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

文献类型：期刊论文

作者Zhang, Liangliang2,3; Zhang, Zhao2,3; Tao, Fulu4,5; Luo, Yuchuan2,3; Cao, Juan2,3; Li, Ziyue2,3; Xie, Ruizhi2; Li, Shaokun1
刊名ENVIRONMENTAL RESEARCH LETTERS
出版日期2021-12-01
卷号16期号:12页码:14
关键词climate change impact adaptation hybrid ideotype food security machine learning
ISSN号1748-9326
DOI10.1088/1748-9326/ac32fd
通讯作者Zhang, Zhao(zhangzhao@bnu.edu.cn) ; Tao, Fulu(taofl@igsnrr.ac.cn)
英文摘要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.
WOS关键词LEAF-AREA INDEX ; WHEAT YIELD ESTIMATION ; CHANGE IMPACT ; MAJOR CROPS ; CHINA ; UNCERTAINTY ; PRODUCTIVITY ; ASSIMILATION ; PARAMETERS ; PHENOLOGY
资助项目National Key Research & Development Programme of China[2017YFD0300301] ; National Key Research & Development Programme of China[2017YFA0604700] ; National Key Research & Development Programme of China[2016YFD 0300201] ; National Key Research & Development Programme of China[2020YFA0608201] ; National Science Foundation of China[42061144003] ; National Science Foundation of China[41977405]
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000725240400001
出版者IOP Publishing Ltd
资助机构National Key Research & Development Programme of China ; National Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/168230]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Zhao; Tao, Fulu
作者单位1.Chinese Acad Agr Sci, Inst Crop Sci, Minist Agr, Key Lab Crop Physiol & Ecol, Beijing 100081, Peoples R China
2.Beijing Normal Univ, Acad Disaster Reduct & Emergency Management, Sch Natl Safety & Emergency Management, Minist Emergency Management, Beijing 100875, Peoples R China
3.Beijing Normal Univ, Sch Natl Safety & Emergency Management, Minist Educ, Beijing 100875, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
5.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Liangliang,Zhang, Zhao,Tao, Fulu,et al. Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning[J]. ENVIRONMENTAL RESEARCH LETTERS,2021,16(12):14.
APA Zhang, Liangliang.,Zhang, Zhao.,Tao, Fulu.,Luo, Yuchuan.,Cao, Juan.,...&Li, Shaokun.(2021).Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning.ENVIRONMENTAL RESEARCH LETTERS,16(12),14.
MLA Zhang, Liangliang,et al."Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning".ENVIRONMENTAL RESEARCH LETTERS 16.12(2021):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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