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Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat

Abstract

Global gridded climate–crop model ensembles are increasingly used to make projections of how climate change will affect future crop yield. However, the level of certainty that can be attributed to such simulations is unknown. Here, using currently available geospatial datasets and a widely employed simulation procedure, we created a wheat model ensemble of 1,440 global simulations of 20 climate scenarios, 3 crop models, 4 parameterization strategies and 3 management inputs of sowing date. We quantified the contributions of climate, model, parameterization and management to the overall uncertainty to predicted responses of yield to warming, then related the results to the latitude of the grid cells. For all warming scenarios, the total uncertainty for mid- and high latitudes is much larger than for low latitudes. Uncertainty arising from crop models was larger than that from the other sources combined. Parameterizing crop models with grid-specific information on wheat cultivars tended to decrease the crop model uncertainty, particularly for low latitudes. Crop model improvements and better-quality spatial input data more closely representing the wide range of growing conditions around the world will be needed to reduce the uncertainty of climate change impact assessment of crop yields.

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Fig. 1: Differences between simulated and reported days to maturity and grain yield under four parameterization strategies.
Fig. 2: Simulated means and uncertainties of global wheat grain yield response to warming during the wheat growing season.
Fig. 3: Simulated yield change and associated uncertainty at different latitudes for three warming levels.
Fig. 4: Combined uncertainty of climate projection, crop model and management input of sowing date averaged across latitudes with four parameterization strategies.

Data availability

All data supporting the simulation and analysis in this study are publicly available from open sources. The historical weather data (1981–2010) are available at https://data.giss.nasa.gov/impacts/agmipcf/; the future climate scenario data (2010–2099) are available at https://esgf-node.llnl.gov/projects/esgf-llnl/. Wheat mega-environment and definition are from https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10625. The spatial data of harvest area, yield, crop calendar and irrigation portion are available at http://mapspam.Info/ (SPAM) and http://www.sage.wisc.edu (SAGE). Historical chemical nitrogen input of wheat is available at http://www.earthstat.org/nutrient-application-major-crops/. The soil data are available from the WISE database (https://www.isric.online/index.php/) and the Digital Soil Map of the World (DSMW). All other generated data (that is, model coefficients for the three crop models), simulation outputs (yield and phenology) and processed data for plotting the figures are available from the corresponding author on request.

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Acknowledgements

We thank J. Yang for help analysing data, and J. Yang and U. A. Schulthess for helpful comments. This work was directly supported by The National Science Foundation of China (grant nos 4147104 and 41171093). This study was also indirectly supported by the CGIAR research programme on wheat agri-food systems (CRP WHEAT) and the CGIAR Platform for Big Data in Agriculture, the World Bank and the Mexican government through the Sustainable Modernization of Traditional Agriculture (MasAgro) project. R. Robertson’s contributions were supported by the CGIAR Research Program on Policies, Institutions, and Markets.

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W.X. and S.A. conceived the study. W.X., I.H.-O., R.R., K.S., D.P., M.R. and B.G. implemented the experiment, W.X., S.A. and G.H. drafted the paper, and all contributed to the writing.

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Correspondence to Wei Xiong.

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Xiong, W., Asseng, S., Hoogenboom, G. et al. Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat. Nat Food 1, 63–69 (2020). https://doi.org/10.1038/s43016-019-0004-2

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