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AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
33
Agriculture Sector Development in Nigeria: Empirical Investigation of Rail
Infrastructure Quality Effect
Ime Okon Utuk
Department of Economics,
Social science faculty,
Akwa Ibom State University, Obio-Akpa Campus,
AkwaIbom State, Nigeria
Email: utuk_ime2003@yahoo.com
Phone:+2348077533543
Enobong Akpan Ekaetor, Ph.D
Department of Economics,
Faculty of Social science
Akwa Ibom State University
Akwa Ibom State, Nigeria.
Email: ekaetoreno084@yahoo.com
Phone: +2348026057189
Ededet Bassey Eduno
Department of Economics,
Faculty of Social science
Akwa Ibom State University
Akwa Ibom State, Nigeria.
Email: eduno.wisdom5@gmail.com; ededeteduno@aksu.edu.ng
Phone: +2349072128666
https://doi.org/10.61090/aksujacog.2024.003
Abstract
For decades, Nigeria’s agriculture intermodal mix has been out of balance, leaning heavily on road
transport. Seventy per cent of agro-produce transportation is through a crumbling road system. For
stakeholders, the return of rail transport services is a good omen. The purpose of this paper was to
investigate the effect of rail infrastructure quality on agriculture sector development in Nigeria
spanning from 2000 to 2022, using time series data. The autoregressive distributed lag (ARDL) method
estimates disclosed that railroad infrastructure quality had an inverse relationship with agriculture
value added in Nigeria during the period covered in this study. Nevertheless, the federal government
of Nigeria should make more effort to rehabilitate old rail lines and the construction of new ones to
complement other modes of transportation across the country. In that regard, effort should be made to
attract potential foreign investors in the construction of rail lines. At the same time, engineers should
be properly trained in diesel locomotive technology and rail track management.
Keywords: Agriculture, rail transport, Nigeria, production function, auto regressive distributed lag
model
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
34
1. Introduction
Agriculture is at the centre of the Nigerian economy, providing the main source of livelihood for the
majority of Nigerians. The farming sector of this West African country employs about 70 per cent of
the entire country’s labour force. Nigeria’s small farms produce 80 per cent of the total food and 33 per
cent of the country’s land is under cultivation for this purpose (Borgenproject.org, 2022). This is the
leading African country in farming because it has the highest levels of productivity and profitability in
this particular sector. Agriculture in Nigeria is the foundation of the economy, as it keeps the people
stable in what they do.
However, Nigeria’s agricultural sector has been hurt by several shocks: sporadic flooding, Boko Haram
(BH) insurgencies, and conflicts between herdsmen and local farmers. Food processing continues to suffer from
a lack of financing and infrastructure. Nigeria relies on $10 billion of imports to meet its food and agricultural
production shortfalls (mostly wheat, rice, poultry, fish, food services, and consumer-oriented foods) (I.T.A, 2021).
Europe, Asia, the United States, South America, and South Africa are major sources of agricultural imports. The
Government of Nigeria (GON) has initiated agricultural programs such as the Anchor Borrowers Program (ABP)
to diversify its economy away from oil.
The railway sector has played a key role in the diversification of economies, facilitating gross domestic
product (GDP) growth and providing a sustainable alternative method of ground transportation. Worldwide,
railways play a very important role in moving grains from farms to mills, food manufacturers and international
markets. The net impact is profound, triggering several effects across industries (The Nation, 2020).
In 2019, according to global research firm, Statista Research Department, global rail freight traffic
amounted to above nine trillion tonnes. Rail freight in Africa, the report says, reached over 150 billion tonnes in
2019, down from over 155 billion in 2018. Indeed, Africa is one of the world’s fastest-growing cargo markets.
Rapid economic growth has seen volumes surging and freight traffic recording strong performance. While
railroads haul various agricultural products, the primary commodities carried are corn, wheat, soybeans, barley,
and sorghum, which account for over 90 per cent of yearly rail farm product tonnages, according to analysts
(Essiet, 2020).
Rail development in Nigeria dates to 1898 when the British colonial government began construction on
a 193-km line connecting Lagos to Ibadan, which was completed in 1901. The Nigerian Railway Corporation
(NRC) traces its roots back to the Government Department of Railways, which was created by amalgamation of
two state-owned rail services in 1912. The NRC was officially created by the Nigerian Railway Corporation Act
in 1955. The core of Nigeria’s current rail network, including the 640-km Bornu extension, running from Kano
to Maiduguri, was completed in 1964, creating a two-line network, commencing in Lagos and Port Harcourt, and
stretching north-east. The NRC reports that the current network comprises a 3505-km route and 4332-km track
of 1067-mm lines, as well as a 19-km, 1067-mm gauge extension from Port Harcourt to the deepsea Onne Port,
and 277 km of standard, 1435-mm gauge track running between Ajaokuta and Warri via Itakpe (Oxford Business
Group, 2022). Rail investment, maintenance and operations have slumped since the 1960s, with cargo volumes
falling from 3m tonnes in 1965 to just 15,000 in 2005 (Oxford Business Group, 2022a).
