中文版 | English
Title

Potential Yield of World Rice under Global Warming Based on the ARIMA-TR Model

Author
Corresponding AuthorCai, Chengzhi
Publication Years
2022-08-01
DOI
Source Title
EISSN
2073-4433
Volume13Issue:8
Abstract
As one of two most important cereals in the world, and with the continuous increase in population and demand for food consumption worldwide, rice has been attracting researchers' attention for improving its potential yield in the future, particularly as it relates to climate change. However, what will be the potential limit of world rice yield in the future, and how does global warming affect the yield of world rice? Therefore, analyzing the potential yield of world rice affected by global warming is of great significance to direct crop production worldwide in the future. However, by far, most modeled estimations of rice yield are based on the principle of production function from static biological dimension and at local or regional levels, whereas few are based on a time series model from a dynamic evolutionary angle and on global scale. Thus, in this paper, both average and top (national) yields of world rice by 2030 are projected creatively using the Auto-regressive Integrated Moving Average and Trend Regression (ARIMA-TR) model and based on historic yields since 1961; in addition, the impact of global warming on the yields of world rice is analyzed using a binary regression model in which global mean temperature is treated as the independent variable whereas the yield is expressed as the dependent variable. Our study concludes that between 2021 and 2030, the average yield of world rice is projected to be from 4835 kg/ha to 5195 kg/ha, the top yield from 10,127 kg/ha to 10,269 kg/ha, or the average yield ranging from 47.74% to 50.59% of the top yield. From 1961 to 2020, through to2030, global warming will exert a negative impact on the average yield of world rice less than that of the top yield, which partly drives the gap between these two yields and gradually narrowed; for world rice by 2030, the opportunities for improving global production should be dependent on both high and low yield countries as the average yield is approaching the turning point of an S-shaped curve in the long-term trend. These insights provide the academic circle with innovative comprehension of world rice yield and its biological evolution for global food security relating to global warming in the future.
Keywords
URL[Source Record]
Indexed By
SCI ; EI
Language
English
SUSTech Authorship
Others
Funding Project
Education Administration of Guizhou Province, China["GZEA2021082","125"]
WOS Research Area
Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS Subject
Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS Accession No
WOS:000846128100001
Publisher
EI Accession Number
20223712709609
EI Keywords
Biology ; Cultivation ; Food supply ; Regression analysis
ESI Classification Code
Atmospheric Properties:443.1 ; Biology:461.9 ; Agricultural Methods:821.3 ; Food Products:822.3 ; Mathematical Statistics:922.2
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/393999
DepartmentSchool of Environmental Science and Engineering
Affiliation
1.Guizhou Univ Finance & Econ, Econ Inst, Guiyang 550025, Peoples R China
2.Guizhou Univ, Econ Sch, Guiyang 550025, Peoples R China
3.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
Recommended Citation
GB/T 7714
Cai, Chengzhi,Yang, Hongyan,Zhang, Lin,et al. Potential Yield of World Rice under Global Warming Based on the ARIMA-TR Model[J]. ATMOSPHERE,2022,13(8).
APA
Cai, Chengzhi,Yang, Hongyan,Zhang, Lin,&Cao, Wenfang.(2022).Potential Yield of World Rice under Global Warming Based on the ARIMA-TR Model.ATMOSPHERE,13(8).
MLA
Cai, Chengzhi,et al."Potential Yield of World Rice under Global Warming Based on the ARIMA-TR Model".ATMOSPHERE 13.8(2022).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Cai, Chengzhi]'s Articles
[Yang, Hongyan]'s Articles
[Zhang, Lin]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Cai, Chengzhi]'s Articles
[Yang, Hongyan]'s Articles
[Zhang, Lin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cai, Chengzhi]'s Articles
[Yang, Hongyan]'s Articles
[Zhang, Lin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.