Alexander Ehimare Omankhanlen1*, Victoria Abosede Akinjare1, Benjamin Ighodalo Ehikioya1, Damilola Ayomiposi Makinde1
1Department of Banking and Finance College of Management and Social Sciences, Covenant University, Nigeria
*Correspondence: Omankhanlen A.E, Department of Banking and Finance, Covenant University, Ota, Nigeria. E-mail: alexander.omankhanlen@covenantuniversity.edu.ng
Received date: 17 March, 2026; Accepted date: 01 April, 2026; Published date: 07 April, 2026
Citation: Alexander, Ehimare Omankhanlen, Akinjare V.A, Ehikioya B.I, Makinde D.A. “Impact Assessment of the Agricultural Credit Guarantee Scheme Fund on Agricultural Sector Growth.” J Glob Entrep Manage (2026): 136. DOI: 10.59462/3068-174X.4.1.136
Copyright: © 2026 Omankhanlen A.E. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The Agricultural Credit Guarantee Scheme Fund was established in 1978 by the military federal government in 1977 to incentivize banks to increase lending to the agricultural sector. However, the lack of repayment of previously lent loans has led to a shortage in the distribution of loans. The primary objective is to analyze the effectiveness of the CBN agricultural intervention program, specifically the Agricultural Credit Guarantee Scheme Fund, over 30 years from 1991 to 2020. Using the Ordinary Least Squares (OLS) method, a linear model was employed to examine the relationship between dependent and explanatory variables. The study's findings reveal that the coefficient of the Agricultural Credit Guarantee Scheme Fund is negative, at the 5% level. This could indicate that beneficiaries in the sector are not making full use of the fund. The report proposes that banks focus solely on agricultural lending to encourage agricultural growth and ensure that loans are allocated toward their intended agricultural purposes to avoid money misuse.
Keywords: Agricultural; Agricultural Credit Guarantee Scheme Fund; Anchor borrowers funds; Funds, Productivity
We all have a shared objective: to swiftly, revive our economy, and achieve robust growth. Intervention involves actively addressing problematic situations to improve or prevent them from deteriorating further. Nigeria's economy is part of the Economic Community of West African States (ECOWAS), operating on a mixed economic model blending elements of capitalism and socialism. It comprises five major sectors: agriculture, industry, construction, trade, and services [1].
The bank has a longstanding involvement in development financing dating back to 1962. Over the years, policies have aimed at improving loan access in critical sectors such as agriculture, manufacturing, and micro, small, and medium-sized enterprises. (MSMEs), and infrastructure. Additionally, efforts have been made to establish and reinforce development finance institutions. As noted, by Osuma [2] banks play a crucial role in disbursing funds to the agricultural sector, preferring to lend based on customers' income levels. Consequently, traditional banks, including agriculture and microfinance banks, are disinclined to lend to peasants and small-scale farmers, who, represent a significant proportion of Nigeria's population [3].
The country's poverty rate is on the rise, prompting the CBN to introduce incentive schemes to encourage farming as a solution. However, the agricultural sector, traditionally responsible for feeding the population, generating export revenue, and supplying raw materials, has recently fallen short of these roles. Obstacles such as insufficient supply and distribution of agricultural goods, restricted adoption of technology, and a deficit in working capital have impeded the sector's ability to realize its full potential. Addressing these structural issues is crucial to putting the economy on a sustainable growth trajectory.
Agriculture serves as the cornerstone of Nigeria's economy, being the largest source of employment with two-thirds of the population relying on it for their livelihood.
Adamgbe [4], governments have implemented various growth improvement programs to stimulate investment and output in the agricultural sector. These programs aim to improve access to finance and agriculture inputs such as fertilizer and seedlings, while also implementing pricing support mechanisms through protective tariffs and tax policies.
Both government agencies and international development partners have been involved in these interventions at all levels. The Central Bank of Nigeria (CBN) has actively participated in agricultural sector interventions to enhance farmers' access to finance, often at single-digit interest rates.
