Analysis of Land Use Efficiency among Women Cassava Farmers in South-West Nigeria

Analysis of Land Use Efficiency among Women Cassava Farmers in South-West Nigeria
AJAERD
Analysis of Land Use Efficiency among Women Cassava
Farmers in South-West Nigeria
*1Akinduko A.K., 2Oseni J.O.
1Federal College of Agriculture, Akure, Nigeria
2The Federal University of Technology, Akure, Nigeria
The study analyzed the land use efficiency among women cassava farmers in Southwest, Nigeria.
The study identified the determinants of access to land and land tenure security status of women
cassava farmers and also compared farm level efficiencies of the land secured and non-land
secured respondents. The study is carried out in Ogun and Ondo State of Nigeria. Primary data
obtained from 300 respondents using multistage sampling procedure is analyzed using
descriptive and inferential statistics. Heckman Probit model reveals that farm size (0.000), primary
occupation (P=0.002), income (P=0.003), number of dependants (P=0.035), proximity to
processing industry (P=0.042) and social group (P=0.068) were positively significant to access to
land while cassava output (P=0.000), access to extension (P=0.001), household size (P=0.019) and
proximity to market (P=0.059) are negatively significant. Data Envelopment Analysis (DEA) shows
that 12.8% of the land secured farmers are technically efficient with the mean efficiency of 0.55
apiece for Constant Return to Scale Technical Efficiency (CRSTE) and Scale Efficiency. The
CRSTE and scale efficiency of non-land secured farmers shows that 3.74% of the respondents
are both technically and scale efficient with the mean efficiency of 0.38 and 0.39 respectively.
Result of paired t-test reveals that there is significant difference in the level of technical efficiency
between land secured and non-land secured women cassava farmers (t=2.29).
Keywords: Land, Efficiency, Tenure, Cassava, Sustainable, Livelihood, Cassava Production, Nigeria.
INTRODUCTION
Agriculture plays a key role in the creation of employment,
foreign exchange, exports, entrepreneurship, food
security, revenue generation, poverty reduction and supply
of labour and raw materials to other sectors of the
economy. Hence, farming constitute a crucial sector that
accrue income of nearly 90% of the rural citizens which put
it at over 70% of Nigeria’s rural population and as well
employs nearly 75% of the labour force vis-a-vis having
nation’s GDP of 40% contributions (Alegre,2006,
Olutumise and Ajibefun, 2019).
Land in Nigeria and Southwest in particular remains the
most important asset in terms of: social, political and
economic structure (Donnelly, 2014; Iheke et al.,
2019).Despite the rapid growth in cassava production, the
cassava subsector in Nigeria is still constrained by a
number of factors such as: shortage of land, pests and
diseases, agronomic problems, shortage of planting
materials, inconsistent government policy measures, poor
market access, limited diversification of processing
options, inefficient extension delivery system and
inadequate access to improved processing technology.
Again, women across the developing world are
disadvantaged relative to men. Under male dominated
social structures and political systems, women do not
derive equal access to land, technology, education and
resources. As a result, rate of illiteracy, malnutrition and
premature death are significantly higher among women
and girls than they are among men (Rahman, 2004). Also,
women do not get the same as men in their access to
critical farm resources and services such as: farm land,
credit and improved inputs due to cultural, traditional and
sociological factors (Tanko, 1994). Concerns about
women’s access to and control over the ownership of land
resources have been raised over the years at different but
*Corresponding Author: Akinduko K. Abimbola. Federal
College of Agriculture, Akure, Nigeria.
E-mail: abimbolak.ojo@gmail.com;
Co-Author 2
Email: oseni156@yahoo.com
Research Article
Vol. 6(2), pp. 744-751, April, 2020. © www.premierpublishers.org, ISSN: 2167-0477
Journal of Agricultural Economics and Rural Development

