TWO ESSAYS ON MAIZE INTRA-CROP DIVERSITY AND USE OF IMPROVED

VARIETIES

by

BRIAN CHIPUTWA

(Under the Direction of GENTIAN KOSTANDINI)

ABSTRACT

This thesis consists of two essays on the factors that affect intra-crop diversity among

maize farmers and the effect of intra-crop diversity on production risk. In the first paper we find

factors such as age, labor, draft power, and number of plots being instrumental in affecting farm

diversity. In the second paper we find that intra-crop diversity affects positively the mean, the

variance and skewness functions of yield distribution suggesting that farmers can diversify crops

in order to reduce risk of crop failure.

INDEX WORDS: Intra-crop diversity, genetic erosion, adoption, Open Pollinated Varieties,

hybrids, landraces,

TWO ESSAYS ON MAIZE INTRA-CROP DIVERSITY AND USE OF IMPROVED

VARIETIES

by

BRIAN CHIPUTWA

BSc., The University of Zimbabwe, Harare, Zimbabwe, 2003

MSc., The University of Zimbabwe, Harare, Zimbabwe, 2006

A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment

of the Requirements for the Degree

MASTER OF SCIENCE

ATHENS, GEORGIA

2011

© 2011

Brian Chiputwa

All Rights Reserved

TWO ESSAYS ON MAIZE INTRA-CROP DIVERSITY AND USE OF IMPROVED

VARIETIES

by

Brian Chiputwa

Major Professor: Gentian Kostandini

Committee: Michael Wetzstein

Cesar Escalante

Electronic Version Approved:

Maureen Grasso

Dean of the Graduate School

The University of Georgia

August, 2011

iv

DEDICATION

This thesis is dedicated to my dad, mom (may your soul rest in peace), siblings (Joseph,

Linda and Shaun) and too many of my friends that I cannot mention by name.

v

ACKNOWLEDGEMENTS

First and foremost, I would like to thank the International Maize and Wheat Improvement Centre

(CIMMYT) in conjunction with the Department of Agricultural and Applied Economics at the

University of Georgia for providing me the financial support to pursue graduate studies. My

heartfelt gratitude and appreciation goes to my major professor, Dr. Genti Kostandini, for all his

encouragement, guidance and direction during the course of this work and throughout my stay in

Athens. I feel honored to have been able to work with you. I would also like to extend my many

thanks to my committee members, Dr. Michael Wetzstein and Dr. Cesar Escalante for all of their

helpful comments and recommendations. I am also deeply indebted to Dr. Augustine

Langyintuo, Dr. Roberto La Rovere, Dr. Wilfred Mwangi, and Dr. Olaf Ereinsten for their

leadership, mentorship and guidance throughout my career at CIMMYT. That data used in this

thesis was collected as part of a broader project, Drought Tolerant Maize for Africa (DTMA),

implemented by the International Maize and Wheat Improvement Centre (CIMMYT) in

collaboration with the International Institute of Tropical Agriculture (IITA) and funded by the

Bill and Melinda Gates foundation and the Howard G. Buffett Foundation. To my friend,

classmate, officemate and roommate, Dawit Kelemework Mekonnen, without you, life in Athens

would not have been much more bearable and enjoyable. Through the spills and thrills you were

always there. Many thanks to all my officemates in 305 Conner Hall namely Dawit, Rebati,

Ajita, Ramesh and Peter. I appreciate all the assistance you provided me.

vi

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS .....................................................................................................v

LIST OF TABLES .............................................................................................................. viii

LIST OF FIGURES ............................................................................................................... ix

1. INTRODUCTION...................................................................................................................1

1.1 Genetic diversity in agriculture ..........................................................................................1

1.2 Importance of maize in sub-Saharan Africa (SSA) .............................................................3

1.3 Measuring intra-specific genetic diversity ..........................................................................4

1.4 Threat to genetic diversity ..................................................................................................4

1.5 Problem statement .............................................................................................................6

1.6 Objectives of the study .......................................................................................................7

1.7 Contribution of each paper .................................................................................................7

1.8 Structure of the rest of the thesis ........................................................................................8

2. DETERMINANTS OF SEED DIVERSITY: THE CASE OF MAIZE FARMERS IN KENYA

...................................................................................................................................................9

2.1 Introduction .......................................................................................................................9

2.2 Modeling determinants of Intra-crop diversity in Maize ................................................... 14

2.3 Conceptual framework ..................................................................................................... 15

2.4 Specification of the regression model ............................................................................... 17

2.5 Problems in estimation ..................................................................................................... 22

2.6 Data and methods ............................................................................................................ 23

vii

2.7 Results ............................................................................................................................. 27

2.8 Conclusions ..................................................................................................................... 34

2.9 Limitations of the study ................................................................................................... 34

3. IMPROVED SEED, GENETIC DIVERSITY AND RISK EXPOSURE IN MAIZE-BASED

SYSTEMS ................................................................................................................................ 35

3.1 Introduction ..................................................................................................................... 35

3.2 Review of pertinent literature ........................................................................................... 38

3.3 Methodology ................................................................................................................... 39

3.4 Survey locations .............................................................................................................. 42

3.5 Econometric estimation.................................................................................................... 47

3.6 Estimation results ............................................................................................................ 49

4. SUMMARY AND CONCLUSIONS..................................................................................... 55

REFERENCES ......................................................................................................................... 57

APPENDIX .............................................................................................................................. 64

viii

LIST OF TABLES

Page

Table 1. Matrix of research questions, objectives and hypotheses ................................................7

Table 2. Diversity indices by district ......................................................................................... 26

Table 3. Summary statistics of variables used in the model........................................................ 27

Table 4. Regression results on count diversity ........................................................................... 30

Table 5. Regression results for factors affecting the Margalef richness diversity in Kenya ......... 33

Table 6. Summary statistics for Ethiopia and Kenya .................................................................. 44

Table 7. A priori expectation of regression model and summary statistics of the independent

variables .................................................................................................................................... 46

Table 8. Regression results of factors affecting the mean, variance and skewness functions in

Ethiopia .................................................................................................................................... 51

Table 9. Estimation results on factors affecting the mean, variance and skewness functions in .. 54

Kenya ....................................................................................................................................... 54

ix

LIST OF FIGURES

Figure 1: Kenya: survey districts ............................................................................................... 24

Figure 2: Use of maize varieties in Kenya ................................................................................. 25

Figure 3: Map of Kenya showing selected survey districts ......................................................... 42

Figure 4: Map of Ethiopia showing selected survey districts ...................................................... 43

1

CHAPTER 1

INTRODUCTION

This study is comprised of two papers that focus on intra-crop diversity and adoption of

improved varieties of maize in sub-Saharan Africa (SSA). The first paper investigates the factors

that affect intra-crop diversity among maize famers in Kenya. The second paper explores the

effects of the genetic diversity of maize on the first three moments of yield distribution in

Ethiopia and Kenya.

1.1 Genetic diversity in agriculture

Despite numerous challenges ranging from disease, pests and climate change, farmers in Africa

have used traditional agricultural systems through years of accumulated experience passed on

from generation to generation, as an adaptive mechanism. McNeety (1995) asserts that through

traditional agricultural mechanisms, some farmers have managed to use labor efficiently,

intensified production with limited resources, and earned maximum returns with low levels of

technology. One core aspect of the traditional knowledge system is through maintenance of

diverse species and traditional varieties that adapt well to pests, different conditions of soil,

rainfall, and sunlight. Isakson (2007) maintains that it is farmers in the developing world that

have been largely responsible for conserving the crop diversity and thus been the main source of

securing the global food supply and providing an invaluable environmental service.

Maintenance of genetic resources is an important source for crop breeding and helps in

improving the genetic diversity. Intra-crop diversity involves the simultaneous planting of

2

varieties (including traditional and improved) with different variances and co-variances of

returns (Bellon & Berthaud (2006)). Farmers use genetic diversity as an ex-ante strategy to

minimize the risk of crop failure in an ever-changing environment. According to Dusen (2000)

and Isakson (2007), crop diversity is believed to provide the raw materials that enable staple

crops such as cereals to evolve with changing environmental conditions hence ensuring that

these crops do not succumb to new pests, emerging plant diseases, and climate change.

Proponents of genetic diversity fear that due to the public good nature of genetic diversity, its

private provision by farmers may come short of the socially optimal level. In such a case, genetic

erosion occurs. Furthermore, the advent of a few genetically uniform, high-yielding varieties

that are used to replace a diverse set of genetically variable crop landraces, increased

participation in modern markets and commercialization of agriculture may further fuel genetic

erosion (Bellon (1996) and Bellon (2001)).

Farmers have a choice of growing traditional varieties usually referred to as landraces or

improved varieties which may consist of open pollinated varieties (OPVs) or hybrid varieties.

Smale (1998) cites numerous reasons believed to drive small scale maize farmers to maintain

crop diversity. Some of the proposed explanations, which are extensions of the neo-classical

model of household decision making, include farmer perceptions, attitudes and behaviour under

risk (yield, price, or consumption), experimentation and learning under uncertainty, missing or

imperfect markets for fertilizer or a jointly-produced output, such as fodder.

In essence, the desire to maintain crop diversity has led to the establishment of publicly

funded gene-bank centres (ex-situ conservation) around the world called the Vavilov centres of

diversity (Bellon (1996)). For example, the centre of origin and diversity for cereal crops such as

wheat, barley and teff is located in Ethiopia (Benin et al. (2004)). However, it is also apparent

3

that there is a need to complement ex-situ conservation with on-farm diversity (in-situ), which

requires public investment (Bellon (2001)). Thus it is important for policy makers to be aware of

some the factors that influence farmers to grow multiple crops.

Gomez et al. (2000) provide a good classification of these different types of varieties

farmers can grow in their fields. They define landraces as varieties that have adapted well “to

farmers‟ conditions through natural and artificial selection.‟‟ Improved OPVs on the other hand,

are varieties that have either naturally or artificially crossed with landraces in farmers‟ fields

over a number years. Farmers‟ decision on what combinations of varieties to grow in what

proportions is a process in which they accumulate numerous phenotypic characteristics, subject

to input availability in order to match bio-physical conditions, consumption preferences or

market requirements amongst other factors (Bellon (2001)).

It is important to note that the source of seed can vary from social networks to the private

sector to input credit schemes run by National Agricultural Research Institutes (NARI) or

International organizations such as the International Center of Maize and Wheat Improvement

(CIMMYT), and The International Crops Research Institute for the Semi-Arid-Tropics

(ICRISAT).

1.2 Importance of maize in sub-Saharan Africa (SSA)

Maize, Africa‟s most important cereal food crop, plays a dominant role in the farming systems

and is life to more than 300 million vulnerable population in rural communities. In order to

improve the lives and livelihoods of farm households, it is crucial for policy to minimize existing

bottlenecks that hinder maize productivity. Smale & Jayne (2004) report that maize evolved as a

4

mainstream crop in Kenya around 1914, after a disease that affected millet resulted in food

shortages, which led to farmers consuming millet instead of planting it.

1.3 Measuring intra-specific genetic diversity

There are numerous indices highlighted in Magurran (1988), which can be adapted to model crop

diversity in empirical analysis. As outlined in Magurran (1988), species diversity is classified

into a trinity of components known as the richness component (which denotes the number of

species encountered in a sampling effort), the abundance component (which illustrates the

distribution of individuals associated with each specie) and the evenness component (which

illustrates how equally abundant the species are).

1.4 Threat to genetic diversity

Crop genetic resources are mostly international public goods and thus the benefits of diversity go

beyond national boundaries (Wale (2011)). Private supply of crop diversity faces the same

predicament as other public goods due to existence of externalities and market failures. As

farmers maintain biodiversity for their private needs they provide a vital service to society, a

phenomenon referred to as „de facto conservation‟ (Wale (2011)). Therefore, depletion of

genetic resources in crops otherwise known as genetic erosion occurs when optimal choices for

private farmers result in levels of crop biodiversity that are not at equilibrium with the socially

optimal threshold. The reduction of on-farm crop diversity results when farmers continually

substitute diverse multiple varieties with a few genetically uniform ones. Many factors have been

cited as causing genetic erosion.

5

The onset of the Green Revolution in the 1950s, in which public research institutes

promoted the international transfer improved and High Yield Varieties (HYVs) of cereal crops to

farmers in developing countries which greatly improved productivity trends in these regions.

HYVs, through evidence from on-farm trials, have been observed to perform better than

traditional varieties especially when other complimentary inputs like water, fertilizer and

herbicides are optimal. Two studies by Langyintuo et al. (2010) and Langyintuo et al. (2008)

investigate factors that limit the production and deployment of improved maize seed in eastern

and southern Africa, estimate that 74% of households in Kenya had adopted improved maize

varieties compared to a regional average of 44% while observed rates in Hassan, et al., 2000

were 71% and 26% respectively. The two studies estimate high adoption rates (over 70%) of

improved maize varieties in countries like Kenya, Zimbabwe and Zambia.

Although it is generally believed that adoption of a few genetically uniform, high-

yielding varieties reduces genetic diversity by replacing a diverse set of genetically variable

traditional varieties (e.g. Bellon (1996)), empirical studies however have shown a rather more

complex relationship (Benin et al. (2004)). Adoption rates of improved maize varieties in SSA,

compared to other cereals like teff, wheat and barley, have generally been on the increase in the

past two decades. In fact, Bellon (2001) asserts that contrary to general view that substitution of

numerous landrace varieties with a few improved varieties contributes to loss in genetic

diversity, the loss is not significant “as long as the alleles and agro-morphological characteristics

are still present in other populations”. Benin et al. (2004) claim that although a-priori

expectations predict a negative relationship between modern varieties and crop genetic diversity,

empirical studies have shown a rather more intricate relationship that still require further inquiry.

Brush (2002) argues that many early reports did not link genetic erosion exclusively with the

6

substitution of traditional varieties, but instead with increased adoption of improved varieties

supporting the argument that HYVs did not necessarily lead to reduction of landraces. Other studies

have found that the introduction of HYVs had broadened the portfolio of varieties held by farmers

(Bellon (1996) and Brush et al. (1992)).

