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Produced for the
27 February – 4 March 2016
Produced for the
Justus Kavoi1, Scolastica Wambua2, Ann Gichangi3, Mercy Mutua4, Eliud Birachi4 and David Karanja1
1Kenya Agricultural and Livestock Research Organization (KALRO) Katumani, Box 340-90100 Machakos, Kenya2KALRO-Headquarters, Box 57811-00200 City Square, Nairobi, Kenya3KALRO-Njoro Research Centre, Private Bag, Njoro, 20107, Kenya4International Centre for Tropical Agriculture, Nairobi, Kenya
Challenges and Opportunities in Bean Production and Marketing in Selected Bean Corridors in Kenya
INTRODUCTIONCommon beans (Phaseolus vulgaris L.) are mainly consumed without much
processing although value addition through precooking has many benefits.
Challenges in common bean production include erratic and poorly distributed
rainfall, use of poor genetic materials, field and storage pests, and degraded
natural resources (especially declining soil fertility). Although small-scale farmers
are prone to food insecurity, they feed more than 80% of the world’s population.
Many of them are in the developing world (Herren et al., 2012). Moreover, many
communities in sub-Sahara Africa depend largely on agriculture. In this region,
relatively slow expansion in cultivated area is due to scarcity of arable land and
is aggravated by rapid population growth (Sibiko, 2012). Thus, development of
resilient and affordable agricultural systems is vital (Njeru, 2013). Most
smallholder farmers in Kenya own less than two hectares of land (Altieri et al.,
2012). Such land sizes are likely to be further reduced due to land fragmentation
and unregulated urban centres’ expansion leading to reduced available
agricultural land. Agricultural extension is the main means of information delivery,
alongside dissemination of new technologies to farmers for increased production
in food and animal products (Kirimi et al., 2013). However, declining government
budgets combined with waning donor interest has led to cuts in public extension
services (Yusuf et al., 2011). Limited farmer access to extension services further
exacerbates farm productivity among smallholder farmers (Chemining’wa et al.,
2014). Additionally, challenges facing smallholder farmers included access to
improved farm inputs e.g. certified bean seed; low household income from crop
sales and distorted bean production, aggregation and marketing systems. facing
smallholder farmers in the selected bean production corridors in Kenya. To
address these challenges, a study was carried out to establish, quantify and
document information related to access to farm inputs; production and
marketing; and identify potential opportunities that can be explored to enhance
production and marketing in selected bean production corridors in Kenya. The
objectives of the study were to:
• Characterise common bean producers in Bomet, Homa Bay, Machakosand Narok Counties
• Establish challenges in common bean production and marketing
• Establish opportunities for increased adoption and production of commonbean and marketing in the bean production corridors
METHODSThe study adopted a cross-sectional survey design to collect data. A multi-stagerandom sampling technique was used to select study sites from County, sub-County, Division, Location, sub-Location and villages as recommended byThompson et al. ( 2011). Two villages were selected in each sub-Location. Withassistance of local public extension officers and local administrators, names ofhouseholds in the selected villages were listed. This formed the sampling framefrom which the desired sample size of 440 respondents was randomly drawnproportionately (70 from Bomet, 238 from Homa Bay, 61 from Machakos and 71from Narok). Homa Bay had more selected respondents than the other countiesdue to its volumes of beans previously produced. A semi-structured questionnairewas used to collect data. Data were analysed using Ordinary List Squares (OLS)regression model:
Y = a+(X1+X2+X3)+ Đ1+ Đ2+ Đ3+ Đ4
Y = Lncrop household income; a = constant; = Predictor of averagechange in Y that is associated with a unit change in X1, X2, X3;
X1 = Quantity of bean seed bought; X2 = Price of bean seed per kg;X3 = Area grown with beans relative to total land owned
Contact: Justus Kavoi,
KALRO-Katumani Research Centre,
P.O. Box 340-90100 Machakos, Kenya,
Cell: +254 726 250 090; +254 733 573 387,
Emails: [email protected]; [email protected]
Certified seed outlets: Agro-vet dealer (left) and smallholder farmers accessing certified
bean seed through a seed fair (right)
CONCLUSION
Quantity of bean seed bought is positively highly correlated to household income.
The more the amount of bean seed bought the higher the household income. Study
advocates for increased bean seed purchases to increase crop sales. Education
plays an important role in timely and accurate decision-making on procurement of
farm inputs and apportioning household owned land under crops. Promotion and
commercialisation of improved bean varieties through well-established public-
private-partnership platform such as community production and marketing system
(COPMAS) has great potential to improve household income, food security and
nutrition from crop sales.
ACKNOWLEDGEMENT
The study was funded by IDRC/ACIAR through the CultiAF project. KALRO and
CIAT allowed use of Institution facilities while planning and carrying out the study.
Authors equally appreciate data collection enumerators for their dedication and the
local extension and administration staff for assistance in data collection. Sponsors
facilitated for participation to the 2016 International Legume conference in
Livingstone, Zambia.
RESULTSHousehold income from crop production is a function of several independent
variables including demographic characteristics of the respondents (education level,
sex, number of household members) and cropped land, amount of seed bought and
supplier of seed. Gender of the respondents was 29% men and 71% women. Most
(64%) had either no education or at least primary education, while 27% had
secondary education. Only 9% had tertiary education. Suggestions from discussions
with respondents during the study showed that organised crop production and
marketing enabled farmers to jointly produce and market their farm produce.
Main challenges included the proportion of owned land grown with beans, which
significantly influenced household income from crop sales (p=0.028). Similarly,
respondents who planted improved (certified) seed gained more from sales relative
to those who planted home or recycled seed (p=0.070) (Table 1). Other challenges
were low prices, transport (walking for long distances to sell farm produce and
procure farm inputs) and market intermediaries interfering with market price stability.
REFERENCES
Altieri, M. A., Funes-Monzote, F., & Petersen, P. (2012). Agro-ecologically efficient agricultural systems for smallholder farmers: Contributions
to food sovereignty. Agronomy for Sustainable Development, 32(1), 1–13. http://dx.doi.org/10.1007/s13593-011-0065-6.
Chemining’wa, G., Kitonyo, O. & Nderitu, J. (2014). Status, challenges and marketing opportunities for canning navy bean in Kenya. African
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Sibiko, W. (2012). Determinants of common bean productivity and efficiency: a case of smallholder farmers in Eastern Uganda, MSc Thesis.
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The study sought to relate variables contributing to household income from crop
sales. Correlation analysis was carried out to establish any relationship between
household income from crop sales and the factors contributing to it. Descriptive
and inferential statistics were used to present the study findings.
A well-established bean field (left) and improved bean variety seeds (right)
Table 1. Effects of selected production factors on household income from
crop sales
VariableCoef. p-Value
t-StatisticStd. Err.
Constant 9.26853236.79
0.2519
Quantity of bean seed bought (kg) 0.00555 0.000*** 5.86 0.00094
Bean seed price in KES/kg 0.001868 0.218 1.24 0.0015
Proportion of owned land under bean 0.019405 0.028** 2.21 0.0087
Used improved bean seed (base is no) 0.236015 0.070* 1.82 0.1296
Household normally sells beans (base is no) 0.545204 0.011** 2.57 0.2122
Household head attained secondary level of
education (base is primary and below) 0.252524 0.040** 2.07 0.1222
*, **, *** Significant at 10%, 5% and 1% level