Small Farms and Agricultural productivity in Nigeria: Empirical Analysis of the Effects of land tenure,
fragmentation and property rights.
Temidayo Apata1, Akintayo Sanusi2, and Victor Olajorin3.
1. Department of Agricultural Economics & Extension, Federal University, Oye-Ekiti,
Ekiti State, Nigeria.
2. Department of Agricultural Economics & Extension, Federal University, Abeokuta
3. Department of Forest Economics, University of Ibadan, Nigeria
[email protected]
Paper prepared for presentation at the
“2016 WORLD BANK CONFERENCE ON LAND AND POVERTY”
The World Bank - Washington DC, March 14-18, 2016
Copyright 2016 by author(s). All rights reserved. Readers may make verbatim copies of this document for
non-commercial purposes by any means, provided that this copyright notice appears on all such copies.
ABSTRACT
Small farm characterize agriculture in Nigeria and seems to be persisting, this is because most of the lands
used are fragmented. With the continuing growing of fragmentation of land holdings, small-size increased
significantly. This study examines the effect of land fragmentation on agricultural productivity in rural
Nigeria. A probability sample of 955 small farmer’s respondents’ were examined but only 826 sample
useful for analysis. Households with most fragmented farms were 49.14% with mean farm size of 1.86ha.
Therefore, high degree of fragmentation is associated with small farms. Land fragmentation index (LFI)
revealed mean of 2.72, thus indicated that small farmers cultivated at least 3 fragmented lands. Farm size
and land fragmentation index are negatively and significantly correlated with net farm income per hectare.
Land fragmentation index negatively affects agricultural productivity and is highly significant at 1percent.
Excessive fragmentation results in uneconomic sub-division of land. Land is the most important resource
in rural areas, where majority do not own or access to land for use, hence, government should formulate
and implement economically viable land reforms policy to ensure that the farmers feel emotional
attachment to the land they cultivate.
Keywords: Technological transfer index, land fragmentation index, Institutional
development, Farm households, Nigeria
INTRODUCTION
Evidence from literature and past studies have identified Sub-Saharan Africa (SSA) as one of the world’s
poorest, and the region’s economies are heavily depended on agriculture as the primary source of income
and food (Alimi, 2012, Spencer, 2002; Poulton et al, 2005; Lipton, 2005). Researchers have also shown
that most of the poorest households in SSA are found in agriculture (Apata et al, 2011; Ikpi, 1989;
Okunmadewa, 2002; Alayande and Alayande, 2004; Apata, 2006). The World Bank revealed that majority
of the rural population produce 84 percent of agricultural value-added (World Bank, 2008). These products
are from numerous smallholder farmers, who on the average, cultivate one hectare of land. Recent estimates
indicated that though 55 percent of smallholder farmers have less than 1 hectare of cultivatable land, there
are about 30,000 estates cultivating between 10 to 500 hectares, but their agricultural outputs compared to
that of small holders who cultivated 1 hectare are insignificant (Olayiwola, L., M., and Adeleye, O. 2006).
Past studies have indicated that in many developing countries, key resource is land, particularly in the rural
areas where they use this significant resource for livelihood activities. Due to traditional system of
inheritance of land and property rights access to land for agricultural purposes is becoming difficult.
Literature have argued that inability of the small holder’s farmers to have leasehold or free title on land
made them to operate on two or more geographically separated expanses of land (Johnson, 1972; Barrows
& Roth, 1990). Past studies have argued that this pushes most of these small holders to have fragmented
land for agricultural purposes and thus affect efficiencies in production ((Lyne & Nieuwoudt, 1991).
Fragmentation result from land scarcity as farmers look beyond for whatever pieces of land may be
available (Gebeyehu, 1995)
Past work on land fragmentation have argued that as population increases, the size of holdings fall, and are
progressively fragmented into small plots, dispersed over a wide area, for residential purposes (Webster &
Wilson, 1980). Evidence from the survey of Nigeria land tenure system revealed that the per capita land
holdings of small farmers declined from 1.53 hectares in 1968 to 0.8 hectares per capita in 2010 (Olayiwola
and Adeleye, 2006, NBS, 2014). However, these farmers play an important role for food security with an
average farm size ranges between 0.7-2.2 hectares. Facts have shown that while proportion of the
population living in poverty in smallholder farming is on the decrease in Asia, the proportion has increased
in SSA. The persistence and even deepening of a type of small farming that is getting smaller all the time
and that demonstrates an even greater orientation toward low-level subsistence than was the case 20 or 30
years ago should be of great concern.
