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The effects of East African low level jet on food security in horn of Africa: A case study of coastal region of Kenya
Abstract
Literature on rainfall variability in Eastern Africa has suggested a significant influence from local factors that control rainfall amounts and distribution in contrast to the global wind circulation systems in oceanic atmospheres. All oceans are associated with unique wind systems that reflect temperature and other physical attributes of the water masses. However, the influence of such systems on Eastern Africa has not been investigated in conjunction with unique climatic phenomena, including the June winds in the coastal region of Kenya. This study involved a review of literature and the analyses of secondary data from studies conducted in the region, including 39 years of meteorological data. The results indicated that only two months in a year, namely April and May, experience a positive net moisture regime. In all other months, predicted evaporation exceeds received precipitation. The results also suggest that the annual June winds create a cyclic depression in rainfall amounts during the long rains season, resulting in decreased soil moisture and therefore adverse effects on annual field crops. The June winds, at critical stages of maize growth, results in depressed crop yields that threaten food supply and food security. Maize yields in the region are associated over time with amounts of rain received during the long rains season. Cyclic patterns indicated that a year of higher rainfall alternates with a year of lower rainfall amounts. The study reveals that June winds causes over 95% in yield loses and suggests that the region can feed itself and export excess grains if only appropriate technologies to counter June winds effects are adopted. Since the occurrence of June winds is strongly linked to the La Niña climatic
phenomenon the study suggests development of a maize yield prediction model for seasonal forecasting based on the onset of June winds during the long rains season.
phenomenon the study suggests development of a maize yield prediction model for seasonal forecasting based on the onset of June winds during the long rains season.