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Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
30 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
ABSTRACT: This study examined the impact of agricultural
sector output on economic growth and sustainability in Nigeria.
The data for the study were extracted from the Central Bank of
Nigeria (CBN) Statistical Bulletin. The methodology adopted in
the research is linear regression with the application of the
Ordinary Least Squares (OLS) Technique. The E-views 10 was the
econometric software used for the research. The major findings of
the study reveal that agricultural output contributes negatively
and insignificantly to economic growth, government agricultural
expenditures contribute negatively and insignificantly to economic
growth, rainfall contributes negatively and insignificantly to
economic growth and foreign direct investment in the agricultural
sector contributes negatively and insignificantly to economic
growth. It is therefore the recommendation of this paper that the
government of Nigeria should encourage farmers by giving soft
loans for agricultural activities. This will help farmers meet with
financial needs in terms of purchasing some seeds, hiring
machines, etc. thereby boosting massive agricultural production
in Nigeria.
KEYWORDS: Agriculture, Economic Growth, Sustainability
AGRICULTURAL SECTOR OUTPUT AND ECONOMIC GROWTH
SUSTAINABILITY IN NIGERIA
Anugwom Chinenye Georgina
Department of Economics, Faculty of Social Sciences, Enugu State University of Science and
Technology (ESUT).
Email: anugwomchinenye@gmail.com
Cite this article:
Anugwom Chinenye Georgina
(2024), Agricultural Sector
Output and Economic Growth
Sustainability in Nigeria.
Research Journal of
Agricultural Economics and
Development 3(1), 30-51.
DOI: 10.52589/RJAED-
OE05P3AI
Manuscript History
Received: 11 Jan 2024
Accepted: 19 Mar 2024
Published: 3 May 2024
Copyright © 2024 The Author(s).
This is an Open Access article
distributed under the terms of
Creative Commons Attribution-
NonCommercial-NoDerivatives
4.0 International (CC BY-NC-ND
4.0), which permits anyone to
share, use, reproduce and
redistribute in any medium,
provided the original author and
source are credited.
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
31 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
INTRODUCTION
The imperativeness of agriculture is underscored by the fact that below a certain level of
nutrition, man lacks not only body energy and sound health but also lacks interest in many
things. He cannot think, function, and rise significantly beyond animal existence and congenital
infantilism. Food is fundamental because it is a necessity one cannot do without it (Anyaoha,
2019). Similarly, a greater proportion of the population, about two-thirds of the total labor force
of the nation (Nigeria), depends on the sector for their livelihood. Also, the rural economy in
particular is propelled by agriculture (Benson, 2019). It is the main source of food for most of
the population and also the dominant economic activity in terms of employment and linkages
with other sectors of the economy; serving as a major source of raw materials for the agro-
allied industries (Moses, 2012).
In common parlance, agriculture has been defined as the production of food and livestock and
the purposeful tendering of plants and animals. Thus, agriculture is the mainstay of many
economies and it is fundamental to the socio-economic development of a nation. This is
because it is a major element and factor in national development. The role of agriculture in
transforming the economic framework of any economy cannot be overemphasized given that
it is the source of food for man and animals and provides raw materials for the industrial sector.
Thus, it plays a significant role in the reduction of poverty (Odoh, 2018).
Over the years in Nigeria, the agricultural sector has been contributing to the Gross Domestic
Product (GDP). In 1981, the agricultural sector's contribution to GDP was N23.80 billion,
which increased to N50.29 billion in 1987. This increase was maintained even from 1987 to
1990 which was N106.63 billion. This increase was also tremendous in the millennium years
(2000s). Figure one below shows that the contribution of the agricultural sector to GDP has
been on the nominal increase. This is demonstrated in the curve sloping upwards from left to
right.
Figure 1:
Source: Data extracted from CBN bulletin, 2020 and graphed with Eviews 10.
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
32 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
The figure shows that even during the millennium years, the contribution of the agricultural
sector has been progressive. This demonstrates the significance of this sector to Nigeria. Hence,
Abayomi (2017) noted that stagnation in agriculture is the principal explanation for poor
economic performance, while rising agricultural productivity has been the most important
concomitant of successful industrialization. Generally, the sector contributes to the
development of an economy in four major ways-product contribution, factor contribution,
market contribution and foreign exchange contribution (Simon, 2016). In realization of this,
the government has embarked on various policies and programmes aimed at strengthening the
sector to continue performing its roles, as well as measures for combating poverty.
