







































 
 

 

10 
© 2023 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 10, No. 1, 10-18, 2023 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/economy.v10i1.4703 

© 2023 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Foreign exchange fluctuations on the performance of agricultural export in Nigeria 

 
Udoh, Francis Sylvanus1    

Inim, Victor Edet2   

Ityav, Doofan Lynda3     
( Corresponding Author)  

 
1Department of Business Administration, Nile University of Nigeria, Abuja, Nigeria. 
Email: slyosly79@gmail.com   
2Department of Accounting, Nile University of Nigeria, Abuja, Nigeria. 
Email: victor.inim@nileuniversity.edu.ng   
3Department of Business Administration, Nasarawa State University, Keffi, Nigeria. 
Email: lityav@ymail.com  

 
Abstract 

The study examines the effect of foreign exchange fluctuation on the performance of agricultural 
export in Nigeria. Despite the emphasis place on foreign exchange, the agricultural export in 
Nigeria is still not performing well. Time frame was from 1986 to 2021 and the adopted research 
design was ex post facto, in which the tool of analysis employed was the ARDL, ECM method, co-
integration and unit root test as finding revealed that foreign exchange fluctuation on the 
performance of agricultural volume and value added has negative and insignificant effect in Nigeria. 
While foreign exchange fluctuation on the performance of agricultural capacity utilization has a 
positive and significant impact. Giving this finding, recommendations are that Nigerian 
government should moderate and regulate the rate of exchange activities in order to make certain 
that it brings about better performance in the agricultural sector. Also, she should strongly attempt 
to make better the stand of the economy internationally with other nations of the world in order to 
expand the market for Nigerian agricultural exports. Finally, the government should change the 
focus of its policy in direction to the external agricultural sector and making sure that it adds in the 
most favourably way to output performance. As an intentional policy, the government should give 
support to rural area agriculture by which investors in distinct communities and commodities 
should be encourage to set up agricultural industries, which will be solely on local raw materials 
comprising equipment and machines.  

 
Keywords: Capacity utilisation and value added, Foreign exchange rate, Output. 

JEL Classification: B27; Q13; Q14; Q17.  
 

Citation | Sylvanus, U. F., Edet, I. V., & Lynda, I. D. (2023). Foreign 
exchange fluctuations on the performance of agricultural export in 
Nigeria. Economy, 10(1), 10–18. 10.20448/economy.v10i1.4703 
History:  
Received: 8 April 2022 
Revised: 15 February 2023 
Accepted: 22 March 2023 
Published: 30 May 2023  
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Authors’ Contributions: All authors contributed equally to the conception and 
design of the study. 
Competing Interests: The authors declare that they have no conflict of 
interest. 
Transparency: The authors confirm that the manuscript is an honest, accurate, 
and transparent account of the study; that no vital features of the study have 
been omitted; and that any discrepancies from the study as planned have been 
explained. 
Ethical: This study followed all ethical practices during writing. 

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 11 
2. Theoretical Framework .................................................................................................................................................................. 12 
3. Methodology ..................................................................................................................................................................................... 13 
4. Data Analysis and Results .............................................................................................................................................................. 14 
5. Discussion of Findings .................................................................................................................................................................... 17 
6. Conclusion and Recommendation ................................................................................................................................................ 17 
References .............................................................................................................................................................................................. 17 
 

 
 

 

mailto:slyosly79@gmail.com
mailto:victor.inim@nileuniversity.edu.ng
mailto:lityav@ymail.com
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/economy.v10i1.4703
https://orcid.org/0000-0002-2141-8751
https://orcid.org/0000-0001-7895-2114
https://orcid.org/0000-0002-7489-9210


Economy, 2023, 10(1): 10-18 

11 
© 2023 by the authors; licensee Asian Online Journal Publishing Group 

 

 

Contribution of this paper to the literature 
This study concentrated on the overall effect of foreign exchange fluctuation and agricultural 
export. However, none of these studies used output, capacity utilization and value added to 
measure agricultural export. Also, the study window is from 1986 to 2021. Based on these 
identifications, the study contributes to literature. 

 
1. Introduction 

The rapidly increasing global economy in today’s world with a constantly changing technology and the laws of 
trade internationally, has affected the way exchange rate plays its role in valuing farm equipment and production. 
For many years, the role of exchange rates as an integral part of agricultural economics was overlooked (Kristinek 
& Anderson, 2002). It was Schuh (1974) in his work titled the role of exchange rates in agricultural trade that brought 
this topic to bare. His evidence in support of the idea is that the drop in agricultural exports due to their relative 
expense in other nations was caused by the overvalued dollar. His view was that while many variables affect 
agriculture, the exchange rate plays a role in all aspects of agriculture (Kristinek & Anderson, 2002).   

Nigeria got her independence in 1960 and during this period, agriculture played a dominant role in her economy, 
but it was soon taken for granted because the government gave it a very little support. This little support provided 
by government for agricultural development was concentrated on export crops like cocoa, groundnut, palm produce, 
rubber and cotton as self-sufficiency in food production seemed not to pose any problem worthy of public attention 
(FMOAWARD, 2018).  

