




































In ternationa l
Scholars
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African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 7 (1), pp. 001-012, January, 2019. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s)                                           Author(s) retain the copyright of this article. 
 
 

 

Full Length Research Paper 

 

Relative importance of common bean attributes 
and variety demand in the drought areas of 

Kenya 

 
Enid Katungi1*, Louise Sperling2, David Karanja3, Andrew Farrow1 and Steeve Beebe4

 
 

1
Centro Internacional de Agricultura Tropical (CIAT) National Agriculture Research laboratories, Kawanda, P. O. Box 

6247, Kampala, Uganda. 
2
Centro Internacional de Agricultura Tropical (CIAT-AFRICA), Arusha, Tanzania. 

3
Kenya Agricultural Research Institute (KARI), P. O. Box 340, Machakos, Kenya. 

4
Centro Internacional de Agricultura Tropical (CIAT) Cali, Colombia. 

 
Accepted 05 September 2018 

 
The analysis assessed the relative importance of production and consumption attributes to different wealth groups 
of households and tested the effect of attribute preference and that of other factors on common bean variety demand 
in the drought areas of Eastern Kenya. Variety demand was conceptualized within the agricultural household 
framework and attributes were incorporated into the model according to the Lancaster (1966) consumer theory. 
Empirical analysis was based on primary data collected from two districts of Eastern Kenya using the stated 
preference and revealed preference methods. A factor analysis was used to cluster a set of common bean variety 
attributes that are highly preferred by households into those related to consumption flavour and yield related 
characteristics. The effect of consumption and production attributes and those of other factors were estimated 
through applying ordinary least squares regression. The study findings reveal that varietal adaptation to 
environmental stresses should also strive to reduce the cooking time as well as enhance the keeping quality and 
grain colour for better benefits to the poor but trade-offs are feasible. Prioritizing the improvement of production 
attributes as a short term goal seems an efficient strategy when multi-attribute based breeding is a long process. 

 

Key words: Common bean, relative importance of variety attributes, variety demand, drought areas, Eastern Kenya. 

 
INTRODUCTION 

 
Common bean (Phaseolus vulgaris L.) is an important 
source of protein for many households in Kenya, but its 
production has not kept pace with demand. In 2007, 
production was about 417,000 metric tons while demand 
was estimated at 500,000 metric ton (FAOSTAT, 2010). 
The supply deficit is attributed to the severity of 
biophysical stresses (such as climatic variability, insect 
pests and diseases; declined soil fertility) that maintain 
productivity at less than 25% of potential yield (Odendo et 
al., 2004; Beebe, Personal Communication).  

The National Agricultural Research Institute of Kenya in 
partnership with Centro Internacional de Agricultura 
Tropical (CIAT) has been conducting researches to  
 
 
 
*Corresponding author. E-mail: e.katungi@cgiar.org. Fax:  
+256(0)414_567635. 

 
 
 

 
produce common bean germplasm that is well adapted to 
the environment. These efforts are still continuing and new 
approaches, such as marker selection, are being exploited 
to meet the farmers‟ needs. While these advances in plant 
breeding have made it possible to develop crop varieties 
with multiple attributes to overcome a range of biotic and 
abiotic constraints without compromising the desirable 
qualities, the process of multi-attribute based breeding 
approach can be often complex, take long to complete and 
provide no guarantee that an ideal variety will be found 
(Bellon, 1996). Therefore, it is important for breeders to 
understand the important variety attributes that drive planting 
decisions in order for them to set breeding priorities.  

The objective of this study was to assess the relative 
importance of common bean attributes to different groups 
of farmers and their contribution to variety demand. 
Sperling et al. (1993) found that common bean variety 

file:///C:\Users\user\Documents\REPUBLICATION\AGRICULTURAL%20SCIENCES\AppData\Local\Temp\www.internationalscholarsjournals.org


  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Figure 1. Predicted and observed amounts of rainfall  (September-November), 1994 to 2003.  
Source: Katumani Meteorological Station. 

 
 

 

choice varies among farmers within a given location 
according to their production conditions, objectives and 
preferences. Their research influenced the integration of 
participatory variety selection into the breeding process. 
However, the information from the participatory variety 
selection regarding the specific consumption and 
production attributes that influence the choice of common 
bean varieties has not been able to provide precise 
weights among a string of desired characteristics. Little is 
known regarding the connection between land allocation 
to common bean varieties (that is, variety demand) and 
relative importance of variety attributes (such as 
consumption or risk reducing characteristics). The paper 
assesses the relative importance of different common 
bean variety attributes to different groups of households 
and tests their effect and that of other factors on the 
variety demand. 
 

 
MATERIALS AND METHODS 
 
Study area and data sources 

 
The study was carried out in Mwala and Kitui districts of Eastern 
Kenya, one of the major bean-producing regions in the country. 
Mwala and Kitui districts are neighbouring towns and are located 
about 50 to 150 km from Nairobi City, on a national road connecting 
Nairobi and Mombasa towns with favourable conditions for the 
marketing of agricultural produce. The areas are elevated between 
1000 to 1500 masl. Highlands above 1500masl exist but are rarely 
used for agriculture because they are either arid or unsuitable for 
animal power technology. The soil types in Eastern Kenya are 
predominantly sandy and murram with patches of black cotton soils 
(vertisols) in poorly drained areas (Mwita Rukandema et al., 1981).  
The area receives a total rainfall of about 800 to 1000 mm per year 
but the records at Katumani Meteorogical Station of Kenya 
Agriculture Research Institute (KARI) indicate that rainfall has 
generally been lower than 800 mm in the last ten years (Figure 1). 
Rainfall variability has also been acute, resulting in famine and 

 
 
 
 
repeated deliveries of food and seed aid (Sperling, 2002). Common 
bean is a short season crop that contributes significantly to food 
and nutrition in the study area. It is managed mainly for subsistence 
requirements but surpluses can be sold in the market.  

The data used in this study were collected for tropical legumes 
project of CIAT/ICRISAT/IITA implemented in 2007 to 2010 in 

South Asia and Sub-Saharan Africa
1
. Mwala and Kitui Districts 

were chosen purposively for the baseline studies on common bean 
because the two districts experience high probability of rainfall 
failure (about 60%), and have high levels of poverty for which the 
project was designed. Geo-referenced maps were used to guide in 
the selection of districts. 

 

Conceptual framework and model specification 
 
The theory of agricultural household (Singh et al., 1986; de Janvry 
et al., 1991; Sadoulet and de Janvry, 1995) was used to derive 
variables used in the econometric analysis. Variety consumption 
attributes were incorporated into the utility function according to the 
consumer theory of Lancaster (1966) also used by others 
(Edmeades 2003, Wale and Mburu, 2005). In the consumer theory 
of Lancaster (1966), the utility derived from the consumption of a 
good is specified as a function of the intrinsic attributes supplied by 
the good rather than the good itself. The specification of intrinsic 
properties of a variety as arguments of a utility function and 
application of an agricultural household model allows for the 
inclusion of variety consumption attributes in the econometric 
estimation of the variety demand.  

Following the two theories, the agricultural household is assumed 
to maximize utility derived from the intrinsic consumption attributes 
of common bean embodied in vector (ϕ c ), the purchased good (G)  
 
 
1 The goal of the project is to enhance the productivity of legumes (i.e common 
bean, pigeon pea, chick pea, ground nuts, cow pea, and soya bean) for 
improved livelihoods of the poor households in drought prone area. It is 
implemented jointly by International centre for Tropical agriculture (CIAT), 
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) 
and International Research and International Institute of Tropical Agriculture 
(IITA) and in collaboration with NARS in participating countries of East and 
Southern Africa and Asia with funding from the Bill and Melinda Gates 
foundation.

 



 
 
 

 
and home time (h). The household chooses the amount of common 
bean to consume from different varieties it produces (V), the 
purchased good and home time subject to income (W), production 
technology (Q), and time endowment (T). Utility is maximized given 
the household preferences, which, in turn, depend on the  

socioeconomic characteristics of the household ( Φ HH  ) and 
 
market conditions ( Φ M ). The theoretical model can be expressed 

as: 

 

MaxU ( x(ϕ 
c
 ), G, h | Φ HH , Φ M  ) 

 

q  f (v(ϕ 
p
 ), l | Φ F , Φ M , 

A) T − l − h  0 
k 

∑vi    1 
i 1  

xi   ≥ 0, qi , vi   ≥ 0 

 
The total amount of common bean consumed (x) can be 
distinguished according to the intrinsic and extrinsic attributes of 
common bean varieties such as taste, cooking time, grain size and  

colour ( ϕ 
c
 ) they possess. The amount consumed of each variety 

 
can vary across households but the levels of intrinsic properties 
each possesses are fixed from the perspective of the household.  

Full income in a single decision making period is composed of 
the net farm earnings (profits) from crop production and income that 
is exogenous to the season‟s crop such as stocks carried over, 
remittances, pension and other transfers from the previous season. 
For goods that are not traded, the prices that govern the choices of 
the household are endogenous to the household, determined by 
internal supply and demand for the good, expressing the 
household‟s valuation of the good. Shadow prices are affected by 
the costs of transacting in the markets influenced by market and 
household characteristics (de Janvry et al., 1991).  

The production technology constraint establishes the output-input 
technical relationships and the production margins. Common bean 
in the study area is produced by combining labour and seed of 
specific varieties on land that is fixed in a single cropping season. 
The choice and allocation of the common bean area to a specific 
variety is influenced by the decision maker‟s perception of the 
variety agronomic attributes such as maturity period, drought 
resistance, yield, tolerance on poor soils or growth habits,  

expressed in vector, v(ϕ 
p
 ) . The demand for variety specific 

 
agronomic attributes emanates from the need for farmers to 
maximize returns from production as well as stabilize income from 
common bean. The land constraint circumscribes the land area 
allocated to common bean, which is fixed to one for every 
household. The household can choose to allocate all common bean 
area to one variety or simultaneously plant multiple varieties if 
certain attributes are unique to a particular variety. Varieties planted 
can vary across households and a variety specific corner solution is 
 
possible. The vector Φ F denotes farm characteristics. The time 

constraint captures the total time available to production and home 

activities.  