The essence of this research is to ascertain the impact of agricultural sector output on economic
growth in Nigeria. In Nigeria, agriculture is disaggregated into four distinct dimensions,
namely: crop production, livestock, forestry and fishing. This research is motivated to ascertain
how each of these dimensions impacts economic growth in Nigeria. One of the basic
parameters to measure economic development in a developing economy like Nigeria is real
gross domestic product. The behavior of the real GDP is a close reflection of the state of
development experienced by a country (especially developing economies). Figure 2 shows that
the rate of real gross domestic product has been expansive.
Figure 2
Source: Data extracted from CBN bulletin, 2019 and graphed with Eviews 10.
Figure 2 clearly shows that Nigeria's real gross domestic product has been on an increasing
path. One begins to wonder whether agricultural output has a positive or negative effect on the
economic growth and development of Nigeria for the years under analysis. It is based on the
foregoing that this paper is aimed at carrying out an empirical investigation on the impact of
agricultural sector output on the economic development of Nigeria covering the period 1981-
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
33 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
2020. Nigeria, at independence, was an agrarian economy, feeding and generating income from
the products of agriculture and exporting her surplus output to other countries of the world.
The major reason was that Nigeria gave much attention to this important sector and also is
highly bestowed with fertile soil that is conducive for varieties of crop production and other
forms of agricultural practices. Agriculture has been the mainstay of the Nigerian economy as
it has contributed greatly to the aggregate gross domestic product. Agriculture sustained the
Nigerian economy at independence.
Despite Nigeria's rich arable land which favors increased agricultural production, the
agricultural sector is still growing at a very slow rate. Only a little over half of the country's
agricultural land is under cultivation (Manyong, 2015), hence contributing to the dwindling
performance of agriculture in the country. The government has over many years formulated
and implemented various policies and projects aimed at putting back the agricultural sector to
its vital place in the economy. The researcher, however, suspects that rainfall or water provision
has not been part of policy considerations when discussing or implementing agricultural
policies. This study therefore suspects rainfall or alternative prioritization becomes the missing
link to other studies estimated.
LITERATURE REVIEW
The Concept of Agriculture Output
Conceptually, agriculture is the production of food, feed, fiber and other goods by the
systematic growing and harvesting of plants and animals. It is the science of making use of the
land to raise plants and animals. It is the simplification of nature’s food webs and the
rechannelling of energy for human planting and animal consumption (Akinboyo, 2018).
Agriculture involves the cultivation of land, raising and rearing of animals, for the purpose of
production of food for man, feed for animals and raw materials for industries. It involves
forestry, fishing, processing and marketing of these agricultural products. Essentially, it is
composed of crop production, livestock, forestry, and fishing. The role of agriculture in
reforming both the social and economic framework of an economy cannot be over-emphasized.
It is a source of food and raw materials for the industrial sector. It is also essential for the
expansion of employment opportunity, for reduction of poverty and improvement of income
contribution, for speeding up industrialization and easing the pressure on balance of payment
(Moses, 2012).
According to Fulginiti and Perrin (2018), agricultural productivity refers to the output produced
by a given level of inputs in the agricultural sector of a given economy. More formally, it can
be defined as “the ratio of value of total farm outputs to the value of total inputs used in farm
production”. Agricultural productivity is measured as the ratio of final output, in appropriate
units to some measure of inputs. Kumar and Manimannan (2018) suggested that “yield per
unit” should be considered to indicate agricultural productivity. Many scholars criticized this
suggestion pointing out that it considered only land as the factor of production with no other
factors of production. Therefore, other scholars suggested that agricultural productivity should
contain all the factors of production, such as labor, farming experiences, fertilizers, availability
and management of water and other biological factors.
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
34 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
Anayo (2017) defines agriculture as the science of making use of the land to raise plants and
animals. It is the simplification of nature’s food webs and the rechanneling of energy for human
planting and animal consumption. Until the exploitation of oil reserves began in the 1980s,
Nigeria’s economy was largely dependent on agriculture. Nigeria’s wide range of climate
variations allows it to produce a variety of food and cash crops.
Agricultural productivity therefore refers to the increase in per capita output of agricultural
produce within an economy during a given period of time. It can be monthly, quarterly or
annually. Most economists and statisticians tend to use the latter (annual trends) due to its
precise and articulate information it tends to offer. The output of agricultural products tends to
fluctuate over a period of time thereby necessitating the need for it to be studied or monitored
closely. In the process of carrying out this research study, agricultural productivity would be
looked at in two forms namely: an increase in the per-capita output of agricultural produce and
a decrease in the per-capita output of agricultural produce. When the per-capita output of
agricultural produce in a given year is greater than that of the previous year we say there is an
increase and vice-versa.
