Effects of Bias-Corrected Regional Climate Projections and Their Spatial Resolutions on Crop Model Results under Different Climatic and Soil Conditions in Austria

Sabina Thaler,Josef Eitzinger,Gerhard Kubu,Miroslav Trnka,Herbert Formayer

doi:10.3390/agriculture11111029

Abstract

The quality, reliability, and uncertainty of Austrian climate projections (ÖKS15) and their impacts on the results of the crop model DSSAT for three different orographic and climatic agricultural regions in Austria were analyzed. Cultivar-specific grain yields of winter wheat, spring barley, and maize were simulated for different soil classes to address three main objectives. First, the uncertainties of simulated crop yields related to the ÖKS15 projections were analyzed under current climate conditions. The climate projections revealed that the case study regions with higher humidity levels generally had lower yield deviations than the drier regions (yield deviations from −19% to +15%). Regarding the simulated crop types, spring barley was found to be less sensitive to the climate projections than rainfed maize, and the response was greater in regions with a low soil water storage capacity. The second objective was to simulate crop yields for the same cultivars using future climate projections. Winter wheat and spring barley tended to show increased yields by the end of the century due to an assumed CO2-fertilization effect in the range of 3–23%, especially under RCP 8.5. However, rainfed and irrigated maize were associated with up to 17% yield reductions in all three study regions due to a shortened growth period caused by warming. The third objective addressed the effects of crop model weather input data with different spatial resolutions (1 vs. 5, 11, and 21 km) on simulated crop yields using the climate projections. Irrigated grain maize and rainfed spring barley had the lowest simulated yield deviations between the spatial scales applied due to their better water supply conditions. The ranges of uncertainty revealed by the different analyses suggest that impact models should be tested with site representative conditions before being applied to develop site-specific adaptation options for Austrian crop production.

Highlights

IntroductionImpact models for agriculture, such as dynamic (process-based) crop growth models, have become useful tools for assessing the impacts of climate change and associated weather extremes on crop production [1,2,3,4,5]
Management interactions, as well as incorporating system feedback measurements [6,7]. These models have been used for estimating the impacts of climate change on crop growth processes and yields at different scales [8,9,10]
Kremsmünster and Poysdorf, on the other hand, showed yield stagnation or even depression in the aggregated input data—up to −10% for winter wheat grown in Poysdorf (IPSL-RCA)

Summary

Introduction

Impact models for agriculture, such as dynamic (process-based) crop growth models, have become useful tools for assessing the impacts of climate change and associated weather extremes on crop production [1,2,3,4,5]. These models are complex, constructed according to biophysical processes, and assess genotype × environment ×. Management interactions, as well as incorporating system feedback measurements [6,7] These models have been used for estimating the impacts of climate change on crop growth processes and yields at different scales [8,9,10]. During the assessment of these impacts, uncertainties about the underlying physical, biological, and socioeconomic processes arise [11,12,13,14,15,16,17,18,19,20,21]

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Results

Discussion

Conclusion