Small farms characterize agriculture in Nigeria and seems to be persisting, this is because most of the lands
used are fragmented. With the continuing growing of fragmentation of land holdings, the average size of
farms fell in the region, while the number of small-size holdings increased significantly. Past studies and
substantial literature have examined the relationship between land fragmentation, on the one hand, and land
productivity, or efficiency at farm level, on the other (Blarel et al., 1992; Bizimana, et al, 2004; Wu et al.,
2005; Van Hung et al., 2007; Thomas, 2007; Rahman and Rahman, 2008; Chen et al., 2009; Corral et al.,
2011, Austin et al, 2012; Sauer et al, 2012). Most of these studies however, empirically have produced
results that helped to shape the concept of this research. For instance, Van Hung et al., 2007 and Corral et
al., 2011, found that land fragmentation is a source of inefficiency or has a negative relationship with farm
profitability other evidence are contained in the following studies (Rahman and Rahman, 2008 and Di Falco
et al., 2010); however, the work of Wu et al. (2005) found out that there is a lack of statistically significant
relationship between land fragmentation and technical efficiency.
Several studies in Nigeria have investigated the persistence of small farms, land fragmentation and small
farms efficiency (Ikpi, 1989, Okunmadewa, 2002, Alimi, 2012, Austin et al, 2012). Most of these studies
were conducted at the Local Government level or at the State level, and these studies are useful because
they helped to identify the structure of land fragmentation at the local and state levels respectively.
However, their application for policy formulation at the national level is limited due to small scope. This
study however, uses national data, and will add to the already existing body of knowledge on agriculture
and land fragmentation.
Substantial studies that examined small farms in Nigeria have argued that agriculture is important in rural
areas because of its role as the main source of income that employs relatively large households in farm
operations (Alimi, 2012, Apata, et al, 2009). Reviewed of similar studies have indicated that accounting
for the cause of land fragmentations among small farms is crucial, since most households in Nigeria has
been identified as resource poor (Alimi, 2012, Apata et al, 2011). Therefore, knowledge on the problems
of traditional land tenure systems and property rights on land in Nigeria and factors influencing increase in
land fragmentation will help to formulate policies that can improve small holder’s access to agricultural
land. The study therefore examine the effects of Effects of land tenure, fragmentation and property rights
on small farms productivity, evidence from rural Nigeria.
METHODOLOGY
STUDY AREA
Nigeria is one of the Sub-Saharan Africa (SSA) nations located approximately between latitude 4° and 14°
North of the Equator, and between longitudes 2° 2' and 14° 30' East of the Greenwich meridian in the
western part of Africa with total geographical area of 923,768 square kilometers and an estimated
population of about 140 million (FRN, 2007). The country has 36 states plus the Federal Capital Territory
(FCT)-Abuja. Nigeria shares its boundary with the Republic of Benin to the west, the Niger republic to the
north, the republic of Cameroon and Chad republic to the east.
Nigeria has a highly diversified agro ecological condition, which makes possible the production of a wide
range of agricultural products. Hence, agriculture constitutes one of the most important sectors of the
economy. The sector is particularly important in terms of its employment generation and its contribution to
gross domestic product (GDP) and export revenue earnings. Despite Nigeria’s rich agricultural resource
endowment, however, the agricultural sector has been growing at a very low rate. Less than 50 per cent of
the country’s cultivable agricultural land is under cultivation. Even then, small holder and traditional
farmers who use rudimentary production techniques, with resultant low yields, cultivate most of this land.
The small holder farmers are constrained by many problems including those of poor access to markets, land
and environmental degradation, and inadequate research and extensions services (ANAP, 2005).
METHOD OF DATA COLLECTION
Data for this study come from Nigerian living Standard Survey (NLSS) and National Consumer Survey.