Akinduko and Oseni 745
interrelated levels (Chikaire et al, 2010). Odoemelam et al.
(2013) examined the effect of tenure security on livelihood
activities of women farmers in Anambra State, Nigeria.
The result of the study showed that major source of
acquiring land for cultivation was through pledge (23%),
followed by inheritance/gift (22%) and the least was
through allocation by State government (2%). Babalola et
al. (2013) analyzed the determinants of farmers’ adoption
of Sustainable Land Management Practices (SLMP) in the
production of maize and cassava in Ogun State Nigeria.
The result shows that farmers had an average of nine
years of formal education, 54% participated in Community
Based Organizations (CBOs), and 91% had access to
extension education, 55% had land tenancy security, 81%
favored the use of Agronomic practices more than other
SLMPs. About 47% of the farmers cultivated undulating
farmlands which were vulnerable to degradation. Raufu et
al. (2012) examined the determinants of land management
practices among crop farmers in Southwest, Nigeria.
Analysis of data was done using descriptive statistics and
Probit model. The result showed that 90 percent of the
farmers are male and married while 85.5 percent of the
household members were literate. Therefore, an accurate
and realistic understanding of people’s assets most
especially women is crucial to be able to analyze how they
endeavor to convert their assets into positive livelihood
outcomes (Feder, 2007). Land tenure issues and its
efficiency among women will help and guide their decision
making on the types of crops to grow; and whether crops
should be grown for subsistence or commercial purpose.
It will influence the extent to which farmers are prepared to
invest on their land or to adopt new technologies and
innovations. Hence, women farmers being the most
vulnerable group to shock and the persistency of these
shocks in the rural areas as well as the need to generate
reliable data base for future plans and interventions in
times of shocks, justifies the importance of this study. In
this paper, analysis of land use efficiency among women
cassava farmers in Southwest, Nigeria is examined. The
study specifically described the socio-economic
characteristics of the women cassava farmers in the study
area; identified the determinants of access to land and land
tenure security status of the respondents and compared
farm level efficiencies of the land secured and non-land
secured women cassava farmers.
METHODOLOGY
This study was carried out in Southwest, Nigeria. Ondo
and Ogun states were selected due to the predominance
of cassava production and processing activities in the
states. Primary data was used for this study. Data were
collected with the aid of structured questionnaire and
complemented with Focus Group Discussion (FGD). Data
were obtained on: socio-economic characteristics of the
women cassava farmers, access to land and land security
status, inputs and output factors, livelihood activities and
constraints to cassava production by the women as
regards land ownership.
A multistage sampling procedure was used for this study.
Stage one involves purposive selection of two out of six
States in Southwest, Nigeria: Ogun and Ondo States as
representatives of the study area due to their ranking as
the highest cassava producing states in Southwest Nigeria
(FAO, 2018) and also for the predominance of women
involvement in cassava production in the areas. Stage two
involved the classification of the study areas according to
agro- ecological zones and selection of two blocks from
each of the zones. The main agro-ecological zones in the
two states that were selected for this study are Rainforest
and Savannah zones, and there was a purposive selection
of one block, each as a representative from the two main
agro-ecological zones that exists in each state chosen for
the study. ImekoAfon and Ijebu North local government
areas (blocks) were selected in Ogun state representing
savannah and rainforest zones respectively while in Ondo
State, Akoko North West and Akure North Local
Government Areas (blocks) were chosen from the
savannah and rainforest agro- ecological zones
respectively. The selection of the local government areas
were based on the massive cassava production,
processing and marketing activities in the zones. In stage
three, 75 respondents were identified and interviewed
from two cells (Afon(45) and Ilara(30)) in ImekoAfon and
75 respondents also interviewed from two cells ( Ijebuigbo
(40) and Omu (35)) in Ijebu North area. In Ondo State,
from the Akure North block, 75 respondents were selected
and interviewed across two cells which are Iju / Itaogbolu
(40) and oba-ile (35)while 75 respondents were also
interviewed from two cells in Akoko North West local
government block comprising of ArigidiAkoko (45) and
OgbagiAkoko (30).The communities were selected with
proportion to the sizes of the blocks. The list of cells which
is made up of all the farming communities, and all the
names and addresses of the women cassava farmers
under each cell, obtained from Ogun State Agricultural
Development Project (OGADEP) and Ondo State
Agricultural Development Project (OSADEP) were used as
the sampling frame from where the respondents were
randomly selected. A total of 300 respondents was finally
selected from the population of women cassava farmers in
Southwest, Nigeria and interviewed to generate
information that were used for this study.
Descriptive statistics was used to describe the socio-
economic characteristics of the respondents. It entails the
use of percentages, mean, standard deviation, maximum
and minimum values, tables and charts.
Heckman’s two-step procedure (Heckman, 1976) was
used to examine factors determining access to land and
the land tenure security status of the women cassava
farmers in the study area. The women cassava farmers
were considered land secured if they engaged in
uninterrupted cassava farming for a period of two years
and above following the assertion of Paul et al, 2002. The
model assumes that there is an existing underlying
relationship that consists of the latent equation given as
equation (1).

