Working Paper No. 2015/07
Impact of Agricultural innovation on improved livelihood and
productivity outcomes among smallholder farmers in Rural Nigeria
Ogunniyi Adebayo1, Kehinde Olagunju2
September 2015
© The authors, 2015
¹ Department of Agricultural Economics, University of Ibadan, Nigeria
2 Szent Istvan University, Institute of Regional Economics and Rural Development, Gödöllő, Hungary
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Impact of Agricultural innovation on improved livelihood and productivity outcomes
among smallholder farmers in Rural Nigeria
Ogunniyi Adebayo1 and Kehinde Olagunju2
1
2
Department of Agricultural Economics, University of Ibadan, Nigeria
Szent Istvan University, Institute of Regional Economics and Rural Development, Gödöllő,
Hungary
Abstract
Agricultural research programs that are driven by Agricultural Innovation System concepts
usually target to change the way in which low income rural agrarian households in a nation like
Nigeria communicate with the market and the decision making strategies pertaining to
development of their agri-business and the scarce resources which are at their disposal. As a
result there has been a shift in the research paradigm in many African countries like Nigeria;
from top down research systems to nonlinear dynamic systems that aim to enhance end users
capacity to obtain and utilize knowledge and research outputs. The aim of this paper was
therefore to assess the extent to which the use of these innovative agricultural research
interventions impact upon the livelihood and productivity outcomes of rural smallholder farmers
in Nigeria using a case study from the South west region of Nigeria. Using propensity score
matching as a means of establishing a valid counterfactual and single differencing to measure
impact, the study establishes that rural incomes and output are significantly impacted upon by
agricultural research interventions that are driven by agricultural innovation systems concepts.
The study however further finds that although participating households had better livelihood and
productivity outcomes and more diversified income portfolios during the implementation of the
innovative research intervention as a result of greater linkages to markets and capacity building
opportunities; phasing out of the research program reduced the diversity of income portfolios and
lead to the erosion of livelihoods. The study therefore concluded that agricultural research
interventions that are driven by agricultural innovation system concepts have the potential to
positively impact upon the livelihood outcomes of rural smallholder farmers in Nigeria however
there is need for greater capacity building of local extension agents and increased budgetary
support to ensure understanding and application of agricultural innovation system concepts by
local level public agricultural extension agents to sustain positive livelihood and productivity
outcomes. In addition agricultural innovation system concepts should be mainstreamed in all
public agricultural extension and research programs to ensure sustained rural innovation and
robust livelihood and improved productivity outcomes.
Key words: Agricultural Innovation Systems, Livelihoods, Productivity, Smallholder‟s farmers.
A paper prepared for presentation at the 5th MSM 5th Annual Research Conference
Managing African Agriculture: Markets, Linkages and Rural Economic Development
4 September 2015, MSM, Maastricht, The Netherlands
1
Introduction
According to World Bank (2000) estimates, 1.2 billion people lived in absolute poverty in 1998,
depending on an income of less than US$1 per day. An additional 1.6 billion lived on less than
$2 per day. The number of people in the former category has remained constant in the last
decade, while there are now an additional 250 millions living on less than $2 per day (Julio and
German, 2002). However, poverty in Africa is predominantly rural. More than 70 per cent of the
continent‟s poor people live in rural areas and depend on agriculture for food and livelihood, yet
development assistance to agriculture is decreasing (IFAD, 2009). Specifically, despite Nigeria's
plentiful agricultural resources and oil wealth, poverty is widespread in the country and has
increased since the late 1990s. Some 70 per cent of Nigerians live on less than US$1.25 a day
(IFAD, 2010). Poverty is especially severe in rural areas, where up to 80 per cent of the
population lives below the poverty line, and social services and infrastructure are limited. The
country's poor rural women and men depend on agriculture for food and income. About 90 per
cent of Nigeria's food is produced by small-scale farmers who cultivate small plots of land and
depend on rainfall rather than irrigation systems (IFAD, 2010).
The severity of rural livelihood and poverty in developing countries like Nigeria has necessarily
informed a drift in her agricultural systems from the strengthening of national research systems
towards systems that enable innovations from individuals and communities, proper transfer of
knowledge, utilization of knowledge and overall transformation. This shift towards an innovation
systems orientation was precipitated by the realization that despite stronger national research
systems, agricultural productivity remained low as a result not only of the lack of appropriate
technologies and the lack of access to those technologies, inputs, credit and access to markets
and rural infrastructure, but also because of gaps in information and skills that prevented rural
producers from effectively utilizing and adopting technologies. The new prevailing agricultural
research paradigm entails that agricultural research innovation system approaches feature highly
in national strategies for many countries working towards promoting long term agricultural
development (Sanginga et al., 2009). Therefore, the role of agricultural innovation in poverty
reduction, improving livelihood and enhancing productivity outcomes cannot be over
emphasized.