The selection of the sample size was based on a two-stage stratified sampling with the 1st stage involving
clusters of housing units called Enumeration areas (EAs), and the 2nd stage involves the housing unit. The
sample size was determined from 120 EAs selected in each of the 36 states of the nation and Abuja which
is the Federal Capital Territory (FCT). Out of these, 4 housing units were selected randomly from each of
the EAs. A total of 240 households were selected randomly in each of the state and FCT, implying that
8,880 households were selected in all, only 955 were captured and 826 data were useful for further analysis.
Nonetheless, data used were collected from selected respondents’ collected in each of the survey
administration. Selection of respondents were based on those whose income sources are provided,
information on livelihood activities, livelihood diversification activities and other relevant information that
were useful to the study.
In addition, the data sets used provides information on plot by plot land use, the number of plots per
household and individual plot sizes, and outputs in quantity. Outputs included in the multi-output multi-
input directional distant function are cassava, maize, vegetables, oil palm, tomatoes, peppers and onions
etc. for inputs sources, land, labour, seeds, fertilizers, herbicides/pesticides used, and rental of land and
farm buildings among others that are used. Concerning labour, the survey examined data about the number
of family members that are engaged on-farm for agricultural production both full- and part-time. Part-time
workers were defined as family members who work at least 20 hours/week on-farm. Also, incorporated is
the question concerning the hours used by hired labour. Collection of data was also based on the allocation
of labour off-farm and off-farm income.
MEASUREMENT OF LAND FRAGMENTATION AND TECHNOLOGICAL TRANSFER
Land Fragmentation Index was captured taking a cue from the work of The Januszewki (JI) index and was
adopted in measuring land fragmentation. This index is located within the range of 0 to 1. The smaller the
LFI value, the higher the degree of land fragmentation. The LFI value combines information on the number
of plots, average plot size and the size distribution of the plots (Jha, et al., 2005). The index is computed
as:
𝐽𝐼 =
√∑𝑎𝑖
𝑎=1 𝑎𝑖
……………………..(`1)
∑𝑛
𝑎=1 √𝑎1
Technology Transfer Index (TTI) was also computed to determine access to technology and adoption and
its influence on agricultural productivity. This index is located within the range of 0 to 1. The higher the
TTI value, the higher the degree of technology transfer. TTI value combines information on technology
accessible to farmer, degree of adoption, outputs and income. This index is computed as follows:
𝑇𝑇𝐼 =
∑𝑛
𝑎=1 √𝑎 𝑖
……………………….(2)
𝑎
𝑖 𝑎
√∑𝑎=1
𝑖
LAND FRAGMENTATION AND TECHNOLOGICAL TRANSFER ON AGRICULTURAL
PRODUCTIVITY
To examine the effects of land tenure, fragmentation and technological transfer on agricultural productivity,
Cobb-Douglas model was chosen. The rational entails in the properties of the model in choosing among
alternative production functions, thus make it an appealing choice, the study is primarily interested on how
land fragmentation affects production (Najafi, 2003). The Cobb-Douglas production is modeled in the
equation below.
𝐴𝑅𝐸𝐴 = 𝐵0 𝐾𝐵1 𝐹𝑆𝐵2 +𝑎𝑖
2𝐿𝐹
𝑇𝑇𝐼 𝐵3
+𝑎22𝐿𝐹
Where AREA = Area of farm operations
𝐿𝐹𝐼𝐵4 𝑇𝐷𝐵5 𝐿𝑀𝐵6 𝐴𝐷𝐵7 𝐸𝑋𝑇𝐵8 𝐶𝑅𝐸𝐵9 ……. (3)
Bo – B9 are partial elasticities
K = Capital facilities (N)
FS = Farm Size (Ha)
TTI = Technology Transfer Index
LFI = Land fragmentation index (Januszewski’s index)
TD = Tenure duration (years)
LM = labour extent (mandays)
AD = Average distance to farmstead (Kilometers)
EXT = Access to extension services (access =1, 0 otherwise)
CRE = Access to credit services (access =1, 0 otherwise)
EMPIRICAL MODELLING
Agricultural productivity is computed using net farm income per hectare of the farm operators, and is
dependant socio-economics and related variables of the operators. These include capital facilities (CF),
farm size (FS) , education (EDU), visits by field extension officer (EXT), land fragmentation index (LFI)
number of plots cultivated (PLT), labour costs per hectare (LCP), access to credit services (CRS) and
distance to farmstead (DF). The study hypothesized Area operated is as endogenous and is estimated from
NFI = f (CF, FS, EDU, EXT, LFI, PLT, LCP, CRS, DF) ………………………… (4)
The dependent variable - net farm income per hectare – reflects returns to management, rent earned on land
and other fixed inputs.
