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FACTORS INFLUENCING RELATIVE PROFITABILITY OF
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6/01 cJCIRC/DateDuepss-p. 15

FACTORS INFLUENCING RELATIVE PROFITABILITY
OF ALTERNATIVE CROPS IN KENYA:
IMPLICATIONS FOR MAIZE POLICY
By

Gerald G. Nyambane

A THESIS

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

MASTER OF SCIENCE
Department of Agricultural Economics

2001

ABSTRACT
FACTORS INFLUENCING RELATIVE PROFITABILITY
OF ALTERNATIVE CROPS IN KENYA:
IMPLICATIONS FOR MAIZE POLICY
By

Gerald G. Nyambane

Relative proﬁtability of maize vis-a-vis that of alternative annual crops in Kenya is
determined using household level data from six districts in three provinces of Kenya.
Taking into account whether farmers are net buyers or net sellers of maize, the
proﬁtability results are used together with other empirical information to determine the
main constraints to agricultural commercialization.

The results show that proﬁtability varies by region, by type of crop enterprise, and
by type of technology employed. The main constraints to agricultural commercialization
are: (1) low relative proﬁtability of some alternative crops compared to that of maize and
its inter-crops; (2) small land size holding and low productivity potential; (3) unavailability
of sufﬁcient water necessary for crop production; (4) lack of adequate market openings
for alternative crops and (5) lack of an enabling environment for commercial agriculture.

This study also found that 73 percent of the farmers would prefer to grow maize
even if it was relatively unproﬁtable because their main concern is to ensure a sufﬁcient
supply of maize, the main staple. The study concludes that it is indeterminate whether
policies that lead to reduced maize prices such as, removal of import tariff will necessarily

lead to reduced maize acreage; and a subsequent shift to cultivation of alternative crops.

ACKNOWLEDGMENTS

I would like to thank all those who have contributed in one way or another to my
successﬁrl completion of this thesis. First my thanks go to my committee composed of
Professors Rick Bemsten, Thom Jayne and John Strauss. I would like to acknowledge
their support in various forms throughout the period of my masters and during the writing
phase of my thesis.

I would also like to acknowledge the Tegemeo Institute for Agricultural Policy and
Development, Nairobi, Kenya, which hosted me during my research. The entire staff at the
Institute were very friendly and supportive of my activities during my stay with them. I am
deeply indebted to them.

Field research was made possible through funding from the USAID via the Food
Security 11, TAMBA project. For that, I am very grateful. I am also thankful to Dr. Wilson
Nguyo, the Director of Tegemeo Institute, who provided me with an appropriate vehicle
and a chauffeur to use while carrying out my research.

My research beneﬁtted most from the tireless effort, dedication, and experience of
the following research assistants: Elliot Mughenyi, Francis Karin, Daniel Musula, Amin
William, Titus Kioko, Betty Landan, and Bwana Ireri alias F irigizi. I will always be

indebted to these individuals.

iii

TABLE OF CONTENTS

ACKNOWLEDGMENTS ................................................ iii
LIST OF TABLES ..................................................... viii
LIST OF FIGURES ..................................................... x
CHAPTER ONE ........................................................ 1
INTRODUCTION ........................................... 1

1.1 Background .......................................... 1

1.2 The situation in Sub-Saharan Africa ....................... 6

1.3 Problem Statement ..................................... 7

1.4 Study Objectives ..................................... 10

1.5 Signiﬁcance of the study ............................... 11

1.6 Organization of Study ................................. 11

CHAPTER TWO ....................................................... 13
BACKGROUND: THE MAIZE NATIONAL SITUATION ............... 13

2.1 Importance of maize in Kenya ........................... 13

2.2.1 Maize production: volume, area and yield ............ 14

2.2.2 Maize Production system ......................... 15

iv

2.2.3 Geographic location of production ................. 16

2.2.4 Geographic location of demand .................... 18

2.3.1 Crop seasonality ................................ 19

2.3.2 Domestic supply and imports ...................... 20

2.3 .4 Government intervention ......................... 23

CHAPTER THREE ..................................................... 27
RELATED STUDIES, THEORY AND METHODS ..................... 27
3.1 Structural Transformation: setting the stage ................ 27

3.2 Structural Transformation: the Sub-Saharan African challenge . 29

3.3 Analytical framework .................................. 31

3.3.1 Land preparation ............................... 32

3.3.2 Planting, weeding, harvesting and other activities ...... 34

3.3.3 Material input costs ............................. 35

3.3.4 Prices ........................................ 37

3.3.5 Yield ......................................... 38

3.3.6 Labor ........................................ 38

3.3.7 Gross margins ..... i ............................ 39

CHAPTER FOUR ...................................................... 41

RESEARCH DESIGN ............................................. 41
4.] Characteristics of survey areas ........................... 41

4.1.1 Uasin Gishu and Trans Nzoia Districts .............. 43

4.1.1.1 Location and size ................... 43

4.1.1.2 Agro-Ecology ...................... 43

4.1.2 Kakamega and Vihiga Districts .................... 46

4.1.2.1 Location and size ................... 46

4.1 .2.2 Agro-ecology ...................... 46

4.1.3 Machakos and Makueni Districts ................... 48

4.1.3.1 Location and size ................... 48

4.1.3.2 Agro-ecology ...................... 49

4.2 Agriculture .......................................... 50

4.3 Data ............................................... 52

4.4 Sampling ........................................... 53

4.5. Survey implementation ................................ 58
CHAPTER FIVE ....................................................... 6O
ANALYSIS AND RESULTS ................................. 60

5.1.1 Mono-crop maize proﬁtability ..................... 6O

5. 1 .2 Maize/bean inter-crop ........................... 65

vi

5.1.3 Beans ........................................ 70

5.1.4 Wheat ........................................ 72
5.1.3 Irish potatoes, cassava and sweet potatoes ............ 74
5.2 How proﬁtable is maize? ..................................... 79
5.3 What if farmers are net maize buyers? ........................... 83
5.4 Farmers’ characteristics and circumstances ....................... 85
5.5 Simulated effect of the removal of maize import tariff on relative
proﬁtability ................................................ 92
5.6 Farmers’ Perceptions ........................................ 95
5.7 Constraints to agricultural commercialization ..................... 99
5.7.1 Land size holding and productivity potential ................ 99
5.7.2 Water availability .................................... 100
5.7.3 Relative proﬁtability ................................. 101
5.7.4 Thin and troubled markets for alternative crops ............ 103
5.7.5 Lack of enabling environment .......................... 105
CHAPTER SIX ....................................................... 107
CONCLUSION ................................................. 107
APPENDIX .......................................................... 1 11
REFERENCES ....................................................... 113

vii

Table 1.

Table 2.

Table 3a.

Table 3b.

Table 4a.

Table 4b.

Table 4c

Table 5a.

Table 5b.

Table 5c.

Table 6a.

Table 6b.

Table 6c .

Table 7.

Table 8.

Table 9.

Table 10.

LIST OF TABLES

Maize production in Kenya by Province, 1998 .................... 18
Total production, marketed production, exports and imports

of maize, 1975/76 - 1995/96, Kenya ............................ 22
Some descriptive information for maize/bean intercrop farmers ....... 36

Percent of crop land under cultivation of major crops,
1992-95 average by District, Kenya ............................. 51

Number of households sampled from each province, 1997 data. ...... 56
Number of households sampled from each province by
household market position and by level of input use on

mono-crop maize plots, 1999 survey. ........................... 57

Number of households sampled from each province by
household market position and by level of input use

on maize/beans inter-crop plots, 1999 survey ...................... 57
Gross Margins for Mono-crop Maize for Rift Valley Province. ....... 61
Gross Margins for Maize Mono-crop for Western Province. ......... 62
Gross Margins for Maize Mono-crop for Eastern Province. .......... 63
Gross Margins for Maize/beans inter-crop for Rift Valley Province. . . . 67
Gross Margins for Maize/beans inter-crop for Western Province. ..... 68
Gross Margins for Maize/beans inter-crop for Eastern Province. ...... 69
Gross Margins for Beans Mono-crop ............................ 71
Gross Margins for Wheat ..................................... 73
Gross Margins for Irish Potatoes ............................... 75

Gross Margins for Cassava ................................... 76

viii

Table 1 1.

Table 12.

Table 13.

Table 14.

Table 15.

Table 16.

Table 17.

Table 18.

Table 19.

Table 20

Table A1.

Gross Margins for Sweet Potatoes .............................. 78
Gross margins for different enterprises by Province (Ksh./acre) ....... 81

Ranking of enterprises according to relative
proﬁtability index by province ................................. 82

Land size (in acres) holdings in the Rift Valley, Western, and Eastern
Provinces,Kenya. .................. 86

Primary occupation of household members of the sample households. . 88

Number of family members per household in the Rift Valley,
Western, and Eastern Provinces, Kenya. ......................... 89

Acreage planted to different crops in the Rift Valley, Western,
and Eastern Provinces, Kenya, 1999 ............................. 91

Ranking of enterprises according to simulated relative proﬁtability
index by province ........................................... 94

The most important crop used as a source of food for
households in Kenya. ....................................... 96

The most important crop used as a source of farm cash income for
households in Kenya ........................................ 97

Maize grain buying and selling median prices, by Region and
by month for 1998/ 1999 crop production period. ................. 112

ix

Figure 1.

Figure 2.

Figure 3.

Figure 2.

LIST OF FIGURES

Relationships between agricultural transformation, agricultural

diversiﬁcation and agricultural commercialization ................... 4
Maize production in Kenya - 1975 - 1996 ....................... 15
Average maize prices for major markets in Kenya -

June, 1998-May, 1999 ....................................... 20
Map of Kenya showing study district ........................... 44

CHAPTER ONE

INTRODUCTION

1.1 Background

Since the advent of market liberalization, spearheaded by the IMF and the World
Bank, there has been increasing debate on its impact on producers and consumers in
developing countries. The proponents of market liberalization anticipated that the process
would create opportunities for farmers to increase their incomes by shifting from
subsistence farming to commercial farming (World Bank, 1997). Liberalization has
involved reforms to macroeconomic and trade policies designed, inter alia, to improve
price incentives for production of tradables (Doward et al.) and, hence, bid resources
away from the production of non-tradables. In the context of the agricultural sector,
tradables are viewed as high-value, export-oriented crops while non-tradables are viewed
as low—value food crops.1

Liberalization also involved reforms to sectoral policies such as the removal of
controls on inter-regional trade and, pan-regional and pan-seasonal prices. The controls
were removed in order to generate spatial and temporal arbitrage, which would in turn

stimulate private trade from surplus to deﬁcit areas, as well as encourage private

 

Tradables are commodities that are traded in the world market and usually include crops that are
commonly referred to as cash crops, commercial crops, export crops or crops with a high ratio of value to
transport costs per unit while non-tradables are commodities that are not traded internationally. They are
usually referred to as food crops, subsistence crops or crops with low value per unit relative to transport
costs.

participation in storage, especially of grain, to smooth commodity supply across time and
space. With trade of food commodities in the hands of the private sector, it was expected
that the ‘pieces’ of the structural transformation paradigm would fall in place and thus
spur economic growth.

While various deﬁnitions of structural transformation are provided in the literature,
this study adopts the deﬁnition put forth by Staatz (1994). Staatz deﬁned structural
transformation as:

" a process by which increasing proportions of employment and output of the

economy are accounted for by sectors other than agriculture. The economy

becomes less agriculturally oriented in a relative sense, although agriculture and,
more broadly, the food system continue to grow absolutely and generate
important growth linkages to the rest of the economy. "

This means that structural transformation entails a net transfer of resources, especially

labor, to other sectors of the economy. But, as Staatz points out, such a transfer of

resources requires the following four conditions in order to occur. These are:

1. Low food prices relative to wages in Off-farm activities;
2. Assurance of a sufﬁcient food supply to urban consumers;
3. Existence of a low-cost exchange system; and

4. Existence of an effective information system.

Low food prices relative to wages in off-farm activities are crucial because those
leaving agriculture need assurance that they will be able to buy food from their wages. If
this assurance is missing, then farmers will be reluctant to give up own production to meet

household consumption needs. Additionally, an assurance of sufﬁcient food supply is

necessary because, not only do workers require the ability to afford food, but they also
require sufﬁcient food to be a available. Existence of a low-cost exchange system and an
effective information system are necessary to reduce transaction costs, which must be low
in order to ensure low food prices.

In the literature, structural transformation is commonly confused with three other
closely related concepts: agricultural transformation, agricultural commercialization,

and agricultural diversiﬁcation. Timmer (1997) distinguishes between these latter

 

Food consumption
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Q
5% International
33‘ Trade
.2. V
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E Agriculture v
Farm leve
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Agricultural transformation

Figure 1. Relatonships between agricultural transformation,
agricultural diversiﬁcation and agricultural
commercialization.

Source: Timmer, PC. 1997. Farmers and markets: The political
Economy of new paradigms. Amer. J. Agr. Econ. 79. 623.

concepts and clariﬁes how they relate to structural transformation. In Figure 1, the
horizontal axis represents the level of agricultural transformation which accompanies
economic growth. Movement to the right reﬂects the fact that with economic growth, the
level of agricultural transformation increases as part of structural transformation.
Agricultural transformation is said to occur whenever the share of agriculture in a
country’s labor force and total output declines both in cross sectional and time series
samples. The vertical axis, on the other hand, represents the degree of agricultural
diversiﬁcation. In the early stages of agricultural transformation, the degree of
diversiﬁcation at the individual farm level is high with little specialization. This phase is
characterized by incomplete markets for local produce with few mechanisms for dealing
with crop production and marketing risks. As markets develop, farmers tend to specialize
in one or just a few crops. Hence, farmers with different resource endowments and agro-
ecological conditions will tend to specialize in different crops. The increasing
specialization by individual farmers leads to decreased crop diversity at the individual farm
level, while leading to increased diversity at the agricultural sector (aggregate) level. As
structural transformation increases, agricultural transformation also increases and the gap
between the levels of diversiﬁcation at the (individual) farm and at the aggregate level
increases. This gap indicates the degree of commercialization. Therefore, the degree of
commercialization is a proxy for economic growth. i. e. the higher the degree of
commercialization, the higher the level of agricultural transformation and by implication,

greater economic growth. Hence, understanding the constraints to agricultural

commercialization is essential for understanding what is slowing down agricultural
transformation in Kenya.

Agricultural commercialization may be deﬁned as the proportion of agricultural
production that is marketed and can be measured along a continuum from zero to 100
percent (Govereh et al. , 1999), where zero percent represents no commercialization and
100 percent represents complete commercialization. Complete commercialization implies
that the farming household sells all the produce (crop) from his farm and consumes none
itself. This would suggest that the farmer cultivates his land and grows crops for sale and
then uses the cash earned to buy what he needs for his household’s consumption.

Figure 1 also shows that as international trade increases, food consumption
patterns become more diversiﬁed than domestic production patterns. Hence, as structural
transformation occurs, the combination of agricultural commercialization and international
trade, which accompanies it, leads to a situation whereby individual farmers depend more
on the market for their food supplies and less on their own production.

The structural transformation paradigm and subsequent advocacy for market
liberalization are mainly based on, among other things, empirical evidence which shows
that all countries that have substantially improved real incomes have gone through
structural transformation (Timmer, 1990)2, whereby their agricultural sector’s share of
GDP declined over time. Hence, some economists believe that if developing nations

implement structural adjustments to their economics as espoused by the transformation

 

2 Cited in Staatz (1994).

paradigm, the economies of these nations will grow absolutely and hence the welfare of

citizens in these nations would improve.

1.2 The situation in Sub-Saharan Africa

After almost two decades of embracing market reforms, economic growth still
remains elusive to most African countries. This has led many social scientists to question
the relevance of the reforms for promoting economic growth in Africa. Following their
study on cash crop marketing, Doward et al. concluded that in sub-Saharan Aﬁ'ica
smallholder agriculture has generally responded weakly to structural adjustment and
market liberalization. Why is this the case? The search for an answer to this daunting
question is currently the central theme of the research agenda for many researchers in
Africa as evidenced by, among others, the presentations at the recent Fourth International
Workshop on Agricultural Transformation in Africa.3 This question is the focus of this

study, with Kenya used as a case study to provide information to answer it.

 

3

The author attended the workshop, which was held in June 27-30, 1999, Nairobi, Kenya, sponsored by
Egerton University’s Tegemeo Institute of Agricultural Policy and Development, Michigan State
University, and The Eastern and Central Africa Programme for Agricultural Policy Analysis
(ECAPAPA).

1.3 Problem Statement

While liberalization occurred in several sectors of the Kenyan economy, the
agricultural sector has received most attention from policy makers and researchers.
Speciﬁcally, the maize sub-sector has drawn tremendous attention in the last few years
because maize is the most important crop in Kenya and policies affecting its availability
greatly impact the farming sector and the food industry.

Liberalization of maize marketing in Kenya, which started in late 19808, mainly
focused on the decontrol of the maize market, albeit very gradually, and the restructuring
of the National Cereals and Produce Board (N CPB), the major maize marketing agency.

In the pre-liberalization period, non permit inter-district maize movement was limited to
two 90-kg bags. This limit was ﬁrst raised to 10, then to 44, and ﬁnally to 88 bags over a
period of nine years before free movement was ﬁnally allowed (Lewa and Hubbard, 1995).
The introduction of free movement has facilitated ease of transfer of maize from surplus to
deﬁcit regions. As a result, the NCPB’S role in maize marketing has been reduced to being
a residual buyer for the purpose of stocking strategic reserves.

There are varying views as to the effects that these liberalization measures have
had on consumers and producers. Jayne et al. (1997) argue that, "liberalization has
conferred substantial beneﬁts to urban consumers" as people are now able to get maize
and mill it themselves. One can also hypothesize that liberalization has also conferred
beneﬁts to producers who now have access to a wider market, enabling them to receive

competitive prices and timely payments for deliveries. However, some government

interventions remain in place that affect, among other things, maize prices. For example,
the government is still involved in the trade of imported maize - both by deciding who
imports (i. e. the government issues import licenses) and by imposing an import tariff.
Also, the NCPB continues to set maize prices for its depots.