Theoretical Literature
Solow Model of Growth
The Solow growth model propounded by Robert Solow (1956) who belongs to the neoclassical
school of thought believes that a sustained increase in capital investments increases the growth
rate only temporarily, because the ratio of capital to labor goes up. He further posits that the
marginal product of additional units is assumed to decline and thus an economy eventually
moves back to a long term growth-path with the real GDP growing at the same rate as the
growth of the workforce plus factor to reflect improving productivity. The Solow model
believes that to raise an economy's long term trend rate of growth requires an increase in labor
supply and also a higher level of productivity of labor and capital. Differences in the rate of
technological change between countries are said to explain much of the variation in growth
rates.
Endogenous Growth Theory
The endogenous growth by Solow (1970) asserts that productivity improvements can be
attributed directly to a faster pace of innovation and extra investment in human capital. They
stress the need for government and private sector institutions to encourage innovation and
provide incentives for individuals and businesses to be inventive. There is also a central role
of the accumulation of knowledge as a determinant of growth i.e. knowledge industries such
as telecommunication, electronics, software, or biotechnology are becoming increasingly
important in developed countries.
Harrod Domar Growth Model
Harrod-Domar (1926) opined that economic growth is achieved when more investment leads
to more growth. The theory is based on a linear production function with output given by capital
stock (K) times a constant. Investment according to the theory generates income and also
augments the productive capacity of the economy by increasing the capital stock. In as much
as there is net investment, real income, and output continue to expand. And, for a full
employment equilibrium level of income and output to be maintained, both real income and
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
35 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
output should expand at the same rate as the productive capacity of the capital stock. The theory
maintained that for the economy to maintain full employment, in the long run, net investment
must increase continuously as well as growth in the real income at a rate sufficient enough to
maintain full capacity use of a growing stock of capital. This implies that a net addition to the
capital stock in the form of new investment will go a long way to increase the flow of national
income. From the theory, the national savings ratio is assumed to be a fixed proportion of
national output and that total investment is determined by the level of total national income.
Empirical Literature
Olabanji, Fakile and Emmanuel (2017) examined the long-run relationship between
agricultural output and economic growth in Nigeria for the period 1981 to 2014 using time
series data. Results from Johansen's maximum likelihood cointegration approach and Vector
error correction model support evidence of a long-run relationship between agricultural output
and economic growth in Nigeria. Granger causality test also confirms the cointegration results
indicating the existence of causality between agricultural output and economic growth in
Nigeria. The nature of the causality however depends on the variable used to measure
Agricultural output. The paper therefore recommends that the government should further
strengthen agricultural policies in the area of funding, storage facilities, and market access to
enhance agricultural production. Policy Strategies that will make agriculture more profitable
and attractive, and less laborious with improved technology should be adopted and promoted
to attract investors and the youths back to agriculture.
Abula and Ben (2016) examined the impact of agricultural output on economic development
in Nigeria using annual time series data spanning 1986 to 2014. Economic development
proxied by per capita income (PCI) was explained by agricultural output (AOUT) and public
agricultural expenditure (PXA). The study employed the Augmented Dickey-Fuller Unit Root
test and the Vector Autoregressive model. The result of the multivariate VAR model indicated
that most of the lags of the variables are not significant. However, the high level of the R2 and
F values in the VAR regression estimates for PCI gave convincing results that collectively all
the lagged terms are statistically significant, implying that agriculture plays an important role
in Nigeria’s economic development. The variance decomposition analysis revealed that the
greater contribution to shocks in economic development apart from feedback shocks was
received from shocks to agriculture. The results of the impulse response function in support of
the variance decomposition analysis showed that per capita income responded positively to
shocks in agricultural output throughout the ten years, while the response of PCI to shocks in
PXA was negative in the first two year period but became positive throughout the last eight
periods.
Ideba, Iniobong, Otu and Itoro (2014) investigated the relationship between agricultural public
capital expenditure and economic growth in Nigeria over the period 1961 to 2010 using annual
data obtained from the Central Bank of Nigeria. The data were analyzed using Augmented
Dickey-Fuller test, Johansen maximum likelihood test, and Granger Causality test. The result
of the Johansen cointegration test showed that there exists a long-run relationship between all
the explanatory variables and the explained variable. The result of the parsimonious error
correction model showed that agricultural public capital expenditure had a positive impact on
economic growth. Also, the Granger Causality test showed a unidirectional relationship
between agricultural public capital expenditure and agricultural economic growth. This means
that agricultural economic growth does not cause expansion of agricultural public capital
Research Journal of Agricultural Economics and Development
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36 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
expenditure; rather it indicates that agricultural public capital expenditure raises the nation's
agricultural economic growth. This investigation did not emphasize policy adjustment as a
factor needed to promote economic growth.
