{"id":921661,"date":"2026-10-09T09:12:27","date_gmt":"2026-10-09T09:12:27","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/921661\/"},"modified":"2026-10-09T09:12:27","modified_gmt":"2026-10-09T09:12:27","slug":"refined-crop-modelling-reveals-intensifying-impacts-of-droughts-and-heatwaves-on-global-wheat-productivity","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/921661\/","title":{"rendered":"Refined crop modelling reveals intensifying impacts of droughts and heatwaves on global wheat productivity"},"content":{"rendered":"

Rezaei, E. E. et al. Climate change impacts on crop yields. Nat. Rev. Earth Environ. 4, 831\u2013846 (2023).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143\u2013147 (2015).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84\u201387 (2016).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fu, J. et al. Extreme rainfall reduces one-twelfth of China\u2019s rice yield over the last two decades. Nat. Food 4, 416\u2013426 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lesk, C. et al. Compound heat and moisture extreme impacts on global crop yields under climate change. Nat. Rev. Earth Environ. 3, 872\u2013889 (2022).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Schewe, J. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat. Commun. 10, 1005 (2019).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

J\u00e4germeyr, J. & Frieler, K. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields. Sci. Adv. 4, eaat4517 (2018).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Noia, R. J. R. et al. The extreme 2016 wheat yield failure in France. Glob. Chang. Biol. 29, 3130\u20133146 (2023).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

J\u00e4germeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873\u2013885 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Webber, H. et al. Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Commun. 9, 4249 (2018).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sun, T. et al. Current rice models underestimate yield losses from short-term heat stresses. Glob. Chang. Biol. 27, 402\u2013416 (2021).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, B. et al. Pathways to identify and reduce uncertainties in agricultural climate impact assessments. Nat. Food 5, 550\u2013556 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tao, F. L. et al. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Chang. Biol. 24, 1291\u20131307 (2018).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wallach, D. et al. Multimodel ensembles improve predictions of crop-environment-management interactions. Glob. Chang. Biol. 24, 5072\u20135083 (2018).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3, 17102 (2017).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xiong, W. et al. Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat. Nat. Food 1, 63\u201369 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

M\u00fcller, C. et al. The global gridded crop model intercomparison phase 1 simulation dataset. Sci. Data 6, 50 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Z., Luo, Y. C., Han, J. C., Xu, J. L. & Tao, F. L. Estimating global wheat yields at 4 km resolution during 1982\u20132020 by a spatiotemporal transferable method. Remote Sens. 16, 2342 (2024).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jones, J. W. et al. The DSSAT cropping system model. Eur. J. Agron. 18, 235\u2013265 (2003).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, E. L. et al. Improving process-based crop models to better capture genotype\u00d7environment\u00d7management interactions. J. Exp. Bot. 70, 2389\u20132401 (2019).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Centre for Research on the Epidemiology of Disasters. EM-DAT. The International Disaster Database https:\/\/www.emdat.be\/<\/a> (University of Louvain, 2025).<\/p>\n

Attia, A. et al. Application of DSSAT-CERES-wheat model to simulate winter wheat response to irrigation management in the Texas High Plains. Agric. Water Manage. 165, 50\u201360 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xie, W. et al. Decreases in global beer supply due to extreme drought and heat. Nat. Plants 4, 964\u2013973 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, T. Y. et al. Increased wheat price spikes and larger economic inequality with 2\u00b0C global warming. One Earth 5, 907\u2013916 (2022).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ewert, F. et al. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agric. Ecosyst. Environ. 142, 6\u201317 (2011).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Huang, J. X. et al. Assimilation of remote sensing into crop growth models: current status and perspectives. Agric. For. Meteorol. 276, 107609 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

R\u00f6tter, R. P. et al. Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes-A review. Field Crop. Res. 221, 142\u2013156 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yin, J. B. et al. Future socio-ecosystem productivity threatened by compound drought-heatwave events. Nat. Sustain. 6, 259\u2013272 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lesk, C. et al. Stronger temperature-moisture couplings exacerbate the impact of climate warming on global crop yields. Nat. Food 2, 683\u2013691 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hao, Z. C. et al. Compound droughts and hot extremes: characteristics, drivers, changes, and impacts. Earth Sci. Rev. 235, 104241 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lesk, C. & Anderson, W. Decadal variability modulates trends in concurrent heat and drought over global croplands. Environ. Res. Lett. 16, 055024 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vogel, E. et al. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zaveri, E. & Lobell, D. B. The role of irrigation in changing wheat yields and heat sensitivity in India. Nat. Commun. 10, 4144 (2019).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mishra, V. et al. Moist heat stress extremes in India enhanced by irrigation. Nat. Geosci. 13, 722\u2013728 (2020).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Siebert, S., Ewert, F., Rezaei, E. E., Kage, H. & Grass, R. Impact of heat stress on crop yield\u2014on the importance of considering canopy temperature. Environ. Res. Lett. 9, 044012 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Webber, H. et al. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: a multi-model comparison. Field Crop. Res. 202, 21\u201335 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, X. H. et al. Global irrigation contribution to wheat and maize yield. Nat. Commun. 12, 1235 (2021).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872 (2016).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, B., Liu, D. L., Waters, C. & Yu, Q. Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia. Clim. Change 151, 259\u2013273 (2018).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

