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ANIMAL HUSBANDRY & VETERINARY SCIENCE | RESEARCH ARTICLE
Effects of fodder production on smallholder
farmers’ household income in Homa Bay County,
Kenya: An application of propensity score
matching
Mary Stacey Ayuko
1
, Job Kibiwot Lagat
1
, Michael Hauser
2
, Kevin Okoth Ouko
3
* and
Dick Chune Midamba
4
Abstract: The global feed production has increased in the past few years. Despite
the growing trend, the current production does not meet the demand in Kenya. The
government of Kenya has initiated several efforts towards promoting fodder pro-
duction to increase milk production and household income. This study analysed the
effects of fodder production on household income in Homa Bay County, Kenya using
the Propensity Score Matching (PSM) technique. The study used primary data col-
lected through structured questionnaires in Homabay County, Kenya from a sample
size of 300 smallholder farmers. Results indicated that years of schooling, herd size,
household size, labour used in land preparation, and land size under fodder had
a positive influence on the probability of farmers to mainly feed their livestock on
Napier grass. On the contrary, the number of extension contacts negatively influ-
enced the probability of farmers feeding their livestock on Napier grass. Results
show that there was a significant difference between the incomes of farmers who
fed their cattle on Napier grass and those who mainly grazed their cattle on natural
grass. Specifically, smallholder farmers who fed their livestock on Napier grass
reported a Kshs. 3,916.67 (USD 25.71) higher income than their counterparts who
grazed their livestock on natural grass reflecting an increase by 24.94%. Thus, the
ABOUT THE AUTHORS
Mary Stacey Ayuko holds a Master of Science in Agricultural and Applied Economics (CMAAE) from
Egerton University, Kenya/University of Pretoria, South Africa.
Michael Hauser works at the Research Program Enabling Systems Transformation, International Crops
Research Institute for the Semi-Arid Tropics (ICRISAT) & Institute for Development Research, University
of Natural Resources and Life Sciences, Vienna
Job Kibiwot Lagat is an Associate Professor of Agricultural Economics in the Department of Agricultural
Economics and Agribusiness Management at Egerton University, Kenya.
Kevin Okoth Ouko is a Research Associate Consultant at WorldFish. He recently completed a PhD in
Food Security and Sustainable Agriculture from Jaramogi Oginga Odinga University of Science and
Technology, Kenya, and an MSc in Agricultural and Applied Economics from Egerton University, Kenya/
University of Pretoria, South Africa. His research expertise includes food systems, food security,
aquaculture value chains, climate change, development finance, and gender and social inclusions.
Dick Chune Midamba is a PhD Candidate in Agricultural Economics at Maseno University, Kenya. He
currently works at Equity Group Foundation as an Enterprises Development Officer. His research
interest includes Technical efficiency, the adoption of sustainable agricultural technologies, Crop
diversity, Resource optimization for cash–food crop production, and Sustainable agriculture.
Ayuko et al., Cogent Food & Agriculture (2024), 10: 2292868
https://doi.org/10.1080/23311932.2023.2292868
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided the original work is properly cited. The terms on
which this article has been published allow the posting of the Accepted Manuscript in
a repository by the author(s) or with their consent.
Received: 20 April 2023
Accepted: 05 December 2023
*Corresponding author: Kevin Okoth
Ouko, Department of Agricultural
Economics and Agribusiness
Management, School of Agriculture
and Food Sciences, Jaramogi Oginga
Odinga University of Science and
Technology, P. O. Box 210 – 40601,
Bondo, Kenya
E-mail: kevinkouko@gmail.com
Reviewing editor:
Pedro González-Redondo, University
of Seville, SPAIN
Additional information is available at
the end of the article
Page 1 of 20
study recommends the need for both the national and county governments to
incorporate fodder production as a key area for livestock development agenda in
their policy plans to improve the farmers’ income.
Subjects: Agriculture; Environmental Sciences; Agriculture and Food;
Keywords: Livestock Development; Napier grass; Farmers; Propensity Score Matching
1. Introduction
Insufficient access to sufficient supplies of high-quality feed has a detrimental impact on the
sustainability of the livestock industry in sub-Saharan Africa. The availability of feed is made worse
by the consequences of climate change. However, in recent times the global feed production has
indicated a positive growth in the past few years. In Africa for example, the dairy feed output
increased by 10%, making dairy one of the few sectors to witness progress across all areas (Altech,
2018). Livestock feedstuff is differentiated into feed concentrates such as grains and oilseeds and
roughage such as grass from pastures and crop residues (Pandey, 2011). Communities living in dry
lands of Kenya are now embracing fodder production to increase their household income and food
security in the occurrence of recurrent droughts as a result of climate variability (CNFA, 2013).
Fodder trees play an important role as a feed source to sustain production in livestock and mitigate
the effects of poor-quality feed on milk, especially during dry seasons in Kenya (Makau et al.,
2020). For example, Place et al. (2009) found that on average, 2 kilograms of Calliandra foliage (dry
matter) fed to a dairy cow daily has been reported to increase daily milk production by approxi-
mately 1 litre. Analysis of gross margin indicates that production of pasture and fodder is
a profitable venture and there is a significant market for it. Conversely, the institutional and
regulatory structure that governs fodder production, processing, and marketing including private
sector support is underdeveloped, leaving vulnerable farmers to unscrupulous market actors
(MoALF, 2017).
Tropical grasses, legumes, and crop residues make up the majority of the forages that go into
animal diets in Kenya (Mwendia et al., 2020). In both of the wet seasons in the predominantly
bimodal regions, the bulk of the feed consists of fodder crops and weeds, while in the dry seasons,
these are supplemented by crop residues (Paterson* et al., 1998). Notably, in developing countries
like Kenya forests provide feed for livestock in the form of fodder for stall-feeding and grazing in
the forest areas, however extraction of fodder resources from forests often leads to forest
degradation (Pandey et al., 2014). In their study, Musalia et al. (2016) found that most animal
feeds came from crop residues such as millet straw, maize stalk, pigeon peas, beans, and sorghum.
The most popular fodder species utilized by dairy producers in a zero grazing system in Kenya are
Napier grass (33%) Rhodes grass (21%), maize (17%), and lucerne (8%). Other species had ratings
of less than 2%. Napier grass makes up about 70% of all forages consumed by smallholders,
making it the most often used fodder when grazing is prohibited (Mwendia et al., 2020). Hay
(produced from Boma Rhodes and Brachiaria grass) and Lucern are the most frequently traded
fodder, whereas Napier grass dominates sales between farmers within close vicinity. As a result of
the shortest value chain of Napier grass, it is directly sold from the producer to the final user
(Auma et al., 2018).
Fodder production has both direct and indirect effects on producer household income. The direct
effect is through the sale of fodder (grass, hay, silage, crop residues) while the indirect effect is
through the sale of milk produced and livestock sales. In smallholder farms in Kenya, the cost of
feeding dairy animals accounts for between 60% and 80% of the total cost of production. Efficient
feeding could significantly increase farmers’ profit margins (Auma et al., 2018). Subsequently,
pastoral and agro-pastoral communities in ASALs in Kenya are gradually taking up the production
of fodder not only in response to pasture inadequacy resulting from recurrent droughts but also as
a supplement to income from livestock production (Ouma, 2017). In Mandera County, the
Enhanced Livelihoods in the Mandera Triangle (ELMT) project reinforced communities in the county
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to improve livestock production through sensitization and provision of inputs used in fodder
production (VSF-Suisse, 2009). Ouma (2017) revealed that selling hay, grass seeds, and leasing
out pasture were the three major ways income was generated translating to an average return of
Ksh. 13500 (USD 88.62) per acre to the fodder farmers. The highest contribution of the household
income was from selling grass as compared to pasture leasing which had the least contribution.
With an increase in fodder production and milk yields, there has been a rise in the income of the
rural smallholder by 25–100 euros (USD 27.19–108.77) per household per year. Women are
observed to take part in the plantation of fodder trees and a lot of them have established fodder
seedling nurseries to earn extra income (SPORE, 2015).
Further, Meyerhoff (2012) found that some dryland areas in Kenya, including Baringo, Marsabit,
and Laikipia, receive about 10 tons of indigenous grass seeds annually. This has contributed to
more income benefits of about Kshs. 1.5 million (USD 9,784.74) per annum, with farmer groups
benefiting from loans worth over Kshs. 750,000 (USD 4,923.51) using rehabilitated fields as
collateral in loan acquisition. Some of the benefits that are obtained from fodder production in
these areas include, hay and leasing out grazing, income received from the sale of grass seeds, use
of grass for thatching, healthier livestock, grazing reserves, and improved livestock productivity
(Wairore et al., 2015). Lugusa (2015) did a study on fodder production and adaptation strategies in
the drylands, in Baringo County Kenya. The study was carried out to map the contribution of fodder
and grass seed value chains on household income. The findings from the study indicated that
fodder production had the potential to address the cash needs of farmers in pastoral communities.
The study emphasized the need to link the fodder farmers to reliable markets to cushion them
from potentially low prices that are offered for the grass seeds. In addition, producers need to
have access to more inputs in the fodder and grass seed value chain to lower the prices of inputs
associated with the input market.
In Kenya, fodder farmers who adopted Brachiaria grass had an increased milk output, for
instance, Maina et al. (2020) found that farmers took on climate-smart push-pull technology
that consumes Brachiaria grass since they observed that it provided feeds for livestock during
the period of drought and this increased milk output. Similarly, Mawa et al. (2014) found that the
cost of fodder produced on farms significantly improved profit efficiency among farmers. An
increase in milk production as a result of feeding fodder shrubs to dairy animals is experienced
within a short time and this in turn facilitates quick evaluation and uptake by the farmers
(Wambugu et al., 2011). Further, it was revealed that increased milk production was one of the
benefits derived from fodder production and the use of silage (Kilelu et al., 2018). Access to quality
fodder all year round is important to addressing the seasonality in fodder supply to meet market
demand, decrease the cost of fodder production, and open the production potential of high genetic
stock. Fodder conserved as hay is important in improving the production of milk (Tolera, 2017).
Feed insecurity associated with prolonged and recurrent droughts remains a perennial challenge
impeding livestock production in Kenya. To promote fodder production, conservation, and market-
ing as well as technology transfer to increase cattle productivity, the Kenyan government works
with research institutions and other development organizations like the Kenya Climate Smart
Agriculture Project (KCSAP), the United States Agency for International Development (USAID),
the Agricultural Sector Development Support Program (ASDSP), and the International Fund for
Agriculture Development (IFAD) (Thomas et al., 2023). In Homa Bay County, Kenya, farmers have
continued to adopt fodder production to address the pasture shortage as well as to have extra
income generated from livestock production (Joshua & Augustine, 2018).
The County Governments in collaboration with the International Livestock Research Institute
(ILRI) have initiated efforts towards promoting fodder production to increase milk production
and improve household income. Farmers involved in fodder farming produced different types
of fodder including Napier grass, Brachiaria grass, crop residues, banana leaves, sweet potato
vines, hay, banana stems, and desmodium. Commercial fodder production by the farmers in
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Homa Bay County is likely to lead to land resilience for increased biomass production; livestock
resilience for increased livestock production, milk, meat, hides, and skins; and livelihood
resilience through improved livelihoods, incomes, and reduced poverty. This study contributes
insights on emerging trends regarding fodder production in Homa Bay County, Kenya, focusing
on households producing Napier grass and those grazing on natural grass on their incomes.
Napier grass is the most preferred fodder as it can withstand considerable periods of drought,
produces greater dry matter (DM) yields than other tropical grasses, and is of high nutritive
value for dairy cattle particularly when supplemented with high-quality feeds such as legumes
(Khan et al., 2014; Nyambati et al., 2010). While there are several studies conducted on the
effects of fodder production on household income and welfare in different regions, such
studies provide varied levels of the effects of fodder production on household income.