Bakare (2013) examined the relationship between sustainable agriculture and rural
development in Nigeria. Vector Auto Regression analytical technique (VAR) was employed
for the empirical study. The a priori expectation is that sustainable agriculture will impact
positively on rural development in Nigeria. The findings of the study show that the past values
of agricultural output could be used to predict the future behavior of rural development in
Nigeria. The main conclusion of this study was that while agriculture remains dominant in the
Nigerian economy, it is unsustainable; the food supply does not provide adequate nutrients at
affordable prices for the average citizen and rural development is deteriorating. The findings
and the conclusion of the study suggested the need for policy makers to promote agriculture to
a sustainable level by driving rural development.
Odetola and Etumnu (2013) investigated the contribution of the agriculture sector to the
economic growth in Nigeria using the growth accounting framework and time series data from
1960 to 2011. The study found that the agricultural sector has contributed positively and
consistently to the economic growth in Nigeria, reaffirming the sector’s importance in the
economy. The contribution of agriculture to economic growth is further affirmed from a
causality test which showed that agricultural growth Granger-causes GDP growth, however no
reverse relationship was found. The resilient nature of the sector is evident in its ability to
recover more quickly than other sectors from shocks resulting from disruptive events e.g. civil
war (1967-70) and economic recession (1981-85) periods. The study also found that the crop
production sub sector contributes the most to agricultural sector growth and that growth in the
agriculture sector is overly dependent on growth of the crop production subsector. This
indicates the importance of this subsector and probably, lack of attention or investment to the
other subsectors.
METHODOLOGY
Research Design
This study adopted the ex-post facto design as the researcher made use of past data in the form
of secondary data to investigate the impact of agricultural sector output on economic growth
in Nigeria. Ex-post facto research is chosen as a suitable research design for this work because
the dataset obtained for analysis were wholly secondary data, which cannot be manipulated.
Model Specification
The model that will guide this study is specified thus:
……………(3.1)
Where;
GDP = Gross Domestic Product (Economic Growth)
AGO = Agricultural Sector Output
Research Journal of Agricultural Economics and Development
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37 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
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GAGEXP = Government Agricultural Sector Expenditures
RF = Rainfall Frequency (Measured in Millimeters)
FDIA = Foreign Direct investment in Agricultural Sector
=s'
The Parameters of the independent variables to be estimated.
= Stochastic Error Term
Unit Root/Stationarity Test
This will be used to test whether a variable’s mean value and variance varies over time. It is
necessary in time series variables in order to avoid the problem of spurious regression. The
Augmented Dickey Fuller (ADF) test will be used for the analysis. Augmented Dickey-Fuller
(ADF) test is used to test existence of unit root when there is autocorrelation in the series and
lagged terms of the dependent variable are included in the equation. The following three models
represent pure random walk, random walk with drift and random walk with drift and trend used
in Augmented Dickey Fuller tests:
=
−− ++=
p
ittitt 111
=
−− +++=
p
itititt 1
10
=
−++++=
p
ittit t
1120
where:
)1( −=
The null hypothesis is
0:
0=
and the alternative hypothesis is
0: a
If the ADF test statistic (t-statistic of lagged dependent variable) is less than the
critical value, we reject the null hypothesis and conclude that the series is stationary (there is
no unit root).
Co-integration Test
In an econometric analysis, there is the need to estimate the long-run relationship of the
variables under consideration. This will be applied to the concept of Cointegration test. One of
the most popular tests for cointegration has been suggested by Engel and Granger (1987). The
process is demonstrated thus; given a multiple regression:
,,...,1,
'Ttxy ttt =+=
where
'
21 ),...,,( ktttt xxxx =
is the k-dimensional I(1) regressors. For
t
y
and
t
x
to be cointegrated,
t
must be I(0). Otherwise it is spurious. Thus, a basic idea is to test whether
t
is I(0) or I(1).