Donors have criticized these government interventions as key obstacles to Kenya’s
agricultural development. However, the government has usually defended the
interventions, arguing that they are in the best interest of the agricultural sector. For
instance, a number of cabinet ministers argued that, "unless the government reverses the
liberalization process, the agricultural sector was bound to collapse" (Daily Nation,
December 14, 1998). In March 1998, the Ministers of Finance and Agriculture jointly
announced the suspension of the maize import tariff, which was at 25 percent during the
month of April, in order to "ensure that imported maize was aﬂordable to consumers"
(Daily Nation, March 11, 1998). However, in October 1998, the President reversed his
ministers’ decision and raised the tariff from 25 percent to 32.5 percent, arguing that "the
(importation) trend can’t be allowed to continue because it leads to increased
dependency on imported goods" (Daily Nation, October 1, 1998). These and many similar
examples, illustrate the erratic nature of government intervention in the maize sub-sector.

Some authors have criticized the import tariff, arguing that the tariff (which raises
maize prices) actually hurts more rural households than it protects because the majority of
rural households (61 percent) are net buyers of maize (Jayne et al. , 1998; 2000). Thus, the
purchasing power of these households is reduced by higher maize prices. Other authors

have argued that high maize prices (or high food costs in general) impede agricultural

commercialization based on non-food crops because high food costs force farmers to
produce food (maize) for themselves, rather than rely entirely on the market for their
supplies. However, this argument is based on the assumption that farmers’ cropping
decisions are based on the expected net returns to the alternative crops and that a stable
market exists for the non-food crops (Owuor, 1998; Omamo, 1998; Jayne, 1994).
Implicitly, the arguments are also based on the assumption that the riskiness of growing
alternative crops is either lower than that for maize, or at least is not important enough to
offset the assumed higher returns that the alternative crops would provide.

Furthermore, the arguments for shifting resources away from food crop production
also assume that food crops and export crops are substitutes in production. Lamb (2000)
provides evidence from several sub-Saharan African countries which shows that only in
the short run, are food crops and export crops substitutes in production. But even in the
short run, as Lamb acknowledges, such shifts in resource allocation might result in
declines in agricultural output leaving countries vulnerable to declines in domestic food
without adequate foreign exchange earnings to purchase food in world markets. A
possible explanation for lowered total agricultural output in the short run is that it may
take several years for export crops such as tea or coffee to produce output for the market
while food crop production falls immediately. It is therefore unclear whether most
countries would successfully go through the intervening short run period of declining
domestic food stocks in order to realize long run impacts of the shifts in resource

allocation.

This study makes an empirical contribution to the search for the reason(s) why
market reforms have not had the anticipated impacts in Kenya. Farm-level data collected
from rural households in Kenya are analyzed to determine the main constraints to
agricultural commercialization-- a necessary ﬁrst step to agricultural and stmctural

transformation.

1.4 Study Objectives

The objectives of this study are:

1. To determine the constraints to agricultural commercialization in Kenya; and
2. To identify strategies for addressing these constraints in order to increase rural
incomes.

To address the objectives of this study, the proﬁtability of maize is estimated and
compared with the proﬁtability of major alternative crops in selected districts in Kenya.
More speciﬁcally, this study uses household-level data that was collected in 1999 to
analyze variations in proﬁtability of maize and major alternative crops across:

(a) regions of high and low potential for maize production; and

(b) different farming categories (i. e. whether households are net buyers or net

sellers Of maize).

This study focuses on maize because ﬁrst, it is the most important food staple for
the majority of Kenyan households and, second, because agricultural commercialization is

anticipated to occur via diversiﬁcation away from maize production towards greater

10

production of higher value crops. The speciﬁc crops that are alternatives to maize vary by
region, depending on agro-climatic conditions, physical infrastructure and institutional
settings unique to each district. In this study, alternative crops are deﬁned as the most

important crop grown in each district besides maize.

1.5 Significance of the study

It is of critical importance to determine the constraints to agricultural
commercialization and identify strategies for addressing the constraints in order to increase
rural incomes in Kenya. First, understanding the constraints faced by farming households
can help policy makers to understand the underlying logic of farmers’ actions. Second, this
study contributes to the pool of information regarding the on—going debate on why

structural transformation has been slow in occurring in developing countries.

1.6 Organization of Study

This thesis is organized as follows: Chapter Two presents background information
about the role of maize in Kenya. First, data are presented to document the importance of
maize as the staple food crop for the majority of the Kenyans and as a source of rural
incomes. Then, maize supply and demand trends over the past two decades are reviewed -

- highlighting the present level of production, geographic location of production and

11

demand, the effect of crop seasonality on prices, and government interventions in the
maize sub-sector which have sought to reverse the decline in maize production.

Chapter Three presents a focused review of maize marketing liberalization in
Kenya from 1980 to date. In addition, relevant empirical studies are cited that highlight the
main concerns in Kenya about maize production and marketing, as they relate to current
attitudes of policy makers. The analytical framework used in this study is also presented in
this chapter.

Chapter Four presents the research design, including details about the
characteristics of the survey areas - location, agro-ecology and agriculture. A brief
description of the data, sampling method and survey implementation is also presented.

Chapter Five reports the empirical results. A detailed discussion of the relative
proﬁtability of various crop enterprises is presented, along with a discussion on the
implications of the results. Inferences based on these results and discussion form the basis

of the conclusions presented in Chapter 6.

l2

CHAPTER TWO

BACKGROUND: THE MAIZE NATIONAL SITUATION

Maize is the most important crop in Kenya and policies affecting its availability
have a great impact on the farming sector and the food industry. Three major issues
concerning maize still remain as challenges for policy makers: the perennial shortage of
domestic maize supply during off-season months, relative to the year round demand; the
inefﬁciencies of the maize distribution system, given the geographically dispersed areas of
supply and demand; and the continuing state of poverty that characterizes the majority of
maize farmers, despite substantial effort by the government to improve farm productivity
and proﬁtability.

This chapter provides a brief description of the national maize situation,
highlighting the importance of the crop, the supply and demand trends and the key issues

concerning the commodity in order to clarify the content and ﬁndings of the study.

2.1 Importance of maize in Kenya

Maize is the most important food crop in Kenya. Over 70% of Kenya’s population
consume it as their main staple and over 75% of rural households cultivate the crop, both
as a source of food as well as a source of farm income (GOK, 1997; Hassan and Karanja,

1997). Because maize forms a major part of the Kenyan diet, while at the same time being

13

a major source of farm income for majority of rural households, the crop has great
economic and political signiﬁcance in the country. Thus, the government places high

priority on increasing maize production, as well as on improving maize farmers’ incomes.

2.2.1 Maize production: volume, area and yield

The area under maize production stabilized in mid-19903 at around 1.4 million
hectares with limited potential for expansion (GOK, 1997). Currently, maize yields
average about 2 mt per hectare, although a potential exists for increasing yields to over 6
mt per hectare (ibid). While maize production has ﬂuctuated greatly from year-to-year,
national production has increased from 1.3 million mt to slightly over 2 million mt over the
last two decades (Fig. 2). Sharp drops in production have mainly been due to severe
drought. For example, the droughts of 1984/85 and 1993/94 are responsible for the low
output in those production periods.

While maize production grew at an average annual rate of 4.4% during the 1980-
90 period, production declined by 1.8% annually during the 1990-95 period (Nyoro and
Jayne, 1999). This declining trend in maize production continues to be a major concern for

the government of Kenya, as well as for other stakeholders in the agricultural sector.

14

l
l

l

l
2500 1
2000 A
l

1500 '

Production ('000 mt)

1000

500 r

0 fr“"“"""—r‘ T“? ”7‘ T—‘FﬂT—T—

75 77 79 81 83 85 87 89 91 93 95
Year

Figure 2. Maize production in Kenya - 1975 - 1996

Source: Kenya MOALDM and CBS, Statistical Abstracts4

 

2.2.2 Maize Production system

Although maize is produced under a variety of farming systems, about 75% of the
crop is produced by small-scale farmers who employ a wide range of farming practices.
While some small-scale farmers use tractors or oxen for speciﬁc activities (i. e. ploughing
and harrowing), most carry out all other farming activities by hand using either family or
hired manual labor. In contrast, large-scale farmers follow a highly mechanized production

system in which they use tractors to carry out most of their farm activities. In addition,

 

4 Figures obtained from Table 1 in Nyangito, H. 1998.

15

they plant hybrid seed, apply fertilizer, and a majority use herbicides for weed control
rather than manual labor.

Labor used for farm activities is available under a variety of arrangements.
Relatively well-off farmers employ one or more permanent workers as laborers, while
most farmers hire labor only for speciﬁc activities such as weeding. While almost all
farming households utilize family members as the main source of labor, they typically hire
labor to supplement family labor during peak activity periods.

Maize cropping regimes are extremely complex. Large-scale farmers mainly
cultivate maize as a mono-crop Qaure stand), but small-scale farmers almost always
practice inter-cropping. Legumes (i. e. beans, cowpeas, and pigeon peas) are most
commonly inter-cropped with maize, although some farmers inter-crop maize with root
crops and tubers. The crop mix ranges from two to six crops on the same plot, with
seeding rates for each crop varying widely - even within a very small geographical area. It
is indeed accurate to say that the crop mixes are as varied and as many as there are

farming households.

2.2.3 Geographic location of production

The bulk of Kenya’s maize is produced in the highlands of the Rift Valley
Province, which is located to the western part of the country. Trans-Nzoia District leads
the Province in maize production, followed by Nandi, Uasin Gishu, Nakuru, Kericho and

Bomet Districts, respectively. Trans-Nzoia, Nandi and Uasin Gichu Districts produce

16

above two million bags (90 kg/bag) of maize each. This ﬁgure of two million bags from a
single district is signiﬁcant because it exceeds the (individual) production level of Central
Province, Coast, Eastern, North Eastern or Nairobi Province. Besides the Rift Valley
Province, Nyanza and Western are the only other provinces, whose production exceeds
two million bags per year. These data highlight the fact that most of the maize production
areas are located in the Western part of Kenya.

In 1998, Nyanza, Western and Rift-Valley Provinces accounted for over 85% of
Kenya’s total maize production (Table 1). Therefore, it is not surprising that the
government usually responds promptly to the grievances of maize farmers from western
Kenya. For example, in 1999, when the price of one 90-kg bag of maize fell to Ksh 600
(330.11/kg)5 -- which farmers saw as being far below their costs of production -- farmers
organized themselves and held back the sale of maize and demanded a price of Ksh 1,500
per bag ($0.28/kg). Farmer protests intensiﬁed, resulting in the government agreeing to
purchase one bag of maize at Ksh 1,200 ($0.22/kg) through the NCPB — which in essence

set a ﬂoor price for maize.

S

The January - March 1999 average exchange rate was Ksh. 60.60 = 1 USD.

17

Table 1.

Maize production in Kenya by Province, 1998

 

 

 

 

 

 

 

Province Area in hectares Production Percent of total

(90-kg bags) production (°/o)
Rift Valley 473,380 14,968,171 54.7
Western 1 84,442 5,172,006 1 8.9
Nyanza 164,982 3,614,499 13 .2
Eastern 480,505 1,808,] 10 6.6
Central 137,377 1,317,098 4.8
Coast 29,3 17 422,848 1.5
Nairobi 2,585 45,601 0.2
North Eastern 3,152 30,565 0.1
Total 1,475,740 1,475,740 100.0

 

Source: Kenya MOALDM

2.2.4 Geographic location of demand

The greatest demand for maize grain comes from large-scale millers who are

mainly based in Nakuru and Nairobi. These ﬁrms mill the grain into ﬂour and pack it in 2-

kg bags for distribution to consumers in urban as well as rural areas across the country.

The majority of maize buyers are based in the central and eastern parts of the country,

which are maize deﬁcit areas. Traders also supply maize grain directly to consumers in

deﬁcit areas. Usually, wholesalers buy maize from Nakuru, Uasin Gishu, Nandi Kericho,

Bomet, Trans-Mara and Trans-Nzoia districts and transport it to the high-demand deﬁcit

areas of Kisumu, Siaya, Nairobi, Machakos and Kitui (Nyoro et al. , 1999). Once in the

18

 

deﬁcit regions, the maize is sold to retailers who re-sell it as grain to consumers or who

ﬁrst mill it and then sell it as maize ﬂour.

2.3.1 Crop seasonality

Generally, the domestic maize supply is largest during the peak harvest months of
August to October and becomes tight from March to May. The extreme seasonality of the
crop causes severe market price ﬂuctuations during the year. On average, maize prices in
the major markets of Kenya gradually rise during the lean months (February to July),
abruptly dropping during the peak harvest months (August to October), and then remain
low until January (Fig.3). Because crop seasonality affects the behavior of maize prices, it
has critical implications for the proper timing of maize import arrivals. If imports arrive
during the peak harvest period, then they may cause prices to fall below what they usually

are and hence reduce the proﬁtability of maize to farmers.

19

O!
S
'5
X
7;; 1000
.N
(U
E
“5
E

500 —i -- ﬁ- — w _,_, - r —7’ ---*—wﬁ——T—-— , W a --- ‘I' —-,———
Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May
Months

Figure 3. Average maize prices for major markets in Kenya - June,
1998-May, 1999

Source: Kenya MOALDM - farm management handbooks, District reports. Various

issues.
2.3.2 Domestic supply and imports

Table 2 shows the domestic production, marketed volumes, exports, and imports
of maize for the period 1975/76 to 1995/966. During the 19703 and 19805, Kenya was
generally self-sufﬁcient in maize production. However, the ﬁrst half of the 19805 was

characterized by considerable shortfalls, due to the droughts of 1981 and 1984, leading to

 

6 These and other data presented in this thesis do not take into account cross-border

trade with neighboring countries.

20

imports during that period. In the 19903, production shortfalls required the government to
import maize in most years, especially after 1992. Also, the demand for maize has been
increasing due to the rapid annual population growth, which is estimated at about 3%.
But, achieving commensurate grth in maize production, estimated at 4% per annum,
remains elusive (GOK, 1997). While other constraints to sustained growth of the maize
sub-sector may exist, the Development Plan of 1997-2000 identiﬁed three key constraints:
unreliable marketing and uncertainty in the sale of surpluses, inadequate farmer use of
improved seeds and fertilizers; and low proﬁtability as a result of low yields and poor

marketing conditions.

21

Table 2. Total production, marketed production, exports and imports of maize,
1975/76 - 1995/96, Kenya

 

 

 

Year Total Marketed Exports Imports
Production production ‘000 mt ‘000 mt
‘000 mt ‘000 mt
1975/76 1375.2 487.8 120.8 0.0
1976/77 1597.1 564.7 113.0 0.0
1977/78 1671.4 424.0 8.0 0.0
1978/79 1620.0 330.2 23.0 0.0
1979/80 1606.5 241.7 120.0 0.0
1980/81 1888.3 217.9 0.0 224.0
1981/82 2560.0 472.9 1.0 77.0
1982/83 2450.1 571.3 1.0 89.0
1983/84 2214.8 637.1 123.0 0.0
1984/85 1500.0 560.6 47.0 405.0
1985/86 2440.3 582.9 18.0 125.0
1986/87 2870.0 669.5 228.0 0.7
1987/88 2400.0 651.9 248.0 0.0
1988/89 3140.0 485.3 167.0 0.0
1989/90 3030.0 625.9 1 10.0 0.0
1990/91 2890.0 509.3 160.0 0.0
1991/92 2252.7 303.5 18.7 0.0
1992/93 2205.0 324.1 0.42 414.4
1993/94 1698.3 241.8 0.1 1 12.9
1994/95 2620.8 316.0 0.17 650.4
1995/96 2369.7 401.0 221.5 10.8
1996/97 2214.0 NA 2.6 1,101.0
1997/98 2430.0 NA 9.1 368.8
1998/99 2250.0 NA 30.5 735
1999/00 1800.0 NA NA NA

 

 

 

 

 

 

NA = data not available.

Source: Kenya MOALDM and CBS, Statistical Abstracts7. FAO Statistical Database.

 

7 Figures obtained from Table 1 in Nyangito, H. 1998. FAO database provided the
ﬁgures for period after 1997.

22

Due to the gap between domestic supply and demand that has existed since the early
19905, maize imports have increasingly been the means by which Kenya mitigates
inadequate domestic supply (Table 2) . In some years, imports have been large enough to
depress domestic prices. For example in 1997, 1.1 million mt of maize were imported, an
amount that is equal to about 50% of Kenya’s total production. Obviously, imports are
inevitable since the country is no longer self-sufﬁcient in maize production. However, in
recent years, farmers have reacted negatively towards importation manifesting their
reaction in protests and riots. Protests and riots by farmers are a key indicator of the
(perceived) impact of imports on producers’ incomes. However, no empirical studies have
been conducted to determine if the alleged low prices, that led to farmer protests, were
mainly due to the arrival of imports. In contrast researchers who have analyzed the
impacts of maize imports have mainly documented their positive impacts on consumers,
due to lower prices, and are silent about the impact on producers (e. g. Argwings-Kodhek,

1999; Argwings-Kodhek and Jayne, 1996).

2.3.4 Government intervention

Considering the economic and political importance of maize, the government has
historically intervened in the maize trade in spite of its policy to liberalize the sub-sector.
The mode of intervention has been varied and sometimes erratic. To date, one way that

the government intervenes in the maize trade is via imposing an import tariff, which it sets

23

at various rates depending on the desired outcome. In addition, in some years the
government has set a ﬂoor price, as happened, most recently in 1999. With respect to the
ﬂoor price, the overriding policy issue is, “at what level should support prices be set to
balance the different interests in the commodity system (including maize producers and

consumers).

Based on his analysis of data from various sources, including farm surveys, Nyangito
(1998) concluded that the country has a comparative advantage in producing maize (as
compared to importing maize), although it does not necessarily have a comparative
advantage in exporting maize.” However, regarding what is needed to ensure growth in
the maize sub-sector, Nyangito provides a list of recommendations that must be
implemented if food security is to become a realizable objective in Kenya”. These

recommendations include:

(a) the need to streamline the farm input delivery system in order to make

affordable inputs available to farmers;

(b) the need to enhance farmers’ knowledge regarding the appropriate use of

inputs;

 

Italics mine.

