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Abstract：
Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.
摘要：
农业,人类文明的基石，面临气候变化带来的日益严峻的挑战，资源限制,和停滞不前的收益率。精确的作物产量预测对于制定贸易政策至关重要，发展战略,人道主义倡议。本研究引入了一个全面的机器学习框架，旨在预测作物产量。我们在适度碳排放情景下利用CMIP5气候预测来评估农业用地的未来适宜性，并纳入气候数据，历史农业趋势，和化肥用量对项目产量的影响。我们的综合方法预测到2028年东南亚作物产量的显着区域变化，确定潜在的耕地利用。具体来说,印度尼西亚的农田面积，马来西亚,菲律宾,如果不采取行动，越南预计将下降10%以上，并且有可能减轻这种损失。此外，越南的大米产量预计将下降19%，泰国将下降7%，而菲律宾可能会看到与2021年相比5%的增长。我们的发现强调了气候变化和人类活动对农业生产力的关键影响，为决策和促进国际合作提供必要的见解。
