No perfect storm for crop yield failure in Germany

Heidi Webber,Frank Ewert,Gunnar Lischeid,Robert Finger,Thomas Gaiser,Claas Nendel,Michael Sommer

doi:10.1088/1748-9326/aba2a4

Abstract

Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.

Highlights

Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity (Battisti and Naylor 2009, Tadasse et al 2016)
While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling
We examined correlations between observations and processes simulated by the process-based model: radiation capture; growing season duration; degree of yield reduction due to insufficient water, further called drought stress; and degree of yield limitation due to crop temperatures above an upper threshold damaging flowering and grain set, further referred to as heat stress (Eyshi Rezaei et al 2015) to understand how much variation could be explained by these processes

Summary

Introduction

Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity (Battisti and Naylor 2009, Tadasse et al 2016). Heavy rains can delay field operations, increase disease loads, cause harvest losses due to lodging (Kristensen et al 2011) or nitrate leaching. Record-low wheat yields in France in 2016 went largely undetected by forecasters until almost harvest and were only later attributed to the combination of an unusually warm autumn and wet spring (Ben-Ari et al 2018). Was this an anomaly or are unusual combinations of seemingly ‘nonextreme’ weather more broadly responsible for yield failures? There is little conclusive evidence of the most frequent and significant causes of yield failures

Methods

Results

Conclusion