These recommendations are indeed similar to those found in most policy
documents such as the National Development Plans of Kenya and sessional papers.

24

(c) the necessity to invest in developing high-yielding maize varieties;
(d) the urgent need to improve Kenya’s road and market infrastructure; and
(e) the need to remove market distortions.

The ﬁrst four recommendations are unequivocally essential for increasing, not only
maize production, but also the production Of all crops because they lead to improved
efﬁciency in crop production and marketing. However, the last recommendation on the
removal of market distortions is moot. Currently, the main distortionary policy in the
maize sub-sector is the import tariff. The debate on this issue is on-going and has garnered
advocates who hold opposite views regarding whether or not the tariff should be removed.
The arguments in support of the tariff hinge on its importance as a tool for protecting
maize producers from competition with imported maize. As a result of the tariff, domestic
prices are usually kept higher than world prices -- which creates an incentive for continued
(and increased) production. The need to encourage domestic production stems from the

fact that maize is the most important crop in Kenya, especially as a staple food crop.

The argument for removing the import tariff is based on (welfare) economic theory.
It is also based on household food security concerns for many households who are net
maize buyers. According to economic theory, removal of the tariff will lead to higher
social welfare. This is because the resulting gain in consumer surplus is far greater than the
loss in producer surplus. In theory, consumers can compensate producers for their loss
and still be left with a surplus. Hence, for the economy in general, the removal of the tariff

is likely to be beneﬁcial. However, in practice it is difﬁcult for consumers to compensate

25

 

producers for their loss because of the complexity of an appropriate transfer program and
the costs of administering such a program may be prohibitively high. Therefore producers
lose out as a result of removing the tariff. Reconciliation of the above views for informing
policy requires a detailed analysis of the distributional impacts on both consumers and

producers which is beyond the scope of this study.

26

 

CHAPTER THREE

RELATED STUDIES, THEORY AND METHODS

This chapter contains a focused review of the literature of related studies, theory,
and analytical methods. It starts with a brief review of the structural transformation
paradigm and how the process works to bring about economic growth. This is followed by
a review of the challenges that the structural transformation process has placed on Sub-

Saharan Africa, followed by a discussion of the analytical framework.

3.1 Structural Transformation: setting the stage

Theoretically, the process of agricultural transformation has four main stages
(Timmer, 1990). The ﬁrst stage is characterized by a predominantly agrarian economy in
which the priority is to ‘jump start’ agriculture. During this stage the rest of the economy,
outside agriculture, is so small that there is a need to tax agriculture in order to kindle the
rest of the economy. However, it is essential in this stage that some of the government’s
revenues are reinvested back into the agricultural sector to improve productivity and

promote growth.

27

In the second stage, the agricultural sector continues to grow and plays a primary
role in the economy, as the main contributor to economic growth. The sector provides,
among other things, food, a market for factor inputs, and raw materials for industry.
During this stage market linkages between agriculture and the rest of the economy are

established.

The third stage is characterized by an agricultural sector that has fully been
integrated into the rest of the macro—economy. In this stage the agricultural sector
becomes more vulnerable to risks and uncertainties caused by events that may occur both
in other sectors of the economy and those that are external to the economy. Also, there is
a push to make agriculture more efﬁcient in an effort to keep farming proﬁtable, while
maintaining low food prices. An efﬁcient agricultural sector becomes necessary to
stimulate the required shift of resources, such as labor from the agricultural to the non-

agricultural sector.

Finally, in the fourth stage of the structural transformation process, the economies of
developing countries resemble those of industrialized nations. In this stage, food
commodities constitute a small share of the consumer’s budgets and, agriculture’s share of
GDP is relatively small. Income distribution becomes a political issue and there are now

greater concerns about the environment and equity issues.

At each stage, a critical set of speciﬁc policies are required to ensure successful
transformation to the next stage. Hence, as a country moves from one stage to the next, it

is essential to develop a dynamic and adaptive policy environment. In reality however,

28

countries tend to protect their agricultural sectors as they become more developed
(McCalla, 1969; Anderson and Tyers, 1989). Thus, it becomes increasingly difﬁcult to
remove subsidies and tariffs initially instituted to promote growth in the agricultural sector
because producers need to be cushioned -- even though free trade is believed to be more

beneﬁcial for the economy as a whole (Bhagwati,l988; Timmer,1990; Krugman, 1991).

3.2 Structural Transformation: the Sub-Saharan African challenge

Many scholars believe that structural transformation is the vehicle through which
rich countries achieved economic growth and there is a substantial literature devoted to
the topic (e. g. Timmer, 1990; Staatz, 1994; Tomich et al. 1995). To support the view that
structural transformation is responsible for transforming poor countries to rich ones, a
number of scholars draw parallels between the success story in East Asian economies with
the failures in African economies. For instance, Gabre-Madhin and Johnston (1999) state
that there are lessons to be learned from Taiwan’s experience for accelerating Africa’s
structural transformation process. Roemer (1991) is also in agreement with the choice of
Taiwan as a role model for African economies, but adds Thailand, Malaysia and Indonesia
to the list. Other scholars have not only embraced the idea that structural transformation is
the process by which economies develop, but they also believe that the process has been

occurring in Africa. Furthermore, they believe that the declining growth rates recorded

29

 

since the mid-19805 are temporary and that African economies are now ‘back’ on the path

to recovery (Badiane, 1999).

However, other economists offer contradictory evidence. For example Dorward et
al. , argue that African economies have responded weakly to structural adjustments. While
the structural transformation paradigm may be a legitimate vehicle for economic growth in
general, they argue that the way neoclassical economists expected it to occur in Africa
was myopic. Thus, they advocate for a paradigm shift towards a theory of “Institutional
Development Policy” that incorporates institutional changes in the transformation process.
Timmer (1997) also argues for the need to develop a new development paradigm that
explicitly takes into account market failures instead of accepting the notion of perfect and
complete markets. In this study, some evidence is provided that, in part, supports this
latter viewpoint, albeit, anecdotally. In contrast, other scholars including Yumkella et al.
(1999) accept the model envisioned by Badiane, take the structural transformation process
as given, and move on to incorporate current forces such as moving towards globalization,

value added, and international trade (especiﬁcally WTO) into their models.

Eicher (1999) however, blames the economic woes of Africa on, inter alia, readily
available food aid, limited political power of farmers, inconsistent and frequently changing
assistance strategies of donors, and a historical focus on industrialization as the engine of
economic growth besides Africa’s own woes such as poor leadership, corruption and civil
wars. Eicher’s view is similar in part to the view held by Schultz (1990) in his article,
‘Investing in people: the economics of being poor’. Schultz attributed the cause of failed

economic policies in deve10ping countries to a failure to focus on the key factors of

30

production, arguing that the decisive factor of production is the improvement of human

capital and not space, energy, and land.

After reviewing the work of Collier and Gunning (1999); Platteau and Hayami
(1998); Hayami and Ruttan (1985); and Tomich et al. (1995) on Africa’s weak economic
performance and on the structural transformation process, Gabre-Madhin and Johnston
(1999) identify an important gap. Missing from the literature is any attempt to account for
adverse effects of what these authors call, ‘a prolonged period of rapid growth of a
country’s labor force on the structural transformation process’. The implication of this is
that an important factor that may help explain why structural transformation has had been

slow in Africa, may have been left out of most scholars’ analyses.

3.3 Analytical framework

This study focuses on assessing the proﬁtability of maize, compared to alternative
crops in several districts of Kenya. The proﬁtability of various crop enterprises was
determined for each crop enterprise using a farm management procedure that is commonly
used for computing gross margins for farm enterprises. Speciﬁcally, the procedures
outlined in Boehlje and Eidman (1984) and the methods recommended by Dillon and
Hardaker (1980) were used to guide the analysis. The speciﬁc procedures followed in

estimating different cost items are discussed below.

31

For the enterprises where it was possible, (i. e. mono-crop maize and maize bean
inter-crop) proﬁtability is estimated by type of technology used and also by the market
position of farming households (i. e. whether they are net maize buyers or net maize
sellers). In addition, a relative proﬁtability index table is presented that provides a
comparison of proﬁtability estimates of maize/beans inter-crop with those of other
enterprises. Also a table is provided that shows a comparison between the observed
cultivation decisions and what the results suggest should be cultivated on the basis of

expected proﬁtability using farm-gate prices.

3.3.1 Land preparation

Land preparation involves land clearing, ploughing, and harrowing to prepare a ﬁeld
for planting. Farmers in all the study areas used a wide range of methods to prepare their
land. Most farmers in the Rift Valley Province used tractors for ploughing and harrowing,
while those in Western and Eastern Provinces used animal draft power or hand hoeing.
Most farmers who used a tractor, hired a private tractor owner to plough their land for a
fee. While the cost of using hired tractor services varied by region and by season, farmers
generally paid about Ksh 1,000/acre in most areas. When computing tractor costs, the
actual amount paid for hiring the tractor was used. For farmers who owned a tractor and
used it to carry out their farm activities, the average rental fee for tractor services in their

villages was used as proxy for the cost of land preparation.

32

Most farmers who used oxen for animal draft power owned their oxen. However,
some farmers hired oxen to plough their land. Where hired oxen were used, the actual
amount paid by the hiring household was used to compute the cost of draft power.
Otherwise, the average cost of hiring oxen in the village where the farmer lived was used

as a proxy for the cost of oxen ploughing services.

A majority of the farmers in the sample mainly used family labor to prepare their
land, although a few used hired labor. Where farmers employed hired labor, the’actual
amount paid for hiring that labor was used as the cost of hired labor. On the other hand,
family labor was valued at its opportunity cost (i.e. the hired wage rate for that activity).

In all the study areas, there was a well-developed market for hired labor for carrying out
different farm Operations. Costing family labor at its opportunity cost was justiﬁed because
a family member could hire out his/her labor services in lieu of working on the family
farm. However, to accommodate the ‘conventional wisdom’ that rural labor markets in
Africa are thin, this study incorporates family labor into the analysis in two different ways.
First, assuming the non-existence of a labor market, family labor is excluded from the
calculation of the production costs and is regarded as a residual claimant after paying for
all cash costs. Second, assuming the existence of a complete labor market, family labor is
explicitly valued at its opportunity cost and included in the computations to estimate

production costs.

Land preparation cash costs varied greatly across regions, depending on whether or
not farmers used hired services (tractor, oxen). These costs were highest in the Rift Valley

Province where hired tractor services for ploughing and harrowing were most commonly

33

used, and lowest in Eastern Province where most farm activities were carried out using
family labor. However, when the cost of family labor is valued at its opportunity cost and
included as a cash cost, land preparation costs are comparable across all enterprises and

regions.

3.3.2 Planting, weeding, harvesting and other activities

Planting, weeding, harvesting, shelling, stooking, pruning, and threshing were
carried out almost entirely using hired and family labor rather than machinery. As these
activities are carried out at different plant growing stages, it is essential that households
schedule their labor to ensure its timely availability. Thus, even households who appear to
have sufﬁcient family labor actually hire labor at some point during the year in order to
complete farm activities such as weeding on time. Weeding, which is carried out two and
sometimes three times per cropping season, is by far the most labor intensive activity and

timeliness in weeding is especially crucial to ensure good yields.

In order for the plants to take full advantage of the initial nitrogen ﬂush, planting
needs to be done within the ﬁrst fortnight after the onset of the rains. A delay in planting
after the onset of the rains reduces the crop’s potential yields.lo However, weeding and
harvesting must also be carried out at speciﬁc stages of plant growth. For instance, if

crops are not weeded or harvested on time, yield reductions are likely to occur; ﬁrst,

 

'0 Author’s knowledge based on training in agronomy and crop production

34

through losing the battle for nutrients and sunshine to weeds and, second, through direct

losses due to spoilage and vermin attack.

The sum of the cost of ploughing, harrowing, planting, weeding, harvesting, and
shelling/threshing equals total operation costs for crop production. While these costs vary
across regions and across different crop enterprises, some commonalities exist that cut
across all enterprises. For example, in this study, the cost of family labor accounts for a
very large share (over 40% in most cases) of total production costs. This underscores the
importance of giving a careful treatment to family labor. If family labor is undervalued or
otherwise excluded as a cost item, then the resulting gross margins would be excessively

large and would provide misleading evidence as to the proﬁtability of the enterprises.

3.3.3 Material input costs

The main purchased inputs used by farmers in this sample were planting materials
(seed) and fertilizer. As only a few farmers (less than 1%), used chemicals-- either for
weed control or pest control-- these costs did not merit formal treatment as separate cost
items. The cost of seed/planting material per acre was computed by multiplying the
divisional average quantity planted per acre by the divisional average price of the seed at
planting time. A similar computation method was used to estimate the cost of fertilizer per

acre. All prices used were the average of divisional level observations. The sum of the

35

seed/planting material costs and fertilizer costs makes up the material costs reported in this

study.

The cost of operations described above, plus seed and fertilizer costs, were summed
to obtain total production costs for each crop enterprise. As a consequence of the two
ways in which family labor is valued, as explained earlier, two estimates of production
costs are reported and used when calculating gross margins, viz, production costs without
family labor included as a cash cost; and production costs with family labor valued at its
opportunity cost. Table 3a below contains a summary of some of the descriptive

information on the sample for this study for the maize/beans intercrop.

Table 3a. Some descriptive information for maize/bean intercrop farmers

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Province
Rift Valley Western Eastern
NBHI NSHI NBHI NSHI NBLI NBHI
number of observations 31 23 16 1 1 12 15
total land size (acres) 11.3 14.0 4.9 10.1 4.2 2.8
area planted to maize/beans 2.2 1.8 1.7 3.3 1.4 1.0
area planted to other crops 2.3 3.0 0.6 0.8 0.8 0.4
% of hhs using hybrid 89.7 96.0 76.2 92.3 16.7 31.3
maize
% of hhs using fertilizer 97.4 100.0 100.0 100.0 16.7 100.0
% of hhs with non-farm 23.1 20.0 14.3 53.8 33.3 25.0
income

 

 

" NBHI is net maize buyer, high input user
NBLI is net maize buyer, low input user

NSI-II is net maize seller, high input user

Source: Survey data, 1999

36

3.3.4 Prices

The prices used to value crop output were based on recall by farmers of what prices
they received when selling produce and what prices they paid when buying produce. The
selling prices are mainly farm-gate because most farmers sold to traders who came to their
farms, while buying prices are retail prices in local markets where farmers obtain their
groceries. To value yield, distinction is made between the prices faced by consumers from
those faced by producers. The farm-gate prices are used to value the produce from net
selling households while retail prices are used to value the produce from net buying
households. Furthermore, we value all commodities at the average district level prices.
With respect to seasonality, we used the weighted mean price across the month in which

the commodity was sold.

During the study period, there was a 25% tariff on maize imports in place and it is
believed that it led to artiﬁcially higher maize prices at that time. In an attempt to account
for the effects of the tariff on relative proﬁtability of crop enterprises, a sensitivity analysis
is carried out on maize prices reﬂecting the 25% import tariff and enterprise proﬁtability is
then estimated using the adjusted prices. The results are reported which show what
proﬁtability would have been without the tariff compared to when there was a tariff in

place.

37

3.3.5 Yield

Yield estimates for the crops planted were obtained by asking farmers to recall the
output they actually obtained from a speciﬁc plot. These plot-level yield ﬁgures were then
converted to per acre yield values and used to estimate total revenue by multiplying them
by the appropriate output prices. Total revenue was computed as a product of the yield in

kilograms per acre and price per kilogram of produce.

3.3.6 Labor

Labor use was estimated by asking farmers what type of labor (i. e. hired or family)
they utilized and how much they used per given activity. With respect to hired labor, the
actual amount of money paid by the farmer was recorded for each activity and included as
part of the production cash costs of the farmer. With respect to family labor, the number
of family members that performed different tasks was recorded together with the time it
took to complete the task. These ﬁgures of family labor use were then converted to
mandays per acre for each activity and valued at the district average wage rate, which was

used as the opportunity cost for family labor.

In computing gross margins, family labor is treated in two ways. First, it is excluded
from calculations as a cost item and regarded as the residual claimant. Returns to family

labor and management are estimated by dividing the resulting ﬁgure of gross margins per

38

 

acre by the total number mandays used. Second, family labor is valued at its opportunity

cost and included as a cost item in the calculations of gross margins.

3.3.7 Gross margins

Gross margins are obtained by subtracting total production costs from total revenue
per acre. Total production costs were estimated as discussed above. Total revenue, on the
other hand, was deﬁned as the value of the total output per acre-- whether or not the
output was sold. Therefore, total revenue includes the output, which is sold, used for
household consumption, used on the farm for seed, and used as in-kind-payments. The
production costs per acre were Obtained as the value of all inputs used in production,
excluding the value of in-kind labor payments of labor (i. e. meals provided to workers).
Family labor, which is valued at its opportunity cost is also included as a cost item.
Although production costs usually also include ﬁxed costs in this analysis they are

excluded.

The difference between total revenue and production costs is deﬁned as the gross
margin per acre. Gross margins are a measure of the return to the family for both its
production management and all capital invested in the land. Therefore, in this analysis,
gross margins represent the proﬁtability of an enterprise per acre. Net margins per acre are
a measure of proﬁtability after accounting for the opportunity cost of family land. Net

margins are estimated as gross margins per acre, minus the land rental rate. They are a

39

measure of proﬁtability that takes into account the farmer’s forgone opportunity to rent

his/her land.

40

CHAPTER FOUR

RESEARCH DESIGN

4.1 Characteristics of survey areas

Kenya, located on the east coast of Africa with the Equator running almost straight
through the middle of the country, has an area of 582,644 sz (224,900 Sq. miles). The
country has a great diversity of physical features, which can be distinctively divided into
ﬁve main zones. The low-lying arid and semi-arid lands of the north and northem—eastem
province cover nearly two-thirds of the country. The coastal belt which runs along the
Indian Ocean from the Kenya-Tanzania border to the Somali border, is a well watered
area receiving rain twice a year from the north-easterly and south-easterly monsoons. The
Nyika Plateau (dry wilderness), situated between the coastal belt and the central highlands,
is a dry area of low rainfall. Here, the vegetation consists of short grass with scattered
acacia trees. This area, which is best described as dry savanna, supports most of Kenya’s

wildlife population.

