Bangelesa Freddy, Pollinger Felix, Sponholz Barbara, Mapatano Mala Ali, Hatløy Anne, Paeth Heiko
Institute of Geography and Geology, University of Würzburg, Germany; Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo.
Institute of Geography and Geology, University of Würzburg, Germany.
Sci Total Environ. 2023 Dec 20;905:167265. doi: 10.1016/j.scitotenv.2023.167265. Epub 2023 Sep 24.
Africa is vulnerable to the impacts of climate change, particularly in terms of its agriculture and crop production. The majority of climate models project a negative impact of future climate change on crop production, with maize being particularly vulnerable. However, the magnitude of this change remains uncertain. Therefore, it is important to reduce the uncertainties related to the anticipated changes to guide adaptation options. This study uses a combination of local and large-scale empirical orthogonal function (EOF) predictors as a novel approach to model the impacts of future climate change on crop yields in West, East and Central Africa. Here a cross-validated Bayesian model was developed using predictors derived from the regional climate model REMO for the period 1982-2100. On average, the combined local and large-scale EOF predictors explained around 28 % of maize yield variability from 1982 to 2016 of the entire study regions. Notably, climate predictors played a significant role in West Africa, explaining up to 51 % of the maize yield variability. Large-scale climate EOF predictors contributed most to the explained variance, reflecting the role of regional climate in future maize yield variability. Under a high-emissions scenario (RCP8.5), maize yield is projected to decrease over the entire study region by 20 % by the end of the century. However, a minor increase is projected in eastern Africa. This study highlights the importance of incorporating climate predictors at various scales into crop yield modeling. Furthermore, the findings will offer valuable guidance to decision-makers in shaping adaptation options.
非洲易受气候变化的影响，尤其是在农业和作物生产方面。大多数气候模型预测，未来气候变化将对作物生产产生负面影响，其中玉米尤为脆弱。然而，这种变化的幅度仍不确定。因此，减少与预期变化相关的不确定性对于指导适应方案很重要。本研究采用局部和大尺度经验正交函数（EOF）预测因子相结合的方法，作为一种模拟未来气候变化对西非、东非和中非作物产量影响的新途径。在此，利用区域气候模型REMO在1982 - 2100年期间得出的预测因子，开发了一种交叉验证的贝叶斯模型。平均而言，局部和大尺度EOF预测因子的组合解释了整个研究区域1982年至2016年玉米产量变异性的约28%。值得注意的是，气候预测因子在西非发挥了重要作用，解释了高达玉米产量变异性的51%。大尺度气候EOF预测因子对解释的方差贡献最大，反映了区域气候在未来玉米产量变异性中的作用。在高排放情景（RCP8.5）下，预计到本世纪末整个研究区域的玉米产量将下降20%。然而，预计东非地区会有小幅增长。本研究强调了将不同尺度的气候预测因子纳入作物产量建模的重要性。此外，研究结果将为决策者制定适应方案提供有价值的指导。
