Rural credit access and contract farming nexus in Ethiopia: A meta-analysis

Abstract

This review aims to assess the variability in empirical research conducted on the assertion that access to credit is an exceptional way to overcome some of the financial obstacles associated with contract farming adoption by farmers. Numerous studies have examined how credit access increases the adoption of agricultural technology at the national level. However, because the researchers' findings are heterogeneous, their applicability at the national level is limited. The random effects model showed that access to credit increases farmers’ contract farming adoption by 1.76 units compared to farmers with no access to credit. Additionally, the results of the meta-analysis revealed that the weighted average of the effect sizes in the studies was 40.01%. This indicates that access to credit encourages smallholder farmers to use different agricultural technologies and high-yielding varieties through contract farming. It recommends significant improvements from the government to encourage contract farming and facilitate credit access to assist smallholder farmers in rural areas.

1. Introduction

Access to financial services such as credit is essential for protecting and improving the livelihoods of rural populations. Rural farmers in developing countries such as Ethiopia still need to obtain credit services to adopt technology and improve their production potential [1]. Accordingly, contract farming is practised as an institutional response to flaws in credit access and the expenses of seeking, screening, and transferring products and services [2]. Consequently, poor rural farmers are offered inputs, access to credit, technical assistance, and market services through contract farming. In addition to raising farmers' income, contract farming may positively affect employment, infrastructure, and market development in the local economy.

Ethiopia has 33 microfinance organisations, 16 commercial banks, and two public banks. However, financial institutions cannot help rural farming populations [3]. The possible reasons for this are an inadequate supply of suitable financial products and services and a low level of financial capacity and awareness. Since most farmers in Ethiopia are among the poorest people in the world, providing credit services helps them rise above poverty and ensure their future [3]. If smaller, poorer farmers are willing to pay more for credit, firms will be incentivised to contract with them. Accordingly, contracts are expected to raise farmers' incomes by providing them with access to capital, better inputs, and technical supervision, thereby increasing yields and improving quality [4].

In Ethiopia, previous researchers have concentrated on using a single econometric model to assess the outcomes of contract farming participation decisions, ignoring the effect of credit on contract farming. In addition, these efforts do not solve the heterogeneity problem at the national level. Therefore, combining all findings from different locations is necessary to gain a common understanding. This study was designed to resolve disagreements among various conflicting investigations and enhance the precision of the results based on these contradictory findings. The following review questions are addressed in this study: What lessons have other countries learned about contract farming in Ethiopia? Second, how does credit availability affect contract farming in Ethiopia? A quantitative analysis (meta-analysis) was conducted to address the review questions and bring about the anticipated national changes. This study has significant implications for policymakers, farmers, researchers, and financial institutions as it concentrates on enhancing rural financial credit services in Ethiopia.

2. Structure of the article

This paper is organised into four sections. Section 1 provides a general introduction to this study. The following section presents an overview of the research methodology and analysis. Section 3 highlights the results, discussion, conclusions, and recommendations. The last section is the reference section.

3. Methodology

3.1. Article identifications

Google Scholar and Science Direct were used to search for literature on this topic. The articles that applied to the case under consideration were identified in the first phase. However, the analysis procedure did not consider exclusion criteria such as duplicated papers, theses, and dissertation work and the absence of credit access and contract farming measurements within articles to maintain methodological similarities between each study. Finally, PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) was used to identify 27 articles for inclusion in the meta-analysis, as indicated by the four-phase flow diagram shown in Figure (1) below.

Fig. 1.

PRISMA flow chart.

Source: Author computation based on the available data

3.2. Study protocol

The inclusion and exclusion criteria were used to identify articles that addressed the goals of the study [5]. The outcome of interest was established before the start of article identification. For this study, it was crucial to identify studies that associated contract farming with access to financial services in Ethiopia and other nations. Accordingly, the inclusion criteria were studies from Ethiopia and other nations published in high-impact journals. This study focuses on the types of contract farming and the implementation of credit access among smallholder farmers in various nations. A scale-up technique for other nations was employed to determine the exclusion criteria based on review papers, books, and other information received from Ethiopian government official [5].

3.3. Data browsing

We conducted a thorough search of the Science Direct and Google Scholar databases. We searched for terms such as credit access and contract farming based on highly regarded publications, and then screened the downloaded articles.

3.4. Data extraction process

Using the standardised form ensured data accuracy and uniformity. Finding relevant research articles in reputable publications considered for a meta-analysis is the goal of data extraction [6]. Data was extracted based on study design, focus, type of output, sample size, number of independent variables utilised, number of significant variables found, and statistical models employed in the reports. Data extraction covered all forms of contract farming; however, this study focused on centralised contract farming, in which one central buyer entered into agreements with several farmers. These were reported as cases after data were removed and aggregated.