The fourth and most productive zone is the central highlands -- a raised volcanic
block split from north to south by the Great Rift Valley, a 8,700 kilometre ditch on the
surface of the earth (sometimes 80 Km wide) which stretches from the Dead Sea in Jordan
to Beira in Mozambique. The eastern wall of the Valley is dominated by Mt. Kenya, a

giant extinct volcano rising to 5,199 metres (17,058 ft.a.s.l.). A combination of good

41

 

rainfall, fertile soils, and suitable climate makes the region one of the richest agricultural
areas in the world. The western slopes, including the Mt. Elgon region, are fertile and
well-watered. They receive most of their rains from the inland sea of Lake Victoria (the
second largest fresh water lake in the world). From the western ﬂanks of the central
highlands, the land slopes down to the lake basin. The lake basin is hot and moist and
receives heavy rainfall from the lake. Its vegetation is mainly savanna woodland. The vast
mass of Lake Victoria creates its own local weather systems. Kenya, one of the most
prosperous agricultural countries in Africa, is the third largest tea producer in the world

and the biggest producer of pyrethrum in addition to a great variety of other crops.

Kenya’s altitude ranges from sea level to 5,199 metres, making its climate vary
greatly from high humid temperatures of the coast to the often cold and wet regions of
Aberdares, Cherangani, and Mau Escarpment, Mt. Elgon, and Mt. Kenya. With the
equator traversing the country, the country has only two seasons - the long rains season
and the short rains season. Sunshine is experienced throughout most days of the year,

although it becomes cooler during the months of June, July and August.

Administratively, Kenya is divided into eight provinces: Western, Nyanza, Rift
Valley, Central, Nairobi, Eastern, North Eastern and Coast. In turn, each province is
divided into several administrative districts. This study was carried out in six districts
which were selected from three provinces. i. e. Uasin Gishu and Trans Nzoia Districts in
the Rift Valley Province, Kakamega and Vihiga Districts in the Western Province, and

Machakos and Makueni Districts in the Eastern Province.

42

4.1.1 Uasin Gishu and Trans Nzoia Districts

4.1.1.1 Location and size

Uasin Gishu and Trans Nzoia Districts are two of the 17 districts in the Rift Valley
Province of Kenya (Fig. 4). Uasin Gishu lies approximately between latitudes 0° 03' and 0°
55' North of the equator and longitudes 34° 50' and 35° 37' East. It shares common
borders with seven Districts: Trans Nzoia (north), Marakwet and Keiyo (east), Koibatek

(southeast), Kericho (south), Nandi (west) and Kakamega (northwest) Districts.

Trans Nzoia District lies approximately between latitudes 0° 52' and 10 18' North of
the equator and longitudes 34° 38' and 350 23' East. It has common borders with the
Republic of Uganda to the west and ﬁve Districts: Bungoma and Kakamega (south), West

Pokot (north), Marakwet (east), and Uasin Gishu (southeast) Districts.

Both Uasin Gishu and Trans Nzoia Districts are divided into six administrative
divisions with the former covering an area of 3,218 square kilometres, while the latter

covers an area of 2,467 square kilometres.

4.1.1.2 Agro-Ecology

Uasin Gishu District is a highland plateau with a terrain that varies greatly with

altitude, which ranges from 1,500m to 2,100 metres above sea level (mas). The district’s

43

general landscape is characterized by an undulating plateau, with no signiﬁcant mountains
and valleys. Annual rainfall in the district is high, reliable, and evenly distributed
throughout the year. Annual rainfall averages 960 mm, with the wettest months being May

and August.

UGANDA

 
 
  

 

 

  

Trans Nzoia . .
asrn Grshu

Kakamegajwfi ’0‘
Vihiga V
A re:

      
 
 

 

‘R
“‘5

   

Figure 4. Map of Kenya showing study districts

44

The district consists of three major agro-ecological zones (AEZs), viz. the Upper
Highland zone (UHZ), the Upper Midland zone (UM) and the Lower Highland zone

(LHZ). Most of the district falls within the LH2 and LH3 zones which are suitable for

growing maize, wheat, barley, sunﬂower and rearing of cattle and sheep.

Trans Nzoia District is also a highland plateau with gentle undulations rising
steadily to Mt. Elgon in the Northwest and to the foot of the Cherangani Hills to the East.
The average elevation for most of the district is 1,800 mas, although the Cherangani Hills
reach an elevation of 3,371 mas. The northern part of the district is quite hilly, making
communication very difﬁcult —- especially during the rainy season when the roads become
practically impassable. Rainfall is fairly well distributed throughout the year, with an
annual average of 1,296 mm. Trans Nzoia District has a pronounced bimodal rainfall
pattern, with the short rains falling during April- June and the long rains falling during

between J uly- October.

The district also has three major agrO-ecological zones: UHZ, UMZ and LHZ. As
expected, each AEZ is more suited for certain agricultural activities and less so for others.
Cattle and sheep are mainly raised in the UHZ (16% Of district), although they are also
raised in the other zones. Partners in the UMZ, which covers 50% of the District, mainly
cultivate maize, sunﬂower, coffee, wheat, barley, and horticultural crops. Farmers in the
LHZ, which covers 34% of the district, mainly cultivate pyrethrum, wheat, tea, maize,

barley, sunﬂower, coffee, and horticulture crops.

45

4.1.2 Kakamega and Vihiga Districts

4.1.2.1 Location and size

Kakamega and Vihiga Districts are two of the six districts in the Western Province
(Fig. 4). Kakamega District lies approximately between latitudes 0° 15' and 1° North and
longitudes 34° 20' and 35° 0' East. It is bordered by seven districts: Busia, Siaya and
Bungoma (west), Nandi and Uasin Gishu (east), Trans Nzoia (north), and Vihiga (south)

Districts.

Vihiga District, which was carved out of Kakamega District, lies approximately
between latitudes 0° and 0° 15' North and longitudes 34° 30' and 35° 0' East. It is
bordered by four districts: Kakamega (north), Nandi (east), Kisumu (south), and Siaya

(southwest) Districts.

Kakamega District is divided into 14 administrative divisions and covers an area of
3,020 square kilometres. Similarly, Vihiga District is divided into six administrative

divisions and covers an area of only 541 square kilometres.

4.1.2.2 Agra-ecology

Kakamega District’s topography ranges from 1,250m to 2,000 mas. The northern,

central, and eastern part of the district is generally hilly, rising gradually to an elevation of

46

1,600m to 2,000m where the district meets the Nandi escarpment. The southern part of

the district is relatively ﬂat, has well drained soils, and heavy rainfall, making it suitable for
sugar cane production. The district has a bimodal rainfall pattern, with the long rains
falling during March-June and the short rains falling during July-September. The driest
months of the district are December and January, when most farmers prepare their land.

On average, annual rainfall varies from 1,000 mm in the northern parts to 2,400 mm in the

southern parts of the district.

Kakamega District falls under four agro-ecological zones. The UMO zone, which is
mainly a forest zone, (i. e. Kakamega forest) supports very little crop production. The UMl
zone is a tea-coffee zone, while maize and sunﬂower are most suited for the UM4 zone.
Sugar cane is mostly grown in the LMl zone, while other minor crops are cultivated to

varying degrees across all the AEZS.

Vihiga District lies on the eastern fringes of the Rift Valley’s lake basin. Its latitude
ranges between 1,300m and 1,500 mas and slopes gently from west to east. The district is
characterized by undulating hills and valleys with very little ﬂat land, mainly in the
southern part of the district. The district’s hilly topography makes accessibility to most
parts very difﬁcult, except for areas that fall within Sabatia, Tiriki and Luanda Divisions.
Annual rainfall, which ranges between 1800m and 2000 mm, is fairly reliable from year-
to-year. Like Kakamega District, Vihiga District also has a bimodal rainfall pattern, with
the long rains falling during April-June and the short rains falling during September-

November.

47

Vihiga District falls into UM1 and LMl agro-ecological zones. The UMI zone has
fertile, well-drained, dark red soils which support tea, coffee and millet production, while

the LMI has soils that mainly support sugar cane and maize production.

4.1.3 Machakos and Makueni Districts

4.1.3.1 Location and size

Machakos and Makueni Districts are two of the 12 districts that comprise Eastern
Province (Fig. 4). From north to south, Machakos District stretches from latitude 0° 45' to
latitude 1O 31' south and lies between longitudes 36° 45' and 37° 45' east. It borders eight
districts: Kitui and Mwingi (east), Makueni (south), Kajiado (west), Nairobi city and

Thika (northwest), Murang’a and Kirinyaga (north), and Mbeere (northeast) Districts.

Makueni District lies between the equator and latitude 10 35' south and longitudes
37° 10' east and 38° 30' east. It borders four districts: Kajiado (west), Taita Taveta

(south), Kitui (east), and Machakos (north) Districts.

Machakos District is divided into 11 administrative divisions and covers an area of
6,051 square kilometres. Makueni District is divided into 14 administrative divisions and

covers an area of 7,440 square kilometres.

48

4.1.3.2 Agro-ecology

Machakos District is largely a plateau that rises from 700m to 1,700 mas with
interruption by an escarpment and a series of hills. The district, which is generally hot and
dry, has a bimodal rainfall pattern. The long rains fall during April—May, while the short
rains fall during October-December. The annual average rainfall ranges from 500mm to
1,3 00mm, with signiﬁcant regional and seasonal variations within the district and very low

rainfall reliability.

Makueni District is generally low lying, rising from about 600m to 1,900 mas above
sea level. The major land features in the district are the Chyulu, Mbooni, and Kilungu Hills
in the western and southwestern part of the district. These hills receive good rainfall and
support coffee and horticultural production. In the rest of the district, rainfall is generally
low -- averaging less than 1,000 mm annually. Makueni District has a bimodal rainfall
pattern. The long rains fall during April-May, while the short rains fall during November-

December. Like Machakos District, Makueni is also hot and dry most of the year.

Machakos District falls under the lower and upper middle agro-ecological zones.
The UM2 zone, which covers the upper slopes of the hill masses of Iveti, Mua and
Kangudo, has an average rainfall of 1,000mm -- which is sufﬁcient to support maize,
coffee and citrus fruit production. The LM3 and UM, zones, which cover the lower slopes
of the hills and parts of Matungulu, have an average annual rainfall of 850 mm -- making

them marginally suitable for maize, beans, pigeon peas and cowpeas production. Most of

49

the district falls under the relatively arid LM,, LM5, UM, and UM5 zones. With an average
rainfall of only 600-650 mm, the main activities in these zones are growing of pigeon peas,

bee-keeping, and ranching.
Makueni District falls under the LM2 - LM6 and UM, - UM6 zones. The LM2 zone

is characterized by red clay soils on the hill masses of Mbooni and Kilungu, where most of
the districts’ coffee, maize and citrus fruits are grown. The slopes of these hills and
adjacent areas of Nziu and Ukia fall under the LM,, UM3, LM, and UM, zones, which
have medium potential for production of maize, coffee and pigeon peas production. About
60% of the district falls in the LM,, LM6 and UM6 zones, which have very low potential
for crop production. In these zones, the most common activity is livestock rearing --

although maize, sorghum and pigeon peas are also cultivated there.

4.2 Agriculture

The cropping patterns in Kenya are greatly diversiﬁed and vary from region-to-
region. As reported in an earlier study (Argwings-Khodek et al., 1999) and conﬁrmed by
this study, maize and maize inter-crops account for over 25% of the cropped area
nationally. However, the proportion of land under maize cultivation is much higher than
the national average (i. e. 25%) in ﬁve of the districts studied, as shown in Table 3b
(below) which summarizes the relative percent of land allocated to major crops between

1992 to 1995 in Uasin Gishu, Trans Nzoia, Kakamega, Vihiga, Machakos, and Makueni

50

Districts. Uasin Gishu and Trans Nzoia Districts have a high potential for crop
production, with over 80% of their combined area classiﬁed as suitable for arable farming.
On the other hand, Kakamega and Vihiga Districts are classiﬁed as having medium
agricultural potential, although small pockets exist within the districts that have high
potential for crop production. Farmers in both Machakos and Makueni Districts mainly
grow food crops such as maize, beans, pigeon peas and cowpeas to meet their household

requirements.

Table 3b. Percent of crop land under cultivation of major crops, 1992-95 average

 

 

by District, Kenya
Crop Uasin Trans Kakameg Vihiga Machakos Makueni
Gishu Nzoia a % % %
°/o °/o °/o

Maize 48 69 44 39 52 8

Wheat 32 3 a a a a

Beans 18 22 26 20 20 38

Cowpeas a a a a 1 1 22

Pigeon peas a a a a 10 16

Tea & coffee a a a 34 a a

Sugar cane a a 26 a a a

Others 2 6 4 7 7 16

 

 

 

 

 

 

 

 

 

a Traces of the crop found in the district or crop not grown at all.

Source: District Development plans, 1997-2001 - various issues.

51

4.3 Data

To address the objective of this study, data were collected via a retrospective farm
survey. A structured questionnaire was used to interview farmers from the selected six
districts in Kenya. The data collected included household socioeconomic characteristics,
land size holding and area planted to major crops, crop output, prices of produce, quantity

and cost Of hired labor, quantity of family labor, and quantity and prices of material inputs.

Crop output data were based on farmers’ recall of what they harvested from a given
plot during the short and long rains seasons preceding the interview date. The recall period
varied from four months in Eastern Kenya to six months in Western Kenya. As farmers
were knowledgeable about the sizes of their plots and the quantities harvested from their

plots, it is possible to accurately estimate farmers’ crop yields.

Retail prices at which farmers sold their crops were gathered from farmers who
actually made sales. In addition, village level selling prices were obtained to corroborate
the price information obtained from farmers. Prices of material inputs were also collected,

both at the farm- and at the village-level.

52

4.4 Sampling

On the basis of average annual rainfall, Kenya’s agricultural land is classiﬁed as
having high or low potential for crop and livestock production. Typically, areas receiving
an annual rainfall of less than 1,000 mm are classiﬁed as low potential, while those
receiving above 1,000mm are classiﬁed as high potential. This broad classiﬁcation was

followed in this study.

A multi-stage sampling procedure was used to select the farmers that were
interviewed. Respondents were selected using an existing sampling frame that includes
farmers in all the provinces in Kenya. This sampling frame was developed in 1997 under
the auspices of the Kenya Agricultural Monitoring and Policy Analysis Project
(KAMPAP), a joint collaboration between Egerton University/Tegemeo Institute, Kenya

Agricultural Research Institute, and Michigan State University.”

To construct the sampling frame, the KAMPAP researchers used a multi-stage
probability proportional to size (PPS) sampling design. First they compiled a list of all
administrative districts and divisions and assigned them an Agro-ecological zone (AEZ)

classiﬁcation based on information reported in Farm management handbooks.I2 The thirty

 

Details regarding sampling frame’s design are contained in Argwings-Kodhek
(1998) and Jayne et al. (1998). And additional details were obtained via personal
communication with Joseph Owuor, the researcher who designed the sampling frame.

Farm management handbooks are annual reports that contain basic agricultural

53

ﬁve distinct AEZs that were identiﬁed were then grouped into eight categories: Coastal
lowlands, Lowlands, Lower Highlands, Upper-Lower Midlands, Lower Midlands, Upper
highlands, Upper Midlandsl , Upper Midland52. From each category, at least two AEZs

were randomly chosen. A total of 17 AEZS formed the ﬁrst stage sampling frame.

In the second stage, the researchers chose at least two divisions'3 from each AEZ.
They then compiled a list of all locations in the chosen divisions and randomly selected
one location to identify the villages to be sampled. In the third stage, one village was

randomly selected from each of the locations chosen in the second stage.

At the village level, a list of all households was compiled with the help of local
leaders. All the households in each village were numbered by the research team to ease the
selection process. Households in each village were then selected using systematic random

sampling.

For this study three provinces were selected, comprised of two districts each (Fig.
4), based on geographical location, agro-ecology and diversity of crops cultivated. These

are:

Rift Valley Province: Uasin Gishu and Trans-Nzoia Districts.
Western Province: Kakamega and Vihiga Districts.
Eastern Province: Makueni and Machakos Districts.

 

published by the Ministry of Agriculture and Livestock Development for each district.

'3 Each district is administratively made up of divisions, which are in turn made up of

locations. The location are themselves made up of villages.

54

For the remainder of this thesis, the Rift Valley Province, the Western Province, and the
Eastern Province will be referred to represent the sampled districts from each province
respectively. Note further that the discussions in this thesis do not in anyway assume that

the results from the sampled districts can necessarily be generalized to the entire province.

A total of 80 households were selected from each province on the basis of whether
they were net maize buyers or sellers, as well as whether they used high or low levels of
inputs for maize production, as determined from the existing KAMPAP 1997 rural
household survey database. While determining which households were net maize buyers or
sellers was fairly straight forward, the classiﬁcation of households into high and low input
users was based on the amount of fertilizer used on maize, as well as the use or non-use of
hybrid maize seed. Households who used less than 20 kg/acre of fertilizer on maize were
classiﬁed as low input users, while those who used 20 kg/acre or more were regarded as
high input users. Table 4a shows the number of households sampled from each province,

based on the category they belonged to.

55

Table 4a. Number of households sampled from each province, 1997 data.

 

 

 

 

 

Province Net maize buyer Net maize seller Total
low input high input low input high input
user user user user
Rift Valley none 40 none 40 80
Western 25 30 none 25 80
Eastern 27 41 none 12 80

 

 

 

 

 

 

 

 

Source: KAMPAP database, 1997

Once the data were collected and analyzed, the households were again classiﬁed
according to whether they were net maize buyers or sellers and whether they were low or
high input users based on the survey results. Tables 4b and 4c Show the number of
households that fell into different categories similar to those in Table 4a above. Table 4b
shows household classiﬁcation based on, among other criteria, the level of input use on
mono-crop maize plots while Table 4c is based on the level of input use on maize/beans
inter-crop plots. The ﬁgures in parentheses are the percentage Of households within that
cell that use hybrid maize. Additionally, it is only these cells with parenthesized

percentages that are used to calculate gross margins presented in Chapter Five.

56

Table 4b. Number of households sampled from each province by household market
position and by level of input use on mono-crop maize plots, 1999 survey.

