Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models.

ABSTRACT: Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yiel

SUBMITTER: Leng G 

PROVIDER: S-EPMC7212054 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature