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Osima, S.E., Indasi, V., Zaroug, M.A.H., Edris, H., Gudoshava, M., Misiani, H., Nimusiima, A., Anyah, R., Otieno, G., Ogwang, B. and Jain, S. (2018) Projected Climate over the Greater Horn of Africa under 1.5 °C and 2 °C Global Warming. Environmental Research Letters, 13, Article ID: 065004.
https://doi.org/10.1088/1748-9326/aaba1b

has been cited by the following article:

• TITLE: Assessing the Impacts of Climate Change and Variability on Maize (Zea mays) Yield over Tanzania

  AUTHORS: Amina Abdalla Lukali, Sarah E. Osima, Yunsheng Lou, Kombo Hamad Kai

  KEYWORDS: Rainfall, Temperature, Climate Variability, Maize Yield, Maize Yield Projections Tanzania

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.11 No.3, July 12, 2021

  ABSTRACT: This study aimed at understanding the impacts of the seasonal hydroclimatic variables on maize yield and developing of statistical crop model for future maize yield prediction over Tanzania. The food security of the country is basically determined by availability of maize. Unfortunately, agriculture over the country is mainly rain fed hence highly endangered by the detrimental consequences of climate change and variability. Observed climate data was acquired from Tanzania Meteorological Authority (TMA) and Maize yield data from Food and Agriculture Organization (FAO). The study used the Mann-Kendall test and Sen’s slope for trend and magnitude detection in minimum, maximum temperature and rainfall at the 95% confidence level. The results have shown that rainfall is decreasing over the country and especially during the growing season but increasing during short rains season. Characteristics of seasonal climatic variables, cycle during growing period were linked to maize yield, and high (low) yield was reported during anomalous wet (dry) growing seasons. This portrays seasonal dependence of maize production. Statistical crop model was built by aggregating spatial regions that have statistically significant relation with maize yield. Results show that, 58.8% of yield variance is linked to seasonal hydroclimate variability. Rainfall emerged as the dominant predictor variable for maize yield since it accounts for 44.1% of yield variance. The modeled and observed yields exhibit statistically substantial relationship (r = 0.78) hence depicting high credence of the built statistical crop model. Also, the results revealed a decreasing trend in Maize yield with further Lessing trend is projected to proceed in the future. This calls for adaptation and implementation of appropriate regional measures to raise maize production in order to feed the burgeoning human population amidst climate change.