Research Journal of Agricultural Economics and Development
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38 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
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Error Correction Model (ECM)
The error correction analysis is an econometric analysis carried out if the variables under
investigation are seen to be cointegrated. The Error Correction Mechanism (ECM) will be used
to estimate the speed of adjustment of the short-run dynamics of the variables and timing to
long run convergence. The ECM is given by the equation:
Where
= First Difference Operator
Granger Causality Model
The Granger causality model is a statistical technique that was carried out in the direction of
causality existing between the dependent variables and the specified independent variables.
The Granger causality model was specified thus:
Research Journal of Agricultural Economics and Development
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39 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
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Decision Rule
If the probability value of an estimated Granger causality is less than 0.05, we reject the null
hypothesis and conclude that a Granger causality exists while if the probability value is greater
than 0.05, we accept the null hypothesis and conclude that there exists no causality relationship
among the variables.
Data Sources
The data required in this research are time series data on aggregate agricultural output, growth
rate of gross domestic product, government expenditure on agricultural sector, rainfall statistics
and foreign direct investment in the agricultural sector covering the period 1981-2020. They
will be sourced from the central bank of Nigeria Statistical Bulletin, 2020 edition and World
Development Index (WDI) 2020.
PRESENTATION AND ANALYSIS OF RESULTS
Empirical Results
Time series data are often assumed to be non-stationary and thus, it is necessary to perform
unit root tests to ensure that the data are stationary. The test was employed to avoid the problem
of spurious regression. Therefore, the Augmented Dickey-Fuller (ADF) unit root test was used
to determine the stationarity of the data to complement each other. The decision rule based on
the ADF test is that its statistic must be greater than Mackinnon Critical Value at 5% level of
significance and in absolute terms. The results of the unit-root test are reported in table 4.1
below.
Unit-Root Test Result
Table 4.1: Unit Root Test Result
VARIABLE
ADF STAT.
CRITICAL VAL.
ORDER
RGDP
-3.124337
-1.949856
I(1)
AGO
-4.661449
-1.949856
I(1)
GAGEXP
-6.978566
-2.943427
I(1)
FR
-7.297295
-1.950117
I(1)
FDIA
-4.640726
-1.949856
I(1)
Source: Author’s Computation Using Eviews 10.
Table 4.1 clearly shows that all the variables are stationary at first difference (I(1)). This means
that the variables have unit-root until differences in the first order.
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Optimal Lag Selection
Table 4.2
VAR Lag Order Selection Criteria
Endogenous variables: RGDP AGO GAGEXP RF
FDIA
Exogenous variables: C
Lag
LogL
LR
FPE
AIC
SC
HQ
0
-1142.303
NA
5.90e+20
62.01635
62.23404
62.09310
1
-974.2102
281.6682*
2.62e+17*
54.28163*
55.58778*
54.74211*
2
-961.3318
18.09937
5.45e+17
54.93685
57.33146
55.78107
3
-933.8790
31.16259
5.90e+17
54.80427
58.28734
56.03222
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
The first step in estimating an econometric model is to select the optimum lag length for the
analysis. Selecting a lag length arbitrarily may lead to estimates that are biased and
inconsistent. As seen from table 4.2, it can be clearly seen the lag length with the highest
priority is lag one. Hence, the analysis will be anchored on lag one.
4.3 Cointegration Analysis (Johansen Methodology)
Table 4.3: Cointegration Test Result
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
Trace
0.05
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Prob.**
None *
0.578564
85.08512
69.81889
0.0019
At most 1 *
0.414215
52.24981
47.85613
0.0183
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At most 2 *
0.341343
31.92729
29.79707
0.0280
At most 3 *
0.256347
16.06030
15.49471
0.0411
At most 4 *
0.118789
4.805427
3.841466
0.0284
Trace test indicates 5 cointegrating eqn(s) at the 0.05 level
Source: Researcher’s Computation Using Eviews
The Johansen method of cointegration was used for the study because all the variables are
stationary at first difference. The Johansen result as displayed in table 4.3 clearly shows
evidence of cointegration as trace statistics test indicates 5 cointegrating equations. Here we
reject the null hypothesis of no cointegration meaning that there exists a long-run relationship
existing between the variables (RGDP, AGO, GAGEXP, RF, FDIA) under study. Given this,
we can now run the unrestricted Vector Autoregression which is the Vector Error Correction
Model (VECM).
