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Cultivar evolution underpins maize yield sensitivity to adverse climate conditions

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Abstract

Cultivar evolution through plant breeding is a cornerstone of contemporary food security, but the extent to which genetic adaptation to climatic variability and shocks contributes to yield gains is not well known. Here, we compile 48,797 cultivar-site-year observations from 2001 to 2020, covering the four prominent maize production regions in China with differing shifts in climatic conditions. The data shows that cultivar evolution underlies long-term yield gains, with productivity increasing by 0.3–2.8 Mg ha-1 per decade. Yields in Northeast China (NEC) and North China (NC) are most vulnerable to heat stress during July and August, whereas high or insufficient precipitation during the growing season is a foremost constraint to yield gains in Southwest China (SWC) and Northwest China (NWC), respectively. Cultivar evolution has significant impacts on yield sensitivity to climate, with genotypic sensitivities to heat stress amplifying in NEC and diminishing over time in NC, respectively. In contrast, yield sensitivity to precipitation increases in SWC and NWC as a result of breeding. These results underscore the importance of breeding climate-resilient cultivars that account for contextualised in situ environmental constraints and climatic adversities in obtaining high yield.

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Data availability

The national unified maize variety test (NUMVT) data and yield data of actual production collected from the Statistical Yearbook of city generated in this study have been deposited in the Figshare database under accession code https://doi.org/10.6084/m9.figshare.29484539. Historical climate data pertaining to maize growth stages from 2001 to 2020 are available at https://data.cma.cn/. Source data are provided with this paper.

Code availability

Code for data manipulation was generated in R v4.3, with packages comprising tidyverse, lme4, lmerTest and boot adopted in data processing, and with ggplot2, metR, ggalt, patchwork and sf operationalised for visualization. The detailed R codes are available at Zenodo repository: https://doi.org/10.5281/zenodo.15816886.

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Acknowledgements

This research was finally supported by the National Key Research & Development Program of China (2023YFD2302300, X.Y.), the Young Talent Promotion Project of China Association for Science and Technology (2019QNRC001, X.Y.) and the 2115 Talent Development Program of China Agricultural University (X.Y. and F.C.). DM was partly supported by the Associate International Laboratory A-AGD (INRAE-CAU). We also thank the National Agro-Tech Extension and Service Center coordinated the national unified maize variety test (NUMVT) of China, and all the scientists and technicians involved in the NUMVT. Moreover, any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the US Department of Agriculture.

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Authors and Affiliations

  1. College of Agronomy and Biotechnology, China Agricultural University; Key Laboratory of Farming system, Ministry of Agriculture and Rural Affairs of China, Beijing, China

    Li Zhang, Zhiyuan Bai, Wang Xi, Datong Zhang, Shanheng Shi, Fu Chen & Xiaogang Yin

  2. Crop Research Institute of Sichuan Academy of Agricultural Sciences (Sichuan Provincial Germplasm Resources Center); Crop Germplasm Innovation and Genetic Improvement Key Laboratory of Sichuan Province, Chengdu, China

    Li Zhang

  3. Department of Agroecology, Aarhus University, Tjele, Denmark

    Jørgen Eivind Olesen

  4. Tasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS, Australia

    Matthew Tom Harrison & Ke Liu

  5. Institute for Sustainability, Energy and Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Carl Bernacchi

  6. Université Paris-Saclay, AgroParisTech, INRAE,UMR 518 MIA PS, Palaiseau, France

    David Makowski

  7. Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    Andrew James McDonald

  8. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

    Ehsan Eyshi Rezaei

  9. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA

    Qi Zheng

  10. School of Geography, Earth and Environmental Sciences (GEES), University of Birmingham, Birmingham, UK

    Yuli Shan

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  1. Li Zhang
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  2. Zhiyuan Bai
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  3. Wang Xi
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Contributions

X.Y., F.C., and L.Z. designed the study. X.Y., Z.B., W.X., D.Z., L.Z., and S.S. created the database. L.Z. led data analysis, X.Y., F.C., J.E.O., M.T.H., C.B., D.M. and E.E.R. analyzed and interpreted the results. L.Z. and X.Y. wrote the original manuscript. M.T.H., D.M.C.B., A.J.M., K.L., Q.Z., Y.S., and J.E.O contributed greatly in revising the manuscript; all authors edited and revised the paper.

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Xiaogang Yin.

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Zhang, L., Bai, Z., Xi, W. et al. Cultivar evolution underpins maize yield sensitivity to adverse climate conditions.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-71045-3

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