After the Nigerian economy fell into recession in 2016, the federal government unveiled a series of bold
plans including the Economic Recovery and Growth Plan (ERGP), which seeks to both revive the country’s non-
oil economy and kick-start new growth. These plans extended to rail development, and in August 2017, the federal
government announced it had begun a $41bn railway expansion plan aiming to boost economic diversification
by improving shipping networks between seaports and the interior. As highlighted by ERGP, the government
plans to build two new railway lines: an 1100-km line connecting its two largest cities, Lagos and the northern
city of Kano, which will carry freight and passengers, as well as a coastal railway connecting Lagos to Calabar
in the east (Oxford Business Group, 2022b).
However, with several initiatives and programmes to improve transport infrastructure as well as public
sector reforms in Nigeria in recent times, it becomes necessary to analyse the impact of rail transport infrastructure
on agricultural output in Nigeria. Some studies have been done in Nigeria in the area of transport infrastructure
and agricultural output (Abdulraheem et al., 2021; Ogunleye et al., 2018; Adepoju & Salman, 2013; Onakoya, et
al., 2012; Tunde & Adeniyi, 2012; Ighodaro, 2011; Inoni & Omotor, 2009), but most of the studies concentrated
on road transport infrastructure since road transport is the most common network linking the villagers to the
market where agricultural products are being sold. Little is known about the relationship between railroad
transportation infrastructure and the agricultural sector in Nigeria. Thus, this study specifically attempts to
examine the effect of rail infrastructure quality on value-added agriculture in Nigeria using recent data. Value-
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
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added agriculture entails changing a raw agricultural product into something new through packaging, processing,
cooling, drying, extracting or any other type of process that differentiates the product from the original raw
commodity (Matthewson, 2007).
The remainder of the paper is organized as follows. In Section 2, the literature review is presented. Section
3 presents the empirical model and data source. In Section 4, results and discussion are carried out. Finally,
section 5 is reserved for summary, conclusion and suggestions.
2. Literature Review
For centuries, agriculture has remained the backbone of the world’s economic development, with nearly 60% of
the population depending on agriculture and related activities for survival. In the United States alone, agricultural-
related industries contribute roughly $ 1 trillion to the country’s annual GDP. This, however, is just a drop in the
ocean. When accounting for the industries that rely on agriculture such as hospitality (which accounts for nearly
10% of the world’s GDP,) textile, fishing, and beverages among others, you will realize agriculture is central to
the economy (Post, 2020). According to Lawal et al. (2018), the role of agriculture in pioneering the growth and
development of the nation’s economy cannot be overemphasized as it fosters sustainability in economic activities;
ensures food security; provides employment to dwellers in rural areas; and reduces poverty; among others. Sekyi
et al. (2017) added that agriculture continues to be the mainstay of most developing countries in Africa with the
majority of the people farming at a subsistent level with very low incomes. Notably, both agricultural and
economic growth can be attributed to effective transportation services.
Like many industries, the agriculture industry cannot function all on its own. The agriculture industry
often needs the support of surrounding industries to speed up production and increase the reach of agricultural
produce. Transportation is one of the most essential facets of successful, bountiful agriculture. The agriculture
industry relies heavily on transportation services to transport agricultural goods short and long distances to keep
food on tables (The Junction LLC, 2022). Therefore, rail transport can catalyze the growth of agriculture. Your
dictionary (2023) defines rail transport as the transport of passengers and goods using wheeled vehicles specially
designed to run along railways or railroads.
Although primitive rail systems existed by the 17th century to move materials in quarries and mines, it
was not until the early 19th century that the first extensive rail transportation systems were set. Rail transportation
has been the product of the industrial era, playing a major role in the economic development of Western Europe,
North America, and Japan, where such systems were first massively implemented (Rodrigue & Slack, 2023). It
represented a significant improvement in land transport technology and has introduced significant changes in the
mobility of freight and passengers. This was not necessarily because of its capacity to carry heavy loads but
because of its higher ubiquity level and speed. Rail transport systems dramatically improved travel time as well
as the possibility of offering reliable and consistent schedules that could be included in the planning of economic
activities such as production and distribution. The coherence of economic activities and social interactions was
thus substantially improved. Rail transportation was the first mode that brought scheduling and reliability to
transportation systems, as its assets and services needed to be planned and geographically allocated.