Vector Error Correction Mechanism (VECM)
Table 4.4
Cointegrating Eq:
CointEq1
RGDP(-1)
1.000000
AGO(-1)
-0.002390
(0.00025)
[-9.40331]
GAGEXP(-1)
-0.142399
(0.04068)
[-3.50024]
RF(-1)
-0.000301
(0.00013)
[-2.25736]
FDIA(-1)
0.033641
(0.00368)
[ 9.15183]
C
-0.336385
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42 Article DOI: 10.52589/RJAED-OE05P3AI
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Error Correction:
D(RGDP)
D(AGO)
D(GAGEXP)
D(RF)
D(FDIA)
CointEq1
0.002374
-149.8193
-0.101171
54.68292
-13.24836
(0.00764)
(19.7818)
(0.90555)
(298.417)
(7.04359)
[ 0.31072]
[-7.57361]
[-0.11172]
[ 0.18324]
[-1.88091]
D(RGDP(-1))
0.674197
-1450.052
13.21300
-2416.246
125.9615
(0.20826)
(539.151)
(24.6808)
(8133.33)
(191.973)
[ 3.23730]
[-2.68951]
[ 0.53536]
[-0.29708]
[ 0.65614]
D(RGDP(-2))
0.551995
-1279.419
-55.01271
-3193.553
21.81578
(0.58244)
(1507.84)
(69.0246)
(22746.4)
(536.889)
[ 0.94774]
[-0.84851]
[-0.79700]
[-0.14040]
[ 0.04063]
D(AGO(-1))
-5.078705
-0.274729
0.000515
0.169817
-0.053981
(7.1E-05)
(0.18292)
(0.00837)
(2.75943)
(0.06513)
[-0.71792]
[-1.50191]
[ 0.06155]
[ 0.06154]
[-0.82880]
D(AGO(-2))
1.793205
-0.410570
0.004942
0.362109
-0.039602
(6.3E-05)
(0.16304)
(0.00746)
(2.45954)
(0.05805)
[ 0.28412]
[-2.51821]
[ 0.66216]
[ 0.14723]
[-0.68217]
D(GAGEXP(-1))
-0.000187
-19.62519
-0.612466
0.933711
-3.304024
(0.00184)
(4.75315)
(0.21759)
(71.7034)
(1.69243)
[-0.10164]
[-4.12888]
[-2.81483]
[ 0.01302]
[-1.95224]
D(GAGEXP(-2))
0.000328
-21.82882
-0.368937
10.11692
-1.604617
(0.00176)
(4.55248)
(0.20840)
(68.6762)
(1.62098)
[ 0.18655]
[-4.79493]
[-1.77033]
[ 0.14731]
[-0.98991]
D(RF(-1))
-3.186506
-0.038844
-0.000117
-0.676460
-0.002408
(5.2E-06)
(0.01351)
(0.00062)
(0.20383)
(0.00481)
[-0.60866]
[-2.87478]
[-0.18919]
[-3.31869]
[-0.50050]
D(RF(-2))
3.820006
-0.018401
-2.10E-05
-0.332687
-0.001001
(5.0E-06)
(0.01285)
(0.00059)
(0.19385)
(0.00458)
[ 0.76987]
[-1.43200]
[-0.03574]
[-1.71620]
[-0.21870]
D(FDIA(-1))
-0.000203
3.477623
-0.002087
-0.344964
0.411562
(0.00025)
(0.64813)
(0.02967)
(9.77732)
(0.23078)
[-0.81099]
[ 5.36563]
[-0.07033]
[-0.03528]
[ 1.78338]
D(FDIA(-2))
-0.000508
3.965001
-0.001362
-3.477779
-0.197283
(0.00028)
(0.71732)
(0.03284)
(10.8211)
(0.25541)
[-1.83429]
[ 5.52751]
[-0.04149]
[-0.32139]
[-0.77241]
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
43 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
C
0.018748
1407.958
1.498426
-115.6788
107.1769
(0.07442)
(192.658)
(8.81936)
(2906.34)
(68.5989)
[ 0.25193]
[ 7.30805]
[ 0.16990]
[-0.03980]
[ 1.56237]
R-squared
0.716234
0.947881
0.349114
0.351575
0.418721
Adj. R-squared
0.591376
0.924948
0.062724
0.066268
0.162958
Sum sq. resids
0.458468
3072716.
6439.028
6.99E+08
389565.7
S.E. equation
0.135420
350.5833
16.04871
5288.704
124.8304
F-statistic
5.736423
41.33336
1.219015
1.232271
1.637144
Log likelihood
28.72876
-262.0531
-147.9462
-362.4611
-223.8453
Akaike AIC
-0.904257
14.81368
8.645741
20.24114
12.74840
Schwarz SC
-0.381798
15.33614
9.168201
20.76360
13.27086
Mean dependent
-0.005502
1005.887
1.873627
2.430297
33.12735
S.D. dependent
0.211847
1279.705
16.57702
5473.161
136.4416
Determinant resid covariance (dof
adj.)