With its abundant natural resources, Nigeria is one of the largest countries in Africa, and agriculture is considered its most significant economic sector. Agriculture employs about 70 per cent of the working population and contributes approximately 60 per cent to the national income [5]. Moreover, agriculture has steadily contributed to Nigeria's GDP growth, with an average share of 20.93 per cent from 2014 to 2019.
Nigeria's economic data indicates an average population of 191 million and an average GDP per capita of USD 2292.2 from 2015 to 2019, with an average unemployment rate of 7.22 per cent and economic growth of 1.24 per cent over the same period [6]. The agricultural programs have led to increased production in various commodities, these initiatives have benefited over 4.8 million smallholder farmers since their inception, as reported by Mojeed [7].
Development financing is a crucial aspect of the central bank's role because sustaining economic growth requires adequate funding. Providing finance to different economic sectors can promote comprehensive growth and welfare development. The CBN's development objectives include policy formulation and execution, product creation, and the provision of competitive, efficient, and sustainable services to support financial institutions' operations. These activities are largely aimed at agricultural, rural, and small and medium-sized enterprises (SMBs), as stated by the CBN in 2007.
The importance of finance in agriculture, as in the industrial and service sectors, cannot be overstated. However, public expenditure on agriculture has often fallen short of meeting the objectives of government agricultural policies, as highlighted by Ayodele [8]. In a developing country like Nigeria, which relies heavily on oil as its primary export, insufficient financing for agriculture poses significant risks for several reasons.
As noted by Ihenacho [9], finance plays an increasingly crucial role in agricultural development in modern times. The Central Bank of Nigeria [10] acknowledges credit as a vital source of finance for agricultural development.
Small farmers constitute approximately ninety per cent of the country's farming population, predominantly employing conventional methods and focusing on subsistence livelihoods. Government interventions aim to enhance productivity and transition these farmers towards mechanized agricultural practices. Commercial farmholders, on the other hand, receive support through credit facilities, input subsidies, capacity building, and export incentives [4].
Agricultural finance, a component of agricultural economics, is concerned with the acquisition and use of capital in agriculture. It examines the demand for and supply of finances to support the agricultural industry, as well as financial intermediaries that secure loanable funds from financial markets and institutions. These subsidies are subsequently distributed to both small-scale and commercial farmers for agricultural purposes. This area has both micro and macro elements, examining the economic and financial links between agriculture and the larger economy, as well as evaluating the impact of economic policies on agricultural performance [11].
Agriculture remains a crucial sector and a key driver of growth in the Nigerian economy. Omankhanlen [12] emphasizes that the agricultural sector significantly contributes to national domestic production, providing livelihoods for Nigerians, alleviating poverty, enhancing employment opportunities, and contributing to overall economic growth, despite the presence of oil.
The Keynesian Economic Theory, developed by British economist John Maynard Keynes in the 1940s, advocates for government intervention to stimulate economies during recessions. Keynes also played a significant role in shaping wartime economics and played a key part in establishing institutions like the International Monetary Fund (IMF) and the World Bank.
The productivity of the oil palm subsector can be evaluated through three measures, influenced by the intervention of the Agricultural Credit Guarantee Scheme Fund (ACGSF): the expansion of palm tree hectares, the output of various palm tree products, notably palm oil, and yield, which represents the output-to-hectares ratio. Palm oil is particularly significant due to its versatile economic importance and global demand. Factors such as rising demand and disproportionate use of edible oils have led to a rapid increase in palm oil demand, as noted by Abdul-Qadir, [13-15].
Medugui [16] conducted a study using the ordinary least squares method to analyze the impact of commercial banks' credit on agricultural output in Nigeria from 1980 to 2018. Their findings revealed that commercial bank credit, along with government expenditure on agriculture, predicts output levels in Nigeria. Additionally, the interest rate showed an inverse relationship with agricultural output, aligning with expectations. The study suggests the need for regulatory policies to encourage commercial banks to lower interest rates, making funds more accessible to farmers at affordable rates, while also increasing government expenditure in agriculture with stringent monitoring.