Similarly, there is a dearth of literature on the effects of fodder production on household
income in the Kenyan context, especially in Western Kenya. Thus, this study aimed to fill the
above gap existing in the literature by determining the effects of fodder production on house-
hold income in Homabay County, Kenya. This study provides policies that can be implemented
by the government of Kenya to improve fodder production which also increases livestock
productivity. With increased livestock productivity, farmers’ income levels also increase.
Finally, the findings from this study contribute to the achievement of Kenya Vision 2030,
Malabo’s commitment to enhancing the resilience of at least 30% of households and produc-
tion systems by 2025, and the United Nations (UN) sustainable development goals (SDGs) of
eradicating poverty, reducing hunger and combating climate change.
2. Methodology
2.1. Study area
This study was undertaken in Homa Bay County, Kenya (Figure 1). The County experiences an
inland equatorial climate modified by the influence of altitude and its proximity to Lake Victoria
which makes the area temperatures range from 17° C to 25°C. Homa Bay County is divided into two
main relief areas namely; the upland plateau which starts at 1,220 meters above sea level and the
lakeshore lowlands. The county experiences long rains starting from late March to June ranging
from 800 mm-1800 m, and the short rains start in August to December and range from 250 mm-
700 mm. Rachuonyo East and Rachuonyo South sub-counties receive reliable rainfall. Ecological
zones in the areas range from Lower Midland2 to Lower Midland4 (LM2–LM4). The County has
about 104,464 hectares for food crops 12,277 hectares for cash crops, 6000 hectares for horticul-
tural crops, and 54 hectares for aquaculture (MoALF, 2016). It has 44,660 small farm holdings,
between 1.2 to 3.0 acres on which food crops such as sweet potato, maize, cassava, and sorghum
are grown. The region covers the upper and lower midland Agro-ecological zone and mainly
consists of Humic Andosols, Orthic and Plinthic Acrisols soil types (MoALF, 2016) Fishing and
agriculture including dairy farming are the main economic activities in the county. The main
livestock kept in the County includes zebu cattle, the red Maasai sheep, the small East African
goat, and indigenous poultry. Cattle, goats, and sheep are kept to cushion households against
vulnerabilities as they can be sold to source for school fees and to offset medical costs whereas
other farmers keep the livestock as a result of social factors such as payment of dowry. In
Homabay County livestock has not been largely commercialized mainly because of limited grazing
land among households. Furthermore, poverty levels as atof023 in Homa Bay county stand at 48%
compared to the national poverty indicator at 45% (CIDP, 2023). Most of the farmers in the County
depend on natural fodder. However, due to the climatic shocks in the County, some of the farmers
shifted to feed conservation and diversification. Farmers in the warm and dry AEZs (LM2, LM3, LM4,
and LM5) tend to farm Napier grass more as they are adversely affected by climate shocks (MoALF,
2016).
2.2. Sample and sample procedure
A multistage sampling technique was used. The first step was the purposive selection of two sub-
counties (Rachuonyo East and Rachuonyo South). These two sub-counties were selected based on
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their response to the uptake of various fodder production technologies that were introduced by the
Accelerated Value Chain Development (AVCD)program with the help of the International Livestock
Research Institute (ILRI) as the lead project implementer in the region. Secondly, a systematic
random sampling procedure was used where the first household to be interviewed was chosen
randomly and the succeeding respondents were systematically selected after every second house-
hold. The sample size was determined using the formula by Cochran (1963) for an infinite popula-
tion (≥50000) as follows;
Where; n = required sample size; Z= confidence level (α = 0.05); p = proportion of the sample con-
taining the major interest; q = 1-p and E=margin of error. Since the proportion of the population is
not known with certainty, p = 0.5 is the assumption and q = 1–0.5 = 0.5, Z = 1.96 and E = 0.0566
(acceptable error term). According to Kothari (2004), an error term of less than 10% is acceptable.
Hence, the study used an error of 0.0566. This error was chosen to get the desired sample size that
was able to fit the budget and the time duration for the study.
A sample of 300 farmers was selected from a population of fodder farmers in the two sub-counties.
The 2019 Kenya National Bureau of Statistics (KNBS) data on the population of dairy cattle farmers in
the 2 Sub-counties of interest (clusters) was used. A proportionate population size of respondents for
each Sub-County was computed to get 300 respondents as shown in Table 1. Further, households
that feed their livestock mostly on Napier grass and natural grass were selected in the ratio of 2:3
respectively. Farmers who mostly practice natural grazing provided the control.
Figure 1. Map of the study area.
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2.3. Data collection
Primary data were collected electronically on Android smartphones and tablets through face-to-
face interviews using a semi-structured questionnaire by a team of trained enumerators. Data was
collected in June and July 2021 in Rachuonyo South and East sub-counties All the Ministry of
Health guidelines against the COVID-19 virus were observed by the enumerators. The data
obtained were then downloaded from Kobocollect as Comma-separated values (CSV) files and
exported to STATA version 16.0 for analysis. To check the understandability and validity of the
questionnaire before data collection, a pre-test was carried out. This helped in assessing the ease
of respondents’ understanding of the questions and their appropriateness under the study context.
It also helped in refining the questionnaire making it farmer-friendly.
2.4. Empirical model
2.4.1. Propensity score matching
This study used Propensity Score Matching (PSM). PSM is most appropriate for obtaining robust
impact assessments (Bii, 2017). Previous studies on the impact of fodder production on household
income have used similar techniques (Tesfaye et al., 2022). Farmers involved in fodder production
produced different types of fodder, including Napier grass, Brachiaria grass, maize fodder, and
desmodium. However, farmers in this study area mostly produced Napier grass with the rest of the
farmers feeding their livestock mostly on natural grass since natural grass is the main source of
feed for the majority of rural smallholder farmers in Homabay County. PSM is a two-step method
where the first step is to determine the probability of participation which is estimated to calculate
the propensity score for farmer households who participate in Napier grass production. In
the second step, each farmer that grows Napier grass is matched with the farmer with
a comparable propensity score to get the average treatment effect on treated (ATT). In this
study, it is the average income effect of farmers that grow Napier grass.
It may not be possible to observe the outcome of the farmers that are involved in Napier grass
production had they not produced Napier grass; hence it may be impossible to approximate the
effect of Napier grass production on the income of each household. To solve this problem, house-
holds need to be assigned to treatment and control in experimental studies. However, for non-
experimental studies, the fodder is not evenly distributed hence households choose what type of
fodder to produce. The decision of a farmer to produce Napier grass or not is most likely grounded
on self-selection since every farmer has different characteristics that may affect their involvement
in decision-making and their welfare.
2.4.2. Estimation of propensity score
The probit model was used to estimate the predicted values of the probability of participation in
Napier grass. The model is specified as in equation 2:
P(X
i
) represents the probability of growing Napier grass, D = 1 for the growers and D = 0 for non-
growers of Napier grass. The regression function is represented as in Equation 3
Table 1. Distribution of sample size
Sub-County Households Proportion Proportionate per
Cluster
Rachuonyo East 1,161 0.59 177
Rachuonyo South 813 0.41 123
Total 1,974 300
Source: KNBS, 2019
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Where φ is the standard normal distribution, β
0
represents the vector of coefficients, X
i
is the
vector of independent variables as in Table 2 and ε
i
is the error term.
There are several techniques used for matching, including local linear matching, radius, kernel
stratification or interval matching and nearest neighbour matching. Rosenbaum and Rubin (1983),
emphasized the use of nearest-neighbour matching where each treatment is matched to the
suitable control with the closest probability given a vector of observed covariates. This study thus
used the nearest-neighbour matching technique.
According to Bryson et al. (2002), introducing a common support state confirms that any
grouping of observed characteristics in the set that is treated can be observed among the control
group. Common support region is therefore the area where the minimum and maximum propen-
sity scores of both the treatments and the controls are contained. In checking the region of
common support, one should compare the minimum values and the maximum values of the
propensity scores in both groups. This approach involves removing all the observations with
propensity scores of less than the minima and more than the maxima in the other set. This
ensured that observations that lie outside the region were rejected from the analysis.
In determining the effect of treating an individual i that is symbolized as λi is referred to as the
difference between the potential outcome for treated and the potential outcome if not treated as
illustrated in equation 4:
Where Y
i
= 1 for treatment (farmers growing Napier grass), Y
i
= 0, for control (farmers depending on
Natural grass). In calculating the Average Treatment on Treated (ATT), the potential income from
livestock output from farmers growing Napier grass is calculated from its counterfactual (farmers
not growing Napier). Average Treatment on Untreated (ATU) is the difference between observed
income and the counterfactual income for the farmers who are not growing Napier grass. The
Table 2. Covariates used for propensity score matching
Variable Description Expected sign
Dependent variable
Participation Choice decision by the farmer to grow fodder
Independent variables
Age Age of household head (years) +
Gender Gender of household head (male=1, female=0) +/-
Education Education level of the household head (years) +
Household size Household size (numbers) +/-
Farm size Land under fodder (acres) +
Group membership Group membership (yes=1, no=0) +
Herd size Number of cattle owned by a farmer +
Volume Milk Volume (liters) +
Off Farm Income Income derived from off-farm activities (Kshs) +
Manure quantity Quantity of Manure (number of wheelbarrows) +
Credit access Access to credit (yes=1, no=0) +
Extension contacts Access to extension services (yes=1, no=0) +
Farming experience Experience in fodder production (years) +
Fertilizer quantity Quantity of fertilizer (kg) +
Farming labour Labour hours (man-hours) +
Fodder training Training on fodder production (yes=1, no=0) +
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impact felt across all the farmers in the population was obtained by calculating the Average
Treatment Effect (ATE) as in Equation 5:
E(λ) represents the expected or average value. The Average Treatment Effect on Treated (ATT)
used in measuring the impact of fodder production on household income for the farmers growing
Napier grass is represented in equation 6:
Average Treatment Effect on Untreated (ATU) as in Equation 7 is used to measure the impact
fodder production will have on the household income of the farmers that did not grow Napier grass
(Counterfactual)
One of the problems is; that all the parameters cannot be observed since they are likely to depend
on the counterfactual effects. For example, the average of a difference measures the difference of
the averages, and the Average Treatment on the Treated was re-written as in equation 8:
Equation 15 represents the average effect that individuals who are treated would most likely
obtain in the absence of the treatment, and this cannot be observed. On the other hand, the Y0
value for the farmers that are not treated is observed. Thus the causal effect (Δ) can be obtained in
equation 9:
The difference between the causal effect (Δ) and ATT was attained by adding and subtracting the
term, as in equations 10 and 11:
SB denotes selection bias which is the difference between the counterfactual for farmers that are
producing Napier grass (treated group) and the outcome that is observed for the controlled
farmers (untreated/not producing Napier grass). If SB is equivalent to zero, then ATT was obtained
by calculating the difference in mean between the observed mean for the treated and the
untreated.
To obtain a better measure of the causal effect, one needs to deal with the selection bias
effect. This can be obtained by pure randomization Successful randomization implies as in
equation 12:
With successful randomization, the t-test would provide statistical insignificance.
2.5. Test for multicollinearity
A multicollinearity test was performed through the computation of the variance inflation factor
(VIF) to ensure that the explanatory variables included in the model were not at all associated with
one another. An estimation of a simple ordinary least square (OLS) regression was done for the
dependent variable and the remaining explanatory variables. In ordinary least square regression,
the VIF gauges how severe the multicollinearity is. Gujarati (2003) states that VIF demonstrates
how the presence of multicollinearity causes an estimator’s variance to be inflated. VIF is calcu-
lated using the formula shown in Equation 13;
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where R2
i is the R
2
of the regression with the i
th
independent variable as a dependent variable.
The results of the VIF are presented in Table 3.
