Journal of Geographical Sciences ›› 2013, Vol. 23 ›› Issue (3): 567-576.doi: 10.1007/s11442-013-1029-3

• Review Articles • 上一篇    

A review on statistical models for identifying climate contributions to crop yields

SHI Wenjiao1,2, TAO Fulu2, ZHANG Zhao1   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 收稿日期:2012-08-19 修回日期:2012-09-17 出版日期:2013-06-15 发布日期:2013-06-15
  • 通讯作者: Zhang Zhao, Ph.D and Associate Professor, E-mail: zhangzhao@bnu.edu.cn E-mail:zhangzhao@bnu.edu.cn
  • 作者简介:Shi Wenjiao, Ph.D and Assistant Professor, specialized in climate change and agriculture, spatial analysis and geostatistics. E-mail: shiwj@lreis.ac.cn
  • 基金资助:

    National Natural Science Foundation of China, No.41001057; The Science and Technology Strategic Pilot of the Chinese Academy of Sciences, No.XDA05090308; No.XDA05090310; Project Supported by State Key Laboratory of Earth Surface Processes and Resource Ecology, No.2011-KF-06

A review on statistical models for identifying climate contributions to crop yields

SHI Wenjiao1,2, TAO Fulu2, ZHANG Zhao1   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2012-08-19 Revised:2012-09-17 Online:2013-06-15 Published:2013-06-15

摘要:

Statistical models using historical data on crop yields and weather to calibrate relatively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale (e.g. global scale, national scale, provincial scale and county scale). Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.

关键词: climate change, crop yield, influence, adaptation, statistical model

Abstract:

Statistical models using historical data on crop yields and weather to calibrate relatively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale (e.g. global scale, national scale, provincial scale and county scale). Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.

Key words: climate change, crop yield, influence, adaptation, statistical model