Post on 10-Mar-2018
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
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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
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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
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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)
__________________________________________________
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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