The agriculture in Nigeria started witnessing some problems and these issues were clearly evident from rising 
food prices, increasing food supply short-fall and declining foreign exchange earnings from agricultural exports. 
However, not much rational concern was shown because the problems were thought to be the temporary effects of a 
series of crises which eventually culminated in the civil war (1967 – 70) (FMOAWARD, 2018).  

After 1960, from 1970 to 1979, the agricultural situation worsened in Nigeria as a result of rising food import 
bills, widening food supply-demand gaps and sharp decrease in government revenue from agriculture, in foreign 
exchange earnings from agricultural exports. The situation was further compounded by the residual effects of the 
civil war, severe droughts in some parts of the country, government fiscal and monetary policies and above all, an 
“oil boom” which created serious distortions in the economy and accelerated the rate of migration of labour from 
agriculture (FMOAWARD, 2018).  

As stated by Abolagba, Onyekwere, Agbonkpolor, and Umar (2010) between 1970 and 1974, agricultural exports 
as a percentage of total exports fell from about 43 percent to slightly over 7 percent. Export of agricultural produce 
in the mid 1970s to the mid 1980s in Nigeria witnessed a sharp decrease by 17 percent. Abolagba et al. (2010) 
emphasized the fact that Nigeria has lost its role as one of the world’s leading exporters of agricultural commodities.  

According to FAOSTAT (2017) in 1961, Nigeria exported 197,000 tonnes of cocoa beans. In 1970, it went up to 
304,000 tonnes and gradually went down to 153,000 tonnes in 1980. However, this number rose up to a staggering 
485,000 tonnes in 2006, and unfortunately decreased to 248,000 tonnes in 2014. Natural rubber was exported to the 
tune of 58,000 tonnes in 1961 and subsequently increased to 147,000 tones in 1990 and in 2014 151,000 tonnes 
FAOSTAT (2017).  

One of the important factor of world trade is exchange rate, which has received much notice in the circumstances 
of world imbalances. The subject of exchange rate fluctuation came to be a topical issue in Nigeria because it is the 
goal of every economy to have a stable rate of exchange with its trading partners (Slowe, 2013). In Nigeria, this aim 
was not achieved not minding the way the government went on underestimating the naira and okayed the Structural 
Adjustment Programme (SAP) in 1986. Not achieving this success, placed the Nigerian agricultural export under 
participating in a constant exchange rate fluctuation. The major goal of SAP was the reorganizing of the production 
base of the economy with a positive inclination for the production of agricultural export. The foreign exchange 
reforms that facilitated a cumulative depreciation of the effective exchange rate were expected to increase the 
domestic prices of agricultural exports and hence boost domestic production (Slowe, 2013). A serious impediment on 
economy development is fluctuation, which makes investment riskier and more problematic. Potential investors will 
invest in a foreign location only if the expected returns are high enough to cover for the currency risk (Gerardo & 
Felipe, 2002).   

On this note, if foreign exchange is properly curtailed or kept low to agriculture, it will help agriculture export 
in Nigeria perform better and contribute to her Gross Domestic Product GDP of the economy. Despite the 
improvement of agricultural products in Nigeria, the performance of agricultural export is below expectation as a 
result of high exchange rate. The major problem however, is that the floating exchange rate from its inception, 
frequency and instability of the exchange rate movements and fluctuation has raise concerns over the effect of such 
movements on the performance of trade flows of agricultural export. It is on this basis that the work examines foreign 
exchange fluctuation on the performance of agricultural export in Nigeria. 

Previous studies such as Adekunle, Tiamiyu, Odugbemi, and Ndukwe (2019) who investigated from 1981 to 2016 
how the dynamics of real exchange rate affect performance of agriculture in Nigeria using the Nonlinear 
Autoregressive Distributed Lag (NARDL) method found a negative relationship between both variables. Also, 
Akinbode and Ojo (2018) who determined the effect of exchange rate volatility on Nigeria’s agricultural export 
performance using annual data from 1980-2015, employed Generalized Autoregressive Conditional 
Heteroscedasticity (GARCH-1,1) model which was used to generate the exchange rate volatility series and 
subsequently incorporated into the Autoregressive Distributed Lag (ARDL) Model for determining factors affecting 
agricultural exports (cocoa and rubber), found an insignificant effect between both variables. However, none of these 
studies used output, capacity utilisation and value added to measure agricultural export. Also, the study window is 
from 1986 to 2021. Based on these identifications, the study fills a research gap. 

The main purpose of this work was to examine the effect of foreign exchange fluctuation on the performance of 
agricultural export in Nigeria. Other related purposes are: to evaluate the effect of foreign exchange fluctuation on 
the performance of agricultural export volume in Nigeria: to examine the effect of foreign exchange fluctuation on 



Economy, 2023, 10(1): 10-18 

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the performance of agricultural export capacity utilization in Nigeria and to determine the effect of foreign exchange 
fluctuation on the performance of agricultural export value added in Nigeria. 

