印度洋偶极子
生产力
气候变化
环境科学
优势(遗传学)
气候学
产量(工程)
作物生产力
厄尔尼诺南方涛动
作物产量
地理
作物
自然资源经济学
海洋学
农学
经济
地质学
生物
林业
宏观经济学
冶金
材料科学
生物化学
基因
作者
Puyu Feng,Bin Wang,Ian Macadam,Andréa S. Taschetto,Nerilie J. Abram,Jing‐Jia Luo,Andrew D. King,Yong Chen,Hao Feng,De Li Liu,Qiang Yu,Kelin Hu
出处
期刊:Nature food
[Nature Portfolio]
日期:2022-10-13
卷期号:3 (10): 862-870
被引量:14
标识
DOI:10.1038/s43016-022-00613-9
摘要
The relationships between crop productivity and climate variability drivers are often assumed to be stationary over time. However, this may not be true in a warming climate. Here we use a crop model and a machine learning algorithm to demonstrate the changing impacts of climate drivers on wheat productivity in Australia. We find that, from the end of the nineteenth century to the 1980s, wheat productivity was mainly subject to the impacts of the El Niño Southern Oscillation. Since the 1990s, the impacts from the El Niño Southern Oscillation have been decreasing, but those from the Indian Ocean Dipole have been increasing. The warming climate has brought more occurrences of positive Indian Ocean Dipole events, resulting in severe yield reductions in recent decades. Our findings highlight the need to adapt seasonal forecasting to the changing impacts of climate variability to inform the management of climate-induced yield losses.
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