Representative Farms in the Three Subregions of Lampung.

 

Resource Use and Produc-
tivity in Subregions

 

 

Item Unit
North Central South
1. Total Land HA 1.25 1.26 1.10
2. Fertilizers Use/Hectare KG 112.5 140.5 0
3. Insecticides/Hectare Liter 2 2 3.3
4. Family Labor/Hectare Manday 195 175 234
5. Hired Labor/Hectare Manday 38 58 S
6. Animal Draft Hired/Hectare AWD 10 10 0
7. Total CrOp Production/HA* KG
Rice KG 1285 1450 567
Corn KG 326 372 207
Soybean KG 258 206 612
Cassava KG 7771 6071 2741

 

Source: Computed.

*Cr0p productivity per hectare is in various mixed crOpping
patterns and not as a sole crop.

115

producing that crop for each subregions of Lampung (Table
4.11). North Lampung has a comparative advantage in pro-
ducing cassava and upland rice in mixed cropping patterns
of upland rice + corn f cassava and mixed cropping of upland
rice - soybeans. Central Lampung has a comparative advantage
of producing rice and cassava with cropping patterns in
mixed cropping of corn % cassava - soybeans, upland rice +
corn { cassava, upland rice f cassava, and low land rice
(irrigated ricefield) as a sole crop. South Lampung has a
comparative advantage in producing soybeans with cropping
patterns consisting of triple crOpping of soybeans in
sequence, and mixed cropping of upland rice + corn - soybean,
upland rice - soybeans. On every crOpping pattern in South
Lampung there is always a soybean crop in it. The compara-
tive advantage is also indicated by the total cr0p pro-
duction and per hectare basis of each crop produced by the
representative farm in each subregion of Lampung (see
Table 4.9 and Table 4.10). But it should born in mind that
the analysis only covered a small area of upland farming in
the study area and it may not represent other crOpping
patterns such as for perennial crops, fruits and vegetables,
lowland rice, and the highly mechanized large scale upland
farming in Lampung.

4.4. The Effects of Releasing Family Labor

Constraints on the Optimal Plan Under
the New Technology
As pointed out in the previous section, the marginal

value product attached to family labor is high in November

116

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117

and January in the Optimal plan after the introduction of
the new technology in North Lampung. So by increasing the
availability of family labor during these months, farm
incOme will increase. Family labor input can be increased
by the family members by working more days and/or more hours
in a day. Assuming the farm family members would consider
an increase family labor input during these months, an
attempt was made to analyse the economic effects of this
family effort. It is hOped that the result of the analysis
will also show the extent to which a family labor is a con-
straint under the new technology.

The number of mandays that the family could supply
will be set equal to the number of mandays of family labor
input in the peak season/month. Other resources, prices,
and technological coefficients are assumed to be unchanged.
The results of the analysis is presented in Table 4.12
through Table 4.14. For North Lampung (see Table 4.12) an
increase in family labor input during the peak season
(November and January), ceteris paribus, has an effect on
the expansion of farm income. Total gross margin increased
from Rp283,089 to Rp303,314, an increase of 7 percent.

Family labor use increased from 232 mandays to 252
mandays, an increase of 9 percent. Hired labor use increased
from 33 mandays to 35 mandays, an increase of 6 percent.
Total labor input increased from 265 mandays to 287 mandays,

an increase of 8 percent.

118

Table 4.12.--Optimal Plan of the Representative Farm Under
New Technology with Existing Resource Levels
and Increased Family Labor in North Lampung.

 

. Existing Increased Change
Item* ' Unit Resource Labor Percent

 

l. C + ULR f CV (TT) HA 0.214 0.185 -13.5
2. C + ULR % CV (NT) HA 0.500 0.500 0
3. ULR - SB (NT) HA 0.536 0.583 9
4. CV (TT) HA - 0.112
Total Land HA 1.250 1.380 10
Total Gross Margin RP 283,089 303,314 7
Family Labor Manday 232 252 9
Hired Labor Manday 33 35 6
Total Labor Manday 265 287 8
Animal Draft AWD 9.5 9.5 0
Hired
Operating Capital RP 39,025 39,975 2
Return/hectare RP 132,692 127,839 3
Return/Manday of RP 1,041 1,032 1
Labor

 

Source: Computed.

*For explanation of the abbreviations see Table C.1,
Appendix C.

119

more family labor is used, there is the expected decrease
in return to labor from Rp104l to Rp1032, indicating
diminishing return to labor. There were no changes in
animal draft hired after the labor constraint was relaxed.
The relaxation of the labor constraint brought more land
into production of food crOps and land became a limiting
factor as shown by the shadow price of land at Rp88,99l.

Returns per hectare decreased from Rp132,692 to
Rp127,839, a decrease of 3 percent, indicating a diminishing
return to land as more land was brought into production.
Operating capital increased from Rp39,025 to Rp39,975, an
increase of 2 percent. This increase in Operating capital
was due to increase in hired labor input.

There were also changes in the crOp enterprise com-
bination. Sole cr0p of cassava entered the Optimal plan
when the family labor constraint was relaxed. Land devoted
to a crOp mixture of corn + upland rice f cassava decreased
from 0.214 hectares to 0.185 hectares, a decrease of about
13.5 percent. Land devoted to a crop mixture of upland rice
- soybeans increased from 0.536 hectares to 0.583 hectares,
an increase of 9 percent. This increase in land devoted to
this crOp mixture of upland rice - soybeans was at the
expense of the decrease in land devoted to the crOp mixture
of upland rice + corn { cassava. Also more land was brought
into production as family labor became more available. The
effects of increasing family labor in Adiluih and Bulusari

villages (representing Central Lampung) is presented in

120

Table 4.13. Total gross margin increased from Rp182,238 to
Rp195,658, an increase of 7 percent. Family labor use
increased from 219 mandays to 235 mandays, an increase of

7 percent. Hired labor also increased from 71 mandays to
78 mandays, an increase of 10 percent. Total labor use
increase from 290 mandays to 313 mandays, an increase of

8 percent. The increase in labor use had an effect on the
decrease in returns to labor from Rp585 to Rp578, an indi-
cation of diminishing return to labor.

Operating capital increased from Rp70,000 to Rp81,300,
an increase of 16 percent. The marginal value product of
dryland was Rp178,954 and the marginal value product of wet-
land/irrigated ricefield was Rp237,996, much higher for
irrigated land. This may indicate that one of the strategies
to increase farm income is to convert dry upland into
irrigated land (assumed technically and economically feasible).
Land becomes more limiting as more family labor is available.

The Optimum farm organization became less diversified
as family labor became available. The crOp enterprise mixture
of upland rice f cassava is eliminated from the optimal plan
as family labor became available in the peak season. More
land was devoted to the cr0p mixture of corn + upland rice f
cassava from 0.262 hectares to 0.574 hectares as more family
labor became available.

The effects of increasing family labor input in
Pugung Raharja (represents South Lampung) are presented in

Table 4.14. Total gross margin increased from Rp192,970 to

121

Table 4.13.--Optimal Plan of the Representative Farm Under
New Technology with Increased Family Labor in
Central Lampung.

 

Existing Increased Change

 

Item* Unit Resources Labor Percent
l. C + ULR f CV (NT) HA 0.262 0.574 119
2. ULR f CV (TT) HA 0.240 -
3. C f CV - SB (TT) HA 0.518 0.446 14
4. LLR (NT) HA 0.240 0.240 0
Dry Land HA 1.02 1.02 0
Wet Land HA 0.24 0.24 0
Total Land HA 1.26 1.26 0
Total Gross Margin RP 182.238 195,658 7
Family Labor Manday 219 235 7
Hired Labor Manday 71 78 10
Total Labor Manday 290 313 8
Animal Draft AWD 12.8 15 17
Hired
Operating Capital RP 70,000 81,300 16
Return/Hectare RP 52,074 55,076 6
Return/Manday of RP 585 578 -1
Labor

 

Source: Computed.

*For explanation of the abbreviations see Table C.1,
Appendix C.

122

Rp195,692, an increase of 1.5 percent. Family labor use
increased from 257 mandays to 268 mandays, an increase of

4 percent. The increase in gross margin was not so signi-
ficant compared to increase in family labor input of 4 per-
cent. It may not be worthwhile to increase family labor
input into the farm by 4 percent if gross income increases
only by 1.5 percent.

Hired labor was eliminated after the family labor
became available. Total labor input increased by 2 percent,
all supplied by the family labor. As the labor input in-
creased, the return to labor decreased, again indicating
diminishing return to labor. Average return to labor de-
decreased from Rp73l/manday to Rp7l7/manday. Operating capi-
tal decreased from Rp7,500 to Rp5,000 due to no more use of
hired labor, and there were no changes in the use of cash
inputs such as for purchasing fertilizers and insecticides.

It seems that the representative farm in South
Lampung subregion has used its resources, e.g., labor, nearly
Optimally in most traditional techniques. The only new input
used by the representative farm was insecticides, with no
use of other modern inputs such as fertilizers and improved
seeds. So the strategy to increase food production and farm
income is to introduce new productive and profitable inputs.
Farmers must be shown a tangible fashion in readily comparable
circumstances, the new production techniques. They must be
shown the risk involved both in yield and price fluctuations.

They must also be aware of the farmer expenditure constraints.

123

Table 4.14.-—Optima1 Plan of the Representative Farm Under
New Technology with Increased Family Labor in
South Lampung.

 

I Existing Increased Change
Item* Unit Resource Labor Percent

 

1. ULR - SB/CV HA 0.325 0.368 13

2. ULR + C - SB/CV HA 0.454 0.411 9

3. SB - SB - SB/CV HA 0.321 0.321 0
Total Land HA 1.100 1.100 0
Total Gross Margin RP 192,970 195,692 1.5
Family Labor Manday 257 268 4
Hired Labor Manday 5 0 -100
Total Labor Manday 262 268 2
Operating Capital RP 75,000 5,000 -33
Return/Hectare RP 63,223 62,420 -1
Return/Manday of RP 731 727 -0.5

Labor

 

Source: Computed.

*For explanation of abbreviations see Table C.1,
Appendix C.

124

4.5 Comparison of Actual and Optimal Plan
of the Representative Farms Under New
Technology with Existing Resources

Table 4.15 through Table 4.17 present the comparison
of actual and optimal plans under the new technology and
existing resources in the three subregions North, Central,
and South Lampung. North Lampung (see Table 4.15) shows a
significant difference between the actual/observed plan and
the Optimal plan. Land devoted to the Optimal plan is
smaller by about 9 percent, 1.38 hectares in the actual plan
and 1.25 hectares in the Optimal plan. This may be due to
the fact that in the actual plan more labor is used (255
mandays) compared with 232 mandays for the optimal plan.

Cr0p enterprise combinations differ significantly
and are less diversified under the Optimal plan. CrOp mix-
tures of corn + cassava, and upland rice f cassava were
eliminated in the Optimal plan. Sole crop of cassava also
does not enter the optimal plan. There was a relatively
very large increase in land devoted to crOp mixture of up-
land rice - soybeans from 0.03 hectares in the actual plan
to 0.536 hectares in the Optimal plan, an increase of 1777
percent.

In North Lampung, total gross margin is higher by
43 percent in the Optimal plan compared to the actual plan
even though land cultivated is smaller. Return to labor is
higher in the Optimal plan by 54 percent than in the actual
plan. Return to land per hectare is very significantly

higher in the Optimal plan, by 158 percent.

125

Table 4.15.--A Comparison of Actual and Optimal Plan of the
Representative Farm Under New Technology and
Existing Resource Levels in North Lampung.