J. Agric. Econ. Rural Devel. 746
j j ijy x u
= + (1)
Where 𝑦𝑗
∗
= latent variable (the tendency of having access
to land or otherwise)
𝑥 = a k-vector of independent variables which include
different factors hypothesized to access to land by a
women cassava farmer.
 = the parameter estimate; and
iju =an error term.
Therefore, only the binary outcome given by the probit
model will be represented as
( 0)probit
j jy y
=  (2)
The dependent variable is observed only if the observation
“j” is observed in the selection equation:
2( 0)select
j j jy z u= +  (3)
1 2~ (0,1); ~ (0,1)u N u N and 1 2( , )corr u u =
where: 𝑦𝑗
𝑠𝑒𝑙𝑒𝑐𝑡
= whether a respondent is land tenure
secured or not,
𝑧 = an 𝑚 vector of independent variables (which include
different factors hypothesized to affect land tenure security
status),
𝛿 = parameter estimate,
𝑢1and𝑢2 = error terms
The explicit function of the model following the approach
used by Ajayi and Olutumise (2017) is shown in equation
(4). Table 1 describes the explanatory variables used for
the Heckman’s probit model.
y*=β0+β1X1+β2X2+β3X3+β4X4+β5X5+…+β14X14+β15X15+β16
X16+ei (4)
Table 1: Description and Measurements of Explanatory Variables Employed for Heckman Probit Model
Variables Description of explanatory variables Type and Measurement of variables Expected signs
X1 Age of the respondents Continuous: Measured in years -/+
X2 Marital status Dummy: Married =1 and 0, otherwise +
X3 Household size Continuous: Number of family members -
X4 Educational status Dummy: Educated =1 and 0, otherwise +
X5 Access to credit Dummy: Yes = 1, No = 0 +
X6 Income of Respondents Continuous: Measured in Naira +
X7 Access to extension services Dummy: Yes = 1, No = 0 -/+
X8 Cassava output Continuous: Quantity of cassava produced
(measured in Tonnes)
+/-
X9 Farming Experience Continuous: Measured in years +
X10 Farm size Continuous: Measured in hectares +/-
X11 Social group belong to Dummy: Yes = 1, No = 0 +
X12 Primary occupation Dummy: Cassava =1, 0 otherwise +/-
X13 Dependants Continuous: Measured in numbers -
X14 Proximity to Market Continuous: Measured in kilometres -
X15 Proximity to city/urban area Continuous: Measured in kilometres -
X16 Proximity to processing industry Continuous: Measured in kilometres -/+
Source: Author, 2019
Data Envelopment Analysis (DEA)was used to measure
and compare the technical efficiency of women cassava
farmers. According to Ajayi and Olutumise (2017), DEA is
a multi-factor productivity analysis model for measuring
the relative efficiencies of a homogenous set of decision-
making units (DMUs).
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝑠𝑢𝑚 𝑜𝑓 𝑜𝑢𝑡𝑝𝑢𝑡
𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝑠𝑢𝑚 𝑜𝑓 𝑖𝑛𝑝𝑢𝑡𝑠
(5)
Using the duality in linear programming as used by
Oguntadeet al. (2011), Ajayi and Olutumise (2017), one
can derive an equivalent envelopment form of this
problem:
𝑀𝑖𝑛𝜃, 𝜆, 𝜃
Subject to:
0,
0,
1 1, 0.
yi Y
xi X
N