 

 

 

 

 

 

 

 

 

 

Province Net maize buyer Net maize seller Total
low input high input low input high input
user user user user
Riﬁ Valley 2 8 (100%) 1 10 (100%) 21
Western 0 9 (77.8%) 3 4 16
Eastern 26 (38.5%) 23 (42.9%) 1 3 53

 

 

 

Source: Survey data, 1999

 

 

 

 

 

Table 4c Number of households sampled from each province by household market
position and by level of input use on maize/beans inter-crop plots, 1999
survey.

Province Net maize buyer Net maize seller Total
low input high input low input high input
user user user user
Rift Valley 4 31 (89.7%) 1 23 (96%) 59
Western 2 16 (76.2%) 1 11 (92.3%) 30
Eastern 12 (16.7%) 15 (31.3%) 1 2 29

 

 

 

 

 

 

 

 

Source: Survey data, 1999

4.5. Survey implementation

The actual administration of the survey of the 240 households turned out to be an
extremely smooth and successful operation. The questionnaire was administered by
myself, with assistance from a team of eight enumerators. All the enumerators had prior
experience in questionnaire administration, which they had gained from their participation
in KAMPAP I and II surveys. Three of them were and still are Research Assistants with
Tegemeo Institute, while the rest were unemployed at the time -- although they had also

participated in the said KAMPAP surveys.

The exercise started with an initial week of ofﬁce training on administering the
questionnaire and pre-testing it in nearby villages. The questionnaire was then reﬁned to
make it easier to administer and more appropriate, based on the pre-test results. Once the
actual survey begun, the entire team met every evening to edit the completed
questionnaires and discuss any difﬁculties that they encountered. The strategy used was to
go into one village as a team and complete the questionnaires for the households in that
village before moving to the next. This strategy proved to be useful in ensuring high
quality work, as I was able to address any issues that were unclear as they arose. In
addition, the strategy led to the development of a strong team work spirit that had
spillover beneﬁts which increased enumerator morale and eliminated of any form cheating

on the questionnaires.

58

The success of the survey was made possible by the availability of sufﬁcient funds
through the Food Security II project in collaboration with Tegemeo Institute. Tegemeo
Institute provided a four-wheel drive vehicle which made it possible for us to access all
households, a feat that would have proved difﬁcult owing to the poor condition of rural
roads in Kenya. In addition, Tegemeo Institute provided ofﬁce space, which made the
coordination and management of ﬁeld resources and activities much easier than they

would have otherwise been.

The survey proper was carried out during a six week period, followed by data entry
and initial cleaning which took two months. All the data were entered in SPSS by one of

the Research Assistants under my supervision.

59

CHAPTER FIVE

ANALYSIS AND RESULTS

The ﬁrst step in data analysis was data cleaning. Data were checked for logical
inconsistencies and veriﬁed against questionnaires to make sure that all data were correctly
inputted. Frequencies and other descriptive statistics were then conducted for all variables

and outliers identiﬁed. All inconsistencies were addressed before beginning detailed analysis.

Data analysis focused on estimating production costs, yields, and gross margins of the
selected crop enterprises in each of the three provinces in which survey data were collected.
Also, analysis was carried out to determine farmers’ perceptions about maize prices and how
these perceptions inﬂuence their decisions at the farm level. Finally, this chapter concludes

with a discussion of the factors that constrain agricultural commercialization in Kenya.

5.1.1 Mono-crop maize proﬁtability

The gross margins for mono-crop maize were computed using data from ﬁelds where
farmers planted maize as a pure stand. The analysis was carried out according to the
contiguous groups depicted in Tables 4b above. Tables 5a, 5b, and 5c report the average
gross margins for mono-crop maize for Rift Valley, Western, and Eastern Provinces

respectively.

60

Table 5a. Gross Margins for Mono-crop Maize for Rift Valley Province.

 

 

 

 

 

 

Item Net maize buyer Net maize seller
High input user High input user
n=8 n=10

YIELD

Harvest (kg/acre) 1,463.50 1,783.50

Price (Ksh/kg) 12.5 8.9
REVENUE

Maize (Ksh/acre) 18,293.75 15,873.15
PRODUCTION COSTS
Seed/planting material

Quantity (kg/acre) 8.90 9.30

Price (Ksh/kg) 91.00 91.00

Total cost (Ksh/kg) 809.90 846.30
Fertilizer/manure

DAP (Kg/acre) @ Ksh 25/kg 46.70 53.00

MAP (Kg/acre) @ Ksh 24/kg 62.50 62.50

20:20/23223 (Kg/acre) @ Ksh 24/kg 24.00 41 .70

Urea (Kg/acre) @ Ksh 17.20/kg 69.00

CAN (Kg/acre) @ Ksh l9/kg 50.00

Total cost (Ksh/acre). 1.322-50 19601-10
Operations costs

Cash costs (Ksh/acref ‘ 49864-70 7.545-30

Family labor (mandays/acre) 24.10 17.60

Family labor (Ksh/acre) 1,687.00 1,232.00

Total cost (Ksh/acre) 6,551.70 8,777.80
ﬂotal Production Costs (Ksh/acre)" 6,997.10 9,993.20

otal Production Costs (Ksh/acre)” 8,684.10 11,225.20

Gross margins (kshs/acre)

Excl. family labor (Ksh/acre) 1 1,296.65 5,879.95

Incl. family labor (Ksh/acre) 9,609.65 4,647.95
Land rent (Ksh/acre) 1,750.00 1,750.00
NET MARGINS

Excl. family labor (Ksh/acre) 9,546.65 4,129.95

Incl. family labor (Ksh/acre) 7,859.65 2,897.95
Return to family labor (Ksh/manday) 396.13 234.66

 

 

I/ 2/

Excluding family labor costs Including family labor costs

The total cost of fertilizer is a weighted average cost that includes all cash costs paid by farmers for
all types of fertilizers/manure. The prices used for computations are divisional level average prices
which differ slightly from the regional level prices reported in the table. Furthermore, the ﬁgure for
total cost also includes cost of fertilizers for which quantities were not reported because farmers
failed to recall them but remembered what they paid.

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling, and all other
miscellaneous farm operations costs.

61

Table 5b. Gross Margins for Maize Mono-crop for Western Province.

 

 

 

 

 

Item Net maize buyer
High ingut user
n=9

YIELD

Harvest (kg/acre) 1,400.00

Price (Ksh/kg) 12.50
REVENUE

Maize (Ksh/acre) 17,500.00
PRODUCTION COSTS
Seed/planting material

Quantity (kg/acre) 15.20

Price (Ksh/kg) 91.00

Total cost (Ksh/kg) 1,383.20
Fertilizer/manure

DAP (Kg/acre) @ Ksh 26/kg 56.70

Urea (Kg/acre) @ Ksh 22/kg 56.60

CAN (Kg/acre) @ Ksh 25.50/kg 50.00

Total cost (Ksh/acre). 1,406-70
Operations costs

Cash costs (Ksh/acre)‘ * 53600-00

Family labor (mandays/acre) 44.10

Family labor (Ksh/acre) 3,087.00

Total cost (Ksh/acre) 8,687.00
Total Production Costs (Ksh/acre)" 8,389.90
[Total Production Costs (Ksh/acre)” 11,476.90
Gross margins (kshs/acre)

Excl. family labor (Ksh/acre) 9,110.10

Incl. family labor (Ksh/acre) 6,023.10
Land rent (Ksh/acre) 2,000.00
NET MARGINS

Excl. family labor (Ksh/acre) 7,110.10

Incl. family labor (Ksh/acre) 4,023.10
Return to famin labor (Ksh/manday) 161.23

 

 

" Excluding family labor costs

*

2/

Including family labor costs

The total cost of fertilizer is a weighted average cost that includes all cash costs paid by farmers for
all types of fertilizers/manure. The prices used for computations are divisional level average prices
which differ slightly from the regional level prices reported in the table. Furthermore, the ﬁgure for
total cost also includes cost of fertilizers for which quantities were not reported because farmers
failed to recall them but remembered what they paid.

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling, and all other
miscellaneous farm operations costs.

62

Table 5c. Gross Margins for Maize Mono-crop for Eastern Province.

 

 

 

 

 

 

Item Net maize buyer Net maize buyer
Low input user High input user ‘
n=26 n=23
YIELD
Harvest (kg/acre) 232.10 341.00
Price (Ksh/kg) 13.00 13.00
REVENUE
Maize (Ksh/acre) 3,017.30 4,433.00
PRODUCTION COSTS
Seed/planting material
Quantity (kg/acre) 8.40 10.00
Price (Ksh/kg) 75.00 75.00
Total cost (Ksh/kg) 630.00 750.00
Fertilizer/manure
DAP (Kg/acre) @ Ksh 30/kg 6.50 12.80
Total cost (Ksh/acre). 195.00 334-00
Operations costs
Cash costs (Ksh/acre). . 2312-70 1491-30
Family labor (mandays/acre) 46.10 59.80
Family labor (Ksh/acre) 2,305.00 2,990.00
Total cost (Ksh/acre) 4,617.70 6,481 .30
iTotal Production Costs (Ksh/acre)" 3,137.70 4,625.30
Total Production Costs (Ksh/acre)” 5,442.70 7,615.30
Gross margins (kshs/acre)
Excl. family labor (Ksh/acre) (120.40) (192.30)
Incl. family labor (Ksh/acre) (2,425.40) (3,182.30)
Land rent (Ksh/acre) 1,500.00 1,500.00
NET MARGINS
Excl. family labor (Ksh/acre) (1,620.40) (1,692.30)
Incl. family labor (Ksh/acre) (3,925.40) (4,682.30)
etum to family labor (Ksh/manday) (35.15) (28.30

 

" Excluding family labor costs

2/

Including family labor costs

The total cost of fertilizer is a weighted average cost that includes all cash costs paid by farmers for
all types of fertilizers/manure. The prices used for computations are divisional level average prices
which differ slightly from the regional level prices reported in the table. Furthermore, the figure for
total cost also includes cost of fertilizers for which quantities were not reported because farmers
failed to recall them but remembered what they paid.

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling, and all other
miscellaneous farm operations costs.

63

The results show that for the Rift Valley and the Western Provinces, farmers who
planted mono-crop maize were all high input users with those in the Western Province
being net maize buyers only. The Rift Valley Province had both net maize buyers and net
maize sellers. The Eastern Province on the other hand, had both low and high input users
although they were all net maize buyers. However, the classification of ‘high input user’
in the Eastern Province was modiﬁed to include households that used at least 10 kg of
fertilizer and above. This was done because of the low level of purchased fertilizer use in

the province on mono-crop maize plots. C.

Table 5a shows that for the Rift Valley Province sample, gross margins average Ksh.
11,297 ( US $ = Ksh. 60.60) per acre, excluding family labor costs for net maize buyers
and Ksh. 5,880 for maize net sellers. However, when family labor costs are included, the
gross margins decline to Ksh. 9,610 and Ksh 4,648 for net maize buyers and net maize
sellers respectively. Further, when land rent is deducted, the gross margins become Ksh.
7,860 and Ksh. 2,898 per acre, with family labor costs included for net maize buyers and

net maize sellers respectively.

Farmers in the Western Province sample realized average gross margins of Ksh.
9,110 per acre excluding family labor costs and Ksh. 6,023 per acre including family labor
costs (Table 5b). These ﬁgures are lower than those realized for net maize buyers in the
Rift Valley Province but are higher than those realized by the comparable group of farmers
in the Eastern Province. The gross margins for net buyers, high input users, in the Eastern
Province sample averaged Ksh. -l92 per acre excluding family labor costs and Ksh. -3,l82

per acre including family labor costs (Table 5c). Similarly, net maize buyers but low input

64

users in the Eastern Province sample realized average gross margins of Ksh. -120 per acre

and Ksh. -2,425 per acre before and after accounting for family labor costs respectively.

These results show that the proﬁtability of the mono-crop maize progressively
declines from areas of high potential to areas of low potential for crop production. In
addition, family labor costs, when included as a production cost, greatly reduce the gross

margins. As a result of including family labor, the gross margins decline by at least Ksh.

11 ..

1,000 in all areas. The results also show that the gross margins realized by net maize
buyers are generally higher than those realized by net maize sellers within the same
province although this is in part driven by the higher retail prices used to value maize

yields for net buyers.

5.1.2 Maize/bean inter-crop

As discussed in the preceding section, some farmers grow maize as a pure stand in
all of the three provinces covered in the study. However, the most common cropping
pattern for growing maize was a maize/bean inter-crop, although farmers also planted
maize and other legume inter-crops. Tables 6a, 6b and 6c report gross margins for

maize/bean inter-crops for the Rift Valley, Western and Eastern Provinces respectively.

Table 6a shows that for the Rift Valley Province sample, gross margins average Ksh.
14,167 per acre, excluding family labor costs for net maize buyers and Ksh. 10,817 per

acre for maize net sellers. However, when family labor costs are included, the gross

65

margins decline to Ksh. 9,134 per acre and Ksh 8,388 per acre for net maize buyers and
net maize sellers respectively. Further, when land rent is deducted, the gross margins
become Ksh. 7,3 84 and Ksh. 6,638 per acre, when family labor costs are included for net

maize buyers and net maize sellers, respectively.

In the Western Province sample, net buying households realized average gross
margins of Ksh. 16,037 per acre excluding family labor costs and Ksh.12,692 per acre
including family labor costs (Table 6b). Net selling households realized average gross
margins of Ksh. 12,882 and Ksh. 1 1,242 with and without family labor costs respectively.
Unlike for the mono-crop maize enterprise, these gross margins are higher than those
realized by farmers in the Rift Valley Province. The gross margins for net buyers, high
input users in the Eastern Province sample averaged Ksh. 2,482 excluding family labor
costs and Ksh. -2,063 including family labor costs (Table 60). Similarly, net maize buyers
but low input users in the Eastern Province sample realized average gross margins of Ksh.

4,048 and Ksh. 1,933 before and after accounting for family labor costs respectively.

These results show that in all Provinces, the gross margins for the maize/bean inter-
crop are substantially greater than the respective gross margins for the maize mono-crop.
This result partially explains why the maize/bean inter-crop is the cropping pattern
followed by most farmers. In addition, the results also show that the gross margins
realized by net maize buyers are generally higher than those realized by net maize sellers
within the same province although this is again, at least in part, due to the higher retail
prices used to value maize yields for net buyers. Again, gross margins decrease

substantially when family labor costs are included at their opportunity cost.

66

Table 6a. Gross Margins for Maize/beans inter-crop for Rift Valley Province.

 

 

 

 

 

Net maize buyen Net maize selle
High input user Hi h in ut use
NIELD n=3 1 n=23
Maize harvest (kg/acre) 1,171.00 1,464.50
Beans harvest (kg/acre) 98.50 104.80
Maize price (Ksh/kg) 12.50 8.90
Beans price (Ksh/kg) 48.00 48.00
VENUE
Maize and beans (Ksh/acre) 19,365.50 18,064.45
RODUCT ION COSTS
eed/planting material
Maize quantity (kg/acre) 9.00 9.10
Beans quantity (kg/acre) 1 1.60 14.60
Maize price (Ksh/kg) 91.00 91.00
Beans price (Ksh/kg) 48.00 48.00
Total cost (Ksh/kg) 1,375.80 1,528.90
Fertilizer/manure
DAP (Kg/acre) @ Ksh 25/kg 42.00 52.50
MAP (Kg/acre) @ Ksh 24/kg 62.50 47.20
20:20/23z23 (Kg/acre) @ Ksh 24/kg 53.30 70.80
Urea (Kg/acre) @ Ksh 17.20/kg 42.60
CAN (Kg/acre) @ Ksh 19/kg 31.30 62.50
Total cost (Ksh/acre)‘ 1,645.00 1,910.00
Operations costs
Cash costs (Ksh/acre)‘ ‘ 2,177.30 3,808.90
Family labor (mandays/acre) 71.90 34.70
Family labor (Ksh/acre) 5,033.00 2,429.00
Total cost (Ksh/acre) 7,210.30 6,237.90
otal Production Costs (Ksh/acre)" 5,198.10 7,247.80
otal Production Costs (Ksh/acre)” 10,231.10 9,676.80
FROSS MARGINS
Excl. family labor (Ksh/acre) 14,167.40 10,816.65
Incl. family labor (Ksh/acre) 9,134.40 8,387.65
Land rent (Ksh/acre) 1,750.00 1,750.00
NET MARGINS
Excl. family labor (Ksh/acre) 12,417.40 9,066.65
Incl. family labor (Ksh/acre) 7,384.40 6,637.65
Return to family labor (Ksh/manday) 172.70 261.29

 

 

 

 

 

" Excluding family labor costs Including family labor costs

The total cost of fertilizer is a weighted average cost that includes all cash costs paid by farmers
for all types of fertilizers/manure. The prices used for computations are divisional level average
prices which differ slightly from the regional level prices reported in the table. Furthermore, the
ﬁgure for total cost also includes cost of fertilizers for which quantities were not reported because
farmers failed to recall them but remembered what they paid.

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling, and all
other miscellaneous farm operations costs.