As in most countries, one of the main advantages of rail transport is the ability to efficiently move large
volumes of goods. Compared to road transport, rail systems have higher capacity and can carry heavier loads over
longer distances. By investing in the development and expansion of rail networks, countries can improve their
logistics infrastructure, reduce transportation costs, and encourage cross-border movement of goods.
Rail transport provides economic benefits predominantly through its cost efficiency. Rail generally has
lower operating costs per ton kilometre than road transport. This affordability translates into lower prices for
consumers and businesses and makes products more accessible and competitive in the marketplace. By choosing
rail as their primary mode of transportation, countries can improve their export capacity, attract foreign
investment, and promote economic diversification (Hellmuth-Sander, 2023).
In addition to its economic benefits, rail transport also contributes to environmental sustainability. Compared
to road or air transport, rail is considered a more environmentally friendly mode of transport due to its lower
greenhouse gas emissions. Trains are more energy efficient and emit fewer pollutants per unit of freight than
trucks or aeroplanes. By shifting freight from road to rail, countries can significantly reduce their carbon footprint
and mitigate the negative impacts of climate change.
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
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2.1 Empirical Review
There is a handful of empirical literature about the rail transport-agriculture development nexus both in
developed and developing countries. For instance, in the United States of America, Atack & Margo
(2011) examined the impact of access to rail transportation on agricultural improvement. Using a new
GIS-based transportation database linked to county-level census data, the study estimated that at least a
quarter (and possibly two-thirds or more) of the increase in cultivable land can be linked directly to the
coming of the railroad to the Midwest. Farmers responded to the shrinking transportation wedge, which
raised agricultural revenue productivity, by rapidly expanding the area under cultivation and these
changes, in turn, drove an increase in farm and land values.
Zhou et al., (2021) used the opening of China’s high-speed railway (HSR) as a quasi-natural experiment
and deployed a multi-period DID model to explore the impact and mechanism of HSR on agriculture-related
enterprises’ exports. The results showed that HSR can promote export growth of agriculture-related enterprises
by 6.9%, and it will reach 10% in 5 years. Furthermore, Herranz-L (2011) examined the role of railways in the
export-led growth of the Uruguayan rural economy between 1870 and 1913 using OLS estimation. The results
showed that Uruguayan railways did produce some positive effects.
Iimi et al., (2019) using a large sample of data comprising more than 190,000 households over eight years
in Ethiopia, estimated the impact of rail transport on agricultural production. With the fixed effects and
instrumental variable techniques combined, an agricultural production function is estimated. It was found that
deteriorated transport accessibility to the port had a significantly negative impact. The use of fertilizer particularly
decreased with increased transport costs.
In Nigeria, Tunde & Adeniyi (2012) examined the impact of road transport on agricultural development
in Ilorin East L.G.A of Kwara State. Descriptive and analytical statistical methods were both employed to analyze
the data gathered. The study found that road transport had both positive and negative impacts on agricultural
development.
Ogunleye et al., (2018) investigated the effects of road transport infrastructure on agricultural sector
development in Nigeria from 1985 to 2014. The study concluded that a positive and statistically significant
relationship exists between road transport infrastructures (LRT). Also, evidence was found of a unidirectional
causality from agricultural sector development to transport infrastructure.
Inoni & Omotor (2009) examined the effect of road infrastructure on agricultural output in Delta State,
Nigeria. The results indicated that rural roads have a significant positive effect on agricultural output.
Abdulraheem et al., (2021) focused on the impact of transportation on agricultural practices and production in
rural areas in Nigeria. From the data collected, some effects were identified as militating against the effective and
productive practice of agriculture in the study area.
Adepoju & Salman (2013) examined access to infrastructure and its effects on agricultural productivity
in Surulere and Ife East Local Government Areas (LGAs) of Oyo and Osun States. The total factor productivity
model used revealed that farm size and labour were positive and significantly affected productivity at 5% and 1%
levels of probability respectively.
Although many studies have analyzed the transportation infrastructure effects on agriculture output in Nigeria as
shown in the literature, little is known about the relationship between rail transportation infrastructure and the
agricultural sector.
3. Empirical Model and Data
3.1 Theoretical Framework
The production function expresses a functional relationship between quantities of inputs and outputs. It shows
how and to what extent, output changes with variations in inputs during a specified period of time. Algebraically
it can be expressed as
Y=F(X1, X2 /X3……Xn) ……………(1)
Where,
Y = Farm Output e.g. maize, cassava, yam, fish, goats, etc.
