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Open Access Library Journal
2023, Volume 10, e9671
ISSN Online: 2333-9721
ISSN Print: 2333-9705
DOI:
10.4236/oalib.1109671 Jan. 16, 2023 1 Open Access
Library Journal
The Level and Determinats of Technical
Efficiency in Fodder Production in
Homa Bay County, Kenya
Mary Stacey Ayuko1*, Job Kibiwot Lagat1, Michael Huaser2
1Department of Agricultural Economics and Agribusiness Management, Egerton University, Egerton, Kenya
2ICRISAT, Nairobi, Kenya
Abstract
Fodder production is important for dairy producers in Kenya as it plays a
major role in the quality and quantity of milk produced. It contributes to im-
proved income through forage sales, milk sales
and livestock sales. There has
been an increasing demand for fodder both locally and in neighboring coun-
tries. This has led to an increase in the production of fodder in the country.
Despite the growing demand of fodder in Kenya, the current production doe
s
not meet the demand. In response, efforts have been initiated by different pro-
grams with the help of the International Livestock Research Institute (ILRI),
towards promoting fodder production with a view to increasing milk produc-
tion. This paper analyzed the level of technical efficiency and its determinants
in fodder production.
Primary data was collected in Rachuonyo East and
South
sub-
counties using structured questionnaires on a sample size of about 300
farmers. Stochastic Frontier model and Tobit model were
used for analysis.
Results of stochastic frontier analysis indicated that land size, quantity of
planting materials, labour for planting, and labor for weeding influenced the
level of technical efficiency. Tobit’s results indicated that herd size,
group
membership, access to credit, household size and access to train
ing affected
the level of technical efficiency of farmers. The study recom
mended the need
for farmers to increase land allocation for fodder, increase planting materials
and the number of man days during planting and weeding to increase fodder
output.
Subject Areas
Economics
Keywords
Fodder, Production Technology, Technical Efficiency
How to cite this paper: Ayuko, M.S.,
Lagat, J.K. and Huaser, M.
(2023)
The Level
and Determinats
of Technical Efficiency in
Fodder Production in Homa Bay County,
Kenya
.
Open Access Library Journal
,
10
:
e9671
.
https://doi.org/10.4236/oalib.1109671
Received:
December 10, 2022
Accepted:
January 13, 2023
Published:
January 16, 2023
Copyright © 20
23 by author(s) and Open
Access Library Inc
.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
M. S. Ayuko et al.
DOI:
10.4236/oalib.1109671 2 Open Access Library Journal
1. Introduction
In smallholder farms in Kenya, the cost of dairy feeding accounts for between 60
and 80 percent of overall production expenses, and efficient feeding has the po-
tential to increase farmers’ profit margins significantly [1]. The production of
fodder is progressively gaining popularity as a source of both feed for livestock
and income for pastoral households. It is another increasingly significant non-
food intervention that is carried out to increase household resilience to drought
and rising food commodity prices [2]. Despite a large number of smallholder far-
mers in Kenya being aware and exposed to different fodder crops, only 55% of
the farmers cultivate at least one type of fodder in their farms [3].
Kenya is experiencing severe fodder shortages estimated to be 70 percent of
the country’s yearly fodder requirements of over 5.5 billion bales being met by
imported fodder. The deficiency is due to insufficient fodder supply and conser-
vation, as well as overgrazing, poor land management techniques and climate
change effects [4]. Additionally, land subdivision as a result of the land inherit-
ance rules, rapid growth in population and the government’s resettlement strat-
egy; have put a strain on animal feed resources in the county [4]. Due to the de-
veloping demand for fodder by neighboring countries, the overall fodder demand
is projected to increase. To produce this amount of fodder, an extra 15 million
acres of land would need to be converted to fodder crops and pastures; however,
this could be achieved by moving to dry and semi-arid regions. It is consequent-
ly recommended that unless targeted strategic fodder interventions are executed
on a national scale, livestock productivity is likely to be affected leading to re-
duced yields of animal products in the medium and long term [5].
Farmers in Homa Bay County, Kenya are progressively adopting fodder pro-
duction not only to address pasture shortage, which is mainly caused by recur-
rent droughts, but also to supplement earnings generated from livestock produc-
tion. Several efforts have been initiated by different programs like Accelerated
Value Chain Development (AVCD), CIAT, KALRO and County Governments
with the help of the International Livestock Research Institute (ILRI), towards
promoting fodder production with a view to increase milk production and im-
prove household income. Rachuonyo East and South sub-counties were among
the sub-counties that benefited from this program. Inputs utilization in fodder
production in Homa Bay County is still unknown. This paper therefore intends
to analyze the level of technical efficiency and its determinants that have not
been evaluated in the county.
2. Literature Review
Different factors have an influence in the production of agricultural commodi-
ties. Such factors include the market factors, socio-economic characteristics, in-
stitutional and external factors. The socio-economic factors that affect the pro-
duction of fodder include age, gender, level of education, experience, off-farm
income, farm size and household size [6]. Institutional characteristics that have
M. S. Ayuko et al.
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an influence on production of fodder include; access to extension services, mem-
bership to a fodder group, land ownership, credit access, contractual arrange-
ments, infrastructure, government policies and laws. The external factors that
the farmers have no control over include natural calamities (flood, drought, fire
outbreak, inversion of pests), which also influence agricultural production. There
are market factors that influence agricultural production such as the prices of
output, means of transport and distance to the market [7].
Lugusa [8] found that in Southern Kenya, financial revenues of three range
grasses were affected by persistent droughts, termite invasion and seed loss. The
primary challenge for fodder production among smallholder farmers is the in-
adequate availability of land. This requires intensification and commercializa-
tion of fodder production [9]. The total household land size determines the land
availability and size that a producer can assign to the production of fodder. Far-
mers in Mexico strongly agreed that land availability and the need for land to be
fertile are necessary to use improved grassland [10]. Households owning bigger
tracts of land are able to set aside some space for fodder production. These
households are more likely to benefit from producing in large quantities that re-
sult in lowering production cost and increasing fodder production [7].
Household size is important in determining the level of fodder production
since labor is mostly provided by the family. Production is more in larger fami-
lies as compared to smaller ones [8]. The age of a household decision-maker is also
considered a key aspect influencing the access to and availability of fodder pro-
duction and livelihood resources [8] [11]. The head of households is more likely
to be richer at above 35 years of age than those who are young, raising their
probability of uptaking new technologies in fodder production.
Gender of the household decision maker is important in determining access to
assets and resources mainly in the African rural setting. In sub-Saharan Africa,
households headed by males have more access to factors of production like land,
livestock and finance as compared to households headed by female. This is due
to the fact that males are household heads as well as have land rights that make
them access these resources [12]. Omollo
et al
. [13] found that 74 percent of
fodder producers were headed by males while 55.3 percent were headed by fe-
males in Baringo and Makueni counties. Further, gender of the household deci-
sion maker was significant and had a positive (p < 0.05) influence on household’s
participation in fodder farming. This implies that households headed by male
were more likely to adopt fodder production as compared to those headed by
female [13]. This can be clarified due to the fact that the males have more access
and control over the factors of production such as land, livestock and finance
than the females [14] [15]. Moreover, this outcome can be linked to the increas-
ing labor requirement of fodder production and the household tasks of women
in the society. This limits their time to access information during agricultural
trainings and extension provision [16] [17] [18]. Marginal effects indicate that
encouraging participation of gender in fodder production can increase the chance
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of fodder production uptake by 20% [13].
3. Research Methodology
3.1. Study Area
This study was undertaken in Homa Bay County, Kenya. Homa Bay is located
in Western region of the country, alongside the south shore of Lake Victoria
lying at latitude of 0.6833˚S and longitude of 34.4500˚E. Homa Bay County is
covering an area of approximately 3183.3 sq. km, having a population of 1,131,950
(male—539,560 and female—592,367), and according to the 2019 National Census
[19]. The County is located about 420 km from Nairobi. It is bordering Migori
county to the south, Kisii county and Nyamira to the east, Kisumu county to the
north and Kericho county to the northeast. Homa Bay County is also bordering
Lake Victoria to the north and west. Figure 1 indicates the map of the study area.
3.2. Sample Size
The sample size was determined using the formula given by Kothari [20] as
Figure 1. Map of the study area.
M. S. Ayuko et al.
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10.4236/oalib.1109671 5 Open Access Library Journal
illustrated below:
2
2
pqZ
nE
=
(1)
where;
n
= required sample size;
Z
= confidence level (
α
= 0.05);
p
= proportion of
the sample containing 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 as-
sumption and
q
= 1 − 0.5 = 0.5,
Z
= 1.96 and
E
= 0.0566 (acceptable error term).
According to Kothari [20], an error term of less than 10% is acceptable. Hence, the
study used an error of 0.0566. This error was chosen so as to get the desired sam-
ple size that was able to fit the budget and the time duration for the study.
2
2
1.96 0.50 0.50 300
0.0566
×× =
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 (clus-
ters) was used. A proportionate to population size of respondents for each
sub-county was computed to get 300 respondents (Table 1).
3.3. Empirical Model
To achieve this objective, Stochastic Frontier Model was used. Various function-
al forms namely; Translog, Cobb-Douglas, quadratic and linear functions can be
used [21]. Cobb-Douglas and the Translog functions are mostly used. Translog
function is flexible though multicollinearity problems may show up. Cobb-Douglas
functional form was used since its simple, self-dual and has been broadly used in
agricultural production technologies in various countries that are still develop-
ing [22]. According to Battese and Coelli [23], the following stochastic frontier
production function of Cobb-Douglas model was used in the estimation of tech-
nical efficiency of fodder production.
01 12 23 3
ln ln ln ln ln
i n ni i
Y X X X X VU
ββ β β β
= + + + ++ +−
(2)
where ln is the natural logarithm,
: 1, 2 , 3, ,in
,
Yi
is the total quantity of fod-
der production in 90 kilograms,
β
0 is the constant term,
β
1 −
βn
parameters to be
estimated,
X
1 −
Xn
vectors of explanatory variables (amount of fertilizer used,
labor, planting material and farm size),
Vi
is a symmetric random error and
Ui
is
half normal error term. Equation (2) above was estimated using the maximum
Table 1. Distribution of sample size.
Sub-County
Households
Proportion
Proportionate per Cluster
Rachuonyo East
1161
0.59
177
Rachuonyo South
813
0.41
123
Total
1974
300
Source: KNBS, 2019.
M. S. Ayuko et al.
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likelihood estimates (MLE).
Technical efficiency (TE) was computed as the ratio of the observed output
Yi
to the frontier output
*
i
Y
.
( )
( ) ( )
*
exp exp
exp
i ii
i
ii
ii
i
X VU
Y
TE U
XV
Y
β
β
+−
= = = −
+
(3)
where
TEi
was the technical efficiency of farmer
i
,
Yi
was the fodder output ob-
served,
*
i
Y
was the maximum potential fodder output. This model was esti-
mated with the use of stochastic production function and the Maximum Like-
lihood Estimates (MLE). FRONTIER 4.1 which is a computer program was used
in the estimation of parameters of the stochastic production function and the ef-
fects of technical inefficiency. Once the socio-economic characteristics of the
fodder farmers and technical efficiency of production was determined, farm in-
puts variables expected to cause variation in fodder production efficiency were
tested as the determinants of the technical efficiency. The explanatory variables
were chosen based on past empirical studies or intuition. The level of TE of the
farmers was regressed on these factors for the purpose of determining the con-
tribution made by each variable [24]. Since technical efficiency scores range from
0 to 1, a Tobit regression model with two limits was used to analyze the link be-
tween socio-economic and institutional factors on technical efficiency. A two-step
method that is frequently utilized was applied in this study. The first step was to
estimate the technical efficiency scores by using the Stochastic Frontier Model.
Second step was to regress the technical efficiency scores on farmer characteris-
tics variables to detect their impact on technical efficiency.
*01
i ji
k
ji
j
UZ
β βµ
=
=++
∑
*
*
1 if 1 (4)
0 if 1 (5)
i
i
U
U
≥
≤
where
*
i
U
is the efficiency scores of the
i
th famer,
β
0 is a constant,
βj
are the pa-
rameters to be estimated,
ui
is the error term,
Zij
are farm and farmer characte-
ristics variables. Table 2 and Table 3 list the variables used in the model.
Table 2. Variables used in Stochastic Frontier Production Model
Variable
Description
Expected sign
Dependent variable
Output (Y)
Total fodder output (kgs)
Explanatory variable
FrmS
Land under fodder (acres)
+
Plantmat
The quantity of planting materials used in kgs
+/−
Llprep
Labor for land preparation (man hours)
+/−
Lpln
Labor for planting (man hours)
+/−
Lwed
Labor for weeding (man hours)
+/−
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Table 3. Variables used in two-limit regression model.
Variable
Description
Measurement
Dependent variable
Technical efficiency (U)
Technical inefficiency measures
Between 0 and 1
Explanatory variable
Age
Age of household head
years
Gender
Gender of household head
male = 1, female = 0
Hhs
Household size
Number
EducLev
Education level of the household head
years
GrpM
Group membership
yes = 1, no = 0
HrdS
Number of dairy cattle owned by a farmer
Number
Acc
Credit access
yes = 1, no = 0
Ext
Access to extension services
yes = 1, no = 0
Trn
Training on fodder production
yes = 1, no = 0
4. Results
4.1. Stochastic Production Frontier Estimates
A stochastic frontier model was used to analyse the level and determinants of
technical efficiency of fodder farmers. The dependent variable was the quantity
of Napier grass produced in 90 kg bags while independent variables used were
land size under fodder, quantity of planting materials, and labor in man days for
land preparation, labor in man days for planting and labor in man days for
weeding.
Table 4 presents the results of Maximum Likelihood Estimates (MLE) of the
Cobb-Douglas stochastic production function of fodder farmers. The diagnostic
statistics such as Sigma-squared, Wald chi-square and log likelihood are presented
together with the results of efficient use of resources (TE). The Wald Chi-square
statistic (3425.90) is statistically significant at 1%, with the implication that all
the variables that were included in the stochastic production function jointly in-
fluenced fodder output.
Results indicate that Sigma-squared is 0.1132, hence lies between 0 and 1. A
value of Sigma-squared equal to 0 implies that technical inefficiency is not
present while a value that is close to 1 implies that the stochastic frontier model
used is appropriate. In addition, the value of Sigma-squared is a measure of com-
posite error distribution and the measure of goodness of fit. The value of lambda
is 1.3442 and is found to be statistically significant at 5 percent significant level.
This is an implication that 134% of the variation in fodder output is attributed to
inefficiency. The value of log likelihood was found to be 13.7709 and is found to
be statistically significant at 1 %, hence indicating the presence of inefficiency in
the data.
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Table 4. Maximum Likelihood Estimates for Stochastic Frontier production function.
Variable
Coefficient
Std. Err
Z-value
p > /z/
Ln_Land size under fodder
0.515***
0.069
7.47
0.000
Ln_Quantity of planting
materials
0.257*** 0.073 3.54 0.000
Ln_Labor for land preparation
0.228
0.041
0.55
0.582
Ln_Labor for planting 0.102* 0.059 1.74 0.082
Ln_Labor for weeding 0.202*** 0.055 3.67 0.000
Constant
3.495
0.225
15.56
0.000
/lnsig2v −3.211 0.251 −12.78 0.000
/lnsig2u
−2.619
0.407
−6.44
0.000
Sigma_v
0.201
0.025
Sigma_u
0.270
0.055
Sigma-squared
0.113
0.022
Lambda
1.344
0.077
Number of observations = 225
Wald Chi2 (5) = 3425.90
Log likelihood = −13.7709
Prob > Chi2
=0.0000
*, **, *** significant at 10%, 5%, 1%.
The results indicate that the sum of the partial elasticity of the factor inputs is
1.304. A function coefficient of less than 1 indicates decreasing returns to scale
while a function coefficient of more than one indicates increasing returns to
scale. On the other hand, a sum of the partial elasticity of 1 indicates constant
returns to scale. Elasticity of 1.304 implies that if all the inputs in Table 4 are in-
creased by 1 %, the fodder output would increase by 1.304%. This means that
most of the farmers were in stage one of the production region, hence every
proportionate increase in unit of factor of production result in more than pro-
portionate increase in fodder output for the farmers. This means that farmers
have the potential to increase their production, thus they are not efficient in al-
location of resources. In other words, the increasing return to scale implies that
the production is inefficient with a room to increase production at an increasing
rate.
The coefficients of land size under fodder, quantity of planting materials used,
labor in man days for planting and labor in man days for weeding were all posi-
tive, indicating that all these inputs were used in the rightful proportions. In ad-
dition, the positive coefficients of these inputs,
ceteris peribus
, would increase
the total fodder output.
The coefficient of land size under fodder was positive and statistically signifi-
cant at 1%, implying that a percent increase in land size would increase the fod-
der output by 0.515. The higher elasticity of land size indicates that land size has
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a strong significant effect on fodder output. This finding is similar to Orewa and
Izekor [25] that farm land expansion increases the technical efficiency of yam
farmers in Edo State, Nigeria. However, this finding is different from Desale [26]
that as the farm size increases, the management ability of farmers’ decrease given
the existing technology, and hence leading to reduced level of efficiency.
The coefficient of quantity of planting materials was positive and statistically
significant at 1% level. This result indicates that a percentage increase in quanti-
ty of planting materials increase fodder output by 0.257. The yield of fodder was
higher with large quantities of planting materials used. This is possible since
good planting materials as well as quality seeds are able to improve the produc-
tion of fodder. This is similar to Kibaara [27] which revealed that improved effi-
ciency of agricultural production output is significant especially in overcoming
problems of low yields by enhanced supply of improved seed variety.
The coefficient of labor for planting and weeding was statistically significant
at 10% and 1% level, respectively. This implies that a percentage increase in the
number of days by farmers in planting and weeding increases the fodder output
by 0.102 and 0.202, respectively. An increase in the number of man-days would
increase fodder output because of improved husbandry practices. This concurs
with Obayelu
et al
. [28] that the availability of family labor provides more man
days hence reduces labor constraints especially during peak of the planting sea-
son. The coefficient of labor for land preparation was positive but insignificant
on the amount of fodder output.
4.2. Distribution of Technical Efficiency Scores of Fodder Farmers
Table 5 presents technical efficiency scores for a sample of 225 fodder farmers in
Homa Bay County.
The results indicate that the best performing fodder farmer had a technical ef-
ficiency score of 0.8 (80 percent) while the least performing farmer had a technical
Table 5. Technical efficiency scores distribution.
TE scores
Freq.
Percent
0 - 0.2
0
0
0.2 - 0.4
5
2.22
0.4 - 0.6
65
28.89
0.6 - 0.8
155
68.89
0.8 - 1
0
0
Total
225
100
Mean 0.7333
Min 0.4
Max 0.8
Std.Dev. 0.1035
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score of 0.4 (40 percent). The mean efficiency score of farmers was about 0.7333.
None of the farmers had technical efficiency scores between 0 to 0.2 and 0.8 to 1.
Results further indicate that majority of farmers (69 percent) had technical effi-
ciency scores ranging from 0.6 (60 percent) to 0.8 (80 percent), while minority (2
percent) had technical efficiency scores ranging from 0.2 (20 percent) to 0.4 (40
percent). The higher technical efficiency scores for majority of the farmers could
be attributed to higher number of man days allocated during planting and
weeding as well as the proportion of land that is allocated for fodder production.
4.3. Factors Affecting Technical Efficiency among Fodder Farmers
Table 6 indicates the sources of variation in technical efficiency scores among
fodder farmers. The technical efficiency scores presented in Table 5 was re-
gressed against the farmer characteristics to get the variation presented in Table
6. Farmer and farm characteristics were treated as independent variables while
technical efficiency scores of each farmer were considered as dependent variable
using Tobit model. Some of the variables that provided positive and significant
coefficients include total herd size, group membership and access to credit while
household size and access to training had negative significant influence on tech-
nical efficiency of fodder farmers. Results indicate that Tobit model explained
about 68.9% of the variations in technical efficiency of farmers. The estimated
probability was higher than the Chi-square value (Prob > Chi2 = 0.0017), imply-
ing that the model has perfect goodness of fit with a strong explanatory power.
Table 6. Two-Limit Tobit regression analysis results.
TE scores
Coef.
Std.err.
t-value
p-value
Age_Hh
0.0007
0.0007
0.93
0.351
Gender
0.0222
0.0152
1.46
0.145
Years of schooling
−0.0017
0.002
−0.85
0.398
Household size
−0.0092**
0.0041
−2.26
0.025
Total herd
0.0083**
0.0037
2.25
0.025
Group membership
0.0429***
0.016
2.68
0.008
Extension
0.0032
0.0178
0.18
0.859
Training
−0.0282*
0.0155
−1.82
0.070
Access to credit
0.0258*
0.0138
1.86
0.064
Constant
0.7046
0.0478
14.72
0.000
Sigma
0.0974
0.0046
Log likelihood=
204.76595
Prob > Chi2=
0.0017
Pseudo R2=
−0.0689
LR Chi2 (9)=
26.41
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Household size was found to be negatively significant on technical efficiency
of fodder farmers at 5% level. An increase in household size by one unit de-
creases the level of technical efficiency of farmers by 0.9%. Farmers with large
household size are able to share the benefits of farming in food consumption and
income needs, hence leaving less benefit to finance fodder farming. This is simi-
lar to the findings by Ndubueze-Ogaraku
et al.
[29] which showed that house-
hold size had a significant negative influence on technical efficiency. However,
this finding contradicts Besseah and Sangho [30] which indicated that household
size had a negative significant impact on technical efficiency of cocoa farmers in
Ghana. A possible reason is that farmers with large household size have enough
labor that would be available to carry out many farming activities, hence making
production activities efficient.
Total herd size was positively significant at 5% level, implying that for every
additional increase in the number of herd size, the technical efficiency would in-
crease by 0.8%. A possible reason is that fodder production is dependent on the
number of herd that the farmers own, hence farmers with large herd size would
be interested to invest more in fodder production to meet the animal feed needs.
In addition, ownership of livestock makes farmers to invest in purchasing fodder
since herd size is directly related to the feed demand. Another possible reason is
that cattle herd is considered as a capital asset hence it can be easily liquidated in
meeting some of the expenditure needs including livestock and family needs
[31]. This finding is in conformity with Otieno
et al
. [32] which argued that beef
herd size is shown to have a positive significant effect on technical efficiency of
farmers in Kenya.
Membership to group was found to be positively significant at 1% level. This
means that farmers who belong to group increases technical efficiency by 4.3%.