ESTIMATION PROCEDURE
Past studies have argued that the use of Ordinary least squares regression analysis (OLS) is a good estimator
if it is assumed that the error term is not correlated with the stochastic variable’ (Judson, and Owen, 1999).
However, to account for possible correlation with the error term, the study replaced the stochastic variable
with an instrumental variable (estimated area operated). Hence, Two-stage least squares (2SLS) regression
analysis was used to estimate eqn (4). The use of 2SLS involves the application of OLS regression analysis
in two stages. It has been argued by literature that the presence of multicollinearity problems can give biased
estimates and there is need to test for multicollinearity (Gujarati, 1995). Hence, the study adopted the use
of Ridge Regression to test for multicollinearity. Ridge Regression (RR) has been indicated as one of
several methods that have been proposed to remedy multicollinearity problems (Neter et al, 1996). The
ridge standardized regression estimators are obtained by introducing into the least squares normal equations
a biasing constant K ≥ 0. The constant K reflects the magnitude of bias in the estimators and usually varies
between 0 and 1. When K > 0, the ridge regression coefficients are biased but tend to be more stable (i.e.
less more variable) than ordinary least squares estimators (Neter et al, 1996).
A commonly used method of determining the optimal biasing constant K is based on the ridge trace and the
variance inflation factors (VIF). Therefore, by examining the ridge trace and VIF values, the smallest value
of K will be chosen where the regression coefficients first become stable in the ridge trace and the VIF
values become sufficiently small.
RESULTS AND DISCUSSIONS
DESCRIPTIVE STATISTICS
Descriptive statistics illustrating a demographic profile of respondents, characteristics specific to land use
and performance indicators, evaluation of sources of farm information and tenure characteristics in the
sample are presented in Table 1.
Table 1. Summary Statistics of Socioeconomic variables of respondents
Variables
Mean
K = Capital facilities (N)
85421
FS = Farm Size (Ha)
2.42
EDU = Education
3.1
AGE (years)
47
TTI = Technology Transfer Index
0.34
LFI = Land fragmentation index
2.72
TD = Tenure duration (years)
45
LM =Labour extent (Man-days)
51
AD = Average distance to farmstead (Kilometers)
4.23
EXT = Access to extension services
0.31
CRE = Access to credit services
0.27
Standard Variation
58230
1.05
2.04
21.8
0.21
2.16
31
27
3.01
0.26
0.21
Table 1 compares the socioeconomics statistical variables of the farm operators in the study areas. The farm
operators appear to be younger (mean age of 47 years), wealthier, better educated, with relatively larger
and less fragmented farms; have greater tenure certainty, and good access to extension services and credit
facilities. Data on education were captured using the scale ranging from zero to four to symbolize; no
education, primary school completed, secondary school completed and post-secondary school completed
respectively. Visits by field extension officers represent frequency of visits on a farm in the last two seasons,
and were captured on a scale as ranging from zero to four (i.e. none; 1-3 times; 4-6 times; 7-9 times; and
10+ times, respectively). The categories of the extension visits variable were determined after a means test
showed significant changes in adoption of farm practices and farm visits by extension officers at the above
intervals. Tenure duration certainty was classified as dichotomous, equal to one if a farm operator is
confident of his long-term tenure, and zero otherwise.
Moreover, Table 1 revealed that show that smallholder agricultural production is practiced in Nigeria. The
average working distance to the farm is 4.23 kilometers and this is important when considering the reason
for the cultivation of small scattered plots. Thus, evidenced established here that most agricultural land are
fragmented. In addition, it is attested here that Land fragmentation index (LFI ) of mean of 2.72 indicated
that small farmers cultivated at least 3 fragmented lands for agricultural activities. The average household
size is seven. Large households results in excessive fragmentation as a result of the need to allocate plots
to male and descendants in the study area. The mean farming experience is 15 years.
