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.
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