The hypotheses of the study are stated in null forms and tested from the purposes of the work: 
Ho1: Foreign Exchange Fluctuation has no significant effect on the Performance of agricultural export Volume in Nigeria 
Ho1: Foreign Exchange Fluctuation has no significant effect on the Performance of agricultural export Capacity Utilization 

in Nigeria 
Ho1: Foreign Exchange Fluctuation has no significant effect on the Performance of agricultural export Value Added in 

Nigeria 
 

1.1. Concept of Foreign Exchange 
When the currency of a country is giving out for the currency of another country at any rate is known as 

exchange rate. The external value of each currency is reflected in the country’s economic conditions in general and 
the purchasing power of the currency relative to that of other currencies in particular (Ani, Ugwunta, & Okanya, 
2013). In other word, for international traders with a given price, the major source of uncertainty is the exchange 
rate at which they can translate their sales revenue in foreign currency into local currency (Adubi & Okunmaadewa, 
2009). 
 
1.2. Concept of Agricultural Export 

Export of agricultural products are better motivation made available by various governments on products 
intended for other country market to support increased in global or other economy trading. In accordance, export 
agriculture refers to money granted by the state which are subject to chance on export performance. They may take 
the form of, for example, cash payments, disposal of government stocks at below-market prices, subsidies financed 
by producers or processors as a result of government actions such as assessments, marketing subsidies, 
transportation and freight subsidies, and subsidies for commodities contingent on their incorporation in exported 
products (FTIS, 2019). 
 
1.3. Concept of Performance 

Taticchi, Balachandran, Botarelli, and Cagnazzo (2008) stated that firm performance is the value, which is 
produced as a result of a certain activity. Each firm is established in order to fulfil specific purposes. When all 
performance factors are effectively utilised, turn out worth gets larger or astronomical than the expected worth, thus 
making the firms to survive or live longer. Competitive markets and the dynamics very likely become better of their 
performance so as to grow their profits and market value of the firm. The production process since the mid1980s 
have been controlled by the firm. In this aspect, firms became aware of that keeping up with continuously changing 
conditions is possible only by understanding firm performance, and they aimed for healthy growth (Taticchi et al., 
2008). 
 

1.4. Empirical Review 
Shuabiu, Usman, and Çavuşoğlu (2021) examines nexus among Competitively Valued Exchange Rates, Price 

level, and Growth Performance in the Turkish Economy; New insight from the Global Value Chains (GVCs) is 
investigated using annual data from 1980 to 2020 within the framework of the ARDL bound test, Bayer and Hanck 
Cointegration (BHC) test, and ECM. It was revealed that the relationship among the variables used induced economic 
performance and external trade competitiveness both in the short and long run. 

Due to a lack of data, Adekunle et al. (2019) looked into the potential asymmetric impact of real exchange rate 
changes on agricultural performance in Nigeria from 1981 to 2016. They decided to use the Nonlinear Autoregressive 
Distributed Lag (NARDL) approach. The ARDL unit root test was used to establish the use of both stationary and 
nonstationary variables. After accounting for several additional factors, the limits test for co-integration indicates 
that there is no long-term relationship between the variables. Results indicated that the study's key foundations were 
that it found both positive and negative relationships between the two variables. 

Using annual data from 1980 to 2015, Akinbode and Ojo (2018) analyze the impact of exchange rate fluctuation 
on Nigeria's agricultural export performance. To identify factors influencing agricultural exports, the Autoregressive 
Distributed Lag (ARDL) Model was utilized to produce the exchange rate volatility series using the Generalized 
Autoregressive Conditional Heteroscedasticity (GARCH-1,1) model (cocoa and rubber). The Bounds test 
demonstrated a long-term link among the variables. This indicates that neither the long-term nor the short-term 
volatility of the currency rate has any beneficial effects on export. 
 

2. Theoretical Framework 
2.1. Clarks’ Neoclassical Theory 

The Clarks (1973) model, which assesses the relationship between exchange rate volatility and trade flows, is the 
theoretical framework that best fits this study. It is predicated on a competitive company without market power that 
produces just one commodity and sells it exclusively to one overseas market without importing any intermediate 
inputs. Since it is assumed that there are no hedging options, such as through advance sales of the foreign currency 
export sales, the company converts the revenues of its exports at the current exchange rate, which varies in an 
unpredictable manner. Furthermore, because it is expensive to change the scale of production, the firm decides on its 
output before the exchange rate is realized. As a result, it is unable to adjust its output in response to favorable or 
unfavorable changes in the profitability of its exports caused by changes in the exchange rate. In this case, the 
exchange rate is the sole cause of the firm's profit unpredictability, and risk has a negative impact on the firm's 
managers, thereby reduced output and hence lower exports occur as a result of increased exchange rate volatility 
without a corresponding change in average level, reducing risk exposure. Similarly, Koren and Szeidl (2003), states 
that exchange rate volatility should have an impact on trade volumes due to the exchange rate's correlation with 
other macroeconomic variables. 

 



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3. Methodology 
Ex-post facto was the adopted research design, because the events the researcher is studying had already taken 

place. This design can also be applicable for studies geared toward ascertaining the cause-effect association between 
the independent and dependent variables (Onwumere, Onodugo, & Ibe, 2013). Evaluating the cause – effect 
relationships is the significant point of this study; hence, the data are time series, gotten from Central Bank of Nigeria 
(CBN) statistical bulletins and NBS, where inflation and interest rate are introduced as control variables covering 
the period 1986 - 2021. The annualised secondary data was analysed using the Autoregressive Distributed lag 
(ARDL) and Error Correction Mechanism (ECM), as well as employing the co-integration method to test for the 
long-run effect among the series. In other words, the underlining postulation is that all variables are integrated of 
order 1 or I (1).  
 