DEGREE OF LAND FRAGMENTATION IN AGRICULTURE IN NIGERIA
The study revealed that there is an existence of Individual and communal ownership of land in most of
Nigerian communities. Hence, it can be argued that traditional tenure system of in-heritance encourages
land fragmentation. Evidence from Table 2 revealed that households with most fragmented farms are in
the category of 0.41-0.60 with 49.14% of small farmers and least fragmented farms 0.21-1.40 of 8.06
percent of the small farmers. The mean farm size uncovers that the households with the most fragmented
farms had low acreage cultivated (1.86ha) compared to 2.71 ha at the most consolidated parcels. Hence, it
can be deduced that high degree of fragmentation is associated with the cultivation of small parcels of land.
Also, it was evidenced that the concentration of a high percentage of the respondents at the lower class that
cultivated the largest acreage in comparison to other classes 2.71 hectares of land on the average.
Table 2. Indication of Land Fragmentation
Fragmentation
index
0.01-0.20
0.21-0.40
0.41-0.60
0.61-0.80
0.81-1.00
Total
No. of observations
51
42
256
105
67
521
Observations
(%)
9.79
8.06
49.14
20.15
12.86
100
Mean farm
size (ha)
1.28
2.71
2.86
2.70
2.18
Mean
of
fragmentation index
0.01
0.28
0.47
0.68
0.81
0.38
LAND FRAGMENTATION AND AGRICULTURAL PRODUCTIVITY
The study adopted the use of Cobb-Douglas (CD) production function of the model to estimates the effect
of land fragmentation and other socio-economic variables on the output of arable crop production. The
results are shown in table 3. In the CD production function farm size and land fragmentation index had a
negative effect on agricultural productivity. Farm size was statistically significant at 10 percent and by
implication negatively affected agricultural productivity. Past studies have argued that Land has remained
the single most important factor of agricultural production but the result obtained could be explained by the
fact that given existing technologies it would be uneconomic to drive agricultural productivity through
increases in farm size. The existing technology is limited to rudimentary implements, hoe and cutlass which
are characterized by high rate of drudgery. As assumed land fragmentation index negatively affects
agricultural productivity and is highly significant at 1percent. Excessive fragmentation results in
uneconomic sub-division of land. Other variables that are significant and positive are; capital facilities,
access to extension and credit facilities and are significant at 5 percent and 10 percent respectively.
Agriculture in the study area is labour intensive with little or no application of mechanization. Hence, an
additional man hour employed in agriculture will cause agricultural productivity increases by 1.3 percent
per hectare cultivated. Also, access to extension and credit facilities will cause agricultural productivity
increases by 1.4 percent and 0.98 respectively per hectare cultivated
Table 3. Results from regression analysis for the use of CD model
Estimates
Coefficients
Constant
6.901
K = Capital facilities (N)
1.329
FS = Farm Size (Ha)
-0.142
TTI = Technology Transfer Index
0.249
LFI = Land fragmentation index
-0.7214
TD = Tenure duration (years)
0.423
LM =Labour extent (Man-days)
0.603
AD = Average distance to farmstead (Kilometers)
-0.045
EXT = Access to extension services
1.410
CRE = Access to credit services
0.981
Note: *, **, *** = Significant at 10, 5 and 1 percent respectively.
R2 = 0.682, Adjusted R2= 0.613, F-ration = 4017***
t- statistics
3.105***
2.562**
-1.942*
4.184
-3.103***
1.301
1.610
-0.937
1.920*
2.110*
RESULTS OF THE AGRICULTURAL PRODUCTIVITY MODEL
The 2SLS regression analysis was found suitable for determining the socio-economic factors contributing
to the agricultural productivity model. The model explains the relationship between area operated, land
fragmentation and agricultural productivity. Results of the 2SLS regression analysis are presented in Table
4.
