Sensitivity of global major crop yields to climate variables: A non-parametric elasticity analysis

Di Liu, Ashok K. Mishra, Deepak K. Ray

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Climate variability controls crop yield variability with impacts on food security at the local, regional and global levels. This study uses non-parametric elasticity to investigate the sensitivity of crop yields of the top four global crops (wheat, rice, maize, and soybean) to three climate variables (precipitation (PRE), potential evapotranspiration (PET), and mean air temperature (TMP)). Trends and serial correlations exist in both climate variables and crop yields over the study period (1961 to 2014). To overcome this limitation, the Trend Free Pre-Whitening (TFPW) method was applied. Crop yields are most sensitive to TMP globally. But the exact sensitivity varies across continents. The highest sensitivity regions are located in parts of the Southeast Asia. Wheat yields are more sensitive to TMP in Western Europe and Northern America, whereas maize has higher sensitivity to TMP for regions located in South America and parts of Eastern and Western Africa. Soybean is more sensitive in North and South America. The elasticities of wheat and rice yields to TMP are negative in most of the regions (i.e. increased TMP decreases yield), whereas maize witnessed positive and soybean witnessed mixed positive and negative signals depending on the region. PRE has lower influence on crop yields. The non-parametric elasticity concept is a simple and an efficient approach that complements the existing linear models methods used to detect climate change impacts on crop yields and can be used to investigate the future consequences of climate change on local to global scale agricultural production.

Original languageEnglish (US)
Article number141431
JournalScience of the Total Environment
StatePublished - Dec 15 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.


  • Crop yield
  • Elasticity
  • Non-parametric statistics
  • Trend analysis


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