1.5 Problem statement

Recently scientists have predicted an unprecedented climatic challenge in the coming years which

will threaten the sustainability of agricultural systems, productivity, food security and human

welfare in general. Traditionally, farmers in the developing world have used crop diversity as a

way to manage such types of risks. However, recent trends such as introduction of HYVs, modern

market integration and commercialization of agricultural have been identified as pre-cursors of

genetic erosion and hence major threats to on-farm diversity. This has led to farmers to shift from

growing traditional varieties to more contemporary improved germplasm.

Wale (2011) points out that there is a misconstrued perception that promotion of traditional

varieties and on-farm diversity perpetuates rural poverty yet it is aimed at ensuring “continuous

survival of traditional varieties of crops amid rural development activities (e.g. use of modern

varieties)”. This thesis is comprised of two papers that explore the determinants of household intra-

crop diversity and the implications of adoption of improved varieties on diversity. Furthermore, the

study investigates the effects of crop diversity on three moments of yield distribution of maize to

determine the relationship between crop-diversity and risk. Table 1 provides an outline of the

research questions, objectives and hypotheses explored in each of the papers.

7

Table 1. Matrix of research questions, objectives and hypotheses Chapter Research Question Research objective Research hypothesis Analytical method One What are

determinants of

on-farm, intra-crop

diversity of maize?

Highlight

significant factors

that influence

farmers decision to

grow multiple

maize crops

Household, farm,

institutional and bio-

physical factors

affect on-farm

maize diversity

Limited dependent

regression models

(poisson and

negative binomial)

Double-hurdle

regression modeling Two How is the

distribution of

maize yield

affected by

different factors?

Determine how

different factors

affect the

distribution of

maize yield

Most production

factors positively

affect the mean

3 Stage Least

Squares (SLS)

regression model

What is the effect

of genetic diversity

on the yield

distribution of

maize?

Investigate the

effect of genetic

diversity on the

yield distribution of

maize

Genetic diversity

increases the

productivity,

reduces variability

and increases

skewness

3 SLS regression

model

1.6 Objectives of the study

The broad objective of the first paper is to highlight the determinants of crop-diversity among

maize farmers in Kenya and establish the effect of adoption of improved varieties on crop

diversity of maize. The second paper investigates the effects of crop diversity of the three

moments of maize yield distribution in order to provide insights on the effects of crop-diversity

on downside risk.

1.7 Contribution of each paper

The first paper will contribute towards understanding some of the factors affecting farmers‟

decisions to maintain intra-specific maize diversify. It will also provide insights on the

relationship and possible trade-offs between adoption of new varieties and maintenance of maize

diversity, a phenomenon that has greatly been debated in literature. The results of this paper

8

explores how different factors affect maintenance of farm diversity and this may provide insights

on the design of national breeding programmes in Kenya and other similar countries in SSA.

Most public breeding programmes are against farmers‟ over-reliance on improved seed produced

commercially by private firms mainly because they are genetically inferior in adapting to pests,

diseases and climate change compared to the traditional varieties.

The second paper will provide insights on the effects of genetic diversity on the yield

distribution of maize. In particular, we investigate the effects of genetic diversity on the mean,

variance and skewness of the yield distribution. Most studies that have focused on production

risk have limited their analysis only to the mean and variance. This chapter goes beyond a simple

mean-variance assessment of the impact of genetic diversity on the yield distribution by

including a higher order skewness effect, which captures farmers‟ exposure to unfavourable

downside risk.

1.8 Structure of the rest of the thesis

The rest of the thesis is structured as follows. Chapter two presents the first paper focusing on the

determinants of intra-specific diversity amongst maize farmers in Kenya. Chapter three focuses

on the second paper, investigating the effects of genetic diversity in maize on the distribution of

yield in Kenya and Ethiopia and chapter four will present the conclusions.

9

CHAPTER 2

DETERMINANTS OF SEED DIVERSITY: THE CASE OF MAIZE FARMERS IN KENYA

2.1 Introduction

Crop diversity is generally referred to as simultaneously planting crops or varieties with different

variances and/or co-variances of returns (Bellon (1996). Crop diversity can be categorized into

intra-crop (growing of multiple varieties of the same crop) or inter-crop (mixing of different

crops). Farmers have generally resorted to diversifying crops and/or varieties mostly as a

response mechanism to changes that are weather related (e.g. droughts and frost), biophysical

(e..g pests and diseases) and market related (e.g. fall in process). This paper focuses on intra-crop

diversity amongst maize farmers in Kenya. Farmers‟ production and maintenance of multiple

maize varieties in SSA has mostly been viewed by many authors as a coping and management

strategy that cushions farmers from risk exposure.

Maintenance of intra crop diversity is a vital component of traditional knowledge system

passed through from generation to generation of farmers. In developing countries, it is an ex-ante

strategy used by small farmers to adapt and cope with ever changing ecological and socio-

economic environments (Bellon, 1991 and 1996 and Brush 1992). It is a way in which farmers

can self-select and isolate different crops and varieties that are more suited to one set of

circumstances (Brush et al. (1992)) as also been argued as a way that preserves and retains some

specific genetic attributes that may be exclusive to certain varieties. In maintaining diversity,

farmers may consider combining two types of varieties; traditional varieties and hybrid varieties.

10

Traditional varieties or landraces are defined as those varieties that a landrace is a dynamic

population(s) of a cultivated plant that has historical origin, distinct identity and lacks formal

crop improvement, as well as often being genetically diverse, locally adapted and associated with

traditional farming systems' (Villa et al. (2005)).

Hybrid seeds are derived from crossing two pure lines. They are generally higher yielding

compared to traditional varieties especially when complimentary inputs such as chemicals and

fertilizers are used optimally. However, hybrid seeds do not as perform well when recycled.

Recycling involves planting of seeds that are saved from the previous cropping season. Crop

diversification is considered an important step in the transition from subsistence to commercial

agriculture. With economic growth, households start to produce for markets and adopt new crops

to meet demand ((Winters et al. (2006)). Bänziger & Diallo (2004) listed soil impoverishment,

food insecurity, high input costs, lack of credit facilities as major drivers for small scale farmers

to grow maize in low input/low risk systems.

Much of the literature on intra-crop diversity (e.g. Gebremedhin et al. (2005); Bellon

(1996); Smale (1998); Di Falco & Chavas (2009)), suggests that it is an ex-ante strategy that

farmers use to support productivity in order to avert and minimize the risk of crop failure in an

ever-changing environment. Crop diversity is a basis for food supply and hence an important

aspect of agriculture in SSA, a region plagued with alarming levels of poverty, malnutrition and

food insecurities amongst the rural poor.

Intra-specific diversity can be a result of conservation on-farm (in-situ) or on-station (ex-

situ). The former refers to the “continued cultivation and management by farmers of crop

populations in the open genetically dynamic systems where the crop has evolved” (Benin et al.

(2004)). This type of conservation, which is the focus of this paper, is shaped by farmers‟

11

livelihood objectives, strategies and goals and how they are intricately intertwined with access to

natural (land, TLU), physical (input and output markets, extension), human (characteristics of

household head, labor), social (networks) and financial capital (credit). However, the

relationship between the adoption of improved maize varieties and maintenance of infra-specific

crop diversity has been a major point of debate in literature. The theoretical hypothesis that the

introduction of improved varieties has led to the loss of genetic diversity is usually obscured by

the traditional seed systems and farmers‟ demand for stability and seed as well as livelihood

resilience (Yemane et al. (2009)). Dyer (2002) challenged the general belief that with the

development of input and output markets, growing traditional varieties has high opportunity

costs.

Winters et al. (2006) argue that risk management, adaptation to dynamically

heterogeneous environmental conditions and matching market demand are the primary drivers

for farmers‟ decision for crop diversification. Benin (2003) further suggests that sometimes

farmers diversify crops as a response mechanism to provide themselves with seeds that have

certain attributes that may not be present in seeds in the formal market. Improved varieties

available on formal markets may also possess certain traits that are not available in local

varieties. Therefore, through crop diversification, small scale producers have the opportunity to

self-select varieties that are best suited to their subsistence needs and the needs of the

commercial markets. Clawson (1985) shows how farmers utilize crops which differ in

maturation periods in order to secure food self-sufficiency.

This paper aims to identify the factors that affect intra-crop diversity of maize in

Machakosi and Makueni districts in Kenya. Intra-crop diversity of maize is assumed be a

function of household characteristics (such as gender, age, education level of household head,

12

TLU), farm factors (farm size, number of plots, fertiliser and manure use, susceptibility to

weeds, pests and diseases) and institutional factors like access to extension and Bio-physical

factors (altitude). Most of these factors are well documented in literature as factors influencing

on-farm diversity such as Benin et al. (2004); Benin et al. (2005); Di Falco et al., (2010);Yemane

et al. (2009); Di Falco et al. (2009); Smale et al. (1998) and Bellon (1996). The main

contribution of this paper is to investigate the effects of adoption improved varieties of maize on

intra-crop diversity in Kenya. A couple of papers that have used similar econometric modelling

like Isakson (2007); Benin (2003); Benin et al. (2004); Benin et al. (2005) and Gebremedhin et

al. (2005) have used data from small farmers in Ethiopia. The results of this study will further

provide more robust empirical evidence on the relationship between adoption of improved

varieties and crop diversity in Kenya. This method has widely been used for cereals for studies in

Mexico and Ethiopia but never in Kenya (see Benin et al. (2004); Benin (2003); Benin et al.

(2004); Gebremedhin et al. (2005)). Since genetic diversity of maize is a public good, its

provision (through on-farm conservation) may not be socially optimal and this may call for

public policy intervention. Results of this paper will provide some insights to social planners in

Kenya and other countries of similar circumstances on harmonizing seed systems policy

frameworks in Kenya which can be up-scaled and adapted to other areas of similar socio-

economic, institutional and biophysical characteristics.

Agricultural productivity and biodiversity

Governments in the developing world are faced with the daunting task and important role of

formulating policies and strategies that enhance agricultural productivity amongst small farmers

and also maintain agro-biodiversity. This effort may require harmonization of seed policies that

13

affect agro-biodiversity, adoption of improved technologies. It is therefore imperative to

understand some of the drivers of genetic erosion in communities in order to formulate such

policies.

In essence, the desire to maintain crop diversity has led to the establishment of publicly

funded gene-bank centres (ex-situ conservation) around the world (Bellon (1996)) for example

Ethiopia is one of the eight Vavilovian centres for cereal diversity (Benin et al. (2005)).

However, it is also apparent that there is a need to complement ex-situ conservation with on-farm

diversity (in-situ), which requires public investment Bellon (2001), thus making it imperative for

policy makers to be aware of some of the factors influencing farmers to grow multiple crops.

Proponents of genetic diversity fear there is a looming genetic erosion of landraces due to the

public good nature of genetic diversity and thus the inherent conflict with farmers‟ private goals

of utility maximization, which may not necessarily be socially optimal, there is often under

provision of this biological service. Genetic erosion of landraces is further compounded by the

advent of a few genetically uniform, high-yielding varieties that are used to replace a diverse set

of genetically variable crop landraces (Bellon (1996) and Bellon (2001)), as market demand

and/or supply evolve/s. Langyintuo et al. (2008) also stresses that there is a major drive on

production and supply of hybrids in Africa resulting in highly skewed share of hybrids at the

expense of other varieties i.e. landraces and OPVs. This is largely attributed to the commercial

incentives that hybrids present to profit maximizing private firms as well as their superior

performance in the view of public breeding programs. Langyintuo‟s paper highlights that

although there are some critics that believe that the shift to hybrids is detrimental to small-scale

farmers, they point out that there is little empirical evidence that corroborates this claim and that

more work needs to done in this regard.

14

2.2 Modeling determinants of Intra-crop diversity in Maize

This study models intra-crop diversity as a function of demographic, socio-economic,

institutional and bio-physical factors of the farmer. The Margalef and Count indices are

computed and make up the dependent variables. A brief description of the variables is given on

the sections below.

Dependent variables

Magurran (1988) illustrates a number of diversity indices that can be used to represent intra-crop

diversity. Various studies like Yemane et al. (2009); Chavas et al. (2010); Di Falco et al. (2010);

Di Falco et al. (2009) and Smale et al. (2003), have applied some these indices. However, the

choice of the indicator depends on a number of factors such as the type of crop under

consideration, the mode of reproduction and the type of data available to the researcher (Dusen

(2000)) as well as the testable hypothesis and the level of analysis (e.g. plant, household, village)

(Smale et al. (2003)). This study uses two indices that represent diversity richness: the Count and

Margalef Indices. The choice of these indices was mainly due to variables in our dataset.

The Count index (C) represents the number of Maize varieties (S) grown by each farmer

less one. C is a count variable starting at zero for farmers growing only 1 variety. It can be

shown by the following expression,

(1)

1 ii SC

15

The Margalef richness index (MI) represents the count of the number of varieties normalized to

the scale of the natural log of the area under all maize (N). The Margalef index has a lower limit

of zero if only one variety is grown

(2)

The indices generated are used as dependent variables regressed against the household‟s socio-

economic and farm characteristics. The Count and Margalef indices1 capture the concept of

species‟ richness and are appropriately used when “diversity is apparent to farmers” (see Meng

(1998); Di Falco et al. (2009)). This notion is further supported by Smale et al. (2003), who state

that farmers choice of varieties is motivated by traits that they can observe as opposed to the

genes they cannot.

In this paper, we assume that in order to reduce the risk of crop failure and to maintain

some desirable characteristics found in specific varieties, farmers will grow multiple maize

varieties that have different yield variances and/or co-variances.

2.3 Conceptual framework

In principle, we follow Rahm & Huffman (1984) model on household utility that assumes that

farmers base their adoption decisions upon the objective of maximizing their utility. In this case

the farmer will grow multiple maize varieties if the utility (UAi) is greater than the utility derived

from growing a single variety (UNi). In general, the utility derivable from growing maize U

1 The larger the index, the greater is the number of maize varieties grown by a household. Besides the indices

discussed above, Shanon Index (which measures richness and relative abundance) and the Berger Parker Index

(which measures relative abundance) are other indices that can be used to model crop diversity (Magurran (1988)

and Smale (2005))

NSMI ii ln)1(

16

depends on M, which is a vector of farmer characteristics (e.g., gender, age and education), farm

characteristics (e.g., farm size, labor), institutional factors (e.g. extension, credit) and bio-

physical factors (e.g. topography, soil type) of the farmer. The preference of adopting one variety

and that of adopting multiple varieties are assumed to be linear in relationship:

We assume farmers base their adoption of maize diversity decisions on utility

maximization

ijiiijij

ijij

eZMFU

ZMU

),(

)),B)I,F,H,|(( MAX

(4)

Where U represents the level of utility the farmer derives

M is a vector of observable explanatory variables affecting maize diversity

H,F,I and B are household, farm, institutional and bio-physical factors that affect maize

diversity, respectively

j is a vector of explanatory coefficients to be estimated of the diversity index

e is a vector of random disturbances of the unobserved factors affecting maize diversity index

j= 1, 2 where 1=adoption of diversity and 2=Non-adoption of diversity

i= 1, 2……n.