X1 and X2 - variable inputs e.g. fertilizer, farmland
X3-------Xn = fixed inputs e.g. labour, capital, etc.
In this model, two variable inputs are combined to produce a given level of farm output.
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
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3.2 Specification of the Model
To examine the relationship between railroad transport infrastructure quality and agricultural value added (Value-
added) is changes made to primary agriculture products (crops and livestock) that increase the product's value) in
Nigeria, equation (1) is specified as follows:
LNAGVALt = Ϫ0 + Ϫ1LNRAILQt + Ϫ2LNPSt + Ϫ3LNREXRt + Ϫ4LNINFRt + Ϫ5LNTECHt + Ϫ6LNMKTt +
Ϫ7LNIHCt + Ѱt ………………….. (2)
where AGVAL is agriculture value added (% of GDP). Value added in agriculture measures the output
of the agricultural sector less the value of intermediate inputs. Also, the quality of railroad infrastructure quality
(RAILQ), 1(low) - 7(high) is one of the components of the Global Competitiveness Index. It represents an
assessment of the quality of the railroad system in a given country. Apart from the rail transport infrastructure
quality ( RAILQ), other variables were included to control for economic factors and various governmental and
industrial policies which could affect agricultural value added: political stability(PS) (Index of Political Stability
and Absence of Violence/Terrorism measures perceptions of the likelihood that the government will be
destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and
terrorism); real exchange rate(REXR); inflation rate (INFR); TECH is technology (it is measured in foreign
direct investment (FDI)); MKT is market size (it is measured in GDP per capita (GDPC). This shows the
purchasing power; the Index of Human Capital per person (IHC) was included because human capital, or an
individual’s collective skill set and knowledge to create economic value, plays a fundamental role in economic
growth and is a keystone to development. The index of human capital per person was based on years of schooling
and returns to education: Ϫ is the error term.
Each variable in equation (2) is expressed in logarithmic terms (LN); therefore, the estimated coefficients
are the relevant elasticities of agriculture value added concerning corresponding variables. The hypothesized signs
of the elasticities are Ϫ1- Ϫ6 > 0.
3.3 Data and Estimation Procedure
This study used secondary data spanning from 2000 to 2022 obtained from World Development Indicators,
theglobaleconomy.com, indexmundi.com, tradingeconomics.com, countryeconomy.com, worlddata.info,
fred.stlouisfed.org, and Statistical Bulletin and Annual Report and Statement of Accounts published by the
Central Bank of Nigeria (CBN). The first step in the estimation involves testing the order of integration of the
individual series under consideration to ascertain whether they are stationary. Variables that are not stationary
can be differenced to make them stationary (Brooks, 2008). Thus, the Augmented Dickey-Fuller unit root tests
were carried out. Furthermore, this study employed the ARDL bounds test approach for cointegration to test for
the existence of cointegration among the variables.
To examine the long-run relationships or the short-run relationships between the independent and
dependent variables, the ARDL (auto-regressive distributed lag) model was applied. The basic form of an ARDL
regression model is:
yt = β0 + β1yt-1 + .......+ βkyt-p + α0xt + α1xt-1 + α2xt-2 + ......... + αqxt-q + εt ,
where εt is a random "disturbance" term, which is assume to be "well-behaved" in the usual sense.
4. Results and Discussion
4.1 Unit Root Test
Time series data were used in this study. Generally, time series data show trending behaviour (scholastic trend),
in other words, there may be a problem of non-stationarity. Therefore, it is necessary to remove such trending
behaviour to obtain valid results. Also, it is a pre-condition to test time series properties to identify whether the
variable is stationary at levels, first difference or second difference. These results of the unit root test will help to
select the appropriate econometric method for the data analysis. This study used Augmented Dicky- Fuller unit
root test out of the numbers of unit root tests in econometric literature.
The results of the ADF unit root test in Table 1 show that the logarithms of some variables are non-
stationary in their level while some variables became stationary after taking their first difference. In order words,
the variables are integrated in a mixed order of I(0) and I(1). The combination of variables which are stationary
at the level and first difference gives the reason for the application of the Auto-regressive Distributed Lag (ARDL)
bounds test for cointegration can be applied.
