Literature DB >> 30798223

Climate-associated rice yield change in the Northeast China Plain: A simulation analysis based on CMIP5 multi-model ensemble projection.

He Zhang1, Guangsheng Zhou2, De Li Liu3, Bin Wang4, Dengpan Xiao5, Liang He6.   

Abstract

Multi-model ensemble climate projections in combination with crop models are increasingly used to assess the impact of future climate change on agricultural systems. In this study, we used a biophysical process-oriented CERES-Rice crop model driven by downscaled future climate data from 28 Global Climate Models (GCMs) under two emissions scenarios: representative concentration pathway (RCP) 4.5 and RCP8.5, for phase five of the Coupled Model Intercomparison Project (CMIP5) to project the effects of climate change on rice yields in three future time periods in the Northeast China Plain (NECP). The results showed that without consideration of CO2 effects, rice yield would increase by 1.3%, 1.3%, and 0.4% in the 2030s, 2060s, and 2090s, respectively, under the RCP4.5 scenario. Rice yield would change by +1.1%, -2.3%, and -10.7% in the 2030s, 2060s, and 2090s, respectively, under the RCP8.5 scenario. With consideration of CO2 effects, rice yield during the 2030s, 2060s, and 2090s would increase by 5.4%, 10.0%, and 11.6% under RCP4.5, and by 6.4%, 12.9%, and 15.6% under RCP8.5, respectively. The rice-growing season would be shortened by 2 to 5 weeks in the future. Overall, the future climate would have positive effects on rice yields in the NECP. Although uncertainties in our study on the impact of climate change on rice might arise from the choice of crop model and GCMs, the results are important for informing policy makers and developing appropriate strategies to improve rice productivity in China.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Future climate change; Impact; Northeast China; Rice; Yield

Mesh:

Year:  2019        PMID: 30798223     DOI: 10.1016/j.scitotenv.2019.01.415

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Research on Rice Yield Prediction Model Based on Deep Learning.

Authors:  Xiao Han; Fangbiao Liu; Xiaoliang He; Fenglou Ling
Journal:  Comput Intell Neurosci       Date:  2022-04-26
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.