ML4WX

Applications & impacts

Influence of Meteo-Climatic Variables and Fertilizer Use on Crop Yields in the Sahel: A Nonlinear Neural-Network Analysis

Antonello Pasini, Giuseppina De Felice Proia, Francesco Nicola Tubiello

Climate ·

PaperPDF

In one sentence. Feed-forward neural networks show heat stress and fertilizer drive maize and millet yields in the Sahel, explaining 59-79% of yield variance.

Abstract

The Sahel is one of the regions with the highest rates of food insecurity in the world. Understanding the driving factors of agricultural productivity is, therefore, essential for increasing crop yields whilst adapting to a future that will be increasingly dominated by climate change. This paper shows how meteo-climatic variables, combined with fertilizers’ application rates, have affected the productivity of two important crops in the Sahel region, i.e. maize and millet, over the last three decades. To this end, we have applied a specifically designed neural network tool (optimised for analysis of small datasets), endowed with feed-forward networks and backpropagation training rules and characterised by generalised leave-one-out training and multiple runs of neural network models in an ensemble strategy. This tool allowed us to identify and quantify the impacts of single drivers and their linear and nonlinear role. The variables analysed included temperature, precipitation, atmospheric CO2 concentration, chemical and organic fertilizer input. They explained most of the variance in the crop data (R2 = 0.594 for maize and R2 = 0.789 for millet). Our analysis further allowed us to identify critical threshold effects affecting yields in the region, such as the number of hours with temperature higher than 30 °C during the growing season. The results identified heat waves and fertilizer application rates playing a critical role in affecting maize and millet yields in this region, while the role of increasing CO2 was less important. Our findings help identify the modalities of ongoing and future climate change impacts on maize and millet production in the Sahel.

Labels

Topic
Applications & impacts, Climate analysis & attribution
Type
Application study
Method
Classical ML (trees, GP, linear, kernels)
Domain
Regional
Data
Other
Status
Research
Identifiers
DOI:10.3390/cli10120193 OpenAlex:W4310681743
Added
2026-10-22

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