Uncertainty in soil data can outweigh climate impact signals in crop yield simulations

Abstract

Paper contact with cynthia festin: festin@iiasa.ac.at

Agraïments: C.F. was partly supported by a Research Fellowship of the Center for Advanced Studies of LMU Munich. We thank Joshua Elliott from the Global Gridded Crop Model Intercomparison (GGCMI) project for processing climate input data and the GGCMI and ISI-MIP project teams for providing various input data used in this study.

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations.