Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany

Abstract

Agricultural production is highly dependent on the weather. The mechanisms of action are complex and interwoven, making it difficult to identify relevant management and adaptation options. The present study uses random forests to investigate such highly non-linear systems for predicting yield anomalies in winter wheat at district levels in Germany. In order to take into account sub-seasonality, monthly features are used that explicitly take soil moisture into account in addition to extreme meteorological events. Clustering is used to show spatially different damage potentials, such as a higher susceptibility to drought damage from May to July in eastern Germany compared to the rest of the country. In addition, relevant heat effects are not detected if the clusters are not sufficiently defined. The variable with the highest importance is soil moisture in March, where higher soil moisture has a detrimental effect on crop yields. In general, soil moisture explains more yield variations than the meteorological variables. The approach has proven to be suitable for explaining historical extreme yield anomalies for years with exceptionally high losses (2003, 2018) and gains (2014) and the spatial distribution of these anomalies. The highest test R-squared (R2) is about 0.68. Furthermore, the sensitivity of yield variations to soil moisture and extreme meteorological conditions, as shown by the visualization of average marginal effects, contributes to the promotion of targeted decision support systems.