PDF(Pubmed)
Abstract：
Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R2 = 0.81) and well reproduced the extreme yield anomalies in 2012 (R2 = 0.79). Further attribution analysis indicated that extreme heat stress was the major cause for yield loss, contributing to 72.5% of the yield loss, followed by anomalies of vapor pressure deficit (17.6%) and precipitation (10.8%). Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012. Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.
摘要：
在大的空间尺度上提供准确的作物产量估算和了解极端气候胁迫下的产量损失是维持全球粮食安全的紧迫挑战。虽然数据驱动的深度学习方法在预测产量模式方面表现出了很大的能力，它检测和归因于极端气候对产量的影响的能力仍然未知。在这项研究中,我们开发了一个基于深度神经网络的多任务学习框架,以估计2006年至2018年美国玉米带县级玉米产量的变化,并特别关注2012年的极端产量损失.我们发现，我们的深度学习模型在2006-2018年（R2=0.81）具有良好的准确性，并很好地再现了2012年的极端产量异常（R2=0.79）。进一步的归因分析表明，极端热胁迫是产量损失的主要原因，造成72.5%的产量损失，其次是蒸气压不足（17.6％）和降水（10.8％）的异常。我们的深度学习模型还能够估计气候因素对玉米产量的累积影响，并确定2012年蚕丝期是影响产量对极端气候胁迫的最关键阶段。我们的研究结果提供了一个新的时空深度学习框架，以评估和归因于数据丰富时代的作物产量对气候变化的响应。