3.1. Model Specification  
Giving the theoretical review, the econometric model employed in this study to examine the effect of foreign 

exchange fluctuation on the performance of agricultural export in Nigeria will be formulated following the study of 
Umaru, Sa'idu, and Musa (2013) and Karimi and Husyin (2015) with modification by including real exchange rate, 
agricultural export volume, agricultural export capacity utilisation and agricultural value added to the contribution 
to GDP. Thus, the model for this study was specified as:  

𝐴𝑉𝐶 = 𝑓(𝑅𝐹𝐸, 𝐼𝑁𝐹, 𝐼𝑁𝑇)                      (1) 
 

𝐴𝐺𝐶𝑈 = 𝑓(𝑅𝐹𝐸, 𝐼𝑁𝐹, 𝐼𝑁𝑇)                     (2) 
 

𝐴𝐺𝑉𝐴 = 𝑓(𝑅𝐹𝐸, 𝐼𝑁𝐹, 𝐼𝑁𝑇)                        (3) 
Equation 1, 2 and 3 above presents the dependent variable Agricultural export proxy by Agricultural Volume, 

Agricultural Capacity Utilization and Agricultural Value Added as a function of the independent variable, given as 
Real Foreign Exchange and control variables of Inflation rate and interest rate. 

Where: 
AVC = Agricultural volume (Output). 
AGCU = Agricultural capacity utilization. 
AGVA = Agricultural value added. 
RFE = Real foreign exchange. 
INF = Inflation rate (Control variable). 
INT = Interest rate (Control variable). 
Incorporating our effect of foreign exchange fluctuation on agricultural export performance relationship into the 

unrestricted ARDL model framework so as to obtain the conditional (restricted) ARDL steady-state model (which 
was accomplished by applying Ordinary Least Square (OLS) methods to estimate the general ARDL model), of the 
form: 

𝛥𝐴𝑉𝐶𝑡 = 𝛼0 + ∑ 𝛼1
𝑖

𝑚

𝑖=1

𝛥𝐴𝑉𝐶𝑡−𝑖 + ∑ 𝛼2
𝑖

𝑛

𝑗=0

𝛥𝑅𝐹𝐸𝑡−𝑗 + ∑ 𝛼3
𝑖

𝑜

𝑘=0

𝛥𝐼𝑁𝐹𝑡−𝑘 + ∑ 𝛼4
𝑖

𝑃

𝑚=0

𝛥𝐼𝑁𝑇𝑡−𝑘 + 𝜆1𝐴𝑉𝐶𝑡−1 + 𝜆2𝑅𝐹𝐸𝑡−1

+ 
𝜆3𝐼𝑁𝐹𝑡−1 + 𝜆4𝐼𝑁𝑇𝑡−1 + 휀𝑡                                                                                                                (4) 

 

 𝛥𝐴𝐺𝐶𝑈𝑡 = 𝛼0 + ∑ 𝛼1
𝑖𝑚

𝑖=1 𝛥𝐴𝐺𝐶𝑈𝑡−𝑖 + ∑ 𝛼2
𝑖𝑛

𝑗=0 𝛥𝑅𝐹𝐸𝑡−𝑗 + ∑ 𝛼3
𝑖𝑜

𝑘=0 𝛥𝐼𝑁𝐹𝑡−𝑘 + ∑ 𝛼4
𝑖𝑃

𝑚=0 𝛥𝐼𝑁𝑇𝑡−𝑘 +

𝜆1𝐴𝐺𝐶𝑈𝑡−1 + 𝜆2𝑅𝐹𝐸𝑡−1 + 𝜆3𝐼𝑁𝐹𝑡−1 + 𝜆4𝐼𝑁𝑇𝑡−1 + 휀𝑡                                                  (5)  

𝛥𝐴𝐺𝑉𝐴𝑡 = 𝛼0 + ∑ 𝛼1
𝑖

𝑚

𝑖=1

𝛥𝐴𝐺𝑉𝐴𝑡−𝑖 + ∑ 𝛼2
𝑖

𝑛

𝑗=0

𝛥𝑅𝐹𝐸𝑡−𝑗 + ∑ 𝛼3
𝑖

𝑜

𝑘=0

𝛥𝐼𝑁𝐹𝑡−𝑘 + ∑ 𝛼4
𝑖

𝑃

𝑚=0

𝛥𝐼𝑁𝑇𝑡−𝑘 + 𝜆1𝐴𝐺𝑉𝐴𝑡−1

+ 𝜆2𝑅𝐹𝐸𝑡−1 + 𝜆3𝐼𝑁𝐹𝑡−1 + 𝜆4𝐼𝑁𝑇𝑡−1 + 휀𝑡                                                                                      (6) 

1 4 −  = Long run multipliers. 

k  = Belonging to identified best lags orders of the variables entering ARDL-ECM. 

1 4 − = coefficients of short run dynamics. 

t = time. 

  = First difference operator. 

0  = Intercept or drift operator. 

t  = Error term. 