Agricultural innovation can have both direct and indirect effects on livelihood and productivity
improvement of the beneficiaries. Which is more important will be determined largely by the
2
relative speed with which a household adopts new technologies or participate in developmental
intervention programmes (such as Growth Enhancement Support Scheme in Nigeria), by the
status of the household as a net food buyer or seller, by the degree of market liberalization
conditioning whether particular products are tradable or non-tradable, and by the institutions and
incentives facing farmers (Julio and German, 2002). This shift towards an innovation systems
orientation was precipitated by the realization that despite stronger national research systems,
agricultural productivity and improved livelihood remained low as a result not only of the lack of
appropriate technologies and the lack of access to those technologies, inputs, credit and access to
markets and rural infrastructure, but also because of gaps in information and skills that prevented
rural producers from effectively utilizing and adopting technologies (Miriam et al, 2011). The
new prevailing agricultural research paradigm entails that agricultural research innovation
system approaches feature highly in national strategies for many countries working towards
promoting long term agricultural development (Sanginga et al., 2009).
In Nigeria, various innovations has emerged from numerous agricultural policies but many failed
as if they were designed to fail owing to political instability, bureaucracy, misappropriation of
funds, poor management among others. Consequently, the federal government through the
Federal Ministry of Agriculture and Rural Development came up with an agenda-innovation
approach by establishing agricultural transformation agenda (ATA) in which growth
enhancement support scheme (GESS) emerged. Fortunately, due to this innovation intervention,
the government has created an enabling environment for the work of agricultural research and
development agencies that, through the use of agricultural innovation systems concepts,
recognize that there is potential for improving rural livelihoods and enhancing productivity by
enabling rural innovation amongst smallholder producers and hence reducing rural poverty.
To the best of authors knowledge, there are few or no empirical studies in the literature that
specifically assess the impacts of agricultural agenda-innovation systems in Nigeria context on
the ability of rural people‟s to efficiently utilize the natural resource base and thus enhance their
production (Gildemacher et al., 2009), increase food security and nutrition (Morris et al., 2007)
and diversify their livelihoods and preserve the ecosystem (UN, 2008). Unfortunately, the few
studies that do exist, the analytical methods employed are mainly qualitative. The problem of
3
possible endogeneity was not solved. Failure to correct the endogeneity will lead to a biased
estimation of agricultural innovation (GESS) impacts on smallholder‟s farmer‟s livelihood and
productivity outcomes. However, this study employs propensity score matching (PSM) to
establish counterfactual information with which outcomes of agricultural innovation (GESS)
participant households are compared. PSM has commonly been used as a non-experimental
technique with cross-sectional data to reduce biases arising from comparing outcomes of
participants and non-participants smallholder‟s farmers who have different characteristics and
attributes.
This paper therefore presents the findings of an empirical study whose objective was to assess
the impact that agricultural innovation (GESS) has on rural livelihoods and productivity
outcomes in Nigeria. The paper contributes towards the ongoing debate pertaining to the impacts
of agriculture innovation systems on rural development and it aims to provide credible evidence
of the impact of agricultural innovation systems interventions on rural livelihoods that can be
used to inform policy.
The Growth Enhancement Support Scheme in Nigeria
The aims of the program are to target beneficiaries through the: provision of affordable
agricultural inputs like fertilizer, hybrid seeds and agro-chemicals to farmers; removal of the
usual complexities associated with fertilizer distribution; shifting the provision of subsidized
fertilizer away from a general subsidy to genuine small holder farmers and making Nigeria self
sufficient especially in rice production and to ban rice importation by 2015 (MANR, 2012).
Under this Scheme, an accredited farmer will receive subsidized agro inputs allocation through
an e-wallet that hosts unique voucher numbers sent to his or her phone, and goes to an accredited
agro dealer to redeem his inputs.
A major policy stance underpinning the implementation of the GES was the withdrawal of the
Federal government from the procurement and distribution of fertilizers and improved seeds in
2011. This is in a bid to decontaminate the input distribution system and promote effective
service delivery. The agricultural transformation agenda (ATA) introduced in 2011 seeks to
tackle the inefficiencies in the distribution of key inputs making them more readily available and
affordable. In this regard the private sector agro-input business enterprises (agro-dealers) are
assigned a critical role especially in the implementation of the Growth Enhancement Support
4
(GES) Scheme (Akinwumi, 2012). They are involved in the procurement, distribution and
delivery of inputs (fertilizers, improved seeds and agro-chemicals) to small-scale farmers. Under
the scheme, farmers are to benefit directly from an innovative electronic system of delivering
subsidized inputs in which the subsidy payments are delivered directly to the beneficiaries
through mobile phones.
The massive distribution of high yielding varieties of seeds to farmers started two years ago and
in particular, efforts have been focused more on the increase in rice production relatively to other
crops. Rice has become a national commodity as majority of the population now live on rice and
their primary food security is entirely dependent on the volume of rice produced (Awotide et al.
2012).Through massive public-private partnership with local seed companies and the Africa Rice
Centre, launched free distribution of Faro 44 and Faro 52 to rice farmers across the country and
two bags of fertiliser per farmer. Thus, in 2013, almost two million farmers had adopted the new
varieties. GES is therefore a cost-sharing arrangement between the beneficiaries and the
governments. It took the government out of direct procurement and distribution of fertilizer.
Today, seed and fertilizer companies sell their products directly to farmers.
Several research findings have pointed to the fact that the easy access to farm inputs, such as
high yielding varieties fertilisers and information that kick-started the Green Revolution in Asia,
could lead to significant increase in agricultural productivity in Africa and stimulate the
transition from low productivity subsistence agriculture to a high productivity agro-industrial
economy (World Bank, 2008). This implies that agricultural productivity growth will not be
possible without developing, disseminating and making accessibility of cost effective yieldincreasing farm inputs to crop farmers, since it is no longer possible to meet the needs of
increasing numbers of people by expanding the area under cultivation (Awotide et al , 2012).
The importance of subsidy on increasing the use of fertiliser, improved seeds and farm
machinery for boosting agriculture productivity and economic growth set back to 1960s during
Asian green revolution. The success of green revolution in Asia was associated with government
support on subsidies, credits and improved infrastructure and uptake of technologies through
research and extension (Danning et al., 2009). Learning from Asia, Africa green revolution was
promoted during 1970s to 1980s in order to overcome limitations which were facing the
agriculture sector. However, due to inefficiencies, budgetary deficit and pressure from donor
5
institutions, subsidies were eliminated in the early 1990s following 1980s market liberalization.
The consequence was higher transaction costs in input markets and complicated processes for
crop-secured loans. Higher transaction costs led to higher fertilizer price affecting the farmers'
input use decision (Winter – Nelson and Temu 2005). In Nigeria fertilizer use declined to an
average of 9kg/ha per year, which is below Africa and world average of 21kg/ha and 100kg/ha
respectively (RickerGilbert and Jayne, 2009). According to World Bank (2013), only ten tractors
are available per 100 hectares of farmland in Nigeria as compared to 241 tractors per hectare in
Indonesia while less than ten percent of Nigerian farmers could access improved seeds. Analysis
of the relative increase in crop yields in developing countries shows that Nigerians crop yields
have the lowest growth rate of 0.2% from 1968 to 2008 as against 1.2 % for China, 2.3% for
Indonesia and 3% for Malaysia (World Bank, 2013). Low adoption and application of fertilizers
and improved seeds in production was associated with low crop productivity, food insecurity and
higher levels of poverty in most developing countries (Danning et al., 2009).
Several attempts have been made over the years to boost farmers‟ productivity. Among these
efforts are the suppliers of farm inputs such as improved seeds, agrochemicals and fertilizers at
subsidized prices to the farmers. However, a large proportion of these inputs could not be
reached to farmers, as a result of the high level of corruption, insincerity and political
interruption in the distribution channels. Adesina (2013) pointed out that the old system used in
supplying inputs to the farmers was weak, inefficient and fraudulent, hence a large proportion of
the farmers could not benefit from it. He stressed that the inputs meant for the farmers were
diverted by political elites to other countries for personal gains. It was also noted that most of the
fertilizers supplied were adulterated, thus damaging the environment.
An attempt to overcome these difficulties and to reverse the declining trend in crop productivity,
and poverty, there has been resurgent interest in subsidy in Africa since mid 2000 (RickerGilbert and Jayne, 2009), the Federal Government of Nigeria introduced the Growth
Enhancement Scheme (GES) in July 2012 which is designed to deliver government subsidized
farm inputs directly to farmers via GSM phones. The GES scheme will be powered by eWallet,
an electronic distribution channel which provides an efficient and transparent system for the
purchase and distribution of agricultural inputs based on a voucher system. The scheme
guarantees registered farmers eWallet vouchers with which they can redeem fertilisers, seeds and
6
other agricultural inputs from agro-dealers at half the cost, the other half being borne by the
federal government and state government in equal proportions. An e wallet is defined as an
efficient and transparent electronic device system that makes use of vouchers for the purchase
and distribution of agricultural inputs (Adesina, 2013).
Over 1.2 million farmers successfully redeemed their seeds and fertilizers using the electronic
wallet system within 120 days of launch of the Scheme. Farmers received 50% subsidy for
fertilizers and 100% subsidy for improved seeds (FEPSAN, 2012). The priority commodities
under this Scheme are rice, cassava, sorghum, cocoa cotton, maize, dairy, beef, leather, poultry,
oil palm, fisheries as well as agricultural extension.
3.0 METHODOLOGY
Scope of the study
The study covers the entire Oyo state, south-western Nigerian. Oyo state has total land area of
28,454 km2 which makes it the 14th largest state by size in Nigeria. It is located on the west coast
of Nigeria and lies between latitude 8oN and 4oE of the meridian. Oyo state has its capital in the
city of Ibadan which is the largest indigenous city in the whole of West Africa. The state is
bordered on the north by Kwara state, on the east by Ekiti and Osun state, in the south by Ogun
state and on the west partly by Ogun state and the Republic of Benin. The state has a total
population of 5,591,589 (National Bureau of Statistics, 2007).
