Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

Bibliographic Data

ID15548507
AuthorsSrishti Vishwakarma (0000-0003-1674-4197, Oak Ridge National Laboratory, corresponding author), Stephanie A Morris (0000-0001-5714-5276, Joint Global Change Research Institute, corresponding author), Stephanie Waldhoff (0000-0002-8073-0868), ABIGAIL SNYDER (0000-0002-9034-9948, Pacific Northwest National Laboratory, corresponding author), Claudia Tebaldi (0000-0001-9233-8903, Joint Global Change Research Institute, corresponding author)
Year2025
Volume20
Issue8
Pages084031-084031
Publication date2025-06-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ade728
OpenAlexW4411545011
LanguageEN
References cited39

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre −1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally

Economics · Geography · Machine learning · Meteorology · Production (economics · Transfer of learning · Computer Science · Environmental Science · Smart Agriculture and AI

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