Transforming South African Agriculture: The Role of Credit in Supporting Value Chain Sustainability

Abstract

Access to credit is essential for transforming South Africa’s agricultural sector, particularly in enhancing value chain sustainability. This study investigated the role of credit access in supporting smallholder farmers’ value chain sustainability, as part of a broader project focused on developing a credit risk model for South African farmers. Data were collected from 223 SAFDA farmers in KwaZulu-Natal and Mpumalanga using a structured questionnaire. The average treatment effects (ATEs) of a propensity score matching (PSM) model was used to estimate the impacts of credit on the following four key variables: farm ownership, farm size, farm income, and farm assets. The results showed that farm ownership was associated with credit access, as ownership provided 1.84 times the chances of loan approval. Additionally, farm income increased by ZAR 2,849,398 for credit recipients compared to non-recipients. This income boost enhances market linkages and food value chain sustainability. This study rejects the hypothesis that credit access has no impact on smallholder farmers, highlighting its vital role in promoting agricultural development and value chain growth. It is recommended that policymakers enhance credit access and risk mitigation strategies to further support smallholder farmers. To improve access to credit for smallholder farmers in South Africa, we recommend the following measures: (1) establishing credit guarantee schemes in partnership with financial institutions to reduce lending risks; and (2) implementing financial education programs for smallholder farmers to enhance their debt management skills. Credit access has the potential to promote positive change across economic, social, and environmental aspects, improving not only the livelihoods of smallholder farmers but also contributing to broader sustainable development goals.

1. Introduction

Agriculture is an inherently risky sector, as well as agricultural lending. Therefore, it becomes even more challenging when lending institutions offer finance to smallholder farmers, who are primarily associated with low-value crops and who have limited-to-no access to the buyer market. Institutions must understand the cash flow of a smallholder farmer, as this aids in the provision of suitable payment terms and risk mitigation tactics. Financing agriculture through the value chain is defined as a flow of financial products and services on different levels of the value chain, which in turn leads to reduced risk and improved efficiency for the participants. This finance is not onerous as credit is based on relationships over collateral with flexible repayment terms. A study assessing the efficiency of these services on 400 smallholder farmers in Ghana showed that participation in agricultural value chains with extension services increased access to formal credit by 64% for participants when compared to non-participants [1,2,3]. A study by [4] on agri-financing by microfinance institutions that provided agricultural lending to smallholder farmers highlighted the following as good practices for lending in this sector:

i.

Understanding the borrower—Lenders should know their clientele; this is not only achieved through upfront market research but also on-going attention to client feedback.

ii.

Flexible products—Lending to smallholder farmers is not a one-size-fits-all phenomenon because some farmers have diversified income through diversified farming products and/or non-agricultural income. Also, the types of crops produced from one farmer to the next can vary in cycles. Therefore, the institution must adapt to this diversification to provide payment terms and risk mitigations aligned to each farmer.

iii.

Cash flow analysis—Cash flow analysis of the borrower aids in accurate repayment terms and determination of the true lending risk involved.

iv.

Diversification of risk management strategies—Due to varied risks in the agriculture sector and lending, risk therefore needs to be mitigated using a variety of approaches such as field client monitoring, portfolio diversification, stress-tested cash flow analysis, production insurance, and use of credit bureaus and/or credit rating agencies. Collateral, such as land, should not be the only main deciding factor in whether to lend to a smallholder farmer or not.

v.

Specialized credit officers—It is imperative to employ credit officers who have an academic background in agriculture. However, where it is impossible to find such officers, agriculture-related training should be of consideration.

vi.

Different targets for commercial and smallholder farmers—To prevent credit officers from only helping commercial farmers, who are easier to lend to and monitor, lending institutions should set two sets of targets for commercial and smallholder borrowers. There should be incentives for credit officers who meet these targets.

vii.

High level of institution buy-in—Successful rural farmer lending requires a specialized approach tailored for the sector; this involves investing in evaluation tools and systems distinct to this sector, which requires a strong institutional commitment.

The South African National Treasury [5] described financial inclusion as a provision and usage of suitable financial services to segments of society who are excluded from these financial services. The country’s government had various funding projects targeted at assisting predominantly emerging farmers, while the commercial banks have, over the years, focused on financing commercial farmers [6]. Despite these governmental funding interventions, smallholder farmers have encountered enormous challenges in finance service accessibility. Several rural smallholder farmers have remained in poverty with restricted availability to credit [7].

According to [8], some of the challenges faced by smallholder farmers have been the failure of land and agricultural policies supporting the sustainable livelihoods of the rural area residents, thus leading to a lack of finance. This remains a crucial challenge. Governmental interventions have collapsed due to a lack of management. Therefore, it is vital to include smallholder farmers in credit access in formal financial institutions. However, to provide credit, an understanding of the smallholder farmer and factors affecting access to credit will enable credit policymakers to adjust credit policies to meet the needs of smallholder farmers [7]. Balana and Oyeyemi [9] acknowledge key constraints as bankable collateral in credit access. However, the study emphasized information availability as a major hindrance as smallholder farmers do not have information on crop insurance, which the Nigerian Government subsidizes with 50%. This, together with extension services, can improve the credit granted, which will in turn increase the adoption of technology that enhances productivity. Credit development is categorized as the crucial component towards agricultural development in developing countries. A successful credit development process is through the agricultural finance value chain.

A value chain involves a full range of activities, from bringing agricultural produce from production to consumption. In the agricultural value chain, credit access is the crucial component for smallholder farmers to participate in the value chain. Value chain finance should not, however, be treated as finance from the traditional banking institutions [10]. It is said that access to credit plays a role in each stage of the value chain. The stages of the value chain are input supply, production, processing, marketing/distribution, and consumer demand. Financial inclusion in input supply involves farmers having access to quality input so as to improve the quality of production. With financial availability, a farmer can decide on the product to produce based on the clientele demand. Finance can enable the processing of the produced commodity and its distribution to the market for consumers. Adequate access to funds in the value chain will lead to a sustainable agricultural industry which is resilient to risks that are prone to farming.

Studies on financial inclusion for smallholder farmers have emphasized that farming is heavily reliant on credit. However, the farmers who received credit provision were positively impacted as follows: (1) the impact on farmers’ socioeconomic factors, which includes positive effects on poverty reduction through food security, increased income, increased savings, and creation of job opportunities; (2) introduction of new technology on the farm which will reduce costs of production and increase output produced and the quality of the produce; (3) the farmer can enhance their personal development through obtaining information on best farming practices, financial and day to day management of the farm; and (4) sustainable agricultural practices despite challenges prone to farming that make farmers resilient [11,12,13,14,15,16]. Therefore, the objective of this paper was to examine if a study in a South African context aligns with the research findings on the impacts of credit.

This paper forms part of a study aimed at assessing credit accessibility for South African smallholder farmers. This study was four-fold, analyzing the status quo of smallholder farmers in credit assessment, factors leading to the decision to apply for a credit application, creating a credit application outcome model, and finally, analyzing the impact credit inclusion has on smallholder farmers. Although the relationship between access to credit and agricultural development is well documented, our study distinguishes itself by analyzing the impact of credit through a propensity score matching model applied to a specific South African context. Additionally, we examine the role of financial institutions and agricultural cooperatives in facilitating credit access, an aspect that has received less attention in the existing literature.

This study is organized into seven (7) sections. The first section introduces the financial inclusion of smallholder farmers in agricultural credit value chains, and the second section provides a literature review with the agricultural value chain as a conceptual framework. The third section describes the study area, data collection, and models that were used to assess credit accessibility for South African smallholder farmers. The fourth section contains the main findings of this study, offering insights into the challenges South African smallholder farmers face in accessing bank credit. This section also discusses the findings in relation to all the set specific objectives. The fifth section provides the conclusions drawn from this study aimed at improving credit accessibility by smallholder farmers based on the study findings. Section 6 and Section 7 focus on the limitations and future study research.

2. Literature Review of Innovative Ways of Financial Inclusion for Smallholder Farmers

Financial institutions are the heart of the country’s capital and money flow. Therefore, the provision of credit to the bank’s clients affects the flow of money in the country and reduces financial crises, as lack of capital or finance can be a significant constraint in the growth of the economy. Credit facilities offered to credit applicants form a huge part of the bank’s assets, which is why policymakers, supervisors, and investors must be attentive to the warning systems of the bank. A significant warning system is a default on a credit facilities payment as it can result in a financial crisis. Banks have, therefore, used various credit rating models to distinguish the probability of repayment and non-repayment before granting the facility to mitigate the risk of non-repayment and to provide credit to customers who have a low risk of defaulting on repayment [17].

According to [17], 15–20% of the agricultural sector credit facilities which were granted from 2007–2017 by the Bank Melli of Iran were either not repaid or had several defaults. It is, thus, crucial to investigate the factors that affect the repayment of agricultural credit facilities. Studies have mentioned the below as some of the determinants for defaulting on credit facilities:

Interest rate: Higher interest rates mean that a monthly repayment will be high, which in turn affects payment in the long run [18]. However, a low interest rate, moratorium, and monitoring of the borrowers post-payout have had a positive influence on the repayment of loans of South African smallholder farmers.

Financial statements: Karimzahdeh et al. [17] highlighted that provision of financial statements by farmers plays a huge role in determining repayment of bank facilities. The financial statements give ratios which banks use to assess repayment. This comparison with past financial statements gives the bank a clearer picture.

Period of repayment: A longer period for repayment of debt provides a smallholder farmer with a lesser cash flow strain, which increases repayment ability.

Total debt exposure: An increase in the total liabilities of the borrower can increase the chances of defaulting, whereas the lower the debt, the better the chances of repaying the credit facility.

