ABSTRACT
There is an increasing need for food security assessment in the wake of today’s challenge of climate change and variability. This study aimed at assessing the impacts climate change and variability on food security in West Pokot County for the period 1980-2012. Objectives of the study were to: characterize rainfall and temperature data for the specified years, evaluate spatial variability in relation to climate change and variability in the county using remote sensing and Geographic Information System (GIS), study the phenology of agricultural vegetation of the area and assess the perception of the household on the relationship between climate variability and food insecurity across three agro-ecological zones in West Pokot County. Household survey, key informant interviews, analyzing rainfall and temperature data and GIS methodologies were adopted. Questionnaires were administered to 124 randomly selected households. LANDSAT and SPOT images were satellite images selected due to their high spatial resolution. The result revealed high inter-annual rainfall variability. The mean rainfall was 973.4 mm p.a. for the years 1980-2011. Years 1984 and 2000 experienced the lowest precipitation of 631.6 mm and 619 mm respectively. Lowlands’ temperatures have increased by 1.25°C and the highlands by 1.29°C respectively over the study period. Majority of respondents strongly believe (68%) that climate variability has occurred in the area with the lowland experiencing a great effect on crop production (75%) followed by the mid potential zone (27%) and finally the highlands (14%). Land cover land use changes showed that cropland has increased by 68% while grassland has reduced by 6%. The mean Normalized Differential Vegetation Index (NDVI) values ranged from 0.36-0.54. There has been a consistent increase in vegetation greenness in the three agroecological zones for the period 2000- 2011. The year 2000 was the lowest in greenness and the peak was in 2011 followed by a decrease in 2012 due to the respective decrease in rainfall. The NDVI time series result show on average low values from January to February (0.36) and then afterwards increases and reaches a peak in June (0.54) before it starts decreasing up to September. April and August are the peak rainfall months in the study locations, May and September are the peak NDVI months. Thus, after rainfall onset, there is a one month lag period for NDVI to reach its peak. In the analysis of variance to show the relationship between rainfall and NDVI value when p