We also assume farmers will diversify maize if and only if

(5) 0

*

iNiAi

iNiA

UUy

UU

Where y* is an unobservable latent variable representing the benefits of diversity. But what we

observe is whether or not the farmer is growing multiple varieties on their farm. Therefore the

17

farmer grows multiple maize varieties, we denote this by Pr (Y=1). Otherwise, Y takes the value

of zero.

..(7)...................................................................... )( )1(Pr

}),(E Pr{

)6....(..........}......... ))(,(ee Pr{

}e ),(e ),(Pr{

)Pr()1(Pr

iii

iiii

ANiiiiNiA

iNiiiNiAiiiA

iAiAi

XFY

ZMF

ZMF

ZMFZMF

UUY

where E = (eA- eN ) is a random disturbance term independently, normally distributed error term

with zero mean and constant variance 2,

β = (αA – αN) is a vector of unknown parameters vector of parameters to be estimated and

interpreted as the net effect of the vector of explanatory variables of maize diversity,

F(Xβ) is cumulative distribution function F evaluated at Xβ.

Equation 6 cannot be estimated directly without knowing the distribution of E that determines

the distribution of F. The functional form of F can be specified with a either a logistic

distribution for a Logit specification or a normal distribution for a probit or Tobit specification.

2.4 Specification of the regression model

Regression modeling of the Count diversity index

To account for the limited dependent variable nature of the count diversity index of maize, we

estimated Poisson and negative binomial models. The two regression models consider the log of

the expected counts of maize diversity as a linear function of the independent variables.

Therefore, we interpret the estimated coefficients as: for a unit change in the independent

variable, the difference in the logs of expected counts is expected to change by the respective

18

regression coefficient, ceteris paribus. The Poisson model is typically restrictive as it imposes

the restriction that the variance of the dependent variable is equal to its conditional mean (equi-

dispersion) while the Negative Binomial model assumes that the two are not equal.

In order to appropriately model the count diversity index, we used a number of

specification tests. Following Hidayat & Pokhrel (2010), we used the Breusch-Pagan test for

homoscedasticity and if the null hypothesis of homoscedasticity among the regressors was not

rejected, we then proceeded to consider count data models that ignore heteroscedasticity. First

we considered the Poisson model for which we used a combination of the likelihood ratio (LR)

statistic to test whether the equi-dispersion condition is met and the election criteria based on the

Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The Model with

lowest BIC and AIC were preferred. The use of the Poisson distribution for the analysis of count

data has been criticized in the past due to the unattractive feature that the conditional mean and

the conditional variance are restricted to be equal, a property also known as equi-dispersion (see,

for instance (Winkelmann (1995)). If this condition is not satisfied, the model will suffer from

over or under dispersion implying that the variance of the count variable is greater or less than its

conditional mean respectively, rendering the estimates of the Poisson model inefficient

(Cameron & Trivedi (1998); Cameron & Trivedi (2009) and Winkelmann (1995)). We then

proceeded to estimate the negative binomial regression model and again used the likelihood ratio

statistic to test for its fit as well as the AIC and BIC.

Regression modeling of the censored Margalef diversity index

When the dependent variable is censored and continuous, as is the case with the Margalef

diversity index in this study, then a Tobit model is the simplest and conventional model to

19

consider. Under the Tobit specification, developed by Tobin (1958), the farmer is assumed to

make two decisions simultaneously; 1) that of growing more than one variety of maize and 2) the

degree or extent of diversity. When the farmer only grows one variety, the Margalef index is

equal to zero. In this study, there are many zero responses which makes the Margalef index, a

left censored dependent variable. The model permits incorporation of all observations including

those of farmers not growing multiple varieties. To take into account all the information in the

limited dependent variable properly, the Tobit estimation method uses maximum likelihood to

combine the probit and regression components of the log-likelihood function Langyintuo (2008).

However, the marginal effect is constrained to have the same directional effects in both parts of

the model. Using a Tobit specification restricts the discrete farmers‟ decision to diversify and the

extent of diversification (as represented by the diversity indices) as joint and simultaneous

decisions. This may not necessarily apply in cases where farmers can decide to grow multiple

varieties mainly based on their farming region but the number of varieties that may be grown

may be negatively affected by access to extension and farmer group association.

As illustrated in Langyintuo (2008), the model can be expressed in terms of a latent

variable:

)8( *

iii uxy

0

00

**

*

ii

i

iyify

yify

,

where yi in our case represents the Margalef index which will either be zero the case for farmers

that only grow one maize variety or positive for farmers growing multiple varieties. The model

combines aspects of the binomial probit for distinction of yi = 0 versus yi > 0 and the regression

20

model for ].,1|[ iii xyyE We could collapse all positive observations on yi and treat this as a

binomial probit (or logit) estimation problem, but doing so would discard the information on

proportion of area under improved varieties by adopters. Likewise, we could throw away the yi =

0 observations but we would then be left with a truncated distribution, with the various problems

that creates. The log likelihood of a given observation as

u

i

iu

xyI

1log)0(),(

u

xyyI u

u

ii

i

2

log2

1log)0(

,

where I(·) = 1 if its argument is true and is zero otherwise. We can write the likelihood function,

summing i over the sample, as the sum of the probit likelihood for those observations with yi = 0

and the regression likelihood for those observations with yi > 0. Since the Tobit model has a

probit component, its results are sensitive to the assumption of homoscedasticity Langyintuo

(2008).

The hurdle model

In order to overcome the restrictive nature of the Tobit model, Cragg (1971) developed an

alternative model called the double hurdle, which decomposes the decision to undertake an

activity (e.g. growing multiple varieties) and the degree of diversification into separate entities.

The first hurdle in our case will be a binary probit regression modeling the whether or not farmer

grows more than one variety using all observations which will later be followed by a left or zero

censored regression. The hurdle model tackles the problem of too many zero responses in the

survey data by giving special treatment to those farmers that grow multiple crops. The model

assumes two hurdles to be overcome to observe positive values.

21

The Heckman method

The Heckman (two-step) model, named after James Heckman, is an alternative generalization of

the Tobit model. It is a two-step procedure which the estimation of the participation decision and

the degree of participation is estimated separately in order to account for sample selection bias,

Heckman (1979). However, as illustrated in Amemiya (1974), the Heckman process take into

account all observations in the second stage by incorporating a measure of the inverse Mill‟s

ratio (IMR) for those farmers that do not participate. The first step involves estimating a discrete

model representing whether or not the farmer selects the activity. After estimation, the inverse

mills ratio parameter is predicted and used as an explanatory variable in the second step.

Amemiya (1974) generalized the Heckman approach to include all observations in the second

step by developing a measure of the inverse Mill‟s ratio (IMR) for the zero observations. In the

second step, the parameters in the linear model are obtained by regressing the observations on

the explanatory variables and on estimates of the predicted values from the first step.

As highlighted in Isakson (2007), the main difference between of the Heckman model

form the double-hurdle, is that the Heckman assumes no zero observations in the second stage,

once the first-stage selection is passed. The double-hurdle, on the other hand, allows for the

possibility of zero responses in the second-hurdle for those individuals‟ deliberate choices or

random circumstances.

In the application of the hurdle model, this study follows the works of Gebremedhin et al.

(2005); Benin et al. (2004) and Benin et al. (2005). The hurdle model modifies the count model

in which two processes are involved, one generating the zeros if the farmer does not adopt a

modern variety and one generating the positive values if the farmer adopts. In the hurdle model a

binomial probit model is estimated in order to calculate the predicted probabilities of adopting

22

improved maize. Once the predicted value is positive, then the conditional distribution of the

positive values is governed by a zero-truncated count model. The predicted probabilities are

exogenous and hence will be appropriate to use in the second-stage regression (Cameron et al.

(1998) and Maddala (1983)). However, due to the censoring problem in the regression, the

predicted values may introduce heteroscedasticity into the model, which causes the estimated

coefficients to be inconsistent (Maddala (1983)). Obtaining the correct standard errors is also

complicated by use of the predicted rather than the observed adoption rates. In order to obtain

correct standard errors through bootstrapping, Benin et al. (2005) suggested Powell's censored

least absolute deviations (CLAD) regression model that is robust to heteroscedasticity. With the

CLAD approach, the coefficients are estimated so as to minimize the sum of the absolute average

deviations from the regression line and makes no assumption about normality or

homoscedasticity (Thomas & LoSasso (2001)).

2.5 Problems in estimation

The first challenge to econometric modeling of diversity indices is that of sample selection or

censoring bias which results from the fact that there are some farmers in the sample that only

grow a single variety of maize. Therefore, the diversity indices will exhibit numerous zero

responses for those farmers that only grow one variety. Consequently, using ordinary least

squares (OLS) or seemingly unrelated regression (SUR) will result in biased and inconsistent

estimates (Gebremedhin et al. (2005); Benin et al. (2004) and Benin (2003)). The preferred

econometric method is to use all available observations through the use of treatment effects

models such as the Heckman model described in the section above (Van Dusen (2005)).

23

The second problem is that using a dummy variable to represent farmers‟ adoption of

improved maize varieties to explain diversity indices presents endogeinity bias to our estimation.

Similar to sample selection bias, including an endogenous explanatory variable will render

estimated coefficients as biased and inconsistent (Greene (2008) and Maddala (1983)). In order

to avoid this problem, we estimate a binomial probit model in the first stage and predict the

probabilities of adoption which are considered exogenous and hence appropriate to use in our

estimation instead of the observed adoption rates. As noted by Benin et al. (2005), even if the

explanatory variables in the first and second stage regressions are identical, because the predicted

probabilities from the first-stage regressions are non-linear functions of the explanatory

variables, the CLAD regression is identified under the normality assumptions of the probit

model.

2.6 Data and methods

Data were collected through a combination of formal surveys and participatory techniques. A

sample of 350 farmers were randomly selected and interviewed by trained enumerators in the

Machakosi and Makueni district in Kenya in the 2007/2008 season. The two districts are located

in Kenya‟s Eastern Province and farmers in these areas are typically engaged in small scale, rain-

fed agriculture and livestock rearing. Survey districts2 in both districts are classified to be in the

medium drought risk zone with a 20-40% probability of failed season in the maize producing

areas. These estimates are based on maps and techniques outlined by Hodson et al. (2002) and

Thornton et al. (2006), respectively. In the formal survey, structured questionnaire were used

2 For full survey details, refer to Muhammad, L. , Mwabu D., R. Mulwa, W. Mwangi, A. Langyintuo and R.

La Rovere 2010. Characterization of maize producing households in Machakos and Makueni districts in Kenya. Kenya. Nairobi: KARI-CIMMYT.

24

designed to capture demographic, socio-economic characteristics of households and agricultural

production activities. The surveys were funded and conducted under CIMMYT‟s Drought

Tolerant Maize for Africa (DTMA) project.

Figure 1: Kenya: survey districts

Figure 2 shows the general use of maize varieties in the two districts. Forty-two percent of the

farmers grow at least one variety of maize reiterating the important role that maize plays as a

subsistence and staple crop in these districts. About 10% of the farmers grew at least four

different varieties. The Count index for the two districts ranges from 0 to 6 as shown in Figure 3.

However, comparing the mean of the Count and Margalef indices, it is evident that the

Machakosi district has more diversity compared to the Makueni district. There is evidence of a

disparity between the mean and variances of the Count index for all the districts and hence there

is need to go beyond the Poisson regression model and consider other models for count depended

variables which are suitable for over or under-dispersed models in the dependent variable.

25

Figure 2: Use of maize varieties in Kenya

The empirical analysis in this paper investigates the determinants of intra-specific

diversity on maize among in farmers in Kenya. Maize crop diversity was modeled as a function

of various explanatory variables that range from demographic, socio-economic, institutional and

bio-physical factors. For the count diversity presented in Table 2, two models were used: 1) the

Poisson model; 2) the Negative binomial model.

147

109

56

21

10

2 1

05

01

00

150

Num

ber

of h

ou

se

ho

lds

0 2 4 6 81 3 5 7Number of varieties grown

26

Table 2. Diversity indices by district

Statistic

Machakosi Makueni Whole sample

Count Margalef Count Margalef Count Margalef

Mean 1.37 0.15 0.60 0.07 0.98 0.11

Max 6.00 0.70 5.00 0.50 6.00 0.70

Min 0.00 0.00 0.00 0.00 0.00 0.00

Range 6.00 0.70 5.00 0.50 6.00 0.70

Std dev. 1.20 0.14 0.89 0.09 1.12 0.12

Variance 1.45 0.02 0.78 0.01 1.26 0.02

Source: DTMA survey data

27

Table 3. Summary statistics of variables used in the model VARIABLE Description Hypothesi

zed effect Mean Std.

Dev. Min Max

Dependent variable

COUNTINDEX Maize Diversity Index 0.983 1.121 0 6

MARGALEF2 Diversity Index (Richness) 0.110 0.124 0 0.70

3 Independent variable

ALTITUDE Altitude (m) (+) 1443.9 153.3 114

6 1840

GENDER Gender of hhd (0=Male,

1=Female) (-) 1.116 0.321 0 1

AGE Age of hhd (+,-) 51.207 15.42 18 90

FARMASSO Hhd member of Famer's

Association (0=No, 1=Yes) (+) 1.661 0.474 0 1

DISTRICT district (0=Machakosi, 1=Makueni)

(+,-) 0.499 0.501 0 1

TOTAREA Total area owned (Acres) 2.746 3.370 0 34.2

5 MANUSE Use of Manure (0=No,

1=Yes) (+,-) 0.840 0.368 0 1

UREA Use of Urea (kgs) (+) 25.067 76.10 0 1150

PLOTS_NO Number of

plots/fragmentation (+,-) 1.828 0.991 0 4

TLU Livestock units (+,-) 3.564 3.787 0 24.8

5 ERRAIN Erratic Rainfall pattern

(0=No, 1=Yes) (+) 0.235 0.425 0 1

PESTNDZZ Problem of pests and disease (0=No, 1=Yes)

(+) 0.338 0.474 0 1

MAIZEPRC Maize prices too low (0=No,

1=Yes) (-) 0.181 0.385 0 1

WEEDS Problem of weeds (0=No,

1=Yes) (+) 0.029 0.167 0 1

IMP_VTIES Predicted adoption

probabilities (+,-)

Source: DTMA survey data

2.7 Results

First, we discuss the results presented in Table 4 for the Count diversity regression disaggregated

by district and for the whole sample. We then focus on the models for the Margalef index for

richness, presented in Table 5. Column 2 and 3 are results of the Tobit regression models

28

disaggregated by district while column 4 illustrates results for the whole sample. The second

hurdle CLAD regression results are presented in Column 5.

Results for the Count diversity Index

Of the socio-economic variables included in the model in Table 4, gender of the household head

was not significant in explaining diversity. However, the farmer‟s age is negatively and

significantly associated with the number of maize varieties grown in both districts and the whole

sample. This may suggest that the younger farmers are more amenable to trying out numerous

varieties as they cushion themselves from risk of crop failure compared to older farmers.

Education level, which is a measure of the farmer‟s literacy level, is significant and positively

correlated with the number of varieties grown by farmers in the whole sample but not in the

individual districts. Our findings on the effects of age and education level are consistent with

Benin et al. (2004).

Of the farm factors, the total farm area did not affect count diversity of the farm. This is

contrary to our postulated effect and that found by (Benin et al. (2004)) that farmers with larger

farms are most likely to try out different combinations of crops. However, farmers with more

land are generally wealthier and may be more willing to try out fewer varieties, but more

productive ones. Households with higher labor capacity, calculated as the Man equivalent units

(MEU)3, are associated with growing more maize varieties. This may be because diversifying

maize varieties may entail greater demand for labor for different farm activities due to the

differences in maturity rates and farm activities among different varieties. This may be because

3 MEUs were calculated after Runge-Metzger (1988) as follows: Household members less than 9 years = 0; 9 to

15 years or above 49 years = 0.7; and 16 to 49 = 1

29

diversifying maize varieties may entail greater demand for labor for different farm activities due

to the differences in maturity rates and farm activities among different varieties.

Farms with greater access to Tropical Livestock Units (TLU) are also positively

associated with a greater number of maize varieties grown. A plausible explanation for this may

be due to the complementary nature between the crop production and livestock sectors with

regards to former providing supplementary fodder to the latter. Farms with more plots are

associated with greater count diversity in both districts. Makueni district and the whole sample at

1% and 10% significance level respectively.

On biophysical factors, altitude and occurrence of erratic rainfall patterns were all

significant albeit with contrasting effects on the maize diversity. Farms on higher altitude were

associated with higher diversity while farms in areas perceived to receive erratic rainfall had

lower diversity. Our finding on rainfall effect on diversity4 is similar to that of Di Falco et al.

(2010) who conclude that rainfall levels hinder the number of crop species grown at farm level

and that farmers who expect less rainfall will diversify their crops more. Di Falco and Chavas

(2008) concluded that increased average rainfall decreases diversity as farmers in high rainfall

tend to specialize on crops that do well in their environments. Surprisingly, the number of

extension visits did not have an effect on maize diversity on farms whilst farmer group

association is associated negatively with diversity. These effects are contrary to our hypothesized

effects.

4 Di Falco et al. (2010) investigated diversity amongst different crops (inter-crop diversity). Our study, on the other

hand, considers diversity within crops (intra-crop diversity).

30

Table 4. Regression results on count diversity

District Whole sample

Makueni Machakosi

VARIABLES Poisson Neg_Binomial Neg_Binomial

GENDER 0.0477 0.123 0.0593

(0.333) (0.224) (0.199)

AGE -0.0171* -0.0120** -0.0156***

(0.00893) (0.00587) (0.00485)

EDUCN 0.548 0.650 0.694**

(0.433) (0.458) (0.334)

HHLABOR 0.0724** 0.0620* 0.0611**

(0.0318) (0.0319) (0.0244)

TOTAREA -0.0117 0.0163 0.00430

(0.0197) (0.0284) (0.0216)

TLU 0.0662*** 0.0121 0.0367*

(0.0245) (0.0208) (0.0196)

PLOTS_NO 0.387*** 0.147*** 0.250***

(0.0983) (0.0540) (0.0513)

ALTITUDE 0.000862 0.000663 0.00171***

(0.000613) (0.000689) (0.000372)

ERRAIN -0.205 -0.422* -0.396**

(0.280) (0.236) (0.175)

PESTNDZZ -0.151 -0.0139 -0.0750

(0.239) (0.143) (0.124)

WEEDS -0.504 0.483 0.346

(0.559) (0.360) (0.348)

CREDIT 0.203 0.602*** 0.344

(0.504) (0.214) (0.306)

EXTNVISITS -0.0159 -0.00331 -0.00213

(0.0226) (0.00907) (0.00757) FARMASSO -0.458* -0.132 -0.235

(0.278) (0.218) (0.181)

CONSTANT -2.180** -1.275 -3.097***

(1.086) (1.151) (0.682)

LNALPHA -15.71*** -3.518

(1.552) (2.466)

Observations 167 160 327

log likelihood -159.4 -231.4 -404.5

chi-square 78.02 45.18 107.3

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Source: DTMA survey data

31

Results for the Margalef diversity Index

The Tobit results for the Margalef index, presented in Table 4, are fairly consistent to those of

the Count diversity index presented in Table 5. The coefficients of the count diversity for

household age, labor capacity, TLU, number of plots and altitude of the farm all have the same

significant effects on the probability and extent of farm diversity. The age of the household head

negatively affects the probability of a farmer growing multiple varieties and the extent to which

they grow multiple varieties. Education, labor capacity, altitude and number of plots all seem to

be positively associated with the probability and extent of a farmer growing multiple varieties.

Similarly number of extension visits, occurrence of pests, diseases and weeds all do not affect

the farmers‟ choice on crop diversification. However, unlike in the Count regression model, the

total farm area positively affects the diversity of maize on farms. A plausible reason for this

could be that farmers with larger tracts of land are willing to experiment and try out new

varieties. Thus there may be several factors that influence how land area and crop diversity are

related.

The CLAD regression model is presented in the last column of Table 4. The first hurdle,

(results not shown), involved running a binary probit model modeling factors affecting adoption

of improved varieties and then using the predicted probabilities from this stage as an explanatory

variable in the second hurdle of the CLAD regression. Female headed farms are associated with

higher diversity. A possible explanation could be that since maize is a staple food crop, women

may take a lead role in trying out multiple varieties that may have different contributions to the

dietary and nutritional needs of the household. Age is shown as having a negative effect on the

probability of a farm being diverse. Household labor, farm area, livestock ownership, farm

altitude and number of plots are all positively associated with farm maize diversity. None of the

32

biophysical factors significantly affect on-farm maize diversity. The predicted probability of

adopting improved maize seeds represented by (Imp_vties) is found to be statistically

insignificant in explaining farm diversity. This finding is consistent with that of Benin (2003)

and Benin et al. (2004) and seem to suggest that farmers‟ adoption of improved maize varieties

does not enhance or inhibit on-farm intra crop diversity of maize.

33

Table 5. Regression results for factors affecting the Margalef richness diversity in Kenya

District Whole sample

Tobit

Whole sample

Machakosi Makueni 2nd „hurdle‟

VARIABLES Tobit Tobit CLAD5

GENDER 0.0354 -0.00442 0.0198 -0.0258*

(0.0455) (0.0484) (0.0352) (0.0135)

AGE -0.00251** -0.00194 -0.00248*** -0.00508***

(0.00115) (0.00122) (0.000868) (0.000654)

EDUCN 0.123* 0.0925* 0.123*** -

(0.0638) (0.0540) (0.0438) -

HHLABOR 0.0132** 0.00852 0.00942* 0.0145***

(0.00649) (0.00619) (0.00485) (0.00244)

TOTAREA 0.000133 -0.00100 -0.00127 0.00381***

(0.00715) (0.00463) (0.00411) (0.00120)

TLU 0.00345 0.00856 0.00599 0.0204***

(0.00435) (0.00548) (0.00367) (0.00188)

PLOTS_NO 0.0288** 0.0532*** 0.0444*** 0.0222***

(0.0119) (0.0183) (0.0102) (0.00778)

ALTITUDE 0.000114 0.000102 0.000300*** 0.000456***

(0.000141) (9.55e-05) (7.07e-05) (6.67e-05)

ERRAIN -0.109** -0.0261 -0.0810*** -0.00325

(0.0428) (0.0380) (0.0292) (0.0174)

PESTNDZZ 0.00531 -0.0437 -0.0200 -0.00877

(0.0291) (0.0356) (0.0230) (0.00964)

WEEDS 0.0941 -0.0535 0.0619 -

(0.0773) (0.0680) (0.0620) -

CREDIT 0.176** 0.0727 0.116 -

(0.0731) (0.0646) (0.0726) -

EXTNVISITS -0.00102 -0.00146 -0.000726 0.000298

(0.00182) (0.00329) (0.00140) (0.000662) FARMASSO -0.0288 -0.0550 -0.0403 -0.0915***

(0.0490) (0.0496) (0.0361) (0.0246)

Imp_vtiesa - - - -0.0575

- - - (0.0548)

Sigma 0.161*** 0.166*** 0.172*** -

(0.0128) (0.0134) (0.00981) -

Constant -0.131 -0.234 -0.463*** -0.437***

(0.236) (0.167) (0.125) (0.0713)

Observations 160 165 325 302 aProbability of growing improved varieties. Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Source: DTMA survey data

5 CLAD regression at the district level failed to converge most likely due to the small sample sizes in each district.

34

2.8 Conclusions

This paper seeks to identify the factors that affect intra-specific diversity of among maize

farmers in two districts in Kenya. In general, the different regressions used to model diversity for

the two districts and the whole sample had more or less the same directional and significance

effect on diversity. The age of household head, access to household labor, access to draft power,

the number of plots was consistently having the same effects on diversity of farms. Therefore,

public policies aimed at promoting on-farm diversity will not conflict with an influx of improved

commercial seed from the private sector. In-fact, government can play a role in identifying and

promoting varieties that are at risk of extinction but yet still provide important attributes that is

beneficial to society.

2.9 Limitations of the study

One weakness of our study is that we make use of cross sectional survey conducted over one

season surveys for both countries. This limits our analysis since we are not able to incorporate

dynamics pertaining to farmers‟ use of multiple varieties and cultivation of improved varieties.

35

CHAPTER 3

IMPROVED SEED, GENETIC DIVERSITY AND RISK EXPOSURE IN MAIZE-BASED

SYSTEMS

3.1 Introduction

Agriculture is a risky sector as farmers are constantly being exposed to biological, climatic and

institutional factors that lead to production uncertainty. In developed countries, farmers are

cushioned from risk exposure through access to well-developed risk management and coping

mechanisms through various instruments like credit and insurance systems and hence are able to

realize higher expected output (Simtowe et al. (2006)). However, this is not the case in

developing nations where production is heavily reliant on rain-fed ecosystems and farmer‟s

exposure to risk and uncertainty is further compounded by the existence of missing or imperfect

markets, information asymmetry and poor infrastructure (Fufa & Hassan (2003);Kumbhakar &

Tsionas (2010)). Presently, there is a lot of attention on the impact of climate change on the

environment and it is predicted that due to frequent occurrence of extreme weather events,

unpredictable weather patterns, production risk will invariably increase.

The World Bank estimates that among the 1.3 billion people living on less than U.S. $1

per day, close to 75% depend on agriculture for survival. In sub-Saharan Africa (SSA) alone,

more than 75% of poor families live in rural areas. It is well known that poor farmers in SSA are

heavily dependent on natural resources such as the soil, its nutrients and rain for their production

activities. Although HYVs are undoubtedly more profitable compared to other traditional

36

varieties, in general they may be riskier and farmers may be more reluctant to adopt these

varieties. In fact, Simtowe et al. (2006) assert that it is common knowledge that hybrid maize

perform far less than the local maize in the absence of fertilizer.

Many studies have investigated issues surrounding production risk such as technology

adoption (see Fufa et al. (2003); Koundouri et al. (2006) and Kumbhakar et al. (2010)) and crop

diversity (e.g. Benin et al. (2004; Di Falco & Chavas (2006; Di Falco et al. (2009)). Most papers

on risk analysis in the maize sector have used the mean-variance analysis to model risk. Since

risk can either be an unexpected good event or bad event, conventional analysis using only mean

and variance functions does not distinguish between these opposite events. Therefore, including

a higher order moment of the maize output distribution, i.e. the skewness function, allows for

investigating the effects of biodiversity on downside risk (see Di Falco et al. (2006) and Menezes

et al. (1980). In-fact, Di Falco et al. (2006) recommend that it is necessary when analyzing risk,

to go beyond a simple variance assessment so as to capture exposure to unfavorable downside

risk as well.

Due to the intricate and momentous relationship between production risk, farmers‟

livelihoods and economic development, it is therefore pertinent for researchers, policy makers

and other stakeholders to design and/or advocate for appropriate risk management policies that

promote farmer‟s productivity. Despite the importance of maize and maize improved varieties in

Africa, there are no studies that investigate the relationship between production risk, maize

diversity and improved varieties in Africa. This study uses the generalized method of moments

on a stochastic production function to investigate the effects of maize genetic diversity on yield

distribution and its implication on farmers‟ risk exposure in Kenya and Ethiopia. The results of

this study provide insights on how maize genetic diversity and other factors affect yield

37

distribution including risk behavior of farmers. Results will help better evaluate the effectiveness

and efficiency of current and future agricultural policies aimed at reducing the exposure by

farmers to production risk. Central to this paper are risk management instruments that are meant

to smooth consumption of farmers so that they can raise farm productivity and profits.

Farmer risk attitudes

It is well documented in empirical studies that farmers, just like most individuals, are risk averse

in nature. This entails two aspects of their behavior. Firstly, farmers generally avoid events that

have a potential of realizing enormous gains but which also leave them even slightly vulnerable

to losses below some critical level (Menezes et al. (1980)). Secondly, these farmers are averse to

downside risk (Antle (1987), Kim & Chavas (2003) and Menezes et al. (1980)). Accordingly, a

farmer that is risk averse will use more of a risk-reducing factor compared to a risk neutral one

(Fufa et al. (2003)). Put simply, this implies that it is inherent in farmers to avoid being exposed

to unpredictably low production yields. Thus, they may be hesitant to engage in investments that

increase the probability of downside risk even if it may increase returns. Di Falco et al. (2009)

noted that “…farmers have an incentive to grow crop cultivars or varieties that affect positively

the skewness of the distribution of returns (thereby reducing their exposure to crop failure in

drought situations)”. Theoretically, risk and uncertainty affects optimal production decisions and

it is expected that risk averse farmers, ceteris paribus, will chose less-risky inputs, ex-ante, to

cushion themselves against unpredictable events and thus realize lower output, profits and

welfare compared to the risk neutral farmers. Therefore, in this context, any factor that positively

and significantly affects the skewness function will subsequently reduce farmers‟ exposure to

downside risk caused by weather variability.

38

3.2 Review of pertinent literature

Hurley (2010) provides a comprehensive review of empirical studies that investigate risk

attitudes of farmers and their response to production risk in developing countries. He points out

that a number of studies have gone further to quantify the effects of various inputs and

production practices on risk (e.g. Di Falco et al. (2007) and Di Falco et al. (2009)). Di Falco et

al. (2009) used a generalized method of moments of a stochastic production function for Barley

farmers in Ethiopia. The moments included the mean, variance and skewness distribution of

yield. The authors concluded that biodiversity increases both farm level productivity and

variability of output but at the same time reduces the risk of crop failure i.e. downside risk

exposure which is measured by the skewness.

Di Falco et al. (2007) estimated a Just-Pope production function for wheat farmers in

Ethiopia. They found genetic diversity in wheat to have an increasing effect on the mean of the

production function, albeit at a decreasing rate in less degraded lands compared to the more

degraded. Genetic diversity in wheat was also found to be more risk-reducing in cases where

degradation is more severe. In their study on wheat farmers in Sicily, Di Falco et al. (2006) used

the variance and skewness function to capture the risk of crop yields and concluded that genetic

diversity does play an important role in increasing wheat output as well as having a positive

effect on the skewness effect which reduces the exposure to downside risk.

Kumbhakar et al. (2010) use multi-stage non-parametric methods to investigate risk and

risk preferences of rice producers in the Philippines when they face uncertainty in production.

They model uncertainty by assuming that producers‟ maximize expected utility of anticipated

profit. Using this approach they find that farmers in this region are in general risk averse; labor is

risk decreasing while fertilizer, land and materials are risk increasing. Koundouri et al. (2006)

39

found empirical evidence that suggests that farmers choose to adopt the new technology in order

to hedge against production risk (e.g. water shortage). Antle (1987), using experimental data,

employed a moment based approach for profit, revenue and output distributions in order to

estimate the distribution of risk preferences amongst of rice farmers in India. They concluded

that there is a positive and high degree of association between farmers‟ attitudes towards

absolute and downside risk aversion.

Smale (1998) proposed a mean–variance investigation of the role of crop genetic

diversity on wheat production in the Punjab of Pakistan. They found that in rain-fed districts,

genetic diversity is positively related to average yield and negatively correlated to yield variance

However, there are other studies that have found contrasting results. For example, Widawsky &

Rozelle (1998) found a negative relationship between genetic diversity of rice with both the

mean and variance functions. They utilized data from rice farmers in China.

3.3 Methodology

This study assumes that farmers are risk averse and they employ a vector of conventional inputs,

x, such as seed, fertilizers, labor, manure. The production function of maize is represented by the

equation:

9).........(.............................. )h(z; + )f(x; =y

where, y is the output level, Vector x and z, are explanatory variables, while β and α are

parameters and ε is the error term with mean zero. The production risk in maize is represented by

the error term ε, whose distribution h(·) is exogenous, hence out of the farmer‟s control. The

production function f(x;β) relates explanatory variables to the mean or deterministic output while

h(z;α)ε relates explanatory variables to the variance (stochastic) components of the production

40

function. The maize input and output market sector in Kenya are mostly controlled by public

institution which renders the prices in these markets exogenous thus rendering climate variability

as the sole risk source. The production function is assumed to be continuous and twice

differentiable. This paper utilizes a more flexible approach to model risk by assuming that

farmers maximize a function of moments of the maize output distribution. This approach has

been adopted in studies by Di Falco et al. (2009); Groom et al. (2008); Kim et al. (2003);

Koundouri et al. (2006) and Ogada et al. (2010). Thus:

(10) )h(z; = Var(y)

The household incurs production risk since yield is affected by uncertain climatic conditions.

This risk is captured by a random variable, ε whose distribution h(.) is exogenous to the

household‟s actions. Prices in input and outputs markets are assumed to be exogenous thus

rendering farmers as price takers and climatic conditions as the unobservable factors affecting

production. In the presence of heteroscedasticity in f(x;β), OLS estimates are inefficient. There is

a need to transform the function by the predicted variance and perform a Weighted Least

Squares (WLS) which will produce consistent and asymptotically efficient parameter values of

the function. The gain in efficiency attained by this procedure ensures valid and desirable

statistical properties tests that allow a proper assessment of the impact of biodiversity on

production risk.

41

The ith central moment of value of maize output about its mean is given by:

(11) ]})(x;f - ).E{[y( = ii

for I = 1, 2, 3

where ε1 represents the mean or first moment of maize output and is assumed to be increasing

and concave in inputs x. The estimated errors from the mean effect regression are estimates of

the first moment of the maize output distribution. The effects of conventional inputs on the

variance of the yield, ∂ε2/∂xi can either be increasing if greater than zero, neutral if equal to zero

or decreasing if less than zero. As noted in Di Falco et al. (2009) and Kumbhakar (2002), the ith

input would contribute to decreasing downside risk exposure when ∂ε3/∂xi > 0 and in the case of

increasing downside risk exposure when ∂ε3 /∂xi < 0. A distribution has more downside risk

than another if it is more skewed to the left and a pure increase (decrease) in risk results in

spreading (contraction) of the probability weight from the center to the tails of a distribution

(Menezes et al. (1980)). It is rational to expect farmers to be averse to downside risk if they are

decreasingly risk averse and hence consider both the mean and the variance of output in

choosing input levels that will obviously deviate from the optimal input level of risk neutral

producers (Di Falco et al. (2009)). As suggested in Antle (1987) the individual farmers‟ attitude

towards risk may vary over time due to interpersonal variation in preferences or by intrapersonal

variation. However, our analysis uses cross-sectional data and does not capture and time effects.

42

3.4 Survey locations

Data were collected through a combination of formal surveys and participatory techniques in

Kenya and Ethiopia. In Kenya, a sample of 350 farmers were randomly selected and interviewed

by trained enumerators in the Machakosi and Makueni district in Kenya. Figure 3 below shows

the location of the two districts6.

Figure 3: Map of Kenya showing selected survey districts

In the case of Ethiopia, 369 farmers were interviewed from two districts: Adami Tulu Jido

Kombolcha (ATJK) and Adama districts which are in East Shewa zone of Ethiopia7. For full

details on the survey data in Ethiopia, refer to Legese et al. (2010). Figure 4 represent a map of

the survey districts in Ethiopia.

6 For full survey details, refer to Muhammad, L. , Mwabu D., R. Mulwa, W. Mwangi, A. Langyintuo and R. La Rovere 2010. Characterization of maize producing households in Machakos and Makueni districts in Kenya. Kenya. Nairobi: KARI-CIMMYT. 7 Legese, G., M. Jaleta, A. Langyintuo, W. Mwangi and R. La Rovere 2010. Characterization of maize

producing households in Adami Tulu - Gido Kombolcha and Adama districts in Ethiopia. In Nairobi DTMA Country Report - Ethiopia. CIMMYT.

43

Survey districts in each of the districts in the two countries are classified to be in the

medium drought risk zone with a 20-40% probability of failed season in the maize producing

areas. These estimates are based on , maps and techniques outlined by Hodson et al. (2002) and

Thornton et al. (2006), respectively. In the formal survey, structured questionnaire were used

designed to capture demographic, socio-economic characteristics of households and agricultural

production activities. The original surveys were part of the Drought Tolerant Maize for Africa

(DTMA) project implemented by the International Maize and Wheat Improvement Centre

(CIMMYT) in collaboration with the International Institute of Tropical Agriculture (IITA) and

funded by the Bill and Melinda Gates foundation and the Howard G. Buffett Foundation.

Figure 4: Map of Ethiopia showing selected survey districts

44

Based on the reviewed literature, expectations on the sign of the variables‟ used in this study are

illustrated in Table 7. In general, all the conventional inputs in the model are expected to increase

the output hence the mean function. New technologies such as fertilizer and new crop varieties

are aimed at increasing the mean yield and reducing the variation in yield variability and these

are normally tested under controlled experiment stations and results are expected to be different

under farmer conditions (Loehman et al. (1995)). However, with regards to production risk,

inputs may increase or decrease the risk.

Table 6. Summary statistics for Ethiopia and Kenya

Variable

Ethiopia Kenya

Mean Std. Dev. Min Max Mean Std. Dev. Min Max

Fertiliser 17.673 48.37 0 500 30.472 59.806 0 500

Labor 3.537 2.143 0 16 5.288 2.250 0 18

Area 6916.35 11217.53 202 10796.660 12389.820 0 138605

Oxen 5.463 7.376 0 53 3.495 3.623 0 24.75

Altitude 0 0 0 1443.938 153.036 1146 1810

Fertuse 0.238 0.427 0 1 0.594 0.492 0 1

Manure 0.358 0.480 0 1 0.841 0.366 0 1

Biodiversity 0.024 0.055 0 0.394 0.108 0.123 0 0.703

Fragmentation 3.111 1.403 0 10 1.841 0.992 0 4

Age 42.203 14.65 20 95 51.225 15.386 20 90

Pestndzz 0.171 0.377 0 1 0.344 0.476 0 1

Weeds 0.163 0.370 0 1 0.029 0.169 0 1

Seedtype 0.663 0.473 0 1 0.302 0.460 0 1

Source: DTMA survey data

As postulated by Roll et al. (2006), higher labor capacity will have risk reducing effects

on maize output since there will be ample labor capacity to carry out field activities such as

weeding, fertilization, harvesting as well as pest and disease scouting and control. We also

assume that the more land allocated to maize the higher the production risk in cases where the

expansion constrains farmers‟ ability to optimally apply complimentary inputs like fertilizers,

45

chemicals and labor. Fertilizer is hypothesized to positively increase mean output as well as

reduce production risk. However, it is important to point out that if applied beyond the

recommended application rates, the fertilizer may in fact increase production risk. Increase in

draft animal is expected to increase mean yields and reduce production risk. High yielding

varieties supplied through commercial seed markets, are likely to have a positive effect on the

average output of maize, but may increase or decrease variability of output depending on the use

and application of complimentary inputs.

We postulate that HYVs will have a positive effect on the skewness of production and

thereby have a risk reducing effect. However, in cases of traditional varieties that have been

recycled over number years, they may increase production risk because of their susceptibility to

pests and diseases as well as failure to withstand harsh climatic conditions like droughts. Manure

application will increase output as well reducing production risk.

46

Table 7. A priori expectation of regression model and summary statistics of the independent

variables

Variable

Variable description Expected signs

Mean Variance Skewness

Fertilizer Fertilizer use in kgs + +/- +/-

Labor Labor capacity in man days + +/- +/-

Area Area in square meters + +/- +/-

Oxen Number of oxen + +/- +/-

Altitude Altitude in meters above sea + +/- +/-

Fertuse Access to fertilizer (1= access) + +/- +/-

Manure Access to manure (1= access) + +/- +/-

Biodiversity Margalef measure of bio-diversity + +/- +/-

Fragmentaion Number of plots +/- +/- +/-

Age Age of household head +/- +/- +/-

Pestndzz Dummy on farmers' perception on

whether pest/disease affect maize

- +/- +/-

Seedtype Dummy on type of seed used

(1=improved, 0=traditonal)

+ +/- +/-

Weeds Dummy on farmers' perception on

whether weeds affect maize

- +/- +/-

47

3.5 Econometric estimation

We model the effects of maize biodiversity and other relevant factors on the three moments

(mean, variance and skewness) of a stochastic production for small-holder farmers in Kenya and

Ethiopia. The production function is specified with the following inputs: labor (L), land (A),

fertilizer (NPK and UREA), livestock and manure. Altitude, biodiversity (Margalef Biodiversity

index) and age of the HHD are also included in the model. The model is developed according to

the method of modeling of production risk proposed by Just and Pope (1978 and 1979). They

proposed a general stochastic specification of the production function which is divided into two

parts; one that is related to the output level and another that is attributed to the variance of the

output, thus allowing for the inputs to be risk increasing or risk decreasing. Due to the restrictive

nature of the Just and Pope model on moments of higher order, Antle (1983), Antle (1987) and

Antle & Goodger (1984) modified the model to a more flexible, moment based approach that

relaxes restrictions on inputs making it more suitable to analyze responses to interventions in

uncertain environments.

The estimated residuals from the mean regression are estimates of the first moment of

value of maize production distribution. The estimated residuals ε are then squared and regressed

on the same set of explanatory variables as in the first equation. The mean, also known as the

first moment of a distribution, is equal to the expected value of the distribution. The second

moment is the variance which is a measure the dispersion of the distribution. Lastly, the third

moment also known as the skewness is a measure of symmetry of the distribution about its mean.

A negatively skewed distribution (i.e. left-skewness) implies that the mean of the data is less

than the median whilst a positively skewed (i.e. right-skewness) indicates that the mean of the

data values is larger than the median.

