ArticleScience advances2018
Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields.
Article in Science advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Refined crop modelling reveals intensifying impacts of droughts and heatwaves on global wheat productivity.Nature food · 2026Article
- Future projections of European maize yields using AquaCrop with an adaptive growing season.European journal of agronomy : the journal of the European Society for Agronomy · 2026Article
- Climate trends and maize production nexus in Mississippi: empirical evidence from ARDL modelling.Scientific reports · 2023Article
- Risks of synchronized low yields are underestimated in climate and crop model projections.Nature communications · 2023Article
- Global crop yields can be lifted by timely adaptation of growing periods to climate change.Nature communications · 2022Article
- The impact of 1.5 °C and 2.0 °C global warming on global maize production and trade.Scientific reports · 2022Article
- From Soil Amendments to Controlling Autophagy: Supporting Plant Metabolism under Conditions of Water Shortage and Salinity.Plants (Basel, Switzerland) · 2022Review
- Climate impacts on global agriculture emerge earlier in new generation of climate and crop models.Nature food · 2021Article
- Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models.Remote sensing · 2019Article
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Authors and funding
2 authors.
Funding
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Abstract
Testing our understanding of crop yield responses to weather fluctuations at global scale is notoriously hampered by limited information about underlying management conditions, such as cultivar selection or fertilizer application. Here, we demonstrate that accounting for observed spatial variations in growing seasons increases the variance in reported national maize and wheat yield anomalies that can be explained by process-based model simulations from 34 to 58% and 47 to 54% across the 10 most weather-sensitive main producers, respectively. For maize, the increase in explanatory power is similar to the increase achieved by accounting for water stress, as compared to simulations assuming perfect water supply in both rainfed and irrigated agriculture. Representing water availability constraints in irrigation is of second-order importance. We improve the model's explanatory power by better representing crops' exposure to observed weather conditions, without modifying the weather response itself. This growing season adjustment now allows for a close reproduction of heat wave and drought impacts on crop yields.
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