Mbelu, and Ifionu [17] conducted a study examining the influence of agricultural financing on economic growth in Nigeria from 1981 to 2019. Their objective was to analyze the long-term relationship between various forms of agricultural financing and Nigeria's economic growth. The study employed stationary tests, co-integration tests, error correction models, and the Granger causality model. All variables were found to be stationary at the first difference, and the co-integration test confirmed a long-run relationship. The findings indicated that the agricultural credit guarantee scheme fund had a positive and significant impact on Nigeria's gross domestic product (GDP) in the long run.
Anh, [18] evaluated the impact of agricultural loan on agricultural GDP in Vietnam between 2004: Q4 and 2016: Q4. Exogenous variables included agricultural labor, government investment, and rainfall. The study's methods included indicator saturation (IS) break tests, autoregressive distributed lag (ARDL) bounds tests with structural breaks, and the Toda-Yamamoto Granger causality test.
Ayeomoni and Aladejana [19] explored the relationship between agricultural finance and economic growth in Nigeria spanning from 1986 to 2014. Employing the Auto-Regressive Distributed Lag (ARDL) approach, they identified significant short- and long-term associations between agricultural lending and economic growth.
The inception of the Agricultural Credit Guarantee Scheme Fund dates back to 1978, established under the Agricultural Credit Guarantee Scheme Decree 1977 (Decree No. 20), later amended on June 13th, 1988 during the federal military administration. Initially, the fund was jointly owned by the federal military government (60 per cent) and the Central Bank of Nigeria (40 per cent), with an initial capital of N100 million. Over the years, the fund has experienced significant growth, increasing its capital to N3.0 billion by March 2001 and further to N50 billion by March 2019 under the Agricultural Credit Guarantee Scheme Amendment Act. Its primary function is to provide guarantees for bank loan facilities extended to farmers, covering up to 75 per cent of the defaulted amount, net of any recovered security. The day-to-day management and operations of the Scheme are under the purview of the Central Bank of Nigeria.
To bolster bank credit in the agricultural sector, the Fund aims to provide guaranteed credit for agricultural loans issued by any bank. The scope of loans covered under the decree includes advances, overdrafts, and all credit schemes, as outlined in guidelines and circulars [20]. The Agricultural Credit Guarantee Fund Board manages the funds, with the Central Bank of Nigeria serving as the managing agency for scheme management. Operations about the scheme are conducted at the central bank's headquarters, overseen by the banks' development department, headed by a director. The location of transactions (central banking office) within each state plays a pivotal role in facilitating the operations of the scheme, aiming to prevent delays.
Given the capital-intensive nature of agricultural activities, loans to the farming sector are essential for transitioning from subsistence to mechanized and commercial farming methods [21]. The federal government is keen on enhancing formal loans to the agricultural sector to promote farm productivity, increase farmers' income, alleviate poverty, and boost the economy's foreign exchange earnings. The objective is to ensure food security, drive transformative rural development, and enhance citizens' nutritional health profiles [22]. The ACGSF is established based on guidelines or a framework designed to address the reluctance of conventional financial institutions to extend credits to farmers.
This chapter will focus on discussing the methods employed in conducting the study. It will outline the various methodologies utilized to obtain unbiased results, ensuring the validity and reliability of the instruments used. The success of any project is heavily influenced by the chosen methodology.
For this study, the Ordinary Least Square method of estimation will be employed. Data will be sourced from published materials, specifically the Central Bank of Nigeria Statistical Bulletins and Indexmundi. These sources will provide the necessary data for analysis and interpretation.
In this study, we outline our model to estimate the effects of CBN initiatives on the agriculture sector, specifically on output. AGOPUT=F (IDF, ACGSF, ABP)
Where;
IDF: Interest Drawback fund
AGOPUT: Agricultural sector (agricultural output)
ABP: Anchors Borrowers Program
F: Functional relationship
We can econometrically state the aforementioned functional form by linearizing it.
AGOPUT= α0 + α1IDF + α2 ACGSF + α3ABP + U
The a priori expectations are that α1 is greater than 0, α2 is less than 0, and α3 is greater than 0.
Agricultural credit guarantee scheme: The agricultural loan guarantee plan will have a positive impact on the agricultural sector's output.
It is shown mathematically as; δY/δ ACGSF < 0
For the Interest Drawback program, we hypothesize a negative relationship with the agricultural sector's output, mathematically expressed as δY/δIDP > 0. Conversely, for the Anchors Borrowers Program, we expect a positive relationship with the agricultural sector's output, mathematically represented as δY/δABP > 0.
Descriptive statistics is used to explain the important features of the data in a study. It includes the mean, median and standard deviation of the distribution (Table 1).
|
|
LAGOPUT |
LIDP |
LACGSF |
ABP |
|
Mean |
9.523158 |
18.73592 |
15.66987 |
3.26E+10 |
|
Median |
9.570090 |
19.08763 |
15.87481 |
0.000000 |
|
Maximum |
9.817282 |
19.84673 |
16.33773 |
9.24E+10 |
|
Minimum |
9.092522 |
15.33108 |
14.54968 |
0.000000 |
|
Std. Dev. |
0.230183 |
1.151899 |
0.489125 |
4.55E+10 |
|
Observations |
17 |
17 |
17 |
17 |
Table 1. Below are the descriptive statistics of LAGOPUT, LIDP, LACGSF, and ABP from 1991 to 2020:
Source: e-view10.
The provided statistics offer a comprehensive overview of the characteristics of the variables being analyzed. They provide essential information about the central tendency, dispersion, and range of the data.
For instance, considering the variable LAGOPUT, the mean value of 9.523158 signifies the average value of the observations. Meanwhile, the median value of 9.570090 represents the middle point of the dataset, indicating that half of the observations are below this value and half are above it. Additionally, the standard deviation of 0.230183 measures the spread of the data around the mean, reflecting the degree of variability within the dataset. Furthermore, the range, spanning from 9.092522 to 9.817282, depicts the extent of the data from its minimum to maximum value.
Similarly, for variables such as LIDP and LACGSF, their mean, median, standard deviation, and range provide valuable insights into their respective distributions and variability. However, for the variable ABP, there is an interesting observation regarding the median value being 0.000000. This suggests that half of the observations in the dataset have a value of zero, while the mean value of 3.26E+10 is significantly higher. The standard deviation of 4.55E+10 indicates a high degree of variability in the data. The range, spanning from 0.0000000 to 9.24E+10, highlights the wide range of values present in the dataset, with some observations being substantially higher than others.
Overall, these statistics provide valuable information for understanding the characteristics of the variables and can guide further analysis and interpretation.
The research will employ the Augmented Dickey-Fuller (ADF) test, a widely used unit root test for evaluating stationarity in time series data. In this context, if the absolute value of the ADF t-statistic exceeds the absolute critical value at the 10 per cent significance level, and if the ADF t-statistic is less than the absolute critical value, the variable is deemed stationary. Stationarity indicates that the statistical characteristics of the variable, such as mean and variance, remain stable over time.
The ADF unit root test will be applied to the variables under study to assess their stationarity. The outcomes of this analysis will be presented in (Table 2), offering insights into whether the variables demonstrate stationary behaviour or not.
|
Variable |
ADF Test Statistics |
10% critical value |
Order of integration |
Remarks |
|
LACGSF |
-4.71 |
-2.63 |
I(0) |
Stationary |
|
LAGOPUT |
-4.89 |
-2.63 |
I(0) |
Stationary |
|
LIDP |
-4.45 |
-2.68 |
I(0) |
Stationary |
|
ABP |
-5.29 |
-2.63 |
I(0) |
Stationary |
Table 2. Augmented Dickey Fuller Unit Root Test Results of Variables
Source: E-views 10. Author’s computation (2024)
The variables exhibit integration in various orders, with the highest order of integration denoted as order (I) and the lowest as order (0).
