Machine learning-based cotton yield forecasting under climate change for precision agriculture

Abstract

The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. The impact of changing climate variables on crop productivity, particularly for key agricultural commodities, raises concerns about future yields. This study examines the potential effects of climate change on cotton production by integrating historical climate data, Global Climate Models (GCMs, CMIP3) projections, and cotton yield data. This study employs a diverse range of machine learning (ML) methods, including multiple regression, k-nearest neighbors (KNN), boosted tree algorithms, and various types of artificial neural networks (ANNs), to investigate the intricate relationship between climate factors and cotton yields. The models are developed and tested using data on climate and crop yields collected from three regions in Punjab, Pakistan, spanning the years 1991 to 2020. To estimate future yield outcomes, climate projections from General Circulation Models (GCMs) are downscaled under the SRA1B, A2, and B1 carbon emission scenarios, enabling forecasts extending to the year 2050. Results show that rainfall has a negligible impact on cotton yield (R = 0.0002), whereas maximum temperature (R = -0.183) is identified as the primary climatic factor influencing yield, followed by minimum temperature (R = 0.248). Among the models, the generalized feedforward (GFF) demonstrated the best performance (R = 0.960, MSE = 0.110, NMSE = 0.187, MAE = 0.269), outperforming probabilistic neural network (PNN), KNN, multilayer perceptron (MLP), and boosted trees. In contrast, linear regression (LR) and multiple regression models performed less effectively. The reliability of GFF and KNN in providing yield estimates ($R^2$ = 0.892, 0.861) supports their potential for accurate predictions. The study forecasts a 4.5% decline in cotton yield by 2050 compared to the highest recorded yield for the region, highlighting the impact of climate change on cotton production and its potential threat to food security. Nevertheless, the adaptive capabilities of the ANN (GFF) models across various climate scenarios present promising tools for integrating ML into climate-resilient agricultural practices, contributing to sustainable agrarian security and mitigating the adverse effects of climate change on food supply.