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Cited by 527

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

Future warming increases probability of globally synchronized maize production shocks

Open access
Tigchelaar, Michelle, Battisti, David S., Naylor, Rosamond L., Ray, Deepak K.·2018·Proceedings of the National Academy of Sciences

Abstract

Significance: Climate-induced shocks in grain production are a major contributor to global market volatility, which creates uncertainty for cereal farmers and agribusiness and reduces food access for poor consumers when production falls and prices spike. Our study, by combining empirical models of maize production with future warming scenarios, shows that in a warmer climate, maize yields will decrease and become more variable. Because just a few countries dominate global maize production and trade, simultaneous production shocks in these countries can have tremendous impacts on global markets. We show that such synchronous shocks are rare now but will become much more likely if the climate continues to warm. Our results underscore the need for continued investments in breeding for heat tolerance.

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Crossref · 10.1073/pnas.1718031115

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Metadata

Authors
Tigchelaar, M., Battisti, D. S., Naylor, R. L., & Ray, D. K.
Venue
Proceedings of the National Academy of Sciences
Published
2018-06-11
Volume
115
Issue
26
Pages
6644-6649
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2808279205
Cited by
527
References
43
FWCI
38.32
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Future warming increases probability of globally synchronized maize production shocks. https://app.cochat.ai/papers/10.1073/pnas.1718031115
The paper
Tigchelaar, M., Battisti, D. S., Naylor, R. L., & Ray, D. K. (2018). Future warming increases probability of globally synchronized maize production shocks. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1718031115

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.