Activities other than farming: Smallholder farmers, who had activities other than farming, such as full-time employment, were likely to default on the credit previously granted [17].

Guarantor: Pishbahar et al. [19] highlighted that credit facilities where a person was a guarantor instead of collateral were more likely to be repaid.

The three innovative funding models proposed are financing farmers, movable collateral, and buyer. International Finance Corporation [20] highlighted that the farmer financing model relies on the farmer’s overall repayment ability, which is established based on cash flow and savings or guarantees. The movable collateral financing model is for movable assets, and the buyer financing model is through value chains or trade finance through assessing the buyer of the smallholder farmers’ produce.

Financing farmers model: The financing farmers’ model can be achieved directly where funds are directly borrowed from the farmer or indirectly, which is also known as a wholesale model where funding is provided through co-operatives that provide inputs. The benefit of direct funding is that it offers a farmer with a wide range of financial services, such as transactional accounts, and value-added banking benefits, while the latter only provides credit [21]. For this model to yield positive results, the lending institution must understand the borrower’s cash flow dynamics, as smallholder farmers usually do not have tangible collateral. Risk can be mitigated by financial institutions putting a cap on the loan amount that can be granted to the farmer. A success story of direct lending is that of the Equity Bank of Kenya. The bank’s target is to finance 2,500,000 developing farmers with minimal to zero collateral needed for the facility. In 2008, the bank had paid out over USD 18 million to 37,000 beneficiaries [20].

Movable collateral financing model: The company’s constitution does not allow reckless lending through extending finance to a company that is insolvent or making historic losses. Therefore, a farmer who wants finance for long-term assets which require collateral may not receive finance if there is no security offered. However, with the financing movables model, there is an option for a smallholder to access leasing assets. This aids in resolving credit constraints and proves to be the safest model because if the farmer defaults or does not pay, the assets will be repossessed by the owner [22].

Buyer financing model: Qwabe [8] highlighted that the Standard Bank of South Africa finances smallholder farmers through collaboration between private and public partnerships where the bank assists with production facilities. The partnerships range from prominent co-operatives and insurance companies which provide insurance as a production risk mitigant. The advantage of financing a farmer in a value chain is that creditworthiness does not rely on the farmer’s credit background but rather on the strength of the buyer through agreed-off-taker agreements. Moreover, the farmer never needs a working facility, and their income becomes easy to predict. The negative side of the buyer financing model is that any negative thing happening on the buyer’s side will affect the farmer’s repayment. A study conducted by [23] highlighted that a successful financial inclusion for smallholder farmers involves de-risking lending products such as (1) the Khula Credit Guarantee Scheme, which is suitable for borrowers who do not have the collateral required by the lender, and (2) the Land Reform Empowerment Facility which makes use of reputable lenders such as wholesalers and commercial banks—this is for land reform beneficiaries.

2.1. Impact of Financial Inclusion on Smallholder Farmers

Although there are constraints in the financing of smallholder farmers globally, there are other countries that have successfully provided credit to their SMEs. This has opened a channel of cross-selling opportunities, not only on the borrowing side but also on the transactional side. Best practices within agricultural finance will result in positive outcomes such as financial sustainability and improved access to clients. Several countries have adopted the strategy of boosting the economy through financing smallholder farmers and non-agricultural small businesses. Countries such as Thailand, Colombia, Bolivia, Brazil, Indonesia, India, Namibia, and Nigeria have continuously provided finance channels [24,25,26,27,28,29].

In Thailand, the Bank of Agriculture and Agricultural Co-operatives (BAAC) was established with the primary role of providing financial assistance to individual farmers, associations, and cooperatives with an aim of uplifting the livelihoods of these farmers. BAAC has continuously implemented ways to better equip the rural poor farmers through finance [24]. In a study conducted by [30], of the 170 respondents, about 93.5% had access to microfinance from BAAC, 84.7% took funeral insurance, and 72.4% had a savings proportion. The bank not only gave credit to these farmers but also provided training courses, such as the production of fertilizers and insecticides. In addition to the cross-selling opportunities that arose, the study found that a portion of farmers defaulted on repayment of the credit received.

The cases of best practices outside the country highlight the important aspects that the commercial bank should consider when dealing with smallholder farmers:

i.

The credit approval approach should not be compared to that of commercial farmers, who are established in the industry with better technology and equipment that aids in good quality produce. This will include having a dedicated department that deals with agricultural small business enterprises.

ii.

The level of interest charged to smallholder farming businesses should be competitive while also protecting the interests of the bank.

iii.

The value-adds such as technical training or financial management play a crucial role in the profitability improvements of a small business enterprise.

Research on the impact of financial inclusion has shown a positive relationship between credit and (1) socio-economic factors, (2) agricultural output through technology adoption, (3) increased income, and (4) the human development index.

