Modern agriculture is heavily dependent on debt which is normally provided in the form of credit. Credit permits farmers to assume new investments and technology [1
], allowing farmers to increase their productivity and efficiency in agricultural businesses. Financial capital has the ability to improve standards of living, expand capital investment, and provide a social service to farmers. Lending in the agricultural sector has several unique characteristics that influence the financial requirements in the sector [2
]. The agricultural sector is regarded as the highest degree of credit risk compared to other sectors in the economy [3
]. One of the aspects in the agricultural sector that needs to be considered is the length of production cycles which has an impact on the frequency that payment on loans can be made [2
], that could pose as a risk factor for financial institutions. Other reasons for the high risks include various factors such as the seasonal nature of agriculture, climate change, modernised technology, excessive division of agricultural land, perishable nature of agricultural products, fluctuation in demand, and prices for products [3
]. Another aspect that plays an important role in the South African agricultural role is the fact that most subsidies in the agricultural sector has been abolished that existed during the apartheid era of the country. These subsidies that, during the apartheid era, assisted in modernising the agricultural sector are no longer available and farmers, commercial farmers, cannot rely on such assistance [4
]. The impact of these factors ultimately affects the repayment ability of borrowers [3
]; hence implying increased risk to credit providers.
Financial institutions are important for the economy as they make credit available to investors who have profitable ideas. Statistics provided by the Department of Agriculture, Forestry, and Fisheries (DAFF) shows that since 2009 on average, 59% of debt in the agricultural sector is provided by commercial banks [5
]. From 2015, the sector received more than 60% of total debt from commercial banks [5
]. This is an indication of the dependence of the agricultural sector on credit from commercial suppliers in South Africa. Credit from the Department of Agriculture has averaged around 0.15% of total credit since 2009, showing that the department is not really a role player in providing credit to farmers. However, the Land and Agricultural Bank’s contribution averaged around 27% for the same period [5
]. Credit is considered as the borrowing capacity provided to a farmer, individual, or institution in the form of a loan. In recent years, formal financial institutions have significantly increased total lending to the South African agricultural sector [6
]. Total South African agricultural debt has increased from R36 443.8 million in 2005 to an estimated R158 342 million in 2017 [5
]. The increase in agricultural debt is caused by a strong reliance on credit to finance capital investments such as machinery, vehicles, livestock, implements, and land [7
]. The rise in demand for credit is caused by an increased demand for capital investment since land and fixed improvements require financial capital in order to use natural resources to their maximum potential. The increase in debt has made credit evaluation more difficult for financial institutions. Therefore, financial institutions have become more aware of the need to improve credit evaluation procedures [8
]. Before credit can be granted, farmers undergo a credit evaluation process, which consists of the collection, analysis, and evaluation of information such as credit repayment history, income, and overall financial status [9
The repayment ability of an applicant can be determined through a subjective or objective approach. The subjective approach is performed on a judgmental basis where a credit analyst determines the creditworthiness of the applicant based on personal knowledge and experience [10
]. This approach leads to inaccuracies, high training costs, and inconsistent decisions made by different credit analysts for the same application [10
]. The shortcomings associated with the subjective approach may be overcome by using more objective approaches. Objective approaches include statistical models, which are available for credit classification.
The objective approach is a quantitative evaluation technique used by financial institutions to evaluate the credit worthiness of an applicant [11
]. Certain personal and business characteristics of the applicant are used in a statistical model to predict the probability of the applicant to be able to repay the loan. As such, this approach provides a credit analyst with an objective tool to assess whether or not credit is to be granted to a particular applicant. The objective approach thus has potential to reduce human judgement, reduce evaluation procedure costs, improve accuracy, improve consistency, and reduce classification errors associated with the subjective approach.
Internationally, researchers have successfully applied neural networks [8
], decision trees [8
], and logistic regression [8
] to determine the factors affecting access to agricultural finance. Of the different methods, a regression framework proved to be the preferred approach [14
]. The challenge of the regression framework, however, is that there are various personal and business characteristics of applicants, which may influence their ability to repay the loan. The inclusion of those characteristics as explanatory variables in the model is of great importance, since they have a direct influence on the accuracy of the model.
