Generative weather for improved crop model simulations
Yuji Saikai
Accurate and precise crop yield prediction is invaluable for decision making
at both farm levels and regional levels. To make yield prediction, crop models
are widely used for their capability to simulate hypothetical scenarios. While
accuracy and precision of yield prediction critically depend on weather inputs
to simulations, surprisingly little attention has been paid to preparing
weather inputs. We propose a new method to construct generative models for
long-term weather forecasts and ultimately improve crop yield prediction. We
demonstrate use of the method in two representative scenarios -- single-year
production of wheat, barley and canola and three-year production using
rotations of these crops. Results show significant improvement from the
conventional method, measured in terms of mean and standard deviation of
prediction errors. Our method outperformed the conventional method in every one
of 18 metrics for the first scenario and in 29 out of 36 metrics for the second
scenario. For individual crop modellers to start applying the method to their
problems, technical details are carefully explained, and all the code, trained
PyTorch models, APSIM simulation files and result data are made available.