Climate Change Impact on Cereal Production in Northern Africa: A Comprehensive Modeling and Control Approach

Abstract

The Earth’s climate is undergoing unprecedented and rapid changes primarily due to human activities, especially greenhouse gas emissions, leading to a temperature rise of 1.1°C to 1.2°C in the last fifty years. In spite of global efforts like the 2015 Paris Agreement which aims to limit the increase in global temperature to 1.5°C above pre-industrial levels, projections indicate that this threshold will likely be exceeded, causing severe climatic disruptions, that will affect vulnerable regions and populations, altering weather patterns and compromising food security, water resources, and economic stability. This study integrates a data-driven downscaling system within an emission control framework aimed at controlling temperature anomalies by reducing CO2 emissions and evaluating its impact on cereal production in North Africa. The approach employs a simplified autoregressive model with exogenous input (ARX) for downscaling. During the validation phase, the ARX models show strong agreement with data, effectively reproducing global temperatures (correlation close to 1 and a normalized mean absolute error around 0.02) and local temperature (correlation of 0.75 and a normalized mean absolute error of 0.2). Moreover, an ARX model is also used to establish links between cereal production, global and regional temperature anomalies and CO2 emissions. This model demonstrates reasonably good agreement with the data, achieving a correlation of 0.72 and a normalized mean absolute error of 0.18. Once validated, the models are integrated into a decision support system based on model predictive control aimed to balance global and regional climate objectives with economic considerations. North Africa is an ideal case study since it is highly susceptible to the impacts of climate change, despite contributing only 1.76% of global carbon emissions in 2022. The region faces significant risks from increased aridity and heat, threatening agriculture, ecosystems, and socio-economic stability. The results of this research highlight the urgent need for environmental and climate-resilient measures to achieve sustainable development and enhance resilience against climate impacts.

Keywords

• Data-driven modeling
• climate change
• optimization
• socio-economic stability