The vector of market conditions ( Φ M  ) captures

 the  role of 
 
market imperfections in variety demand. Particular households can 
choose to be self sufficient in common bean variety and/or its seed 
if faced with wide price bands (Sadoulet and deJanvy, 1995).  

Common bean markets in Kenya are characterized by high price 
fluctuations with the price of varieties those are well adapted 

  
  

 
 

 
to the fluctuations with the price of varieties those are well adapted 
to the communities more likely to increase by two fold at planting 
time.  

Common bean is bulky and markets for grain and seed are 
located far away from the farming communities, with implications of 
high transport costs. Households differ in their abilities to overcome 
transport costs even when located in the same geographical area. 
Majority of the households produce seed on-farm and separate it 
from grain harvest. The family is the main source of labour used in 
common bean production. Thus, for many households, production 
decisions are likely to be influenced by the endogenous shadow 
prices rather than the exogenous market prices.  

Kuhn-Tucker conditions are used to derive optimal demand of 
varieties. Given that the conditions are met, the following reduced 
form equation defines the optimal share of common bean area 

allocated to variety i . 
 

v 
*
 i    v(ϕ 

c
 ,ϕ 

p
 , 

p a , p g , I , Φ HH , Φ F , Φ M ) (2) 
 

        

 
Equation 2 is the basis of the econometric estimation. The share of 

common bean area allocated to variety i is derived as a function of  

variety consumption and production attributes (ϕ 
c
 ,ϕ 

p
 ) , the 

 

socio-demographic  characteristics  of  the  household  ( Φ HH  )  and  

characteristics of the farm ( Φ F ) and market  

characteristics ( pa , p g , Φ M  ) . 
 

The impact of consumption and production attributes will depend 
on distribution of these attributes in the varieties selected for 
analysis. A positive relationship is hypothesized for varieties with 
respective high supply of the attribute. For example, varieties with 
high supply of consumption attributes will be highly demanded by 
those households who demonstrate high preferences for 
consumption attribute while low supply of such attributes will result 
in low demand for the variety. The same relationship is expected for 
the production attributes.  

Household characteristics such as household composition and 
assets are expected to influence crop variety production decisions 
through their effect on consumption demand and risk preferences. 
High dependency ratio is associated with the vulnerability of the 
household and this may increase the risk of starvation and hence 
favour demand for crop varieties that are risk reducing. Dependants 
in form of children may also have special consumption 
requirements, which could stimulate demand for varieties that meet 
special consumption needs. Household wealth enhances the 
household‟s ability and willingness to take risks that could favour 
the demand of the risk increasing varieties.  

Farm characteristics include the scale of operation and diversity 
of soil types on the farm. The effect of scale on variety demand 
cannot be determined a priori. Larger scale of operation, defined as 
the total area under common bean in a given season, enhances the 
household‟ ability to bear the risk of failure. Scale of operation also 
reflects the importance of the crop in meeting the household‟ 
livelihood requirements. Research elsewhere suggests that farmers 
with a high degree of soil heterogeneity tend to match the crop 
varieties with soil types (Bellon and Taylor, 1993). By so doing, the 
area share allocated to each variety might be reduced.  

Market failures are household specific depending on shadow 
prices (de Janvry et al., 1991). In the study area, markets may fail 
due to high transport costs or risk aversion. In an environment 
where price risks are high, households who commercialize are risk 
neutral and are expected to demand market varieties. In Eastern 
Kenya, donkeys are highly used in transportation of agricultural 
goods to and/or from the market and households in possession of 
this asset are expected to take less time to the market and can 



 
 
 

 
travel there more frequently. This can easily influence the 
household‟ ability to depend on the market and hence choice and 
demand of specific varieties. 
 
 
Data collection and measurement of empirical variables 
 
Data on socioeconomic household characteristics (education 
dependency ratio, exogenous income and household wealth 
assets), farm characteristics (scale of operation, soil types) market 
conditions (production orientation, market value of donkeys) and 
land allocation of varieties was collected from randomly selected 
households between June and August, 2008. A total of 120 
households randomly selected from three villages with a moderate 
to high potential for common bean production in each district were 
interviewed as part of the baseline survey. Morphological 
characteristics such as seed colour and size were used to 
distinguish varieties grown by farmers with the help of seed 
samples. Data on consumption and production attributes important 
to the household were collected in April 2010 as part of project 
monitoring. Due to limited budget, only 60 households randomly 
selected from the original baseline sample were interviewed for 
tracking varietal diffusion.  

The data on common bean area share allocated to the selected 
varieties (that is, the variety demand) was determined in two steps. 
In the first step, all varieties grown in the study area were solicited 
from respondents and the popularity of each in terms of percentage 
of households growing the variety determined. About 15 varieties of 
different seed sizes and colour were identified in the sampled 
villages (Table 1) with an average of 2 varieties per farm. These 
varieties were landraces and the improved varieties released in 
1980s. Although many varieties are grown in the study area, only 
two were popular among the selected households. These were 
GLPx92 (grown by 87% of the households) and GLP2 large red 
mottled (grown by 72% of the households) (Table 1). Other varieties 
were grown by less than 40% of the households. Based on this, the 
two popular varieties, GLPx92 and GLP2 large red mottled, were 
selected for the econometric analysis.  