Socio-economic factors: South Africa’s inequality Gini coefficient is 0.63, which makes it one of the most unequal countries in the world. South Africa thus suffers high levels of poverty and unemployment [31]. According to [16], access to finance for smallholder poultry farmers had positive impacts on the creation of employment through increased production size. Therefore, improvement in employment levels can serve as a measure to reduce poverty levels in rural areas and contribute to measures taken to resolve the country’s inequality.

Agricultural output through technology adoption: Teye and Quarshie [32] conducted a study on the impact of finance on technology adoption, agricultural productivity, and rural household economic well-being in Ghana. The study highlighted that credit has acted as a catalyst in technology adoption; 61% of the farmers in the study emphasized that with finance, they were able to invest in high-quality seeds for the farming produce and improved technologies that enhance production cost efficiency.

Research on the impact of credit access showed evidence of improved productivity [33,34], but a study conducted in Afghanistan emphasized that credit accessibility had positive impacts on the farmer’s income generated. However, this effect is improved when credit provision is in conjunction with extension services [35]. The study highlighted the importance of credit access coming from formal sources as informal agricultural credit showed a decline in net income. This could be a result of this type of finance being expensive (higher interest rate), and there is a significant decrease in farming revenue for farmers with no credit accessibility. Farming input costs also increased with credit as farmers invest in improved inputs, which translated to improved productivity.

Increased income: A study by [36] stated that credit participation increased farmers’ turnover from USD 116.61 to USD 136.89. The increase in turnover could enable savings of the surplus income, which can be used to acquire assets, invest in entrepreneurial education, build resilience, and mitigate any shocks or stresses that enhance the participants’ livelihoods [37]. A substantial increase in income can unlock untapped markets by smallholders where these producers can invest in improved packaging, and therefore, their produce can be sold in chain stores and or export markets, thus bringing foreign currency into the country.

Human Development Index (HDI): This index is used to measure sustainable development in a country. Therefore, the higher the HDI, the better and more sustainable development of the country is. This index includes life expectancy, education, and per capita income. A study by [38] posited a positive relationship between financial inclusion and HDI that has an impact on sustainable development. Men et al. [18] stated a need for simplified credit documentation from credit providers as this is one of the constraints in credit accessibility, including high interest rates, agricultural credit information, and collateral.

2.2. Conceptual Framework: The Role of Credit in Agricultural Value Chains

This research draws on insights induced from the financial inclusion theory. Financial inclusion has been categorized as an enabler for seven of the seventeen Sustainable Development Goals (SGBs). Inclusion is defined as the means by which individuals and/or businesses have access to meaningful and reasonably priced financial services and products tailored to their needs and available in a maintainable manner [39]. Financial inclusion has been limited for smallholder farmers for a number of reasons, such as (a) the distance from the farms to the nearest commercial bank, that is, there are no banks in the rural areas; therefore, smallholder farmers are hindered from obtaining these services [40] and (b) trust that smallholder farmers can repay debt granted should financial institutions finance this sector for technological investments. This mistrust has resulted in barriers to obtaining through high pricing (interest rate), high security, or capital requirements [32]. However, theories in financial inclusion have highlighted who should be a beneficiary of financial inclusion above, the four theories of beneficiary, as follows: public good theory, dissatisfaction theory, vulnerable group theory, and system theory [41].

Ozili [41] explained that under the public good theory, financial inclusion is viewed as a service, which should be enjoyed by all persons and businesses. This is achieved through free transactional costs for a transactional account. The system should then be triggered to offer other debit or transactional cards where the government subsidizes the banks so that all customers can enjoy free banking. The biggest issue with this theory is that public funds used as a subsidy could have been used for important economy-stimulating growth projects, which are needed in many developing countries. The dissatisfaction theory revolves around the idea that it is easy for financial service providers to bring back clients who once banked with the institution but left due to dissatisfaction. This theory prioritizes individuals who were once serviced by the institution. The theory, however, does not mention making financial services available to all and assumes that lack of participation is due to dissatisfaction. The vulnerable group theory argues that financial inclusion should be for vulnerable communities such as the poor, women, and the elderly. Issues with this theory are that even persons who are not vulnerable need access to finance, and targeting only these individuals will increase the inequalities prevalent in developing countries. The last beneficiary stated by [41] is the systems theory. This theory acknowledges existing economic, social, and financial structures. It highlights that inclusion outcomes are achieved through these existing structures and that they are all interlinked, where if something happens on one subsystem, the whole subsystem will be affected.

The financial inclusion theory emphasizes the country’s government’s responsibility to ensure that the delivery of financial services reaches all. The figure below (Figure 1) shows the three pillars of financial inclusion that the South African government has proposed. The pillars are deepening financial inclusion for individuals and financial inclusion extended to SMMEs. This is the pillar the smallholder farmers will be operating under, as well as a more diversified provider and distributor base. The National Treasury [5] has proposed ways of ensuring financial inclusion for South Africans.

i.

