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Climate drives variability and joint variability of global crop yields
Ehsan Najafi1, Indrani Pal2, Reza Khanbilvardi1
1Civil Engineering Department, The City College of New York, The City University of New York, 10031 New York City, USA; NOAA Center for Earth System Sciences and Remote Sensing Technologies (NOAA-CREST), The City College of New York, The City University of New York, 10031 New York City, USA.
The Science of the Total Environment
|January 29, 2019
Summary
Global crop yields for maize, rice, sorghum, and soybean (MRSS) are linked to climate patterns like ENSO and NAO. Understanding these climate-yield connections helps predict agricultural variability.
Area of Science:
- Agricultural Science
- Climate Science
- Data Science
Background:
- Long-term national crop yields (maize, rice, sorghum, soybean) exhibit complex variability.
- Understanding drivers of yield fluctuations is crucial for global food security.
Purpose of the Study:
- To decompose long-term crop yield data and identify associations with climate variability.
- To explore co-varying patterns among countries and crops influenced by climate.
Main Methods:
- Robust Principal Component Analysis (RPCA) applied to national yield data (1961-2013).
- Analysis of climate indices (SSTa, ATa, PDSI) and oceanic/atmospheric indices.
- Identification of principal components (PCs) linked to persistent yield anomalies.
Main Results:
- Large-scale climate patterns, notably El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO), strongly correlate with crop yield variability.
- Specific crops showed regional yield variations linked to local climate (e.g., maize in Europe/N. America, rice in S. America/Asia).
- Sorghum yield variability demonstrated significant correlations with numerous climate indices; co-variation observed across different crops and countries.
Conclusions:
- Climate variability is a significant driver of MRSS yield fluctuations globally.
- Identifying co-varying countries and crops aids in understanding predictable climate-driven agricultural patterns.
- This analysis provides insights for nations to manage climate-related risks in crop production.
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