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
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Table 1: Augmented Dickey Fuller (ADF) Unit Root Results
Variable
ADF test
Order of
Integration
Levels
1st difference
Intercept
Trend &
Intercept
Intercept
Trend &
Intercept
LNAGVAL
-1.912313
-2.516866
-3.573747**
---
I(1)
LNRAILQ
-1.537543
-3.912056**
---
---
I(0)
LNREXR
-1.876691
-2.819644
-5.521139*
---
I(1)
LNINFR
-3.466959**
---
---
---
I(0)
LNPS
-2.245170
-1.210952
-5.230434*
---
I(1)
LNFDI
-3.121809**
---
---
---
I(0)
LGDPC
-2.906720***
---
---
---
I(0)
LNIHC
-2.307898
-1.023337
-3.504476**
---
I(0)
Note: ADF test was performed using Schwarz information criterion and the automatic lag selection set as 4 lags. Also, *,
** and *** imply statistical significance at 1%, 5% and 10% levels respectively.
Source: Author’s computation using Eviews 10
4.2 ARDL Bounds Test (Cointegration Test)
In carrying out the ARDL bounds testing, the model is specified in its original form where LNAGVAL is the
dependent variable and LNRAILQ, LNPS, LNREXR, LNINFR, LNFDI, LNGDPC and LNIHC are independent
variables. Due to the sample size, the study chose a maximum lag length of 1 for the dependent variable and
independent variables. In addition, the specification was with Unrestricted Constant and No Trend, and the model
selection criteria was the Akaike information criterion. The rule of ARDL bounds testing is that if the computed
F-statistic falls below the lower bound we would conclude that the variables are I(0), so no cointegration is
possible, by definition. If the F-statistic exceeds the upper bound, we conclude that we have cointegration. Finally,
if the F-statistic falls between the bounds, the test is inconclusive.
The bounds test for cointegration in Table 2 indicates that the computed F-statistic of 6.183965 is greater
than the lower and upper bounds critical values of 2.96 and 4.26, respectively, at the 1 per cent significance level.
Therefore, the null hypothesis of no cointegration is discarded, meaning that there is evidence of a long-run
relationship among LNAGVAL, LNRAILQ, LNPS, LNREXR, LOGINFR, LNFDI, LNGDPC and LNIHC. The
next stage of the procedure would be to estimate the coefficients of the long-run relations and short-run (as well
as the associated error correction model (ECM)) using the ARDL approach.
Table 2: F-Bounds Test
Null Hypothesis: No levels relationship
Test Statistic
Value
Signif.
I(0)
I(1)
F-statistic
6.183965
10%
2.03
3.13
K
7
5%
2.32
3.5
2.5%
2.6
3.84
1%
2.96
4.26
Source: Author’s computation using Eviews 10
4.3. ARDL Estimates
4.3.1 Presentation of Result
Table 3 delineated both long-run and short-run coefficients for the model specification. From Table 3, the
coefficient of the parameter of error correction mechanism (ECM) (-0.718408) has the hypothesized negative
sign and is statistically significant at 1% level. This showed that about 71.84 per cent of disequilibria in the
agriculture value added in the previous year were corrected for in the current year. It, therefore, follows that the
ECM could rightly correct any deviations from short run to long-run equilibrium relationship of the dependent
and the explanatory variables.
In the long run, as shown in Table 3, all the variables are significant in influencing agriculture value
added in Nigeria. However, railroad infrastructure quality (LNRAILQ), political stability (LNPS), inflation
(LNINFR), and market size (proxy by GDP per capita (LNGDPC) had a negative impact on agriculture value-
added, while exchange rate (LNREXR), foreign direct investment (LNFDI) (a proxy for
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
39
technology (TECH)), and the index of human capital per person (LNIHC) had a positive impact on agriculture
value added.
Specifically, the coefficient of railroad infrastructure quality (LNRAILQ) in the long run is negatively
signed (not in line with apriori expectation) and statistically significant at a 10 per cent significance level. This
means that a percentage increase in railroad infrastructure quality will reduce agriculture value added by
1.017078%.
The analysis equally showed that the coefficient of political stability (LNPS) is inversely related to agriculture
value added in the long run. Thus, a percentage increase in political stability will decrease agriculture value added
by 2.538704%. This result is statistically significant at a 10% significance level. The same negative sign is
maintained in the short run but the statistical significance is at 1% significance and the coefficient of the current
value of political stability is -1.255178.
Similarly, a 1% increase in the inflation rate (LNINFR) decreases agriculture value added by
approximately 0.641707% at a 10% significance level in the long run but in the short run agriculture value added
decreases by 0.234163% for every 1% increase in the current value of inflation rate. In the same vein, for every
1% increase in market size (proxy by GDP per capita (LNGDPC)), agriculture value added declines by
4.374691% in the long run. This same negative effect is observed in the short run but the magnitude of the
coefficient of the current value of market size is small (-0.910437) and statistically significant at the 1%
significance level.
