Crop traits enabling yield gains under more frequent extreme climatic events
Yan, H and Harrison, MT and Liu, K and Wang, B and Feng, P and Fahad, S and Meinke, H and Yang, R and Liu, DL and Archontoulis, S and Huber, I and Tian, X and Man, J and Zhang, Y and Zhou, M, Crop traits enabling yield gains under more frequent extreme climatic events, Science of The Total Environment, 808 Article 152170. ISSN 0048-9697 (2022) [Refereed Article]
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.
climate change, crop model, model parameters, wheat ideotypes, waterlogging stress