Articles | Volume 25, issue 12
Research article
22 Dec 2021
Research article |  | 22 Dec 2021

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

Michael Peichl, Stephan Thober, Luis Samaniego, Bernd Hansjürgens, and Andreas Marx

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Cited articles

Albers, H., Gornott, C., and Hüttel, S.: How do inputs and weather drive wheat yield volatility? The example of Germany, Food Policy, 70, 50–61,, 2017. a, b, c
Apley, D. W. and Zhu, J.: Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models, arXiv [preprint], arXiv:1612.08468, 2016. a, b, c
Auffhammer, M., Hsiang, S., Schlenker, W., and Sobel, A.: Using Weather Data and Climate Model Output in Economic Analyses of Climate Change, Tech. Rep. 2, National Bureau of Economic Research, Cambridge, MA,, 2013. a
Bachmair, S., Svensson, C., Hannaford, J., Barker, L. J., and Stahl, K.: A quantitative analysis to objectively appraise drought indicators and model drought impacts, Hydrol. Earth Syst. Sci., 20, 2589–2609,, 2016. a
Bachmair, S., Svensson, C., Prosdocimi, I., Hannaford, J., and Stahl, K.: Developing drought impact functions for drought risk management, Nat. Hazards Earth Syst. Sci., 17, 1947–1960,, 2017. a
Short summary
Using a statistical model that can also take complex systems into account, the most important factors affecting wheat yield in Germany are determined. Different spatial damage potentials are taken into account. In many parts of Germany, yield losses are caused by too much soil water in spring. Negative heat effects as well as damaging soil drought are identified especially for north-eastern Germany. The model is able to explain years with exceptionally high yields (2014) and losses (2003, 2018).