The VIF value of the predictor variables should neither be greater than 10 nor less than one
(Gujarati, 2003). VIF value greater than 10 indicates multicollinearity (Gujarati, 2003). The mean
VIF was 1.88. The VIF of the explanatory variables ranges from 1.09 to 3.97. The independent
variables’ VIF is less than five. No significant correlations between any of the independent variables
were established, ruling out the possibility of multicollinearity.
3. Results and discussion
3.1. Descriptive statistics
The household characteristics affecting the use of Napier grass and natural grazing among
households raising cattle in Homa bay are household size, age, gender, education level, main
occupation, group membership, types of cattle kept, total farm size and farm size under fodder.
The results are presented in two tables: categorical variables in Table 4 and continuous variables
in Table 5.
Results in Table 4 show that among the farmers who mainly fed their livestock on Napier grass,
male farmers were 69% and females 31%, while male farmers were 72% and 28% female who
grazed their cattle on natural grass. The chi-square results indicate that the gender of the Napier
grass and natural grass farmers was statistically insignificant. This implies that the gender of the
farmers was equally distributed among these two groups of farmers, but dominated by men.
According to Maina et al. (2020), male-headed households have access to land ownership, as well
as resources and information that ensure that they can easily adopt new technologies.
Among Napier grass farmers, (3%) had no formal education, primary (18%), secondary (32%)
and tertiary (47%) levels of education, respectively. On the other hand, 6%, 33%, 30% and 31% of
the farmers who fed their cattle mainly on natural grass had no formal education, primary,
secondary and tertiary level of education, respectively. The chi-square results indicate that
Table 3. Variance inflation factor (VIF) results
Variable VIF 1/VIF
Land under fodder (acres) 3.97 0.2517
Fertilizer quantity (Kgs) 3.45 0.2900
Land preparation (man days) 3.41 0.2936
Extension contact 1.75 0.5704
Manure quantity (wheelbarrows) 1.67 0.5977
Group membership 1.64 0.6079
Years of schooling 1.51 0.6607
Age of household head (years) 1.51 0.6618
Total herd size (numbers) 1.36 0.734
Household size (numbers) 1.35 0.742
Access to training 1.28 0.7826
Volume of milk (litres) 1.22 0.8194
Access to credit 1.1 0.9089
Gender 1.09 0.9193
Mean VIF 1.88
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education was statistically significant at a 1 per cent significance level, with implications that
farmers who mainly fed their herd on Napier grass were more educated than those who grazed on
natural grass. This finding is similar to that obtained by Mutimura et al. (2018) which indicated that
farmers who grew fodder had higher levels of education.
In terms of the main occupation, 29% of Napier grass and 40% of natural grass farmers
respectively depended on business. On the other hand, 40% and 15% of Napier and natural
grass farmers depended on formal employment. The result of the chi-square on main occupation
was statistically significant at a 1% significance level, with the implication that Napier grass
farmers had more resources in terms of occupation than natural grass farmers. This confirms
the results by Njima (2016) which indicated that the main occupation influences the decision of
the farmers to grow fodder.
Results on group membership indicated that 65% of Napier grass farmers belonged to
a cooperative group. On the other hand, 13% of natural grass farmers belonged to a group. The
chi-square results indicated that group membership was statistically significant at a 1% signifi-
cance level. This implies that the majority of farmers who mainly fed their cattle on Napier grass
belonged to the cooperative group rather than those grazing on natural grass. According to
Table 4. Categorical variables affecting use of Napier and natural grazing among farmers in
Rachuonyo East and South sub-counties
Variables Category Percentage Chi-square Sig
Napier Natural grazing
Gender Male 69.17 72.22 0.327 0.568
Female 30.83 27.78
Education level None 3.33 5.56
Primary 17.50 32.78 12.227*** 0.007
Secondary 31.67 30.56
Tertiary 47.50 31.11
Main
occupation
Business 28.33 39.44 0.000
Livestock 18.33 13.33 35.804***
Crop cultivation 8.33 22.22
Casual Labour 5.00 10.00
Formal
employment
40.00 15.00
Group
membership
Yes 65.00 13.33 85.651*** 0.000
No 35.00 86.67
*** indicates a significance level at 1%
Table 5. Continuous variables affecting use of Napier and natural grazing among farmers in
Homa Bay County
Variables Mean t-test p-value
Napier Natural grazing Combined
Household size (Numbers) 4.575 5.027 4.847 −2.070** 0.039
Age (Years) 44.300 47.417 46.17 −2.212** 0.028
Herd size (Numbers) 3.225 3.356 3.303 −0.500 0.618
** indicates a 5% significance level
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Nchinda et al. (2010), farmers who belong to groups are exposed to a variety of ideas as well as
have easy access to information concerning new technology and innovation. In addition, these
farmers have greater bargaining power, especially when they are purchasing farm inputs as well as
marketing their farm output
In Table 5, the mean household size was 4 members for farmers who mainly fed their cattle on
Napier grass and 5 members for farmers who practised natural grazing. The overall average
household size was 4.85 members, which was higher than the national average of 3.4 persons
(KNBS, 2019). Results of the t-test indicate that household size was significant at 5 per cent, with
the implication that farmers who mainly fed their cattle on Napier grass had a smaller household
size than farmers who mainly graze their cattle on natural grass. The larger household size
provides family labour necessary for grazing animals. This is similar to the findings by Umeh
et al. (2016) which established that larger household size is the source of labour in agricultural
production.
The average age of the farmers who mainly fed their cattle on Napier grass was 44 years, while
those who grazed their cattle on natural grass were about 47 years. The t-test result indicates that the
age of farmers was significant at a 5 per cent significance level. This implies that farmers who fed their
cattle on Napier grass were younger than those who grazed cattle on natural grass. Younger farmers
are responsive to fodder production technologies, including new types, such as Brachiaria, lucerne, and
Boma Rhodes which have the potential to increase milk production among lactating cattle. This is
contrary to the results obtained by Mutimura et al. (2018) which found that older farmers were more
likely to grow Brachiaria since they have more experience than younger farmers.
