Integrated prediction and optimization of sugarcane yield: Analyzing climate impact, agricultural practices, and optimal harvest timing in Bangladesh

Abstract

This study presents a comprehensive approach for predicting and optimizing sugarcane yield in Bangladesh by incorporating climate dynamics, agricultural practices and optimal harvest timing. Highly accurate yield predictions are made that identify the optimal harvest periods using sophisticated machine learning models trained on detailed historical data encompassing the years 1971 to 2024. The results demonstrate that temperature and precipitation can strongly influence crop productivity and provide useful solutions to manage the impact of climate variability. This research serves a critical need for farmers and policymakers, offering practical methods to maintain or increase crop yields as well as agricultural resilience to extreme climate change.