In the second step, area allocated to each of the selected variety 
was determined. Given the multiple varieties grown and the 
intercropping system, determining common bean area occupied by 
a single variety was not straight forward. Farmers were able to 
recall the amount of seed for each variety that was planted during 
the cropping season of 2008/2007 and their usual seeding rate. The 
amount of variety seed and the seeding rate were then used to 
compute the area under each variety selected for the analysis. The 
descriptive statistics are presented in Table 2.  

Farmers were presented with a predetermined list of common 
bean attributes (cooking time, keeping quality, flatulence, grain size 
and grain colour, drought tolerance, early maturing, pest resistance, 
tolerance to poor soils and growth habit) derived from the previous 
participatory variety selection studies (Sperling et al., 1996). Each 
farmer was asked to rate each attribute on a three point scale (3= 
very important and 1= not important) according to his/her 
preference. A higher score reflects high preference and vice versa. 
The 10 variety attributes were subjected to a factor analysis 
procedure using principal components method to analyze 
correlation between them and determine whether they could be 

represented by a smaller number of components
2
. Based on the 

criterion of an Eigenvalue greater than unity, the 10 variety 
attributes were grouped into two independent attributes according 
to two unobserved factors interpreted as: consumption flavour and 

yield related characteristics
3
.  

 
2
 The attribute preferences tend to be collated and including all them would 

induce multicollinearity a well as the degrees of freedom.  
3 The results of the factor analysis are summarized in appendix A. The 2 factors 
explain 100% of total variance in the stated preferences for traits.

 

 
 
 
 

 
Factor one, explained about 56% of the variance in the 10 

selected attributes subjected to factor analysis. Consumption 
attributes (high keeping quality, less flatulence and less cooking 
time) loaded heavily on this index and the index was interpreted as 
consumption flavour. Drought tolerance, early maturing, pest 
resistance and tolerance to poor soils were highly correlated with 
factor two interpreted as yield related characteristics. The two 
underlying latent factors were recovered from the data using the 
scoring after factor analysis command in Stata 8.0 and included in 
the analysis as explanatory variables (Table 2).  

Variables representing individual and household characteristics 
(education, household wealth, dependency ratio, exogenous 
income), farm characteristics (scale of operation and soil type 
heterogeneity index) and market conditions (production orientation 
and value of donkey) were computed based on primary data 
gathered through interview of individual households. For each plot, 
the soil type and plot area were recorded. Then, soil type diversity  

was represented by the Shannon index ( Di
s
 ) computed as: 

 
 S S   S    

 

Di −∑j ij In ij   
 

    
A

i   
A

i  
 

D 
s
 ≥ 0      (3)  

        
 

S is the total area occupied by soil type j on farm i .  A is the total 
 

 ij      i 
   

cropped area on farm i . The Shannon index has a lower limit of 

zero when only one soil type exists on the farm. The list of utilized 
variables, their definition and descriptive statistics is presented in 
Table 2. 

 
Data analysis 
 
The importance for common bean attributes and distribution of 
attributes in the popular varieties found in the study area were 
analyzed using descriptive statistics based on the average ratings. 
The Kruskal Wallis one-way analysis of variance by ranks (a 
nonparametric statistical procedure) was used to test for differences 
among the ratings and determine whether or not each rating for an 
attribute was statistically equal among the three livelihood groups. 
The criteria used to classify households into livelihood groups are 
summarized in Table 3. A paired sample t-test was used to 
compare attribute supply by the two popular varieties chosen for the 
analysis.  

The effect of the attributes ratings and other factors on area 

share occupied by variety i was determined using econometric 

analysis. The choice of the econometric estimation method was 
dictated by the nature of the data on the dependent variables. The 
area shares allocated to GLPx92 and GLP2 large red mottled differ 
across households but were observed for almost all households in 
the data set used in the analysis and censored for very few 
households (2 for GLPx92 and 8 for GLP2 large red mottled). 
These observations were too few to allow calculation of the total 
effect of the explanatory variables on the explained variable from a 
tobit model. For this reason, the Ordinary Least Squares (OLS) 
regression model was preferred over a tobit regression for the 
estimation of area shares allocated to GLPx92 and GLP2 large red 
mottled.  

Denote Vi * the optimal demand of variety i expressed as a 

function of all the variables hypothesized (Z) to explain variation in 

the area share occupied by the variety i , cast on the right hand 

side of Equation 2, and the random component ( ε i ): 
 

y 
*
   β ' Z  ε 

i 
ε 

i 
| Z  ~ N (0,1) (4) 

 

i    
 



 
  

 
 

 
Table 1. Varieties grown, their morphological characteristics, local names, incidences and the year of release.  