One of the possible reason is that group membership provides farmers with so-
cial capital hence they can pool resources for collective action. In addition, group
membership makes farmers to exchange ideas as well as learn about the ideas
concerning benefits of fodder production. Some of the reasons why group mem-
bership increases technical efficiency of farmers include, group membership en-
hances access to fodder for livestock, trainings, joint input purchase, group
marketing and alternative income sources [33]. Instances where farmers are able
to work together in groups, new skills are able to be developed including, skills
in managing groups, technical skills, economic cooperative, problem solving
skills, book keeping, grass root democracy [34]. This would potentially make
fodder farmers have market oriented production, hence diversify their incomes
and increase their production.
Access to training had a negative significant influence on technical efficiency
at 10% level. This implies that access to training decreases the technical efficien-
cy of farmer by 2.8%. These trainings could be done by semi-skilled profession-
als that do not have adequate content on fish production. As a result, these far-
mers did not obtain adequate training services, particularly on agronomic prac-
tices that are related to fodder production, hence making training services ineffi-
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cient. For other reasons, farmers who got training services were trained on other
crops other than fodder, making fodder production inefficient. This finding is
however different from the study by Dessale [26] who argued that training is
important in building the management capacity of most of farmers. Training of
farmers improves skills in using improved seeds, postharvest handling, resource
management, farm management as well as other farm productivity. In addition,
training services are designed to enlighten the farmers on the benefits of group
membership, coupled with effective extension services in delivering capacity
building in using production inputs and access to credit facilities that are neces-
sary in increasing technical efficiencies.
Access to credit was positively significant on technical efficiency at 10% level.
This indicates that access to credit increases technical efficiency by 2.6%. Credit
is important to farmers in purchase of inputs used in fodder production, pur-
chase of production facilities such as fertilizers, planting materials and bailing
equipment. In addition, credit makes fodder farmers to meet their cash needs in
the production cycle. Farmers who have access to credit is important in improv-
ing the incomes of farmers by mobilizing resources thus they would have more
productive resources. Moreover, credit increases the efficiency of farmers since it
capable of solving shortage of liquidity capital. In addition, availability of credit
is capable of shifting the cash constraint outwards, hence would make farmers to
make purchases that they could not afford using their own resources as well en-
hance the use of agricultural inputs hence leading to more technical efficiency.
The finding is similar to the study by [26] [35] [36] that found a positive signifi-
cant relationship between access to credit and technical efficiency. The findings
from this study reveal the technical efficiency of farmers by having better alloca-
tion of some of the available resources that including land allocated for fodder,
quantity of planting materials as well as labour for land preparation and plant-
ing.
5. Conclusion
The findings establish that inputs used in Napier grass production had an elas-
ticity of about 1.304%, implying that farmers were in stage one of production.
These farmers have the potential to increase their production, hence are not effi-
cient in the allocation of their resources. Land size under fodder, quantity of
planting materials used, labor in man days for planting and labor in man days
for weeding were all positively influencing the farmer’s level of technical effi-
ciency scores in fodder production. The higher elasticity of land size implies that
land size had a strong significant effect on fodder output. It was important to
note that majority of the farmers had higher technical efficiency scores implying
that Napier grass production has the potential of improvement both in the short
run and in the long run. Further, results from Tobit model indicated that total
herd size, group membership and access to credit were positive and significant
on technical efficiency among fodder farmers. On the other hand, household size,
and access to training was found to be negative and significant on technical effi-
M. S. Ayuko et al.
DOI:
10.4236/oalib.1109671 13 Open Access Library Journal
ciency among these farmers.
6. Recommendations
Farmers need to increase land under fodder, planting materials and the number
of man days during planting and weeding since they were significant in increas-
ing farmers’ fodder output. In addition, the Government and input suppliers should
ensure that the planting materials are readily available to Napier grass farmers at
affordable prices. These planting materials should be of good quality so as to
improve the production of fodder. Furthermore, there should be efficient distri-
bution of these materials to ensure that they are accessible to all farmers. Far-
mers should be encouraged to join some of the existing fodder groups through
which training and extension services are provided.
Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Auma, J., Kidoido, M. and Kariuki, E. (2016) Report of the Baseline Survey in
Western and Eastern Kenya, August 2016: Feed the Future Kenya Accelerated Live-
stock Value Chain Development—Dairy Component (AVCD-Dairy), Kenya, Nai-
robi.
[2] USAID (2012) Mother of Five Invests in Fodder Production in Kenya’s Arid Lands.
U.S. Agency for International Development.
https://reliefweb.int/report/kenya/mother-five-invests-fodder-production-kenya%E
2%80%99s-arid-lands
[3] SNV (2013) Study on the Kenyan Animal Feed and Fodder Sub-Sectors; Kenya Feed
Industry Policy and Regulatory Issues.
[4] Musalia, L.M., Odilla, G.A., Nderi, O.M. and Muleke, V. (2016) Current Status of
Fodder Production, Conservation and Marketing in the Arid and Semi-Arid Lands
of Tharaka Nithi County, Kenya.
African Journal of Agricultural Research
, 11,
2337-2347.
[5] MoALF (2017) Repositioning the Fodder Value Chain for Sustainable Livestock
Production in Kenya.
[6] Mutimura, M., Guthiga, P. and Haug, R. (2018) Socio-Economic Status Affecting
Smallholder Farming and Food Security: A Study from Six Case Countries in Africa.
Horizon 2020, European Union Funding for Research & Innovation.
[7] Ouma, O.E. (2017) Analysis of Fodder Production and Marketing in the Rangelands
of Southern Kenya. Thesis, University of Nairobi, Nairobi.
[8] Lugusa, K.O. (2015) Fodder Production as an Adaptation Strategy in the Dry Lands:
A Case Study of Producer Groups in Baringo County, Kenya. Thesis, University of
Nairobi, Nairobi.
[9] Rademaker, C.J., Bebe, B.O., Lee, J.V., Kilelu, C. and Tonui, C. (2016) Sustainable
Growth of Kenyan Dairy Sector.
[10] Galdino, C., Garcia, M., Dorward, P. and Rehmar, T. (2013) Factors Influencing the
Adoption of Improved Grassland Management by Small-Scale Dairy Farmers in
Central Mexico and the Implications for Future Research on Smallholder Adoption
M. S. Ayuko et al.
DOI:
10.4236/oalib.1109671 14 Open Access Library Journal
in Developing Countries.
Livestock Science
, 152, 228-238.
https://doi.org/10.1016/j.livsci.2012.10.007
[11] Wasonga, V.O. (2009) Linkages between Land-Use, Land Degradation and Poverty
in Semi-Arid Rangelands of Kenya: The Case of Baringo District. University of Nai-
robi, Nairobi. Thesis, University of Nairobi, Nairobi.
[12] Adesina, A., Mbila, D., Nkamleu, G.B. and Endamana, D. (2000) Econometric
Analysis of the Determinants of Adoption of Alley Farming by Farmers in the For-
est Zone of Southwest Cameroon.
Agriculture
,
Ecosystems & Environment
,
80, 255-265.
https://doi.org/10.1016/S0167-8809(00)00152-3
[13] Omollo, E.O., Wasonga, O.V., Elhadi, M.Y. and Mnene, W.N. (2018) Determinants
of Pastoral and Agro-Pastoral Households’ Participation in Fodder Production in
Makueni and Kajiado Counties, Kenya.
Pastoralism, 8,
Article No. 9.
https://doi.org/10.1186/s13570-018-0113-9
[14] Saito, K. and Spurling, D. (1992) Developing Agricultural Extensions for Women
Farmers. World Bank Discussion Chapter No. 156, World Bank, Washington DC.
[15] Olila, D. (2014) Economic Evaluation of Factors Affecting Farmers Participation in
Development Groups: A Case of Trans-Nzoia County, Kenya.
Agricultural Econom-
ics
,
Extension and Rural Development
, 2, 74-81.
[16] MacOpiyo, L., Irungu, P. and Elhadi, Y.M. (2013) Study of Livestock, Fodder, Milk
Production and Marketing in Arid and Semi-Arid Lands of Kenya Under-Promotion
and Strengthening Enterprises and Market Systems in Drought-Prone ASAL Areas.
Baseline Report for Kajiado, West Pokot and Narok Counties.
[17] GoK (2015) National Policy for the Sustainable Development of Northern Kenya
and other Arid Land (Draft).
[18] Kidake, B., Munyeki, J., Kubasu, D. and Mnene, W. (2016) Promotion of Range
Fodder and Fodder Production among the Pastoral and Agro-Pastoral Communi-
ties in Kenyan Rangelands: Experiences and Lessons Learnt.
Livestock Research for
Rural Development
,
28, Article No. 51.
http://www.lrrd.org/lrrd28/8/kida28151.html
[19] KNBS (2019) 2019 Kenya Population and Housing CensusResults: Volume 1. Nai-
robi. https://www.knbs.or.ke/?p=5621/
[20] Kothari, C. (2004) Research Methodology Methods & Techniques. 2nd Edition,
New Age International (P) Limited, New Delhi.
[21] Addison, M., Yankyera, K.O. and Antoh, E.F. (2016) Gender Role, Input Use and
Technical Efficiency among Rice farmers at Ahafo Ano North istrict in Ashanti Re-
gion of Ghana.
Journal of Food Security
,
4, 27-35.
[22] Bravo-Ureta, B.E. and Pinheiro, A.E. (1993) Efficiency Analysis of Developing
Country Agriculture: A Review of the Frontier Function Literature.