48

In general, the first part of the model is treated as an ordinary regression problem and the

method of least squares is used to estimate model parameters and the initial step involves testing

for production risk by performing the White and Breusch-Pagan-Godfrey tests for

heteroscedasticity in order to ascertain the presence of production risk. Once the null hypothesis

(homoscedasticity) is rejected, OLS estimation in this case yields asymptotically inconsistent and

inefficient estimators, albeit unbiased. Dummy variables representing fertilizer usage exhibit

many zero responses and we incorporate this into the model following a method illustrated in

Battese (1997). This method specifies fertilizer usage as β0D + β1ln(NPK + D), where D=1 if the

farmer uses NPK fertilizer and D=0 if the farmer does not use NPK and β0 and β1 are

parameters.

Therefore if the primary focus was on the mean yield of maize, then using a

Heteroscedastic Consistent Estimator (HCE) would lead to efficient and asymptotically

consistent estimates (Greene (2008)). In the presence of production risk, it is standard procedure

to transform the data and re-estimate the production function as a weighted function. In cases

where the model is correctly specified, residuals are assumed as capturing variation of

unobserved factors that the farmer cannot control. The predicted y in the re-estimated model

represents the mean function, while the squared residual represents the variance function.

However, it is worth noting that this may be a signal of omitted variables. Therefore it is

important to test for this. Since in this study we are concerned with the risk structure of

production, there is need to re-estimate the production function with a suitable weighing matrix

in order to obtain consistent, asymptotically normal estimators using flexible functional forms of

the mean, variance and skewness functions.

49

If the tests confirm heteroscedasticity, then it shows that production risk is present and

we need to re-estimate the mean, variance and skewness functions to see how they are affected

by production factors like labor, TLU, Manure and Fertilizer and some socio-economic factors.

As highlighted earlier, due to the risky nature of agricultural production, the variance of

production is assumed to be heteroscedastic and hence follows some functional form

specification. In the case of models that are linear in parameters, such as the Cobb-Douglas,

estimation requires only conventional procedures such as generalized least squares or three-stage

least squares.

Antle (1983) illustrated that in estimating such models, the first step involves running an

OLS and testing for the presence of heteroscedasticity. If the null hypothesis of constant variance

is rejected, then the second stage involves re-estimating the model using a weighted least squares

method which will lead to unbiased, efficient and asymptotically consistent estimators and

predict the mean function. The third stage involves squaring the variance term and running OLS

on the predictors and again predicting the second moment-the variance function. The third

moment, the skewness function, is obtained by running OLS on the cube of the residuals against

the predictors in the first stage. After obtaining the three functions, we use the 3 stage least

squares regression which takes into account for the depended variables which are assumed to be

endogenous.

3.6 Estimation results

Results for Ethiopia

Regression results for Ethiopia are presented in Table 8 and they seem to suggest that

conventional inputs in Ethiopia (fertilizer, land, labor capacity and cattle) all have positive and

50

significant effects on the mean function as hypothesized. Increase in these inputs will increase

the mean output of maize. Improved seed has a positive and significant effect on the mean output

of maize. Age of the farmer, which in this case is used as proxy for farmers‟ experience, is also

positively related to the mean function and the variance of yield. The positive and significant

effect on the skewness function indicates that the use of improved varieties decreases downside

risk. Therefore, a risk-averse farmer will use more of the improved maize seed varieties as

opposed to traditional varieties. With regards to biodiversity, the results show that the use of

multiple varieties significantly increases the mean output. However, they do not have a

significant effect on the variability of yields. Plot fragmentation and use of manure showed a

negative and significant effect on the mean function. An increase in either one of them, ceteris

paribus, reduces the average yield. With regards to the variance function, labor, area, manure and

fragmentation seem to increase the variability of the maize yield. On the other hand, cattle, use

of fertilizer, and experience significantly reduce variance of maize yields. Farmer perceptions

that their plots are infested with pest and disease seem to reduce the mean and increase

variability of maize yields. Farmer perceptions that weeds are a problem have a positive effect on

the mean and variance of maize yields. Fertilizer intensity, labor, area, access to manure,

fragmentation are negatively and significantly related to the skewness function. Cattle and access

to fertilizer have a positive effect on the skewness effect. This implies that they reduce farmers‟

exposure to downside risk and farmers will use more of them.

Biodiversity is positively and significantly associated with the mean and skewness

function which implies that greater variation in maize varieties grown increase the mean output

and also reduces the exposure to downside risk. This result is consistent to that of Di Falco et al.

(2009) in which they concluded that barley farmers in Ethiopia maintain larger number of

51

varieties that support productivity and reduce risk. Even though new maize varieties increase

yields and reduce variability, they are still considered risky because they require complementary

application of fertilizer. Therefore it will be logical for farmers to spread their risk by growing

multiple varieties that include traditional ones in order to secure some level of food security.

Table 8. Regression results of factors affecting the mean, variance and skewness functions in

Ethiopia

(1) (2) (3)

VARIABLES Mean

Function

Variance

Function

Skewness

Function

Fertiliser 0.0400*** -8.05e-05 -0.511***

(0.000568) (0.0416) (8.92e-09)

Labor 0.0172*** 0.189*** -0.136***

(0.000664) (0.0487) (1.04e-08)

Area 0.0444*** 0.629*** -2.367***

(0.000378) (0.0277) (5.94e-09)

Oxen 0.0551*** -0.159*** 0.400***

(0.000407) (0.0298) (6.40e-09)

Fertuse -0.0542*** -0.500*** 2.460***

(0.00176) (0.129) (2.76e-08)

Manure -0.00610*** 0.716*** -2.307***

(0.000675) (0.0495) (1.06e-08)

Biodiversity 0.0132*** -0.00232 0.370***

(0.000383) (0.0280) (6.01e-09)

Fragmentaion -0.0419*** 0.537*** -2.126***

(0.000725) (0.0531) (1.14e-08)

Age 0.0150*** -1.177*** 3.200***

(0.00105) (0.0772) (1.65e-08)

Pestndzz -0.0211*** 0.143** -0.389***

(0.000798) (0.0585) (1.25e-08)

Weeds 0.0389*** 0.155** -2.193***

(0.000843) (0.0618) (1.32e-08)

Seedtype 0.00717*** 0.227*** 1.357***

(0.000644) (0.0472) (1.01e-08)

Constant 1.722*** 3.416*** -11.17***

(0.00367) (0.269) (5.76e-08)

Observations 330 330 330

log likelihood 6017 6017 6017

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity chi2(1) = 5.85**

Source: DTMA survey data

52

Results for Kenya

Regression results for Kenya (Table 9) show that conventional inputs such as fertilizer, labor,

fragmentation, area and oxen all have positive and significant effects on the mean output of

maize while the dummy for fertilizer access is shown to have a negative effect on the mean

function. Age of farmer is shown to negatively affect the mean output. This implies that older

farmers realize reduced average yields compared to younger farmers and there are number of

possible reasons for this. One of them is could be that younger farmers are more amenable and

open to try out new strategies and techniques that may improve their yields compared to the

older farmers. The area under maize, access to fertilizer, and use of manure all increase

variability of yield distribution.

Fertilizer, area and manure all have positive effects on the skewness function suggesting

that risk-averse farmers would use these inputs in order to reduce risk of crop failure. Use of

Improved seeds (Seedtype) increases the mean, variance but is shown to have a negative effect

on the skewness function. This result implies that use of improved varieties by farmers increases

the average maize yields, increases the variability of output and increases the the risk of crop

failure by farmers.

Biodiversity is positively and significantly associated with the mean, variance and

skewness functions. This also implies that growing of multiple maize varieties increases the

average maize yield, its variability and also reduces farmers‟ exposure to risk of crop failure.

Comparative analysis of results in Kenya and Ethiopia

Comparing the results of the two countries, most of the conventional inputs like fertilizer, labor,

area and oxen all have a positive effect on the mean function. Intra-crop diversity in both

53

countries affects the mean and the skewness functions positively. However, the results also

reveal that biodiversity positively and significantly affects the variance function for Kenya but

yet does not have any significant effects for Ethiopia.

With regards to the Seedtype, results for Kenya show that the use of improved seed has

positive effects on the mean and variance functions in both countries. There are however

contrasting results when it comes to the skewness functions in which seed type is positively

affects the skewness function in Ethiopia while in Kenya it has a negative effect. This implies

that access to improved seed will be risk reducing in Ethiopia while in Kenya will have risk

increasing effect to farmers.

54

Table 9. Estimation results on factors affecting the mean, variance and skewness functions in

Kenya

(1) (2) (3)

VARIABLES Mean

Function

Variance

Function

Skewness

Function

Fertilizer 0.0418*** -0.0275 0.695***

(0.00136) (0.0180) (0.00874)

Labor 0.0219*** -0.0338 -3.144***

(0.00304) (0.0401) (0.0195)

Area 0.0874*** 0.226*** 0.671***

(0.00164) (0.0216) (0.0105)

Oxen 0.0134*** -0.00577 -0.233***

(0.000807) (0.0106) (0.00518)

Fertuse -0.101*** 0.409*** -2.840***

(0.00504) (0.0664) (0.0323)

Manure 0.0111*** 0.142*** 1.859***

(0.00299) (0.0395) (0.0192)

Biodiversity 0.00443*** 0.314*** 0.326***

(0.00120) (0.0158) (0.00771)

Fragmentaion 0.0108*** -0.195*** -1.532***

(0.00244) (0.0322) (0.0157)

Age -0.0235*** -0.163*** 2.505***

(0.00380) (0.0501) (0.0244)

Pestndzz 0.0223*** 0.161*** 0.418***

(0.00231) (0.0304) (0.0148)

Weeds 0.0507*** -0.146* 1.938***

(0.00667) (0.0880) (0.0428)

Seedtype 0.0214*** 0.250*** -1.107***

(0.00266) (0.0351) (0.0171)

Constant 1.180*** -0.950*** -12.06***

(0.0171) (0.225) (0.110)

Observations 331 331 331

R-squared 0.962 0.659 0.996

log likelihood 1586 1586 1586

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Source: survey data

55

CHAPTER 4

SUMMARY AND CONCLUSIONS

This study is comprised of two papers that focus on intra-crop diversity and adoption of

improved varieties of maize in sub-Saharan Africa (SSA). Maintenance of Intra-crop diversity by

individual farmers contributes to global crop genetic resources which are classified as

international public goods. It is therefore common that private supply of on-farm diversity falls

short of the socially optimal level of supply. This has been made worse by the advent of new and

improved varieties which farmers may use to substitute diverse multiple varieties. Through intra-

crop diversity, farmers reduce the risk of crop failure in order to adapt to the emergence of new

diseases, pests and changes in climate. The first paper analyzed the factors that affect intra-crop

diversity among maize famers in Kenya. The second paper explored the effects of genetic

diversity on the mean, variance and skewness of the yield distribution and made a comparison

between maize farmers in Ethiopia and Kenya.

We generally find that farms with younger household heads, high education level, high

labor capacity, high number of plots and in areas that experience low rainfall that are associated

with higher intra-crop diversity. Therefore, efforts aimed at promoting breeding programs that

support crop diversity in Kenya should be designed to target farmers that may have similar

socio-economic characteristics.

56

The second paper uses the generalized method of moments on a stochastic production function

and we find that that intra-crop diversity increases the mean, variance and skewness functions for

maize farmers in Kenya and Ethiopia. Results show that maintenance of maize diversity by

farmers increases the average maize yield, its variability and also reduces farmers‟ exposure to

risk of crop failure. These findings confirm the fact that on-farm conservation of genetic

diversity in Ethiopia and Kenya play an important part in reducing the variability and cushioning

maize farmers against unfavorable production risks and uncertainties. Therefore policies

designed to enhance on-farm diversity of maize will lead to more stable yields that enhance food

security in both countries and other countries of similar agro-ecological and socio-economic

settings.

We also find that the use of improved varieties increases both the mean and the variance

in the two countries. However, when it comes to the skewness function, adoption of improved

maize shows a positive effect in Ethiopia while in Kenya the effect is negative. However, we are

not able to make insightful conclusions for policy on the overall impact of farmers‟ use of

improved varieties on risk, due to the contrasting effects of that cultivation of improved varieties

has on the variance and the skewness effects.

One limitation of our study is that we make use of cross sectional survey conducted over

one season surveys for both countries. This limits our analysis since we are not able to

incorporate dynamics pertaining to farmers‟ use of multiple varieties and cultivation of improved

varieties.

57

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63

64

APPENDIX

A. Survey Questionnaire

Drought Tolerant Maize for Africa

Household level survey: Questionnaire for Farmers

65

Questionnaire for Farmers

1. Enumerator: _____________________ 2. Date of interview: __________________

3. Country: _____________________ 4. Region/Province/State: _________________

5. District/LGA: _____________________ 6. Village/Community/PA: _________________

GPS coordinates at the house of respondent

7. Latitude: ______________ 8. Longitude: ____________ 9. Altitude: _____________

A. GENERAL INFORMATION

[The respondent must be the head or de-facto head of the household]

10. Name of respondent: ______________________________

11. Gender of respondent: [1] Male [2] Female

12. Age of respondent (in years): ______________

13. Is the respondent head of the household? [1] Yes [2] No

If NO continue from Q14, BUT if YES, skip to Q18.

14. Name of household (HH) head: ______________________________

15. Gender of HH head: [1] Male [2] Female [3] N/A

16. Age of HH head (in years): ______________

17. Where is the household head? [1] Temporarily away from the house

[2] Absent from home at least 6 months in a year

18. Who is the main decision maker on farming activities? [1] household head [2] Spouse

[3] Children [4] Household head and spouse [4] Household head and children

[5] Spouse and children [6] All members

19. Marital status of HH head: [1] Single [2] Married [3] Divorced [4] Separated [5] Widowed

20. Educational level of HH head: [1] Illiterate [2] Primary sch. [3] Sec. sch. [4] Post sec.

[5] Adult education

66

B. HOUSEHOLD COMPOSITION

21. We are interested in knowing more about the composition of your household (all the people living in the same compound, eating from

the same “pot” and working on the family farm)

Name

Gender

1=F

2=M

Age

in

Years

Relation

to head:

(See Code

below)

Marital

Status

(See Code

below)

Literacy

status

(See Code

below)

Indicate type of

off-income HH

member is

earning

(Code below)

Months

living at

home in

the last 12

months?

Number of

months (in a

year) available

for farm work

Table 21 (Cont.)

