Crop traits enabling yield gains under more freq.uent extreme climatic events

Haoliang Yan, Matthew Tom Harrison, Ke Liu, Bin Wang, Puyu Feng, Shah Fahad, Holger Meinke, Rui Yang, De Li Liu, Sotirios Archontoulis, Isaiah Huber, Xiaohai Tian, Jianguo Man, Yunbo Zhang, Meixue Zhou

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)

Abstract

Climate change (CC) in central China will change seasonal patterns of agricultural production through increasingly frequent extreme climatic events (ECEs). Breeding climate-resilient wheat (Triticum aestivum L.) genotypes may mitigate adverse effects of ECEs on crop productivity. To reveal crop traits conducive to long-term yield improvement in the target population of environment, we created 8192 virtual genotypes with contrasting but realistic ranges of phenology, productivity and waterlogging tolerance. Using these virtual genotypes, we conducted a genotype (G) by environment (E) by management (M) factorial analysis (G × E × M) using locations distributed across the entire cereal cropping zone in mid-China. The G × E × M invoked locally-specific sowing dates under future climates that were premised on shared socioeconomic pathways SSP5–8.5, with a time horizon centred on 2080. Across the simulated adaptation landscape, productivity was primarily driven by yield components and phenology (average grain yield increase of 6–69% across sites with optimal combinations of these traits). When incident solar radiation was not limiting carbon assimilation, ideotypes with higher grain yields were characterised by earlier flowering, higher radiation-use efficiency and larger maximum kernel size. At sites with limited solar radiation, crops required longer growing periods to realise genetic yield potential, although higher radiation-use efficiency and larger maximum kernel size were again prospective traits enabling higher rates of yield grains. By 2080, extreme waterlogging stress in some regions of mid-China will impact substantially on productivity, with yield penalties of up to 1010 kg ha−1. Ideotypes with optimal G × M could mitigate yield penalty caused by waterlogging by up to 15% under future climates. These results help distil promising crop trait by best management practice combinations that enable higher yields and robust adaptation to future climates and more extreme climatic events, such as flash flooding and soil waterlogging.

Original languageEnglish
Article number152170
JournalScience of the Total Environment
Volume808
DOIs
Publication statusPublished - 20 Feb 2022
Externally publishedYes

Bibliographical note

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© 2021 The Authors

Keywords

  • Climate change
  • Crop model
  • Model parameters
  • Waterlogging stress
  • Wheat ideotypes

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