 

Actual Optimal Change

 

Item* ’ Unit Plan Plan Percent

1. c + ULR % CV (TT) HA) 0.214)
) 0.620 ) 15

2. c + ULR { CV (NT) HA) 0.500)
3. ULR - SB (NT) HA 0.03 0.536 1777
4. ULR i CV (TT) HA 0.19 - -100
5. C f CV (TT) HA 0.09 - ~100
6. CV (TT) HA 0.37 - -100
7. GNDT (TT) HA 0.08 - -100
Total Land HA 1.38 1.250 -9
Total Gross Margin RP 197,620 283,089 43
Family Labor Manday 255 232 -9
Hired Labor Manday 29 33 14
Total Labor Manday 284 265 -7
Animal Draft Hired AWD 6.5 9.5 46
Operating Capital RP 29,925 39,025 30
Return/Hectare RP 51,528 132,692 158
Return/Manday 677 1,041 54

 

Source: Computed.

*For explanation of abbreviations see Table C.1,
Appendix C.

126

These differences can be attributed probably to the
fact that North Lampung is a relatively newly Opened area
for transmigrants and use of modern inputs was just recently
introduced to the representative farm. So the representative
farm has not yet sufficient time to make all necessary adjust-
ments to achieve Optimum farm organization. The other factor
that may have contributed to these differences between Optimal
and actual plan of the representative farm is the data limita-
tion. Information obtained was based on the "memory" of the
respondent farmers which, in the interview, covered a whole
crOp year of 1978/79. Problems may have develOped in
measuring, for example, the area of land cultivated and
yields.

If we assume that the results of the linear program-
ming applications are valid, the implication from the analy-
sis is that there is some potential for achieving an increase
in farm income and improvement in the efficiencies of
resource use measured by the average return to resources
such as land, labor, and Operating capital. This increase
in farm income could be realized through reallocation of
resources among crOp production activities as indicated by
the optimal plan.

The slow process of adjustment in the farm organiza-
tion toward the optimal plan may be due to the limited
knowledge of the representative farmer. A more intensive
extension effort backed by field research could probably

Speed up the process.

127

The comparison of Optimal and actual plans under
existing resource levels for Central Lampung is presented
in Table 4.16. The table shows that there was no significant
increase in farm income from the actual, even though there
is a significant change in the crOp enterprise combination.
Sole crOps of upland rice and cassava were eliminated in the
optimal plan, while there was a significant change in the
land devoted to crOp mixtures. For the crOp mixture of corn
f cassava - soybeans, land devoted to it in the optimal
plan covered 0.518 hectares and in the actual plan only
0.13 hectares, an increase of 298 percent. The increase of
land devoted to this crOp mixture was at the expense of the
crOp mixture of corn + upland rice f cassava and the single
crOps of upland rice and cassava. There was an increase of
20 percent of land devoted to crop mixture of corn - soybeans
+ cassava under the new technology.

Family labor use was decreased in the Optimal plan
compared to the actual plan by 11 percent. Returns to labor
increased in the optimal plan by 3 percent compared to the
actual plan.

Hired labor use increased by 51 percent and use of
animal draft power increased by 35 percent. The impact on
Operating capital was to increase its use in the Optimal
plan compared to the actual plan by 29 percent. Return to
land was higher in the optimal plan by 25 percent.

If the results of the linear programming are valid

then there is a small potential to increase farm income,

Table 4.16.--A Comparison of Actual and Optimal Plan of the

128

Representative Farm Under New Technology and
Existing Resource Levels in Central Lampung.

 

 

Actual Optimal Change

Item* Unit Plan Plan Percent
l. C + ULR f CV (NT) HA 0.340 0.262 -23
2. C + ULR f CV (TT) HA 0.200 0.240 20
3. C - SB f CV (NT) HA 0.130 0.518 298
4. LLR (NT) HA 0.240 0.240 0
5. ULR (TT) HA 0.100 - -100
6. CV (NT) HA 0.250 - ' -100
Total Land HA 1.260 1.260 0
Total Gross Margin RP 178,500 182,238 2
Family Labor Manday 245 219 -11
Hired Labor Manday 47 71 51
Total Labor Manday 292 290 -1
Animal Draft Hired AWD 9.5 12.8 35
Operating Capital RP 54,250 70,000 29
Return/Hectare RP 41,548 52,074 25
Return/Manday Labor RP 568 585 3

 

Source: Computed.

*For explanation of abbreviations
Appendix C.

see Table C.1,

129

but high potential to increase efficiency of the resource

use such as land (as indicated by the average returns to
land). This high increase in return to land resource but
small increase in farm income could be achieved through the
reallocation of resources among crOp enterprises as indicated
in the optimal plan. Then the question may arise is whether
the small increase of 2 percent in farm income is worthwhile
to make the changes with a decrease in family labor input by
11 percent (absolute decrease by 26 mandays) while increases
in use of hired labor by 51 percent (absolute increase by

24 mandays), increase in use of hired animal draft by 35 per-
cent (absolute increase by 3.3 animal working days) and its
consequences of increasing operating capital by 29 percent
(absolute increase by Rp15,750). Taken into account risk
factor, it is likely that the farmer would not make any
changes in order to obtain a nonsignificant increase in his
income.

The comparison of actual and Optimal plans under
existing resources in South Lampung is presented in Table
4.17. The table shows that gross farm income in the optimal
plan is higher compared to the actual plan by 6 percent.
There were changes in the farm organization. The crOp mix-
ture of upland rice + corn - soybeans was eliminated in the
optimal plan. Sole crOp of cassava was eliminated in the
optimal plan.

There was a significant change in the land devoted

to each crOp mixture. Land devoted to the cr0p mixture of

130

Table 4.17.--A Comparison of Actual and Optimal Plan of the
Representative Farm Under New Technology and
Existing Resource Levels in South Lampung.

 

 

Actual Optimal Change
Item* Unit Plan Plan Percent
l. ULR - SB/CV (NT) HA 0.360 0.325 -11
2. SB - SB - SB/CV (NT) HA 0.240 0.321 34
3. ULR + C - SB/CV (NT) HA 0.280 0.454 62
4. ULR + C/CV (TT) HA 0.200 - -100
5. CV (TT) HA 0.020 - -100
Total Land HA 1.100 1.100 0
Total Gross Margin RP 181,425 192,970 6
Family Labor Manday 288 257 -ll
Hired Labor Manday 27 5 -440
Total Labor Manday 315 262 -17
Operating Capital RP 18,000 7,500 -58
Return/Hectare RP 49,623 63,223 27
Return/Manday Labor RP 664 731 10

 

Source: Computed.

*For explanation of abbreviations
Appendix C.

see Table C.1,

131

upland rice - soybeans was smaller by 11 percent in the
Optimal plan compared to the actual plan. Triple crOps of
soybeans increased by 34 percent compared to the actual
plan, and land devoted to the crOp mixture of upland rice +
corn - soybeans increased by 62 percent compared to the
actual plan. The crOpping pattern was less diversified in
the Optimal plan than in the actual plan.

There was a decrease in family labor input in the
Optimal plan compared to the actual plan by 11 percent.
Hired labor use declined to about one-fifth of the level of
the actual plan and total labor use decreased by 17 percent
in the Optimal plan compared to the actual plan. There was
also a decrease in Operating capital by 58 percent in the
Optimal plan compared to the actual plan due to reduced hired
labor input in the Optimal plan. Even though there were
decreases in resource use in the Optimal plan compared to
the actual plan, gross income and returns to resources were
higher in the Optimal plan. Returns per hectare were higher
by 27 percent and returns to labor were higher by 10 percent.

Assuming that the results of the linear programming
analysis are valid, then there was only a small potential to
increase farm income and a small increase in returns to
labor, but a substantial Opportunity to increase the produc-
tivity of the land resource. These increases can be obtained
through the reallocation of resources among the cr0p enter-
prises as indicated in the Optimal plan under the new tech-

nology. It should also be noted that the only new input

132

which has been used after the introduction of the new tech-
nology on the representative farm in South Lampung is
insecticides.
4.6 Summary of the Results from
Linear Programming Analysis

The results of the analysis from the empirical data
collected in the study area has been presented in this
Chapter. The findings indicated that some of the cropping
patterns have a better competitive position in the Optimal
plan under the new technology than under the traditional
one, i.e., in North Lampung this would be mixed cropping of
upland rice - soybeans, in Central Lampung this would be
mixed cropping of corn i cassava - soybeans, and for South
Lampung this would be mixed cropping of upland rice + corn -
soybeans. The crOpping pattern is less diversified under
the new technology. There is a comparative advantage in
producing a specific crop with specific crOpping patterns
in each subregion as shown by the results of the linear
programming analysis as well as by observations.

North Lampung has a comparative advantage in pro-
ducing cassava and upland rice with crOpping patterns con-
sisting of crOpping mixtures: corn + upland rice i cassava.
Central Lampung has a comparative advantage in production of
rice and cassava with crOp mixtures: corn - soybeans % cas-
sava, and corn + upland rice f cassava. South Lampung has

a comparative advantage in producing soybeans with crOp

133

mixtures: corn + upland rice - soybean, upland rice - soy-
beans, and triple crOps of soybeans in one crOpping year.

The introduction of currently available new techno-
lOgy to the upland farming in Lampung indicates from the
results of the linear programming analysis that there is
some potential to increase food production and income of the
farmers, and increase resource use and productivity. The
highest potential is in North Lampung which is a relatively
newly settled transmigration area. This may be due to the
fact that the representative farm has not yet enough time
to make required adjustments towards the Optimal farm organi-
zation. For the representative farms in Central and South
Lampung the results of the analysis indicated that there is
little potential to increase farm income and food production
through the adOption of the currently available new techno-
logy. It is also noted that in Pugung Raharja village
(South Lampung) insecticides is the only new input used by
the representative farm.

The results of the analysis of the representative
farm in Central Lampung show that the marginal value product
of irrigated land is much higher than the marginal value
product of dryland. This may imply that the conversion of
upland/dryland into irrigated land is one of the strategies
to increase both food crOp production and farm income of the
small upland farmers.

Labor during the peak demand months is a limiting

factor in increasing food crOp production and farm income

134

in the study area, eSpecially for weeding under the new
technology, and for land preparation under the traditional
technology. Hired labor is limited in the peak seasons
since there are no landless farmers in the study area.

The use of animal draft power and other source of power may
release part of this labor constraint. Medium to long term
credit may be needed for investment in additional sources
of power (e.g., cows, small tractors).

The representative farmers need no credit for Oper-
ating capital if they practice a mixed crOpping pattern
which distributes cash income more evenly through the year.
This is accomplished by selling their products after harvest
of cassava in September, soybeans in February, corn in March,
and rice in May.

In this Chapter the analysis emphasized the compari-
son of the Optimal farm organization under traditional and
under the new technolOgy to present the likely effects of
the introduction of the new technology. The comparison of
the effects of the new technology with existing resource
levels between the three subregions sheds some light on the
comparative advantage in producing specific crOps with
Specific cropping patterns. The analysis also sheds some
light on the importance of some of the limiting resources
in the Optimal plan under the new technology. In the follow-
ing Chapter the normative supply functions and estimates of

price elasticities of the major crOps will be discussed.

CHAPTER V

NORMATIVE SUPPLY FUNCTIONS FOR MAJOR FOOD
CROPS UNDER TRADITIONAL AND

NEW TECHNOLOGIES

5.1 Introduction

The previous chapter dealt with the analysis of the
changes in the optimal plan of allocation of farm resources
as a new technology is introduced to the traditional farm-
ing. The analysis emphasized the effects of the new tech-
nology on farm income, crop enterprise combinations, resource
use and their productivities, and identification of some of
the constraints. In this chapter the examination will be
on the effect of the new technology on the major food crops
supply functions and elasticities on the representative farms
in the study area.

In Indonesia the major food crops are rice, maize
or corn, and cassava. For 1977 rice harvested covered 8.8
million hectares and total production 23.9 million tons of
unhulled rice, corn harvested included 2.6 million hectares
and total production of 2.8 million tons, and cassava har-
vested was 1.36 million hectares and a total production of

12.17 million tons (Asian Development Bank, 1978). In terms

135

136

of human consumption, rice is predominant providing about
one half of all calories. Corn provides about a quarter
and cassava about one-fifth of all calories.

For example, if the relative price of rice declined
relative to the other staple food crops, rice will be con-
sumed in a larger quantity, and vice versa. Income elas-
ticity of rice in Indonesia is 0.6 which is very high
(Timmer, 1974; Parhusip, 1976). Rice production has bene-
fitted from the government investment program such as irri-
gation, credit and extension programs (BIMAS), and research
programs in Indonesia. Rice production is still lagging
behind the increasing demand due to the rapid population
and income growth. The average annual import of rice is
about two million tons since the late 19703 and now Indo-
nesia has become the largest rice importer in the world.

In Lampung, rice, corn, and cassava are also the
major food crOps. Area harvested and total production of
these crops for Lampung province is presented in Table B1
through Table B3 of Appendix B. Corn production has deteri-
orated since 1973 and cassava production only has recently
had a significant increase. Lampung is still a rice deficit
area and local rice production cannot compete with the
increasing demand for rice. Corn is produced mostly for
domestic consumption. Some corn is exported and locally
traded. For example, in 1977, 3,888 tons were exported and
15,801 tons were traded to the other provinces, especially

to Java. Cassava is also produced mostly for domestic

137

consumption in the rural area as well as in the urban
areas. Exports and other uses of cassava are fairly small,
probably less than 15 percent of total production (Dixon,
1979; Jones, 1978).

The low prices received by the farmers for these
crops have often been cited as one of the causes of the
slow growth or even deterioration in their output. Many
studies have shown that small farmers are rational and
respond positively to increases in commodity prices
(Gotsch and Falcon, 1974). Given this small farmer's
behavior, then policy makers have sometimes been called
upon to raise producer prices in order to increase food
production. An accurate knowledge of the price elastic-
ities of supply of these major crops is required to make a
correct decision in a right direction and appropriate pro-
ducer price increases in order to increase output by the
required amount.

The adoption of the new technology shifts the pro-
duction function and many times changes the farmer's
response to commodity price changes (Gotsch and Falcon,
1974). Where rapid technological change is being experi-
enced by the farmers, the previous estimates (if any) of
price elasticities will not be a very good guide to the
future supply response of the farmers in producing these
crops. A recent estimate of price elasticities of these
major crops may be needed in order to provide a basis for

meaningful public decisions.

138

According to Heady (1961) there are two major direc-
tions from which empirical estimates of agricultural supply
have been attacked in the past. The positive or descrip-
tive approach is the prediction of the quantitative rela-
tionship among variables as they actually do exist at a
point of time, or have existed over a period of time. The
other is the normative or predictive approach which refers
to what ought to exist, under certain assumptions. The
major tools for positive analysis are regression procedures,
e.g., econometric analysis, which attempts predictions from
observations drawn out of the "actual operating world."

The major tools for normative analysis include budgeting

and programming. Here, certain assumptions are usually made
about goal(s) and actions of decision makers. Both positive
and normative approaches have been used and are being used
because of the limitations of the estimates derived by each.

The most traditional positive approach is econo-
metric analysis of aggregate time series data. This approach
is descriptive since the estimation of the parameters is
based on the past behavior of producers in response to pro-
duct price changes and other relevant economic variables.
This positive analysis of supply functions indicates
responses to the real world in the past but is restricted
in its ability to estimate what might be in the future when
there are changes in structures or new variables.

A normative approach to estimation of supply func-

tions is by programming individual farms for optimal

139

solutions, indicating farmer's potential response under the
assumptions of farm income maximization, perfect knowledge
about prices, technological changes and institutional envi-
ronmental factors. To the extent that these assumptions
fail, a farmer's actual decisions may sometimes differ
markedly from those indicated as optimum.

Both approaches have their advantages and limita-
tions, and they are supplements rather than substitutes for
each other. However, when rapid technological change is
experienced by the farmers, and when there is a lack of
accurate time series data on production and prices at the
farm level, then there are definite advantages in the appli-
cation of the programming approach. These advantages have
been summarized by Buckwell and Hazel (1972). Programming
provides a wealth of information on the farm level which is
very useful for evaluation of the likely impact of policy
on many small farm problems, embodied in a causal system of
how individual farms function. It becomes easier to evalu-
ate the future impact of policies. The fact that most
farmers produce many products and use many resources with
various enterprise combinations renders this approach well
suited to evaluate the total impact of changes in relative
prices on the supply of the individual crops.

These advantages may be weighted against the enor—
mous data requirements of programming. The supply function
estimated by the programming is normative in the sense that

it indicates what farmer should produce in order to maximize

140

income. The power of the analysis is dependent on the
degree of correspondence between the assumptions made and
reality including the structure of constraints, activities
included in the model, technological coefficients, prices
of outputs and inputs, and how risk factors are taken into
account in the model (Heady, 1961). The normative approach
by its nature may lead to an upward biased estimate of the
supply function and elasticities (Anderson et‘al., 1965;
Sheehy e5 21., 1964). There is no clear way to estimate

to what extent normative quantities should be adjusted to
closely approximate the actual supply response. Krenz et
31. (1962) have suggested that the supply function could be
made "less normative" or more realistic by including in the
linear programming only production activities that the
farmer is likely to consider.

The normative supply function estimated for the
representative farm is derived from the linear programming
model presented earlier. Parametric (or variable price)
programming is used to derive the optimal output of the
commodity concerned as its price is varied within an
appropriate range while other prices are held constant.
Variable price programming derives a supply function that

can be formalized as follows (Krenz et 31., 1962):

QA = f (P1, P2, PA, . . . Pn, R1, R2, . . . Rn,

C1, C2, . . . Cn) in which:

141

QA = quantity of A produced (PA varied)

P1 to Pn = prices of factors and products at the farm
level

1R1 to Rn = the fixed resources of the farm (firm)

C1 to Cn = coefficients of production on the farm in

all production alternatives considered.
The supply function also considered alternative products
that may be produced with the given resources, at specified
prices for factors, and technology of the farm under con-
sideration. The supply function obtained is normative in
the sense that it indicates what a farmer would plan to
produce if he intended to maximize profits.

The functional relationship of price and quantity
of the commodity derived from linear programming is dis-
continuous and in the form of a "step" function. Burt
(1964) defined a step function as "a function such that a
range is divided into a finite number of intervals with
the dependent variable constant on a given interval."
Graphically a step function appears as a series of steps
as shown in Figure 5.1. By putting a consumption constraint
or household requirement in the model, then that amount
should be produced irrespective of its price (subsistence
production). According to Kottke (1967) the stepped supply
function consists of two parts. The horizontal segment
depicted in Figure 5.1 is identified as marginal cost.

The vertical segment of the step is identified by applying

142

 

 

 

 

 

 

P'
(MC)
SP + SM
S? J:“’
Mcc ._ f -----
MCb an ~ ‘— ~ .4
SP = Subsistence
Production
Mca ‘”
------ SM = Marketed Surplus
I ‘r 4* ‘
Os 03 Qb QC Q

(Quantity of Output)

Figure 5.1. Farm-Firm Step Supply Function.

143

the profit maximizing criterion of MR 3 MC. The vertical
segments or optimal solutions and price ranges for the

steps can be formulated as follows:

f(P) = Qs for 0 i P i MCa
= Qa for MCa < P i MCb
= Qb for MCb < P : MCc

Qc for MCc < P

where MC is the marginal cost of producing Q, and P is the
price of Q.

The range of the vertical segments of the supply
function is based on the profit maximizing criterion P =
MR = MC. The optimum crop enterprise combination, and
the optimum output, holds for all the prices included in
the range of the vertical portion of any one step. The
"stepped" characteristic of the supply function results
from the finite number of production alternatives and
rigid resource restrictions in the programming model. The
number of steps and corners are the function of the number
of production alternatives and restrictions. Including
more production alternatives and restrictions will generate
a normative supply function with more and smaller steps.

A supply function derived by means of parametric
programming is static and in partial equilibrium in that no
changes are assumed other than the price of the product
(e.g., no changes in asset structure, no changes in tech-

nological coefficients). Supply elasticities associated

144

with normative supply functions are biased upwards when
compared with those obtained from time series data in which
implicit are time lags, risk and uncertainty, and other
behavioral factOrs that influence farmer's decisions to
produce the output (Heady et 31., 1961).
5.2 Normative Supply Function of Rice,
Corn, and Cassava

In this section the supply functions for rice, corn,
and cassava derived from the linear programming models
described in Chapter III will be presented. Normative sup-
ply functions for the three crops were derived under tra-
ditional and new technologies with existing resource levels.
The effects of releasing family labor constraints under the
new technology were also examined. In the interpretation
of the results caution must be taken since they were
derived from one representative farm based on a purposive
sample that was drawn from a limited number of samples and
limited geographical areas. As a complete case only the
analysis of the representative farm of North Lampung will

be presented.

5.2.1 Rice Supply Function

In order to obtain the normative supply function
for rice, the price of rice in the selling activities were
varied over the range of Rp 100 per kilogram to Rp 300 per

kilogram and then corresponding optimal solutions were

145

obtained.1 The quantity of rice produced at each price
level was obtained from optimal solutions. The comparison
of the results under traditional and new technologies within
exiSting resource levels and new technology with labor
increased is presented in Table 5.1. The comparison shows
the likely impact of the new technology on the supply of
rice on the representative farm in North Lampung.

The introduction of the new technology resulted in
a shift in the supply curve outward to the right, indicat-
ing that the new technology has the effect of increasing
the quantity of rice produced at each price. The increase
of rice output comes from the expansion of acreage under
cultivation of rice and an increase in rice yields.

The table also shows that the normative supply
functions for rice under the new technology and increased
availability of family labor increased the quantity of rice
produced at each price level. The effectiveness of the new
technology was enhanced by increasing the availability of
family labor during the peak seasons. The effects of the
new technology and the increase in availability of family
labor are also shown in Figure 5.2.

The effect of relaxing credit constraints was not

analyzed here, on the consideration that operating capital

 

1The prevailing rice price in the study area is Rp
100 per kilogram. The price range used in the supply anal—
ysis may not represent the expected range of price increase,
due to the government control on rice price.

146

Table 5.1.--A Comparison of Normative Supply Functions for Rice Under
Traditional and New Technologies with Existing Resources
of the Representative Farm in North Lampung.

 

 

 

 

 

 

 

 

Traditional. New Technology
Technology
with Existing Existing Resource Labor Increased
Resources
Price Price
Price Range Quantity Range Quantity Range Quantity
(RP) (Kg) (RP) (Kg) (RP) (K9)
0-85 500 0-97 500 0-85 500
86-121 1144 100-121 1569 86-115 1602
122-257 1144 122-320 1569 116-363 1602
258-900 1153 321-666 1539 364—1326 1689

 

Source: Computed.