The climatic situation within the state which is equatorial has also favoured agriculture with two
notably predominant seasons, namely the wet and dry season. The wet season lasts from April to
October while the dry season starts in November and ends in March. Average daily temperature
ranges between 25oC and 35oC almost throughout the year.
Agriculture is the main occupation of the people of Oyo state. The climate favours the cultivation
of crops like maize, yam, cassava, millet, rice, plantain, cocoa, palm produce, cashew etc. A
number of government farm settlements are located in Ipapo, Ilora, Eruwa, Ogbomosho,
Iresaadu, Ijaiye, Akufo and Lalupon. A number of international and federal agricultural
institutions are also located in the state.
7
Type and source of data
Primary data was used for this study. Personal Interview and well-structured questionnaires was
used to obtain data from the maize, yam and cassava farmers, which include socio-economics
characteristics such as age (years), sex, educational level (years of formal education), household
size(number in house of farmer), occupation, farming experience (years), marital status, etc.,
inputs such as labor cost (Naira), seed cost (naira), farm size (hectares), quantity of fertilizer used
(kg), quantity of fungicide used (kg) , value of maize output, value of cassava output, value of
yam output(both in kilogram/hectare) value of awareness characteristics and adoption.
Sampling Technique and Sample size
Multistage sampling technique was used for the selection of respondents.
Three local
governments were purposively selected in the state which is Ibarapa, Akinyele and Ogbomoso
because farmers which are more concentrated in these areas. However, considerable
counterfactual are available in these areas. Two communities were randomly selected from each
LGA using simple random sampling technique. Simple random sampling was used to select 20
households from each community and this making a total of 120 households from the three
LGAs for each crop farmers observed in the study. Therefore, 360 households (120 maize
farmers, 120 yam farmers and 360 cassava farmers) were sampled.
Impact Assessment: Propensity Score Matching
In determining the impact of an intervention; an impact assessment must estimate the
counterfactual; that is, what would have happened had the intervention or program never taken
place or what otherwise would have been. To determine the counterfactual, it is essential to net
out the effect of the intervention from other factors. This is accomplished through the use of
control groups which are compared with the treatment group. The control groups should
resemble the treatment group except in program participation. The choice of a good
counterfactual is therefore crucial in impact assessment. Propensity scores are an alternative
method to estimate the effect of receiving treatment when random assignment of treatments to
subjects is not feasible. Propensity score matching (PSM) refers to the pairing of treatment and
control units with similar values on the propensity score, and possibly other covariates, and the
discarding of all unmatched units. It is primarily used to compare two groups of subjects but can
be applied to analyses of more than two groups. Diaz and Handa (2004) suggests that PSM
8
works well as long as the survey instrument used for measuring outcomes is identical for
treatment and control participants Hence, the success of PSM hinges critically on the data
available, as well as the variables used for matching.
The concept of PSM was first introduced by Rosenbaum and Rubin (1983) while Heckman
(1997) also played a role in the development of propensity score matching methods. He focused
on selection bias, with a primary emphasis on making casual inferences when there is nonrandom assignment. He later developed the difference-in-differences approach which has
applications to PSM. As a program evaluation technique, PSM is based on the idea of comparing
the outcomes of program participants with the outcomes of “equivalent” non-participants. Since
the two groups are comparable on all observed characteristics with the exception of program
participation, the differences in the outcomes are attributed to the program. The estimated
propensity score, for subject e(xi), (i = 1,…, N ) is the conditional probability of being assigned
to a particular treatment given a vector of observed covariates xi Rosenbaum and Rubin, 1983):
e(xi) = Pr (zi = 1│xi) ……………………………………. ………..(2)
and
(
)
∑
* + *
* ++
………………………... (3)
Where
zi= 1 for treatment
zi= 0 for control
xi= the vector of observed covariates for the ith subject
The propensity score is a probability, it ranges in values from 0 to 1.Thus, if propensity score
matching was used in a randomized experiment comparing two groups, then the propensity score
for each participant in the study would be 0.50. This is because each participant would be
randomly assigned to either the treatment or the control group with a 50% probability. In study
designs where there is no randomization, such as in a quasi-experimental design, the propensity
score must be estimated. Propensity score values are dependent on a vector of observed
covariates that are associated with the receipt of treatment.