Results on the average number of cattle kept by farmers indicated that farmers feeding their cattle
on Napier farms kept fewer animals (3.2) than those who mainly graze their animals on natural grass
(3.4). However, the result of the t-test indicates that the total number of cattle was insignificant, thus
explaining that there was no difference between the number of cattle owned among farmers who
mainly fed their herd on Napier grass and those who mainly grazed on natural grass.
3.2. Estimation of the probability propensity scores
The probability of the households feeding their livestock on Napier grass and natural grass was
estimated using the Probit model as in Table 6. All the observable covariates affecting participation
and livestock income were considered in the model.
Results indicated that the estimated model performed well for the intended matching method
with a Pseudo R
2
of 90.45 per cent. The log-likelihood ratio for the model is −19.28 and the
probability value of 0.000 implied that the regression coefficients were not equal to zero.
Increased household size decreases the farmers’ probability of feeding their livestock on Napier
grass by 43.17% at a 10 % significance level. This is probably because farmers with large house-
hold sizes use large proportions of their land to invest in crops that they consume directly while
allocating less land for fodder production. This is similar to the findings by Davies and Gouveia
(2010) who found a negative significant relationship between household size and farmers’ parti-
cipation in fodder production. However, this finding is different from Gebremedhin et al. (2015)
who mentioned that the size of farmers’ households is directly proportional to the demand for food
as well as other income necessary to cater for other necessities, hence increasing the farmer’s
participation in fodder production.
Increased herd size decreases the probability of farmers feeding their livestock on Napier grass by
39.31% at a 10% significance level. A possible reason is that farmers allocated small land sizes for
fodder production thus the quantities of fodder produced remains insufficient to feed large herd size.
This is different from the findings by Omollo et al. (2018) who established that larger herd size
influences the decision of farmers to grow fodder hence they would feed their cattle on fodder.
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Labour required for land preparation was found to be statistically significant on the decision by
the farmer to mainly feed their livestock on Napier by 67.69% at a 10% significance level. During
land preparation, labour is important in incorporating fertilizers, reducing weeds, and increasing
aeration as well as porosity in the soil (Baudron et al., 2019). In addition, more man-days for labour
during land preparation makes the soil settle and facilitates the decomposition of soil organic
matter (Baudron et al., 2019). Similarly, Turinawe et al. (2012) mentioned that the demand for
labour has a direct impact on the demand for fodder technologies with larger families likely to
grow fodder.
Land size under fodder was found to increase the chances of feeding the livestock mainly on
fodder by 1433% at a 1% significance level. Land is a productive asset that is used as collateral in
accessing credit in investments for production activities (Komarek, 2010). The large size of land
under fodder makes farmers produce more fodder which is adequate to feed their livestock and
excess can be sold to get income that can cater for food and other necessities. This is in line with
the study by Omollo et al. (2018) which indicated that land size informs the decision of the farmer
to grow fodder crops.
Access to extension contacts was found to decrease the likelihood of farmers feeding their
livestock on Napier grass by 239.1%. This is contrary to previous findings where access to extension
is likely to enhance the ability of farmers to join groups, where they receive training on fodder
agronomy, fodder utilization and markets for their livestock output. As a result, these farmers
decide to feed their livestock on various types of fodder, and hence high milk volume which
translates to high household income. Our results are thus not in line with those obtained by
Bahta and Bauer (2007) which indicated that farmers who had more extension services were
exposed to more information, especially on the current farming technology.
3.3. Region of common support
The region of common support is used to ensure that treatments have closer observations in the
nearby propensity score distribution (Heckman et al., 1997). Results in Table 7 and Figure 2 indicate
that there were about 106 cases in the treatment group that were outside the region of common
Table 6. Probit Estimation of factors influencing the decision of farmers to mainly feed their
livestock on Napier grass
Variable Coefficient Std. Err. Z P >/Z/
Gender −0.5901 0.7567 −0.78 0.435
Age (years) −0.0131 0.0299 −0.44 0.662
Years of schooling 0.1619* 0.0950 1.70 0.088
Household size (number) −0.4317* 0.2496 −1.73 0.084
Herd size (number) −0.3931* 0.2206 −1.78 0.075
Manure quantity(wheelbarrows) −0.1834 0.1924 −0.95 0.340
Land preparation (man days) 0.6769* 0.3216 2.10 0.035
Land size fodder (acres) 14.3386*** 3.8932 3.68 0.000
Volume of milk(litres) −0.1594 0.1173 −1.36 0.174
Extension access −2.3994* 1.0995 −2.18 0.029
Fodder training 0.9276 0.6008 1.54 0.123
Access to credit −0.7982 0.6307 −1.27 0.206
Group membership −0.4863 1.0402 −0.47 0.640
Fertilizer quantity (Kg) 4.8430 22.5206 0.22 0.830
Constant −2.0706 1.7689 −1.17 0.242
Number of observations= 300, LR Chi
2
= 365.25, Pseudo R
2
= 0.9045, Prob>Chi
2
=0.0000, Log likelihood= −19.280672
***, **, * represents significance levels at 1%, 5% and 10% respectively.
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support. The individuals between treatment and control groups in which the characteristics were
observed could be compared since they fall in the region of common support, hence the inferences
can be made about causality.
Before determining the impact of fodder production on livestock income, balancing properties of
propensity scores need to be taken into consideration in ensuring that observations have
a distribution of the propensity scores or not. The balancing check of covariates is used to compare
any significant differences between the matching algorithms by use of the nearest neighbour
matching technique (Tolemariam, 2022). The balancing power between the estimators of the
matched and unmatched households that feed their livestock on fodder was estimated using
test methods such as reduction in mean, percentage reduction in bias and equality of means using
the t-test.
3.4. Testing for covariate balance between treated and control groups
The covariate imbalance was checked after matching with a propensity score-test command. It
shows a percentage reduction in bias, referred to as a standardized bias. According to Rosenbaum
and Rubin (1983), a good bias reduction is below 5%, although a reduction of below 10% is
reasonable.