67

 

 

 

 

 

 

 

 

Table 6b. Gross Margins for Maize/beans inter-crop for Western Province.
Item Net maize buyer Net maize seller
High input user igh input user
YIELD n=16 n=1 1
Maize harvest (kg/acre) 1498.7 1332.2
Beans harvest (kg/acre) 78.8 112.6
Maize price (Ksh/kg) 12.5 9.4
Beans price (Ksh/kg) 56 56
REVENUE
Maize and beans (Ksh/acre) 23,146.55 18,828.28
PRODUCTION COSTS
Seed/planting material
Maize quantity (kg/acre) 9.9 8.8
Beans quantity (kg/acre) 14.1 9.5
Maize price (Ksh/kg) 91 91
Beans price (Ksh/kg) 56 56
Total cost (Ksh/kg) 1,690.50 1,332.80
Fertilizer/manure
DAP (Kg/acre) @ Ksh 26/kg 52.10 53.50
20:20/23z23 (Kg/acre) @ Ksh 24/kg 50.00
Urea (Kg/acre) @ Ksh 22/kg 44.90 25.50
CAN (Kg/acre) @ Ksh 25.50/kg 33.30 48.70
Total cost (Ksh/acre)‘ 2,503-30 29103-70
Operations costs
Cash costs (Ksh/acre). * 2,9112 2,5050
Family labor (mandays/acre) 66.9 32.8
Family labor (Ksh/acre) 3,345.0 1,640.0
Total cost (Ksh/acre) 6,256.2 4,145.0
otal Production Costs (Ksh/acre)" 7,110.00 5,946.50
otal Production Costs (Ksh/acre)” 10,455.00 7,586.50
ROSS MARGINS
Excl. family labor (Ksh/acre) 16,036.55 12,881.78
Incl. family labor (Ksh/acre) 12,691.55 11,241.78
and rent (Ksh/acre) 2,000.00 2,000.00
ET MARGINS
Excl. family labor (Ksh/acre) 14,036.55 10,881.78
Incl. family labor (Ksh/acre) 10,691.55 9,241.78
etum to family labor (Ksh/may) 209.81 331.76
" Excluding family labor costs 2’ Including family labor costs

The total cost of fertilizer is a weighted average cost that includes all cash costs paid by farmers
for all types of fertilizers/manure. The prices used for computations are divisional level average
prices which differ slightly from the regional level prices reported in the table. Furthermore, the
ﬁgure for total cost also includes cost of fertilizers for which quantities were not reported because
farmers failed to recall them but remembered what they paid.

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling, and all

other miscellaneous farm operations costs.

68

Table 6c . Gross Margins for Maize/beans inter-crop for Eastern Province.

 

 

 

 

 

 

 

 

Item Net maize buyer Net maize buyer
Low input user High input user
n=12 n=15

YIELD

Maize harvest (kgacre) 212.6 239.9

Beans harvest (kg/acre) 78.3 69.3

Maize price (Ksh/kg) 13.0 13.0

Beans price (Ksh/kg) 51.0 51.0
REVENUE

Maize and beans (Ksh/acre) 6,757.10 6,653.00
PRODUCTION COSTS
Seed/planting material

Maize quantity (kg/acre) 12.0 8.9

Beans quantity (kg/acre) 12.6 15.3

Maize price (Ksh/kg) 75.0 75.0

Beans price (Ksh/kg) 51.0 51.0

Total cost (Ksh/kg) 1,542.60 1,447.80
Fertilizer/manure

DAP (Kg/acre) @ Ksh 30/kg 9.7 67.3

Total cost (Ksh/acre)‘ 291-00 2,019-00
Operations costs

Cash costs (Ksh/acre)‘ ' 875-8 704-0

Family labor (mandays/acre) 42.3 90.9

Family labor (Ksh/acre) 2,1 15.0 4,545.0

Total cost (Ksh/acre) 2,990.8 5,249.0
ﬂotal Production Costs (Ksh/acre)" 2,709.40 4,170.80

otal Production Costs (Ksh/acre)” 4,824.40 8,715.80

GROSS MARGINS

Excl. family labor (Ksh/acre) 4,047.70 2,482.20

Incl. family labor (Ksh/acre) 1,932.70 (2,062.80)
Land rent (Ksh/acre) 1,500.00 1,500.00
NET MARGINS

Excl. family labor (Ksh/acre) 2,547.70 982.20

Incl. family labor (Ksh/acre) 432.70 (3,562.80)
Re_turn to family labor (Ksh/manday) 60.23 10.81

 

 

ll

2/

Excluding family labor costs Including family labor costs

The total cost of fertilizer is a weighted average cost that includes all cash costs paid by farmers
for all types of fertilizers/manure. The prices used for computations are divisional level average
prices which differ slightly from the regional level prices reported in the table. Furthermore, the
ﬁgure for total cost also includes cost of fertilizers for which quantities were not reported because
farmers failed to recall them but remembered what they paid.

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling, and all
other miscellaneous farm operations costs.

69

5.1.3 Beans

Because very few of the sampled farmers grew beans as a mono-crop, only in the
Rift Valley and in the Eastern Provinces were there enough cases to analyze this
enterprise. Table 7 reports gross margins for mono-crop beans. Gross margins per acre
averaged Ksh.8,861 for the Rift Valley Province and Ksh. 6,737 for the Eastern Province,
excluding family labor costs. When family labor costs are included as a cost, gross
margins decrease to Ksh.3,582, and Ksh.2,412 for the Rift valley and the Eastern
Provinces, respectively. However, for the Eastern Province, the gross margins remain
positive whereas, for the maize mono-crop and, for high input users, the maize/bean inter-
crop, gross margins were negative when family labor was valued at its opportunity cost.
These results also show that beans are more proﬁtable in the high potential area than in the

low potential area.

70

Table 7. Gross Margins for Beans Mono-crop

 

 

 

 

 

 

 

 

Item Rift Valley Eastern Province
Province

YIELD (n= 18) (n=37)

Harvest (kg/acre) 280.00 218.00

Price (Ksh/kg) 48.00 51.00
REVENUE

Beans (Ksh/acre) 13,440.00 1 1,118.00
PRODUCTION COSTS
Seed/planting mate rial

Quantity (kg/acre) 34.70 35.30

Price (Ksh/kg) 48.00 51.00

Total cost (Ksh/kg) 1,665.60 1,800.30
Fertilizer/manure

DAP (kg/acre) 61.10 1,215.00”

Price (Ksh/kg) 25.00 1.10

Total cost (Ksh/acre) 1,527.50 1,336.50
Operations costs

Cash costs (Ksh/acre)' ' 1,386.20 1,244.20

Family labor (mandays/acre) 75.40 86.50

Family labor (Ksh/acre) 5,278.00 4,325.00

Total cost (Ksh/acre) 6,664.20 5,569.20
Total Production Costs (Ksh/acre)" 4,579.30 4,381.00
Total Production Costs (Ksh/acre)” 9,857.30 8,706.00
GROSS MARGINS

Excl. family labor (Ksh/acre) 8,860.70 6,737.00

Incl. family labor (Ksh/acre) 3,582.70 2,412.00
Land rent (Ksh/acre) 1,750.00 1,000.00
NET MARGINS

Excl. family labor (Ksh/acre) 7,] 10.70 5,737.00

Incl. family labor (Ksh/acre) 1,832.70 1,412.00

Return to family labor (Ksh/manday) 94.30 66.30
" Excluding family labor costs 2’ Including family labor costs”

Manure

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling,
and all other miscellaneous farm operations costs.

71

5.1.4 Wheat

Wheat was grown only in the Rift Valley Province, where farmers have relatively
larger land holdings and the agro-ecology supports this crop. As wheat production is
relatively capital intensive, only households which can ﬁnance relatively higher cash
outlays cultivate this crop. Therefore, wheat may not be substitutable in production with
maize except for households with sufﬁcient capital resources. Table 8 reports gross
margins for wheat, which averaged Ksh. 11,333 without accounting for family labor and
Ksh. 11, 053 when it is included at its opportunity cost. These ﬁgures are not very
different from one another, reﬂecting the relative low importance of family labor in wheat

production.

Compared with the crop enterprises discussed earlier (i. e. mono-crop maize,
maize/beans inter-crop, and mono-crop beans), wheat appears to be the most proﬁtable
enterprise for farmers in the Rift Valley Province, although these farmers do not allocate
as much land for wheat as they do for maize and beans. While other reasons may explain
why farmers allocate only a small share of their land to wheat, these results suggest that
the main factor is a capital (cash) constraint. A relatively higher cash investment is
required to cultivate wheat and labor cannot be easily substituted for these machinery

costs”.

 

'4 The total costs of production for wheat, excluding family labor are Ksh. 8,313

compared to an average of Ksh. 6,205 (i. e. 25% lower) for mono-crop maize.

72

 

 

 

 

 

Table 8. Gross Margins for Wheat
Item Rift Valley Province
YIELD (n=3l)
Harvest (kg/acre) 893.00
Price (Ksh/kg) 22.00
REVENUE
Wheat (Ksh/acre) 19,646.00
PRODUCTION COSTS
Seed/planting material
Quantity (kg/acre) 83.90
Price (Ksh/kg) 22.00
Total cost (Ksh/kg) 1,845.80
Fertilizer/manure
DAP (kg/acre) 67.80
Price (Ksh/kg) 25.00
Total cost (Ksh/acre) 1,695.00
Operations costs
Cash costs (Ksh/acre)‘ ' 4,772.40
Family labor (mandays/acre) 4.00
Family labor (Ksh/acre) 280.00
Total cost (Ksh/acre) 5,052.40
Total Production Costs (Ksh/acre)" 8,313.20
Total Production Costs (Ksh/acre)” 8,593.20
GROSS MARG INS
Excl. family labor (Ksh/acre) 11,332.80
lncl. family labor (Ksh/acre) 11,052.80
Land rent (Ksh/acre) 1,750.00
NET MARC INS
Excl. family labor (Ksh/acre) 9,582.80
Incl. family labor (Ksh/acre) 9,302.80
Return to family labor (Ksh/manday) 2,395.7

 

1/

2/

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling,

Excluding family labor costs

Including family labor costs

and all other miscellaneous farm operations costs.

73

 

5.1.3 Irish potatoes, cassava and sweet potatoes

Gross margins for irish potatoes are presented in Table 9. In the Rift Valley
Province, gross margins average Ksh. 13,950 per acre before accounting for family labor
and decrease to Ksh. 5,956 per acre after accounting for it. In the Eastern Province, gross
margins average Ksh. 6,151 per acre and Ksh. 2,777 per acre before and after accounting
for family labor respectively. First, these results show that irish potatoes are more
proﬁtable in high potential areas than in low potential areas. Second, they indicate that
family labor costs, as a percent of total production costs, is large (i. e. 49 % and 51% for
the Rift Valley and Eastern Provinces respectively). In addition, in the Rift Valley
Province, production of irish potatoes is relatively more labor intensive than wheat. Thus,
proﬁtability analysis based only on cash costs shows that irish potatoes are more proﬁtable
than wheat production in Rift Valley. However, when family labor costs are taken into

account, the proﬁtability falls to one-half of the corresponding ﬁgure for wheat.

Cassava, which is a drought tolerant crop, is mainly grown in the Western and in
the Eastern Provinces. In the Western Province, the gross margins for cassava are Ksh.
8,415 and Ksh. 2,330 while in the Eastern Province, the gross margins are Ksh. 4,701 and
-1,329 (Table 10), before and after accounting for family labor, respectively. These results
also show that cassava production’s main input is family labor. Therefore, analysis of
proﬁtability of cassava that does not explicitly account for family labor, overestimates its

relatively proﬁtability.

74

Table 9.

Gross Margins for Irish Potatoes

 

 

 

 

 

 

 

Item Rift Valley Province Eastern Province

YIELD (n=15) (n=8)

Harvest (kg/acre) 2,030.70 721.90

Price (Ksh/kg) 1 1.00 13.00
REVENUE

Irish Potatoes (Ksh/acre) 22,337.70 9,384.70
PRODUCTION COSTS
Seed/planting material

Quantity (kg/acre) 448.90 159.40

Price (Ksh/kg) 1 1.00 13.00

Total cost (Ksh/kg) 4,937.90 2,072.20
Fertilizer/manure

DAP (kg/acre) 85.30

Price (Ksh/kg) 25.00

Total cost (Ksh/acre) 2,132.50
Operations costs

Cash costs (Ksh/acre)' ' 1,317.50 1,161.90

Family labor (mandays/acre) 1 14.20 48.20

Family labor (Ksh/acre) 7,994.00 3,374.00

Total cost (Ksh/acre) 9,311.50 4,535.90
Total Production Costs (Ksh/acre)” 8,387.50 3,234.10
Total Production Costs (Ksh/acre)” 16,381.50 6,608.10
GROSS MARGINS

Excl. family labor (Ksh/acre) 13,950.20 6,150.60

Incl. family labor (Ksh/acre) 5,956.20 2,776.60
Land rent (Ksh/acre) 1,750.00 1,000.00
NET MARGINS

Excl. family labor (Ksh/acre) 12,200.20 5,150.60

Incl. family labor (Ksh/acre) 4,206.20 1,776.60
Return to family labor (Ksh/manday) 106.80 106.90

ll 2/

it

Excluding family labor costs

Including family labor costs

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling,
and all other miscellaneous farm operations costs.

75

 

 

 

 

 

 

 

Table 10. Gross Margins for Cassava
Item Western Eastern Province
Province
YIELD (n=18 ) (n=8)
Harvest (kg/acre) 1,801.70 916.30
Price (Ksh/kg) 6.00 6.00
REVENUE
Cassava (Ksh/acre) 10,810.20 5,497.80
PRODUCTION COSTS
Seed/planting material
Quantity (kg/acre) 1,360.00 530.00
Price (Ksh/kg) 1.00 1.00
Total cost (Ksh/kg) 1,360.00 530.00
Operations costs
Cash costs (Ksh/acre)“ " 1,035.40 266.70
Family labor (mandays/acre) 121.70 120.60
Family labor (Ksh/acre) 6,085.00 6,030.00
Total cost (Ksh/acre) 7,120.40 6,296.70
Total Production Costs (Ksh/acre)” 2,395.40 796.70
Total Production Costs (Ksh/acre)” 8,480.40 6,826.70
GROSS MARGINS
Excl. family labor (Ksh/acre) 8,414.80 4,701.10
Incl. family labor (Ksh/acre) 2,329.80 (1,328.90)
Land rent (Ksh/acre) 1,750.00 1,000.00
NET MARG INS
Excl. family labor (Ksh/acre) 6,664.80 3,701.10
Incl. family labor (Ksh/acre) 579.80 (2,328.90)
Return to family labor (Ksh/manday) 54.80 30.70

 

 

 

" Excluding family labor costs

2’ Including family labor costs

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling,
and all other miscellaneous farm operations costs.

76

Sweet potatoes, which mainly serve the role of a ‘food security’ crop in most
households’ diets, are mostly cultivated in the lower potential areas of the Western and the
Eastern Provinces. In the Western Province, gross margins for sweet potatoes average
Ksh. 5,327 and Ksh. 1,962 (Table 11) before and after accounting for family labor,
respectively, versus Ksh. 3,741 and Ksh. 136, in the Eastern Province, before and after
accounting for family labor, respectively. Just like the other root tubers (irish potatoes and
cassava) discussed above, sweet potato production also uses relatively more labor in

proportion to capital.

77

Table 11.

Gross Margins for Sweet Potatoes

 

 

 

 

 

 

Item Western Province Eastern Province
YIELD (n=39 ) (n=18 )
' Harvest (kg/acre) 2,390.50 1,370.60

Price (Ksh/kg) 3.00 3.50
REVENUE

Sweet Potatoes (Ksh/acre) 7,171.50 4,797.10
PRODUCTION COSTS
Seed/planting mate rial

Quantity (kg/acre) 1,153.00 889.20

Price (Ksh/kg) 1.00 1.00

Total cost (Ksh/kg) 1,153.00 889.20
Operations costs

Cash costs (Ksh/acre)’ ' 691.30 166.70

Family labor (mandays/acre) 67.30 72.10

Family labor (Ksh/acre) 3,365.00 3,605.00

Total cost (Ksh/acre) 4,056.30 3,771.70
Total Production Costs (Ksh/acre)” 1,844.30 1,055.90
Total Production Costs (Ksh/acre)” 5,209.30 4,660.90
GROSS MARGINS

Excl. family labor (Ksh/acre) 5,327.20 3,741.20

Incl. family labor (Ksh/acre) 1,962.20 136.20
Land rent (Ksh/acre) 1,750.00 1,000.00
NET MARGINS

Excl. family labor (Ksh/acre) 3,577.20 2,741.20

Incl. family labor (Ksh/acre) 212.20 (863.80)
Return to family labor (Ksh/manday) 53.20 38.00

 

1/

2/

Cash costs are costs of ploughing, harrowing, planting, weeding, harvesting, shelling,

Excluding family labor costs

Including family labor costs

and all other miscellaneous farm operations costs.

78

 

 

5.2 How proﬁtable is maize?

The main focus of this study was to determine how proﬁtable maize production is
vis-a-vis that of alternative crop enterprises. The results on enterprise proﬁtability that
have been presented in the preceding section are summarized in Table 12, which reports a
summary of the gross margins and Table 13, which reports the relative rankings of crop
enterprises based on a relative proﬁtability index. The gross margins from each enterprise
is divided by that of the maize/bean (NBHI) ( net buyer, high input user) enterprise to
obtain a ratio that serves as a relative proﬁtability index. NBHI was chosen because it was
the most common among the sampled farmers. An index of less than, equal to or greater
than unity indicates that the enterprise is less, equally or more proﬁtable than the
maize/beans inter-crop (N BHI). The results show that in the Rift valley province,
maize/bean inter-crop (N BHI) is the most proﬁtable enterprise excluding family labor
costs but ranks third when family labor costs are included. Wheat followed by mono-crop
maize are ﬁrst and second respectively when family labor costs are included. Maize/beans
inter-crop (N SHI) (net seller, high input user) is fourth followed irish potatoes. In the
Western Province, maize/beans inter-crop (NBHI) is the most proﬁtable both before and
after including family labor costs. The second most proﬁtable enterprise is also a
maize/bean inter-crop (N SHI) followed by mono-crop maize (NBHI) with cassava and
sweet potatoes being the least proﬁtable. In the Eastern Province, the most proﬁtable
enterprise is irish potatoes after including family labor costs. The second most proﬁtable

enterprise is beans followed by maize/beans inter-crop (NBHI) and maize/beans inter-

79

crop(NBLI) (net buyer low input user). Sweet potatoes are only marginally proﬁtable
while cassava, mono-crop maize (NBLI) and mono-crop maize (N BHI) all have negative

1‘ CIUI’I'IS.

The inference we draw is that proﬁtability of maize is low relative to alternative
crops, but can be greatly improved if maize is inter-cropped with a legume, in this case,
beans. Hence, proﬁtability analysis that does not explicitly take into account maize inter-
crops should indeed ﬁnd that maize is less proﬁtable than alternative crops; and that the
continued cultivation of the crop by farmers can only be for: (1) subsistence reasons; or
(2) at for other reasons that are not based on proﬁtability. Earlier studies which have
concluded that maize production is unproﬁtable, relative to other crops, may have been
looking at maize proﬁtability in Kenya from this ‘narrow’ lens. The results presented in
this study suggest that maize production is relatively more proﬁtable, or at least as
proﬁtable as other alternative enterprises, if maize is grown as an inter-crop with beans.
The fact that the maize/bean inter-crop is the most common cropping pattern in Kenya,

suggests that farmers have reached a similar conclusion.

