Journal Article

Uncertainty in simulating wheat yields under climate change

Plant Production Research ; Agrifood Research Finland, Shcherbak, I, Gayler, S, Supit, I, Department of Agroecology ; Aarhus University [Aarhus], Brisson, Nadine, WESS-Water and Earth System Science Competence Cluster ; Eberhard Karls Universität Tübingen = University of Tübingen, Challinor, A. J, Cammarano, D, Stratonovitch, P, Martre, Pierre, Hunt, L. A, Institute for Climate and Water ; Instituto Nacional de Tecnología Agropecuaria (INTA), Nendel, C, Ecosystem Sciences ; Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO), Osborne, T. M, Plant Production Systems and Earth System Science-Climate Change ; Wageningen University and Research [Wageningen] (WUR), Biological Systems Engineering ; Washington State University (WSU), Institute of Soil Ecology ; Helmholtz Zentrum München = German Research Center for Environmental Health (HMGU), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE) ; Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS) ; University

2013

Overview

Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

• Publisher: Nature Publishing Group,CCSD,Nature Publishing Group UK
• Subject: 704/106/694/1108 / 704/106/694/2739 / 706/1143 / Agricultural and forest climatology and meteorology. Irrigation. Drainage / Agricultural and forest meteorology / Agricultural production / Agronomy. Soil science and plant productions / Biological and medical sciences / Carbon dioxide / Climate Change / Climate Change/Climate Change Impacts / Climatology, meteorology / Climatology. Bioclimatology. Climate change / crop models / Crop yield / crops / Earth, ocean, space / Emissions / Environment / Environmental impact / Environmental Law/Policy/Ecojustice / Exact sciences and technology / External geophysics / Farm buildings / Fundamental and applied biological sciences. Psychology / General agronomy. Plant production / General Circulation Models / Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production / global warming / grain yield / greenhouse gas emissions / Greenhouse gases / letter / Life Sciences / mathematical models / Meteorology / quantitative analysis / simulation models / temperature / Triticum aestivum / uncertainty / uncertainty analysis / variance / Wheat
• ISBN: 0003268161000