Inclusion of the correct and subjective variables has the ability to improve performance through increased classification accuracy and greater cost-effectiveness, and it also contributes to a better understanding of the underlying process, which generates the data [15
]. The importance of the selection of variables is echoed by Bolton [16
] who argues that significant improvements in classification is most likely to come from including new or additional predictive characteristics in credit classifications decisions. The benefit of improving the accuracy of the model through the selection of appropriate variables may be substantial. West [17
] states that even a fraction of a percentage increase in credit scoring accuracy could be regarded as a significant accomplishment. While such an improvement may not seem large, if considering the number and associated monetary value of credit applicants that are assessed, a small improvement will make a noteworthy contribution in the agricultural credit sector.
The specific information to include in the credit scoring models has received attention from researchers in the past. While, traditionally, financial information is mainly considered in loan applications, there are several other factors, typically non-financial, that also affect the ability of a borrower to repay a loan. Such information includes cyclical performance, seasonal production patterns, farm typography, commodity, geographic location, participation in government programs, leasing farmland, high capital intensity, and annual payments of loans [18
]. In an attempt to understand the factors used in credit evaluations, Henning and Jordaan [20
] employed a Delphi study to investigate the factors used by a financial institution. The results indicated that there are additional factors that are being used in credit evaluations compared to what is generally found in research. The factors are characteristics that influence the decision whether or not to grant credit including age, account standing, type of farming enterprise, client’s success factor compared to competitors, farm experience, collateral, credit record, education/qualification, farm ownership, past and current financial information, experience, reputation, product market projections, sustainability of the enterprise, and industry projection risk. Interestingly, information and research related to factors influencing access to credit is often collected from the applicants instead of the financial institutions who provide the credit [20
]. The problem is that there could be a mismatch in how applicants see their creditworthiness compared to the view of the credit provider. Ultimately, it is the view of the credit provider that determines whether an application for credit is successful. It is therefore important to understand the factors that financial institutions use to evaluate loan applications in order to gain a better understanding of the requirements that applicants have to meet.
The aim of this research is to determine the different factors used in evaluating loan applications in the agricultural sector by a credit institution. The research explores the factors used by a financial institution as indicated by Henning and Jordaan [20
] in granting credit to applicants. The research determines whether factors such as years as client, number of enterprise diversification, credit record, account standings, and associated industry risk do have an impact on the final decision. These factors will be explored in conjunction with other popular factors found in credit research such as age, education, collateral, financial performance, loan amount, and payback period amongst others. The research contributes to literature by expanding on the traditional factors used in literature and therefore provides a better understanding of the factors used by a credit institution to guide decisions on granting credit to farmers. Thus, a better understanding of the complex mechanisms that influence credit decision making is provided which could also assist the applicants to consider factors that could have an impact on the success of their loan applications.
The following section of the paper discusses the method and data used in the research. Firstly, the data collection and a short description is provided which is followed by the procedures in terms of the principal component. A discussion of the factors identified is also presented in the section. The final section of the paper concludes the findings from the research and how the findings can contribute to the agricultural credit industry and suggestions for future research.
The results from the logistic PC procedure are given in Table 5
. It is important to note that the coefficients and significance in Table 5
do not indicate that there is a difference in importance between the variables. The results provide an indication of the influence of the variation observed for each variable in relation to the variation observed in the classification, specific to the present case. By implication, the model identifies the variables that are more likely to have an influence on applications being accepted or rejected.
The results shown in Table 5
indicate that 15 of the 30 variables are significant at a 10% level. These variables include loan amount, years of business with bank, account standings, credit history, collateral, production costs, interest expense ratio, number of enterprises on the farm (diverse two, diverse three and more), ownership of the farming business, age, experience, and education level (no education, post graduate, and no indication). From the results, it can be seen that factors from all three categories—loan, financial, as well as farm and personal characteristics—are considered in the granting of credit to agricultural applications.
In terms of loan characteristics, the results indicate that larger loan-amount applications may be less likely to be classified as approved (p
< 0.1). The behaviour of the applicant and the relationship between the applicant and the financial institution may also have an influence on the possible classification of the applicant [12
]. This is indicated by the negative influence of account standing and credit history of the applicant (p
< 0.01), research should, therefore include the credit providers assessment on the clients account standings as opposed to just an indication whether savings exists. Motsoari et al. [14
] also mention that a positive relationship is expected between repayment record (credit record) and access to credit, meaning that a good record would increase the changes of access to credit in the future. Apart from an overall credit history, financial institutions also look at the years of business of their clients. This fact is also illustrated in the results where more loyal clients of the financial institution (clients who held accounts or were with the bank for longer periods) are more likely to be classified in the approved category (Business, p
< 0.1). These findings is similar to the findings of Awunyo-Vitor et al. [23
] and provide a wider indication of factors such as period of being a client as opposed to only having a good credit record or savings. The number of years with the institution should thus be considered in future research on access to credit by farmers. Research has shown that an additional year of savings with a formal lender could reduce the chance that an application be rejected [23
]. Savings and loyalty to the lender is thus not only important to be possibly more successful with loan applications as mentioned by Awunyo-Vitor et al. [23
]. The authors further mention that savings can be used as collateral for loans. These results illustrate that a good, long-standing relation with the financial institution, repaying loans, and investing money in their facilities (savings) and doing business by using their account facilities, do provide an advantage when applications are made for loans.
Collateral is an important factor to be considered in loan applications, as this is the security provided to the institution in default situations. One expects collateral to be significant in the classification of applications to ensure that enough security is provided. Previous research has also considered this aspect in different forms, especially land tenure, because the applicant owns the land [14
]. This is also reflected in the results where applicants in the ‘other than good’ category are less likely to be classified in the approved category (p
< 0.01). The result is a good indication that the credit institution has to ensure that enough security is provided to cover the loan, or total debt exposure, should the client default. This confirms the thought of Ghosh et al. [33
] that collateral is a prerequisite for formal institutions and that resource poor farmers may therefore struggle to access credit capital. However, this result is contradictory to research of Motsoari et al. [14
] who found that ownership of land (providing security) has a negative relationship with access to credit. The authors provide a possible reason for their finding in that most of the borrowers participated in government programs, which does not require ownership (security) for approving loans. These results will have quite a serious impact on smaller and emerging farmers who do not necessarily have the resources to provide collateral or security for a new loan. Especially when there are no specific credit products from institutions aimed at smallholder and/or emerging farmers whereby collateral is provided or considered in different forms.
Previous research (for example Ogundeji et al. [21
]) found that keeping records do have an impact on accessing credit. It can, however, be argued that the keeping of records is only the initial requirement and that accurate and reliable information from the financial statements do also have an impact on loan applications. These could be in terms of financial analysis that are traditionally used in credit analysis. The research explored this by including a ratio analysis calculated from the financial information provided rather than just asking, whether financial records were kept. The significant interest expense ratio (p
< 0.01) of the farm indicates that the higher the interest expense ratio, the less likely an application is to be approved. Considering that higher interest payments are an indication of larger levels of debt, this result could be interpreted as an indication that the debt levels of the applicant is already high. As a result, the farmer may be taking on too much debt when the current application is considered. This result is similar to previous indication by Ogundeji et al. [21
] where it was found that higher interest rates are negatively related to farmers accessing loans for financing institutions. The production costs ratio (p
< 0.05) has a positive influence on the likelihood of being classified as approved. This is a rather strange observation, since a higher production cost ratio indicates that a greater percentage of the gross farm income has been spent on production costs, thus leading to lower net farm income. In general, the results do indicate that keeping record do assist in accessing loans, but that the financial statements are used for purposes more than just being able to prove that the financial statements exists and for actual analysis of performance and repayment ability of the applicant.