3.5. Statistical analysis

STATA and SPSS software version 14 were used for the data analysis. Qualitative and quantitative data were collected. Frequency and percentage metrics were used to analyse the qualitative data. The intervention variable in this study was access to credit services, while the outcome variable was contract farming adoption. Three models can be used in a meta-analysis: random, fixed, and mixed effects. The random effects model was chosen based on the variability of the investigations [7].

In meta-analyses, heterogeneity can occur because of variations in the way the intervention variable affects the outcome variable, which can be brought about by variations in the study areas, target groups, sample sizes, product types, and the number of independent variables used in various studies [8]. A value of 12 was used to measure heterogeneity (12 = 0, no heterogeneity; 12 = 0–50 %, medium; and 12 = > 75 % solid heterogeneity) [9]. A random-effects model is used when heterogeneity is observed. In contrast, low heterogeneity necessitates the use of either a fixed effect or a combined effect model. Given the significant heterogeneity among the studies, a random-effects model was used in this analysis. The odds ratio log represents the effect size of the pooled 27 observations. Harbord and Peters tests were also performed, along with a meta-regression analysis using the log odds ratio as the dependent variable and the other four covariates [1].

4. Results and discussion

4.1. Article selection overview

Table 1 shows that the studies' median sample size was 281.88, with minimum and maximum sample sizes of 75 and 610, respectively. The studies also employed a mean of 12.11 independent variables, with a minimum of 8 and a maximum of 19, as well as cross-ponding to a minimum of 2 and a maximum of 9 independent variables that were significantly impacted by the availability of credit while smallholder farmers engaged in contract farming. A review of the findings reveals that smallholder farmers of various crops earned an average credit of 35.64 %, with a standard deviation of 22.64. This demonstrates that most smallholder farmers in rural areas need help to obtain credit access to purchase agricultural inputs during the farming seasons. This finding aligns with that of [1], who found that credit strength improves farmers' financial capacity to purchase inputs such as seeds, equipment, fertilisers, and dairy feed through contract farming.

Table 1.

Descriptive summary of continuous variables.

Variables	Mean	St. Deviation	Minimum	Maximum
Sample Size	281.88	137.74	75	610
Number of Independent Variables	12.11	2.78	8	19
Number of significant variables	6.26	1.87	2	9
Credit access used in percentage	35.64	22.64	10	94.2

Source: Author computation based on the available data

The research review employed both inclusion and exclusion criteria to select papers for the meta-analysis. Study's focus was on access to credit through contract farming, which was considered for the review. Finally, 27 studies were included in this meta-analysis (Table 2). Firms select contract farming for various reasons, and the types of contracts they select reflect their goals. One of the inclusion requirements for this study was a contract that provided resources. According to the chosen sample article, resource-providing contracts force the processor to provide various forms of input, extensions, or credit in return for a marketing agreement. Most researchers agree that firms can obtain such deals because premier farmers are willing to pay for them. In exchange for financing, businesses can purchase raw agricultural products at costs below the market [10]. The study's conclusions are consistent with the idea that enterprises are incentivised to contract rather than use spot markets based on the transaction costs associated with the credit market [11]. Businesses can employ several institutional arrangements to supply raw products for processing or sale claims [12]. The degree to which each input supply and production step is integrated under contract farming ownership varies across these arrangements.

Table 2.

The final identified studies for the meta-analysis.

Authors	Product Type	Model used	Study area	Sample Size	Credit access (%)	No. of Independent Variables Used	No. of significant variables
[13]	cashew	Probit	Ghana	391	16.1	18	9
[14]	Seed Corn	Probit	Indonesia	350	10.9	14	8
[15]	Food	probit	Vietnam	460	14.8	12	5
[11]	Vegetable	Probit	Ethiopia	384	25	9	6
[16]	Honey	OLS	Ethiopia	195	42	12	6
[17]	Honey	Probit	Ethiopia	180	35.5	11	5
[18]	Honey	Probit	Kenya	180	47.6	13	4
[12]	Barley	Login	Ethiopia	312	94.2	13	7
[19]	Food	Probit	Ethiopia	262	12.8	13	6
[20]	Food	Probit	Cambodia	75	15.5	10	4
[21]	Sugar	Logit	Ethiopia	383	25.5	12	8
[22]	Sesame	SEM	Ethiopia	200	18.5	8	3
[23]	Barley	Probit	Ethiopia	384	10	11	6
[24]	Barley	Login	Ethiopia	398	35.5	15	7
[25]	Food	Logit	Benin	400	17.5	10	2
[26]	Avocado	Probit	Kenya	100	81	15	8
[27]	Chickpea	Login	Ethiopia	150	27.5	11	4
[28]	Honey	OLS	Ethiopia	412	35	12	7
[29]	Poultry	Login	Kenya	180	43.5	14	6
[30]	Beef Cattle	PSM	China	610	68.4	10	7
[31]	Cotton	Login	Zimbabwe	100	42.5	9	7
[32]	Rice	Stochastic Frontier	Ghana	350	26.5	9	5
[33]	Maize	Probit	Nigeria	361	64.3	19	9
[34]	Tobacco	Login	Zimbabwe	388	29.6	13	9
[35]	Tobacco	Logit	Tanzania	150	34.5	10	5
[36]	Coffee	multinomial logistic (MNL) regression	Vietnam	183	43.7	8	6
[37], 2020	Barley	Logistic	Ethiopia	98	67.5	12	6

Source: Author computation based on the available data

4.2. Contract farming adoption in africa

In Ethiopia, contract farming has been practised for over 20 years, but still needs improvement. For the last ten years, contract farming has been an agricultural commercialisation instrument in Ethiopia that transforms substance farming into high-value, export-oriented agrarian output [24]. It is a business tool for increasing food security, knowledge sharing, and loan accessibility. Although Ethiopia has minimal experience with contract farming in terms of regulations, the government stated its most profound concern and saw it as a method to raise the value of the nation's exports [38].

According to Fig. 2, four African countries, Ethiopia, Kenya, Zimbabwe, and Benin, have extensive experience in contract farming for crops, including honey (14.81 %), barley (14.81 %), food (14.81 %), and vegetables (7.41 %), through agreements by agreeing with producers and firms. Another East African country, Kenya, is well known for its contract farming of avocados, chickens, and honey [29]. The Zimbabwe Cotton and Tobacco Company, another public company founded in 1969, was the largest company when it began its contract farming programs in 1992. Ghana and Benin, two Western African nations, are well-known for their extensive contract agricultural operations that produce large amounts of rice, food, and cashews. Farmers' ability to boost productivity and income is limited by the need for more credible information on effective production technologies and market prospects, because Sub-Saharan Africa's agricultural market could be more stable and frequently better. Even if the information is accessible, individuals are still denied credit access because there is no collateral, and interest rates are too high. They cannot afford to embrace new technology because it is too expensive. As a result, contract farming gives farmers, especially smallholder farmers in Sub-Saharan Africa, access to high-quality production services, credit, the right equipment, and market opportunities that they would otherwise not have had [39].

Fig. 2.

Contract farming practices in different countries.

Source: Author computation based on the available data

4.3. The effect of access to credit on contract farming

Fig. 3 shows the results of the random effects model for the impact of loan availability on contract farming among smallholder farmers in rural areas. The log odds ratios describe the average effect size or login. According to the model's results, contract farmers who have access to funding adopt these practices at a 48 % higher rate than those who do not. Thus, farmers may finance the acquisition of different inputs such as chemicals, fertilisers, pesticides, and advanced agricultural technology to improve their standard of living. This outcome aligns with findings [40] that show that credit enhances farmers' capacity to finance the purchase of state-of-the-art farming equipment, thus increasing the amount and calibre of agricultural production.

Fig. 3.

Random effects model output.

Source: Author computation based on the available data

Furthermore, the model yielded an I2 value of 95 % within the heterogeneity range, with a P value of 0.000. This implies that the influence of finance availability on contract farming adoption differs (is heterogeneous). Consequently, the output of the random effects model should include a succinct explanation of the possible reasons for the disagreement between studies. Table 3 illustrates the results of the meta-regression analysis used to determine the causes of heterogeneity. The results confirm the finding that credit access enhances technology adoption [1], in which the analysis was performed based on the random effects model.

Table 3.

Meta-regression analysis output.

Log(0R)	Coefficient	Standard Error	t	P>/t/	[95 % Confidence Interval]
Distance from Contract farm	−0.02**	0.08	0.25	−0.87	−0.13	0.002
Extension Contact	0.12**	0.03	4	0.02	0.21	0.04
Total Income	0.01**	0.001	10	0.00	0.01	0.013
Constant	1.16	0.31	3.74	0.002	0.63	1.78
Several obs. = 27			Adj R-squared = 97.95 %
tau square = 0.05			Model F(5,21) = 93.3
I-squared residual = 31.32 %			Prob > F = 0.0000

***p < 0.01, **p < 0.05.

Source: Author computation based on the available data

4.4. Meta-regression analysis

The results of the meta-regression analysis are presented in Table 3, which explains the heterogeneity among the studies. More than 90 % of the studies, based on data already available, found that the main factors influencing the effect of credit availability on contract farming were annual income, engagement with extension agents, and proximity to contract farming. These three criteria were employed as indicators to consider various treatment results. Nearly all the differences between the studies appear to have been caused by all the factors, according to Table 3's lower I-squared value of 31.32 % and square value of 0.05. A P-value of 0.000 for each variable in the joint test indicated a possible correlation between the treatment effect and at least one covariate.