The results indicate that LACGSF, LAGOPUT, LIDP, and ABP are integrated of order I (0), suggesting stationarity at levels. According to the rule of thumb for unit root tests, if the absolute value of the test statistic exceeds the critical value at 1%, 5%, or 10% significance levels, we reject the null hypothesis of stationarity. Conversely, if the absolute value of the test statistic is lower than the critical value, we accept the null hypothesis.
The empirical results of the OLS estimates of parameters for the model in Chapter 3 are as follows:
AGOPUT = α0 + α1LIDP+ α2LACGSF+ α3ABP
The t-values are presented in parentheses below the coefficients. The reported data include R², modified R², F-statistics, and Durbin-Watson (DW) (Table 3).
LAGOPUT = 7.874330 - 0.134229LIDP - 0.061193LACGSF + 2.84E-12ABP
(11.28533) (4.955949) (-0.976541) (5.374886)
R2=0.901648
R-2=0.878951
F stat=39.72596
D.W=1.754075
|
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
LIDP |
0.134229 |
0.027084 |
4.955949 |
0.0003 |
|
LACGSF |
-0.06119 |
0.062663 |
-0.97654 |
0.3466 |
|
ABP |
2.84E-12 |
5.29E-13 |
5.374886 |
0.0001 |
|
C |
7.87433 |
0.697749 |
11.28533 |
0 |
Table 3. Presents the Regression Findings Utilizing the Ordinary Least Squares Method
|
Value |
Statistic |
Value |
|
|
R-squared |
0.901648 |
Mean dependent var |
9.523158 |
|
Adjusted R-squared |
0.878951 |
S.D. dependent var |
0.230183 |
|
S.E. of regression |
0.080085 |
Akaike info criterion |
-2.00912 |
|
Sum squared resid |
0.083378 |
Schwarz criterion |
-1.81307 |
|
Log likelihood |
21.07753 |
Hannan-Quinn criterion |
-1.98963 |
|
F-statistic |
39.72596 |
Durbin-Watson stat |
1.754075 |
|
Prob(F-statistic) |
0.000001 |
Table 4. The analysis of the table reveals several key insights into the estimated model
The R-squared value, at 0.901648, suggests that roughly 90.16% of the fluctuations in agricultural output can be accounted for by alterations in the independent variables. This suggests a strong relationship between the independent and dependent variables (Table 4).
Following adjustments for degrees of freedom, the adjusted R-squared value remains at 0.878951, indicating that 87.89% of the variations in agricultural output are accounted for by the independent variables. This adjusted value reaffirms the robustness of the model and its suitability for estimation.
The F-statistic of 39.72596 demonstrates statistical significance at the 10% level, confirming the validity and reliability of the overall model. This suggests that the independent variables collectively have a significant impact on the dependent variable, further supporting the model's validity.
The probability associated with the F-statistic is reported as 0.000001, which is significant at the 5% level. This confirms the statistical significance of the model and strengthens the confidence in its reliability.
The Durbin-Watson statistic, measuring autocorrelation, is reported as 1.754075. A value near 2 signifies the absence of autocorrelation in the model, indicating that the independent variables are not correlated with each other. This further enhances the model's credibility.
In conclusion, based on the diagnostics provided, the estimated model appears to be robust and reliable, demonstrating a good fit for the data and offering valuable insights into the relationship between the independent variables and agricultural output.
The analysis of the probability values at the 5% significance level provides insights into the significance of each variable in impacting the dependent variable:
LACGSF: The probability value associated with LACGSF is 0.3466, indicating that it is not statistically significant at the 5% level. With a probability value of 34.66%, the impact of LACGSF on the dependent variable is deemed non-significant.
