Abstract:
This research was conducted to test the business potential of the decision support tool known as Livelihood Early Assessment and Protection (LEAP). The initial intension with LEAP development was to monitor the climate and crops through growing season for food security analyses in Ethiopia. In this study the performance of the tool was tested over agriculturally high productive areas of Ada’a and Ambo Zuria woredas in central Ethiopia, using key indicator data sets the satellite based rainfall estimates, in conjunction with station rainfall data and maize in Ambo Zuria and teff data in Ada’a districts was used. Evaluated LEAP for its capability to monitoring climate, the period of 2009-2013 JJAS dekadal rainfall data were validated. According to the analytical result the satellite merged TAMSAT_SEDI (r2=0.76 and 0.73) and NMA_GRID (r2=0.79 and 0.67) performed slightly better than TAMSAT (r2=0.72 and 0.61) whereas RFE2 (r2=0.62 and 0.52) and ARC2 (r2=0.57 and 0.46) was less performed than TAMSAT over Ada’a and Ambo Zuria respectively. Similarly, LEAP performance in monitoring crop yields through linking a grid-cell-based dekadal water requirement satisfaction index (WRSI) into the observed crops yield data (2003-2013) shows that WRSI of both crops was found to be highly significant and positive with R2 (> 0.48, p < 0.05). From the results provided it can be observed that, the WRSI in both study districts has been in the range of best case scenario. Finally, regarding potential of LEAP for translating seasonal rainfall prediction using historical (2083-2015) rainfall and ENSO data, JJA SST anomaly exhibited stronger correlation in predicting JJAS rainfall. The result shows, Ada’a (r = -0.66, -0.59, -0.56 and -0.28) and Ambo district (r = -0.32, -0.46, -0.25 and -0.21) of NMA station, TAMSAT, NMA_Grid and ARC2 respectively. TAMSAT as rainfall source is in good agreement with NMA_station and NMA_Grid, district wise Ada’a is strongly correlated than Ambo. Hence, this study indicated, pixel-by-pixel detailed spatial analysis of satellite rainfall and crop WRSI on various time scales is needed to obtain finer information, which could also be easily expanded other areas of the country relevant for localized agricultural activities.