R\u00f6tter, R. P. et al. Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crop. Res. 133, 23\u201336 (2012).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Karger, D. N. et al. CHELSA-W5E5: daily 1km meteorological forcing data for climate impact studies. Earth Syst. Sci. Data 15, 2445\u20132464 (2023).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

You, L. Z., Wood, S., Wood-Sichra, U. & Wu, W. B. Generating global crop distribution maps: from census to grid. Agric. Syst. 127, 53\u201360 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Han, E., Ines, A. V. M. & Koo, J. Global High-Resolution Soil Profile Database for Crop Modeling Applications (International Food Policy Research Institute, 2015).<\/p>\n

Gao, Y. J. et al. Cost-effective adaptations increase rice production while reducing pollution under climate change. Nat. Food 6, 260\u2013272 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Heinke, J., M\u00fcller, C., Mueller, N. D. & J\u00e4germeyr, J. N application rates from mineral fertiliser and manure. Zenodo https:\/\/doi.org\/10.5281\/zenodo.4954582<\/a> (2021).<\/p>\n

Li, Y. B. et al. Wheat traits and the associated loci conferring radiation use efficiency. Plant J. 112, 565\u2013582 (2022).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ringeval, B. et al. Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences. Geosci. Model Dev. 14, 1639\u20131656 (2021).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182\u2013197 (2002).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Parent, B., Turc, O., Gibon, Y., Stitt, M. & Tardieu, F. Modelling temperature-compensated physiological rates, based on the co-ordination of responses to temperature of developmental processes. J. Exp. Bot. 61, 2057\u20132069 (2010).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jagermeyr, J. et al. GGCMI Phase 3 crop calendar. Zenodo https:\/\/doi.org\/10.5281\/zenodo.5062513<\/a> (2021).<\/p>\n

International Food Policy Research Institute (IFPRI). Global spatially-disaggregated crop production statistics data for 2010 version 2.0. Harvard Dataverse https:\/\/doi.org\/10.7910\/DVN\/PRFF8V<\/a> (2010).<\/p>\n

Portmann, F. T., Siebert, S. & D\u00f6ll, P. MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, GB1011 (2010).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Naab, J. B., Boote, K. J., Jones, J. W. & Porter, C. H. Adapting and evaluating the CROPGRO-peanut model for response to phosphorus on a sandy-loam soil under semi-arid tropical conditions. Field Crops Res. 176, 71\u201386 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zscheischler, J. & Seneviratne, S. I. Dependence of drivers affects risks associated with compound events. Sci. Adv. 3, e1700263 (2017).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tabari, H. & Willems, P. Trivariate analysis of changes in drought characteristics in the CMIP6 multimodel ensemble at global warming levels of 1.5 \u00b0, 2 \u00b0, and 3 \u00b0C. J. Clim. 35, 5823\u20135837 (2022).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Han, J. C. et al. Threat of low-frequency high-intensity floods to global cropland and crop yields. Nat. Sustain. 7, 994\u20131006 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kornhuber, K. et al. Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions. Nat. Clim. Change 10, 48\u201353 (2020).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Garc\u00eda-Herrera, R., D\u00edaz, J., Trigo, R. M., Luterbacher, J. & Fischer, E. M. A review of the European summer heat wave of 2003. Crit. Rev. Environ. Sci. Technol. 40, 267\u2013306 (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Iizumi, T. et al. Impacts of El Ni\u00f1o Southern Oscillation on the global yields of major crops. Nat. Commun. 5, 3712 (2014).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Luo, Y. et al. Accurately estimating global wheat yields at 4-km resolution during 1982\u20132020 (GlobalWheatYield4km). Figshare https:\/\/doi.org\/10.6084\/m9.figshare.10025006<\/a> (2023).<\/p>\n

Cao, S. et al. Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.2). Zenodo https:\/\/doi.org\/10.5281\/zenodo.8281930<\/a> (2023).<\/p>\n

Liu. R. et al. GLOBMAP global leaf area index since 1981. Zenodo https:\/\/doi.org\/10.5281\/zenodo.4700264<\/a> (2021).<\/p>\n

Luo, Y. Code for \u2018Refined crop modeling reveals intensifying impacts of droughts and heatwaves on global wheat productivity\u2019. Figshare https:\/\/doi.org\/10.6084\/m9.figshare.30846617<\/a> (2026).<\/p>\n","protected":false},"excerpt":{"rendered":"Rezaei, E. E. et al. Climate change impacts on crop yields. Nat. Rev. Earth Environ. 4, 831\u2013846 (2023).…\n","protected":false},"author":2,"featured_media":921662,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22],"tags":[64,63,44754,130206,75,1320,1321,128],"class_list":["post-921661","post","type-post","status-publish","format-standard","has-post-thumbnail","category-environment","tag-au","tag-australia","tag-climate-change-impacts","tag-ecological-modelling","tag-environment","tag-humanities-and-social-sciences","tag-multidisciplinary","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/921661","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/comments?post=921661"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/921661\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media\/921662"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media?parent=921661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/categories?post=921661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/tags?post=921661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}