To deepen inclusion for individuals, it was proposed that the benefits of making use of transactional bank accounts should be promoted rather than not banking one’s cash. Therefore, the aim is to explore reasons for not having accounts and to provide effective cash-in methods for individuals; also, through improving financial institutions’ efficiencies in the onboarding of clients so as to ensure less documentation is required for onboarding as the bank’s system should be linked to a centralized validation system of Home Affairs.

ii.

Extending financial services access to SMMEs by improving access to credit by building a credit infrastructure for small businesses, including smallholder farmers. This can be achieved by reforming security/collateral requirements for required credit facilities through the development of suitable specific agricultural insurance.

iii.

Solidification of financial co-operatives and development of the microfinance sector will help in achieving the third pillar of financial inclusion. This includes permitting new entrants who aim to provide financial services to join the banking industry.

Figure 2 illustrates the central role of credit in transforming agricultural value. Credit access is the core enabler mechanism at all stages of the value chain, namely input, production, processing, marketing, and consumer demand. Credit access not only promotes transformation within the agricultural sector but, in turn, leads to sustainable agricultural practices and creates farmers that are resilient to changes that come with uncertainty in the agricultural sector.

3. Methodology

3.1. Research Design and Rationale

The objective of this study was to examine the role of credit in ensuring a sustainable value chain. This research is part of a broader effort aimed at developing a credit risk model for smallholder farmers in South Africa and to present innovative finance ways that financial institutions can adopt to promote financial inclusion for these farmers. Due to the nature of the study objectives, a descriptive research approach was adopted [42]. In addition, the data for this study were collected at a single point in time, classifying the research as cross-sectional.

3.2. Description of Study Areas

The research was conducted in two provinces of South Africa, Mpumalanga and KwaZulu Natal. These provinces were selected due to their significant contributions to the agricultural sector. These provinces rank third and fourth amongst households involved in agricultural activities in the country [43] and diverse agricultural activities are practiced in these provinces. Primary data were collected from the South African Farmers Development Association (SAFDA) because of its pivotal role in enhancing rural economies by not only focusing on the sugar cane industry but also aiming to influence smallholders’ greater participation in different sectors. Since its recognition in the sugar industry, the association has delivered much-needed changes to its members, such as reduced haulage costs, reduced fertilizer costs, and reduced diesel [44]. Moreover, the SAFDA database consists of economically active smallholder farmers. Figure 3 below shows the nine provinces of South Africa, highlighting Mpumalanga and KwaZulu-Natal, where this study was conducted.

3.2.1. KwaZulu Natal

KwaZulu Natal is the second most populous province in South Africa, after Gauteng, with a population of over 11 million. Approximately, 18.2% of the households in the province are actively involved in agriculture [43]. Spanning an area of 94,361 km², KwaZulu Natal contributes about 16% to the country’s gross domestic product (GDP), making it the second-largest economy in South Africa [45]. The province boasts diverse agricultural activities, with sugarcane being among the largest in the country, yet there is an enormous potential for agricultural expansion, which if fully utilized, could drastically increase yields [45].

3.2.2. Mpumalanga

Mpumalanga Province, although the second smallest in South Africa after Gauteng, is the fourth-largest economy in the country. According to [46], the area size of the province is 76,495 km^2, with a total population of over 4 million. Mpumalanga Economic Growth Agency (MEGA) [47] highlighted that the province is one of the country’s’ most productive and important agricultural regions, which also exports produce such citrus and nuts. In addition, 28.1% of households in the province are involved in agricultural activities [43].

3.3. Sampling Procedure, Sample Size and Data Collection Process

The target population for this study consisted of farmers in Mpumalanga and KwaZulu Natal who were affiliated with the SAFDA. As of 2017, SAFDA had over 2500 members in KwaZulu Natal and Mpumalanga [44]. Using a 95% degree of precision, 5% marginal error and a 0.5 variance in the 2500 SAFDA members, this study was initially designed to include 334 participants. However, upon liaising with SAFDA’s Chief Operating Officer, the researchers were advised that due to the POPI Act, SAFDA could not share the member database. As a result, the researchers relied on referrals from farmers who belonged to SAFDA known to the researchers. Although the initial target sample included 334 respondents, the requirement to obtain explicit consent and the challenges in accessing data due to the restrictions imposed by the POPI Act led to a reduction to 223 participants. Nevertheless, the final sample remains representative of the target population, ensuring reliable results.

According to [48], a scientific sampling method which was used was a non-probability snowballing technique. This technique has disadvantages such as being uncontrolled, and surveys through a referral system mostly have similar characteristics to each other as respondents. However, the technique is valuable in cases where access to the population size for random selection is not possible (in regards to this study, the SAFDA member list was not made available to the researchers by the organization).