67

Name

Gender

1=F

2=M

Age

in

Years

Relation

to head:

(See Code

below)

Marital

Status

(See Code

below)

Literacy

status

(See Code

below)

Indicate type of

off-income HH

member is

earning

(Code below)

Months

living at

home in

the last 12

months?

Number of

months (in a

year)

available for

farm work

0=Head

1=Spouse

2=Parent

3=Child/grand

child

4=Nephew/Niece

5=Son/daughter-

in-law

6=Brother/Sister

7=other relative

0=Single

1=Married

2=Widowed

3=Separated

4=Divorced

0=Minor

1=Illiterate

2=Primary

3=Secondary

4=Post sec

5=Adult

education

0=Petty trading

1=Teaching

2=Masonry/carpentry

3=Nursing

4=Art and craft

5=Driving

6=Fitting mechanic

7=Farm labor

8=Other

9=N/A

68

C. HOUSEHOLD RESOURCES

(We would like to know a little bit about the resources your household owns)

22. What type of dwelling do you live in?

1) Mud hut with grass thatch roof 2) Mud hut with asbestos/iron roof

3) Brick house with grass thatch roof 4) Brick house with asbestos/iron roof

5) Block house with grass thatch roof 6) Block house with asbestos/iron roof

7) Pole and dagga with grass thatch 8) Other (specify) _____________________

69

23. How many of the following assets does the household own and how many did it buy or sell in

2005/06 crop season?

Item purchased Units

owned

(or pairs)

Units

bought in

2005/06

Buyer Units sold

in 2005/06

Seller

Motor vehicle

Motor cycle

Bicycle

Tractor

Tractor plough

Tractor harrow

Draft animals**

Animal plough

Animal harrow

Animal scotch cart

Wheel barrow

Television

Radio

Private well

Private borehole

Water pump

Cultivator

Diesel pumps

Water tanks

Generator

Mobile Phones

Fixed phone

Other:

*Buyer/seller codes: 1) HH head 2) Spouse 3) Parent 4) Sibling 5) Child

6) Other dependent 7) Jointly purchased/sold

** Draft animals = Bullock/oxen/donkeys/horse

70

D. INSTITUTIONAL SETTINGS

(We want to know the different facilities at your disposal within the Village/Community)

24. Are there times you have critical shortage of available funds for agricultural activities?

[1] Yes [2] No

25. If YES, during which months of the year? [1] Jan – Mar [2] Apr – Jun [3] Jul – Sep [4] Oct -

Dec

26. Did you receive any cash and/or input (formal and informal) credit in the 2005/06 crop

season for crop or livestock production or household consumption? [1]=Yes [2] = No

27. If No to Question 26 please say why:

[0] = N/A [1] = No source of credit in vicinity [2] = Did not look for credit

[3] = No collateral to guarantee credit [4] = No collateral to guarantee credit and

No source of credit in vicinity [5] High interest rate [6] Other (specify):_______________________

71

28. If Yes to Q26, provide information on the cash and input credit you received

Item Amount (local

currency)/quan

tity (kg)

Source1 Interest

rate

Form of

repayment2

Was credit

received on

time?

Yes = 1No=2

Production cash credit

Consumption cash credit

Input credit –maize seed (List varieties)

1.

2.

3.

Other seeds (specify)

1.

2.

3.

Input credit- Fertilizer

Basal (e.g., NPK)

Top dress (e.g., urea)

1Source of credit: 0= N/A 2Repayment: 1= Seed

1= Financial institution 2= Grain

2= Money lender 3= Cash

3= Neighbor 4= Other ____

4= Relative

5= NGO

6= Government program

7= Other: __________

72

29. Have you benefited from any of the following governmental and/or non-governmental

organization (NGO) programs within the last two years?

Organization Number of times

you benefited

Benefit package (what

was given to you?)

Benefit package:

1) Food relief

2) Seed relief

3) Fertilizer relief

4) Seed and

fertilizer relief

5) Livestock

breeding stock

6) Other

________________

World Vision International

Action Aid

Sasakawa Global 2000

Catholic Relief Services

Care International

Africare

Government Starter Pack

PAM

World Food Program

Agricultural Dev’t Projects

GTZ

Self Help International

Save the Children (US)

Kulima

Land O lakes

Heifer International

Government Safety Net

Others:

30. Do you belong to any farmers’ associations/cooperatives in your Village/Community?

[1]=Yes [2] = N0

31. If YES, to Question 30 how many years have you been a member? ______________

73

32. During the 2005/06 cropping season did you attend field days/demonstrations organized by

staff of the following organizations?

Organization No. of field days

attended

0=None

No. of field

demonstrations

attended

0=None

Number of

times you

discussed maize

crop

production

0=None

Agricultural Extension Services

Agricultural Research Institute

An NGO (specify)

Seed Company

Cotton Company

Tobacco Company

Other agric. development agency

33. What are your frequent sources of extension messages?

[1] Agric extension staff [2] Extension bulletins [3] News paper [4] Radio

[5] Television [6] Other (specify): _________________________________

34. How many times did you interact with agricultural extension workers on crop and livesock

production in 2005/06 season? _____

74

E. AGRICULTURAL PRODUCTION

(We need to discuss your agricultural production practices beginning with crop production and then livestock production)

E.1 CROP PRODUCTION

35. What is the total size of the farm land you have/own?

Size of plot

Crops grown Tenure system

If rented-in

this year how

much did you

pay?

If rented-out

this year, how

much did you

earn?

If share-

cropped this

year, what %

of harvest did

you pay?

Main water

source?

How long

does it take

you to get to your farm on

foot (minutes,

one way) from the

homestead? Number of

units

Unit of

measure

Plot abandoned

Plot under fallow

Pasture land

Tree crop plot

Plot cropped (1)

Plot cropped (2)

Plot cropped (3)

Plot cropped (4)

Plot cropped (5)

75

Plot cropped (6)

Plot cropped (7)

Plot cropped (8)

Plot cropped (9)

Plot cropped (10)

1=ha

2=acre

3=lima

4=timad

Crops codes

1=local maize

2=improved OPV

3=hybrid

4= Rice

5=Sorghum

6=Pearl millet

7=Finger millet

8=Cowpea

9=Beans

10=Groundnuts

11=Tuff

12=yam

13=Cassava

14=Soybean

15=Other

Tenure codes

1= Own land

2= Land rented in

3= Land rented out

4= Sharecropped

5= Family land

6=Outright

purchase

7=Communal

8=Other

Source codes

1=Irrigated

2=Rain-fed

3=Swamp 4=Water

harvesting

0= <1 minute 1=1-30 2=31-60 3=more than 60

76

36. Approximately how many years do you crop your land before putting it to fallow? _________

37. Approximately, how many years do you fallow a piece of land? _________________

38. Which crop(s) is/are grown following a fallow period?

1=Maize 2=Rice 3=Sorghum 4=Pearl millet 5=Finger millet 6=Cowpea

7=Beans 8=G’nuts 9=Cassava 10=Soybean 11=Tree crop

12=N/A 13=Other

39. Rank the three most important factors that determine how large your cultivated farm should

be in any season (1 = most important, 3=less important)

---- 1) Expected family labor availability ---- 2) Cash availability to hire labor

---- 3) Cash availability to purchase other inputs ---- 4) Current grain prices

---- 5) Expected grain prices after harvest ---- 6) Food needs

---- 7) Availability of seed ---- 8) Other: ______________________

40. How does the last maize season area compare with the previous ones and why?

1) Same 2) Larger 3) Smaller

Reason

1)Rainfall pattern unchanged

2) Pests and diseases

3) Weeds

4) Yield

5) Market price

6) Seed quantity unchanged

7) Seed price

8) Labor force unchanged

9) Cash for inputs unchanged

10) Land size unchanged

11) Not interested in expanding

12) Other

Reason

1) Enough seed

2) Enough labor

3) Enough cash to buy inputs

4) Enough land to expand

5) Interested in expanding

6) Better rainfall

7) Other

Reason

1) Inadequate seed

2) Reduced labor force

3) Reduced cash for inputs

4) Reduced land available

5) Interest in intensive

farming

6) Poor rainfall

7) Floods

8) Pests and diseases

9) Other________________

77

41. Give the quantities of maize varieties1 you purchased in 2005/06?

Name of maize

variety

1 = Local

2 = Imp.

Seed

quantity

purchased

(kg)

Month of

purchase

Amount

paid (LC)

Transport

charge for

seed (per

kg)

Name

of

seller

Major season

Minor season

Note: Varieties mean both OPVs and hybrids (Hybrids and OPVs will be discussed later)

42. What quantities of the following inputs did you purchase in the 2005/06 season?

Input Quantity

purchased

Month of

purchase

Amount

paid

Transport

charge for

input

Name of

seller

Major season

Other cereals (kg)

Other legumes (kg)

Tubers (number)

Root cuttings

Basal (NPK) fertilizer

(kg)

Top dress (urea)

fertilizer (kg)

Herbicides (l)

Insecticides (l/kg)

Manure ( )

Others ( )

78

Minor season

Other cereals (kg)

Other legumes (kg)

Tubers (number)

Root cuttings

Vegetable seeds (kg)

Basal (NPK) fertilizer

(kg)

Top dress (urea)

fertilizer (kg)

Herbicides (l)

Insecticides (l/kg)

Manure ( )

Others ( )

79

43. What quantities of seed did you plant in the 2005/06 crop season?

Crop Quantity of seed

(kg) planted in

major season

Quantity of seed (kg)

planted in minor

season

Local variety of maize

Improved variety of maize

Rice

Beans

Cowpea

Soybean

Groundnuts

Yam setts

Cassava cuttings

Other crop ( )

80

44. What quantities of the following fertility inputs did you apply to the following crops in 2005/06

crop season?

Crop NPK (basal)

(kg)

SA/Urea (top-

dress) (kg)

Animal

manure

(carts)

Other

(____________)

Major season

Local Maize

Improved maize

Millet

Sorghum

Rice

Other crops

Minor season

Local Maize

Improved maize

Millet

Sorghum

Rice

Other crops

81

45. What were the main sources of labor for the various field operations on your maize fields

(Please indicate the proportions used during the past season(s)

Operation Family Hired Communal Shared crop

labor

Land preparation (Manual)

Land preparation (Draught)

Land preparation (Tractor)

Planting

Weeding

Fertilization

Harvesting

Threshing

82

E2. MAIZE PRODUCTION

46. Do you know the different types of maize known as improved OPV and hybrid? [1] Yes [2] No

47. List all the maize varieties you know; indicate if you grow them or not and in which season

1 2 3 4 5 6 7 Name of variety Say whether

it is a

1=Hybrid

2=Improved

OPV

3=Landrace

4=Don’t know

How many

years have

you grown

it?

(0=never

grown it)

If grown

how long

do you

recycle the

seed? (N/A

= never

grown it;

0=No

recycling)

If grown, did you

grow it in

2005/2006?

No=0

Yes=1

(N/A = never

grown it)

If NO in

column 5

say why1

(N/A =

never

grown it)

What is

your

perception

of drought

tolerance

of the

variety

(1=lowest,

5=highest)

Major Minor

1Why no longer growing: 1) Poor grain yield 2) Poor grain storage 3) Poor grain price

4) Expensive seed 5) Poor food taste 6) Seed not available

7) Other (specify)

__________________________________________________

83

48. Which maize varieties have you planted over the years? [List in order of importance in terms

of planted area, and recall as good as possible the yield, especially if it failed]

Crop

season

Variety Quantity of seed

planted (kg)

Area planted (ha) Production (kg)

Season 1 Season 2 Season 1 Season 2 Season 1 Season 2

2005/06

1

2

3

4

5

6

2004/05

1

2

3

4

5

6

2003/04

1

2

3

4

5

6

2002/03

1

2

3

4

5

6

2001/02

1

2

3

4

5

6

84

Q48:

Varieties:

1. ____________________________________

2. ____________________________________

3. ____________________________________

4. ____________________________________

5. ____________________________________

6. ____________________________________

7. ____________________________________

8. ____________________________________

9. ____________________________________

10. ____________________________________

11. ____________________________________

12. ____________________________________

13. ____________________________________

14. ____________________________________

15. ____________________________________

16. ____________________________________

17. ____________________________________

18. ____________________________________

19. ____________________________________

20. ____________________________________

21. ____________________________________

22. ____________________________________

85

49. Choose your best OPV, hybrid and landrace and compare them in terms of the following

attributes

Known Best Improved OPV: ______________Best hybrid: ________________Best Landrace: __________

Best Imp.

OPV

versus

Best

Hybrid

Best Imp.