Table 8 indicates that the matching was less biased in the covariates which were below 10%.
The standardized bias difference before matching was ranging between 2.2% and 169.5%, with
a statistical difference before matching. After matching, results indicated that the standardized
bias difference was between 0.3% and 49.2% in absolute terms. This shows that there was
Table 7. Region of common support
Treatment
assignment
Off support On support Total
Untreated 0 180 180
Treated 106 14 120
Total 106 194 300
0 .2 .4 .6 .8 1
Propensity Score
troppusnO:detaerTdetaertnU
Treated: Off support
Figure 2. Region of common
support from the propensity
score matching.
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Table 8. Propensity score test for covariates based on nearest neighbor matching technique
Variables Sample Mean % reduction t p>|t|
Treated Control % bias bias
Gender Unmatched 0.6917 0.7222 −6.7 −0.57 0.569
Matched 0.7143 0.7857 −15.6 −133.8 −0.42 0.676
Age(years) Unmatched 44.3 47.417 −26.5 −2.21 0.028
Matched 40.429 44.286 −32.8 −23.8 −0.99 0.329
Years of schooling Unmatched 12.417 9.9833 57.2 4.77 0.000
Matched 12.714 13.857 −26.9 53.0 −1.05 0.302
Household size Unmatched 4.575 5.0278 −24.7 −2.07 0.039
Matched 4.6429 4.9286 −15.6 36.9 −0.62 0.538
Herd size (numbers) Unmatched 3.225 3.3556 −5.9 −0.50 0.618
Matched 2.9286 3.2143 −13.0 −118.8 −0.54 0.595
Quantity of manure
(wheelbarrows)
Unmatched 1.45 1.0417 18.4 1.67 0.097
Matched 3.25 3.7143 −20.9 −13.7 −0.51 0.611
Labour on land preparation(man
days)
Unmatched 5.5417 1.1111 137.3 12.84 0.000
Matched 2.8571 3.3571 −15.5 88.7 −1.41 0.171
Land under fodder
(acres)
Unmatched 0.5388 0.0766 169.5 15.87 0.000
Matched 0.3009 0.3018 −0.3 99.8 −0.02 0.988
Volume of milk
(litres)
Unmatched 3.55 2.3903 43.5 3.87 0.000
Matched 3.7857 4.7857 −37.5 13.8 −0.72 0.480
Extension contacts Unmatched 0.3583 0.40 −8.6 −0.73 0.469
Matched 0.3571 0.2857 14.7 −71.4 0.39 0.699
Fodder training Unmatched 0.4417 0.4833 −8.3 −0.71 0.480
(Continued)
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Table 8. (Continued)
Variables Sample Mean % reduction t p>|t|
Treated Control % bias bias
Matched 0.5 0.5714 −14.3 −71.4 −0.37 0.717
Access to credit Unmatched 0.45 0.4611 −2.2 −0.19 0.850
Matched 0.2857 0.5 −42.9 −1828.6 −1.15 0.262
Group membership Unmatched 0.3917 0.3056 18.1 1.54 0.124
Matched 0.2143 0.2143 0 100 0 1.000
Quantity of fertilizer (Kgs) Unmatched 14.113 0.0056 149 14.14 0
Matched 0 0 0 100 0
Sample Pseudo R2 LR Chi2 P> chi2 Mean Bias Med Bias
Unmatched 0.905 365.25 0.000 48.3 21.5
Matched 1.000 38.82 17.9 15.5
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a significant difference between the matched and unmatched data, hence the high degree of
covariate balance between treatment and control groups was created.
3.5. Average treatment effects on income
The average income earned by farmers was compared for the farmers who mainly fed their
livestock on Napier grass and those who practised direct natural grazing to determine the impact
of fodder production on household income. The results indicate that there was a significant
difference (p > 0.08) between the incomes of farmers who mainly fed their livestock on Napier
grass and those who practised natural grazing (Table 9). The average income of farmers who
mainly fed their livestock on Napier grass in a season was Ksh. 15707.04 (USD 103.11) which was
a higher figure compared to the average income of farmers who mainly fed their livestock on
natural grass, which was Kshs. 11790.48 (USD 77.40). This means that feeding livestock on Napier
grass increases the income of farmers by Kshs. 3916.67 (USD 25,71) or by 24.94% after controlling
for the differences in both socio-economic and institutional factors for both the treated and control
groups. This suggests that fodder production and utilization play a significant role in improving the
income status of livestock farmers in Rachuonyo East and Rachuonyo South Sub-counties.
Farmers who mainly feed their livestock on Napier grass fetch higher income from livestock
production compared to those who feed their livestock on natural grass. However, the difference in
income was 24.94%, which could be explained by the adequate food supply for the livestock from
fodder and the different forms of fodder utilized. The values of the controls and the treated
outcome variable were close, with implications that the confounder can be simulated to provide
a large outcome value. The study indicates that the ATT estimates for the income of the household
are robust indicators of the effects of fodder production on livestock income.
3.6. Sensitivity Analysis
Since the PSM technique cannot fully adjust for unobservable characteristics, Aakvik (2001) pro-
posed using the Mantel and Haenszel (1959) test statistic for detecting hidden bias (Tagel and
Table 9. Average treatment effects on income
Variable Sample Treated Controls Difference S.E t-stat
Income effects Unmatched 13130 7381.17 5748.83 1650.62 3.48
ATT 15707.14 11790.48 3916.67 9045.82 0.08
USD = 152.34 Kshs
Table 10. Mantel- Haenszel for household income
Gamma (Γ) Q_mh+ Q_mh- P_mh+ P_mh-
1 4.1325 4.1325 0.000018 0.000018
1.05 4.1696 4.2908 0.000015 8.9e-06
1.1 4.113 4.3499 0.00002 6.8e-06
1.15 4.0597 4.4072 0.000025 5.2e-06
1.2 4.0093 4.4628 0.00003 4.0e-06
1.25 3.9615 4.5169 0.000037 3.1e-06
1.3 3.9161 4.5695 0.000045 2.4e-06
1.35 3.8729 4.6207 0.000054 1.9e-06
1.4 3.8318 4.6707 0.000064 1.5e-06
1.45 3.7924 4.7195 0.000075 1.2e-06
1.5 3.7548 4.7673 0.000087 9.3e-07
Note: MH bounds using STATA 13. Γ = 1≈no “hidden” bias(odds of differential assignment due to unobserved factors).
