  Accurate prediction of crop yield supported by scientific and domain-relevant
insights, can help improve agricultural breeding, provide monitoring across
diverse climatic conditions and thereby protect against climatic challenges to
crop production including erratic rainfall and temperature variations. We used
historical performance records from Uniform Soybean Tests (UST) in North
America spanning 13 years of data to build a Long Short Term Memory - Recurrent
Neural Network based model to dissect and predict genotype response in
multiple-environments by leveraging pedigree relatedness measures along with
weekly weather parameters. Additionally, for providing explainability of the
important time-windows in the growing season, we developed a model based on
temporal attention mechanism. The combination of these two models outperformed
random forest (RF), LASSO regression and the data-driven USDA model for yield
prediction. We deployed this deep learning framework as a 'hypotheses
generation tool' to unravel GxExM relationships. Attention-based time series
models provide a significant advancement in interpretability of yield
prediction models. The insights provided by explainable models are applicable
in understanding how plant breeding programs can adapt their approaches for
global climate change, for example identification of superior varieties for
commercial release, intelligent sampling of testing environments in variety
development, and integrating weather parameters for a targeted breeding
approach. Using DL models as hypothesis generation tools will enable
development of varieties with plasticity response in variable climatic
conditions. We envision broad applicability of this approach (via conducting
sensitivity analysis and "what-if" scenarios) for soybean and other crop
species under different climatic conditions.