Following position of  Menike (2016) the relationship between foreign exchange fluctuations and agricultural 
exports is specified as: 

𝛥𝐴𝑉𝐶𝑡 = 𝛼0 + ∑ 𝛼1
𝑖

𝑚

𝑖=1

𝛥𝐴𝑉𝐶𝑡−𝑖 + ∑ 𝛼2
𝑖

𝑛

𝑗=0

𝛥𝑅𝐹𝐸𝑡−𝑗 + ∑ 𝛼3
𝑖

𝑜

𝑘=0

𝛥𝐼𝑁𝐹𝑡−𝑘 + ∑ 𝛼4
𝑖

𝑝

𝑚=0

𝛥𝐼𝑁𝑇𝑡−𝑘 + 𝛿𝑒𝑐𝑡𝑡−1

+ 휀𝑡                                                                                                                              (7) 

𝛥𝐴𝐺𝐶𝑈𝑡 = 𝛼0 + ∑ 𝛼1
𝑖

𝑚

𝑖=1

𝛥𝐴𝐺𝐶𝑈𝑡−𝑖 + ∑ 𝛼2
𝑖

𝑛

𝑗=0

𝛥𝑅𝐹𝐸𝑡−𝑗 + ∑ 𝛼3
𝑖

𝑜

𝑘=0

𝛥𝐼𝑁𝐹𝑡−𝑘 + ∑ 𝛼4
𝑖

𝑝

𝑚=0

𝛥𝐼𝑁𝑇𝑡−𝑘 + 𝛿𝑒𝑐𝑡𝑡−1

+ 휀𝑡                                                                                                                                 (8) 



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𝛥𝐴𝐺𝑉𝐴𝑡 = 𝛼0 + ∑ 𝛼1
𝑖

𝑚

𝑖=1

𝛥𝐴𝐺𝑉𝐴𝑡−𝑖 + ∑ 𝛼2
𝑖

𝑛

𝑗=0

𝛥𝑅𝐹𝐸𝑡−𝑗 + ∑ 𝛼3
𝑖

𝑜

𝑘=0

𝛥𝐼𝑁𝐹𝑡−𝑘 + ∑ 𝛼4
𝑖

𝑝

𝑚=0

𝛥𝐼𝑁𝑇𝑡−𝑘 + 𝛿𝑒𝑐𝑡𝑡−1

+ 휀𝑡                                                                                                                                   (9) 
Equations 4 to 9 above presents the logged dependent variable Agricultural export proxy by Agricultural Volume, 

Agricultural Capacity Utilization and Agricultural Value Added as a function of the independent variable, given as 
Real Foreign Exchange and control variables of Inflation rate and interest rate. 
 

4. Data Analysis and Results 
4.1. Testing for Unit Root 

Data from time series are generally described by a stochastic pattern that can be eliminated by differentiation. 
Therefore, the unit root is a test of the non-stationary or stationary existence of the data employed in this description. 
This is to find out whether there is a spurious or nonsensical relationship between foreign exchange fluctuation and 
performance of agricultural export in Nigeria. Thus, as shown in Table 1, the study employed Augmented Dickey-
Fuller (ADF) techniques to test and verify the series unit root property and model stability. 
 

Table 1. Unit root of 1st and 0 order test result of the variables. 

Variable 
  

ADF test statistics 

ADF Critical value Order of integration 

AVC -6.978246 -4.309824 I (1) 
AGCU -3.508251 -3.207094 I (1) 
AGVA -5.840353 -4.252879 I (1) 
INT -4.204566 -3.580623 I (1) 
INF -3.707572 -3.562882 I (0) 
RFE -3.477945 -3.204699 I (0) 

Note: The tests include intercept and trend. 

 
From Table 1 it could be observed that the results from ADF showed that four of the variables (which are AVC, 

AGCU, AGVA and INT) are integrated at order one; while two of the variables (which are INF and RFE) are 
integrated at order zero. 

The variables which were found to be stationary at first difference, have their ADF test statistics as: -6.978246, 
-3.508251, -5.840353, -4.204566; and they were found to be greater than the critical values of: -4.309824 (at 1%); -
3.207094 (at 10%); -4.252879 (at 1%); -3.580623 (at 5%) respectively. 
 

4.2. Co-integration Test (Bound Test Approach) Results 
If there is equilibrium relationship or a long term in variables, it means that they are co-integrated. To avoid 

false or fake regression situations there must be a pre-test. Table 2 presents the summary results of ARDL bounds 
test for Co-integration for the three models (agricultural volume model, agricultural capacity utilization model; and 
for agricultural value-added model) using Akaike Information Criterion (AIC) recommended lags. 
 

Table 2. Bound test-co-integration results of lower (I(0)) and upper bound (I(1)). 

AVC-model 

F-statistic 3.642942   Decision 

Significance 5% 
I (0) 2.39 

Co-integrated 
I (1) 3.38 

AGCU-model 

F-statistic 3.793283   
Co-integrated 

Significance 10% 
I (0) 2.39 

I (1) 3.38 

AGVA-model 

F-statistic 15.31406   
Co-integrated 

Significance 5% 
I (0) 2.39 

I (1) 3.38 
Note: ** significant at 5%. 

 
Foreign currency rates and AVC have a long-run or equilibrium relationship, according to the co-integration test 

result from Table 2. This was shown by the F-statistic value of 3.642942, which at the 5% level of significance was 
found to be greater than the lower (I(0)) and upper bound (I(1)) critical values of 2.39 and 3.38, respectively. 

The F-statistic value of 3.793283, which is greater than the lower (I(0)) and upper bound (I(1)) critical values of 
2.39 and 3.38, respectively, and also at the 5% significant level, revealed the existence of a co-integrating connection 
between foreign exchange rates and AGCU. 

Finally, the F-statistic value of 15.31406 is greater than the lower (I(0)) and upper bound (I(1)) critical values of 
2.39 and 3.38 respectively at the 5% significant level, providing evidence of a co-integrating relationship between 
foreign exchange rates and AGVA. The analysis consequently comes to the conclusion that the independent and 
dependent variables have a long-term, or equilibrium, connection in Nigeria during the time period under 
consideration; as a result, the study moves forward using error correction models. 

 

4.3. Model Estimation and Results Evaluation  
The study has established that there is a co-integrating connection between foreign exchange fluctuation and 

performance of agricultural export in Nigeria; as such, the study moves to calculate the long-run models and error 
correction. The ARDL-ECM result examines in what manner the ARDL model changes to the long-run equilibrium. 
The study utilised a general-to-specific modelling approach to derive a satisfactory reduced short-run dynamic policy 
captured in Table 3, 4, and 5. 

Hypothesis one: Foreign Exchange Fluctuation and Performance of agricultural export Volume in Nigeria. 



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The ect(-1) depicts adjustment of the speed to bring back the long run in the activity model coming after an 
interruption. The coefficient of the estimated ect(-1) equals -0.5601 puts forward a prompt speed of adjustment back 
to the long-run equilibrium. The coefficient is appropriately signed and to a greater degree significant at the 1 percent 
significance level. This hugely significant ect is emphasised fact of the existence of a stable long-term relationship.  

 
Table 3. Auto-regressive distributive lag regression result. 

Dependent variable: D(AVC) 

ARDL error correction regression 

Variable Coefficient Std. error T-statistic Prob. 

D(AVC(-1)) -0.7055 0.184346 -3.82706 0.0123 
D(RFE) 9.164678 1.482621 6.181402 0.0016 
D(RFE(-1)) -0.98868 1.094113 -0.90363 0.4076 
D(RFE(-2)) 4.472565 1.236556 3.616953 0.0153 
D(INF) 9.212627 2.723365 3.38281 0.0196 
D(INF(-1)) -11.9096 2.45619 -4.8488 0.0047 
D(INF(-2)) 7.366478 2.241361 3.28661 0.0218 
D(INT) -14.3228 3.202153 -4.47287 0.0066 
D(INT(-1)) 21.32017 3.551261 6.003549 0.0018 
D(INT(-2)) 21.43774 3.505539 6.115392 0.0017 
CointEq(-1)* -0.560133 0.087799 -6.379758 0.0014 
R-squared 0.841708 Mean dependent var 0.85 

Adjusted R-squared 0.611466 S.D. dependent var 2.836829 

F-statistics (and P-value) 
9.768266 
(0.00002) Akaike info criterion 4.257852 

Durbin-Watson stat 2.007263 Schwarz criterion 5.066691 
Note: * p < 0.1. 

 
The coefficient of determination (R-square) indicates that the model was reasonably fit in prediction. It showed 

that 84.17% changes in AVC were unanimously owed to RFE, INF and INT, while 15.83% not included variations 
was represented as the error term.  

The overall importance of regression model, which is the F-statistic reveal a significant result as examined. The 
value of the F-statistic captures it at 9.76 and its associated value of 0.000002 at 5% level was found to be significant. 

It is further proof in the result that among the variables, there is absence of autocorrelation as proven by Durbin 
Watson (DW) statistic of 2.00. It showed that the data can be depended upon and are impartial.  

Hypothesis Two: Foreign Exchange Fluctuation and Performance of agricultural export Capacity Utilization in Nigeria. 
 

Table 4. ARDL regression result. 

Dependent variable: D(AGCU) 

ARDL error correction regression 

Variable Coefficient Std. error T-statistic Prob.    

D(AGCU(-1)) -0.45659 0.094461 -4.83358 0.0013 
D(RFE) -6.02E-05 0.000361 -0.16661 0.8718 
D(RFE(-1)) 0.002052 0.000488 4.204631 0.003 
D(RFE(-2)) -0.00069 0.000517 -1.33548 0.2185 
D(RFE(-3)) 0.001405 0.000497 2.827868 0.0222 
D(INF) -0.0029 0.000633 -4.5786 0.0018 

D(INF(-1)) 0.00344 0.000834 4.12341 0.0033 

D(INF(-2)) 0.003392 0.000708 4.793683 0.0014 

D(INF(-3)) 0.003382 0.000742 4.556027 0.0019 

D(INT) 85.12271 17.93108 4.747215 0.0015 

D(INT(-1)) 45.25082 10.14136 4.462009 0.0021 

D(INT(-2)) 37.72435 12.42301 3.03665 0.0161 

D(INT(-3)) 30.40713 10.16939 2.990065 0.0173 

CointEq(-1) -0.38839 0.056976 -6.81672 0.0001 
R-squared 0.930433     Mean dependent var -0.03316 

Adjusted R-squared 0.845958 
Standard deviation 

dependent var 
4.213092 

F-statistics (and P-value) 
8.2556 
(0.000) 

    Akaike info criterion 4.142062 

Durbin-Watson stat 2.22199     Schwarz criterion 4.966539 

 
The Error Correction Model (ECM) parameter is negative, less than unity and significant at 5% level as expected. 

The ECM is an error correction term in the model to restore back equilibrium, and validates that there exists a long 
run equilibrium relationship among the variables. The value of the ECM is 38.83%, meaning that the system corrects 
(or adjusts to) equilibrium in the following year at speed of 38.83% which is good.  

To show the elucidatory capacity of the model and the reliability of the estimates, the coefficient of determination 
(R-square) was deployed. It indicates how the model was in a sensible way fit in forecasting. It emphasized that 93.04 
percent alterations to AGCU were collectively due to RFE, INF and INT, at the same time 6.96% represents the 
white noise.  

To determine the whole importance of the regression model in the same extent, the F-statistic was used to 
evaluate it and was revealed that the results are significant. 8.25 captures the value of the F-statistic and its affiliated 
probability value of 0.000 having been discovered to be significant at 5% level. The Durbin Watson (DW) statistic 
of 2.22 in the model emphasized that there is absence of autocorrelation between the independent and dependent 



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variables (as it fell within the acceptable range of 1.5 and 2.4). This proves that unbiased estimates can be depended 
on to make decision on policy. 

Hypothesis Three: Foreign Exchange Fluctuation and Performance of agricultural export Value Added in Nigeria. 
The lagged error correction term (ECT(-1)) was considerably statistically significant at 5% less than unity and 

negative, as expected. The coefficient revealed that once the system is out of equilibrium, it will require an average 
(high) speed of 34.68% for it to return to a state of long-run equilibrium. The calculated model is reasonably good at 
making predictions, according to the coefficient of determination (R-square), which was used to assess the goodness 
of fit of the model. It revealed that RFE, INF, and INT together were responsible for 98.53 percent of changes in 
AGVA, whereas the error term was able to explain for 1.47 percent of unaccounted fluctuations. 

In addition, the full model is likewise significant at the 5% level, according to the F-statistic value of 10.233 and 
its associated probability value of 0.000. The Durbin Watson (DW) statistic of 2.29 from the model also showed that 
the variables did not exhibit any autocorrelation. This demonstrated that the estimations were objective and could 
be trusted for making policy judgments as well. 
 

Table 5. ARDL error correction regression. 

Dependent variable: D(AGVA) 

ARDL error correction regression 

Variable Coefficient Std. error t-Statistic Prob. 

D(AGVA(-1)) 1.037668 0.086699 11.96858 0.0013 
D(AGVA(-2)) 0.035936 0.056903 0.631519 0.5725 
D(AGVA(-3)) -2.63654 0.20328 -12.97 0.001 
D(REF) 29.08825 1.894067 15.35756 0.0006 
D(REF(-1)) -16.9905 1.243886 -13.6592 0.0008 
D(REF(-2)) 12.38867 0.987773 12.54202 0.0011 
D(INF) 27.31271 2.244818 12.167 0.0012 
D(INF(-1)) -29.02 3.057898 -9.49018 0.0025 
D(INF(-2)) -25.5089 2.359674 -10.8104 0.0017 
D(INF(-3)) -44.9569 2.767935 -16.242 0.0005 
D(INT) 9.28747 2.710911 3.425958 0.0417 
D(INT(-1)) 34.74633 2.701423 12.86223 0.001 
D(INT(-2)) -4.44554 3.163681 -1.40518 0.2546 

D(INT(-3)) -45.0603 3.766716 -11.9628 0.0013 
CointEq(-1) -0.34682 0.019339 -17.9331 0.0004 
R-squared 0.985399 Mean dependent var 0.125625 
Adjusted R-squared 0.949709 S.D. dependent var 5.336756 

F-Statistics (and P-value) 
10.233 
(0.000) Akaike info criterion 3.366169 

Durbin-Watson stat 2.29617 Schwarz criterion 4.419666 

 

4.4. Statistical Test of Hypotheses 
H01: Foreign Exchange Fluctuation has no significant effect on the Performance of  agricultural export Volume in Nigeria. 

 
Table 6. Wald test results on foreign exchange fluctuation and 
performance of agricultural export volume in Nigeria. 

Test statistic Value Df Probability 

F-statistic 1.280560 (9, 5) 0.4122 
Chi-square 11.52504 9 0.2414 

 
The Wald-test in Table 6 indicated that the calculated F-value for the relationship between Foreign Exchange 

Fluctuation and the Performance of agricultural export Volume in Nigeria is 1.280560, and its probability value is 
0.4122. For the reason that the probability value is greater than 0.05 at 5% level of significance, it means it falls in 
the region of acceptance and as a consequence, hypothesis one in a null form (H01) was accepted. The result 
emphasizes that Foreign Exchange Fluctuation has no significant effect on the Performance of agricultural export 
Volume in Nigeria. 

H02: Foreign Exchange Fluctuation has no significant effect on the Performance of agricultural export Capacity 
Utilization in Nigeria. 
 