147

pares
(RP)6000

 

'I'T/ER NT/ER NT/gZIV

5009

400»

 

3001

 

200"

 

 

 

100v ‘_ _____ _.._ _.._ _ - .. - .. .. _... _ _ -

 

 

 

 

oLA’Ngbo 600 760 800 900 1000 1100 1100 1300 $400 1500 1600 1700
QUANTITY (KG)

Figure 5.2. Normative Supply Functions for Rice Under Traditional and

New Technologies of the Representative Farm in North

Lampung.

Explanation of Abbreviations:

TT/ER = Traditional Technology with Existing Resources
NT/ER = New Technology with Existing Resources
NT/LV = New Technology with Increased Family Labor

148

Table 5.2.--A Comparison of Farm Income Under Traditional
and New Technologies with Rice Price Varied on
the Representative Farm in North Lampung.

 

 

 

 

 

Price Quantity Sold (Kg) Farm Income (Rp)

(RP) TT/ER NT/ER NT/LV TT/ER NT/ER NT/LV
100 644 1069 1102 172302 283089 303314
115 644 1069 1102 181966 299118 319848
150 644 1069 1102 204514 336518 358418
400 653 1039 1089 366873 577925 611120

 

Source: Computed.

Explanation of Abbreviations:

TT/ER = Traditional Technology with Existing Resource
NT/ER = New Technology with Existing Resource Levels
NT/LV = New Technology with Increased Family Labor

Income = Gross Margin

149

is not a constraint due to a more even distribution of
cash income to the farmers as they practiced multiple
cropping.

Table 5.2 shows a comparison of farm income under
traditional and new technologies under existing resources
and with labor increased. Both rice production and farm
income are increased under the new technology at all price
levels. These increases are generated by the increase in
the availability of family labor under the new technology.
The implication is that the introduction of the new tech-
nology has a potential for increasing rice production and
farm income. These increases are enhanced by the increase
in the availability of family labor.

5.2.2 Corn or Maize Supply
Functions

The normative supply functions for corn were also
derived by variable price programming (parametrically vary-
ing the price of corn) over the range 60 Rupiah per kilo-
gram to 561 Rupiah per kilogram, then obtaining the corres-
ponding optimal solutions.1 Table 5.3 shows a comparison
of the quantity of corn produced at each price level under
traditional and new technologies with existing resource

levels and increased family labor under the new technology.

 

1The prevailing price of corn in the study area is
Rp 60 per kilogram, so this price range may be unrealistic.
The price range should be wide in any case in order to see
the effect of price changes on corn production.

150

Table 5.3.--A Comparison of Normative Supply Functions for Corn Under
Traditional and New Technologies with Existing Resources and
Labor Increased for the Representative Farm in North

 

 

 

 

 

 

 

 

Lampung.
New Technology
Traditional
Technology Existing Resource Labor Increased
Existing Resource
Price Price
Price Range Quantity Range Quantity Range Quantity
(RP) (KG) (RP) (KG) (RP) (KG)
0-37 150 0-46 150 0-47 150
37-80 238 47-79 501 48-94 491
81—212 268 99-135 499 94-200 484
212-300 268 145-300 502 201-300 484
301-561 276 301-717 502 301-421 484

 

Source: Computed.

151

With results similar to the case of rice, the adoption of
the new technology increased the quantity of corn produced
at all price levels (shifted the supply curve outward to
the right). The increase in corn production is the result
of an increase in yields and the increase in the acreage
under corn cultivation in a mixture. These supply func-
tions are presented in Figure 5.3. An increase in family
labor does increase in corn production and farm income.

The results of the analysis show that there is some
potential for increasing corn production and farm income
through the adoption of the new technology. The increase
in family labor availability will enhance corn production
and farm income. Under the new technology corn is produced
in a mixed cropping system.

Table 5.4 shows a comparison of farm income under
traditional and new technologies with existing resources and
labor increased. Both production and farm income are
increased under the new technology as in the case of rice.
This is also true for corn. The increase in the avail-
ability of family labor enhances corn production and farm
income. The implication is that the introduction of the
new technology has a potential to increase both corn pro-
duction and farm income. These increases are also enhanced

by the increase in the availability of family labor.

PRICE

(RP)

550

500

450

400»

350

300

250

200

150
100

50

152

 

 

Figure 5.3.

r
I

 

 

 

 

 

NT/ER
TT/ER '
NT/LV
r—
F
150 250 350 450 550 650
QUANTITY (KG)

Normative Supply Functions for Corn Under
Traditional and New Technologies of the
Representative Farm in North Lampung.

Explanation of Abbreviations:

TT/ER
NT/ER

NT/LV

Traditional Technology with Existing Resources
New.Technology with Existing Resources

New Technology with Increased Family Labor

 

153

Table 5.4.--A Comparison of Farm Income Under Traditional
and New Technologies with Corn Price Varied
for the Representative Farm in North Lampung.

 

 

 

 

 

Price Quantity Sold (KG) Farm Income (RP)
(RP) TT/ER NT/ER NT/LV TT/ER NT/ER NT/LV
60 88 351 336 172302 283089 303658
100 113 349 334 176811 297032 316685
200 113 352 334 188081 332152 350113
300 113 352 334 199352 367352 383541
350 121 352 334 205402 384952 400255

 

Source: Computed.

Explanation of Abbreviations:

TT/ER = Traditional Technology with Existing Resources
NT/ER = New Technology with Existing Resource Levels
NT/LV = New Technology with Increased Family Labor

Farm Income = Gross Margin

154

5.2.3 Cassava Supply Functions

The normative supply functions for cassava were
obtained by parametrically varying the price of cassava over
the range of 17 Rupiah per kilogram to 60 per kilogram,1 and
then obtaining the corresponding optimal solutions.

Table 5.5 shows a comparison of the quantity of cassava
produced at each price level under traditional and new tech-
nologies and at an increased level of labor under the new
technology. At all price levels the production of cassava
is higher under the new technology than under the tradi-
tional technology. An increase of family labor availability
has a positive impact on cassava production.

The supply curve of cassava under the new technology
shift upward to the right as shown in Figure 5.4. The
likely impact of the adoption of the new technology will
increase cassava production and farm income. Table 5.6
shows a comparison of farm income under the traditional
and new technologies. Farm income is higher under the new
technology than under the traditional technology at all
price levels. Increased family labor availability enhanced

both cassava production and farm income.

 

1The prevailing price of cassava in the study area
is Rp 17 per kilogram, the price range is realistic since
demand for cassava is strong due to a recent dramatic
increase in the capacity of cassava processing industries
in Lampung. Also the prospects for cassava prices are
favorable in domestic markets and abroad.

155

Table 5.5.--A Comparison of Normative Supply Functions for Cassava Under
Traditional and New Technologies with Existing Resource and
Labor Increased for the Representative Farm in North

 

 

 

Lampung.
Traditional Technology New Technology
Existing Resource Existing Resource Labor Increased

 

 

 

Price Range Quantity Price Range Quantity Price Range Quantity

 

 

 

 

(RP) (KG) (RP) (KG) (RP) (KG)
0-11.0 3000 0-16 3000 0-7 3000
11-18.5 4314 16.2-18.3 9718 7-9 10384
19-21 5169 19.7-21.5 9720 19.5-20 11482
24-28 5380 24.3-31.6 9778 20-48 11482
28-34 6151 34-48.6 10729 48-168 13111
36-47 8415 48.6-79.6 13011 168-283 13111
47-90 8523

 

Source: Computed.

‘l

156

 

 

 

 

 

 

 

PRICE
(RP)
110 ' NT/LV
. 1001, F
90)» Tr/[ER- _—
801»
700
NT/ER
60h
50m ......
40" r - ‘-
- - - - - NT/LV
301- I - f
20” _----' NT/ER -----
1. (I J
0v . . . . . j , , , I r
O 3000 4000 5000 6000 7000 80009000 10000 12000 14000

11000 13000

QUANTITY (KG)
Figure 5.4. Normative Supply Functions for Cassava under Traditional
and New Technologies of the Representative Farm in North

Lampung.

Explanation of Abbreviations:

TT/ER = Traditional Technology with Existing Resources
NT/ER = New Technology with Existing Resources
NT/LV = New Technology with Increased Family Labor

 

157

Table 5.6.--A Comparison of Farm Income Under Traditional
and New Technologies with Cassava Price Varied
for the Representative Farm in North Lampung.

 

 

 

 

 

Price Quantity Sold (KG) Farm Income (RP)
(RP) TT/ER NT/ER NT/LV TT/ER NT/ER NT/LV
17 1314 6718 7384 172302 283089 303658
20 2169 6720 8482 176663 303237 325903
25 2380 6778 8482 188023 337076 368368
30 3151 6778 8482 201369 370925 410784
40 5415 7729 8482 249015 445598 495618
50 5523 7729 10111 303457 526166 583711
60 5523 7729 10111 358687 603458 684822

 

Source: Computed.

Explanation of Abbreviations:

TT/ER
NT/ER

NT/LV

Farm Income = Gross Margin

Traditional Technology with Existing Resources

New Technology with Existing Resources

New Technology with Increased Family Labor

158

The effects of commodity relative price increases
under the new technology show that increases in relative
price of cassava has the largest impact on farm income.

For example a 50 percent increase in the relative price of
cassava (from Rp 17 to Rp 25.50 per kilogram), ceteris
paribus, farm income increases by 20 percent from Rp 283,089
to Rp 340,465. An increase in the relative price of corn
by 50 percent (from Rp 60 to Rp 90 per kilogram), ceteris
paribus, farm income increases by 4 percent from Rp 283,089
to Rp 293,542. An increase in relative price of rice by 50
percent (from Rp 100 to Rp 150), ceteris paribus, farm
income increases by 19 percent from Rp 283,089 to Rp
336,518.

5.3 The Effects of the New Technology on

the Price Elasticities of the Supply
of the Major Food Crops

The ultimate purpose of the supply function analy-
sis generally is the estimation of price elasticity. To
complete the analysis of the supply functions of the major
food crops, i.e., rice, corn, and cassava, the estimation
of the price elasticities of these supply functions will
proceed. For the "stepped" supply function, estimation of
the price elasticity is not as clear-cut as the estimation
of the price elasticity of smooth supply functions. Esti-
mation of the stepped supply function is meaningless unless
the function is transformed into a smooth or continuous

supply curve.

159

Some methods are available to transform a "stepped"
supply function into a smooth supply function. Krenz (1960)
used optimum quantities and their corresponding prices as
the observations to be used as data for a least square
regression analysis to estimate price elasticity as in a
continuous function. According to Burt (1964) the mid-
points of the vertical portions of the steps are more stable
with respect to price changes and therefore are used as
values for price (independent variable). This method will
be used in this section to estimate price elasticities of
supply of the major food crops under traditional and new
technologies.

A continuous supply function is fitted to the data
presented as stepped supply functions in section 5.2. The
model is based on only a single independent variable since
it is assumed there are no changes in factors affecting
supply except its price. The general functional form is

as follows:

Q=f(P)
where:
Q = quantity of commodity produced in kilograms
per crop year
P = price of commodity in Rupiah per kilogram

Each vertical part of the step function is treated as an
observation on the dependent variable. It has been men-

tioned before that price production is stable at the

160

midpoints of the vertical parts of the steps (and therefore
these midpoints will be used as values for the independent
variable). But it should be born in mind that the data
generated by this procedure may not meet the normality and
independence assumptions, so that statistical inferences
and probability statements cannot be made.

Functional relationships to be considered are as in

the following forms:

Q = 80 + 31 P (linear relationship)

Q = BO + 81 P + 82 P2 (quadratic relationship)

Q = Bo + 81 log P (semi-log relationship)
1n Q = 80 + 81 log P (double-log relationship)

The criteria to be used for choosing the "best" fit of the
functional relationship are: size of adjusted R square, the
sign of the price coefficient (81) and F- value for the
regression mean square. For estimating these functional
relationships by using the SPSS (Statistical Package for
Social Sciences) by Nie, 1975.