Nonetheless, a 1% increase in the exchange rate (LNREXR) will boost agriculture value added by about
4.476596%. This outcome shows statistical significance at the 5% significance level. The same positive effect is
experienced in the short run. Also, at a 5% significance level, every 1% increase in technology (proxy by foreign
direct investment (LNFDI) spurs agriculture value added by 1.216797% in the long run but in the short run by
0.527005% at a 1% significance level. Similarly, agriculture value added is substantially improved (by
17.22446%) for every 1% increase in the index of human capital per person (LNIHC) in the long run but in the
short run, it dropped slightly to 14.56502% for every 1% rise in the index of human capital per person (LNIHC).
Table 3: ARDL Result
Variable
Coefficient
Std. Error
t-Statistic
Prob.
Long Run Esitmates
LNRAILQ
-1.017078
0.497754
-2.043335
0.0803***
LNPS
-2.538704
1.092838
-2.323038
0.0532***
LNREXR
4.476596
1.790822
2.499744
0.0410**
LNINFR
-0.641707
0.287331
-2.233338
0.0607***
LNFDI
1.216797
0.378119
3.218023
0.0147**
LNGDPC
-4.374691
1.402228
-3.119814
0.0168**
LNIHC
17.22446
5.830429
2.954236
0.0213**
Short Run Estimates
D(LNPS)
-1.255178
0.203763
-6.159986
0.0005*
D(LNREXR)
0.654751
0.099702
6.567050
0.0003*
D(LNINFR)
-0.234163
0.037150
-6.303232
0.0004*
D(LNFDI)
0.527005
0.052868
9.968299
0.0000*
D(LNGDPC)
-0.910437
0.121658
-7.483565
0.0001*
D(LNIHC)
14.56502
1.683432
8.651980
0.0001*
ECM(-1)
-0.718408
0.072223
-9.947031
0.0000*
Note: *, ** and *** imply statistical significance at 1%, 5% and 10% levels respectively.
Source: Author’s computation using Eviews 10
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
40
4.3.2 Discussion of Result
The results show that in the long run railroad infrastructure quality has a negative impact on agriculture. This is
not surprising because poor transport infrastructure has long been a big hindrance to economic development in
Nigeria. As Oluwagbemi (2016) observed, after a prolonged neglect of the nation’s railway, recent efforts by the
government to revive and modernise the transport mode have not yielded the desired results. Many resources
have been committed to increasing food production in Nigeria in recent years; however, the challenge of post-
harvest losses remains a nagging reality. This could have been properly addressed by using rail transport to
address the enormous wastages that occur while moving commodities from rural areas to urban centres (Ojewale,
2021). The Agriculture Promotion Policy of the Federal Ministry of Agriculture highlighted that current post-
harvest loss rates are as high as 60% for perishable crops. In Tomatoes for instance, out of an annual demand of
2.2 million metric tonnes, the country’s actual production is 1.5 million tons but 0.7M tons (almost half) is lost
post-harvest (Ojewale, 2016). There was a short-lived ray of hope in 2017, when a tomato shipment from Kano,
arrived in Lagos by train for the first time in 58 years. For those in the value chain, however, more still needs to
be done to address postharvest losses by using trains, particularly refrigerated carriages to freight more
commodities across the country. However, as noted by Huso (2016), the American Association of Railroads in a
publication on “The Environmental Benefits of Moving Freight by Rail” noted that railroads are the most
environmentally sound way to move freight over land. On average, trains are four times more fuel-efficient than
trucks. They also reduce highway gridlock, lower greenhouse gas emissions, and reduce pollution.
Similarly, political stability (LNPS) was shown to inversely relate to agriculture value added in the long
run. This result agrees with Messer et al.'s (1998) estimate that during periods of conflict, agricultural production
drops. Political instability is a common occurrence in Nigeria which always affects the unity and peaceful co-
existence of the country as a nation. The increasing farmers-herdsmen conflict in the country disrupts the supply
and distribution of inputs and outputs, creates price shocks and causes massive displacement of labour. These
compounding challenges make agricultural investments difficult to maintain in politically volatile environments.
Though inflation rate is not new in Nigerian economic history, the recent rates of inflation have been a
cause of great concern to many. The result from this study shows that the inflation rate (LNINFR) has a negative
effect on agriculture value added. This implies that inflation has a generally negative effect on the agricultural
sector compared to any other sector since it is highly competitive, most of the outputs are perishable and that is
the least sector able to pass input cost increases directly into higher output prices (Obasi, 2007). This results in a
decline in agriculture sector performance. More so, market size (proxy by GDP per capita (LNGDPC)) revealed
a negative effect on agriculture value added. This result is contrary to Ubi & Udah, 2019 whose results indicated
that market size can drive agricultural sector performance in Nigeria.
However, an exchange rate (LNREXR) was found to be positively associated with agriculture sector
development. Exchange rate changes impact Nigerian agriculture export prices, the price of imported inputs, and
the competitiveness of the Nigerian agriculture sector. This result aligns with Ogunjobi et al., 2021; Awolaja &
Okedina, 2020 that real exchange rate appreciation has a significant positive effect on the agricultural sector. This
implies that as the exchange rate experiences upward fluctuation due to the depreciation of the local currency, it
causes the imports to become dearer while the exports of local commodities become cheaper in the international
markets. This forces down Nigeria's agricultural commodity prices due to the excess demand created by the
devaluation. Invariably a decrease or depreciation of the local currency will make producers more competitive
and generally would increase exports and improve agriculture sector development. Additionally, technology
(proxy by foreign direct investment (LNFDI)) spurred agriculture value added as shown in this study. This result
conforms to Akinwale et al., (2020) and Ogbanje & Salam's (2022) studies which showed that foreign direct
investment had a significant effect on the agricultural sector in Nigeria. In a similar manner, agriculture value
added is substantially improved by index of human capital per person (LNIHC). The outcome of this study
supports Aderounmu & Osabohien (2018) and Osinowo et al., (2021) findings which revealed that human capital
positively and significantly influences agricultural sector development in Nigeria.
4.4 Diagnostic Analysis
This study went further to conduct various diagnostic tests to ascertain the validity, appropriateness and
stability of the estimated model as well as the robustness of the results. Initially, two residual tests were
deployed: Correlograms Q-Statistics and the Breusch-Godfrey LM tests. From the Correlogram in Table
5, the Q-statistics are significant at all lags, indicating significant serial correlation in the residuals of
the model. That is, there is the presence of serial correlation. This was confirmed by the Breusch–
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
41
Godfrey test Obs*R-squared (16.17408) with Prob. Chi-Square (2) of 0.0003 which is significant, thus
the hypothesis of no serial correlation is rejected (see Table 6).
Table 7 shows that the null hypothesis that no heteroskedasticity exists cannot be rejected. This is
because of the high and insignificant probability value of 0.775037 for the computed F-statistic (0.6766),
and also the Obs*R-squared (13.37280) with Prob. Chi-Square (14) of 0.4974 which is insignificant.
The Jarque-Bera normality test statistics (0.881991) in Figure 1 indicates that the residual of the model
is normally distributed since the p-value of 0.643396 is greater than the significance level of 5%, i.e.,
0.643396 > 0.05.
Finally, the cumulative sum of recursive residuals (CUSUM) and CUSUM of square tests were
also applied to assess parameter stability. Figures 2 and 3 plot the results for CUSUM and CUSUMSQ
of squares tests. The CUSUM Test graph is fitted inside the 5% significance strip, defined by the upper
and lower lines which is an indication of stability. However, the CUSUM of the square tests graph
appeared at some point to be outside the upper and lower lines even though in the later part remained
stable inside the 5% significance strip.