OPV

versus

Best

Landrace

Best

Hybrid

versus

Best

Landrace

1 Seed price (same=0; cheaper=1; more exp=2)

2 Seeds availability

(same=0; readily=1; not readily=2)

3 Market price for grain

(same=0; lower=1; higher=2)

4 Disease tolerance (same=0; more=1; less=2)

5 Field pests resistance (same=0; more=1; less=2)

6 Storage pests resistance (same=0; more=1; less=2)

7 Early maturing (same=0; earlier=1; later=2)

8 Yield potential (same=0; highest=1; least=2)

9 Performance under low soil fertility

(same=0; highest=1; least=2)

10 Performance under low soil moisture

(same=0; highest=1; least=2)

11 Resistance to lodging(same=0; better=1; worst=2)

12 Cob size (same=0; largest=1; smallest=2)

13 Grain size (same=0; largest=1; smallest=2)

14 Palatability (same=0; more=1; less=2)

15 Poundability (same=0; easier=1; difficult=2)

16 Nshima/palp/tuwo quality (same=0; better=1;

worst=2)

17 Maputi/chiwaya quality (same=0; better=1; worst=2)

18 Porridge quality (same=0; better=1; worst=2)

19 Injera quality (same=0; better=1; worst=2)

20 Roasted green maize palatability (same=0; better=1;

worst=2)

21 Samp quality (same=0; better=1; worst=2)

86

50. List the three most important characteristics you desire in your ideal maize variety? ___ ___ ___

[1] Yield potential [2] Pest/disease resistance [3] Performance under poor soils

[4] Performance under poor rainfall [5] Superior storage pest resistance

[6] Cob size [7] Grain size [8] Cob filling

[9] Plant height [10] Yield stability [11] Resistance to lodging

[12] Early maturity [13] Drought tolerance [14] Number of cobs per plant

[15] Husk cover [16] Grain color [17] Other ( )

51. Have you ever planted any improved variety of maize during the last five years?

[1] Yes [2] No

52. If NO to question 51, why have you never planted any improved maize variety?

[1] N/A

[2] Not heard of any improved varieties

[3] Can’t get the seeds to buy

[4] No money to buy the seeds

[5] Satisfied with the local varieties I plant

[6] Simply not interested in experimenting with new varieties

[7] Not seen any demonstration to show superiority of improved varieties

[8] Other: __________________________________________________

53. How many years ago did you plant an improved variety of maize for the first time? _________

54. What was the name of the improved variety you planted for the first time? _________________

[See Question 48 for list of varieties]

87

55. What was the source of information about the improved variety?

[1] Fellow farmer [2] Local retail shop

[3] Ministry of Agric. Extension agent [4] Seed company staff ___________

[5] Staff of a Research Institute [6] NGO (specify) _______________

[7] Radio [8] Television

[9] Newspaper [10] Other (specify) ______________

56. What was your source of seed?

[1] Saved from last season’s harvest [2] Free seed from a neighbor

[3] Free seed from government program [4] Free seed from an NGO program

[5] Purchased from a Seed company [6] Purchased from NGO

[7] Purchased from Ministry of Agriculture [8] Purchased from another farmer

[9] Purchased from market [10] Purchased at a seed fair

[11] Purchased from an agro-dealer [12] Other: ________________________________

57. What was the reason for your choice of seed source?

[1] N/A [2] Cheaper source [3] Available source

[4] Lack of cash [5] Near homestead [6] Free source

[7] Other: _________________________________________________

58. Have you been planting improved maize varieties since then (continuously)? [1] Yes [2] No

[If “Yes”, go to Question 64, but if No continue with Queston 59]

59. If No to Question 58, how many years ago did you discontinue planting? ________________

88

60. If No to question 58, why did you discontinue planting?

[1] N/A [2] Preferred seed no longer available

[3] No cash to purchase seed [4] Not satisfied with performance of the varieties

[5] Depressed prices [5] Other: ___________________________________

61. After discontinuing when did you resume planting any improved maize variety? _________

62. Which variety did you plant when you resumed planting? _______________

[See Question 48 for list of varieties]

63. Why did you resume planting improved maize varieties?

[1] N/A [2] Improved varieties satisfied my demand

[3] Local varieties performing too poorly [4] Convinced by extensionist

[5] Other: ______________________________________

64. Did you plant an improved variety in the last cropping season? 1) Yes 2) No

65. If YES to Q64, which variety did you plant? __________________

[See Question 48 for list of varieties]

66. What was the source of information about the improved variety that you planted last

season?

[1] Fellow farmer [2] Local retail shop

[3] Ministry of Agric. Extension agent [4] Seed company staff ___________

[5] Staff of a Research Institute [6] NGO (specify) _______________

[7] Radio [8] Television

[9] Newspaper [10] Other (specify) ______________

67. What was your source of seed?

[1] Saved from last season’s harvest [2] Free seed from a neighbor

[3] Free seed from government program [4] Free seed from an NGO program

89

[5] Purchased from a Seed company [6] Purchased from NGO

[7] Purchased from Ministry of Agriculture [8] Purchased from another farmer

[9] Purchased from market [10] Purchased at a seed fair

[11] Purchase from an agro dealer [12] Other: ________________________________

68. What was the reason for your choice of seed source?

[1] N/A [2] Cheaper source [3] Available source

[4] Lack of cash [5] Near homestead [6] Free source

[7] Other: _________________________________________________

69. List three factors you consider when selecting maize varieties to plant

[1] High yield potential [2] Disease/pests resistance [3] Drought resistance

[4] Resistance to storage pests [5] Maturity period [6] Husk cover

[7] Good performance on poor soils [8] Number of cobs per plant [9] Cob size

[10] Ease of poundability [11] Taste of meal [12] Cost of seed

[13] Other ________________________________________

70. If you did not purchase maize seed in the 2005/06 season at all, say why not

[1] N/A [2] No cash to purchase seed

[3] Could not obtain preferred seed [4] No seed retailer within locality

[5] Satisfied with the seed stock I have [5] Other (specify):_____________________

90

E.3 LIVESTOCK PRODUCTION AND MARKETING

71. List the livestock you have at home or on your farm

Livestock

Number owned

(now)

Total value

(local curr)

During the last 12 months, how many

were…

consumed

Number

sold

Number

purchased/

received

Number

Cows – Local

Bulls – Local

Young bulls-Local

Heifer –Local

Calves –Local

Cows – Improved

Bulls – Improved

Young Bulls - Improved

Heifer –Improved

Calves –Improved

Goat – Local

Pigs

Sheep

Transport animals

Chicken – Local

Chicken –Improved

Other:______________

F. PERCEPTIONS ON RISK

72. We would like to find out about your crop profitability and riskiness

Crop

Rank the

profitability of

each of these

crops on the scale

of

1. Most profitable

2.

3.

4.

5. Least profitable

0. No experience

What is the

trend in

profitability?

1. Increasing

2. Constant

3. Decreasing

What are your plans

to improve

profitability?

1. Increase

production

2. reduce costs

3. grow profit. crops

4. Diversify

5. other

Rank in order

of importance

the crops (list

in col. 1) that

suffer most

from drought

stress

1=most

important,

2=next, etc

What do you do in

case of crop failure?

1. Sell some assets

2. Assets remain

unaffected

3. More assets are kept

What happens to

your assets such as

livestock if your crop

yield increases?

1. Sell some assets

2. Assets remain

unaffected

3. More assets are

kept

Local land race

Improved OPV

Hybrid maize

Millet

Sorghum

Beans

Groundnuts

Cowpeas

Teff

Yam

Cassava

92

73. How many years out of 10 do you experience crop failure due to drought? _________________

93

74. Did you have to sell assets (e.g. livestock, house, land…) this year because of difficulties? 1= Yes 2= No

75. If the answer to the above question is “yes”, give reason: [1] To buy food [2] To pay debts (e.g. on credit), [3] To pay

taxes, [4] Family events [5] other (specify): __________________ [6] Not applicable (if answer is No)

76. How will you change your crop area given changes in grain price, yield, and fertilizer price and credit availability*?

If

Price less

than normal

Price

higher

than

normal

Yield less

than

normal

Yield higher

than normal

Fertilizer is

available and

affordable

Fertilizer less

available and

unaffordable

Credit is

readily

available and

affordable

Local land race maize

Improved OPV maize

Hybrid maize

Sorghum

Millet

Groundnuts

Beans

Teff

Yam

Cassava

*Codes: How will you change your crop area? [1] Decrease [2] Same area [3] Increase area

77. What are your crop production risk coping strategies?

Crop

Rank how risky the

following are in

terms of yield

fluctuations?

1. Most risky

.

.

.

6. Least risky

What is the most important

strategy you use to reduce or

eliminate production (yield) risk of

each crop?

1. agric. diversification

2. non –agric. diversification

3. asset accumulation

4. participate in NGO/gov’t

programs

5. Other (specify)

Where do you

access information

on production risk of

each crop?

1. Extension officer

2. other farmers

3. NGOs

4.Radio/newspaper

5. Field days

Local land

race maize

Improved OPV

maize

Hybrid maize

Millet

Sorghum

beans

Groundnuts

Cowpeas

Teff

Yam

Cassava

95

78. We are interested in finding out your perceptions about output price (or marketing) risk

Crop

Is the selling

price for this

crop an

important

factor in

determining

how much of

the crop you

sell or not?

1. Yes

2. No

How will you

change your

maize sales if

the selling

prices of these

crops are higher

than normal?

1. Less

2. Same

3. More

Which crop would

you sell more or less

(given the change

in col 3)

0 = N/A (col. 3 = 2)

1 = maize

2 = millet

3 = sorghum

4 = beans

5 = groundnuts

6 = cowpea

7 = other (specify)

How would

your fertilizer

and other input

use change if

the selling price

was attractive

for this crop?

1.Increase

2. Same

3. Decrease

Would you

acquire

more credit

if the selling

price was

attractive

for this

crop?

1. Yes

2. No

What

happens to

your assets

such as

livestock if

your crop

prices

decrease?

1. Sell some

2.

Unaffected

3. Keep

more

What

happens to

your assets

such as

livestock if

your crop

prices

increase?

1. Sell some

2.

Unaffected

3. Keep

more

Local land race

Improved OPV

Hybrid maize

Millet

Sorghum

Beans

Groundnuts

Cowpeas

Yam

Cassava

Teff

96

79. We would like to know about your price risk coping strategies

Crop

Rank how risky the

following crops

are in terms of

selling price

fluctuations?

1. Most risky

.

.

.

5. Least risky

What is the most important

strategy do you use to reduce

or eliminate price risk?

1. asset accumulation

2. participate in NGO/gov’t

programs

3. Forward contracting

4. Informal insurance

5. Other ( specify)

0. N/A

Where do you

access information

on price risk?

1. Extension officer

2. Other farmers

3. NGOs

4. Radio

5. Newspaper

6. Field days

7. Extension/farmers

8. Radio/Newspaper

9. Other

combinations

10. Other

Local land race

maize

Improved OPV

maize

Hybrid maize

Millet

Sorghum

beans

groundnuts

Cowpeas

Yam

Cassava

Teff

Other

97

G. AGRICULTURAL MARKETING DECISIONS

80. How did you dispose of your crops harvested in the 2005/06 season (kg/tubers)?

Quantity

harvested

Quantity

Consumed

Quantity

Sold

Quantity

Given out

as gift

Quantity

reserved as

seed for

next season

Quantity

loss due to

handling

Local land

race maize

Improved

OPV maize

Hybrid

maize

Millet

Sorghum

Paddy rice

Cowpeas

Groundnuts

Teff

Yam

Cassava

81. When do you sell your grains?

Quarter of the year Quantity sold Place of sale* Av. Price per unit Buyer**

Maize

Soon after harvest

Six months after

harvest

Just before planting

Other cereals

Soon after harvest

Six months after

harvest

Just before planting

*Places codes: [1] At home [2] In a market [3] Market cooperatives

**Buyer codes: [1] Trader [2] Middlemen [3] Established agent [4] Marketing co-ops [5] Millers

98

82. Who fixed the prices of grains you sold in 2005/06 season?

[1] N/A [2] Yourself [3] The buyer [4] Government

83. If prices were fixed by you, say how you determined them

[1] N/A [2) I used prices in neighboring markets

[3] I used published prices in the news papers [4] I used prices announced on the radio

[5] I used cost of production [6] Other (specify): ________________________

84. List the problems you encounter during storage of grain/seed and how you try to solve them

Crop Major storage problem* Quarter of

year when

problem is

very serious

What do you do to combat the

problem?

Maize grains

Maize seeds

Other cereals

grains

Other cereals

seeds

Cowpea grains

Cowpea seeds

Other Legumes

grains

Other Legumes

seeds

*Storage problems: [1] Weevils [2] Rats [3] Moulds [4] Other (specify)

Solution codes: [1] Early sales

99

H. INCOME AND EXPENDITURE PROFILE

85. What are the sources of income for your household in 2005/06?

Category Amount (local

currency

Category Amount (local

currency

Crops (grains/seeds) sales Paid employment

Fruits and vegetables sales Self employed

Livestock/fish sales Remittances

Petty trading Other (specify)

86. Approximately how much did the household spend on the following items in 2005/06?

Expenditure category Amount

(in local currency)

Food and beverages

Staple foods/snacks

Tobacco/Alcohol

Other expenses

Educational fees

Medical expenses

Clothing

Fuel - wood, paraffin, etc

Remittances to relatives

Social contributions

Miscellaneous (bicycle repairs, gifts, etc)

100

I. LIVELIHOODS AND POVERTY

87. Livelihood outcomes: what does the household seek to do to improve its livelihoods?

Type of typical livelihood outcomes

Rank importance Specify the type of

action sought

Increase agricultural production

Reduce agricultural production risk

Reduce marketing risk

Increase food security

Improve health status of members

Increase volume of household assets

Increase education level of household members

Increase land ownership

Improve its social status

Increase its income / Reduce income risk

Increase job opportunities / earn wages

Get out of agriculture

88. What are the three most serious threats for livelihoods of your household? (e.g., droughts,

food insecurity, etc.)

[1]--------------------------------- [2] --------------------------------- [3]---------------------------------

89. What are the three most serious constraints for improving the livelihoods of your household?

(e.g., production, output marketing, input markets, health, soil conditions, transportation, etc.)

[1]--------------------------------- [2] --------------------------------- [3]---------------------------------

90. Did all members of your household have enough and adequate food in the last year?

1= Yes 2= No

91. If No to Q90, for how many months didn’t the household have enough adequate food?

________________ Months

92. If NO to the Q90, during which month(s) wasn’t there enough and adequate food this year?

101

93. What are the most important coping mechanisms against food shortage in your household?

[1] Reduced frequency of food intake [2] Withdrawing children from school

[3] Reducing other expenditure [4] Selling small animals

[5] Selling cattle [6] Selling farm equipments

[7] Selling other assets [8] Working more off-farm

[9] Working at Food-for-Work [10] Receiving food aid

[11] Other (specify): _______________________________________________

94. Has your household been affected by a serious shock* in the last 10 years?

Specific shocks Rank the five most

serious shocks

(1=most,

5=least important)

Indicate in

which year it

occurred out

of the last 10

Has this risk/shock

affected maize

directly? (1=Yes,

2=No)

Drought

Too much rain or flood

Land slide

Frost or hailstorm

Plant pests and diseases

Livestock diseases

Destruction of crops by animals

Dangerous weeds

Large increases in input prices

Large drop in maize prices

Large drop in wheat prices

Large drop in yam prices

Large drop in cassava prices

Large drop in other prices

Loss of farm land

Death or loss of livestock

Death of breadwinner or wife

Illness/disability of

breadwinner/wife

Theft of property (other assets)

102

Q94 (Cont.)

Burning of property (or arson)

Household’s breakdown

Erratic rainfall

Birds

Conflict

Other______ _________

Risk/shock on livestock:

___________

Risk / shock on off-farm

income____

* An event that led to a serious reduction in the household’s asset holding, and/or substantial

income fall resulting in a significant reduction in consumption

End of interview: Thank you for your cooperation