 
 
− + 
− 
= 
(6)
where𝜃 is a scalar 𝜆 is a 𝑁 × 1 vector of constants.
This envelopment form involves fewer constraints than the
multiplier form( 𝐾 + 𝑀 < 𝑁 + 1), and hence is generally
the preferred form to solve. The value of 𝜃 obtained will
be the efficiency score of the 𝑖-th decision making unit
(DMU). It will satisfy𝜃 ≤ 1, with a value of 1 indicating a
point on the frontier and hence a technically efficient DMU
(Farrell, 1957).
The model was run separately for the secured and non-
secured women cassava farmers and results explored
technical efficiencies of the land secure and non-land
secured respondents.
Paired Sample Test
This was used to test null hypothesis (H0) that states:
There is no significant difference between efficiencies of
land secured and non-land secured respondents.

𝑡 =
𝑥1− 𝑥2
√1
𝑛1⁄ + 1
𝑛2⁄
𝜎
(7)
𝜎 = √
𝑛1 𝑠1
2+ 𝑛2 𝑠2
2
𝑛1+ 𝑛2− 2
(8)
Where t = the student’s t – distribution with 𝑣 = 𝑛1 + 𝑛2 −
2 degrees of freedom.
𝑥1 = mean efficiency of land secured respondents’ farms
𝑥2 = mean efficiency of non-land secured respondents’
farms
𝜎 = Standard deviation of 𝑥1 and 𝑥2
𝑛1 = number of land-secured respondents sampled
𝑛2 = number of non-land-secured respondents sampled
𝑠1 = standard deviation of efficiency of land secured
respondents’ farms
𝑠2 = standard deviation of efficiency non-land secured
respondents farms
RESULTS AND DISCUSSION
The results of the socio-economic characteristics as
indicated in table 2 shows the mean age of 47.4 years old
of the women cassava farmers which is an indication that
majority of the respondents are still within the economically
active age. The results is in agreement with Olayemi
(2004) who opined that for farmers to be productive in farm
chores, they must be young and active in order to
contribute meaningful labour input into all the stages of
production for efficient output realization which in turn
results in consumptive and income opportunities with
proportional household welfare. Majority (42.0%) of the
respondents had at least primary school education which
implies the respondents are educated and would easily
adopt innovations that would enhance their productivity.
This is in support of studies by Oduntan et al. (2015) who
posited that majority of farmers (73.3%) had one form of
formal education or the other. The average household size
of the respondents was 6 persons. This aligns with the
findings of Balogun and Obi-Egbedi (2012) who reported a
mean household size of 6 persons in their study carried
out among cassava farmers in Southwest, Nigeria. Thus,
majority of the sampled farmers had moderate family
members that could be of assistance if they are willing to
help in the production process thereby reducing labour
wage bill and production cost in the long run.
The farmers had considerable farming experience with a
mean of 15.4 years and standard deviation of 6. most
(54.7%) of the cassava farmers claimed to have access to
adequate farmland, while about 45.3% of the farmers had
no access to adequate farmland in the study area.
According to Fasoranti (2006), poor accessibility to
adequate farmland coupled with other inputs may result to
inability of farmers to employ modern farm implements
which could lead to a resultant poor productivity by the
farmers.
Table 2: Socio-Economic Characteristics of Respondents (N=300)
Socio-economic variables Frequency Percentages Mean Standard deviation
Age (Years)
< 30 6 2.0 47.4 8.8
30 – 39 50 16.7
40 – 49 125 41.7
50 – 59 76 25.3
60 – 69 42 14.0
≥ 70 1 0.3
Household Size
1-5 207 69.0 5.8 5.8
6 – 10 1 – 5 22.0
11 – 15 27 9.0
Farming Experience(Years)
01-10 117 39.0 15.4 6.0
11-20 118 39.3
21-30 51 17.0
31-40 14 4.7
Farm Size (Ha)
0.01-0.09 109 36.3 1.04 0.6
1.0-1.99 151 50.3
2.0-4.0 40 13.4
Access to Adequate Farmland
Yes 164 54.7
No 136 45.3
Source: Field survey, 2019