Table5: Correlogram
Source: Author’s computation using Eviews 10
Table 6: Breusch-Godfrey Serial Correlation LM Test:
F-statistic
6.940575
Prob. F(2,5)
0.0361
Obs*R-squared
16.17408
Prob. Chi-Square(2)
0.0003
Source: Author’s computation using Eviews 10
Table 7: Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
0.775037
Prob. F(14,7)
0.6766
Obs*R-squared
13.37280
Prob. Chi-Square(14)
0.4974
Scaled explained SS
1.244387
Prob. Chi-Square(14)
1.0000
Source: Author’s computation using Eviews 10
Autocorrelation
Partial Correlation
AC
PAC
Q-Stat
Prob*
****| . |
****| . |
1
-0.520
-0.520
6.8046
0.009
. |* . |
.**| . |
2
0.102
-0.231
7.0813
0.029
. *| . |
.**| . |
3
-0.105
-0.230
7.3859
0.061
.**| . |
*****| . |
4
-0.254
-0.624
9.2802
0.054
. |*** |
.**| . |
5
0.417
-0.291
14.691
0.012
.**| . |
***| . |
6
-0.207
-0.376
16.105
0.013
. |* . |
.**| . |
7
0.212
-0.292
17.693
0.013
. *| . |
. *| . |
8
-0.099
-0.186
18.060
0.021
. *| . |
. *| . |
9
-0.148
-0.132
18.949
0.026
. |* . |
. | . |
10
0.148
0.039
19.912
0.030
. *| . |
. | . |
11
-0.190
-0.001
21.654
0.027
. |**. |
. | . |
12
0.226
0.042
24.358
0.018
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
42
0
1
2
3
4
5
6
7
-0.10 -0.05 0.00 0.05
Series: Residuals
Sample 2001 2022
Observations 22
Mean 1.29e-15
Median -0.000106
Maximum 0.071404
Minimum -0.092965
Std. Dev. 0.039853
Skewness -0.218669
Kurtosis 2.838283
Jarque-Bera 0.199298
Probability 0.905155
Figure 1: Normality Test
Source: Extracted from Eviews 10
-8
-6
-4
-2
0
2
4
6
8
2016 2017 2018 2019 2020 2021 2022
CUSUM 5 % Significance
Figure 2: CUSUM Test
Source: Extracted from Eviews 10
-0.4
0.0
0.4
0.8
1.2
1.6
2016 2017 2018 2019 2020 2021 2022
CUSUM of Squares 5% Significance
Figure 3: CUSUM of Squares Test
Source: Extracted from Eviews 10
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
43
5. Summary, Conclusion and Recommendations
Transportation is an essential component of economic development and quality of life considerations. In recent
years, Nigeria has embarked on what is easily the most ambitious railway rehabilitation and expansion programme
in its history. The purpose of this paper was to investigate the effect of rail infrastructure quality on agriculture
sector development in Nigeria. The data for the research from 2000 to 2022 were gathered from World
Development Indicators, theglobaleconomy.com, indexmundi.com, tradingeconomics.com,
countryeconomy.com, worlddata.info, fred.stlouisfed.org, and Statistical Bulletin and Annual Report and
Statement of Accounts published by the Central Bank of Nigeria (CBN). The period was decided due to data
availability.
This study used an augmented Dicky- Fuller unit root test to check for stationarity of the variables. Based
on the feature of the unit root tests at the level and first difference, the Autoregressive Distributed Lag (ARDL)
estimation method is applied to estimate the short and long-run relationships.
As the result shows, the coefficient of railroad infrastructure quality (LNRAILQ) in the long run is
negatively signed and statistically significant at a 10 per cent significance level. The coefficient of political
stability (LNPS) is inversely related to agriculture value added
in the long run. The same negative sign is maintained in the short run. Similarly, a 1% increase in the inflation
rate (LNINFR) decreases agriculture value added by approximately 0.641707% at a 10% significance level in the
long run but in the short run agriculture value added decreases by 0.234163% for every 1% increase in the current
value of inflation rate. In the same vein, for every 1% increase in market size (proxy by GDP per capita
(LNGDPC)), agriculture value added declines by 4.374691% in the long run. This same negative effect is
observed in the short run.
Nonetheless, a 1% increase in the exchange rate (LNREXR) will boost agriculture value added by about
4.476596%. A positive effect is also experienced in the short run. Also, at a 5% significance level, every 1%
increase in technology (proxy by foreign direct investment (LNFDI) spurs agriculture value added by 1.216797%
in the long run but in the short run, by 0.527005% at a 1% significance level. Similarly, agriculture value added
is substantially improved (by 17.22446%) for every 1% increase in the index of human capital per person
(LNIHC) in the long run but in the short run, it dropped slightly to 14.56502% for every 1% rise in the index of
human capital per person (LNIHC).
Conclusively, railroad infrastructure quality has an inverse relationship with agriculture value added in
Nigeria during the period covered in this study. Nigeria is a large country in terms of land mass, so the distances
over which agricultural product is shipped are sufficiently great so much so that the rail transport system should
be of economic advantage. The fact is that there is a need to put in place effective and efficient rail transportation
in the country. This is because railroads are crucial to nearly every aspect of agriculture, including the movement
of products essential to farming, such as finished farming equipment and agricultural chemicals, as well as the
food found on grocery shelves and dinner tables across the country and around the world. As such, the federal
government of Nigeria should make more effort to rehabilitate old rail lines and construct new ones to link the
other modes of transportation across the country. In that regard, effort should be made to attract potential foreign
investors in the construction of rail lines. At the same time, engineers should be properly trained in diesel
locomotive technology and rail track management.
AKSU Journal of Administration and Corporate Governance, Volume 4 Number 1, April 2024
44
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