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

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.

Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

“…Given the diverse distribution of stations with varying rainfall patterns and geographical locations, a station-by-station data analysis was conducted, rendering the homogeneity test inapplicable for this study. For more detailed procedures, refer to [11,19,53,54] and [55]. Table 2 displays the results, indicating that the data fulfll the mentioned assumptions and can be used for further analysis.…”

“…Te fooding also led to the destruction of bridges and roads, impacting transportation in various areas [16]. Generally, extreme rainfall events have already taken a severe toll on the country's agricultural sector, causing signifcant losses in maize and rice production in the Southern Highlands [15,[17][18][19]. Tanzania and other countries that rely heavily on agriculture, the impact of extreme rainfall is already posing great challenges for their economies.…”

A case study on the impacts of future climate change on soybean yield and countermeasures in Fujin city of Heilongjiang province, China

“…Global climate change may have disastrous impacts on nature and human social systems, especially on agriculture industry and food security (Tao et al, 2016;Baldos et al, 2020). Climate plays a significant role in agricultural production, and changes in climatic conditions directly affect crop development and final yield (You et al, 2008;Guo et al, 2009;Zhou and Wang, 2015;Kassaye et al, 2021;Lukali et al, 2021). Studies show that 32%-39% of the annual yield variability of corn, soybean and other crops globally is caused by climate variability (Ray et al, 2015).…”

Assessing the Relationship between October-November-December Rainfall and Indian Ocean Dipole in Recent Decades over Tanzania Following the 2011 Abrupt Change

“…In recent decades, East Africa has witnessed a surge in extreme climate events, notably droughts and floods, with profound socio-economic ramifications (Anande & Luhunga, 2019;Kimambo et al, 2019;Lukali et al, 2021). Specifically, studies in Tanzania have highlighted an increased frequency of floods during the October-November-December (OND) rainy season (Mbigi & Xiao, 2021), in contrast to a decadal decrease in flood events during March-April-May (MAM) rainy season in the late 1990s (Makula & Zhou, 2022;Umutoni et al, 2021).…”