Current Research Journal of Social Sciences 4(4): 314-322, 2012
ISSN: 2041-3246
© Maxwell Scientific Organization, 2012
Submitted: May 16, 212
Accepted: June 23, 2012
Published: July 30, 2012
Multinomial Logit Analysis of Small-Scale Farmers’ Choice of Organic Soil
Management Practices in Bungoma County, Kenya
Oscar I. Ayuya, Waluse S. Kenneth and Gido O. Eric
Department of Agricultural Economics and Agribusiness Management, Egerton University,
Box 536-20115, Egerton, Kenya
Abstract: Bungoma County is one of the areas in Kenya where maize is produced on small-scale basis; however,
the County isfacing soil nutrient depletion due to continuous and unsustainable cultivation of land. Various
interventions have sensitized farmers into adopting organic soil management techniques of enhancing soil fertility
and upholding environmental sustainability. The study was aimedatestablishing the most preferred organic soil
managementtechniques among farmers and the factors influencing the choice of these techniques. This wasbased on
an exploratory study of small-scale organic maize farmers in Bungoma County.Asimple random sampling approach
was used to select a sample of 150 smallholder maize farmers and primary data was collected using a semistructured questionnaire. In the analyses, descriptive statistics and a multinomial Logit model were employed using
STATA computer program. The results indicated that extension, farm size household size, gender, age, education,
credit, group membership, land tenure, farm distance and slope of land significantly influenced the choice of
different techniques. Therefore the study recommends that policies in support of organic soil management should
disaggregate farmers according to their socioeconomic, farmer and farm characteristics in order to achieve their
intended objectives. Further there is need to increase extension visits to improve farmer awareness on the advantages
of the various techniques.
Keywords: Choice, multinomial logit, organic soil management practices
conventional farms leading to more fertile topsoil
important for agricultural production.
A quarter of the global organic land is located in
developing countries (Willer et al., 2009). However,
organic farmingin Kenya is still relatively small though
it is fast growing. About 0.69 % of the total agricultural
area in Kenya (over 182,000 ha) is under organic
management. In addition, about 30,000 farms have
changed from conventional farming techniques to
organic cultivation methods (Ifoam and Fibl, 2006).
Maize is the dominant staple food in Kenya whose
consumption is estimated at 98 kg/capita/year and it is
produced by over 90% of the rural households. Smallscale maize production accounts for approximately 70%
of the total maize produced in the country (Ministry of
Agriculture) (MOA, 2006). Bungoma County is one of
the areas in Kenya where maize is produced on smallscale basis. The County is experiencing soil nutrient
depletion due tocontinuous and unsustainable
cultivation of land (Tungani et al., 2002). The
government of Kenya and non-governmental
organizations, particularly SACRED AFRICA and One
Acre Fund, has been promoting organic farming as a
means of promoting sustainable maize production.
INTRODUCTION
Small-scale farming in Kenya, particularly in
western Kenya, is primarily constrained by continuous
soil nutrient mining, which affects the production of
adequate food for family subsistence needs and surplus
for commercial purposes (Tungani et al., 2002;
Marenya and Barrett, 2007). One of the strategies
employed by farmersto solve the problem of soil
nutrient mining is by practicing organic agriculture
through the adoption of Organic Soil Management
Practices (OSMP). Organic agriculture relies strongly
on closed on-farm nutrient cycling and a set of
management practices by avoiding the use of the
synthetic pesticides and fertilizers. It combines
traditional and modern techniques of enhancing soil
fertility to benefit the shared environment and uphold
high-quality life for all (Ifoam, 2009a; Fibl and Ifoam,
2011). Organic agriculture production systems are
based on four principles; health, care, fairness and
ecology (Ifoam, 2009b). Farmer’s uptake of OSMP is
important in enhancing soil fertility which leads to high
productivity. Reganold et al. (1987) found out that soil
erosion was less frequent on organic farms than in
Corresponding Author: Oscar I. Ayuya, Department of Agricultural Economics and Agribusiness Management, Egerton
University, Box 536-20115, Egerton, Kenya
314
Curr. Res. J. Soc. Sci., 4(4): 314-322, 2012
As a result of this initiative, some small-scale
maize farmers have adopted organic farming through
uptake of OSMP. These farmers choose among the
different OSMP technologies available to them to
adopt. However, it is not clear whichof the adopted
OSMP are mainly preferred by farmers and
whichinstitutional, farm and farmer characteristics
influence the choice of the main OSMP in Bungoma
County. Therefore, the study aims to fill this knowledge
gap based on an exploratory study among small-scale
organic maize farmers in Bungoma County. From the
results, relevant policy recommendations will be
proposed to organic farming stakeholders so that they
can be relied upon when promoting specific OSMP as a
soil fertility enhancement initiative.
