Machine learning approaches can reduce environmental data requirements for regional yield potential simulation

Abstract

Abstract The crop growth model (CGM) widely contributes to studying the impacts of regional climate change on crop growth status and yield. However, it requires high-quality daily weather data for the model establishment and verification, and the low data availability limits the application of CGM at the regional scale. With the rapid development of machine learning techniques, adapting data-driven machine learning algorithms to build a meta-model with environmental feature variables at the desired spatio-temporal scale provides a new method for regional yield simulation. In this study, we developed four machine-learning-based meta-models to simulate regional yield potential (RYP) of wheat in China with the selected environmental feature variables and assessed four different machine learning algorithms, including multiple linear regression (MLR), artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The research aimed to verify whether these meta-models can outperform CGM in simulating RYP. The results showed that the meta-models could reduce the requirements of the number of input variables and the amount of data for RYP simulation and maintain the simulation accuracy because monthly weather variables could replace daily weather variables in the meta-models. Although all four meta-models can well reveal the mean RYP, the meta-model based on RF has the best performance. In the RF-based meta-model, longitude, latitude, altitude, and the averaged maximum temperature in March are the top four ranked essential variables. However, the generalizability of meta-models is affected by the training dataset, and the meta-models cannot adapt appropriately to new unseen data. Moreover, there is no unique optimal machine learning algorithm used for building meta-models, and this will increase the workload of similar research in the future.