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Concurrent Precipitation Extremes Modulate the Response of Rice Transplanting Date to Preseason Temperature Extremes in China
oleh: Yiqing Liu, Weihang Liu, Yan Li, Tao Ye, Shuo Chen, Zitong Li, Ran Sun
Format: | Article |
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Diterbitkan: | Wiley 2023-01-01 |
Deskripsi
Abstract Understanding how crop phenology responds to climate change is critical for enabling agricultural adaptation measures. Pre‐season temperature alone leads to well‐understood changes in crop phenology. Nevertheless, the modulation effect of concurrent precipitation extremes on the response to temperature extremes has been largely under‐addressed. Here, we investigate the response of rice transplanting dates to pre‐season temperature extremes and reveal the modulation effects of concurrent precipitation extremes by using station‐observed rice transplanting dates from 1981 to 2018 across mainland China. We also evaluate the performance of a remotely sensed phenology product, ChinaCropPhen1km, in reproducing the above temperature responses and modulation effects. Our results showed that transplanting dates tended to advance in response to an extremely hot pre‐season, while concurrent extreme drought offset the advance by up to 2.6 days. Transplanting dates tended to be delayed in response to an extremely cold pre‐season, while concurrent extreme precipitation exacerbated the delay by up to 1 day. Responses to temperature extremes and modulation effects were divergent across different regions. Under extremely hot conditions, transplanting dates advanced further in hotter and wetter regions, while under extremely cold pre‐seasons, transplanting dates delayed less in colder and drier regions. Transplanting dates from the ChinaCropPhen1km product underestimated the responses to temperature extremes by up to 4.7 days and detected the opposite modulation effect compared to those obtained from observations. Our results highlight that the need to improve our understanding and modeling of modulation effects of precipitation extremes on temperature–phenology relationship, which benefits the field of agriculture risk analysis and climate change adaptation.