farmers in Bungoma County. The model was preferred
because it permits the analysis of decisions across more
than two categories in the dependent variable; hence it
becomes possible to determine choice probabilities for
the different OSMP’s. On the contrary, the binary
probit or logit models are limited to a maximum of two
choice categories (Maddala, 1983). The MNL was
preferred for this study because it is simple to compute
than its counterpart, the multinomial probit model
(Hassan and Nhemachena, 2008).
MATERIALS AND METHODS
where, y denotes a random variable taking on the values
{1, 2, …, J} for a positive integer J and x denote a set
of conditioning variables. X is a 1xK vector with first
element unity and βj is a K×1 vector with j = 2, …, J. In
this case, y denotes organic soil management practices
or categories while x denotes specific household and
institutional characteristics of the maize farmer. The
inherent question is how changes in the household and
institutional characteristics affect the response
probabilities P(y = j/x), j = 1, 2, …, J . Since the
probabilities must sum to unity, P(y = j/x) is
determined once the probabilities for j = 1, 2, …, J are
known. For this study, the OSMPs used in the study
area were characterised, after which the most common
techniquespreferred by farmers (or decision categories)
were identified.These techniques comprised the
decision categories for the multinomial Logit model.
In order for the parameter estimates of the MNL
model in Eq. (1) to be unbiased and consistent, the
Independence of Irrelevant Alternatives (IIA) is
assumed to hold (Deressa et al., 2008). The IIA
assumption requires that the probability of using one
OSMP by a given maize farmer must be independent of
the probability of choosing another OSMP (that is,
Pj/Pk is independent of the remaining probabilities).
The basis of this assumption is the independent and
homoscedastic disturbance terms of the basic model in
Eq. (1).
The parameter estimates of the MNL model only
provide the direction of the effect of the independent
variables on the dependent (choice) variable; thus the
estimates represent neither the actual magnitude of
change nor the probabilities. Instead, the marginal
effects are used to measure the expected change in
probability of a particular technique being chosen with
respect to a unit change in an independent variable from
the mean (Greene, 2000). To obtain the marginal effects
Study area, sampling and data: The study covered
Bungoma County which occupies approximately
2,068.5 km2 with a population of roughly 1,630,934
people and a population density of 482 persons/km2
(KNBS, 2009). The County is located between
longitude 34° 21.4′ and 35° 04′ East and latitude 0°
25.3 and 0° 53.2′ North. There is a bimodal rainfall
pattern; the long rains (March–July) and the short rains
(August-October). The annual rainfall ranges between
1250 and 1800 mm. The altitude ranges between 1200
and 2000 m Above Sea Level (A.S.L) and temperature
ranges between 21-25°C during the year (GoK, 2005).
The County is endowed with well-drained, rich and
fertile arable soils but poor husbandry methods and a
bulging population have resulted in declining yields,
deforestation and soil erosion. Small scale crop and
livestock production has been an important component
of agricultural activity in this area.
Bungoma County was selected because it is one of
the areas in Kenya where maize farming is practiced on
small scale basis and where OSMPhave been widely
promoted and adopted through interventions by nongovernmental organizations such as SACRED Africa.
A sample of 150 respondents was selected from a
population of small scale maize farmers in Bungoma
County. Out of the seven administrative districts in the
County, two districts were purposivelyselected;
Bungoma South and Bungoma Central. Primary data
was collected through observations and interviews,
using a semi-structured questionnaire during the months
between April to May 2011.
Model specification; Multinomial logistic regression:
The Multinomial Logit (MNL) model is used to analyse
the factors influencing choice of OSMP among maize
The MNL model is expressed as follows:
⁄
315 exp
1
∑
exp
12, … . .
