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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2758

Advisors: Dr. K.V.SURYABHAGAVAN (Assistant Professor)
Keywords: Agricultural drought,
Remote Sensing,
Copyright: Jun-2010
Date Added: 7-May-2012
Abstract: Abstract Climate has always been a dynamic entity affecting natural systems through the consequence of its variability and change. Agriculture is the most vulnerable and sensitive sector that is seriously affected by the impact of climate variability and change, which is usually manifested through rainfall variability and recurrent drought. In dryland semiarid areas of Ethiopia, including large part of East Shewa zone, agricultural drought and crop failure have been common, and farmers inhabiting the area experience extreme temporal and spatial variability of rainfall in cropping season with frequent and longer dry spells. This makes them vulnerable to the risk of agricultural drought. Thus, in order to adapt and/or mitigate the impact of agricultural drought, agricultural drought assessment has to form one dimension of research to be done whereas the use of remote sensing and GIS techniques provides wide scope in drought risk detection and mapping. Consequently, this study was conducted in East Shewa zone with the objective of assessing agricultural drought risk and preparing agricultural drought risk zone map using satellite data. To assess and examine spatiotemporal variation of seasonal agricultural drought patterns and severity, three drought indices namely, Water requirement satisfaction index (WRSI), Standard precipitation index (SPI) and NDVI anomaly are applied. A time series advanced very high resolution radiometer (AVHRR) NDVI and rainfall estimate (REF) satellite data for the years 1996- 2008 were utilized as input data for the indices while grain yield data was used to validate the strength of indices in explaining the impact of agricultural drought. The result derived from indices for the study period has shown that the 2000 to 2005 cropping seasons experienced enhanced agricultural drought with observed spatial difference in severity level within East Shewa zone. However, the severity level was higher in 2000 and 2002 cropping seasons whereas 2002 being the most severe of all. The impact of agricultural drought on crop production was measured through estimation of yield reduction. Compared to other cropping seasons of the analysis period, yield reduction for the years 2000 to 2005 was also higher in the East Shewa zone. Similarly, the year 2002 had highest reduction followed by that of the year 2000. Generally it is revealed that index results are in agreement with results of yield reduction depicting that yield reduction is largely attributed to agricultural drought. In order to evaluate the strength of the indices for expressing the existence of agricultural drought, simple regression analysis of indices results with total grain yield was computed. The result revealed that WRSI, SPI and NDVI anomaly express 76, 64 and 54 percent of variability of the grain yield in that order. Thus, WRSI can be a good indicator for occurrence of agricultural drought. Agricultural risk map of East Shewa zone was produced by integrating the drought frequency maps derived from the three drought indices in order to guide future prioritization of adaptation and mitigation options for agricultural drought prone areas. The result indicates that East Shewa zone is classified into slight, moderate and severe agricultural drought risk zone covering 17.18, 41.32 and 42.50 percent of the total geographical area respectively. Thus, this agricultural drought risk mapping can be useful to guide decision making process in drought monitoring and to reduce the risk of drought on agricultural production and productivity.
URI: http://hdl.handle.net/123456789/2758
Appears in:Thesis - Earth Sciences

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