In this study, the propensity score matching (PSM) was used to evaluate the impact of
agricultural innovation (Growth Enhancement support Scheme) on improved livelihood and
9
productivity outcomes among smallholder farmers in Rural Nigeria. The PSM allows evaluators
to calculate the mean effect of treatment (livelihood and productivity) on the treated. Household
welfare, diversification and income were used as proxy variables to check the livelihood
outcomes of the household while value of outputs (yam, maize and cassava in kilogram) and
fertilizer usage pattern were used as proxy for productivity. If Y1 denotes the potential outcome
conditional on participation and Y0 denotes the potential outcome conditional on nonparticipation, the impact of program is given by:
………………………………………………………….. ………
(4)
i) Estimating the Propensity Score (PS)
The propensity score is defined as the conditional probability of receiving a treatment given pretreatment characteristics (Rosenbaum and Rubin, 1983). The propensity scores were computed
using binary logit regression models given as:
( )
*
+
*
……………...(5)
+
Where,
D= (0, 1) is the indicator of exposure to treatment characteristics (dependent variable)
That is, D=1, if exposed to treatment and D=0 if not exposed to treatment.
X is the multidimensional vector of observed characteristics (explanatory variables).
These explanatory variables are those which are expected to jointly determine the probability to
participate in the treatment and the outcome. The explanatory variables considered in this study
were based on theory and from review of studies on agricultural innovation, productivity and
livelihood.
ii) Matching the unit using the Propensity Score
After the propensity score is estimated and the score computed for each unit, the next step is the
actual matching. Nearest neighbor matching method was used to match. Nearest to neighbor
matching uses the propensity score of similar individuals in the treated and control group to
construct the counterfactual outcome. One major advantage of this approach is the lower
variance which is achieved because more information is used. The matching estimator is given
as:
10
∑
{
…………………………………………….. (6)
∑
}
{∑
∑
} …………………………………….(7)
∑
denotes the numbers of controls matched with observation and define the
weights
()
otherwise. M stands for nearest neighbour matching and
the number of units in the treated group is denoted by NT.
One of the major advantages of this method is that, the absolute difference between the estimated
propensity scores for the control and treatment groups is minimized.
iii) Estimating the impact (Average Treatment Effect on the Treated)
The matched sample was used to compute the Average Treatment Effect for the treated (impact).
It is estimated as follows:
ATT= Ε(∆ | D=1, Χ)
= Ε(Y1 – Y0| D = 1, Χ)……………………………………. ….(8)
= Ε(Y1 | D = 1, Χ) - Ε(Y0 | D = 1, Χ)…………………….....................(9)
where D = 1 denotes program participation (treatment) and Χ is a set of conditioning variables
on which the subjects were matched. Equation 9 would have been easy to estimate except for the
equation Ε(Y0 | D = 1, X). This is the mean of the counterfactual and denotes what the outcome
would have been among participants had they not participated in the program. PSM provides a
way of estimating this equation.
A unique advantage of PSM is that instead of matching subjects on a vector of characteristics,
we only need to match on a single item, the propensity score that measures the probability of
participating in the program. Given that the Conditional Independence Assumption and the
common support assumption holds, then we estimate the mean effect of the treatment through
the mean difference in the outcomes of the matched pairs:
ATT= Ε[Y1 | D = 1, P(X)] = Ε[Y0 | D = 0, P(X)]……………………………..(10)
11
Equation 10 is applicable to single treatment programs where the treatment variable is a
categorical variable that has only two mutually exclusive categories. However, the equation is
easily generalized to multiple treatment programs (Imbens, 2000; Lechner, 1999, 2001). The
ATE, i.e. the average effect of the treatment for an individual drawn at random from the overall
population at random is
………………………………….. (11)
Where N1 is the number of treatment group and N0 is the number of control group. The above
illustration shows the relationship between ATT (average treatment on the treated), ATE
(average treatment effect on an individual) and ATU (average treatment on the untreated).
RESULT AND DISCUSSION
Validity of the logit model of Growth Enhancement Support Scheme (GESS) programme
participation.
To obtain the propensity score matching estimator through the logit regression, individual
socioeconomic status was used to form matched pairs of observational similar individual
characteristics. Individual in households participating in GESS (the treatment cases) and
households not participating (the controls) are considered. Therefore in this study, the logit
model of GESS participation that was estimated was found to be a good predictor of
participation as demonstrated by the results of two alternative tests of goodness of model fit, the
Hosmer and Lemeshow (H-L) static and the chi square test. The H-L goodness of fit test static
was 40.540 and it was non-significant (p=0.342), depicting that the model is a good fit, as the
rule of thumb for accepting a logit model is that the H-L static must be greater than 0.05 and
should show non-significance (Miraim et al, 2011). Secondly, the model has a chi-square static
of 45.52, which is statistically significant at the 1 % confidence level, therefore implying that all
the predicators that have been included in the model are capable of jointly predicting
participation in the GESS programme.
Matching was done on the individual propensity score of treatment. The propensity score was
operationalized as the predicted probability of participation estimated from a logistic regression
of GESS status on the predictors. The coefficients from this model, which are presented in
12
Table1, show that the likelihood of participation rises with gender of the farmers, marital status,
age, schooling years, farm size, and household size. The propensity score is a probability, so the
average probability in the treatment for all households are 65.5% i.e. the probability that a
particular household will be a participant (treatment assignment) is 65.5% with respect to the
outcome variable (livelihood and productivity).