The probability value for ABP is recorded as 0.0001, signifying high significance at the 5% level. With a probability value of 0.01%, ABP is considered highly significant in influencing the dependent variable.
Likewise, the probability value for LIDP is noted as 0.0003, suggesting high significance at the 5% level. With a probability value of 0.03%, LIDP is deemed highly significant in its influence on the dependent variable.
Additionally, the coefficients associated with each variable further elucidate their impact:
LACGSF: The coefficient for LACGSF is reported as -0.061193, which signifies a negative relationship. However, with a value of 6.1193%, it is deemed highly insignificant statistically.
ABP: The coefficient for ABP is 2.84E-12, indicating a positive relationship. This coefficient is statistically significant at the 5% level.
LIDP: The coefficient for LIDP is 0.134229, representing a positive relationship. It is also statistically significant at the 5% level.
In summary, while LACGSF is found to be statistically non-significant and negatively related to the dependent variable, both ABP and LIDP are highly significant and positively related to the dependent variable. These findings provide valuable insights into the impact of each variable on agricultural output.
Breusch-Godfrey serial correlation lm test: The Breusch-Godfrey (BG) test serves as a diagnostic tool to detect autocorrelation of errors in a regression model. It evaluates whether there is serial correlation in the residuals of a regression analysis. The test extends beyond the limitations of the Durbin-Watson test by detecting autocorrelation up to a specified order, denoted as "p."
The null hypothesis of the BG test is that any order up to p does not exhibit serial correlation in the errors. In other words, it assumes that there is no autocorrelation present in the model. The test statistic generated from the residuals of the regression model is compared to a critical value to determine the significance of the autocorrelation.
Unlike the Durbin-Watson test, which primarily detects first-order autocorrelation, the BG test can identify autocorrelation up to the specified p-order. Moreover, the BG test is more flexible in accommodating a broader range of regressors.
A low p-value resulting from the BG test indicates that the null hypothesis of no serial correlation is rejected. This suggests that autocorrelation is present in the model, and adjustments may be necessary to address this issue for accurate interpretation of the regression results.
|
Value |
Probability Distribution |
Probability Value |
|
|
F-statistic |
0.280582 |
Prob. F(2,11) |
0.7606 |
|
Obs*R-squared |
0.825158 |
Prob. Chi-Square(2) |
0.6619 |
Table 5. presents the results of the Breusch-Godfrey Serial Correlation LM Test
The Prob. F and Prob. Chi-Square values in the table above serve to assess the presence of autocorrelation in the regression model (Table 5).
Since the Prob. F and Prob. Chi-Square values are less than 5%, it indicates autocorrelation.
Conversely, since the F Prob. and Chi-Square values exceed the 5% significance level, it indicates no autocorrelation in the model.
In this case, the Prob. F value is reported as 0.7606, which is above the 5% significance level. Similarly, the Prob. Chi-Square value is 0.6619. Since both values are higher than 5%, it suggests that there is no autocorrelation present in the regression model.
The study looked at how three CBN intervention initiatives - the interest drawback, the Agricultural Credit Guarantee Scheme Fund (ACGSF), and the Anchors Borrowers Program - affected the agricultural industry from 1991 to 2020.
A notable correlation was observed between the interest drawback program and agricultural output.
ACGSF demonstrates a negative association with agricultural output.
Conversely, the Anchors Borrowers Program demonstrates a positive relationship with agricultural output.
These results emphasize the significance of financial support and credit facilities offered by the CBN in fostering agricultural sector growth. Particularly, the interest drawback and Anchors Borrowers Program exhibit positive correlations with agricultural output, highlighting their role in promoting sectoral development.
Therefore, encouraging the CBN to play a proactive role in providing finance and loans could lead to increased agricultural productivity in Nigeria. By enhancing the contribution of banks and improving access to loans for farmers, the agricultural output of the country can be further boosted.