Telephonic interviews were conducted in which a researcher read out the consent form to the participant and completed the form. Interviews were conducted from August 2022 until April 2023. Due to referrals and time limitations, interviews were conducted with 223 consented participants. Primary data were collected using detailed copies of a semi-structured questionnaire. The questionnaire was pre-tested with a small sample of participants to ensure its accuracy, appropriateness, and reliability. All data collection procedures adhered to the ethical guidelines, and permission was given by the North West University Ethics Committee to conduct this research.

3.4. Data Analysis

Upon primary data collection from smallholder farmers, the information was coded, cleaned, and stored in a Microsoft Excel 365 spreadsheet. Subsequently, the coded data were exported to SPSS version 29 and STATA version 18. The research made use of the commonly used regression models to assess credit application outcomes. The details of how the analytical tools were applied are provided below.

Propensity Score Matching (PSM)

Similar observable characteristics of smallholder farmer participants who have not received credit from the financial institutions were grouped and matched with those who have received credit at banks using propensity score matching (PSM) to assess the impacts of credit provision.

According to [49], the first step in constructing PSM is to use the probit or logit model to estimate propensity scores as two groups as follows:

Y = β₀ + β₁X + ϵ

(1)

where Y is a binary variable (having received credit from a financial institution or not); X represents the covariates that determine credit approval; β is a vector of coefficients to be estimated; and ϵ represents a vector of random unobserved factors affecting credit approval. Christian and Obi [50] expressed the average treatment effect on the treated (ATT) as follows:

Ʈ_ATT = E (Ʈ | D = 1) = E [Y (1) |D = 1] − E [Y (0) |D = 1]

(2)

where ATT is the average treatment effects between participants who applied for credit and received approval against those who applied by applications were declined. The propensity score was therefore used to match recipients to non-recipients of credit from commercial banks and access to credit using the nearest neighbor matching of PSM. When interest is on comparing outcomes for having received credit (T = 1), the propensity score is estimated as follows:

E [Y (1) |D = 1] − E [Y (0) |D = 0] =Ʈ_ATT + E [Y (0) |D = 1] − E [Y (0) |D = 0]

(3)

Caliendo and Koeinig [51] stated the difference between the left hand side of Equations (2) and (3) is the ‘self-selection bias’ when Ʈ_ATT is defined as follows:

Ʈ_ATE = E [Y (1) − Y (0)]

(4)

The propensity score P (D = 1|X) = P(X), i.e., the probability for an individual to participate in a treatment given his observed covariates X, is one possible balancing score. Therefore, PSM for ATT can be written as follows:

$${Ʈ}_{ATT}^{PSM}=E_{P\left(X\right)\mid D=1}\{E[Y(1)\mid D=1;P(X)]-E[Y(0)\mid D=0;P(X)]\}$$

(5)

Table 1. Explanatory variables used in the credit risk model for credit outcome.

Variable Name	Type of Measurement	Prior Expectations
Application outcome (Approved/Decline)	Dependent variable (Binary)
Farmer’s gender	Farmer’s sex (Female = 0; male = 1) (Dummy)	+
Farmer’s age	Actual number in years (Continuous)	−
Farmer’s education	Grade 12 and lower = 0, Post matric = 1 (Dummy)	+
Farming experience	Actual number in years (Categorical)	+
Farm ownership	Owned = 1, Not owned = 0, Leased = 2 (Categorical)	+
Farm Size	Farm size in hectares (Categorical)	+
Tangible collateral	Presence of collateral (Yes = 1, No = 0) (Dummy)	+
Financial	Presence of financial records (Yes = 1, No = 0) (Dummy)	+
Farming income	Actual amount in Rands (Categorical)	+/−
Current assets	Actual amount in Rands (Categorical)	+
Years banking	Actual number in years (Categorical)	+
Account conduct	Well conducted account = 1, Not well conducted = 0 (Dummy)	+/−
Loan Purpose	Reason for loan (0 = Short term, 1 = Long term,2 = Not applied) (Categorical)	+/−
No. of times of application	Number of times a farmer applied (Categorical)	−
Required loan amount	Actual amount in Rands	−
Loan repayment term	Actual number in years (Continuous)	−
Account defaults	Defaulted on credit (Yes = 1, No = 0) (Dummy)	+/−

+/− represents the direction of influence (either positive or negative) Source: Authors.

presents a summary of the explanatory variables used for credit application outcomes; these were variables used for propensity score matching. The questionnaire used had four sections, which looked at attributes of the 5Cs of credit, which form the basis of bank’s credit application assessment. The 5Cs of credit are (1) collateral the client provided, (2) capacity repayment/affordability, (3) capital owners’ contribution on the requested finance, (4) character attributes of the borrower, and (5) conditions for the facility.

Section A—Farmer’s characteristics: This section was used to understand the farmer better, as it addressed features such as the farmer’s age, level of education, and years of farming experience. The researchers further asked about the farmer’s interest in progressing from smallholder to commercial farmer.