The estimated supply equations for these major
food crops are presented in Table 5.7, Table 5.8, and
Table 5.9 for rice, corn, and cassava respectively. For
the rice supply equations the R2 are low and all coeffici-
ents are not significant at 5 percent level. Corn supply
equation R25 are also low and all coefficients are not

significant at the 5 percent level, except the corn supply

equation under traditional technology. This equation has

161

Table 5.7.--Estimated Supply Functions for Rice Under
Traditional and New Technologies for the
Representative Farm in North Lampung.

 

Technology and Functional B 8 -2
Resource Level Form 0 1 a,Y

 

Traditional Tech- Semi-log -86.9 501.5 0.30 2.3
nology and Exist-
ing Resources

New Technology Double log 2.1 0.44 0.45 3.5
and Existing

Resources

New Technology Double-log 2.4 0.31 0.25 2.0

and Labor Increased*

All of the coefficients are not significant at 5 percent
level

 

Source: Computed.

*It was assumed that the family members are willing
to work each month as long as in the months of the peak
season.

162

Table 5.8.--Estimated Supply Functions for Corn Under
Traditional and New Technologies for the
ReSpresentative Farm in North Lampung.

 

 

Technology and Functional B B §2 F
Resource Level Form 0 l a,y
Traditional Tech- Semi-log 60.9 87.0* 0.80 16.8

nology and Exist-
ing Resources

New Technology Smi-lOg -47.0 232.1 0.43 4.0
and Existing
Resources

New Technology Double-log 1.76 0.40 0.56 6.1
and Labor
Increaseda

 

Source: Computed.

*Significant at 5 percent.

aIt was assumed that the family members are willing
to work as long as in the months of peak season.

163

Table 5.9.--Estimated Supply Functions for Cassava Under
Traditional and New Technologies for the
Representative Farm in North Lampung.

A

 

Technology and Functional B B §2 F
Resource Level Form 0 l a,y
Traditional Tech- Double-log 3.04 0.5** 8.95 111.2
nology and Exist-

ing Resources

New Technology Semi-log -2909.9 8712** 0.82 28.8
and Existing

Resources

New Technology Semi-log 3055.9 4721.7** 0.57

and Increased

Labora

 

Source: Computed.
*Significant at 5 percent.

**Significant at 1 percent.

aIt was assumed that the family members are willing
to work as long as in the months of peak season.

164

R2 = 0.80 and the 81 coefficient is significant at the 5

percent level. The R2 for the cassava supply equations
are high at 0.95, 0.82, and 0.57 under traditional technol-
ogy-with existing resources, new technology with existing
resources, and under the new technology with increased
family labor respectively. All coefficients for cassava
supply equations are significant at 1 percent and at 5 per-
cent levels.

Elasticities of supply of the major food crops with

respect to their own prices were calculated by using the

following formula:

Price elasticities for rice supply were calculated at vari-
ous price levels ranging from 42.5 Rupiah to 579 Rupiah per
kilogram. These calculated price elasticities for rice

are shown in Table 5.10. All elasticities are positive

and indicate the percentage increase in quantity of rice
produced in response to a 1 percent increase in rice price.
Under traditional technology, the price elasticity for rice
supply varies from 1.0 at the price of 42.5 Rupiah per
kilogram to 0.44 at the price of 579 Rupiah per kilogram.
Price elasticities of rice are lower than one (except for
rice price lower than the existing price of 100 Rupiah per
kilogram). Rice supply functions can be considered inelas-

tic.

165

Table 5.10.--Estimated Price Elasticities of Rice Supply
Functions Under‘Traditional and New Techno-
logies for the Representative Farm in North
Lampung.w

 

 

 

Elasticities

Price .
(Rp/KG) Tradtional Tech- New Technology New Technology

nology and Ex15t- and EXIsting and Increased

ing Resources Resources Labor

42.5 1.00 0.44 0.31
103.5 0.44 0.44 0.31
139.5 0.44 0.44 0.31
579 0.44 0.44 0.31

 

Source: Computed.

166

The price elasticities of rice supply under tradi-
tional and new technologies with existing resources are con-
stant at 0.44. Price elasticities of rice supply are also
conStant at 0.31 under the new technology with increased
family labor. The supply of rice under the new technology
is inelastic with constant elasticity irrespective of price
levels (the best fit is the supply equation in double
logarithmic form).

As family labor increased under the new technology,
the supply of rice became more inelastic. This means that
response of rice supply to a 1 percent change in the price
of rice is lower under the new technology than under the
traditional technology, but it is not necessarily true in
terms of absolute changes of rice output, as can be seen
later in this section. In all cases the price elasticities
of rice supply functions are constant within the price range
from 100 Rupiah per kilogram (existing rice price in the
study area) to 579 Rupiah per kilogram.

Table 5.11 shows the price elasticities of corn
supply functions. All coefficients are less than one
except at a corn price at 18.5 Rupiah per kilogram under
the new technology and existing resources. With current
price of corn at 60 Rupiah per kilogram in the study area,
it is likely that the supply of corn is inelastic. The
supply of corn is more inelastic under traditional tech-
nology. 80 under the new technology, corn supply is more

responsive to its price changes than under the traditional

167

Table 5.ll.--Estimated Price Elasticities of Corn Supply
Functions Under Traditional and New Techno-
logies for the Representative Farm in North

Lampung.

 

 

 

Elasticities
Price . .
(RP/KG) Traditional Tech- New Technology New Technology
nology and EXIst- and EXisting and Increased
ing Resources Resources Labor
18.5 0.58 1.54 0.40
63.0 0.37 0.46 0.40
146.5 0.32 0.46 0.40
256 0.32 0.46 0.40
476 0.32 0.46 0.40

 

Source: Computed.

168

technology. The effect of the adoption of the new tech-
nology on the price elasticity of corn supply is different
from rice. Increases in the price of corn under the new
technology is mOre effective in increasing production than
is the case with rice.

Table 5.12 shows the price elasticities for cassava
supply functions at the price range from 7.2 Rupiah per
kilogram to 91 Rupiah per kilogram. All elasticities are
less than one, except at the price level of 7.2 Rupiah per
kilogram (which is much lower than the 17 Rupiah per kilo-
gram existing price) under the new technology. 80 the
supply of cassava is also inelastic but less inelastic than
corn and rice supply curves. Supply of cassava is less
inelastic under the new technology compared to the tradi-
tional technology. This implies that the increase in price
of cassava is more effective in increasing its production
under the new technology than under the traditional tech-
nology. In the case of the new technology, the price elas-
ticities of cassava decreased as its price increased. This
means that price increases for cassava is less effective to
increase production at higher price levels.

In case the policy or program objective is to
increase food production, the absolute increase in pro-
duction in response to increases in crop prices is more
relevant than the price elasticity itself as indication of
the effectiveness of the price incentive. These absolute

changes or increases in the major food crop production in

169

Table 5.12.--Estimated Price Elasticities of Cassava Supply
Functions Under Traditional and New Techno-
logies for the Representative Farm in North
Lampung.

 

 

 

Elasticities
Price d' ' 1 h N h 1 N T h 1
(Rp/KG) Tra itiona Tec - ew Tec no ogy ew ec no ogy
nology and EXISt- and EXISting and Increased
ing Resources Resources Labor
7.20 0.50 2.90 1.57
14.75 0.50 0.89 0.45
20.75 0.50 0.89 0.41
26.00 0.50 0.89 0.41
41.50 0.50 0.81 0.36
91.00 0.50 0.66 0.36

 

Source: Computed.

170

response to a 1 percent increase in its price are shown in
Table 5.13, Table 5.14, and Table 5.15 for rice, corn, and
cassava respectively.

Table 5:13 shows that the absolute increase of rice
output in response to a 1 percent increase in its price is
highest under the new technology. This implies that price
increase is more effective to induce rice production in an
absolute sense under the new technology with existing
resources than under the traditional technology. Table 5.14
shows that the absolute increase in corn output in response
to a 1 percent increase in its price is much higher than
under traditional technology. This implies that price
increase is much more effective to induce an increase in
production of corn under the new technology than under the
traditional technology.

Table 5.15 shows that the absolute increase in
cassava output in response to a 1 percent increase in its
price under the new technology with existing resource was
more than triple the level under the traditional technology.
Absolute increases in cassava output under the new tech-
nology and increase in labor availability is more than
double the level of the traditional technology. These
calculations imply that price increases are much more
effective in expanding cassava production under the new
technology than under the traditional systems. So even
though the price elasticity of cassava supply under the new

technology with increase in labor is lower than under the

171

Table 5.13.--Absolute Increases in Production of Rice in
ReSponse to a 1 Percent Increase in Its Price
Under Traditional and New Technologies for the
Representative Farm in North Lampung.

 

Increase in Output (KG)

 

Traditional Tech- New Technology New Technology

 

IRpiiG) nology and Exist- and Existing and Increased
ing Resources Resources Labor
42.5 5.00 2.20 1.55
103.5 5.03 6.90 4.97
139.5 5.03 6.90 4.97
579.0 5.07 6.77 4.93

 

Source: Computed.

172

Table 5.14.--Absolute Increases in Corn Production in
Response to a 1 Percent Increase in Its Price
Under Traditional and New Technologies for the
Representative Farm in North Lampung.

 

Increase in Output (KG)

 

 

IRliiG) Traditional Tech- New Technology New Technology
p nology and Exist- and Existing and Increased
ing Resources Resources Labor
18.5 0.87 2.31 0.60
63.0 0.88 2.30 1.96
146.5 0.86 2.30 1.94
256.0 0.86 2.30 1.94
476.0 0.86 2.30 1.94

 

Source: Computed.

173

Table 5.15.--Absolute Increases in Cassava Production in
ReSponse to a 1 Percent Increase in Its Price
Under Traditional and New Technologies for the
Representative Farm in North Lampung.

 

Increase in Output (KG)

 

 

Price Traditional Tech- New Technology New Technology
(RP/KG) nology and Exist- and Existing and Increased
ing Resources Resources Labor
7.20 15.0 87.0 47.1
14.75 21.6 86.5 46.7
20.75 25.8 86.5 47.1
26.00 26.9 87.5 47.1
41.50 30.7 86.9 47.2
91.00 42.1 85.8 47.2

 

Source: Computed.

174

traditional technology, the absolute increase in cassava
production in response to a 1 percent increase in its price
is higher.

In the next chapter the major findings from the
empirical analysis on the effects of the new technology on
the key farm variables will be summarized and policy impli-
cations of these findings will be discussed. The limita-
tions of the study and suggestions for further study will

be presented.

CHAPTER VI

SUMMARY, POLICY IMPLICATIONS, LIMITATIONS
OF THE STUDY AND SUGGESTIONS

FOR FURTHER RESEARCH

6.1 Summary

The present performance of Indonesian agricultural
food crop subsector can be considered unsatisfactory. Food
crop production is inadequate, resource productivity and
farmers incomes are low. Food production has not kept pace
with the growing population, so that Indonesia has to rely
on food imports, especially rice. The problem of increas-
ing food production is also aggravated by the concentration
of population on the island of Java. These conditions
hampered the capacity of the agricultural food crOp sub-
sector to contribute significantly to general economic
development and especially to the industrial sector which
relies on agriculture as a source of its raw materials.