Table 1: Logistic Regression Model for participation in the GESS programme
Coefficient
Standard Error
Education
0.739**
(0.314)
Age of household head
-0.021
(0.762)
Marital status
-0.953
(0.704)
Household size
0.607***
(0.314)
Farm size
0.429**
(0.119)
Gender
0.696
(0.052)
Occupation
-1.749**
(0.825)
Source: Author’s computation, 2015
Number of observation= 360
Pseudo-R2= 0.1104
Chi2 = 45.52
Log likelihood= -78.389735
*, **, *** = 10%, 5% and 1% respectively
Table 2: Propensity Score
Variable
Observation
Propensity score
360
Mean
Std. Dev.
Min
Max
0.6547
0.2930
0.069
0.999
Source: Author’s computation, 2015
To further test for balancing i.e. quality of match, common support graph was drawn. This test is
effective because it shows visual presentation of overlap of propensity scores between the treated
and control cases. A larger proportion of overlap implies a good match of treated and control
cases (Dehejia and Wahba, 2002).
From the graph below there is a considerable overlap of propensity scores between the treated and
control cases, this implies that the match is good and balanced.
Graph 1
13
0
.2
.4
.6
Propensity Score
Untreated
.8
1
Treated
Figure 1: common support graph
Source: Author’s compilation (2015).
Using propensity scores for participation generated by the logit regression model, households in
the intervention were matched on the basis of the proximity of their propensity scores of
participation to households in the counterfactual. All other households whose propensity scores
for participation were different from the range of scores for the intervention households were
dropped from the analysis. By dropping all the counterfactual households whose probability of
participation was very different from the households in the intervention, differences in livelihood
and productivity outcomes were then compared between households that were more similar and
therefore comparable and as such any differences in outcome variables between the participants
and non-participants are attributed to the intervention (Ravallion, 2003; Miriam et al, 2013).
Impact of GESS on Livelihood Outcomes
The impact of GESS on the farmers (cassava, yam and maize) livelihood was proxied by
household welfare (per capita expenditure) , income diversification and income. However, the
14
effects of the GESS programme were found to be statistically significance in many aspect of
livelihood of the considered farmers (yam, cassava and maize) while it was not significance in
some of the livelihood outcomes among the considered farmers. This is with an implication that
there was dynamism in the impact of the programme on the participant livelihood outcomes.
Impact of GESS on Welfare (Per Capita Expenditure)
The empirical result of the impact of GESS on welfare proxy by per capita household
expenditure for the entire households of the maize, cassava and yam farmers in the southwest
Nigeria is presented in Table 3. The average impact estimation shows that GESS have a
significant and positive impact on the welfare of all categories of individuals considered in the
study. The treatment effect on the treated on the average had a positive impact and increases the
household per capital expenditure {welfare} by 12411.95, 21209.12 and 4445.43 which implies
that GESS positively impact household welfare by ₦12411.95, ₦ 21209.12 and ₦4445.43 for
maize, cassava and yam farmers respectively. ATE, i.e. the average effect of the treatment for a
household drawn from the overall population at random is somewhat smaller with a value
₦10977.24k, ₦13213.24k and ₦1977.24 compared to the treated category. The ATU was
estimated by matching a similar treated household to each non-treated household. Thus, average
treatment effect on the untreated {ATU} had a significant positive impact on welfare, this is the
counter factual outcome of the treated had it been they were not treated. The positive influence
of GESS on household per capital expenditure {welfare} corroborates the findings of the study
of Awotide et al (2012) and Ogunniyi and Salman (2015). Furthermore, the study revealed that
the impact of GESS was much higher on cassava farmers compared to the yam farmer‟s
counterpart. With the findings of the study, there is an indication that the objective of the
agricultural innovation (GESS) was achieved in the study area in relation to household welfare.
Table 3: Average Impact Estimates of Propensity Score Matching of GESS on Welfare
PCE{welfare} (₦)
Sample
Treated
Control
Difference
t-stat
Maize farmers
Unmatched
71937.37
56328.33
15609.04
2.17**
ATT
71937.37
59525.42
12411.95
2.82*
ATU
56328.33
44003.11
12325.22
ATE
10977.24
15
Cassava farmers
Unmatched
111937.45
90525.42
21411.70
2.87*
ATT
111937.45
90728.33
21209.12
5.22*
ATU
89078.33
73003.11
16075.22
ATE
Yam farmers
13213.24
Unmatched
41345.92
36753.10
4592.82
3.67*
ATT
41345.92
36900.49
4445.43
4.60*
ATU
26678.90
24083.98
2594.92
ATE
1977.24
* Significant at 10 % level, ** Significant at 5 % level, *** Significant at 1 % level
Source: Authors’ computation 2015. Kernel matching
Impact of GESS on Income
The empirical result of the impact of GESS on income for the entire households in the study area
is presented in Table 4. In rural areas of Nigeria, and in this study, there is a norm in relation to
income generation. Therefore, household income is not synonymous with cash income but is a
computed value that includes cash income earned from various on-and-off employments; which
are summarily grouped based on the sources which are agriculture wage employment, agriculture
self-employment,
non-agriculture
self-employment,
non-agriculture
wage
employment,
remittances. In this study, different sources of household income were identified and used to
compute a household‟s total income. As it is depicted in Table 4 below, households who
participated in the GESS intervention had on average ₦50381.66k and 19412.45k more total
income than their counterparts in the counterfactual for maize and cassava farmers respectively.
