Evidence map›Paper›PMID 42850446›Full record

ArticleNature food2026

Refined crop modelling reveals intensifying impacts of droughts and heatwaves on global wheat productivity.

Yuchuan Luo, Zhao Zhang, Juan Cao, Jichong Han, Qiang Tang, Jonas Jägermeyr, Fulu Tao

Abstract read
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In one paragraph

Article in Nature food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yuchuan Luo *Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China.ORCID http://orcid.org/0009-0008-8392-7392
Zhao Zhang *Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, School of National Safety and Emergency Management, Beijing Normal University, Zhuhai, China. zhangzhao@bnu.edu.cn.ORCID http://orcid.org/0000-0002-5697-8011
Juan CaoKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Jichong HanCollege of Geography and Planning, Chengdu University of Technology, Chengdu, China.
Qiang TangChongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China.
Jonas JägermeyrNASA Goddard Institute for Space Studies, New York, NY, USA.
Fulu TaoKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. taofl@igsnrr.ac.cn.ORCID http://orcid.org/0000-0001-8574-0080

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42061144003
6 · The paper itself

Abstract

Extreme weather events, including heatwaves and droughts, are becoming more frequent and intense, yet their impact on crop productivity remain highly uncertain. Here we jointly improve the structure and parameterization of a crop model to enhance its ability to reproduce climate impacts on global wheat productivity at 10-km resolution. Compared with a multimodel ensemble, the improved parameterization increases model skill by 30%, 20% and 7% in simulating global wheat yield losses from droughts, heatwaves and their compound events, respectively, during 1981-2015. Over the same period, the improved modelling reveals global average wheat yield losses of 9.7%, 5.8% and 13.3% caused by droughts, heatwaves and their compounds, respectively. Further analysis underscores that irrigation represents an effective adaptation option to offset these adverse impacts; however, such countervailing effects has markedly weakened over time with disaster intensifying. Our framework enhances crop models' ability to assess the impacts of extreme weather on global crop productivity, supporting climate-risk assessment and the development of effective adaptation strategies.

Identifiers

PMID42850446

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.