Use of GIS-Based Sampling to Inform Food Security Assessments and Decision Making in Kenya

Kenya relies on agricultural production for supporting local consumption and other processing value chains. With changing climate in a rain-fed dependent agricultural production system, cropping zones are shifting and proper decision making will require updated data. Where up-to-date data is not available it is important that it is generated and passed over to relevant stakeholders to inform their decision making. The process of generating this data should be cost effective and less time consuming. The Kenyan State Department of Agriculture (SDA) runs an insurance programme for maize farmers in a number of counties in Kenya. Previously, SDA was using a list of farmers to identify the crop fields for this insurance programme. However, the process of listing of all farmers in each Unit Area of Insurance (UAI) proved to be tedious and very costly, hence need for an alternative approach, but acceptable sampling methodology. Building on the existing cropland maps, SERVIR, a joint NASA-USAID initiative that brings Earth observations (EO) for improved environmental decision making in developing countries, specifically its hub in Eastern and Soutehrn Africa developed a High Resolution Map based on 10m Sentinel satellite images from which a GIS based sampling frame for identifying maize fields was developed. Sampling points were randomly generated in each UAI and navigated to using hand-held GPS units for identification of maize farmers. In the GIS-based identification of farmers SDA uses 1 day to cover an area covered in 1 week by list identification of farmers. Similarly, SDA spends approximately 3,000 USD per sub-county to locate maize fields using GIS-based sampling as compared 10,000 USD they used to spend before. This has resulted in 70% cost reduction.