Section B—Farm’s attributes: This section aimed at evaluating farm ownership status, the size of the farm, the type of farming products produced on the farm, and the reasons for farming the commodity, be it for household consumption or as income-generating produce. For efficient farming, a farmer needs farming equipment and machinery; Section B therefore enquired about assets on the farm, mainly to also identify if these were financed through commercial banks or not.

Section C—Financial management: This research study focused on credit accessibility; therefore, understanding the financial position of the participants played a significant role in addressing some of the objectives.

Section D—Loan features: For the researchers to conclude if smallholder farmers had credit access or not, Section D was one of the important parts of the questionnaire, as the questions focused on (1) access to banking, (2) information on the requested loan, (3) the credit history of the applicant, (4) collateral information, and (5) challenges faced when applying for funding [48].

Table 1 thus specifies the measurement type and outlines the theoretical relationship posited between these variables and the dependent variable studied. The following personal attributes are expected to have a positive relationship with the likelihood of approval of a loan application—(education, farming experience, and a male gender). An increase in a farmer’s age is expected to have a negative relationship with approval. The presence of tangible collateral, land ownership, and a unit increase in farm size is expected to have a positive relationship with credit approval, thus increasing the likelihood of obtaining finance. Financial features (cash flow, solvent and profitable financials, income, and asset values) are expected to have a positive influence on the likelihood of credit approval. The longer the banking relationship with a well-conducted business account is the greater the likelihood of obtaining finance. The higher the loan amount and the longer the loan repayment terms, the higher the chances of defaulting; therefore, the researcher expects these variables to have a negative relationship with credit approval. The presence of defaulting history decreases the chances of obtaining credit.

4. Results and Discussion

The impact of access to credit on smallholder farmers.

The propensity score matching (PSM) method was used to assess the impact that credit access has had on smallholder farmers who applied for credit at a commercial bank and received approval. Among a sample of 223 participants, 62 farmers had applied for credit at one of the five banks (Absa, FNB, Land Bank, Nedbank, and Standard Bank). Out of these 62 applicants, only 11 successfully secured a loan. The purpose of this analysis is to compare the outcomes of farmers who were granted credit to those who did not have access to credit in order to evaluate its effectiveness.

To estimate the impact of credit access, average treatment effects (ATEs) on those treated were calculated using nearest neighbor matching methods. The analysis focused on four key aspects: (1) farm ownership, (2) farm size, (3) farm income, and (4) farm assets. These aspects were selected based on evidence that highlights the significant impact of credit on the socio-economic factors of smallholder farmers. The benefits often include positive effects such as poverty reduction through enhanced food security, increased income, increased savings, creation of job opportunities, and the use or adoption of technology in farming operations.

Table 2. The impact of credit access on smallholder farmers.

Outcome	Coefficient	Standard Error	Z	p > |z|	[95% Confidence Interval]
Farm ownership	1.843	0.434	4.25	0.000 *	0.992	2.693
Farm size	55.547	7.464	7.44	0.000 *	40.918	70.176
Farm income	2,849,398	376,995	7.56	0.000 *	2,110,502	3,588,295
Assets	35,943	193,589	0.19	0.853	−343,484	415,371

Notes: * means significant at 1%, levels. Source: Survey, (2023).

presents the PSM with farm ownership, farm size, farm income, and farm assets between farmers with access to credit and those that did not have credit. The results indicate that farm ownership is positively associated with loan approval. Specifically, farmers who owned land were 1.84 times more likely to have their loan application approved compared to those who did not. This relationship is statistically significant at both the 0.05 and 0.01 levels. Credit accessibility may enable land acquisition [52]. Research on credit assessment lists farm ownership as one of the important considerations, as this can be used as collateral to cover the risk the bank takes on borrowing the requested financial facilities.

Additionally, Table 2 shows that on average, loan approval is associated with an increase in farm size by approximately 55.5 ha. This variable is statistically significant at both the 0.01 and 0.05 levels. A larger farm size provides farmers with the opportunity to engage in a commercial-scale production compared to those with smaller farms. The average farm size for farmers that accessed formal credit is 7.33 ha in Cambodia. The average farmer’s age who had credit access was 47 years, with farming experience of 22 years [18]. Although land size affects credit access, ref. [53] found land ownership to negatively affect credit access, meaning farmers with title deeds tend not to apply for credit, which is in contrast to [54] study, as one would expect land to be used as collateral and increase access to credit.

A farm income is essential for enabling a farmer to produce on a larger scale while effectively managing operational costs to ensure profitability. Once operational costs have been covered, the remaining free cash flow is used to service any acquired debt. Table 1 shows that farm income increased by ZAR 2,849,398 for farmers who have received credit from a financial institution. This variable is statistically significant at 0.05 and 0.01 levels. Therefore, income is a critical component in credit approval as it enables credit recipients to invest in better technology that can improve the quality of their produce. The findings are in line with those of [36], which reported an increase in farm income among Lesotho farmers who had credit access. Similarly, a study of 4210 agricultural households in Nigeria found that farmers with access to credit had yields three times higher compared to farmers who did not receive agricultural credit [55]. Furthermore, ref. [56] found that farmers involved in agroforestry production who had access to credit saw a 254 kg increase in output compared to those without credit. In Ghana, credit access in cocoa production led to increased yields by 16.98–26.26% and net income increased by 20.29–29.84% [57].

