Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning.

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

SUBMITTER: Zhong R 

PROVIDER: S-EPMC11197588 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature