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ABSTRACT Amid the quest for sustainable agriculture, this study explores key ecological and technological factors influencing crop production under climate change. We conduct a comprehensive assessment of temperature, biomass, farmer education, renewable energy devices, greenhouse gas emissions and their effects on rice yields in Granma, Cuba, from 1989 to 2022. The novelty of this study lies in developing precise forecasting models via machine learning techniques, including Bayesian neural network, support vector machines, and pattern recognition neural network that integrate multi‐variable historical data for highly accurate predictions. Furthermore, farmer education plays a vital role in adopting these technologies, enhancing overall productivity. Quantile autoregressive distributed lag analysis reveals that biomass boosts long‐run rice production by 1.54%, while a 1% increase in greenhouse gases reduces it by 0.2%. Rising temperature significantly lowers short‐term yields by 0.48% per 1% increase, which is positively offset by renewable energy devices. This approach not only reduces environmental impact but also ensures long‐term food security in the region. The study's contributions include policy recommendations that align sustainable development goals with strategies to cut emissions through enhanced water management, low‐emission techniques, and low‐energy sources. The adoption of machine learning and smart practices like heat‐tolerant cultivars and controlled fertilization is recommended to mitigate ecological risks. Additionally, investing in education and extension services can empower farmers to implement these practices effectively. For policymakers, key strategies encompass improved water management, low‐emission practices, renewable energy adoption, education investments, and climate‐smart farming practices to foster sustainable rice production. The study highlights the importance of integrating technology with ecological considerations for sustainable agriculture. Overall, our findings provide a roadmap for other regions facing similar challenges.
Dar et al. (Sun,) studied this question.