Q_ mh+=Mantel-Haenszel statistic (assumption: overestimation of treatment effect); Q_ mh- =Mantel-Haenszel
statistic (assumption: underestimation of treatment effect); P_ mh+= Significance level (assumption: overestimation
of treatment effect); P_ mh_=Significance level (assumption: underestimation of treatment effect).
Ayuko et al., Cogent Food & Agriculture (2024), 10: 2292868
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Anne, 2015). Thus, the purpose of sensitivity analysis is to determine the robustness of the
estimated treatment effects. Mhbounds was used in computing Mantel- Haenszel Bounds for
variable treatment in checking for sensitivity analysis of the average treatment effects as well as
the critical hidden bias. The Mantel-Haenszel non-parametric test compares the successful number
of participants against the same expected number given that participation is zero. According to
Becker and Caliendo (2007), the hidden bias arises when unobserved factors influence the decision
to participate in an activity. Sensitivity analysis was used to compare the baseline treatment
effects together with the simulated treatment effects by comparing the values of the outcome
effects and selection effects.
Table 10 has the results of the Mhbounds where ┌ = 1 shows the absence of unobserved factors
and they were increased by 0.05. Results of the sensitivity analysis indicate that the estimated
treatment effects were insensitive to the unobserved bias, with gamma ranging from 1 to 1.5.
A gamma level of 1.5 implies that all individuals with the same independent variables vector differ
from those who feed their livestock on natural grass by a factor of about 0.5 per cent. The different
levels of bounds indicate the degree to which unobserved negative and positive selection effect
becomes significant. Results show that Q_mh+ and Q_mh- test statistic has almost similar results
across the bounds as a result of the unobserved factors. The positive values of Q_mh+ imply
a positive selection bias, where farmers that mainly feed their livestock on fodder have a high
income from livestock. The insignificant values of P_mh+ and P_mh- indicate that there was no
bias, hence there were no cases of underestimation and overestimation of the treated effect.
Results indicated that the study was insensitive to bias that could make changes in income from
livestock as a result of fodder production.
4. Limitations of the study
The study is limited by the use of questionnaires to collect primary data since respondents rely on
recall information, which may not be correct but is a frequent limitation of surveys of a similar
nature. This study’s use of primary cross-sectional data and exclusive focus on fodder farmers in
Homabay County, Kenya, is another drawback. An examination of how impact may change over
time in the region might be possible using a longitudinal approach and qualitative data as well.
Despite its limitations, this study offers some interesting viewpoints into the effects of fodder
production on smallholder farmers’ household income in Homabay County, Kenya, and provides
baseline information for conducting related case studies in other settings within the country and
beyond.
5. Conclusion and policy recommendations
The findings of this study revealed that the quantity of labour put into land preparation, the area of
land utilized for fodder production, the number of extension contacts, and accessibility to training
all had a favourable influence on farmers’ likelihood of primarily feeding their livestock on Napier
grass. On the other hand, the likelihood that farmers would feed their cattle with fodder was
negatively influenced by household size, herd size, and the quantity of manure used. The findings
reveal a significant income disparity between farmers who mostly grazed their cattle on natural
grass and those who fed their cattle Napier grass. Compared to farmers who largely let their cattle
graze on natural grass, those who predominantly fed their herds Napier grass made much more
money. The use of various types of fodder and the sufficient supply of fodder are credited with this.
Farmers require access to information media, including television, radio, and other print
media, thus the government and policymakers need to focus on these media sources. As
a result, farmers would have more information on the different types of fodder, the volume
produced, and the benefits. To increase farmers’ income, the national and county governments
should make fodder production a priority for livestock development in their policy plans. This is
because there was a significant difference in the incomes of farmers who mostly fed their
cattle Napier grass against those who primarily grazed their cattle on natural pastures, indi-
cating the potential for fodder to increase farmers’ income.
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Author details
Mary Stacey Ayuko
1
ORCID ID: http://orcid.org/0000-0001-9240-0252
Job Kibiwot Lagat
1
Michael Hauser
2
Kevin Okoth Ouko
3
E-mail: kevinkouko@gmail.com
ORCID ID: http://orcid.org/0000-0001-9894-5042
Dick Chune Midamba
4
ORCID ID: http://orcid.org/0000-0003-4467-419X
1
Department of Agricultural Economics and Agribusiness
Management, Egerton University, Kenya.
2
The International Crops Research Institute for the Semi-
Arid Tropics (ICRISAT), Nairobi, Kenya.
3
Department of Agricultural Economics and Agribusiness
Management, School of Agriculture and Food Sciences,
Jaramogi Oginga Odinga University of Science and
Technology, Bondo, Kenya.
4
Department of Rural Development and Agribusiness and,
Faculty of Agriculture and Environment, Gulu University,
Kampala, Uganda.
Data availability statement
The data that support the findings of this study are
available from the corresponding author upon reasonable
request.
Contributions
All the authors contributed to this work.
Ethics approval
Ethical approval was granted by the Egerton University
Ethics Review Committee and the National Commission
for Science, Technology and Innovation (NACOSTI).
Disclosure statement
No potential conflict of interest was reported by the
author(s).
Citation information
Cite this article as: Effects of fodder production on small-
holder farmers’ household income in Homa Bay County,
Kenya: An application of propensity score matching, Mary
Stacey Ayuko, Job Kibiwot Lagat, Michael Hauser, Kevin
Okoth Ouko & Dick Chune Midamba, Cogent Food &
Agriculture (2024), 10: 2292868.
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