Book

Description

This study aimed at the assessment of climate change and its singular effect on cereal yield in Sétifois Region-Algeria by 2070. Two predictive statistical methods namely the Artificial Neural Network (ANN) and the Stepwise Multiple Linear Regression (SMLR) were used, according two Representative Concentration Pathways (RCPs) scenarios (2.6 and 8.5) outputs that were adopted by the IPCC for generation of climate model results for the fifth assessment in 2014. Over Sétifois region, climate predictions show a spatial pattern with high trends to drought and warming under RCP 8.5, while, trends are less alarming under RCP 2.6. Predictive statistical models expect a relative association between climate trends and grain yield variations; meaning that the impact on expected yields vary from one province to another. The overall grain yield decline over Sétifois region will be around 33% according to RCP 8.5 outputs and about 16% under the RCP 2.6. Locally, the expected temperature increase and rainfall drop point out that climate change will have a negative impact on water resources and therefore a direct effect on cereal yields, which highlights the need to plan integrated mitigation strategies for the agricultural sector to ensure food security and achieve long-term sustainable development.