Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning

文献类型： 外文期刊

第一作者： Zhang, Liangliang

作者： Zhang, Liangliang;Zhang, Zhao;Luo, Yuchuan;Cao, Juan;Li, Ziyue;Xie, Ruizhi;Zhang, Liangliang;Zhang, Zhao;Luo, Yuchuan;Cao, Juan;Li, Ziyue;Tao, Fulu;Tao, Fulu;Li, Shaokun

作者机构：

关键词： climate change impact; adaptation; hybrid ideotype; food security; machine learning

期刊名称：ENVIRONMENTAL RESEARCH LETTERS （ 影响因子：6.947； 五年影响因子：8.414 ）

ISSN： 1748-9326

年卷期： 2021 年 16 卷 12 期

页码：

收录情况： SCI

摘要： Crop hybrid improvement is an efficient and environmental-friendly option to adapt to climate change and increase grain production. However, the adaptability of existing hybrids to a changing climate has not been systematically investigated. Therefore, little is known about the appropriate timing of hybrid adaptation. Here, using a novel hybrid model which coupled CERES-Maize with machine learning, we critically investigated the impacts of climate change on maize productivity with an ensemble of hybrid-specific estimations in China. We determined when and where current hybrids would become unviable and hybrid adaptation need be implemented, as well as which hybrid traits would be desirable. Climate change would have mostly negative impacts on maize productivity, and the magnitudes of yield reductions would highly depend on the growth cycle of the hybrids. Hybrid replacement could partially, but not completely, offset the yield loss caused by projected climate change. Without adaptation, approximately 53% of the cultivation areas would require hybrid renewal before 2050 under the RCP 4.5 and RCP 8.5 emission scenarios. The medium-maturing hybrids with a long grain-filling duration and a high light use efficiency would be promising, although the ideotypic traits could be different for a specific environment. The findings highlight the necessity and urgency of breeding climate resilient hybrids, providing policy-makers and crop breeders with the early signals of when, where and what hybrids will be required, which stimulate proactive investment to facilitate breeding. The proposed crop modelling approach is scalable, largely data-driven and can be used to tackle the longstanding problem of predicting hybrids' future performance to accelerate development of new crop hybrids.

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