 

Variety, local name(s) 
Researcher classification/ 

Year of release/origin Household share (%) 
 

 

Morphological characteristics 
 

 

    
 

Nyayo or  Maina GLP2 large red mottled Early 1980s 71.5  
 

Amini GLP2 Very large red mottled  4.9  
 

Rosecoco GLP2 medium purple mottled Early 1980s 13.8  
 

Nyayo short, saitoti or short maina GLP2 medium red mottled  17.9  
 

Kakunzu Local Purple stripes on cream  8.9  
 

Mwezimoja GLP1127or GLP1004 Medium purple or grey speckled Early 1980s land race) 7.3  
 

Katumbuka, Mwitemania, Katinga or Maddu GLPX92 Medium Pinto Early 1980s land race) 87.0  
 

Wairimu, Katune or Kamusina GLP585 Small red haricot Early 1980s 12.2  
 

Kitui GLp24 Large dark red kidney Pre-released 1993 14.6  
 

Kitui small; GLP24 Small dark red kidney - -  
 

Kayellow, Kathika, or Ka-green KatB1 Medium yellow/green round shaped Pre-released 1985 34.6  
 

Ikoso, Ngoloso or Itulenge Local Black with white stripes  15.5  
 

Kamwithiokya Local Black    
 

 
Source: Kenyan seed company in Spilsbury et al. (2004) updated with survey data. 

 

 

β  is a vector of parameters to be estimated while ε i   is a 
 
vector of random component assumed to be randomly 
distributed with zero mean and constant variance IN  
( 0,σ 

2
 ) . 

 
There is a possibility that one household grows both 

GLPx92 and GLP2 large red mottled. This implies that the 
factors that influence demand of one variety are also likely 
to influence demand for the other. This is likely to induce 
correlation between the unobserved heterogeneity in the 
two variety demand equations. However, according to 
Greene (2000, p. 616) there is no gain in statistical 
efficiency from estimating the two equations jointly when 
the set of explanatory variables is identical in both 
equations (Greene, 2000, p. 616). Based on this, each 
variety demand equation was treated separately. 

 

RESULTS 
 
Assessing the relative importance of common 
bean attributes to farmers 
 
Generally, farmers  prefer  many common bean 

 
 

 

attributes. Four production attributes and one 
consumption attribute were rated important with 
an average rating of more than 2.5 on a three 
point scale (Table 4). This reflects the wide range 
of production constraints faced by the households 
in the drought areas of Kenya and the importance 
of varietal adaptation. The Kruskal Wallis one-way 
analysis of variance by ranks indicates no 
variation in the relative importance for all the 
production attributes across household categories 
defined by livelihood groups. This means that 
these production attributes are preferred equally 
across households in the study area because 
production constraints affect them in the same 
way. Similarly, the relative importance for high 
keeping quality and grain colour does not show 
significant variation across household categories, 
which implies that preferences for these attributes 
do not depend on the household socioeconomic 
background.  

However, ratings for a less cooking time variety, 

 
 

 

grain size and low flatulence, also consumption 
attributes, vary across household categories, with 
the households in the middle stratum attaching 
relatively less importance on less cooking time as 
compared to those in the top and bottom strata. 
Households in the top stratum also expressed 
high preference for low flatulence and grain size 
than the poorer households. 
 

 
Distribution of the important attributes in the 
varieties grown 

 

Farmers were asked to rate each variety 
according to the selected production and 
consumption attributes. Results summarized in 
Table 5 shows that varieties differ in their supply 
of attributes preferred by households. GLPx92, 
the most popular variety is rated above average 
(3.7 on a 5 point scale) for drought tolerance, yield 
and tolerance to poor soils. On the other hand, the 



 
 
 

 
Table 2. Definition of empirical variables included in the analysis, hypothesized effects and the summary of the descriptive statistics.  

 
Variable Definition Expected effect Mean SD 

Dependent variable     

GLPx92 Share of common bean allocated to GLPx92 variety during the  0.250 0.217 
 2008/2007 cropping year    

GLP2 large red mottled Share of common bean allocated to GLP2 large red mottled  0.186 0.142 
 variety during the 2008/2007 cropping year    

 
Attribute 

 
Consumption flavour 

 

 
Production related 

 
 

Household characteristics 

Education 
 

Dependency ratio 
 

 
Household wealth 

 

 

Exogenous income 

 

Farm characteristics 
 

Scale of production 

Diversity of farm soil types 

 
Market access  
Value of donkey 

 

Production orientation 

  
 

An index derived from factor analysis of attributes where +/- 0.114 0.723 
consumption attributes loaded heavily    

An index derived from factor analysis of attributes where +/- 0.319 0.585 
production attributes loaded heavily    

Years of schooling, household head + 7.70 3.66 

Proportion of household members below 15 years and above +/-   
64 years of age  0.538 0.406 

Total value in (K.sh) of consumer durables (i.e. bicycle, radio) +   
and livestock  62567.3 22911.3 

A dummy capturing whether a household receives +/-   
remittances, credit, or non farm/labour income  0.277 0.452 

Total area (ha) allocated to common bean in a year +/- 0.147 0.180 

A Shannon index of soil types on the farm cropped area - 1.350 0.610 

Total value in (K.Sh.) of the donkey if sold at the time of survey + 1255.3 2900.3 

Dummy(=1) if the farmer „s primary objective of producing +/-   
common bean is for sale and 0 otherwise  0.468 0.504  



  
 
 

 
Table 3. Criteria used for categorizing households in wealthy strata.  