Figure 1: Distribution of respondents by level of education
Factors Influencing Access to Land and Land Tenure
Security Status of Women Cassava Farmers
Figure 2 and 3 presented the distribution by access to land
and land tenure security status of the women cassava
farmers in the area. The results revealed that about 89%
of the sampled respondents had access to adequate land
in the area, while only 11% do not have access to
adequate land. An examination of the land tenure security
status of those that had adequate access to cassava farm
land revealed that 71.3% of the respondents were not land
tenure secured, while 28.7% of them were land tenure
secured.
The outcome of this result formed the dependent variables
(two-stage regression) for the Heckman probit regression
model.
Fig 2
Fig 3
Figure 2 and 3: Distribution of Respondents by Access to
land and Land Tenure Security Status
The Heckman probit model showed the presence of
sample selection problem (dependence of the error terms
from the outcome and selection models) therefore,
extenuating the use of Heckman probit model with rho
significantly different from zero (Wald χ2=0.0079, with
P=0.001) as depicted in Table 3. Furthermore, the
likelihood function of the Heckman Probit model was
statistically significant (Wald χ2=32.78, with P<0.0000)
showing a strong explanatory power of the model. The
results from the selection model revealed that household
size, income, access to extension service, cassava output,
farming experience, farm size, members of social group,
dependent, proximity to market and proximity to
processing industry are the factors that statistically
influenced the probability of the farmers’ access to
adequate land. In the same vein, the results of outcome
model indicated that marital status, educational status,
access to credit, access to extension service, farming
experience, farm size, dependent, proximity to market and
proximity to processing industry influenced statistically the
likelihood of being land secured or not in the study area.
Farm Level Efficiencies of the Land Secured and Non-
land Secured Women Cassava Farmers
Table 4 and 5 showed the results of the input oriented DEA
analysis of the cassava production under Land-Secured
and non-Land Secured women cassava farmers in
Southwest, Nigeria respectively, for constant return to
scale technical efficiency (CRSTE), variable return to scale
technical efficiency (VRSTE) and scale efficiency. In DEA,
it should be noted that efficiency score ranges between 0
and 1. Efficiency scores of less than 1 but greater than 0
are identified as inefficient while the scores at unity are
termed efficient. Table 5 reveals the measures of
efficiency of land secured farmers and indicates that both
CRSTE and scale efficiency have mean efficiency scores
of 0.55 apiece. About 12.79% of the land secured cassava
farmers were technically efficient while the remaining land
secured farmers were technically inefficient, though, at
different levels. Considering the CRSTE and scale
efficiency of non-land secured farmers, 8 interviewed
farmers (3.74%) were both technically and scale efficient
with 0.38 and 0.39 mean efficiency, indicating about 62%
and 61% inefficiency for land secured and non-land
secured farmers respectively. Though only 12.79% of land
secured and 3.74% of non-land secured farmers’ category
were operating at the most productive scale size but land
secured farmers had 38% of farms that was ≥ 0.5
efficiency score, while the non-land secured farmers had
39% within the same range for scale efficiency. This result
was in line with Taraka et.al (2010), who investigated
technical efficiency of rice using DEA approach on rice
farms in crop year 2009/2010, their results showed that
most farms operate at lower level of technical efficiency.
The inefficiencies of the many farmers may be due to
misallocation of resources since the mean scale efficiency
of the sample farms is relatively on the average (0.55) for
the land secured farmers and 0.38 for the non-land