Furthermore, depending on the market position of the farmer and the type of
technology regime employed, the ‘same’ enterprise may seem proﬁtable to one group of
farmers while it is unproﬁtable to another group. For example in the Rift Valley Province,
mono-crop maize is more proﬁtable than maize/beans inter-crop for net-buying
households while it is relatively unproﬁtable to net selling households. Hence, proﬁtability
analysis that involves the major food staple needs to be done within a context that

recognizes the dual nature of the market position (i.e. either net maize buyers or net maize

80

sellers) of farmers as well as taking into account different input use regimes.

 

 

 

 

 

 

 

 

 

 

Table 12. Gross margins for different enterprises by Province (Ksh./acre)
Crop enterprise Rift Valley Western Province Eastern Province
Province
Ksh/acre Ksh/acre Ksh/acre
Return Gross Return Gross Return Gross
to Margins to Margins to Margins
Family Family Family
labor labor labor
Maize mono-crop (NBH1)" 396 9.610 161 6,023 (28) (3,182)
Maize mono-crop (N BLI) - - - — (35) (2,425)
Maize mono-crop (N SHI) 235 4,648 - - - -
Maize/bean inter-crop (NBHI) 173 9,134 210 12,692 1 1 (2,063)
Maize/bean inter-crop (N BLI) - - - - 60 1,933
Maize/bean inter-crop (N SHI) 261 8,3 88 332 1 1,242 - -
Beans 94 3 ,5 83 - - 66 2,412
Wheat 2,396 1 1,053 - - - -
Irish potatoes 107 5,956 - - 107 2,777
Cassava - 55 2,330 31 (1,329)
Sweet potatoes - 53 1,962 38 136
" NBHI is net maize buyer, high input user

NBLI is net maize buyer, low input user

NSHI is net maize seller, high input user

81

 

Table 13.
province

Ranking of enterprises according to relative proﬁtability index by

 

 

 

 

 

 

 

 

 

 

Crop enterprise Rift Valley Western Province Eastern Province
Province
Ksh/acre Ksh/acre Ksh/acre
Return Gross Return Gross Return Gross
to Margins to Margins to Margins
Family Family Family
labor labor labor
Maize mono-crop (NBH1)" 2.29 1.05 0.77 0.47 (2.55) (1.54)
Maize mono-crop (NBLI) - - - - (3.18) (1.18)
Maize mono-crop (NSHI) 1.36 0.51 - - - -
Maize/bean inter-crop l .00 1 .00 1 .00 l .00 1 .00 1 .00
(N BHI)’2
Maize/bean inter-crop (NBLI) - - - - 5.45 0.94
Maize/bean inter-crop (N SHI) 1.51 0.92 1.58 0.89 - -
Beans 0.54 0.39 - - 6.00 1.17
Wheat 13.85 1.21 - - - -
Irish potatoes 0.62 0.65 - - 9.73 1.35
Cassava - - 0.26 0.18 2.82 (0.64)
Sweet potatoes - - 0.25 0.15 3.45 0.07
" NBHI is net maize buyer, high input user

NBLI is net maize buyer, low input user

NSHI is net maize seller, high input user

’2 Maize/bean inter-crop (NBHI) = 100%

82

 

 

5.3 What if farmers are net maize buyers?

In this section a slightly different argument is presented that incorporates the
characteristic of the farming household. In section 1.3, a paper by Jayne et al. was cited,
which reported that over 61% of the rural households in Kenya were net maize buyers.
Also, Jayne et al. (2000) report that a high percent (52%) of the rural small holders in
Kenya, are net maize buyers. Both of these papers argue that smallholders are hurt by high
maize prices, since they are net buyers of maize. However, these papers do not address the
implications of the farmers’ net buying position on maize production. Since gross margins
(farm proﬁts) are the difference between revenue and production costs, their magnitude is
directly related to the output price. When the crop of concern serves both the role of being
a food crop as well as a cash crop, the output price affects the farming household in two
ways. First, it is the price at which their produce is valued and therefore a high price
would be preferred. Second, it is the price at which the household would buy the crop for
consumption and would therefore prefer a low price. Maize in Kenya falls in this category

of crops.

If the household is a net maize seller, then the selling price (farm gate price) of
maize is the relevant price to use when computing the monetary value of that households’s
produce. In this case, the farmer looks at what price at which he/she can actually sell
his/her surplus in the market place. However, if the household is a net maize buyer, then
the selling price is not the appropriate price to use. Rather, the buying price should be

used because this farmer, values his/her produce at the savings his/her own produce

83

accords the household in lieu of making purchases for consumption (Jayne 1994).

This study revealed that a great divergence exists between the buying prices and
the selling prices of maize in all the study areas. Appendix Table A1 reports the 1998/1999
median maize buying and selling prices, by month for the province covered in this study.
These data show signiﬁcant differences between the prices at which farmers were able to
sell their maize, versus the prices at which they were able to buy maize. In this study, we

were unable to establish the reason(s) why this divergence exists.

The results on proﬁtability presented earlier incorporate an illustration of the
impact of using buying prices and selling prices, to estimate maize proﬁtability for different
categories of farmers. The maize from net buying households was valued at buying prices
while maize from net selling households was valued at selling prices. The results showed
that on average proﬁtability was higher for net buying households than for net selling
households. When buying prices are used to value maize, then the proﬁtability ﬁgures
represent what the proﬁtability of maize production is from the standpoint of a net buying
household. Now, looking at maize proﬁtability through this ‘lens’ of the net buying
households, it is hard to conclude that maize production is unproﬁtable, relative to
alternative crops. The superiority of the maize/bean inter-crop gets greatly reinforced by

these ﬁgures.

Given the foregoing discussion, it seems reasonable to conclude that farmers who
are observed growing maize, rather than alternative ‘high value’ crops, are in fact rational.
Thus, one can conclude that, what at ﬁrst glance seems to be production decisions based

on non-economic principles, are in fact based on the optimization principle. However,

84

these farmers incorporate their consumption requirement in their optimization constraint.
This result is consistent with similar analysis and empirical ﬁndings from Zimbabwe

(Jayne, 1994).

5.4 Farmers’ characteristics and circumstances

In order to understand the factors (in addition to proﬁtability) that inﬂuence
farmers decisions regarding what crops to grow, the farmers’ characteristics and their
perceptions were examined. This section synthesizes some of the characteristics examined.
The ﬁrst, and most important factor in determining what crop and how much of it a farmer
grows, is the amount of land that the farmer has available for crop production. Table 14
reports the land area owned by farmers in the whole sample and in the study provinces.
Land sizes average 7 acres per household for the whole sample, compared to 11, 6, and 4
acres per household for the Rift Valley, the Western, and the Eastern Provinces,
respectively. Although the actual acreage varied greatly, ranging from 0.10 acres to 81
acres, the median acreage indicates that at least 50 per cent of the sampled households
owned 4 acres or less. The median acreage for the Rift Valley, the Western, and the
Eastern Provinces are 5, 4, and 3 acres per household, respectively, indicating that most of
the farmers in the sample are land constrained, hence, the reference to these farmers as
‘small holders’ or ‘small-scale farmers. To supplement the family-owned land,
approximately 22 percent of the farmers in the whole sample rented an average of 2 acres

(per renting household) with the median acreage being 1 acre. The amount of land rented

85

also varied across renting households, ranging from a minimum size of 0.05 acres to a

maximum of 9 acres.

 

 

 

Table 14. Land size (in acres) holdings in the Rift Valley, Western, and Eastern
Provinces, Kenya.
Region Number Mean Median Std. Min. Max.
Dev.
Rift Valley 80 10.71 5.00 16.05 0.10 81.00
Western 80 6.04 4.00 7.52 0.25 48.00
Eastern 80 4.44 3.00 4.29 0.50 30.00
Whole sample 240 7.07 4.00 10.83 0.10 81.00

 

 

 

 

 

 

 

 

Source: Survey data, 1999

The second factor that inﬂuences household decisions regarding what crops to

grow is the number of people that depend on family-owned land for their provisions.

Farmers use their land to provide their family members with the basic needs of life - food,

shelter, and clothing. For agrarian communities like the ones from which the sample was

drawn, their most important concern is to have enough food for the family members. The

fact that the households depend mostly on land for their livelihood, rather on other sources

of livelihoods (incomes), is evidenced by the low percent of people involved in off-farm

income-generating activities. Of the over 1,600 members of the 240 sampled households,

48 percent were students and 39 percent were engaged in farming as their primary

occupation (Table 15). Hence, only 17 percent of all the members in the sampled

households depend, in part, on non-farm sources of income. This fact underscores the

86

 

importance of land as a source of livelihood for majority of the rural small holders in

Kenya.

The third factor that inﬂuences household decisions regarding what crops to grow
is an interaction between the number of family members that depend on the family land for
food and the ability of alternative crops to generate sufﬁcient revenue necessary for
meeting the household’s food needs. Although the process by which farming households
make decisions on cropping patterns is complex, I hypothesize that they do so in a step-
wise fashion. First, I hypothesize that they consider crops that will provide them with
food, usually their staple. Second, they consider alternative crops that could potentially be
grown to provide them with income, which they can use to purchase their staple food.
Third, they consider the relative proﬁtability of the alternative crops, compared to that of
the staple food crops. If the alternative crops are more proﬁtable then they are, ceteris
paribus, considered potential challengers for the land grown to the staple food crop.
Finally, farmers determine if the revenue from the more proﬁtable alternative crops will in
fact be sufﬁcient to purchase the staple food. If the revenue from the alternative crops is
not sufﬁcient to pay for the food staple, farmers would choose to grow the staple food

themselves.

87

Table 15. Primary occupation of household members of the sample households.

 

 

Primary occupation Frequency Percent1 Cumulative
Percent
farmer 559 34.8 34.8
farm labourer 62 3 .9 38.7
teacher 40 2.5 41 .2
civil servant 15 0.9 42.1 1
other employment on salary 79 4.9 47.0 I 1
artisan 20 1.2 48.3
trader 27 1.7 49.9 1" .
student 766 47.7 97.6
butcher 2 0. 1 97.8
religious activities 9 0.6 98.3
unskilled non-agric. worker 19 1.2 99.5
homemaking 8 0.5 1 00.0
Total 1,606 100.0

 

 

 

 

 

 

Source: Survey data, 1999

For the whole sample, each household had an average of 8 members that were
supported by the family farm (Table 16). In the Rift Valley Province, the average family
size is 8.5 members while in the Western and the Eastern Provinces, average family sizes
are 8 and 7.2 members, respectively. The median number of members per household is 8
members for the whole sample, implying that at least 50 percent of all the households have
a family size of 8 or more members to feed, house, and clothe. In additiOn, on average
these 8 members per household, depend on 7 acres (Table 14) of land with at least 50

percent of the households depending on less than 4 acres of land.

88

Table 16. Number of family members per household in the Rift Valley, Western,
and Eastern Provinces, Kenya.

 

 

 

 

 

Region Number of household family members
Number Mean Median Std. Dev.
Rift Valley 80 8.49 9.00 2.83
Western 80 8.01 8.00 2.71
Eastern 80 7.23 7.00 2.08
Whole sample 240 7.91 8.00 2.61

 

 

 

 

 

 

Source: Survey data, 1999

In order to determine whether the revenues from alternative crops are sufﬁcient to
pay for household food needs, a detailed analysis of consumption is necessary. However,
while this is beyond the scope of this study, by studying the cropping choices that farmers
made in 1999 (Table 17), one can hypothesize on what the reality faced by farmers could

be.

In the Rift Valley Province, farmers that are net maize buyers allocate an about
40% of their land to maize/beans inter-crop and 16% to mono-crop maize while about
38% of the land was allocated to wheat. All other crops, such as, irish potatoes,
pyrethrum, bananas, and sweet potatoes are allocated less than 5% of the total land. In the
Western and the Eastern Provinces, a similar pattern of cropping choices are evident.
Farmers allocate the largest share of their crop land to maize and maize/bean inter-crops
while they allocate less than 5% of total area to most of the alternative crops. This land
allocation pattern suggests that farmers believe that alterative crops can not generate

sufﬁcient revenue to pay for their food. Nevertheless, further analysis that looks at the

89

consumption patterns of households and how these affect cropping decisions need to be
done. In addition, it should be noted that during the survey period, there was a tariff of

25% on maize imports which may have inﬂuenced the cropping choices that farmers made.

 

90

Table 17.

Acreage planted to different crops in the Rift Valley, Western, and
Eastern Provinces, Kenya, 1999.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Region Market Input Crop enterprise Percent of total area
position use
Rift Valley Net maize High Maize/beans inter-crop 39.73
buyer input
user Wheat 37.72
Mono-crop maize 15.88
Irish potatoes 1.54
Net maize High Maize/beans inter-crop 44.96
seller input
user Wheat 23.84
Mono-crop maize 22.33
Pyrethrum 4.53
Western Net maize High Maize/beans inter-crop 55.05
buyer input
user Sugarcane 20.48
Mono-crop maize 6.30
Sweet potatoes 2.44
Net maize High Maize/beans inter-crop 60.35
seller input .
user Mono-crop marze 19.84
Sugarcane 8.49
Millet 2.97
Eastern Net maize High Maize/beans inter-crop 47.26
buyer input .
user Mono-crop marze 8.67
Coffee 8.32
Beans 5.72
Low Maize/beans inter-crop 56.57
input .
user Mono-crop marze 13.69
Maize/pigeon peas 6.08
Maize/beans/cassava 4.56

 

Source: Survey data, 1999

91

 

5.5 Simulated effect of the removal of maize import tariff on relative proﬁtability

During the study period, a 25% maize import tariff was in place that is thought to
have affected maize prices and therefore affected the proﬁtability of maize and hence the
relative proﬁtability of other crop enterprises. The price of maize was reduced by 25% and
used to compute gross margins for the mono-crop maize and maize/beans inter-crop in
order to see what happens to relative proﬁtability in without-tariff setting. The simulation
results were then used to compute a relative proﬁtability index for each enterprise and the

results are presented in Table 18.

The results show that in general the proﬁtability of alternative crops increases in
the without-tariff setting compared to when there was the tariff. For example the relative
proﬁtability index for cassava in the Western province increases from 0.52 with the tariff
to 0.74 without the tariff. In terms of ranking ordering of enterprises according to relative
proﬁtability index, some changes also occur. In the Rift Valley Province, wheat is still the
most proﬁtable enterprise but irish potatoes now become the second most proﬁtable
instead of mono-crop maize (N BHI). Maize/beans inter-crop (NBHI) is third followed by
Maize/beans inter-crop (N B81) and then mono-crop maize (N BHI). In the Western
Province, maize/beans inter-crop (N SHI) becomes the most proﬁtable taking the place of
maize/beans inter-crop (N BHI) with the least proﬁtable being the mono-crop maize
(N BHI). In the Eastern Province, irish potatoes are still the most proﬁtable and, again, is
followed by beans just like in the situation with tariff. Maize/bean inter-crop (NBLI) is

third and sweet potatoes are fourth with the other remaining enterprises having negative

92

indices reﬂecting the negative returns from these enterprises.

These results show that even with the removal of the import tariff, wheat and irish
potatoes are still the most proﬁtable enterprises in the Rift Valley and Eastern Provinces
respectively. In the Western Province, the most proﬁtable enterprise is maize/beans inter-
crop (N SHI) in place of the maize/bean inter-crop for net buyers, high input users. The
inference that can be drawn from these simulation results is that the removal of the import
tariff, does not substantially alter the ranking of crop enterprises based on relative

proﬁtability.

93

Table 18.

index by province

Ranking of enterprises according to simulated relative proﬁtability

 

 

 

 

 

 

 

 

 

 

Crop enterprise Rift Valley Province Western Province Eastern Province
Ksh/acre Ksh/acre Ksh/acre
Return Gross Return Gross Return Gross
to Margins to Margins to Margins
Family Family Family
labor labor labor
Maize mono-crop (NBHI)" 1.91 0.92 0.63 0.21 (1.16) (1.51)
Maize mono-crop (NBLI) - - - - (1.01) (1.12)
Maize mono-crop (NSHI) 0.74 0.12 - - - -
Maize/bean inter-crop 1.00 1 .00 l .00 l .00 l .00 (1.00)
(N BHI)’2
Maize/bean inter—crop - - - - 4.24 0.44
(N BLI)
Maize/bean inter-crop 1.49 0.94 1.75 1.01 - -
(N SHI)
Beans 0.80 0.65 - - 4.16 0.85
Wheat 19.39 2.01 - - - -
Irish potatoes 0.84 1.09 - - 6.81 0.98
Cassava - - 0.41 0.29 2.08 (0.47)
Sweet potatoes - - 0.47 0.25 2.77 0.05
" NBHI is net maize buyer, high input user
’2 Maize/bean inter-crop (NBHI) = 100%

NBLI is net maize buyer, low input user

NSHI is net maize seller, high input user

Source: Survey data 1999

94

 

5.6 Farmers’ Perceptions

To corroborate some of the conclusions drawn from the results presented in this
study, farmers were asked questions to express their perceptions on several issues about
which analysts are concerned. To determine if land owned was perceived as a constraint,
farmers were asked if they rented land and, if so, how much. The results reveal that 23
percent of the whole sample rented land in 1999. Farmers were then asked what crops
they grew on the rented land. The results reveal that 64 percent of the farmers’ rented land
was planted to maize. Wheat was grown on 11 percent of the rented land, followed by
sugar cane, (5 percent), and cassava (5 percent), among others. These results reinforce the
conclusions above that as they make cropping decisions the main concern for households

is to ensure a sufficient supply of the staple food (maize) for their households.