 
Sources of livelihood Wealth category 

Crops, any type of livestock and/ off farm employment Top 

Crops and small Livestock Medium 

Crops and local chicken only Bottom 
 

 
Table 4. Average rating of importance of common bean attributes by household wealthy category in Eastern Kenya, 
2008.  

 
 Variety attribute Top=11 Med=15 Bottom=21 Total P-value 

 

 
Drought tolerance 

2.82 3.00 2.90 2.92 
0.877  

 
(0.60) (0.00) (0.30) (0.35)  

   
 

 
Early maturing 

2.82 3.00 2.81 2.88 
0.876  

 
(0.60) (0.00) (0.60) (0.49)  

   
 

 
Pest resistance 

2.64 2.60 2.71 2.67 
0.871  

 
(0.81) (0.74) (0.72) (0.72)  

   
 

 
Tolerance to poor soils 

2.64 2.53 2.62 2.60 
0.917  

 
(0.67) (0.74) (0.74) (0.71)  

   
 

 
Growth habit (upright) 

2.18 2.87 2.29 2.48 
0.160  

 
(0.98) (0.52) (0.92) (0.85)  

   
 

 
Less cooking time 

2.45 1.80 2.71 2.38 
0.029**  

 
(0.82) (0.94) (0.64) (0.87)  

   
 

 
High keeping quality 

2.40 2.87 2.76 2.72 
0.599 

 

 
(0.97) (0.52) (0.62) (0.68)  

   
 

 
Low flatulence 

1.91 1.33 2.19 1.88 
0.033**  

 
(0.94) (0.72) (0.87) (0.91) 

 

   
 

 
Grain size 

2.36 1.33 1.86 1.83 
0.039** 

 

 
(0.92) (0.72) (0.91) (0.93) 

 

   
 

 
Grain colour 

2.09 1.80 2.20 2.06 
0.561  

 
(1.04) (0.94) (1.01) (0.99) 

 

   
 

 
**, * denote 5% and 10% levels of significance, respectively. 

 

 

variety is rated inferior in all the consumption attributes. 
The rating for GLP2 large red mottled was in the reverse 
order. It is rated above average for consumption 
attributes, but below average for production attributes. A 
paired sample t-test further revealed that these varieties 
significantly differ in their supply of the selected attributes 
as perceived by farmers. GLPx92 outperforms GLP2 
large red mottled in all production attributes but evaluated 
inferior to GLP2 large red mottled in terms of 
consumption attributes (Table 5). 

 
 

 

Effect of attributes and other factors on variety 
demand 

 

An OLS regression was used to estimate the marginal 
proportion of common bean area allocation to GLPx92 
and GLP2 large red mottled for each explanatory variable 
and the respective elasticities. Missing data across the 
variables reduced the usable sample to 40 observations 
in analyzing the predicted effects. The results summa-
rized in Table 6 show that the two models were significant 



 
 
 

 
Table 5. Evaluation of popular common bean variety in different preference criteria and paired samples t-test results between the varieties.  

 
 

Attribute 
Average rating of relative performance of variety (5 point scale) GLPx92-GLP2 large red mottled 

 

 

GLPx92 GLP2 large red mottled Mean score difference P-value 
 

  
 

 Drought tolerance 4.575 2.855 1.725 0 
 

 Early maturing 0 0 0  
 

 Pest resistance 0 0 0  
 

 Tolerance to poor soils 3.89 2.2 1.69 0 
 

 Cooking time 2.265 3.9 -1.635 0 
 

 Low flatulence 2.25 3.815 -1.57 0 
 

 Keeping quality 2.34 3.565 -1.225 0 
 

 Seed size 2.69 4.155 -1.465 0 
 

 Seed colour 2.29 4.15 -1.855 0 
 

 
 

 

at less than 1% with a respective R
2
 of 0.48 and 

0.51, which implies a good fit for a cross sectional 
data. Test for multicollinearity using the variable, 
inflation factor, also shows that all variables had a 
VIF of less than 2.5, suggestive of stable results.  

A joint test of the hypothesis that consumption 
attributes and other household demographic 
factors do not matter in land allocations to 
varieties was rejected, providing support to the 
agricultural household mode l used to derive 
variables for the econometric estimation. As 
expected, a high index of the rating of production 
attributes had a positive and significant effect on 
the area share allocated to GLPx92, which is 
generally evaluated superior to other varieties in 
production attributes. The index had a negative 
and significant impact on the area share allocated 
to GLP2. The GLP2 large red mottled was rated 
below average for the supply of almost all 
production attributes.  

The analysis of elasticity revealed that farmers 
in the drought parts of Kenya are more responsive 
to production attributes than they are to 
consumption attributes. Results indicate that 1% 
increase in the index for the production attributes 

 
 

 

increases the area share of GLPx92 by 0.35% 
and reduces that of GLP2 large red mottled by 
0.24% (Table 5). On the other hand, a similar 
increase in preference index for consumption 
attributes reduces area share for GLPx92 by 
0.06% and increases that of GLP2 large red 
mottled by 0.07%.  

