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ENSO climate risk: predicting crop yield variability and coherence using cluster-based PCA

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Abstract

The El Niño–Southern Oscillation (ENSO) has, in recent years, contributed to increases in the yields of major agricultural (annual) crops like wheat and barley in Canada. How such forcing alters the pattern of yield variation across different geographic scales and across large agricultural landscapes like the Canadian Prairies is less understood. Yet, such questions are of major importance in forecasting future cereal crop production. We explore the potential impact of ENSO on wheat and barley across the Canadian Prairies/Western Canada using a multi-scale, cluster-based principal component analysis (PCA) model that integrates machine-learning (K-means clustering) to predict areas of high climate risk. These risk areas are separable clusters of subregions that show similar ENSO-yield correlation response (spatial coherency). Benchmarking this spatial model to non-spatial models indicates that spatial coherency leads to gains in prediction skill. Incorporating spatial coherency increased the skill in predicting crop yield; reducing RMSE error by up to 26–34% (spring wheat) and 2–4% (barley). We infer that accounting for spatial coherency improves the accuracy and reliability of crop yield forecasts.

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Source: NOAA/ESRL Physical Sciences Division, Boulder Colorado, https://www.esrl.noaa.gov/psd/data/correlation/

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Notes

  1. International Organization of Vine and Wine (OIV).

  2. http://www.foodsecurityportal.org.

  3. G20 Action Plan on global food price volatility, http://www.foodsecurityportal.org/g20-action-plan-highlights-agriculture-and-food-price-volatility.

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Acknowledgements

This study was funded by the Growing Forward Two Federal Research Program (Agriculture and Agri-Food Canada, AAFC) (Project No. J-000179.001.02) and assistance of Canada’s Federal Research Affiliate Program (RAP). Agro-climatic and CAR crop yield data used in this study were provided by partners in Statistics Canada and Environment and Climate Change Canada (ECCC). We thank Dr. Aston Chipanshi (AAFC) and Dr. Tracy A. Porcelli for their helpful review and feedback on earlier manuscript drafts, and Dr. Alex Cannon (ECCC) for input data guidance, and anonymous reviewers for their providing thoughtful comments and suggestions.

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  1. Department of Geography, University of Victoria, David Turpin Building, 3800 Finnerty Road, Victoria, BC, V8P 5C2, Canada

    Weixun Lu & David E. Atkinson

  2. Science and Technology Branch, Agriculture and Agri-Food Canada, Summerland Research and Development Centre, 4200 Highway 97 South, Summerland, BC, V0H 1Z0, Canada

    Nathaniel K. Newlands

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  1. Weixun Lu
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Correspondence to Weixun Lu.

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Lu, W., Atkinson, D.E. & Newlands, N.K. ENSO climate risk: predicting crop yield variability and coherence using cluster-based PCA. Model. Earth Syst. Environ. 3, 1343–1359 (2017). https://doi.org/10.1007/s40808-017-0382-0

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