This study was designed to use of local level seasonal climate prediction for rain feed crop production planning by providing valuable support for decision-making strategies in the Upper Awash Basin (UAB). A rainfall data recorded over 24 well-distributed rain-gauge stations confined within UAB covering time period of 1980 to 2014 were used for regionalization of homogeneous rainfall pattern. Principal component analysis was used to classify the study area into different homogenous rainfall regions with seasonal rainfall characteristics and spatial continuity. The first four leading rotated principal components (RPCs) explained Belg rainfall variability from 25% to 80% and for kiremt ranges from 18% to53% of variance, respectively. According to the finding, three climatological sub-divisions were found in the Upper Awash Basin. On the other hand from 1970 to 2014 years of daily rainfall data of eight selected stations from UAB were analyzed to identify variation of rainfall characteristics such as onset and cessation dates, probability of dry spell frequency, seasonal rainfall amount and its temporal as well as spatial distribution. Besides, the risk of dry spells with varying number of days was computed using first-order Markov chain model. The result indicates that zone three has a relatively higher dry spell risk as compared to the other zones. April is start of small rainy season while September is the cessation of main rainy season. The small rainy season has higher dry spell risk and onset variability than main rainy season. The major seasonal predictors for the study area were found to be the Nino regions and Indian Ocean dipole phase (IOD). Due to variability of ENSO phases, rainfall amounts in the major rainy season were high in La Niña but low in El Niño years. The By using weighted average rainfall from homogenous zones and zonal crop yield data of major crops obtained for the periods of 1995 to 2013,the rainfall- crop relationship result generated from this study revealed that, excess rainfall would reduce crop yield in zone one while relatively better for zone three. Hence, use of seasonal climate prediction is enable to maximize agricultural rain feed crop productivity while minimizing the crop risk associated with seasonal rainfall. Crop yield risk analysis compared to the ENSO-based approach using cumulative density function refers that, during normal ENSO phase has resulted in preferable yield value and less crop risk by first stochastic dominance and second stochastic dominance senses. Hence, Normal phase is found to have the best risk efficient set identified for each three Zones crop productions planning. Therefore, users in the study area can take comparisons based on alternate ENSO forecasts for further insight to crop risk planning and management strategies. This research therefore had given attention to the response farming activities with scientific seasonal climate prediction information respect to ENSO phase. It is customary to note that seasonal climate prediction provides the best opportunity for farmers to adjusting time of sowing date, cultivar selection in the rain feed crop production.
Date of publication:
RUFORUM Theses and Dissertations
Dr. Diriba Korecha, Dr. Muktar Mohammad