In Indonesia, formal credit led to increased productivity, with farmers who received credit producing 2898 kg more than those who did not receive credit and 1403 kg more than those who received informal credit from informal providers. Technical efficiencies were measured through the use of fertilizers and pesticides, improved seeds, and labor. Farmers who had credit access had a positive adoption of technical efficiencies [53,58]. In contrast to studies in credit space that highlight access as a catalyst that enhances productivity, a study in Congo found that credit access did not lead to an increase in farm productivity. The study further indicated that having more fields also led to less credit access [59]. One could argue that bigger fields are associated with more revenue, thus there is no need for credit. Additionally, credit availability in KwaZulu-Natal influenced the use of information communication technology (ICT), which in turn positively impacted the financial well-being of smallholder farmers [60].

Lastly, Table 2 presents the PSM results for the value of assets owned by farmers. The results show that for a loan to be approved, the applicant’s average asset value should increase by ZAR 35,943 compared to the applicants who did not receive credit from a bank. However, the larger standard error of 193,589 relative to the estimated effect suggests a high level of uncertainty. As a result, the variable tested is therefore insignificant. This is in line with the reasons why financial institutions do not prefer general notarial bonds “security over lose assets” as a good security as this type of security can be shifted from one area to the next without notifying the creditor [61].

While credit positively impacts agricultural productivity and income, there is a risk of over-indebtedness, particularly in contexts of high agricultural price volatility. Some studies have highlighted that in the absence of risk mitigation measures, such as agricultural insurance or sustainable interest rates, credit can become a burden rather than an opportunity for smallholder farmers. Therefore, it is crucial to consider risk management strategies to ensure that credit remains a tool for sustainable development.

5. Conclusions and Recommendations

This study primarily evaluated the impact that credit inclusion has on smallholder farmers. The findings emphasize the importance of financial inclusion, showing a positive impact of financial inclusion on (1) socio-economic factors, (2) agricultural output through technology adoption, (3) increased income, and (4) human development index. Findings of this study from the PSM showed the positive impacts that credit has on farming size, as it enabled farmers to produce on a bigger scale, increased farming income, and led to farm ownership. It is recommended that financial inclusion not only be traditionally provided by commercial banks but also consideration of funding models, namely financing farmer, movable collateral, and buyer. This inclusion of finance will positively influence the sustainability of farming. Moreover, to improve access to credit for smallholder farmers in South Africa, we recommend the following measures: (1) establishing credit guarantee schemes in partnership with financial institutions to reduce lending risks; (2) implementing financial education programs for smallholder farmers to enhance their debt management skills; and (3) promoting public–private partnerships to develop tailored financial instruments that meet the specific needs of the agricultural sector. The South African Department of Agriculture should collaborate with well-established agricultural cooperatives, such as BKB, Obaro, TWK, VKB, and the like, to provide short-term production facilities. This, however, needs to be monitored closely to ensure that individuals who need funds have access to them, and it should be at favorable interest rates. Credit access has the potential to promote positive change across economic, social, and environmental aspects, improving not only the livelihoods of smallholder farmers but also contributing to broader sustainable development goals.

6. Study Limitations

The data collection technique was initially designed as a probability, where each member of SAFDA stood a chance of being selected for the survey through random selection. However, due to the restrictions imposed by the POPI Act, the researcher was not granted this permission and thus resolved to use the snowball technique, which relied on referrals. During the 2020–2021 period, the COVID-19 pandemic led to widespread disruptions, with numerous businesses experiencing significant challenges due to lockdown measures. Consequently, the financial data available for analysis may not accurately reflect the true state of these businesses, as many were still in the recovery phase at the time the data were collected and also due to the recall bias as the questionnaire relied on farmers’ recall of previous production seasons. On the technical side of the research, several smallholder farmers do not have financial statements prepared by accountants—this means that this study lacked the financial health tests (such as liquidity ratio, and interest cover ratio, to mention a few), which play a huge role in credit provision from financial institutions. Another crucial ratio, DSCR, which determines debt serviceability, was also not calculated as this value depended on annual financial statements.

7. Future Areas of Study

A study that aims at credit risk model development with data that were received by the big financial institutions so as to compare if the outcome per applicant will be similar to that of the borrower in terms of credit approval or rejected application.

Developing a credit model from data from developing countries that have been successful in financing smallholder farmers and integrating the model into the South African credit assessment.

Research that takes into account loss given default and exposure at default models in credit modelling.

A long-term comparison of developing farmers who have received credit in commercial banks to determine the success or failure of credit accessibility over time.

A study that evaluates the affordability of credit from commercial banks for smallholder farmers