1.27E+17
Determinant resid covariance
1.79E+16
Log likelihood
-954.8135
Akaike information criterion
55.12505
Schwarz criterion
57.95504
Source: Researcher’s Computation Using Eviews 10
From table 4.4, it can be clearly seen that the numerical coefficient of agriculture output (AGO)
yielded a negative value at the magnitude of -5.078705. This entails that agricultural output
contributes negatively to economic growth for the period under analysis. Hence, a 1% increase
in agricultural output reduces economic growth by -5.078705. This practically entails that
agricultural output is not contributing positively to economic growth in Nigeria. This is clearly
because the agricultural sector is not performing optimally.
Government expenditure to the agricultural sector (GAEXP) yielded a negative numerical
coefficient at the magnitude of -0.000187. This entails that government agricultural
expenditure contributes negatively to economic growth in Nigeria. It entails that a 1% increase
in government agricultural spending yields a 0.000187% decrease in economic growth.
Rainfall (RF) contributes negatively to economic growth in Nigeria as the numerical coefficient
yielded -3.186506. This entails that rainfall does not contribute positively to economic growth
in Nigeria. Hence, rainfall is not sufficient enough to improve economic growth and
productivity.
Foreign direct investment on the agricultural sector (FDIA) yielded a negative numerical value
(-0.000203). This entails that foreign investment in the agricultural sector does not lead to a
positive increase in economic growth in Nigeria for the period under analysis. This does not
also conform to economic a priori expectations.
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
44 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
The R-squared value yielded 0.716234 which is more than 60%. This means that the
explanatory power of the independent variables is high. It practically entails that variations in
the dependent variable are explained by changes in the independent variable by approximately
72%. This shows the model has good fitness.
The F-statistics is a statistical tool employed in checking statistical significance of the entire
regression plane. From the regression, it can be clearly seen that the probability value of the F-
statistics yielded 5.736423. This means that the test is statistically significant at the entire
regression plane.
Diagnostic Tests
Block-Wald Causality Test
Table 4.6
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 04/01/22 Time: 00:34
Sample: 1981 2020
Included observations: 37
Dependent variable: D(RGDP)
Excluded
Chi-sq
df
Prob.
D(AGO)
0.666826
2
0.7165
D(GAGEXP
)
0.034819
2
0.9827
D(RF)
0.641086
2
0.7258
D(FDIA)
3.435581
2
0.1795
All
15.58704
8
0.0487
Source: Researcher’s Computation Using Eviews
From table 4.6, it can be clearly seen that the Chi-Square probability of AGO yielded 0.7165
> 0.05. This entails that agricultural output (AGO) does not have any causal effect on RGDP.
The table also reveals that GAGEXP does have any causal effect on economic growth because
its Chi-Square probability yielded 0.9827 > 0.05. The Chi-Square probability of rainfall (RF)
yielded 0.7258 > 0.05. This entails that RF does not have a causal effect on RGDP. From the
table, we can conclude that FDIA does not have a causal effect on RGDP because its Chi-
Square probability yielded 0.1795 > 0.05. Jointly, AGO, GAGEXP, RF and FDIA have short-
run causal effects on RGDP.
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
45 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
Model Stability (AR Unit-Circle)
Table 4.7
Source: Researcher’s Computation Using Eviews
There is a need to carry out a stability diagnostic to make sure the model is dynamically stable.
The condition for stability is that no inverse root dot should be outside the unit circle. Judging
from the inverse roots of the AR characteristic polynomial, the model is stable as no dot lies
outside the enclave of the unit circle.
Serial Correlation Test
Table 4.8
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
1.100195
Prob. F(2,22)
0.3201
Obs*R-squared
6.223502
Prob. Chi-Square(2)
0.2302
Source: Researcher’s Computation Using Eviews 10.
The serial correlation test was carried out to ascertain the presence of serial correlation in our
model. However, it is recalled that the null hypothesis states that there is no serial correlation.
Based on the serial correlation test, it can be clearly seen that the probability of Chi-Square
yielded 0.2302 > 0.05. This entails the acceptance of the null hypothesis and we therefore
conclude that there is no evidence of serial correlation in our residuals.