Table 3: Results of the Heckman’s Probit Selection Model
Explanatory Variable Outcome equation
Land Security status
Selection equation
Access to land
Coefficient P-value Coefficient P-value
Age of the respondents 0.00288 0.560 0.00691 0.488
Marital Status 0.12769*** 0.000 0.20568 0.155
Household Size -0.02599 0.470 -0.10468*** 0.019
Educational status 0.00532** 0.026 -0.11187 0.275
Access to credit 0.00419*** 0.000 0.24715 0.348
Income of Respondent 9.92E-07 0.459 3.77E-06*** 0.003
Access to Extension services 0.00331*** 0.001 -0.95098*** 0.001
Cassava Output -3.81E-06 0.862 -0.00006*** 0.000
Farming Experience 0.00610*** 0.008 0.02936** 0.032
Farm size 0.75837*** 0.010 1.18985*** 0.000
Social group belong to -0.00839 0.954 0.41056* 0.068
Primary occupation 0.07811 0.665 0.54897*** 0.002
Dependants -0.00776*** 0.019 0.16237** 0.035
Proximity to Market -0.06814** 0.024 -1.43551** 0.059
Proximity to city/urban area -0.00418 0.853 -0.03643 0.458
Proximity to processing industry 0.01268*** 0.000 0.78319** 0.042
Constant 0.78047 0.028 0.76801 0.226
Total observations 300
Censored 161
Uncensored 139
Wald chi square (zero slopes) 32.78***
Wald chi-square 0.0079
*** Significant at 1% level, ** significant at 5% level and * significant at 10% level.
Source: Computed from Field Survey Data, 2019
secured farmers. The mean levels of technical efficiency
are almost in the same level with other findings on
technical efficiency. For instance, Oguntade et al (2011) in
the measure of technical efficiency of cocoa farms in Cross
River State, Nigeria obtained mean technical efficiency
between 59% and 87%, Oyewole (2011) obtained the
technical efficiency of maize production in Oyo State at
59%, Seyoum et al. (1998) found the mean technical
efficiency of maize producers in Ethiopia to be 79%,
Wierand Knight (2000) found mean efficiency levels of
about 55% among cereal crop producers.
Table 4: Efficiency Scores by CRSTE, VRSTE and scale for Non-Land Secured Farmers
Efficiency CRSTE VRSTE SCALE
Frequency Percent Frequency Percent Frequency percent
< 0.10 27 12.62 0 0.00 27 12.62
0.10 – 0.19 33 15.42 1 0.47 29 13.55
0.20 – 0.29 20 9.35 4 1.87 18 8.41
0.30 – 0.39 38 17.76 1 0.47 38 17.76
0.40 – 0.49 45 21.03 0 0.00 45 21.03
0.50 – 0.59 11 5.14 0 0.00 12 5.61
0.60 – 0.69 20 9.35 0 0.00 21 9.81
0.70 – 0.79 11 5.14 0 0.00 12 5.61
0.80 – 0.89 1 0.47 0 0.00 2 0.93
0.90 – 0.99 0 0.00 0 0.00 2 0.93
1.00 8 3.74 208 97.20 8 3.74
Total 214 100.00 214 100.00 214 100.00
Minimum 0.04 0.16 0.04
Maximum 1.00 1.00 1.00
Mean 0.38 0.98 0.39
Standard deviation 0.24 0.13 0.24
Source: Computed from Field Survey, 2019