,
(1)
Curr. Res. J. Soc. Sci., 4(4): 314-322, 2012
Table 1: Variables used in the MNL model and their expected signs
Variables
Definition and measurement
OSMPchoice
Choice set of organic soil management techniques
Educ Yrs
Number of years of formal education of the household head.
Age
Age in years of the chief decision maker (continuous)
Gender
Gender of the chief decision maker ( Dummy 1 = Male, 0 = Female )
H/h Size
Number of household members (continuous)
Fm Size
Size of the farm available in hectares (continuous)
Fmg Exp
Number of years of farming experience of the household head (continuous)
Off Inc
Amount of off-farm income received in a year (continuous)
Ld Tenure
Land ownership by title deed (1= owned by title deed, 0 = Otherwise
Credit
Amount of credit access by the farmer in thousands Kenya Shillings (continuous)
Extn
Number of extension contacts access by the farmer, (continuous)
Training
Number of training sessions the farmer attended (continuous)
Slop Erosn
If the slope of the land leads to soil erosion(1 = Yes, 0 = Otherwise)
Gm Ship
If a farmer belong to agricultural related group (1 = belong to a group, 0 = Otherwise)
Fm Dista
Distance in kilometers of the farm from the farmer’s homestead (continuous)
Percep
Farmer perception towards organic farming activities. (5 = strongly agree 4 = Agree 3 = Neutral
Undecided 2 = Disagree 1 = strongly disagree)
for the model, Eq. (1) is differentiated with respect to
the explanatory variables as shown in Eq. (2):
∑
(2)
It has also been noted that the signs of the marginal
effects and respective coefficients may be different
(Hassan and Nhemachena, 2008), since the former
depends on the sign and magnitude of all other
coefficients.
The empirical specification for examining the
influence of explanatory variables which are described
in Table 1 on the choice of OSMP
is given as
follows:
,…
(3)
RESULTS AND DISCUSSION
Socioeconomic characteristics: The summary of the
socio-economic characteristics is presented in Table 2.
A comparative analysis of the mean and proportion of
the socio-economic variables between adopters of
OSMP and non-adopters showed that, there exist
significant differences between four variables. These
are; age of the household head, household size,
education level of the household head and the number
of extension visits received. In addition, adopters of
OSMP projected higher mean and proportion values in
the four significant variables. The other variables (farm
Expected sign
±
±
±
±
±
±
±
+
−
−
+
±
±
±
±
size, farming experience, farmer training and farm
distance from the homestead) indicated insignificant
difference between adopters and non-adopters of
OSMP.
Figure 1 shows how farmers adopted different
OSMP. Use of Farm Yard Manure (FYM) was
practiced by majority of the respondents (21%)
indicating that farmers appreciate its importance in
maize production as a meansof improving soil fertility.
In addition to supplying nutrients to the soil, FYM
improves the physical, chemical and biological
properties of the soil which helps to maintain the soil
productivity and soil health (Tolessa and Friesen,
2001).
Furthermore, the use of FYM helps farmers to
avoid the high costs of purchasing inorganic fertilizers.
However, incorporation of crop residues in the farm
was least practiced among adopters (5%) of OSMP.
This could be attributed to the fact that many farmers in
the area practice mixed farming and therefore prefer to
use crop residue as fodder for their livestock because
their farm sizes are small and therefore they cannot
afford to cultivate enough feed for their animals.
Moreover, about 27% of the respondents (inorganic
farmers) reported not to have adopted any of the OSMP
due to shortage of land, labor and organic inputs as well
as pest and disease challenges as shown in Fig. 2.
About 40% of the non-adopters of OSMP indicated
that shortage of organic inputs such as crop residues
and compost materials was a major constraint hindering
them from adopting OSMP. Approximately 31% of the
non-adopters of OSMP indicated that their small pieces
of land did not allow them to invest in these activities.