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
46 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
Heteroscedasticity Test (Breusch-Pagan-Godfrey)
Table 4.9
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
0.324963
Prob. F(15,20)
0.5803
Obs*R-squared
13.34488
Prob. Chi-Square(15)
0.8810
Scaled explained SS
16.36427
Prob. Chi-Square(15)
0.6968
Source: Researcher’s Computation Using Eviews 10
The heteroscedasticity test was carried out to ascertain the presence of homoscedasticity in our
model. The probability of the Chi-Square yielded 0.8810 > 0.05 and this means that there is no
evidence of heteroscedasticity in our residuals. This is good and desirable.
Normality Test (Jaque-Berra)
Table 4.10
Component
Skewness
Chi-sq
df
Prob.
1
-3.669511
85.28029
1
0.0000
2
-0.389658
0.961610
1
0.3268
3
1.032298
6.749044
1
0.0094
4
1.061511
7.136440
1
0.0076
Joint
100.1274
4
0.0000
Component
Kurtosis
Chi-sq
df
Prob.
1
20.15820
466.1392
1
0.0000
2
3.337493
0.180344
1
0.6711
3
4.714106
4.652086
1
0.0310
4
7.986682
39.37275
1
0.0000
Joint
510.3444
4
0.0000
Research Journal of Agricultural Economics and Development
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47 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
Component
Jarque-Bera
Df
Prob.
1
551.4195
2
0.0000
2
1.141953
2
0.5650
3
11.40113
2
0.0033
4
46.50919
2
0.0000
Joint
610.4718
8
0.0000
Source: Researcher’s Computation Using Eviews 10.
The VEC normality test was carried out to ascertain if the residuals are normally distributed.
The joint probability value of the Jarque-Bera yielded 0.0000 which is obviously less than 0.05.
This compels us to accept the null hypothesis of normal distribution. Hence, we conclude that
the residuals are normally distributed.
SUMMARY, CONCLUSION AND RECOMMENDATION
Summary of Findings
This study has been able to estimate the impact of agricultural output on economic
sustainability in Nigeria covering the period 1981-2020. In the course of the study, data for the
study was collected from the Central Bank of Nigeria (CBN) statistical bulletin, 2020. The
linear regression with the application of Ordinary Least Squares (OLS) was used and the major
findings of the study are as follows:
1. Agricultural output contributes negatively and insignificantly to economic growth.
2. Government agricultural expenditures contribute negatively and insignificantly to
economic growth.
3. Rainfall contributes negatively and insignificantly to economic growth.
4. Foreign direct investment on the agricultural sector contributes negatively and
insignificantly to economic growth.
Conclusion
The study has been able to carry out an empirical analysis of the impact of agricultural output
on economic sustainability in Nigeria ranging from 1981-2020. In the course of the research,
it was discovered that agricultural components have a negative and insignificant impact on
economic growth in Nigeria for the period under analysis. The conclusion drawn from this
Research Journal of Agricultural Economics and Development
Volume 3, Issue 1, 2024 (pp. 30-51)
48 Article DOI: 10.52589/RJAED-OE05P3AI
DOI URL: https://doi.org/10.52589/RJAED-OE05P3AI
www.abjournals.org
study is that Nigeria is yet to build and develop its agricultural sector. This is a reflection of
the low budgetary allocation to the agricultural sector over the years. The discovery of oil is
indeed a disadvantage to the agricultural sector of Nigeria.
Recommendations
Based on the findings of the study, the following recommendations are recommended:
1. The study discovered that agricultural output contributes negatively but insignificantly
to economic growth. Hence the government of Nigeria should encourage farmers by giving
soft loans for agricultural activities. This will help farmers meet up with financial needs in
terms of purchasing some seeds, hiring machines, etc thereby boosting massive agricultural
production in Nigeria.
2. In the course of the study, it was also discovered that government agricultural
expenditures contribute negatively but insignificantly to economic growth. Hence; for
government agricultural expenditure to exhibit the desired results in the economy, government
expenditure needs to be closely monitored. This will help ensure that agricultural budget
allocations are channeled into the required targets that will help improve the economy.
3. Instead of relying entirely on rainfall, Non-Governmental Organizations (NGOs), and
Private Public Partnership (PPP) mechanisms, should provide farmers with drip irrigation
systems to deliver water directly to a plant’s roots, and hence reduce the evaporation that
happens with spray watering systems.
4. It was discovered in the course of the study that foreign direct investment on the
agricultural sector contributes negatively but insignificantly to economic growth. The need to
attract FDI into Nigeria's economy cannot be over-emphasized. Massive investment for the
provision of power is needed to achieve this growth.
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