4.7 Article

Forecasting global crop yields based on El Nino Southern Oscillation early signals

Journal

AGRICULTURAL SYSTEMS
Volume 205, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.agsy.2022.103564

Keywords

ENSO; Climate anomaly; Yield variability; Crop loss; Food security

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The study aims to examine the synchronous impacts of El-Niño Southern Oscillation (ENSO) and the probability of simultaneous ENSO-related crop loss on global crop yields, and investigate the predictability of finer-scale variation in crop yields based on ENSO-related large-scale climate precursors. Using updated crop census data, the study finds significant negative (positive) associations between wheat, rice, maize, soybean harvest areas and El Niño (La Niña). ENSO reduces global-mean crop yield for wheat, rice, and maize, but increases it for soybean. The findings suggest that reliable crop yield prediction based on ENSO indexes can be developed in limited harvest areas, particularly in ENSO-sensitive regions.
CONTEXT: The El-Ni (n) over tildeo Southern Oscillation (ENSO), one of the most well-known climate modes, can lead to large-scale climate variability and subsequent crop loss, posing a severe risk to global food security. OBJECTIVE: The study's main goal was to examine the synchronous impacts of ENSO and the probability of simultaneous ENSO-related crop loss on the global yields of major crops and investigate the predictability of finer-scale variation in crop yields based on ENSO-related large-scale climate precursors. METHODS: Here, using updated crop census data for similar to 12,000 political units, the study first investigated the synchronous impact of ENSO on yield variability of major crops (i.e., maize, rice, wheat, and soybean) using Synthetic Analysis and bootstrap method, and then estimated the probability of simultaneous crop loss in the top five crop-producing countries by copula approach. Finally, multiple regression was developed to identify the best forecast model, the corresponding ENSO indices, and the lead time for each political unit based on pre-occurred ENSO indices. RESULTS AND CONCLUSIONS: The results show that 12.8% (2.1%), 13.4% (6.4%), 11.8% (10.2%), and 8.4% (18.3%) of wheat, rice, maize, and soybean harvest areas were significantly negatively (positively) associated with El Ni (n) over tildeo, respectively; and 7% (11.7%), 20.2% (3.4%), 5.8% (5.6%), and 14% (6.4%) with La Ni (n) over tildea. El Ni (n) over tildeo reduced global-mean crop yield by 1.32%, 1.33%, and 0.37% for wheat, rice, and maize, respectively, but increased it for soybean by 1.9%. La Ni (n) over tildea reduced the global mean yield for rice (2.1%), maize (1.5%), and soybean (1.3%) but increased it for wheat (1.0%). Rice (6.6%) had the highest probability of simultaneous loss during the El Nino phase, whereas La Ni (n) over tildea is soybean (5.9%). Based on the early ENSO signals, crop yield could be reliably forecasted for similar to 32.05%, similar to 42.2%, similar to 21%, and similar to 26.37% of global harvest areas, with R-2 being 0.24, 0.26, 0.24, and 0.23 and a lead time of 1-12 months, for wheat, rice, maize, and soybean, respectively. The results suggest that although the reliable yield prediction based on ENSO indexes alone can be developed in a limited proportion of harvest areas, it is skillful in the ENSO-sensitive regions. SIGNIFICANCE: The findings improved the understanding of ENSO-induced crop yield variability and developed novel approaches to forecast global crop yields based on early ENSO signals.

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