Leveraging Geospatial Technology for Improved Smallholder Farmer Credit Scoring

Abstract

A small holder farmer is generally understood to be one that farms on a small piece of land, often taken as 2 ha or less, and largely for subsistence; however, this size threshold varies from country to country, depending on the prevailing ecological and demographic conditions; for example in Kenya it is about 0.5 ha. According to the Food and Agriculture Organization (FAO) of the United Nations, there are about 500 million small holder farmers in the world, and in the developing countries, such farmers produce about 80% of the food consumed there; their farming activities are therefore critical to the economies of their countries, and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions; but even where they are on registered land, the fear of losing such land should they default on loan payments often prevents them from applying for farm credit; and even if they apply, they still get disadvantaged by low credit scores (measure of credit worthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer, good seeds among others. This depresses their yields, and in turn has negative implications for the food security in their communities and in the world, hence making it difficult to realize the UN Sustainable Development Goal No.2 (no hunger). This study aimed at demonstrating how geospatial technology can be used to leverage farm credit scoring for the benefit of small holder farmers. A survey was conducted within the study area to identify the small holder farmers and farms. Further investigation was conducted to establish the extent to which small holder farmers are financially excluded and the results obtained from statistical analysis revealed that indeed the farmers were financially excluded to a large extent. A sample of 101 surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used, and the results showed that over 40% of the farmers could not qualify for the credit. When non-financial geospatial data, namely NDVI was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that introduction of the NDVI variable into the traditional scoring model could improve significantly the small holder farmer chances of accessing credit. Possible approaches by which this new model could be fine-tuned have been suggested, and should the model be adopted by industry, the technical and institutional issues that could feature in the implementation are discussed.