The problems of unequal distribution of population
and low farm income has been partly tackled by the trans-
migration program from Java to the outer islands, especi-
ally Lampung in South Sumatra. These programs are expected

to have the dual strategies of easing Java's population

175

176

pressure and also developing the outer islands. The
intended impact is to increase food production, farm income,
and growth centers outside Java which will then hopefully
attract new migrants.

Traditional agriculture and traditional techniques
have been identified as the primary causes of the poor per-
formance of the agricultural food crop subsector. In order
to increase food production, especially rice, the govern-
ment has launched a series of programs aimed at increasing
food production. The "BIMAS PROGRAM" (mass guidance for
increasing rice production) has been underway since 1964/65
with special emphasis on irrigated rice production in Java.
A major strategy for technological improvement has been the
introduction of modern inputs such as chemical fertilizers,
improved seed varieties, insecticides/pesticides and
improved cultural practices. These programs to some extent
have been successful and have been extended to other food
crops such as corn, soybeans, and cassava in the upland
farming in the outer islands.

Lampung province received considerable attention
from the Central Government for its strategic location as
a major recipient of the transmigrants from Java and for its
great potential for food crop production. Programs for
increasing upland food crop production such as upland rice,
corn, and cassava have been carried out more intensively in
Lampung compared to the other outer islands. Besides the

BIMAS program there is a Japanese technical assistance

177

program called "TANI MAKMUR PROJECT" in Lampung which is
introducing new technology into the traditional cropping
system. This project also enhances the farmer's organiza-
tional abilities to cope with the introduction of the new
technology, especially farmer's training, distribution of
agricultural inputs, and introduction of revolving funds.
The Central Research Institute for Agriculture (CRIA)
received technical assistance from the International Rice
Research Institute (IRRI) and has conducted multiple crop-
ping experiments in Lampung to provide information on the
new highly potential cropping systems for the local area
in order to increase food production and farm income.

Agricultural development theory focuses on the avail-
ability and introduction of new high return agricultural
inputs. The use of these new inputs could significantly
alter the resource use, crop enterprise combinations, as
well as farm income. These new technological and economic
conditions could have a significant impact on the key farm
variables such as cropping patterns, crop enterprise combi-
nations, farm income, employment, resource use and resource
productivity.

This study was conducted in Lampung province where
the transmigrants are concentrated and upland farming
systems for upland food crop production are the main
agricultural activities, and new technology for food crop
production has been introduced. The study emphasis is on

the small subsistence family farms based on the presumption

178

that the development of small scale farms in Indonesia will
be the necessary strategy for increasing food production

and incomes of millions of small farmers. The large scale,
highly mechanized commercial farms are important to increase
agricultural production, but they are excluded in this study
for the reasons discussed in the introduction.

This study was the first in Lampung with linear pro-
gramming approach in which data was obtained from periodic
visit survey on small farm operators. The ultimate objec-
tives of the government program are to raise farm incomes,
output and resource productivity. Quantitative information
on the effects of technological changes on farm income,
cropping patterns and crop enterprise combinations, resource
use and productivity is needed. This information is useful
as a basis for the evaluation of current agricultural
policies and strategies.

The purpose of this study was to explore the extent
to which new inputs have been incorporated on the small
upland food crops farm and the changes in farm income, crOp
enterprise combinations, resource use and productivity, and
to estimate price elasticities of the normative supply func-
tions of the major food crops.

The linear programming approach was used as the
framework for analyzing the economics of resource use under
the traditional and new technologies. The linear pro-
gramming structure is presented in Chapter III. The model

was designed to maximize gross margins subject to meeting

179

the minimum requirements for family food crop consumption.
The activities in the model included crop production and
selling activities, labor hiring activities, fertilizer and
insecticide buying activities, food crop consumption and
buying activities, capital borrowing activities, and trans-
fer activities. Transfer activities are included in order
to transfer surplus capital from one month to the next.
This model was used to generate optimal plans both under the
traditional and new technologies. A comparison of these
two optimal plans was used to obtain the likely effects of
the adoption of the new technology on the traditional farm-
ing system.

The data used for this empirical analysis was

obtained from both secondary and primary sources but mostly
from primary sources. The primary data were collected by
interviewing a number of sample representative farmers.
The sample farmers were selected by the stratified simple
random sampling method. Four villages were selected and
from each village twenty-three sample farms were selected
at random, so we had ninety-two sample farms at the
beginning of the interview. Due to several farms which
dropped out for various reasons, there were only eighty-
five sample farms used in the final analysis. These sample
farms were interviewed every month from August through
December 1979.

The unit of analysis was a representative farm

based on average data from the sample farms from each

180

village. The representative farm sizes were 1.48 hectares,
1.44 hectares, 1.45 hectares, and 1.38 hectares for Adiluih,
Bulusari, Pugung Raharja, and Tatakarya villages respec-
tively. About 1.27 hectares, 1.25 hectares, 1.10 hectares,
and 1.38 hectares of these areas were devoted to food crop
production respectively.

The family size on the representative farms was 5.3
persons, 5.9 persons, 5.6 persons, and 6.0 persons for
Adiluih, Bulusari, Pugung Raharja, and Tatakarya villages
respectively. The educational level of the household head
of the representative farms was 3.4 years, 3.6 years, 3.0
years, and 3.2 years for these villages. The average family
labor available including the farm operator were 2.8, 2.8,
2.9, and 2.t adult male equivalents for Adiluih, Bulusari,
Pugung Raharja, and Tatakarya villages respectively.

The results of the analysis from the empirical data
collected in the study area indicated that there is little
potential to increase farm income and food production
through the adoption of the currently available new tech-
nology on the representative farms in Central and South
Lampung. Most of the currently available new technology
has been adopted. Some .potential was shown to be present
for further adoption of currently available new technology
for the representative farm in Tatakarya Village (North
Lampung) a relatively newly settled transmigration area
compared to the other two subregions of Central and South

Lampung. This is maybe due to the lack of time for the

181

representative farm to make the required adjustments toward
the optimal farm organization.

The findings also indicated some of the cropping
patterns have a better competitive position in the optimal
plan after the introduction of the new technology than under
the traditional technology. These are the mixed cropping
of upland rice - soybeans in North Lampung, mixed cropping
of corn + cassava - soybeans for Central Lampung, and mixed
cropping of upland rice + corn - soybeans in South Lampung.

There is a comparative advantage in producing a
specific crop with specific cropping patterns in each sub-
region as shown by the results of the linear programming
analysis under the new technology and by observations.
North Lampung has a comparative advantage in producing
cassava and upland rice with cropping patterns consisting of
crop mixtures: corn + upland rice f cassava and upland rice
- soybeans. Central Lampung has a comparative advantage in
producing rice and cassava with crop mixtures: corn 7
cassava - soybeans and upland rice + corn 7 cassava. South
Lampung has a comparative advantage in producing soybeans
with crop mixtures: corn + upland rice - soybeans, upland
rice - soybeans, and a triple crop of soybeans in sequence.

Turning to resource constraints, labor during peak
demand months as expected is a major limiting factor in
increasing crOp production and farm income in the study
area, especially for land preparation and weeding. The

increase of family labor input by working more days and/or

182

more hours in a day would increase farm income significantly.
There is also a limited supply of hired labor during the
peak seasons which may be due to the absence of landless
farmers in the Study area. The use of animal draft power
may release part of this labor bottleneck for land prepara-
tion.

Long term credit may be required by the representa-
tive farms for investment in livestock such as cows or
buffaloes for animal draft as additional source of power.
The introduction of simple mechanical equipment for weed-
ing operations need also to be considered to overcome labor
bottlenecks for weeding. The analysis of representative
farms shown no credit need for operating capital if they
practice mixed cropping patterns which distribute their
cash income more evenly by selling their products after
harvest (cassava in September, soybeans in February, corn
in March, and rice in May). The analysis shows that in
production of rice, corn, and cassava a mixture of these
crops is more profitable than sole crops under the new
technology.

For estimation of the normative supply schedules of
the three major food crops under traditional and new tech-
nologies variable price programming was used. The results
of this price variable programming are the step supply
functions. The results of the analysis for the representa-
tive farm in North Lampung show that the adoption of the

currently available new technology resulted in a shift in

183

the supply curves outward to the right, indicating that the
new technology has the effect of increasing output of rice,
corn, and cassava at each price level (shift in supply
curves). For example with existing prices for these crops,
adoption of new technology caused the supply of rice to
increase by 37 percent, corn by 68 percent, and cassava by
123 percent, on the representative farm in North Lampung.

The effectiveness of the new technology to increase
production would be enhanced by the increase in the avail-
ability of labor during the peak seasons.

In examining the supply response to a relative
price change for each crop the step supply functions were
transformed into smooth or continuous supply functions by
means of regression analysis. From these continuous supply
functions own price elasticities of the three major food
crops were estimated. The results of the supply functions
estimates show that double-logarithmic and semi-logarithmic
forms of relationship were the best fitting for the three
major food crops both under traditional and new technol-
ogies.

The following results should be interpreted with
care since not all of the functional forms had a high R2
and significant coefficients. For example rice normative
supply functions had a low R2 and insignificant coefficient.
The analysis shows that the price elasticities for rice,
corn, and cassava are less than one for price ranges up to

three times the present price for both traditional and new

184

technologies. Although the normative supply functions for
the three major food crops are inelastic in all cases, the
normative supply function for cassava is less inelastic.
Rice price elasticities are equal under the traditional and
new technologies with the existing resource levels. Corn
price elasticities are higher under the new technology than
under the traditional technology. This is also true for
cassava. These results may imply that price increases would
be more effective to increase the three major food crops
production under the newtechnology. The results of the
analysis also show that the price elasticities of the three
major food crops are nearly constant even when their prices
are tripled. Therefore relative price increases for these
food crops as an incentive to increase food production

appears likely to be effective for a broad range of prices.

6.2 Policy Implications

The policy implications derived from this study are
limited to the assumptions made, data reliability, analy-
tical framework used, and the construction of the repre-
sentative farms. The quantitative estimates of the
parameters may differ from their true magnitudes but at
least this study has indicated the likely economic effects
of the introduction of the new technology on traditional
small upland farms in Lampung.

To release population pressure in Java, Lampung has

a great potential as a transmigration area due to its

185

strategic location and great potential for food crop pro-
duction. Large areas still available are not yet farmed

in Lampung province. The analysis of a small representative
farm (1.38 hectares) in North Lampung which is a relatively
newly settled transmigration area shows that there is poten-
tial to increase food production and farm income through the
adoption of currently available new technology. Therefore,
the develOpment of small upland farm in the newly settled
area in Lampung would partly tackle the problem of popula-
tion distribution, and the problem of increasing food pro-
duction and increasing small upland farm income.

For Central and South Lampung regions there is
limited potential for increasing the output and farm income
through the ad0ption of the currently available new tech-
nology. Therefore efforts should be emphasized on the
production and the introduction of other new technology not
currently available such as high yielding varieties, new
cropping patterns and new crops.

The study also shows that an increase in the avail-
ability of labor could enhance the increase in output and

farm income in the adoption of the new technology. Hired

labor is also limiting since there are no landleSS farmers
in the study area. The use of animal draft for land prep-
aration could release part of the labor constraints in this
peak period as could other sources of power such as trac-

tors for land preparation. Other selective simple

186

mechanization of some of the farm operations also need to
be considered..

The analysis of the representative farm in Central
Lampung, where irrigated rice production was also included
in the model, shows that the marginal value product of irri-
gated land is much higher than the marginal value product
of upland. Therefore it is important to explore the pro-
fitability of converting upland into irrigated land to
increase both food production and farm income.