Both these differences in household incomes of the maize and yam farmers were statistically
significant at the 1 % confidence level. Increased cash incomes can be convincingly attributed to
the fact that the GESS intervention focused on assisting smallholder farmers in the study area
which is part of the transformation agenda of the intervention, to develop profitable and resource
efficient agro-enterprises in order to meet existing market opportunities as opposed to them
marketing any surplus that they grew for subsistence. Hence, intervention communities
conducted an analysis of existing market opportunities prior to the onset of the cropping year in
order to determine the type of agro-enterprises that would be most profitable.
16
Table 4: Average Impact Estimates of Propensity Score Matching of GESS on Income
Income (₦)
Sample
Treated
Control
Difference
Maize farmers
Unmatched
129907.08
66328.33
63578.75
4.09*
ATT
129907.08
79525.42
50381.66
3.22*
ATU
90876.04
66754.89
24121.15
ATE
Cassava farmers
18907.09
Unmatched
178007.97
96368.93
81639.04
1.45
ATT
178007.97
98245.02
79762.95
1.00
ATU
117808.00
81903.81
35904.19
ATE
Yam farmers
t-stat
22007.24
Unmatched
110937.54
90998.98
19948.56
8.03*
ATT
110937.54
91525.09
19412.45
9.27*
ATU
96328.33
89983.17
6345.16
ATE
6977.24
*** Significant at 10 % level, ** Significant at 5 % level, * Significant at 1 % level
Source: Authors’ computation 2015. Kernel matching
Impact of GESS on Income diversification
Rural income diversification has generally occurred in the study area as a result of an increased
importance of off-farm wage labor in household livelihood portfolio or through the development
of new forms of on-farm/on-site production of non-conventional marketable commodities. In
both cases, diversification ranges from a temporary change of household livelihood portfolio
(occasional diversification) to a deliberate attempt to optimize household capacity to take
advantage of ever-changing opportunities and cope with unexpected constraints (strategic
diversification). The GESS intervention was found to positively impact the rate of income
diversification in the study area. However, the statistically significant differences were observed
on the maize and yam farmers but found non-significant on the cassava farmers. By implication,
GESS intervention increased the rate of income diversification for participating households by
23.45 percent and 3.35 for the maize and yam farmers respectively. Cassava farmers have a
higher rate of income diversification than yam farmers but unfortunately it was found not
statistically significant. Coincidentally or not, the estimate was in line with the positive impact of
GESS on the household income for both maize and yam farmers (see table 4).
17
Table 5: Average Impact Estimates of Propensity Score Matching of GESS on rate of Income diversification
Rate of diversification (%)
Sample
Treated
Control
Difference
t-stat
Maize farmers
Unmatched
79.47
52.56
26.91
4.17*
ATT
79.47
56.02
23.45
3.80*
ATU
63.28
48.03
15.25
ATE
Cassava farmers
12.04
Unmatched
59.22
46.93
12.29
1.17
ATT
59.22
49.08
10.14
1.22
ATU
53.99
45.01
8.98
ATE
Yam farmers
9.77
Unmatched
32.37
26.83
5.54
2.87*
ATT
32.37
29.02
3.35
2.62*
ATU
23.78
18.11
5.67
ATE
3.24
*** Significant at 10 % level, ** Significant at 5 % level, * Significant at 1 % level
Source: Authors’ computation 2015. Kernel matching
Impact of GESS on Productivity
The impact of the GESS intervention programme on productivity was observed in two ways
namely; output and fertilizer usage pattern of the maize, cassava and yam farmers. The estimates
of the impact of GESS were found to be statistically significant on all cases of the value of
output but with a variation on the pattern of fertilizer usage.
Impact of GESS on Value of output
The impact of GESS participation on maize, cassava and yam farmers‟ was also estimated
through the propensity score matching. Results presented in Table 6 show that GESS
intervention had a positive and significant effect on output of all the three categories of farmers
considered in the study. However, the impact is higher among the yam farmers while the maize
famers receive the lowest impact of GESS on their output. Participation in GESS increased the
output (measured in kilogram per hectare) of participants. Therefore, the Average Treatment
Effect on the treated (ATT) in the entire sub-population of participants was 120.95kg/ha, 295.39
kg/ha and 352.42kg/ha for maize, cassava and yam farmers‟ respectively. The ATE on the entire
population in the study area i.e picking any farmers at random was 109.24, 169.89 and 177.09.