Table 5: Efficiency scores by CRSTE, VRSTE and scale for Land Secured Farmers
Efficiency CRSTE VRSTE SCALE
Frequency Percent Frequency Percent Frequency Percent
< 0.10 2 2.33 0 0.00 2 2.33
0.10 – 0.19 8 9.30 0 0.00 8 9.30
0.20 – 0.29 10 11.63 0 0.00 10 11.63
0.30 – 0.39 10 11.63 0 0.00 10 11.63
0.40 – 0.49 16 18.60 0 0.00 16 18.60
0.50 – 0.59 5 5.81 0 0.00 5 5.81
0.60 – 0.69 6 6.98 0 0.00 6 6.98
0.70 – 0.79 9 10.47 0 0.00 9 10.47
0.80 – 0.89 4 4.65 0 0.00 3 3.49
0.90 – 0.99 5 5.81 1 1.16 6 6.98
1.00 11 12.79 85 98.84 11 12.79
Total 86 100.00 86 100 86 100.00
Minimum 0.09 0.95 0.09
Maximum 1.00 1.00 1
Mean 0.55 1.0 0.55
Standard
deviation
0.28 0.01 0.28
Table 6: Hypothesis Testing Between Land Secured and Non-land Secured Women Cassava Farmers
Paired variables T-test value P value
(2 tailed)
Decision rule
Land Secure efficiency and non-Land Secure efficiency 2.29 0.023** Reject HO
Land Secure CRSTE and non-Land Secure CRSTE 4.21 0.010*** Reject HO
CONCLUSION AND RECOMMENDATIONS
The study concluded that women cassava farmers had
access to adequate land for cultivating cassava but not
many of the respondents were land secured in the study
area. Farmers that are land secured were more efficient
than those that were not land secured in the area. Despite
that, not all of them are efficient at operating at the most
productive scale size. The land secured households are
efficient in the use of resources for optimal cassava
production than their non-land secured counterpart. Also,
access to extension agents, farming experience, farm size,
number of dependents, and proximity to processing
industry are significant and germane in determining factors
affecting access to land and the land tenure security status
of the women cassava farmers in the study area. The study
recommended that women participation in agricultural
activities should be encouraged by way of equal provision
of production resources, access to land, technology and
education to them as it applies to their male farmer
counterparts to enhance their contribution to sustainable
livelihood in supporting their household survival and also
abate poverty and social instability around the world and
Government should make policy reforms on land
management and development programs that enhance
women economic opportunities and voice in decision
making process to enhance land tenure security which is
crucial for women’s empowerment.
REFERENCES
Ajayi, C.O. and Olutumise, A.I. (2017): Determinants of
Food Security and Technical Efficiency of Cassava
Farmers in Ondo State, Nigeria. International Food and
Agribusiness Management Review, 21(7): 915 – 928.
Alegre J., Lapiedra R. and Chiva R. (2006): A
measurement scale for product innovation
performance. European Journal of Innovation
Management. Vol 9 no 4 pp 333-346.
Babalola D.A and Olayemi J.K., (2013): Determinants of
farmers’ preference for sustainable land management
practices for maize and cassava production in Ogun
State, Nigeria. Publication of the 4th International
Conference of the African Association of Agricultural
Economists, September 22-25, 2013, Hamet, Tunisia
Chikaire J.U., Onoh, P.A. and Echetama J.A. (2010):
Perceived causes of crop farmers and pastoralists’ land
use conflicts in South East agro- ecological zone of
Nigeria. Nigerian journal of Agriculture, Food and
Environment. 18(4): 7-15
Donelly K. (2014): The other average man;Science
Workers in Quetelet’sBelgium.SAGE Journals,
https//doi.org/10./117710073275314559331.
Fasoranti, M.M. (2006): A Stochastic Frontier Analysis of
Efficiencies of Cassava-Based Cropping Systems in
Ondo State, Nigeria. Unpublished Ph.D Thesis, Federal
University of Technology, Akure, Nigeria. Pp 109.