Shortage of land was attributed to high population
pressure in the area, forcing farmers to intensively farm
on small plot of land. This hindered them from
316 Cuurr. Res. J. Socc. Sci., 4(4): 3114-322, 2012
Table 2: Farm
mer’s socio-econom
mic characteristicss
Mean
----------------------------------------------------Characteristicc
Adopters
N
Non-adopters
48.35
Age (years)
4
43.23
Household sizze (number)
8.06
4
4.88
Education (yeears)
1
12.03
14.31
Farm size (haa)
2
2.11
1.85
Experience (yyears)
1
16.45
18.98
Extension (coontacts)
0
0.18
2.11
Training (conntacts)
1
1.58
2.81
Farm distancee (km)
1
1.43
0.75
***: Significaant at 1% level; **
*: Significant at 5 level
Use
U of compost
manure
16%
Incorporation
I
of crop
residues
5%
Agroforestry
A
15%
Overall
46.98
7.21
13.70
1.92
18.45
1.59
2.48
0.93
t-ratio
-2.039**
-6.018***
-4.749***
1.114
-0.829
-4.219***
-1.910
1.648
Sig.
0.043
0.000
0.000
0.267
0.409
0.000
0.580
0.102
No adoption
27%
Crrop rotation
9%
Planting
leguminouss
crops
7%
Use of FYM
21%
Fig. 1: Farm
mers’ choice of organic
o
soil manaagement practicees
40%
30%
20%
10%
0%
Shortage Shorttage Pest & Shortage
S
disease of
o labour
of organic of laand
challenge
inputs
Fig. 2: Connstraints to adopttion of organic soil managementt
pracctices
practicing organic techniiques such as crop
c
rotation and
a
agroforestrry which requ
uire large agricultural land. In
addition, 18%
1
of the non
n-adopters of OSMP indicatted
that it is difficult
d
to con
ntrol pests and diseases usiing
biological methods undeer OSMP. Shoortage of labour,
o the farmersas a contraint to
was identified by 11% of
practicing OSMP sincce the techniique is labouurintensive particularly
p
in the
t preparationn and applicatiion
of FYM and
a compost manure.
m
Therefore, househollds
with low number
n
of mem
mbers workingg on the farm are
a
less effective since these practices aree labor-intensiive
(Douglas et
e al., 2005).
Empiriical results: Table
T
3 presennts the results of the
multinoomial Logit moodel which inddicated that 11 out of
15 varriables used in the modeel were statistically
significcant at 10% levels. The chi-square
c
vallue of
168.77 showed that liikelihood ratioo statistics are highly
h
significcant (p<0.0001) suggestingg the model has a
strong explanatory power.
p
The psseudo-R squarre was
0.4218 indicating thhe explanatoryy variable expplained
4
of the variiation in choicee of OSMP.
about 42%
Geender of the household
h
heaad had a significant
effect on the chooice of com
mpost manuree and
agroforrestry practicess at 5% and 1%
% level, respectively.
Male-hheaded househholds had a hiigher probabillity of
practiciing agroforestrry techniques by
b 31.02%; how
wever,
they had a lower probability
p
of practicing coompost
manuree by 31.93%. Male-headed
M
hoouseholds havee been
found to
t undertake risky
r
businessees. In additionn, they
have a higher access to resources and
a informatioon that
give thhem greater caapacity to adoppt new technoologies
than fem
male-headed households
h
duee to traditional social
barrierss (Kaliba et al.,
a 2000; Asfaw and Adm
massie,
2004; Odendo
O
et al., 2009). On thhe other hand, Gopal
and Kaanokporn (2011) indicated thhat more femalle than
male-headed househoolds planted orgganic vegetables.
Thhe results show
wed that age off the householdd head
significcantly influencced the likelihoood of choosinng not
317 Curr. Res. J. Soc. Sci., 4(4): 314-322, 2012
Table 3: Marginal effects from the multinomial logit on the choice of OSMP
No adoption
Crop rotation
Crop residues
FYM
--------------------------------------------------------------------------------------------------------------------p-level
x/ y
p -level
x/ y
p -level
x/ y
p -level
Explanatory variables x/ y
Gender
0.0725
0.177
0.0119
0.407
-0.0052
0.924
-0.0694
0.420
Age
0.0108*
0.071
0.0017
0.327
0.0108**
0.027
-0.0132**
0.010
Education
0.0063
0.524
-0.0016
0.647
-0.0114
0.128
0.0048
0.665
Household size
-0.0746**
0.030
-0.0052
0.526
-0.0295**
0.047
0.0372*
0.063
Experience
-0.0012
0.733
-0.0014
0.278
0.0011
0.661
-0.0014
0.757
Perception
-0.0275
0.573
-0.0117
0.482
-0.0562
0.107
0.084
0.107
Farm size
0.0521*
0.071
0.0194*
0.057
-0.0166
0.295
-0.0537
0.198
Off-farm income
0.1304
0.205
0.0889
0.283
0.0967
0.415
-0.1036
0.229
Extension
-0.1323***
0.001
-0.0321*
0.070
0.0248*
0.070
0.0286
0.129
Training
-0.0035
0.764
-0.0011
0.726
0.0009
0.908
-0.0119
0.255
Credit
0.0138**
0.027
-0.0072
0.340
-0.0066
0.445
0.0391***
0.007
Group membership
-0.0549
0.521
-0.0032
0.867
-0.1582
0.221
0.1496*
0.065
Land tenure
-0.0969*
0.083
0.0199
0.364
-0.0178
0.838
0.0716
0.389
Farm distance
0.0277
0.292
0.0110
0.290
0.0154
0.365
-0.0948**
0.041
Slope erosion
-0.0560
0.332
-0.0043*
0.083
0.0907
0.342
0.0332
0.774
Leguminous crops
Compost manure
Agroforestry
-------------------------------------------------------------------------------------------Explanatory variables x/ y
p-level
x/ y
p -level
x/ y
p -level
Gender
-0.0007
0.919
-0.3193**
0.010
0.3102***
0.002
Age
-0.0008
0.214
-0.0099
0.246
0.0005
0.956
Education
0.0071*
0.086
0.0147
0.423
-0.0128
0.570
Household size
0.0016
0.225
0.0148
0.697
0.0557**
0.045
Experience
-0.0005
0.409
0.0034
0.575
-0.0001
0.995
Perception
0.0042
0.396
0.0440
0.644
-0.0368
0.731
Farm size
-0.0054
0.301
-0.1743***
0.005
0.1601**
0.021
Off-farm income
0.0050
0.586
-0.0823
0.502
-0.1351
0.359
Extension
0.0027*
0.068
0.1094***
0.002
0.0043
0.902
Training
-0.9680
0.999
0.0274
0.105
-0.0117
0.541
Credit
0.0014
0.330
0.0035
0.845
-0.0166
0.478
Group membership
0.0087
0.393
-0.0209
0.853
0.0789
0.591
Land tenure
0.0077
0.316
-0.1713
0.263
0.0070*
0.060
Farm distance
-0.0046
0.395
-0.0251
0.618
0.0704
0.256
Slope erosion
0.0009
0.893
-0.2838**
0.015
0.2106**
0.028
Number of observations: 150; Wald chi2 (90): 301.52; Prob > Chi2: 0.0000; Pseudo R2: 0.4218; Log pseudolikelihood : -168.77. ***: significant
at 1% level; **: significant at 5 level; *: 10 level
to use any OSMP at 1% level. In addition, age
significantly influenced the chances of choosingto use
crop residues and FYM at 5% level. An increase in age
by one year increased the probability of choosing nonadoption of OSMP and use of crop residues by 1.08%.
It also decreased the probability of choosing FYM by
1.32%. Young household heads are more interested in
trying out new agricultural technologies because of
their risk taking character. Older household heads are
risk averse hence they are rigid in adopting new
technologies (Khanna, 2001). In contrast, Odendo et al.
(2009) found out that age had a positive correlation
with traditional practices such as manure.
Education level of the household head had a
positive and significant effect on choice of leguminous
crops. An increase in education level by 10 years
increased the probability of choosing planting
leguminous crops by 7.1%. Higher education gives
farmers the ability to interpret and respond to new
information much faster than their counterparts with
lower education (Feder et al., 1985). These results tally
those with of Mbaga-Semgalawe and Folmer (2000)
and Odendo et al. (2009)where they found out that
adoption of improved natural resource conservation
technologies such as OSMP requires an understanding
of the environment in which this activities are taking
place thus,household heads with higher education levels
have a higher probability of adopting new technologies.
However, other studies have indicated that education
level of the household head negatively influencesthe
use of soil and water conservation measures and
organic agricultural system (Gould et al., 1989; Furruh
et al., 2007).
The effect of household size was significant for
non-adoption, adoption of crop residues, FYM and
agroforestry technique. An increase in the household
size by one member decreased the probability of
choosing non adoption and use of crop residues by 7.46
and 2.95%, respectively. However, it increased the
probability of choosing FYM and agroforestry
technique by 3.72 and 5.57%, respectively. Household
size has been used as a proxy measure for the numbers
of members available to provide farm labor. Large
household size positively influences adoption of labor318 Curr. Res. J. Soc. Sci., 4(4): 314-322, 2012
intensive agricultural technologies since they have the
capacity to relax the labor constraints required during
introduction of new technologies (Croppenstedt et al.,
2003; Birungi, 2007; Nyangena, 2007; Odendo et al.,
2009). On the other hand, households with large
families may be forced to divert part of the labour force
to off-farm activities in an attempt to earn more income
in order to ease the consumption pressure imposed by a
large family (Tizale, 2007; Yirga, 2007).
The choice of non-adoption, crop rotation, compost
manure and agroforestry technique was significantly
influenced by the size of farm. An increase in farm size
by one hectare increased the probability of choosing
non-adoption, crop rotation and agroforestry technique
by 5.21, 1.94 and 16.01%, respectively and decreased
the probability of choosing compost manure by
17.43%. Small land holding hinder the usage of
technologies compared to large land holding. A large
farm size allows a farmer to experiment new
technologies on a portion of land without worrying
about compromising the family food security. In
addition, the benefits from large-scale adoption of new
technologies are higher for larger farms (Zepeda, 1994).
The number of extension contacts received by
farmers significantly influenced the choice of nonadoption as well as use of crop rotation, crop residues,
leguminous crops and compost manure. An increase in
extension contacts by one visit decreased the
probability of choosing non-adoption of OSMP and
adoption of crop rotation by 13.23 and 3.21%,
respectively. However, a similar change in the number
of extension contacts increased the probability of using
crop residues, leguminous crops and compost manure
by 2.48, 0.27 and 10.94%, respectively. Agricultural
extension agents provide different information and
alternatives depending on prevailing activities which
impacts farmers differently and they are expected to
choose an option that suits them best (Baethgen et al.,
2003). The number of contacts with extension officers
is a proxy measure for access to agricultural
information and this positively contributes to awareness
and subsequent adoption of new technologies (Adesina
et al., 2000; Abdulai and Huffman, 2005; Menale et al.,
2009; Tizale, 2007; Yirga, 2007). However, a study by
Ayuya et al. (2011) contrasts this result where
agricultural extension services were more focused on
intensifying crop and livestock production at the
expense of tree planting.
Amount of credit accessed by farmers positively
influenced choice of FYM at 1% significance level and
non-adoption of OSMP at 5% significance level. An
increase in credit amount by KES 1,000 increased the
likelihood of not adopting OSMP by 1.38% and choice
of FYM by 3.91%. The amount of credit dissuaded
non-adopters from engaging in OSMP because they
were able to purchase inorganic fertilizers instead of
using organic fertilizers whose preparation and
application is perceived to be labor-intensive. A rise in
use of FYM is attributed to an increase in the
purchasing power as a result of the credit accessed
hence farmers with higher amount of credit would
acquire FYM from external sources and supplement
what they prepare on-farm. Other studies have also
indicated that there is a positive relationship between
the intensity of use of various technologies and the
availability of credit (Degu et al., 2000; Kandlinkar and
Risbey, 2000; Feleke and Zegeye, 2006; Tizale, 2007;
Yirga, 2007).
Group membership had a positive influence on
choice of FYM at 10% significance level. Farmers who
belong to an agricultural related group had a higher
chance of choosing FYM by 14.96%. Membersin a
farmer group may influence one another tochoosebetter
technologies. Membership in groups exposes farmers to
a wide range of ideas and sometimes gives farmers the
opportunity to have better access to information,
through training and extension services, which may
positively change their attitude toward an innovation
(Nkamleu, 2007). Similar studies found a positive
relationship between group membership and adoption
of organic and inorganic fertilizers as well as the
intensity of use of improved yam seed technology
(Nkamleu, 2007; Nchinda et al., 2010).
The choice of not adopting OSMP and agroforestry
technique by farmers was significantly influenced by
land tenure at 10% level. Farmers who possessed land
title deeds had lower chances of choosing not to adopt
OSMP by 9.69%. On the other hand, possession of land
with security of tenure increased the probability of
choosing agroforestry technique by 0.7%. Land title
deeds serve as a security tool to acquire credit facilities
from financial institutions. In addition, farmers who
have full ownership of land are assured of future access
to returns of investments (Menale et al., 2009). These
results tally with those of Mwirigi et al. (2009) and
Ayuya et al. (2011) where land tenure security
positively influenced adoption of a new technology.