Table 7. Wald test results on foreign exchange fluctuation and 
performance of agricultural export capacity utilization in Nigeria. 

Test statistic Value Df Probability 

F-statistic 12.25883 (7, 5) 0.00255 
Chi-square 14.25369 5 0.00293 

 
The Wald-test in Table 7, indicated that the calculated F-statistic value for the relationship between Foreign 

Exchange Fluctuation and the Performance of agricultural export Capacity Utilization in Nigeria was found to be 
12.25883 and its probability value was 0.0025. For the reason that the probability value is less than 0.05 or 5% level 
of significance (and fell in the rejection region), hypothesis 2 in the null (H02) was rejected. The study concludes, 
Foreign Exchange Fluctuation has a positive and significant effect on the Performance of agricultural export 
Capacity Utilization in Nigeria. 

H03: Foreign Exchange Fluctuation has no significant effect on the Performance of agricultural export Value 
Added in Nigeria. 



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Above all, the Wald-test in Table 8, the indicated that the F-value for the relationship between Foreign Exchange 
Fluctuation and the Performance of agricultural export Value Added in Nigeria was found to be 7.160760; with an 
associated probability value of 0.0657. Since the probability value is greater than 0.05 or 5percent level of significance, 
the third null hypothesis (H03) was accepted. The study thus concludes that Foreign Exchange Fluctuation has no 
significant effect on the Performance of agricultural export Value Added in Nigeria. 

 
Table 8. Wald test results on foreign exchange fluctuation and the 
performance of agricultural export value added in Nigeria. 

Test statistic Value Df Probability 

F-statistic 7.160760 (8, 5) 0.0657 
Chi-square 85.92912 5 0.0000 

 

5. Discussion of Findings 
That Foreign Exchange Fluctuation was seen to have insignificant effect on the performance of agricultural 

export Volume in Nigeria. This is in agreement with the results of Akinbode and Ojo (2018) whose findings revealed 
the volatility of exchange rate does not affect export significantly in the long and short-run. This may be to a limited 
extent ascribed to the inelastic qualities of agricultural commodities’ supply most importantly in the short run. It 
was also exposed that there exists insignificant relationship among agricultural export and GDP, world prices, 
exchange rate and inflation. The findings further agreed with Omojimite (2014) whose study showed that foreign 
exchange fluctuations through spread of interest rate was found to have no positive and significant effect on 
agricultural output in Nigeria. 

Furthermore, discovery from the analysis shows that foreign Exchange Fluctuation has a significant effect on 
the Performance of agricultural export Capacity Utilization in Nigeria. It showed that the significant fundamentals 
were real exchange rate, real appreciation and depreciation (after some lags), has significant effect on agricultural 
export capacity utilization in Nigeria (after some lags) in the short run. This aligned with the findings of Shuabiu et 
al. (2021) whose study showed that there is a relationship among between the variables. 

Foreign Exchange Fluctuation has no positive effect on the Performance of agricultural export Value Added in 
Nigeria. The implication of this findings is that, unstable exchange rates impacted ineffectively on Performance of 
agricultural export Value Added in Nigeria. This is in agreement with Brownson, Vincent, Emmanuel, and Etim 
(2012) whose study showed that in both long run and short run, real exports, real external reserves, inflation, and 
external debt have insignificant negative effects on agricultural productivity, whereas industrial capacity utilization 
and nominal exchange rate promote agricultural productivity in Nigeria. The study is in line with the Clarks (1973) 
theory, which views exchange rate and trade flows as a perfect way for firms to earn foreign currency. 
 

6. Conclusion and Recommendation 
Empirical result disclosed no effect between foreign exchange fluctuation and agricultural volume, which is 

the output sector in Nigeria in the long run. The study also concluded that foreign exchange fluctuation does not 
cause agricultural volume to increase or perform well, which would have led to corresponding increase in agricultural 
output at 5% level of significance. Also, the second null hypotheses revealed that foreign exchange fluctuation has no 
negative and insignificant effect on the performance of agricultural capacity utilization in Nigeria. Based on the 
findings it is established that foreign exchange fluctuation impact on agricultural capacity utilization as its optimum 
capacity utilization causes foreign exchange to increase in Nigeria within the period of reviewed. Finally, the study 
concludes that foreign exchange fluctuation does not affect value added in Nigeria. with this, the study concludes 
that foreign exchange fluctuation does not cause agricultural value added to contribute to the GDP of the economy 
and that foreign exchange does not influence agricultural sector value added to grow and conclude that their 
relationship is insignificant and negatively related. Based on these conclusions, the study recommends that Nigerian 
government should moderate and regulate the rate of exchange activities in order to make certain that it brings about 
better performance in the agricultural sector. Also, she should strongly attempt to make better the stand of the 
economy internationally with other nations of the world in order to expand the market for Nigerian agricultural 
exports. Finally, the government should change the focus of its policy in direction to the external agricultural sector 
and making sure that it adds in the most favourably way to output performance. As an intentional policy, the 
government should give support to rural area agriculture by which investors in distinct communities and 
commodities should be encourage to set up agricultural industries, which will be solely on local raw materials 
comprising equipment and machines. Hence, this will increase and advanced the market capacity utilization and value 
added locally. 
 

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