In terms of the farm and personal characteristics, the number of enterprises influenced the possibility to be classified approved. Research on small enterprises by Nikaido et al. [34
] found that enterprises with diversified activities are more likely to receive formal credit. Diversified activities were also explored in the research with Diverse 1 used as the base category where applicants had only one enterprise, relying on one enterprise (no diversification). Interestingly, applicants with two enterprises (p
< 0.01) were less likely to be classified as approved compared to a single enterprise (Diverse 1). On the other hand, applicants with three or more enterprises (p
< 0.01) were more likely to be classified as accepted than applicants with one enterprise (Diverse 1). The results do indicate that it is important to consider more than just the enterprises of the farming business, but also the number of enterprises. The more enterprises in the business or farming business in this case, the more the risk may be spread to ensure potential income and be more resilient in difficult times [34
]. There is, however, a chance that this may also lead to the possibility that one enterprise may receive preferential attention over another, leading to a potential decline in the performance of the neglected enterprise, resulting in higher risk and potential losses. The result may be an indication that diversification is a method by which risk can be spread, and the results indicated that spreading the risk to more than two enterprises or alternatively, to be focused on one enterprise, may be beneficial in loan applications. This should, however, be further investigated in future research to have a better understanding on the impact of the number of enterprises.
Differences in personal factors, which influenced the classification of applicants included ownership, age, experience, and education. Ownership (p
< 0.01), age (p
< 0.1) and experience (p
< 0.05) contributed positively to being classified in the approved category. In terms of education, the result indicated that where there was no indication that education level had a negative influence on being classified in the approved category (p
< 0.01) compared to applicants that had a Grade 12 (matric) education. This result is expected since evidence has shown that education has a very important role as part of human capital [34
The aim of this research was to determine the influence of different factors used in evaluating loan applications in the agricultural sector by a formal credit institution. Results from the principal component logistic regression indicated that factors resulting in successful applications included older more experienced farmers, sufficient collateral, years of business with the credit provider, acceptable credit history, smaller loan amounts, lower levels of debt (lower interest expense ratio), higher production cost ratios and enterprise diversification. These factors were classified into two broad categories. The first was small loans; smaller loan amounts were more likely to be awarded. The second category was the ability of the applicant to carry or manage risk, and thereby their ability to repay the loan. One of the risks faced by credit providers is the default of an applicant. As a result, the credit provider would like to ensure that sufficient collateral is available, and that the current debt levels are within acceptable ranges. Historical repayment performance is also considered to ensure that the applicant does not have a history of default. From the research, it is found that information such as the period the applicant has been a client of the institution, account standings, and number of enterprise diversification should be considered in future research on farmers access to credit. It is thus not only collateral and a good credit history, but also staying with your banking institution for longer periods and maintaining good account standings, could assist in positive outcomes in credit applications. Diversification of farming enterprises is a well-known strategy to manage risk. As a result, applicants with different diversification strategies have different loan application outcomes. The results indicate that applicants who focussed on one enterprise (Diverse 1) were more likely to be approved for a loan than an applicant with two farming enterprises. Applicants with three or more farming enterprises were more likely to be successful in their application compared to farming with a single enterprise. It can therefore be argued that applicants who diversify in more than two enterprises are more likely to be successful in loan applications. A possible explanation could be that the farming business has moved from an operation with a single enterprise to a more diversified farming enterprise. Thereby, the risk is spread and consequently the probability of defaulting on the loan is possibly minimised. However, this is a matter that requires further investigation in the future.
The conclusion is that applicants must trade-off the loan amount applied for against the size of the loan amount needed, the current debt structure and repayment ability to ensure affordability of the loan over the loan duration. Advice to smallholder farmers would be to ensure that they are able to provide information on repayment ability with strong financial evidence instead of merely providing proof of existing financial statements. The research also provides a picture of credit applications without the assistance of receiving any subsidies from the government, indicating that the farmers only have their farming businesses or other non-farm income sources and no subsidies as sources of income. Countries that are thus experiencing reduction or possible removal of subsidies could learn from the South African experience. Future research should investigate the factors that affect an applicant’s ability to repay loans as used in credit evaluations by expanding the number of credit providers (financial institutions) and total number of applicants considered in the analysis. Further consideration can also be given to differences in credit granting between branches, farming enterprises, and economical scale of farms in relation to one specific financial institution compared with others.