With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.

翻译：面对数十亿人口面临中度或重度粮食不安全问题，由于气候变化和地缘政治事件的影响，全球粮食供应的韧性日益受到关注。本文提出一个框架，通过结合遥感技术、深度学习、作物产量建模和粮食分配系统的因果建模，以更准确地识别粮食安全热点区域。虽然我们认为这些方法可适用于世界其他地区，但本分析聚焦于供应全球大量人口的印度北部小麦主产区。我们基于法国经筛选的遥感数据，对用于小麦农场识别的深度学习领域自适应方法进行了定量分析。我们利用现有作物产量建模工具WOFOST模拟气候变化对作物产量的影响，并通过纵向惩罚函数回归识别作物模拟误差的关键驱动因素。本文还描述了印度粮食分配系统的系统动力学模型，以及基于预测作物产量数据驱动该模型所得到的粮食不安全识别结果。