The planning of agricultural development should be
continued toward the expansion of the adoption of the new
technology on the small upland farm in the newly settled
areas. To facilitate the process of diffusion of the new
technology to the small upland farms, a number of programs
were needed including research to produce new profitable
inputs, farmer training for the use of these new inputs,
farmers organization for the distribution of these new
inputs and for marketing their agricultural products. The
efforts such as of the Tani Makmur Project in this regard
need to be continued and expanded.

The normative supply functions of the major food
crops are inelastic, i.e., at the existing price levels the
rice supply price elasticity is about 0.44 both under the
traditional and new technologies; corn supply price elas-
ticity is 0.37 under the traditional technology and 0.46

under the new technology; and cassava supply price

187

elasticity is 0.50 under the traditional technology and 0.89
under the new technology.

The price elasticity of supply of cassava is less
inelastic compared to rice and corn both under traditional
and new technologies. This may imply that price incentive
is more effective to increase cassava production than rice
or corn.

The response of the output of corn and cassava to a
1 percent increase in their prices is higher under the new
technology than under the traditional technology. This may
imply that price incentive is relevant to increase food pro-
duction in the areas where the new technology have been
adopted.

The analysis also shows that the increase in the
relative price of cassava by 50 percent (from Rp 17 to Rp
20.50 per kilogram, ceteris paribus), has the largest impact

on farm income, i.e., an increase of 20 percent. This may

imply that the most effective price policy to increase
income of the small upland farmers in North Lampung is by
increasing the relative price of cassava.
6.3 Limitations and Suggestions
for Further Study
The effects of technological changes are locational
and time specific due to differences in the physical,
economic, and institutional environment in which the

changes take place. The study only covered a one year crop

188

cycle on the very limited representative areas. For better
information of the effects of the new technology on the key
farm variables, similar studies are needed on other regions
and other cropping years in order to obtain a more compre-
hensive picture.

The linear programming approach is static in its
nature while farming is dynamic. The linear programming
approach is also within partial equilibrium conditions.

The path of the technological diffusion from one equilibrium
to another could not be identified by the static approach.
This knowledge is important for policy makers as is the
knowledge of the equilibrium conditions. The partial
equilibrium conditions assumes that the price of one com-
modity can increase while the price of others are kept con-
stant. This is an unrealistic situation, especially for
crops which are competitive or complement any and supple-
mentary in the production process.

For example the increase in the price of rice and an
increase in its production will compete for the use of land
and other resources with the production of corn and cassava.
The increase in production of rice may be at the expense of
the decrease in land devoted to corn production. As the
supply of corn decreases, its price may rise. Hence there
is a positive correlation between the rice price and the
price of corn. A rise in the price of rice tends to pull
the price of the competitive products in the same direction.

This condition has obviously violated the analysis in this

189

study, in which the price of one crop was increased while
the price of its competitive crops are held constant.

The aggregate price effect of the technological
changes is not considered. As the aggregate production
increases the price of that crop may fall, so the price
range used in the normative supply of the major food crops
may be unrealistic. Perennial crops such as clove which
recently experienced a dramatic increase in price is a
strong competitor for land resources. This was not taken
into consideration in this study. A study on the farming
system which includes the perennial crops is needed.

On the input side, the circumstances of the tech-
nological change virtually guarantees that well established,
well stocked and efficiently functioning input markets for
the adoption of the new technology will take a considerable
time to establish. Technical information that must accom-
pany the sale of the new inputs is rarely possessed by the
traditional shopkeeper. This is also not taken into con-
sideration in this study.

The study does not take into account the distribu-
tive effect of the new technology. This is an important
study to undertake in order to shed some light on the likely
effect of the introduction of the new technology in achiev-
ing the equity objective of the government. Development
strategies which are based only on economic efficiency may

be disastrous in the long run.

190

To overcome the labor constraints in the peak sea-
sons, the introduction of alternative sources of power is
needed. For this purpose a study of the potential impacts
of the alternative forms and level of mechanization on
cropping patterns, output, labor and other resource use
and productivity, as well as on farm income, is needed.

The last but possibly the most important limitation
of the study is that the data obtained by interviewing the
small number of sample farmers once a month within a half I

of a complete cropping cycle depended on the farmer's memory

 

recall. To reduce this problem a larger number of sample
farmers and more frequent interviews in a shorter period is
needed. For example, future studies may well be designed
for once or twice a week interviews covering the whole year
of a cropping cycle. This method, called "cost route
survey," has been used by some researchers in other devel-
oping countries, but has been very rare in Indonesia.

Most of the study limitations mentioned are not
restricted to the farm planning and supply estimation tech-
nique used in this study. Other techniques of farm planning
and supply estimates also face similar limitations. Despite
these limitations, the study has shed some light on the
likely effects of the technological change on the key farm
variables of the small upland farm, and has broadened our
knowledge concerning the role of the new technology as a
strategy for the development of the traditional small

upland farm in Lampung.

 

WI‘ um..-

APPENDICES

APPENDIX A

MAPS OF INDONESIA AND LAMPUNG PROVINCE

 

191

 

 

 

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APPENDIX B

MAJOR FOOD CROP PRODUCTION BY

SUBREGIONS OF LAMPUNG

 

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196

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I I 000H NOH 0N 0m mmoa N00 000a 0N0 memnnzxaU .ON
00 omH vmmm mmH H0 m0 000m aqua momva @000 onomo:03 .0H
I I om m CH m end 00 mmmma Hmmw 0:a0¢ 000M .ma
I I 0H®N ovm omm mom ommm omha mawv mo0a 0:500:00 Sufism .ba
I I 00m mm vow mmm ovm ovm omooa mmmm 0:00000:0Hm9 .oH
om om 00mm mod Hma ova mhma 0mm mmoHH 000m :000H00mm .mH
mod mom mvwm 0mm mam mom woma 0N0 mmmm vaa omuwsoxnm .vH
I I I I I I I I 0000H 0000 030000000 .0H
I I N00 00 I I I I 000HH H000 0000000000 .NH
I I muoa 00H H0 00 000 N00 0mhm mmvm mxzmmvmm .HH
I I mom om m0 m0 comm coma 0m00a 000m 0:00:000M .OH
0m mm mamo mam mmma mum 00mm ooom womaa omam :00009 .00 .0
00H avm momma onH mmmm mmma 00mm 00mm mmam moam 00002 .m
00 00 mommma mvmma 00m on 00mm m0om 0NHm NO0H :oumvmx .0
ma ma 0mm 0m mm 00 Hmmw mmvm 000m 00nd :05000 .00 .0
0m 00 000m 00H Nmm Nov boom ooom mvaa 0mv 0:500m .9 .m
mvm 0m0 00mm mama omhm vmvm vOMvH mmob 00mm 000m 0:5nwumx .0
I I 0mm 00 N0 mm mma ow 0mov omma 00:00H0M .m
00 00 @0000 0mm 000 How omom oooa mmom mmmH :0:00:0:0m .N
ma 0H 0m00 omo 00m mma Nomm mama onNH mmmm mmamm .H
0:09 0m 0:09 mm 0:09 mm 0:09 mm 0:09 mm
:0 05000 :00000
0:00:000 0>00000 :000 000m 000m 0 M\ . 99m
0:0HQD 0:0H304

 

.mhma Mom 0::QEMA £0500 :0 :0000039m an :000090000 0000 HOnMZII.mm 0Hn09

 

APPENDIX C

EXPLANATION OF ABBREVIATIONS USED IN CROPPING

PATTERNS OF THE REPRESENTATIVE FARMS

IN LAMPUNG

APPENDIX C

EXPLANATION OF ABBREVIATIONS USED IN CROPPING

PATTERNS OF THE REPRESENTATIVE FARMS

IN LAMPUNG

 

Cropping Patterns/
Abbreviations

Explanations

 

C+ULR¥CV

ULRfCV

ULR

CV

LLR

ULR-SB

C+SB¥CV

Corn and upland rice planted at the
same time/or within a week and
cassava planted one month later on
the same piece of land.

Upland rice and cassava planted on
the same piece of land but cassava
planted a month later after rice.

Upland rice as a pure stand, only
one crop a year.

Cassava planted as a sole crop,
only one crop a year.

Lowland rice as a sole crop planted
in fully or partly irrigated land.
Two crops a year is usually the
practice.

Upland rice and soybeans planted on
the same piece of land, but soybeans
planted after upland rice harvested.

Corn and soybeans planted at the
same time/or within a week and one
month later cassava planted on the
same piece of land.

197

 

198

 

Cropping Patterns/
Abbreviations

Explanations

 

C+ULR/CV

SB-SB-SB/CV

C/COC

GNDT

PEP

CfCV

ULR-SB/COC

COF+COC

CLO+COC

COC/FC

COF/FC

PEP+CLO

Corn and upland rice planted at
the same time or within a week and
after one month cassava planted
surrounding each block of land
where corn and upland rice planted.

Three crops of soybeans planted in
sequence, after one crop harvested
the second crop planted and the
third soybean crop planted after
the second crop harvested. Cassava
was planted a month later after

the first crop planted surrounding
every block of land.

Corn is planted between coconut
trees.

Groundnut as a sole crop.

Pepper (perennial) crop as a sole
crop.

Cassava is planted after one month
later after corn planted on the
same piece of land.

After upland rice harvested then
soybeans planted and these two crops
were planted between coconut trees.

Coffee and coconut both are peren-
nial crops planted together on the
same piece of land arranged in rows.

Clove planted between coconut trees.

Food crops were planted between
coconut trees.

Some food crops planted between
coffee trees.

Pepper and clove planted together
on the same piece of land.

 

199

 

Cropping Patterns/

Abbreviations Explanations

 

CLO/PC ' Some food crops were planted between
clove trees.

 

APPENDIX D

EXPLANATION OF ABBREVIATIONS USED IN THE

LINEAR PROGRAMMING TABLE OF THE REPRE-

SENTATIVE FARMS IN LAMPUNG

 

APPENDIX D

EXPLANATION OF ABBREVIATIONS USED IN THE

LINEAR PROGRAMMING TABLE OF THE REPRE-

SENTATIVE FARMS IN LAMPUNG

 

Row No./

 

 

 

Column No. Abbreviations Complete Headings
Restrictions
1. Land Land operated/under
cultivation
2. FLXAUG Family Labor in August
3. FLXSEP Family Labor in
September
4. FLXOCT Family Labor in October
5. FLXNOV Family Labor in
November
6. FLXDEC Family Labor in
December
7. FLXJAN Family Labor in
January
8. FLXFEB Family Labor in
February
9. FLXMAR Family Labor in March
10. FLXAPR Family Labor in April
11. FLXMAY Family Labor in May
12. FLXJUN Family Labor in June
13. FLXJUL Family Labor in July
14. HLXAUG Hired Labor in August
15. HLXSEP Hired Labor in September
16. HLXOCT Hired Labor in October
17. HLXNOV Hired Labor in November
18. HLXDEC Hired Labor in December
19. HLXJAN Hired Labor in January
20. HLXFEB Hired Labor in February
21. HLXMAR Hired Labor in March

200

 

Row No./

 

