Methodology to assess the changing risk of yield failure due to heat and drought stress under climate change

Tommaso Stella,Nándor Fodor,Alfredo Rodríguez,Clara Gabaldón-Leal,Heidi Webber,Sravya Mamidanna,Marco Bindi,Jon Lizaso ,Mohamed Jabloun,Stefan Fronzek,Loïc Manceau ,Brian Collins,Pierre Stratonovitch,Kurt Christian Kersebaum ,Pierre Martre,Mikhail A Semenov ,Roberto Ferrise,Claas Nendel,Alex C Ruane ,Jørgen E Olesen ,Simone Bregaglio,Ignacio J Lorite ,Margarita Ruíz-Ramos ,Babacar Faye,Frank Ewert

doi:10.1088/1748-9326/ac2196

Abstract

While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.

Highlights

Impacts of climate change on crops are already observed across world regions (Ray et al 2019)
This work aimed at providing a methodology to assess how climate change may translate into changes in risk of yield failures
Relative distributions were used to study the changes occurring across the entire yield distribution under climate change, providing insights that are missed by common analyses of average shifts in crop yields

Summary

Introduction

Impacts of climate change on crops are already observed across world regions (Ray et al 2019). The reliance on only historical yield distributions of crop yields is challenged as interactions between CO2 concentrations, temperatures and crop water demand present challenges to extrapolating historical data with statistical models (Lobell and Asseng 2017) Such interactions are considered by process based crop models, which dynamically simulate crop resource capture and yield formation in response to environment and management drivers under future climate scenarios (Ewert et al 2015). Webber et al (2018) presented a crop model ensemble analysis that allowed to distinguish the effects of drought stress, heat stress and mean warming on crop yields in Europe with climate change Over all years, they projected increased yield losses due to drought for maize, and that drought would drive yield losses for maize and wheat in low yielding years. Our study adds to this research by trying to distinguish the differential impacts of climate change on upper and lower tail of the yield distribution (Malikov et al 2020)

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