Leveraging Geospatial Technology for Smallholder Farmer Credit Scoring

作     者：Susan A. Okeyo Galcano C. Mulaku Collins M. Mwange Susan A. Okeyo;Galcano C. Mulaku;Collins M. Mwange

作者机构：Department of Geospatial and Space Technology University of Nairobi Nairobi Kenya 

出 版 物：《Journal of Geographic Information System》 (地理信息系统（英文）)

年 卷 期：2023年第15卷第5期

页      面：524-539页

学科分类：120301[管理学-农业经济管理] 12[管理学] 1203[管理学-农林经济管理] 

主　　题：Credit Scoring Machine Learning Geospatial Technology Migori 

摘      要：According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in 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 when they are on registered land, the fear of losing such land that they should 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 (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and 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 for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of 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 credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing cred