| The broad objective of this research was to develop a spatial model which provides both timely and quantitative regional maize yield estimates for real-time Early Warning Systems (EWS) by integrating satellite data with ground-based ancillary data. The Food and Agriculture Organization (FAO) Crop Specific Water Balance (CSWB) model was modified by using the real-time spatial data that include: dekad (ten-day) estimated rainfall (RFE) and Normalized Difference Vegetation Index (NDVI) composites derived from the METEOSAT and NOAA-AVHRR satellites, respectively; ground-based dekad potential evapo-transpiration (PET) data and seasonal estimated area-planted data provided by the Government of Kenya (GoK).; A Geographical Information System (GIS) software was utilized to: drive the crop yield model; manage the spatial and temporal variability of the satellite images; interpolate between ground-based potential evapo-transpiration and rainfall measurements; and import ancillary data such as soil maps, administrative boundaries, etc. In addition, agro-ecological zones, length of growing season, and crop production functions, as defined by the FAO, were utilized to estimate quantitative maize yields. The GIS-based CSWB model was developed for three different resolutions: agro-ecological zone (AEZ) polygons; 7.6-kilometer pixels; and 1.1-kilometer pixels. The model was validated by comparing model production estimates from archived satellite and agro-meteorological data to historical district maize production reports from two Kenya government agencies, the Ministry of Agriculture (MoA) and the Department of Resource Surveys and Remote Sensing (DRSRS).; For the AEZ analysis, comparison of model district maize production results and district maize production estimates from the MoA (1989-1997) and the DRSRS (1989-1993) revealed correlation coefficients of 0.94 and 0.93, respectively. The comparison for the 7.6-kilometer analysis showed correlation coefficients of 0.95 and 0.94, respectively. Comparison of results from the 1.1-kilometer model with district maize production data from the MoA (1993-1997) gave a correlation coefficient of 0.94. These results indicate the 7.6-kilometer pixel-by-pixel analysis is the most favorable method. Recommendations to improve the model are finer resolution images for area planted, soil moisture storage, and RFE maps; and measuring the actual length of growing season from a satellite-derived Growing Degree Day product. |