Agriculture Re-
source Economic Review
, 22, 88-101. https://doi.org/10.1017/S1068280500000320
[23] Battese, G. and Coelli, T. (1993) A Stochastic Frontier Production Function Incor-
porating a Model of Technical Inefficiency Effects. Working Papers in Economet-
rics and Applied Statistics, 69.
[24] Fita, L., Trivedi, M.M., Patel, A.M., Tassew, B. and Joshi, C.G. (2013) Determinants
of Technical Efficiency of the Dairy Farmers in Ada’a District of Oromia State,
Ethiopia.
Iranian Journal of Applied Animal Science
, 3, 595-565.
[25] Orewa, S.I. and Izekor, O.B. (2012) Technical Efficiency Analysis of Yam Produc-
tion in Edo State: A Stochastic Frontier Approach.
International Journal of Devel-
opment and Sustainability
,
1, 516-526. https://isdsnet.com/ijds-v1n2-35.pdf
M. S. Ayuko et al.
DOI:
10.4236/oalib.1109671 15 Open Access Library Journal
[26] Dessale, M. (2019) Analysis of Technical Efficiency of Small Holder Wheat-Growing
Farmers of Jamma District, Ethiopia.
Agriculture and Food Security
, 8, Article No.
1. https://doi.org/10.1186/s40066-018-0250-9
[27] Kibaara, B.W. (2005) Technical Efficiency in Kenya’s Maize Production: An Appli-
cation of the Stochastic Frontier Approach. Master Science Thesis, Colorado State
University, Fort Collins, Co.
[28] Obayelu, A.E., Moncho, C.M.D. and Diai, C.C. (2016) Technical Efficiency of Pro-
duction of Quality Protein Maize between Adopters and Non-Adopters, and the
Determinants in Oyo State, Nigeria.
Review of Agricultural and Applied Economics
,
19, 29-38. https://doi.org/10.15414/raae.2016.19.02.29-38
[29] Ndubueze-Ogaraku, M.E., Adeyoola, O.A. and Nwigwe, C.A. (2020) Determinants
of Technical Efficiency of Small-Holders’ Yam Farmers in Nigeria
. Review of Agri-
cultural and Applied Economics
,
24, 13-20.
https://doi.org/10.15414/raae.2021.24.01.13-20
[30] Besseah, F.A. and Sangho, K. (2014) Technical Efficiency of Cocoa Farmers in Ghana.
Journal of Rural Development
,
37, 159-182.
[31] Barrett, C., Mcpeak, J., Luseno, W., Little, P., Osterloh, S., Mahmoud, H. and Gebru,
G. (2004) Pastoralist Livestock Marketing Behavior in Northern Kenya and South-
ern Ethiopia: An Analysis of Constraints Limiting Off-Take Rates.
https://doi.org/10.2139/ssrn.611064
[32] Otieno, D.J., Hubbard, L. and Ruto, E. (2012) Determinants of Technical Efficiency
in Beef Cattle Production in Kenya.
The International Association of Agricultural
Economics
(
IAAE
)
Triennial Conference
, Foz do Iguacu, Brazil, 18-24 August 2012,
18-24.
[33] Sala, S.M. (2019) An Analysis of the Factors Influencing Participation of Pastoralists
in Commercial Fodder Value Chain for Livelihood Resilience in Isiolo County,
Kenya. Master Thesis, Department of Agricultural Economics, Faculty of Agricul-
ture, University of Nairobi, Nairobi.
http://erepository.uonbi.ac.ke/handle/11295/107167
[34] Emmanuel, T., Hajime, K., Matsumura, I., Mohamed, E. and Siddighi, B.B. (2015)
Economic Analysis of the Role of Farmer Organizations in Enhancing Smallholder
Potato Farmers’ Income in Middle Guinea.
Journal of Agricultural Science
,
7, 123-137.
https://doi.org/10.5539/jas.v7n3p123
[35] Mussa, E.C., Obare, G.A., Bogale, A. and Simtowe, F.P. (2012) Analysis of Resource
Use Efficiency in Smallholder Mixed Crop-Livestock Agricultural Systems: Empiri-
cal Evidence from the Central Highlands of Ethiopia.
Journal of Developing Coun-
tries Study
,
2, 30-40.
[36] Biam, C.K., Okorie, A. and Nwibo, S.U. (2016) Economic Efficiency of Small Scale
Soybean Farmers’ in Central Agricultural Zone, Nigeria: A Cobb-Douglas Stochas-
tic Frontier Cost Function Approach.
Journal of Development Agricultural Eco-
nomics
,
8, 52-58. https://doi.org/10.5897/JDAE2015.0688
