Observational constraint of process crop models suggests higher risks for global maize yield under climate change

Abstract

Abstract Projecting future changes in crop yield usually relies on process-based crop models, but the associated uncertainties (i.e. the range between models) are often high. In this study, a Machine Learning (i.e. Random Forest, RF) based observational constraining approach is proposed for reducing the uncertainties of future maize yield projections by seven process-based crop models. Based on the observationally constrained crop models, future changes in yield average and yield variability for the period 2080–2099 are investigated for the globe and top ten producing countries. Results show that the uncertainties of crop models for projecting future changes in yield average and yield variability can be largely reduced by 62% and 52% by the RF-based constraint, respectively, while only 4% and 16% of uncertainty reduction is achieved by traditional linear regression-based constraint. Compared to the raw simulations of future change in yield average (−5.13 ± 18.19%) and yield variability (−0.24 ± 1.47%), the constrained crop models project a much higher yield loss (−34.58 ± 6.93%) and an increase in yield variability (3.15 ± 0.71%) for the globe. Regionally, the constrained models show the largest increase in yield loss magnitude in Brazil, India and Indonesia. Our results suggest more agricultural risks under climate change than previously expected after observationally constraining crop models. The results obtained in this study point to the importance for observationally constraining process crop models for robust yield projections, and highlight the added value of using Machine Learning for reducing the associated uncertainties.