However, another study indicated that privatization of
land does not automatically increase investment in
more sustainable agricultural practices (FAO, 2001).
Farm distance from the farmer’s homestead
negatively influenced the choice of FYM at 5%
significance level. An increase in farm distance by one
kilometer reduced the probability of choosing FYM by
9.48%. Perhaps this could be a factor hindering
adopters of OSMP from using FYM. This is be
attributed to the high laborunits required to prepare,
carry and apply FYM to far distant fields hence, this
319 Curr. Res. J. Soc. Sci., 4(4): 314-322, 2012
would be more convenient for fields closer to the
homestead. Farms that are located close to the
homestead positively influence adoption of new
innovations (Chukwuji ad Ogisi, 2006; Alene et al.,
2008; Olowale et al., 2009)
The decision to choose crop rotation, compost
manure and agroforestry technique was significantly
influenced by the slope of land. Farmers who possessed
steep land had a lower probability of choosing crop
rotation and compost manure by 0.43 and 28.38%,
respectively. However, steep land increased the
likelihood of choosing agroforestry technique by
21.06%. The likelihood of households choosing to
practice soil conservation depends onhow the farmer
perceives slope of the farm since steeper slopes are
more prone to soil erosion (Menale et al., 2009).
Sustainable agricultural systems are intuitively sitespecific hence plot characteristics influence the decision
to adopt conservation tillage (Lee, 2005). The slope of
land influences adoption as well as the type of
technology to be adopted such as the decision to
combine use of compost and conservation tillage
(Menale et al., 2009).
CONCLUSION AND POLICY
RECOMMENDATIONS
The study used a Multinomial Logit (MNL) model
to investigate the factors influencing a household’s
decision to choose an OSMP. In the model, the
dependent variables included six choice options while
the explanatory variables included different household,
institutional and social-economic factors. Farmers
adopted organic soil management by using different
techniques the main ones being; use of farm yard
manure, agroforestry, crop rotation, compost manure,
leguminous crops and crop residues. These techniques
comprised the choice set for the multinomial Logit
model. Farmers who did not adopt OSMP indicated that
shortage of land, labor and organic inputs as well as
pest and disease challenges were the major
impediments to adoption of OSMP.
The study used a Multinomial Logit (MNL) model
to investigate the effects of socioeconomic, farmer and
farm characteristics on the choice of OSMP techniques
as a way of addressing the current prevalent problem of
soil nutrient mining. The results from the model
indicate that most of the variables used in the model
significantly influenced the choice of a technique.
These include; gender, age and education level of the
household head. Other variables include household size,
number of extension visits, farmer access to credit,
group membership, land tenure, farm distance from the
homestead and the slope of land. However, the most
outstanding determinants of choice of OSMP were
number of extension visits, farm size and household
size.
Therefore the study recommends that policies in
support of organic soil management shoulddisaggregate
farmers according to their socioeconomic, farmer and
farm characteristics in order to achieve their intended
objectives.For instance, to enhance the choice of crop
rotation technique, the relevant stakeholders should
target farmers with large land holdingas well as those
with relatively less sloppy land. When promoting the
use of crop residues, the promoters should target older
farmers with small household size and support them
with more extension contacts. To promote the use of
farm yard manure, which is highly labor intensive
especially in its preparation and application, the study
recommends that young women headed households
should be targeted. In addition, farmers should be
encouraged to form groups to overcome the high labor
demand. In the case of compost manure the target
population could be farmers with small and less sloppy
land holding. Frequent extension contacts should also
be provided to enhance its choice. Agro-forestry
technique should be promoted by targeting male headed
households with larger land holding andhousehold
membership. In addition, there is need to ensure
security of tenure for the land through provision of land
title deeds.
ACKNOWLEDGMENT
We are indebted to the African Economic Research
Consortium (AERC) for their financial support in
carrying out of this study. The dedicated enumerators
are highly acknowledged for collecting data. We also
thank the farmers who answered our questions. The
opinions expressed in this study are those of the
authors.
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