Farmers were also asked to identify their most important food crop. An
overwhelming majority (91 percent) said that it was maize while, the others said beans (3
percent), bananas (3 percent), sweet potatoes (1 percent), and cassava (1 percent) ( Table
19). When farmers were asked what crop served as their most important source of (cash)
income, the results were less overwhelming (Table 20). However, maize was ranked as the
most important source of income by 22 percent of the households, followed by sugarcane
(15 percent), wheat (12 percent), bananas (10 percent), coffee (8 percent), and beans (5
percent). These results are consistent with the conclusions drawn in this study — that maize
is proﬁtable and that households use maize both as a cash crop and food crop. However,

some other studies on Kenyan agriculture have arrived at different conclusions. For

95

example, Jayne et al. (2000) argue that, “the idea of small rural farms relying mostly on

grain crops for their incomes is an outdatedperception They suggest that the most

important sources of farm income are horticulture, sugarcane, tea, and coffee. However,

the paper acknowledges that in Trans Nzoia and Uasin Gishu districts of the Rift Valley

Province, maize appears to be the most lucrative cash crop. This is consistent with the

ﬁndings of this study. Nonetheless, in the Western Province which is the area most

suitable for growing sugarcane, the data from this study do not show that sugarcane is the

most important source of cash income (see Table 20).

 

 

 

 

 

 

 

 

 

 

 

 

Table 19. The most important crop used as a source of food for households in

Kenya.
Most importan REGION OVERALL

source of foo
RIFT WESTERNI EASTERN
VALLEY

maize 90.0%I 92.4% 90.0% 90.8%
maize/beans 1 .3%l - - .4%
beansr 7.5% 1.3%. 1.3% 3.3%
sorghum - 1.3% - .4%
bananas - 2.5% 6.3% 2.9%
cassava - - 2.5% .8%
sweet potatoes] 1.3% 25%| - 1 .3%

 

 

 

 

 

Source: Survey data, 1999

96

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 20 The most important crop used as a source of farm cash income for
households in Kenya
Most importantI REGION OVERALL
source 0f income FT VALLEY WESTERN] EASTERN
maize 48.6% 14.30/ 4.1% 22.2%
maize/beans 1 .4% .5%
beans 2.8%. 4.3% 8.1% 5.1%
millet 1.4% .5%
bananas 1 .4%] 27.0% 9.7%
coffee 10.0% 13.5% 7.9%
teal 10.0% 3.2%
whea 36.10/ 12.0%
sugarcane 1.4%l 41 .4%: 4.1%. 15.3%
pyrethnun 8.3%1 2.8%
cowpeas 1.4°/ .5%
french beansl 7.1%1 8.1%l 5.1%
cassava 2.9% 5.4%] 2.8%
tobacco l .4%1 .5%
tomatoes 4. 1% 1 .4%
kalesi 1.4% 2.9%: 4.1% 2.8%
oranges 1.4°/ .5%
onion 4.1%1 1.4%
cabbages 2.7% .9%
pigeon peas 8.1%1 2.8%
sweet potatoes 1.4%. 1.4% 1.4% 1.4%
avocado l .4% .5%
napier grass 1.4% .5%

 

 

 

 

 

 

Source: Survey data

97

 

To examine the perceptions of farmers regarding maize price policy and how they
perceive maize prices to affect their cropping decisions, farmers were asked an open ended
question, ‘If the price of maize went down, what would you do with the land that is under .
maize production? ’ A majority (73 percent) of the respondents said that they would
continue growing the same quantity of maize while, 14 percent said that they would
consider reducing maize acreage for another crop, 10 percent said that they would
consider changing to another crop altogether, and 3 percent said they would consider
changing to dairy farming. A follow-up question was asked to those who said that they
would continue growing the same quantity of maize as they did before the price decline,
asking why they would do so. The majority (73 percent) of the respondents answered,
because “maize is their main food staple”. Others said that they would continue to grow
maize because, “they produce maize strictly for home consumption and not for sale” (8
percent), that instead of switching crops they, “would rather speculate, hoping to sell
maize later at a higher price” (6 percent), that “there is no alternative food crop and that
maize is the only viable option” (5 percent), and that “the next best alternative crop is
capital intensive” ( 2 percent). These results suggest that it is indeterminate whether lower
maize prices would lead to a substantial shift in cropping patterns from maize to

alternative crops.

98

5.7 Constraints to agricultural commercialization

One objective of this study is to identify the factors that explain why the structural
transformation process seems to be slow in stimulating agricultural commercialization in
Kenya (i.e. increasing the share of total production that farmers market). Further, a higher
degree of commercialization is thought to enhance the process of structural transformation
by encouraging changes in cropping patterns in favor of crops with high (cash) value. A

factor is considered a constraint to agricultural commercialization if :

1. it hinders shifts in cropping patterns from production of low-value to high

value crops;
2. it hinders an increase in the share of marketed crop output ; and
3. if it encourages ‘subsistence’ type of agricultural production system.

The following summary of the factors thought to be constraining agricultural
commercialization is based on the results discussed above and information gathered

through observation and interviews with subject matter specialists in Kenya.

5.7.1 Land size holding and productivity potential

Access to arable land was the most constraining factor to commercialization for

most of the household studied. Over 30 percent the respondents owned 2 acres, or less

99

with 10 percent owning less than 1 acre. Considering that the average family with 8
members depend on family land for their livelihood, then it is clear that land is a constraint
to agricultural production, given the technology currently in use. For some districts, such
as Vihiga District (in the Western Province), the largest parcel of land owned by a single
household is 3 acres. Moreover, it is on the same land that the homestead is built. Most
households visited had three to four houses built on their land to provide shelter for family
members. With land sizes this small, and family sizes this large, surpluses for the market

are seldom achieved. In fact, sufﬁcient food for the household members is seldom

 

produced. Thus, the size of land holding is one of the constraints to agricultural
commercialization. This argument is however based on the assumption that the main

objective for the farmers is to feed their family.

5.7.2 Water availability

Land availability is more of a constraint in the Rift Valley and the Western
Provinces than it is in the Eastern Province. In the Eastern Province, low land productivity
is the issue of most concern. Most of the land in Eastern Province is regarded as
‘unproductive’ simply because of poor rainfall, hence, the classiﬁcation of this region as a
low potential area. In the study areas of the Eastern Province, most of the land was left
fallow and covered by acacia trees, savanna shrubs, and grass. Farmers cleared small
sections of their land, planted crops on these cleared sections, but left most of the land

fallow. However, some farmers whose lands are served by seasonal rivers, used water

100

from the seasonal streams to grow a variety of crops such as onions, cabbages, and
tomatoes which they sell in the local markets and to traders from outside their districts.
During interviews, farmers frequently asked, “where is the water to grow those crops you
are talking about ?” Hence, the issue of concern in the Eastern Province is not whether
they can produce for the market, but rather whether they can produce at all. As articulated
by Owuor (1999), the alternative high value crops such as horticulture can only become
viable options if substantial investments are made to develop water infrastructure. Thus,
access to water is a major constraint to agricultural production and commercialization,

especially in the low potential areas of the Eastern Province.

5.7.3 Relative profitability

In order for farmers to shift from growing one crop, such as maize, to an
alternative crop, the proﬁtability of the alternative crop needs to be convincingly higher
than the proﬁtability of the original crop. In this study, the analysis of the relative
proﬁtability of maize versus alternative crops, yielded mixed results in the three provinces
studied. In the Riﬁ Valley Province, wheat is more proﬁtable than maize and in the
Eastern Province, irish potatoes and beans were more proﬁtable than maize with the gross
margins for maize being negative. However, in the Western Province, maize/beans inter-
crop was the most proﬁtable enterprise. Thus, based on proﬁtability, one would expect
that farmers in the Rift Valley would allocate more land to wheat and irish potatoes than

to maize, and that farmers in the Eastern Province would allocate more land to growing

101

irish potatoes and beans than maize. However, this study revealed that farmers allocate
more land to maize than any other crop in all the three Provinces, in spite of some

alternative crops yielding higher gross margins.

The explanation for this apparent discrepancy is twofold. First, while alternative
crops such as wheat may be more proﬁtable than maize, because they are highly capital
intensive, they are not viable options for cash-strapped households. This explanation is
consistent with conclusions drawn by other scholars who have analyzed the cultivation of
highly capital intensive crops. For example, Kodhek (1995) states that a lot of small-scale
farmers are reluctant to adopt crops like tea and coffee because of their inability to self-

ﬁnance the establishment of such crops. Similar conclusions are drawn by Owuor (1999).

Second, some alternative crops which seem more proﬁtable than maize (and are
therefore commonly referred to as ‘high value’ crops), are only more proﬁtable when
maize is valued at its selling (farm gate) prices. But, as discussed in section 5.4 above, to
accurately assess its proﬁtability, maize should be valued at its buying prices because it
serves the dual role of a cash and food crop. This is because poor households who depend
mostly on their family land for their food and other provisions, value maize in terms of the
saving to their households, (i. e. money saved by not having to purchase maize) rather than
in terms of the cash income they could potentially earn from selling maize. When maize is

valued at the buying prices, maize is a proﬁtable enterprise.

102

5.7.4 Thin and troubled markets for alternative crops

The key to agricultural commercialization is the existence of a reliable market for
farm produce. In Kenya, the strategy put forth for transforming smallholder agriculture

into a more commercial orientation is based on encouraging farmers to shift from

 

production of maize, sorghum, millet, and cassava towards the production of horticulture, 1'.

r a
coffee, tea, sugarcane, cotton, and dairy farming (World Bank, 1997). However, some of "
these crops are not straight forward alternatives because of, among other reasons, the ii f

absence of a stable market.

The markets for sugarcane, cotton, and milk (dairy farming) have many problems.
Due to lack of enforcement of the sugar import duty, imported sugar has ﬂooded the
supermarkets and left local industries burdened with excessive inventory. As a result,
farmers are unable to sell their sugarcane harvested on time, leading to tremendous losses.
During the survey period, most sugarcane farmers’ cane had been in their ﬁelds for over
24 months, when the optimum harvesting age is 18 months. This is a disincentive to
current and potential sugarcane farmers. As for cotton, massive imports of duty free used
clothes have ﬂooded the market with cheap garments, resulting in a decline in demand for
domestic textile products. By the time of the research, three textile industries had closed
and the last surviving one was struggling. As a result, growing of cotton has declined
tremendously. Thus, cotton is no longer a viable alternative for farmers. Similar

observation concerning the cotton and sugarcane industry are made by Owuor (1997).

Horticulture provides a lucrative alternative to growing maize, but its market is too

103

thin to beneﬁt majority of the farmers. The main horticultural crops produced for export
are cut ﬂowers, french beans, and Asian vegetables. These crops are grown under contract
from exporting companies, which in turn sell these crops in very competitive export
markets in Europe. Depending on seasonal supply and demand, these companies purchase
varying quantities of horticultural crops from farmers and sometimes, when the European
market is ﬂooded, fail to buy at all. The following account, even though anecdotal, is

illustrative of the challenges of relying on horticulture as a source of a family’s livelihood.

 

In 1998, a horticultural export company contracted farmers in Mutuyu Village, Makueni 1
District, to grow french beans and provided them with the inputs to do so. The
experience was good and farmers got good returns for their land and labor. In the next
season, many additional farmers in the village shifted to the production of french beans
and were also provided with seed by the exporting company. However, this next season
coincided with a period of heavy rainfall throughout the country as a result of the El-
Nino phenomenon. French beans in locations nearer to Nairobi than Mutuyu, performed
well and exporters were able to procure them cheaply» including the exporter in
question. The company failed to show up in Mutuyu Village to collect the french beans
produced by Mutuyu farmers on its behalf. These farmers lost. They lost an opportunity
to grow their own food and there was hunger in Mutuyu Village that season. However, a
few farmers who had grown cabbages instead of french beans, made money as the
demand within the village was high since most farmers did not grow their own. In 1999,
farmers in Mutuyu planted cabbages and abandoned planting of french beans. To use the
words of one farmer, “...cabbages were so many that you could get a bag free of charge

even the cows could no longer eat any more cabbages”".

The above account is indicative of the uncertainties that are prevalent in the horticultural
industry. When farmers have such experiences, it is hard to convince them to make the
shift to relying on horticultural crops for their livelihoods because of the potential danger

of not having any food for the family.

 

'5 Quote from Jacob Kimanthi, a primary school teacher and farmer in Mutuyu

Village, Makueni District.

104

5.7.5 Lack of enabling environment

In Chapter One, four conditions are listed that must be met for the structural
transformation process to occur. These are: low food prices, relative to wages in off-farm
activities; assurance of a sufﬁcient food supply to consumers; existence of a low-cost
exchange system; and existence of an effective information system. As discussed above,
the buying price of maize is sometimes substantially higher than the selling price. The great E
divergence of the buying and selling prices is in part due to high marketing costs. At the
time of the research, the road network in Kenya was in very poor condition. The tarmac
roads were ridden with potholes that made communication very difﬁcult and expensive.

Also the rural roads were in poor condition and most of them were unmotorable.

Also, inherent in the structural transformation process, is the need for a vibrant
non-farm sector to absorb the labor released from agriculture and hence lead to high
productivity per agricultural worker. Unfortunately, Kenya’s non-farm sector is not
vibrant. The public service commission stopped employing new workers in 1993 and there
are thousands of university graduates that are unemployed, among other potential
workers. The pool of unemployed workers continues to grow each year. Because of the
high rate of unemployment, household members ﬁnd themselves dependent on the family

farm for their provisions.

Therefore, given the foregoing, it is not surprising that structural transformation
has not occurred very rapidly in Kenya. Liberalization and other structural changes that

the government has implemented notwithstanding, there is lack of an enabling environment

105

for structural transformation to occur. Unless the above bottlenecks are removed or at
least mitigated, agricultural commercialization is likely to remain low and the process of

structural transformation will continue to remain elusive.

106

.1 .

CHAPTER SIX

CONCLUSION

The goal of this study was to determine the relative proﬁtability of maize vis-a-vis

that of alternative annual crops in Kenya. A main contribution of the study was to provide

9"”
-’-'l

an empirically determined list of constraints to agricultural commercialization in Kenya.

-..

This study also provides information that helps in understanding the constraints faced by

I. I

1

farming households. A more general contribution of this study is that it adds to the pool of
information regarding on-going debate on why the structural transformation process has

been slow in developing countries.

The results show that proﬁtability varies by region, by type of crop enterprise, and
by type of technology employed. Crop enterprises are generally most proﬁtable in the high
potential districts of the Rift Valley Province followed by the middle potential districts of
the Western Province and ﬁnally by the low potential districts of the Eastern Province.
The results also show that family labor constitutes a signiﬁcant share of the production
costs of most crops, suggesting that proﬁtability results need to be interpreted with

caution— taking into account whether or not family labor costs are explicitly considered.

Analysis of proﬁtability for the mono-crop maize and for the maize/bean inter-crop
was carried out for three contiguous groups each— net maize buying high input using
households (N BHI), net maize buying low input using households (NBLI), and net maize
selling high input using households. The results reveal that on average, the high input

using households were able to generate net surpluses that could be sold. The advantage of

107

this classiﬁcation of households according to their market position is that it makes it
possible to take into account the fact that maize farmers are not a monolithic group, an
assumption that is inherent in most of the previous studies. Hence, maize proﬁtability is
different, even within the same geographic area, depending on the level of input use by
farming households, and also depending on whether a household is a net maize buyer or

net maize seller.

Relative to the maize/beans inter-crop, wheat was more proﬁtable in the Rift
Valley Province followed by mono-crop maize. In the Eastern Province, Irish potatoes
were the most proﬁtable followed by beans while in the Western Province, maize/beans
inter-crop was the most proﬁtable enterprise. These results show that relative to maize,
there are enterprises that are more proﬁtable in two of the three provinces studied.
However, maize/beans inter-crop was still the most common enterprise grown by farmers
because some of the more proﬁtable alternative crops are capital intensive while others do

not have a stable market.

This study also found that although the majority of farmers said that they would
prefer to grow maize, there were some who said that they would be willing to consider
alternative ventures if the price of maize were lowered. However, since this study found
that maize was still cultivated even in regions where it was clearly unproﬁtable to do so, it
remains unclear whether in reality farmers would shift from maize cultivation in the face of
lower maize prices. In addition, this study found that the main concern for households is
to ensure a sufﬁcient supply of maize, the main staple, and as a result a high percent (73%)

of the interviewees said that they would continue to grow it even if prices declined. This

108

suggests that policies that lead to reduced maize prices such as, removal of import tariff
may not substantially affect maize acreage and the expected subsequent shift to cultivation

of alternative crops.

Several factors were found as the main constraints to agricultural
commercialization in Kenya. First, proﬁtability of alternative crops is not signiﬁcantly
higher than that of maize or maize/bean inter-crop. Hence, to the extent that maize is
regarded as a subsistence crop, relative proﬁtability results suggest that alternative crops
do not offer much opportunity for farming households to shift from maize and depend on

alternative for food provision.

The second constraint is small land size holding and low productivity potential.
Over 30 percent of the respondents owned two acres or less of land and a whole family,
often a large one, depended solely on this piece of land for its livelihood. Such a small
parcel of land is often insufﬁcient for the production of food for home use and generate

surpluses for sale.

The third constraint is the unavailability of sufﬁcient water necessary for crop
production. Although farmers in the Eastern Province owned relatively large pieces of
land, they usually leave their farms fallow because most of the Province is semi-arid. The
Province does not receive sufﬁcient rainfall to grow crops in most years and the limited
irrigation that is currently being done using water from seasonal streams is inadequate for

substantial crop production.

The fourth constraint identiﬁed was the lack of adequate market openings for

alternative crops such as cotton, sugarcane, and horticultural crops. In part, due to laxity

109

 

in the enforcement of import duty policies on sugar and textile products, Kenyan markets
are ﬂooded with these products. Most of the cotton and sugar industries have closed

down and those that still remain continue to be faced with difﬁculties of tied up capital in
unsold inventories. The horticultural produce market for most of the exported crops such

as french beans and cut ﬂowers is highly volatile and unpredictable.

The ﬁfth constraint identiﬁed was the lack of an enabling environment for
commercial agriculture. For commercialization of agriculture to be a realizable objective, a
vibrant non-farm sector that absorbs the labor force released from agriculture is necessary.
Poor transport and communication to most parts of the country raise the marketing costs

of agricultural produce hence hindering agricultural commercialization.

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