Masters Degrees (Agricultural Economics)

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Browsing Masters Degrees (Agricultural Economics) by Advisor "Henning, J. I. F."

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    Agricultural credit models: identifying high risk applications
    (University of the Free State, 2017) Bougard, Dominique Alyssa; Henning, J. I. F.; Jordaan, H.
    The objective of the research was to explore the performance of various statistical credit-scoring models, in order to identify a model that will minimise the misclassification of high-risk applicants, and identify the characteristics that influence repayment ability. The study was conducted in South Africa, with the use of a case study of a South African financial organisation serving the agricultural sector. The data gathered for this study was gathered through a formal agreement with a commercial financial organisation. Logistic regression (LR), probit analysis (PA) and neural network (NN) were used to construct the credit-scoring models that can be used to classify credit applications in the agricultural sector. Results of the LR indicate significance at 10% of the following variables, which may have an impact on classification: medium-term loan, credit history, debt to assets (DTA), net farm ratio, diverse 2, high risk, ownership and experience. The PA results demonstrate the following variables at 10% significance: credit history, DTA, net farm ratio, diverse 2, ownership and experience. The identification of characteristics provides confirmation of characteristics that are of importance to credit research. Financial organisations can use the identification of important characteristics as a method to provide guidance to applicants who apply for loans. Doing so will ensure that the organisation will identify characteristics that ensure that the applicant is accepted by the financial organisation. Applicants for loans can ensure that they possess characteristics that correspond to important characteristics identified by the statistical model. The results from the NN are not easily interpretable; due to “black-box” qualities it was not easy to identify the variables that have an influence on the predicted outcome. The NN did, however, outperform the LR and PA in terms of classification accuracy. Neural networks achieved the highest correctly predicted overall accuracy and a lower percentage of Type II error classifications. Logistic regression and PA have overall classification percentages of 96.06% and 3.94% respectively for classifying Type II errors. The NN had an overall classification accuracy of 98.43% and Type II classification error of 1.54%. The main conclusion from this research is that the statistical methods are able to classify credit applications in the agricultural sector and have the ability to improve accuracy in correctly classifying agricultural applicants. Further research is need to ensure that the correct variables are included in the classification. The classification results of the models are tested and monitored over a period of time to ensure that the accuracy and prediction are acceptable according to the financial organisations. Further research is needed to select the correct variables to be used when supplying credit to smallholder farmers and financial organisations can use the identified important characteristics to provide recommendations and guidance when evaluating applications for loans. Credit applicants can also use these identified important characteristics as a point of reference before applying for the loan at the financial organisation.
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    Entrepreneurial characteristics and financial performance
    (University of the Free State, 2016-02) Niewoudt, Simone; Henning, J. I. F.; Jordaan, H.
    The main objective of the study is to explore the relationship between entrepreneurial competencies of a farmer and the financial performance of said farm to determine whether initiatives focussed on improving entrepreneurial competencies of farmers will contribute towards improving their financial performance. The study was conducted in South Africa, and the data used within the study was gathered through a formal agreement with a commercial financial organisation. The financial performance of the farmers was calculated by means of farm financial ratios and then used to determine a single measurement namely the operating efficiency. The operating efficiency was calculated using a mathematical linear programming technique, this technique is a financial based Data Envelopment Analysis (DEA). It was hypothesised that entrepreneurial competencies of farmers will have an effect on the financial performance of the farm. The entrepreneurial competencies instrument used by Man (2001), was identified and used to measure the entrepreneurial competencies of the farmers. Entrepreneurial competencies were identified in terms of the statements that displayed high-factor loadings for each of the competencies. Farmers displayed an average of 70% or above for all the individual entrepreneurial competencies, indicating entrepreneurial behaviour among the farmers. . To determine the relationship between entrepreneurial competencies and financial performance the operating efficiency scores were regressed against the competencies scores. An Ordinary Least Squares (OLS) model was used within the Principal Component Regression (PCR) to regress the dependent and independent variables due to the nature of the dependent variables. The results from the financial based DEA showed that there were inefficient farms within the sample, however more than half of the farms had an efficiency score above 0.855, indicating high levels of operating efficiency. Therefore, the majority of farms were operating close to efficiency compared to one another, however not all were efficient. The entrepreneurial competencies scores indicated that all the farmers displayed entrepreneurial competencies. In determining the relationship between the operating efficiency and all of the entrepreneurial competencies as a combined index there was a positive significant relationship, for a single entrepreneurial competencies index. On further investigation a t-test was used to determine if there was a statistical difference between each individual competencies and the financial performance. It was found that individual competencies have a larger positive relationship on the operating efficiency of the farm. The results show that each of the individual entrepreneurial competencies have a positive relationship with the operating efficiency of the farm. Operating inefficiencies can be improved by increasing the individual entrepreneurial competencies where a farmer is lacking.
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    Factors affecting adoption of alternative pineapple production systems in Ghana
    (University of the Free State, 2015-07) Badu-Gyan, Farida; Henning, J. I. F.; Grove, B.; Owusu-Sekyere, E.
    The main objective of this research was to examine farmers’ decision and choice of production systems for pineapple production in order to determine the effect of factors within the social, physical and institutional environment that the farmers in the Central Region of Ghana operate under. An integrated value chain (VC)–New Institutional Economics (NIE)–Structure-Conduct-Performance (SCP) framework (‘VC-NIE-SCP framework’) was used to identify and describe the characteristics and requirements of the different production systems in the pineapple production sector. The integrated VC-NIE-SCP framework allows for comprehensive analysis of the behaviour and performance of small-scale pineapple farmers in their social, physical and institutional environment. A multinomial logit model was used to determine the factors that will influence farmers’ decision and choice of pineapple production system in Ghana in order to assess the relationship between social, physical and institutional factors and farmers choice behaviour. The results show that there are three pineapple production systems in the Central Region, namely certified organic, non-certified organic and conventional pineapple production systems. The majority of the farmers are conventional pineapple producers. Participation by women in the pineapple sector is very low. All the categories of farmers are credit-constrained. Most of the certified organic farmers have either written or oral contracts with pineapple exporters or processors. Most of the farmers in all the three categories have basic education. The empirical results reveal that farmers’ choice of certified organic pineapple production is positively influenced by the farmers’ concern for the environment, organic premium perception, and contracts with certified organic pineapple exporters or processors, training on organic production, access to support services from governmental or non-governmental organisations, and availability and access to the certified organic market. Within the institutional environment, farmers’ knowledge on institutional factors, such as level of knowledge on land tenure systems, level of knowledge on phytosanitary regulations of importing countries, and level of knowledge about the traditional norms, taboos and beliefs in the farming communities, all have positive influence on farmers’ choice of certified organic pineapple production system. Social capital index has a positive influence on farmers’ choice of certified organic pineapple production. However, personal factors, such as senior high school, training college and undergraduate university levels of education, household size, off-farm activity and wealth of farmers, have negative influence on farmers’ choice of certified organic pineapple production. Farmers’ choice of certified organic pineapple production is negatively influenced by access to government-subsidised inputs. Among the physical environment factors, farm size and distance from farm to organic market negatively influence farmers’ choice of certified organic pineapple production, compared with conventional production methods. Owned land tenure system has a negative influence on farmers’ choice of certified organic pineapple production, compared to conventional production methods. The main conclusion from this research is that, for the growth and development of the certified organic pineapple production sector in Ghana, policy makers should take the above factors into consideration when designing policy documents and sustainability strategies for the development of the pineapple sector.