What is it about?

Climate change is having serious effects on agriculture, especially in countries like Morocco where many people depend on farming. In this study, we used machine learning techniques to analyze how changes in temperature, rainfall, and other climate factors affect Morocco’s agricultural economy. Our findings show that rising temperatures and irregular rainfall patterns can significantly reduce the contribution of agriculture to Morocco’s GDP. Using machine learning allowed us to predict these impacts more accurately and provide useful information for decision-makers to help protect farmers and food security.

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Why is it important?

Our study is one of the first to apply advanced machine learning models to predict the economic impact of climate change on Morocco’s agricultural sector. Unlike traditional approaches, machine learning can capture complex, non-linear relationships between climate variables and agricultural GDP. This provides more accurate, data-driven forecasts to support policymakers. Given the increasing climate uncertainty in North Africa, these insights are both timely and essential for building resilience in Morocco’s farming economy.

Perspectives

Working on this article was a rewarding experience, as it allowed me to combine two fields I am passionate about: climate economics and innovative data science. I believe using machine learning to address real-world challenges like climate change makes the research more relevant and impactful. Morocco’s agriculture is not just an economic sector—it supports millions of livelihoods. I hope this study encourages more evidence-based decisions to help vulnerable communities adapt and thrive despite growing climate risks.

Pr Mariem Liouaeddine
Universite Ibn Tofail Kenitra

Read the Original

This page is a summary of: Impact of climate change on agricultural GDP in Morocco using machine learning techniques, Modeling Earth Systems and Environment, June 2025, Springer Science + Business Media,
DOI: 10.1007/s40808-025-02486-w.
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