Feder G. and Jock A. (2007): Policy making towards
extension Investments: Hand book of Agricultural
Economics. Vol.3, pp 2343-2378.
Heckman J. J. (1976): The Common Structure of
Statistical Models of Truncation, Sample Selection and
Limited Dependent Variables and a Simple Estimator
for Such Models. Annals of Economic and Social
Measurement 5: 475-492.
Iheke O.R, Nto P.O. and Onyeneke I.Q. (2019): Effect of
Tenure Security and Farmers’ Socio-economic
characteristics and Farm Investment of arable crop
farmers in Imo State, Nigeria. SCIREA Journal of
Agriculture Vol4, Issue 2, pp 18-34.
Odoemelam L.E, Osahon E., and Nechedo E. (2013):
Effect of tenure security on livelihood activities of
women farmers in Anambra State, Nigeria. Journal of
Agriculture and Social Research. Vol 13, No2.
Oduntan, O., Amos, T.T. and Oseni, J.O. (2015).
Efficiency and Profitability of Small-Scale Cassava
Production in Akure Area of Ondo State, Nigeria.
Journal of Applied Tropical Agriculture. Vol. 20 No. 2:
55-61.
Oguntade, A.E., Okafor, C., Mafimisebi, T.E. and
Fatunmbi, T.E. (2011): Technical Efficiency of Cocoa
Farms in Cross River State, Nigeria. African Journal of
Agricultural Research 6(22): 5080–5086.
Olayemi, J. K. (2014). Principle of Micro-Economics for
Applied Economic Analysis. Ibadan, Nigeria: SICO
Publishers. Pp 67-92.
Olutunmise A.I and Ajibefun I.A, (2019): Evaluation of
poverty status and time-taken to exit poverty among
food crop farmers in Nigeria. Journal of Agricultural
faculty of Gaziosmanpasa University. Vol 36(2), 97-
106.
Oyewole, I. O. (2011). Technical Efficiency of Maize
Production in Oyo State. Journal of Economics and
International Finance Vol. 3(4), pp. 211-216.
Paul M. F., Paolo G., Adriana H., Jonathan L., Paul M. and
David P. (2002): Food and Agricultural Organization
(FAO) Land Tenure Studies; Land Tenure and Rural
development. Publishing Management Service,
Information divisions, FAO, VialedelleTerme di
Caracalla 00100 Rome, Italy.
Rahman A.A, Abdul R.Z. and Sambasivav M. (2004): The
influence of organizational culture on attitudes towards
organizational change. Leadership and Organization
Development Journal. Vol 25, No2 pp 161-179.
Raufu M.O.and M.O Adetunji (2012): Determinant of Land
Management Practices among crop Farmers in South-
Western Nigeria. Global Journal of Science Frontier
Research, V12, Issue 1 Version 1.
Seyoum, E.T; Battese G.E; and Fleming. E.M. (1998).
Technical Efficiency and Productivity of Maize
Producers in Eastern Ethiopia: A study of Farmers
Within and Outside the Sawakawa–Global 2000
Project. Agricultural Economics (19): 341-348.
Taraka, K.; Latif, A., I; and Shamsudin, N., M. (2010). A
Nonparametric Approach to Evaluate Technical
Efficiency of RiceFarms in Central Thailand
chulalongkorn journal of Economics 22, 1-14.
Weir. S. and Knight,J. (2000). Education Externalities in
Rural Ethiopia: Evidence from Average and Stochastic
Frontier Production Functions. Working Paper No. 4,
2000. Centre for Study of African Economies,
University of Oxford.
Accepted 3 April 2020
Citation: Akinduko AK, Oseni JO (2020). Analysis of Land
Use Efficiency among Women Cassava Farmers in South-
West Nigeria. Journal of Agricultural Economics and Rural
Development, 6(2): 744-751.
Copyright: © 2020: Akinduko and Oseni. This is an open-
access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

Analysis of Land Use Efficiency among Women Cassava Farmers in South-West Nigeria

Recommended

Recommended

More Related Content

What's hot

What's hot (17)

Similar to Analysis of Land Use Efficiency among Women Cassava Farmers in South-West Nigeria

Similar to Analysis of Land Use Efficiency among Women Cassava Farmers in South-West Nigeria (20)

More from Premier Publishers

More from Premier Publishers (20)

Recently uploaded

Recently uploaded (20)

Analysis of Land Use Efficiency among Women Cassava Farmers in South-West Nigeria