18
This implies that the participants had an increase of 109.24 kg/ha, 169.89 kg/ha and 177.09 kg/ha
in maize, cassava and yam productivity respectively. The findings corroborate the study of
Awotide(2012).
Table 6: Average Impact Estimates of Propensity Score Matching of GESS on output (kg/ha)
Output (kg/ha)
Sample
Treated
Control
Difference
Maize farmers
Unmatched
719.37
328.33
391.04
2.09**
ATT
719.37
595.42
120.95
2.99*
ATU
563.33
440.11
123.22
ATE
Cassava farmers
109.24
Unmatched
890.81
563.63
327.18
10.17*
ATT
890.81
595.42
295.39
8.09*
ATU
568.73
440.31
128.42
ATE
Yam farmers
t-stat
169.89
Unmatched
937.87
573.83
364.04
2.67*
ATT
937.87
585.45
352.42
2.72*
ATU
628.00
440.11
187.89
ATE
177.09
*** Significant at 10 % level, ** Significant at 5 % level, * Significant at 1 % level
Source: Authors’ computation 2015. Kernel matching
Impact of GESS on Fertilizer usage pattern
Agricultural inputs distribution is part of the major of the objectives of the innovation. The
impact of the GESS intervention on fertilizer use patterns on the beneficiaries was estimated by
evaluating the differences in the number of bags that farmers used per hectare of farm land.
Inorganic fertilizers, in combination with hybrid seeds and good rainfall, play a crucial role in
ensuring high maize, cassava and yam production and eliminating food insecurity among the
smallholder farmers in Nigeria. Therefore, for farming household purchasing inorganic fertilizer
demonstrates a household‟s decision appraising patterns in relation of capital reinvestment in
their agribusinesses. The estimated impact shows that there were significant differences between
the amounts of inorganic fertilizer applied between intervention and counterfactual households
for the maize and cassava farmers at 1 % confidence for both categories while GESS was found
not to have any statistical significance on the fertilizer usage pattern of the yam farmers.
19
For both maize and cassava farmers, intervention households have increase on the pattern of
fertilizer usage with an increase application of 2.02 and 1.17 respectively on the average number
of bag of inorganic fertilizer as compared to households in the counterfactual. This difference
can be attributed to the GESS intervention, as the increased market outcomes acted as incentives
for households to reinvest in their farm enterprise in order to sustain their agro-enterprise.
Table 7: Average Impact Estimates of Propensity Score Matching of GESS on Fertilizer usage pattern
Fertilizer usage pattern
Sample
Treated
Control
Difference
t-stat
Unmatched
3.97
1.88
2.09
2.17**
ATT
3.97
1.95
2.02
2.82*
ATU
2.93
1.44
1.49
(no of bags used)
Maize farmers
ATE
Cassava farmers
1.24
Unmatched
5.19
3.63
1.56
18.00*
ATT
5.19
4.02
1.17
13.22*
ATU
3.28
2.43
0.85
ATE
Yam farmers
0.97
Unmatched
9.97
5.63
4.34
1.54
ATT
9.97
5.95
4.02
1.30
ATU
6.33
4.11
2.22
ATE
2.65
*** Significant at 10 % level, ** Significant at 5 % level, * Significant at 1 % level
Source: Authors’ computation 2015. Kernel matching
CONCLUSION AND RECOMMENDATION
The diversity of livelihoods through agricultural intervention and innovation is an important
feature of rural survival but often overlooked by the architects of policy. Diversity is closely
allied to flexibility, resilience and stability. Therefore, it is the submission of this paper that
agricultural research interventions that use an innovation systems approach have a strong
positive impact on some but not all aspects of rural livelihoods and productivity outcomes of the
maize, cassava and yam farmers, with stronger positive impacts being seen for welfare proxied
by per capita expenditure, incomes, and output (measured in kg/ha). In addition, weaker positive
20
impacts are seen for rate of income diversification and fertilizer usage pattern but still
considerably substantial with an indication that if intensify, stronger impact can be experienced.
Innovative agricultural research interventions therefore have the potential to positively influence
the output, incomes, and welfare of rural households in Nigeria.
Over the years in Nigeria, sustainability of programme effects is threatened, however, by phasing
out of the interventions, as local agricultural extension agents lack the human and financial
capacity to maintain the higher levels of contact and innovative strategies employed in
implementing interventions using agricultural innovation systems concepts. Hence, to ensure
sustainability of the positive effects on rural livelihoods and the use of agricultural innovation
systems concepts, there is the need for agricultural research organizations to invest more in
building the capacity of local public extension agents for understanding and applying agricultural
innovation systems concepts. Secondly, there is the need to mainstream agricultural innovation
systems concepts in all public agricultural development initiatives. This, however, will require
that there be deliberate and greater budgetary support towards innovation systems mainstreaming
in all public agricultural extension and research programmes. In order for mainstreaming to be
effective, it must be done concurrently with capacity building efforts and budgetary support,
without which mainstreaming of innovation systems concepts in public agricultural policies runs
the risk of becoming rhetorical, with no real implementation.
21
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