Table 4: Results of 2SLS and ridge regression analysis of economic efficiency model
Variable
2SLS Regression
Ridge Regression
Coefficient
t-values
Coefficient
t-values
K = Capital facilities (N)
20.31
5.21***
18.03
4.18***
FS = Farm Size (Ha)
-3.14
-3.402**
-3.01
-2.99**
TTI = Technology Transfer Index
14.35
4.318***
13.81
4.110***
LFI = Land fragmentation index
-31.23
6.01***
-29.15
5.98***
Education
25.08
5.512***
21.45
4.992***
Farming experience
-5.02
1.472
-4.12
1.054
TD = Tenure duration (years)
8.13
1.512
7.11
1.325
LM =Llabour extent (Man-days)
7.15
1.647
6.83
1.603
AD = Average distance to farmstead 6.02
1.438
5.93
1.342
(Kilometers)
EXT = Access to extension services
18.54
6.013***
17.93
5.913***
CRE = Access to credit services
23.21
6.151***
21.55
5.995***
F statistics = 41.381***
K= 0.10
R2 = 0.673
F statistics = 38.47***
2
Adjusted R = 0.645
R2 = 0.628
Adjusted R2 = 0.581
Note: ***, **, * denote statistical significance at the 1, 5, and 10% levels of probability, respectively
Table 4 reviews the results of the agricultural productivity. Evidence results from the 2SLS regression
analysis indicated consistency with the hypothesized relationships. This is particularly true with respect to
the significant and strongly positive effects of capital facilities, education, and access to extension and credit
facilities. On the other hand, farm size and land fragmentation index had strong negative effect on net farm
income per hectare. This results in line with outputs of Sundqvist and Anderson, (2004) and Daniel et al,
(2010). Beta coefficients indicate that farm access to extension and credit facilities have the strongest
impacts on net farm income per hectare, which indirectly reflects agricultural productivity. In absolute
terms, the results suggest that a unit (hectare) increase in access to extension and credit facilities will
increase net farm income per hectare by 6.013 and 6.151 percent respectively. Farm size and land
fragmentation index are negatively and significantly correlated with net farm income per hectare, indicating
that land fragmentation leads to small and uneconomic size of operational holdings (Gebeyehu, 1995, Chen
and Ravallion, 2004). This implies that efficiency of very small-scale farms can be enhanced by land
consolidation.
2
Despite the relatively high R statistic and the relatively high t-statistics of the estimated regression
coefficients, the Condition Index of 20.6 indicates the presence of a moderate to high multicollinearity in
the 2SLS regression equation (4) (Gujarati, 1995). The Condition Index is the square root of the ratio of the
largest eigenvalue to the minimum eigenvalue. Following Maddala (1992) and Neter et al (1996). Hence,
ridge regression (RR) was used to remedy for multicollinearity in the original equation (2). RR overcomes
the multicollinearity problem by adding a biasing constant; K ≥ 0 to the least squares normal equations and
then by estimating the standardized ridge estimators (Neter et al, 1996).
CONCLUSIONS AND IMPLICATIONS
There is overwhelming evidence that food security and Land tenure security determines whether people
will invest in land, adopt new technologies and access credit. Without secure land rights, investment
(financial and labour) and the up-take of new technologies in agriculture and sustainable land management
is undermined. Insecure land rights is the major source of social inequality and instability in Africa. The
“Land Question “is a fundamental issue for African development. Land is not just an economic asset. It has
deep social and cultural significance. The presence of property rights eliminates the anxiety and uncertainty
of expropriation that encourage the farmers to make long term investment decision on land and to adopt the
best cropping system. Pattern of land ownership affects net per acre output by affecting the efficient use of
inputs.
The tenancy status of respondents as follows 36% of the land is operated by owners (i.e., who do not rent
out any land), 28% by owner-tenants, and just 7% by pure tenants. One third of total operated area is under
some kind of tenancy arrangements with sharecropping covering about one-half of the land. The study
indicated that poor agricultural production of small farmers is the structure of land tenure, the lack of proper
land ownership as well as lack of improved agricultural technology and changing climatic conditions. Land
is the most important resource in rural and agricultural country like Nigeria. Without owning or having
access to land, people cannot sustain themselves. Over the past 40 years, the dispossession of small peasant
producers from their land has increased dramatically. Today at least 60 per cent of rural families are
landless. These people are turned into seasonal laborers, working or sharecropping on land belonging to
others. So, the government should formulate and implement economically viable land reforms policy to
ensure that the farmers feel emotional attachment to the land they cultivate. Enforcing land reform and
agricultural development polices, restrict informal settlements of agricultural land, austerity in acquisition
of land: land ownership policy is vital in order for vast majority of population living in rural areas whose
income is dependent on farming.
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