The Impact of Rice Blast Disease, its Mapping and Suitability Analysis for Rice Growing Sites in the Greater Mwea Region

Abstract

Rice is one of the most important cereal crops in Kenya coming third after maize and wheat. It forms a very important diet for a majority of families in Kenya and is the source of livelihood in the Greater Mwea region. The demand for rice in Kenya has increased dramatically over the last few years while production has remained low. This is because rice production has been faced by serious constraints notably plant diseases of which the most devastating is rice blast. Disease mapping and applications of GIS provide a systematic way to spatially link known epidemiologic data on disease systems with relevant features in the environment to develop maps that can then be used, by extrapolation, to predict risk of disease over broad geographic areas where data are not available. Land suitability analysis is a prerequisite to achieving optimum utilization of the available land resources. Lack of knowledge on the best combination of factors that suit production of rice has contributed to low production. The aim of the study was to determine the impact of rice blast disease on the livelihood of the local farmers, map the spatial distribution of rice blast disease and develop a suitability map for rice crop based on physical and climatic factors of production using a Multi-Criteria Evaluation (MCE) & GIS approach. The study methodology employed a questionnaire survey which was subjected to sample population of households in the 7 sections with 70 blocks within Mwea region. The collected data was analysed using SAS Version 9.1. Descriptive statistics were used to summarize the household characteristics, the farm characteristics and the farmers‘ perceptions of rice blast disease. In the questionnaire, farmers‘ response on whether they had been affected by the rice blast disease and the total production per acreage was used to develop an attribute table with GPS points. The GPS points were interpolated to create a geographical distribution map of rice blast disease. Biophysical variables of soil, climate and topography were considered for suitability analysis. All data were stored in ArcGIS 9.3 environment and the factor maps were generated. For MCE, Pairwise Comparison Matrix was applied and the suitable areas for rice crop were generated and graduated. The current land cover map of the area was developed from a scanned survey map of the rice growing areas. From the survey farming was the mainstay economic activity (73.4% of the respondents) of virtually all the respondents selected for this assessment. The remaining respondents were engaged as casual labourers 12.8%, while 7.4% and 3.1% were engaged in business and formal employment, respectively. Among them, formal employment has the highest income earning per annum. The research revealed that almost all the farmers‘ 98% had awareness and knowledge of rice blast disease. Out of the 98% with knowledge and awareness 76% had been affected by the disease, while 24% had never been affected. The month of October had a higher disease prevalence compared to the other months and 87% of the farmers were first affected by rice blast in the year 2009. Majority of the farmers interviewed (72%) did not engage themselves in any other socio-economic activity even after being affected by the rice blast disease. According to disease mapping results 33.4% of the study area had a moderately high disease density and only 13.7% of the study area was under very low disease density. The present land cover map indicated that rice cultivated area was 13,369 ha. The crop-land evaluation results of the present study showed that, 75% of total area currently being used wasunder highly suitable areas and 25% was under moderately suitable areas. The results showed that the potential area for rice growing was 86,364 ha and out of this only 12% was under rice cultivation. This research provided information at local level that could be used by farmers to select cropping patterns and suitability. Key words: rice farming, socio-economic status, climatic data, land use land cover, disease mapping, multi-criteria evaluation.