Additionally, an adjusted R2 value of 97.95 % from the regression analysis indicated that these factors were primarily responsible for the variation in the study. This is consistent with the findings of [16], who found that farmers' ability to use credit for contract farming is influenced by credit access. Moreover, the log odds ratio and total livestock holdings had an inverse relationship that was significant at the 1 % level.

At the 95 % confidence level, the findings demonstrate a negative relationship between smallholder farmers' distance from contract farms and the log odds ratio. Due to growing transaction costs, the benefits of contract farming diminish as one moves farther away from the nearest contract farm. This conclusion is consistent with that of [40], who highlighted how transportation costs increase with the distance from contract farms. Furthermore, contract farmers cannot travel to remote locations because of the lack of infrastructure in rural areas, which limits their capacity to reach agreements. Furthermore, a significant correlation was found between the frequency of extension contact and the log odds ratio at the 95 % confidence level. Based on the sample articles used in this study, most researchers agreed that regular extension contacts and advisory services are essential for sharing information on dairy contract farming, including product prices, contract farming locations, and agreement requirements. The spread of information on credit access and contract farming is directly affected by the frequency of extension contact [41], and our results confirm these findings. Therefore, total income has a favourable impact on the log odds ratio and is considerable at the 5 % level. The model's output demonstrated that a unit increase (thousand) in total revenue causes a 0.01 ceteris paribus increase in the log odds ratio. This confirms the results of [42], who discovered that a high total income demonstrates farmers' wealth and allows them to obtain credit to practise contract farming. Impoverished rural farmers have a greater chance of addressing credit availability. Contract farming requires the assistance of smallholder farmers.

4.5. Publication bias diagnostic test

Asymmetric funnel plots are often used to assess whether meta-analyses have a publishing bias. However, by definition, this kind of visual interpretation is subjective. For minor research effects, Galbraith plots and other statistical tests were used. Publication bias can be caused by various factors including chance, true heterogeneity, poor methodological quality, and reporting bias. Based on these findings, 95 % of the studies were nearly identical and closely matched the regression line (red line). The slope of the regression line over the origin in Fig. 4 represents the total effect size. Because every study in this review followed a regression line, no outliers were likely to stand out. We need to look for similarities between these study findings. This aligns with the findings of a study [43]. Larger sample sizes in most studies are apparent at the top of the graph and are grouped around the mean effect size. The graphs show studies with smaller sample sizes that were usually distributed over a broad range of values. The meta-analysis indicates that asymmetry in research is typically distributed toward the origin. This finding confirms the validity of this study. The studies shown in Fig. 4 did not exhibit publication bias because all effect sizes were accurate. An asymmetric funnel plot suggests that other factors should be investigated, which calls into question the validity of this simple meta-analysis.

Fig. 4.

Galbraith plot.

Source: Author computation based on the available data

5. Conclusion and recommendation

Access to credit is a critical factor in improving contract farming. This can increase the income of those living in poverty and advance rural development. This study focused on loan availability for smallholder farmers across several nations and contract farming arrangements determined by inclusion and exclusion criteria. Data were extracted in a standardised form to ensure accuracy and uniformity. Data were processed based on the study design, focus, type of output, sample size, number of independent variables used, number of significant variables found, and statistical models used in the reports. Three models–random, fixed, and mixed effects–were employed in the meta-analysis, based on the variability of the investigations. STATA and SPSS software version 14 were used for all analyses.

Based on the findings and random effects model output, financing availability increases contract farming adoption by 48 % compared to farmers without access to credit. Farmers with credit access can employ better crop varieties and modern agricultural technologies, because they are practically applied in Ethiopia. According to regression analysis, more than 90 % of the studies revealed that proximity to contract farms, interaction with extension agents, and annual income were the primary determinants of the impact of credit access on contract farming. Accordingly, income, frequency of extension contact, and distance from contract farm were the primary variables that explained how access to credit affected smallholder farmers' ability to engage in contract farming. According to the Galbraith plot, 95 % of studies were nearly identical and closely matched the regression line (red line). Based on these results, the government should create rural financial institutions by enlisting private organisations that offer only lending and saving services to farming communities. Information and training centres for farmers are also necessary to increase farmers' awareness and credit usage.

Data availability

Data will be made available on request.

CRediT authorship contribution statement

Fikiru Temesgen Gelata: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Jiqin Han: Writing – review & editing, Visualization, Validation, Resources, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.