Column No. Abbreviations Complete Headings

22." HLXAPR Hired Labor in April

23. HLXMAY Hired Labor in May

24. HLXJUN Hired Labor in June

25. HLXJUL Hired Labor in July

26. AHXAUG Animal Hired in August

27. AHXSEP Animal Hired in September

28. AHXOCT Animal Hired in October

29. AHXNOV Animal Hired in November

30. SEEDRC Quantity of Rice Seed
Used

31. SEEDXC Quantity of Corn Seed
Used

32. SEEDSB Quantity of Soybean Seed
Used

33. SEEDCV Quantity of Cassava
Stick Used

34. FERTXN Quantity of Fertilizer-N
Used

35. FERTXP Quantity of Fertilizer-P
Used

36. INSECTD Quantity of Insecticides
Used

37. OCXAUG Operating Capital in
August

38. OCXSEP Operating Capital in
September

39. OCXOCT Operating Capital in
October

40. OCXNOV Operating Capital in
November

41. OCXDEC Operating Capital in
December

42. OCXJAN Operating Capital in
January

43. OCXFEB Operating Capital in
February

44. OCXMAR Operating Capital in
March

45. OCXAPR Operating Capital in
April

46. OCXMAY Operating Capital in May

47. OCXJUN Operating Capital in June

48. OCXJUL Operating Capital in July

49. OPTRICE Quantity of Rice Produced

50. OPTCORN Quantity of Corn Produced

51. OPTSOBN Quantity of Soybeans Pro-

duced

 

202

 

 

CgifimgoNo. Abbreviations Complete Headings

52.- OPTCASV Quantity of Cassava Pro-
duced

53. CONSRICE Minimum Quantity of Rice
for Household Consumption

54. CONSCORN Minimum Quantity of Corn
for Household Consumption

55. CONSCASV Minimum Quantity of

' Cassava for Household

Consumption

56. AMT-BC Total Amount Borrowed

57. LMT HL-AUG Limited Hired Labor
Available in August

58. LMT HL-SEP Limited Hired Labor
Available in September

59. LMT HL-OCT Limited Hired Labor
Available in October

60. LMT HL-NOV Limited Hired Labor
Available in November

61. LMT HL-DEC Limited Hired Labor
Available in December

62. LMT HL-JAN Limited Hired Labor
Available in January

63. LMT HL-FEB Limited Hired Labor
Available in February

64. LMT HL-MAR Limited Hired Labor
Available in March

65. LMT HL-APR Limited Hired Labor
Available in April

66. LMT HL-MAY Limited Hired Labor
Available in May

67. LMT HL-JUN Limited Hired Labor
Available in June

68. LMT HL-JUL Limited Hired Labor
Available in July

69. LMT AH-AUG Limited Hired Animal
Available in August

70. LMT AH-SEP Limited Hired Animal
Available in September

71. LMT AH-OCT Limited Hired Animal
Available in October

72. LMT AH-NOV Limited Hired Animal
Available in November

73. LMT BCAUG Limited Borrowed Capital
in August

74. LMT BCSEP Limited Borrowed Capital

in September

203

 

Row No./
Column No.

Abbreviations

Complete Headings

 

75.

76.

77.

78.

79.

80.

81.

82.

83.

84.

LMT BCOCT
LMT BCNOV
LMT BCDEC
LMT BCJAN
LMT BCFEB
LMT BCMAR
LMT BCAPR
LMT BCMAY
LMT BCJUN

LMT BC JUL

Limited Borrowed Capital
in October

Limited Borrowed Capital
in November

Limited Borrowed Capital
in December

Limited Borrowed Capital
in January

Limited Borrowed Capital
in February

Limited Borrowed Capital
in March

Limited Borrowed Capital
in April

Limited Borrowed Capital
in May

Limited Borrowed Capital
in June

Limited Borrowed Capital
in July

Activities

 

1.

AlC+ULRfCV

A2C+ULR¥CV

A3C+ULR¥CV

A4C+ULR¥CV

(TTl)

(TT2)

(NTl)

(NTZ)

A5ULR+CV (TT)

Produce Corn, Upland
Rice and Cassava in
Mixture with Manual Labor
for Land Preparation
Produce Corn, Upland
Rice and Cassava in
Mixture with Animal Power
for Land Preparation
Produce Corn, Upland
Rice and Cassava in
Mixture with Use of
Fertilizer Level 1 and
Use of Insecticides as
Recommended.

Produce Corn, Upland
Rice and Corn in

Mixture with Use of
Fertilizers Level 2 with
Use of Insecticides at
Recommended Level
Produce Upland Rice and
Cassava with Traditional
Technology

 

204

 

Row No./
Column No.

Abbreviations

Complete Headings

 

10.

ll.

12.

13.

14.

15.

16.

A6ULR+VC (NT)

A7C+CV (TTl)

A8C+CV (TT2)

A9C+CV (NT)

A10ULR-SBT

AllULR-SBN

AlZCVTTl

A13CVTT2

A14CVNT

AlSHLXAUG

A16HLXSEP

Produce Upland Rice and
Cassava in Mixture with
the Use of Fertilizers
(New Technology)
Produce Corn and Cassava
in Mixture with Manual
Labor for Land Prepara-
tion and no Use of New
Inputs (Traditional
Technology)

Produce Corn and Cassava
in Mixture with Animal
Power for Land Prepara-
tion, no Use of New *
Inputs (Traditional

Technology)

Produce Corn and Cassava

in Mixture with the Use

of the New Inputs (New

Technology)

Produce Upland Rice and

Soybeans in Sequence with

no Use of New Inputs

(Traditional Technology)

Produce Upland Rice and

Soybeans in Sequence with

Use of New Inputs (New

Technology)

Produce Cassava with

Manual Labor for Land

Preparation, no Use of

New Inputs (Traditional

Technology)

Produce Cassava with

Animal Power for Land

Preparation, no Use of

New Inputs (Traditional

Technology)

Produce Cassava with the

Use of New Inputs (New

Technology

Hiring Labor Activities

in August

Hiring Labor Activities

in September

 

205

 

 

ngzmgofié. Abbreviations Complete Headings

17. A17HLXOCT Hiring Labor Activities
in October

18. AlBHLXNOV Hiring Labor Activities
in November

19. A19HLXDEC Hiring Labor Activities
in December

20. A20HLXJAN Hiring Labor Activities
in January

21. AZlHLXFEB Hiring Labor Activities
in February

22. A22HLXMAR Hiring Labor Activities
in March

23. A23HLXAPR Hiring Labor Activities
in April

24. A24HLXMAY Hiring Labor Activities
in May

25. A25HLXJUN Hiring Labor Activities
in June

26. A26HLXJUL Hiring Labor Activities
in July

27. A27AHXAUG Hiring Animal for Land
Preparation in August

28. A28AHXSEP Hiring Animal for Land
Preparation in September

29. A29AHXOCT Hiring Animal for Land
Preparation in September

30. A30AHXNOV Hiring Animal for Land
Preparation in November

31. A31BFERT-N Buying Fertilizer Activ-
ities in October

32. A32BFERT—P Buying Fertilizer Activ-
ities in October

33. A33BPSTD Buying Insecticide Activ-
ities in October

34. A34BRCXSEP Buying Rice Activities
for Consumption in
September

35. A35BRCXOCT Buying Rice Activities
for Consumption in
October

36. A36BRCXNOV Buying Rice Activities
for Consumption in
November

37. A37BRCXDEC Buying Rice Activities

for Consumption in

December

206

 

Row No./

 

Column No. Abbreviations Complete Headings

38.’ A38BRCXJAN Buying Rice Activities
for Consumption in
January

39. A39BOYXFEB Buying Oyek Activities
for Consumption in
February

40. A40BOYXMAR Buying Oyek Activities
for Consumption in March

41. A41CONSRICE Family Household Rice
Consumption

42. A42CONSCORN Family Household Corn
Consumption

43. A43CONSCASV Family Household Cassava
Consumption

44. A44SRCMAY Selling Rice Activities
in May

45. A45 SCMAR Selling Corn Activities
in March

46. A4GSSBFEB Selling Soybeans Activ-
ities in February

47. A47SCVSEP Selling Cassava Activ-
ities in September

48. A480RCTOSSRC Rice Seed from Own Pro-
duction

49. A49OCTOSSC Corn Seed from Own Pro-
duction

50. ASOOSBTOSSB Soybean Seed from Own
Production

51. A51FL/NOVTOSSCV Family Labor Activities
in November to Produce
Cassava Sticks

52. ASZBCAUG Borrowing Capital Activ-
ities in August

53. A53BCSEP Borrowing Capital Activ-
ities in September

54. A54BCOCT Borrowing Capital Activ-
ities in October

55. A55BCNOV Borrowing Capital Activ-
ities in November

56. A56BCDEC Borrowing Capital Activ-
ities in December

57. A57BCJAN Borrowing Capital Activ-
ities in January

58. ASBBCFEB Borrowing Capital Activ-

ities in February

 

207

 

 

CEIEmHONo. Abbreviations Complete Headings

59. A59BCMAR Borrowing Capital Activ-
ities in March

60. A60BCAPR Borrowing Capital Activ-
ities in April

61. A61BCMAY Borrowing Capital Activ-
ities in May

62. A62BCJUN Borrowing Capital Activ-
ities in June

63. A63BCJUL Borrowing Capital Activ-
ities in July

64. A64TC-AS Transfer Capital from
August to September

65. A65TC-SO Transfer Capital from
September to October

66. A66TC-ON Transfer Capital from
October to November

67. A67TC-ND Transfer Capital from
November to December

68. A68TC-DJ Transfer Capital from
December to January

69. A69TC-JF Transfer Capital from
January to February

70. A70TC-FM Transfer Capital from
February to March

71. A71TC-MA Transfer Capital from
March to April

72. A72TC-AM Transfer Capital from
April to May

73. A73TC—MJ Transfer Capital from
May to June

74. A74TC-JJ Transfer Capital from
June to July

75. A75PY OFF Pay Off Activities in

July

 

APPENDIX E

MONTHLY MAJOR FOOD CROPS PRICES AT THE

NEAREST MARKET TO THE REPRESENTATIVE

FARMS IN LAMPUNG

 

 

 

 

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mm mm om om om om ma ma ma ma m n w>mmmmu .v
oaa oaa oHH OHH oaa mad OHH mm mm mm em on cuoo .m
oaa oaa OHH OHH OHH OHH mod ooa mm mm ooa OOH mowm owaasnco .m
mam mam mam cam cam mmm oom mna mna mna oma mha mowm pmaasm .H
cmumawm memcnm .H
omo >oz poo mom msfi How can >82 Ham Hm: bum coo mmfluflooafiou
on\mm. moflum manucoz mowum>< cam uoauumflonsm
*.mhma MOM .mcomfimq susomv OmHmnoxam
ocm.Amc:mqu cunozv cmumamm memcsm CH mmOAHm mono boom Mono: manucozlu.am manna

UZDQZflA ZH wzmflm W>HB<Bmemmmmm

mmB OB Emmmdz Emmm<mz mmfi Ed mmonm mmomo 900m mOh<E NAEBZOE

m xHQmem4

208

209

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afloawoé How mumospoum How umxumfi umwummc mzu ma oflumcoxnm .Amcsmemq suuoz.

mmmaafl> mmuwxmume HOm umxuma m.umosooum ummumm: may we cmumamm flmxmcsm«

.memmcoocH .ocsmfimq mo >ocmm¢ coflmcwuxm amusuHsOAHm< "mousom

 

 

mam cam com mum mum mum mum mum omm com com mmm cmwnmow .m

mm mm om ma mm om ma ha ma ma ma NH m>mmmmu .v

OHH OHH OHH OHH moa mm ms ms mu om om Hm cuoo .m

ooa OOH ooa ooa ooa 00H mm mm om co mm mm oofim pmaasncb .N

com mom mom mom com com com mud mma oma moa 05H moafi pmaasm .H
onumnoxsm .HH

own >oz poo mom wad ado can wmz Had no: Dom coo

 

AOM\mmv moaum wanucoz momuw>¢

mmfluHQOEEoo
cam vowuumflvnam

 

.omscwucooln.am manna

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