The concept of the marginal rate of substitution 
was also applied to assess the farmers‟ valuation 
of consumption attributes in terms of production 
attributes so as to draw implications for possibility 
of trade-offs. Results show a marginal rate of 
substitution of consumption attributes for 
production attributes of 0.42 for GLPx92 and 0.67 
for GLP2, further demonstrating the high valuation 
for production attributes as compared to 
consumption attributes. This result implies that at 
the current level of utility, farmers are only willing 
to give up less than one unit of utility from 
production attributes for gains in one unit of utility 
from consumption attributes.  

Among the household characteristics included in 
the analysis, education and household wealth 
were only significant. Education had a positive 
effect on the area share allocated to GLPx92 and 

 
 

 

a negative effect on GLP2 large red mottled. This 
result could be interpreted that educated people 
tend to allocate their land designated for common 
bean to varieties that perform relatively well in 
terms of yield and use the market to access 
varieties they prefer but do relatively poorly on 
their farm.  

Farm characteristics represented by the scale of 
operation and the heterogeneity in farm soil types 
are also important determinants of variety 
demand. Scale of operation was positively related 
with the area share allocated to both varieties but 
the effect was only statistically significant for 
GLP2. The positive and significant effect of scale 
on the area share occupied by GLP2 implies that 
farmers with access to land tend to compensate 
for low yield from GLP2 with area expansion.  

Variables included as proxies for market 
conditions show the expected signs. Households 
with commercial production orientation tend to 
allocate a bigger proportion of their common bean 
land to GLP2 (market variety), and less to 
GLPx92, a risk reducing variety but with inferior 
consumption attributes. The value of donkeys, 
shows a positive relationship with the area share 



  
 
 

 
Table 6. Results of the estimation of the ordinary least squares regression of the effect of variety attributes and other factors on the area 
share allocated to GLPx92 and GLP2 large red mottled.  

 

Variable 
GLP2 large red mottled  GLPx92  

 

Coefficient Elasticity t-value Coefficient Elasticity t-value 
 

 
 

 0.089 0.071  -0.099 -0.061  
 

Index of consumption attribute (0.040) (0.033) 2.22** (0.057) (0.036) -1.72* 
 

 -0.132 -0.249  0.237 0.347  
 

Index of production attributes (0.073) (0.139) -1.82* (0.104) (0.156) 2.29** 
 

 0.006 0.018  -0.039 -0.089  
 

Dependency ratio (0.055) (0.162) 0.11 (0.078) (0.179) -0.5 
 

 -0.022 -0.899  0.023 0.755  
 

Education (0.009) (0.402) -2.29** (0.013) (0.441) 1.74* 
 

 0.098 0.245  -0.169 -0.327  
 

Production orientation (0.050) (0.128) 1.95* (0.072) (0.143) -2.36** 
 

 -0.001 -0.005  -0.141 -0.765  
 

Soil diversity (0.042) (0.295) -0.02 (0.060) (0.337) -2.34** 
 

 3.3E-01 0.274  -0.071 -0.046  
 

Scale of operation (1.4E-01) (0.119) 2.37** (0.198) (0.128) -0.36 
 

 2.0E-05 0.094  -5.1E-06   
 

Value of donkey (8.9E-06) (0.044) 2.2** (1.3E-05)  -0.4 
 

 1.4E-07 0.048  -2.0E-07   
 

Household wealth (1.8E-07) (0.061) 0.79 (2.5E-07)  -0.78 
 

 -2.3E-02   0.039   
 

Exogenous income (6.1E-02)  -0.38 (0.087)  0.45 
 

 2.6E-01   0.301   
 

Constant (9.8E-02)  2.64*** (0.139)  2.16** 
 

Observations 38   38   
 

F( 10, 27) 2.88   2.46   
 

Prob > F 0.014   0.0308   
 

R2 0.5161   0.4766   
 

Adj. R
2
 0.3369   0.2827   

 

Root MSE 0.11748   0.16744   
 

 

 

allocated to GLP2 (a market variety), but the magnitude is 
too small to matter. 
 

 

DISCUSSION 

 
This paper highlights important variety attributes, their 
supply by varieties grown, their effect and that of other 

 

 

factors on the variety demand in drought parts of Kenya. 
Although the sample was taken from Eastern Kenya, 
results have implications for other areas with similar  
socioeconomic and agro-ecological conditions. 
Consistent with the findings of the previous research 
(Sperling et al., 1993), a high number of variety attributes 
is preferred. This preference is largely derived from the 
high incidences and severity of biophysical stresses and 



 
 
 

 

socioeconomic problems faced by farmers in the study 
area (Katungi et al., 2010). All farmers attach high 
importance to all production attributes analyzed in this 
paper, while they significantly differ in their stated 
preferences for cooking time, low flatulence and grain 
size. To wealthier households, low flatulence and large 
grain size are very important while the medium and the 
poorer households do not mind any size. On the other 
hand, poorer households prefer a bean variety with less 
cooking time because these households lack the required 
resources to support the extended cooking time. This is a 
key result and has not been reported from other 
preference studies on common bean.  

The study findings indicate that stated consumption and 
production preferences significantly explain the revealed 
land allocations to two popular varieties grown in Eastern 
Kenya. This is consistent with the agricultural household 
model that was used in this study. Edmeades (2003) also 
applied an agricultural household model to study variety 
choice among banana producers in Uganda and obtained 
similar results. The variety attributes have coefficients of 
larger magnitudes, implying that variety preferences are 
the important determinants of land allocation to a 
common bean variety, underscoring the importance of 
clear understanding of farmers‟ preferences. 
 

The study has elaborated that households attach high 
importance on production and consumption attributes, 
implying that genetic improvement that incorporates both 
desirable production and consumption traits would 
enhance the welfare of these people, especially the 
poorer households. Therefore, breeding effort should 
target producing varieties with superior production 
attributes that are compatible with farmers‟ consumption 
preferences. Results further revealed that farmers in 
drought areas are more responsive to production 
attributes and value them highly compared to 
consumption attributes. This implies that introduction of 
varieties with superior production attributes than is 
currently supplied by the popular varieties will be widely 
adopted even when their consumption attributes are the 
same as that of the existing varieties or reduced slightly.  

The study has also demonstrated that in addition to 
variety attributes, education of the production decision 
maker, farm size, diversity of soils on the farm and 
production orientation significantly explain variation in the 
extent of variety planting across households. Study 
results indicate that the area share allocated to a risk 
increasing variety (GLP2 red mottled) increases with farm 
size because larger farmers have the ability to diffuse risk 
through better access to credit (Feder, 1980; Feder et al., 
1985; Knight et al., 2003). Controlling for farm size and 
other factors, education seems to reduce land allocation 
to the risk increasing variety. Although education has 
been demonstrated to act through its positive effect on 
information (Schultz, 1975), we do not expect this 
mechanism to be important in land allocation to the two 

 
 
 
 

 

varieties considered in this study since these varieties 
have been grown for long in the community and almost 
every farmer is considered to have information about 
them. Instead, the result implies that as drought in Kenya 
increasingly becomes more important (Sperling, 2002) 
and bean markets increasingly rely on imports, better 
educated decision makers choose to allocate more land 
to varieties less vulnerable to drought while they depend 
on the markets for varieties considered more vulnerable 
to drought as a way of enhancing productivity of their 
resource. Several empirical studies reviewed by Feder et 
al. (1985) have confirmed the positive effect of education 
on allocative efficiency in agriculture, particularly, under 
changing environment.  

Results also show that area allocation to each variety 
reduces with increase in soil type heterogeneity on the 
farm which is consistent with the findings in literature that 
farmers match their crop varieties with soil types (Bellon 
and Taylor, 1993) and this could result in each variety 
occupying a small portion of the land. Furthermore, 
farmers with commercial production orientation tend to 
trade-off expected yield with high marketability. The 
implication is that as markets develop and market 
attributes become more important, area under common 
bean cultivation may shift to more marketable varieties 
even in drought prone areas. This could further reduce 
common bean productivity. 
 

 

Conclusions 

 

The study findings have important implications for 
breeding, as well as variety dissemination. For breeding, 
the study findings support the conclusion that varietal 
adaptation to environmental stresses that also strive to 
reduce the cooking time, enhance the keeping quality and 
grain colour will greatly benefit the poor. However, when 
multi-attribute breeding is not feasible or is long term, 
prioritizing the improvement of production attributes as a 
short term goal seems an efficient strategy. Trade-off 
between desirable production attributes and marketability 
need to account for exogenous factors that are important 
in variety planting decisions but subject to change. For 
example, the study findings point to a possibility of a 
future shift in land to well adapted varieties as farm sizes 
become smaller due to increases in population pressures 
and household access to markets in neighbouring 
countries with a comparative advantage in the production 
of market varieties improves. In the long term, there is 
need for breeding strategy that simultaneously enhances 
the production, as well as marketability of varieties even 
in drought prone areas.  

Study findings also imply that during variety 
dissemination, information related with less tangible 
variety attributes such as cooking time and keeping 
quality should be packaged and disseminated alongside 
varieties to facilitate adoption decision making process 



 
 
 

 

and faster diffusion of varieties. 
 

 

ACKNOWLEDGEMENTS 

 

This study was done as part of a project funded by Bill 
and Melinda Gates Foundation. The good collaboration of 
the International Centre for Tropical Agriculture (CIAT), 
International Crops Research Institute for the Semi-Arid 
Tropics (ICRISAT) and International Institute of Tropical 
Agriculture (IITA) and KARI contributed greatly to the 
success of this project. The Kenya Agriculture Research 
Institute in Katumani greatly facilitated data collection and 
the initial analysis of the study. 
 

 
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APPENDIX 

 
Table 1. Scores after factor analysis of variety attributes.  

 
 

Attribute 
Scoring coefficient  

 

 

Consumption flavor Yield related characteristic Proportion of variation explained 
 

  
 

 Consumption   0.568 
 

 Less cooking time 0.10 0.02  
 

 High keeping quality 0.41 0.09  
 

 Less gas 0.28 0.03  
 

 Grain size -0.12 0.04  
 

 Grain colour -0.04 0.03  
 

 Production attribute   0.471 
 

 Drought tolerant 0.05 0.39  
 

 Early maturing -0.05 0.23  
 

 Pest resistance 0.28 0.22  
 

 Tolerate poor soils -0.05 0.20  
 

 Growth habits (upward) 0.13 0.07  
  


