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Cited by 185Climate change impacts on agricultureClimate variability and modelsClimate Change Policy and Economics

The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.

Negative impacts of climate change on cereal yields: statistical evidence from France

Open access
Gammans, Matthew, Mérel, Pierre, Ortiz-Bobea, Ariel·2017·Environmental Research Letters
DOI 10.1088/1748-9326/aa6b0c
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Abstract

In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring–summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.

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Registry

Crossref · 10.1088/1748-9326/aa6b0c

Cites

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Statistics

Figures

Figure 1 Historical yield -temperature relationships during warm months. Graphs at the top of each frame represent changes in log yield if one day at 0 ° Cor below is replaced by one day at a given temperature, holding the total number of days in the season constant. The 95% con fi dence interval for the polynomial regression is shown in gray and accounts for spatial correlation. Histograms at the bottom of each frame show the average temperature exposure across all departments and years in the sample.
Figure 1 Historical yield -temperature relationships during warm months. Graphs at the top of each frame represent changes in log yield if one day at 0 ° Cor below is replaced by one day at a given temperature, holding the total number of days in the season constant. The 95% con fi dence interval for the polynomial regression is shown in gray and accounts for spatial correlation. Histograms at the bottom of each frame show the average temperature exposure across all departments and years in the sample.CC-BY · From the PDF
Figure 2 Historical yield -temperature relationships during fall months. Graphs at the top of each frame represent changes in log yield if one day at 6 ° Cor below is replaced by one day at a given temperature, holding the total number of days in the season constant. The 95% con fi dence interval for the polynomial regression is shown in gray and accounts for spatial correlation. Histograms at the bottom of each frame show the average temperature exposure across all departments and years in the sample.
Figure 2 Historical yield -temperature relationships during fall months. Graphs at the top of each frame represent changes in log yield if one day at 6 ° Cor below is replaced by one day at a given temperature, holding the total number of days in the season constant. The 95% con fi dence interval for the polynomial regression is shown in gray and accounts for spatial correlation. Histograms at the bottom of each frame show the average temperature exposure across all departments and years in the sample.CC-BY · From the PDF
Figure 3 Climate-change impacts under various climate models and scenarios. Graphs display predicted changes in crop production, holding current growing areas and technology constant, by the middle and the end of the century relative to the period 1977 -2005. Dots represent point estimates and whiskers show the 95% con fi dence interval that accounts for spatial correlation. The colors correspond to the yield speci fi cations of fi gure 1. The scale on the vertical axis differs across crops.
Figure 3 Climate-change impacts under various climate models and scenarios. Graphs display predicted changes in crop production, holding current growing areas and technology constant, by the middle and the end of the century relative to the period 1977 -2005. Dots represent point estimates and whiskers show the 95% con fi dence interval that accounts for spatial correlation. The colors correspond to the yield speci fi cations of fi gure 1. The scale on the vertical axis differs across crops.CC-BY · From the PDF
Figure 4 Temperature determinants of warming impacts. The fi gure shows the contribution of exposure to each 2 ° C interval during warm months to the impact of a þ 3 ° Cuniform warming, with a 95% con fi dence interval that accounts for spatial correlation, based on the polynomial speci fi cation. The change in exposure to each 1 ° C interval between the reference period and a þ 3 ° C uniform warming scenario is represented at the bottom of the graph. Green (resp. blue) shades represent absolute increases (resp. decreases) in exposure to a temperature interval, with shade intensity commensurate with the magnitude of the change.
Figure 4 Temperature determinants of warming impacts. The fi gure shows the contribution of exposure to each 2 ° C interval during warm months to the impact of a þ 3 ° Cuniform warming, with a 95% con fi dence interval that accounts for spatial correlation, based on the polynomial speci fi cation. The change in exposure to each 1 ° C interval between the reference period and a þ 3 ° C uniform warming scenario is represented at the bottom of the graph. Green (resp. blue) shades represent absolute increases (resp. decreases) in exposure to a temperature interval, with shade intensity commensurate with the magnitude of the change.CC-BY · From the PDF

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Citations

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  1. Lobell D B, 2017, Environ. Res. Lett., 12

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Metadata

Authors
Gammans, M., Mérel, P., & Ortiz-Bobea, A.
Affiliations
  • University of California, Davis (US)
  • Giannini Foundation of Agricultural Economics (US)
ORCID
Venue
Environmental Research Letters
Publisher
IOP Publishing
Published
2017-05-01
Volume
12
Issue
5
Pages
054007
ISSN
1748-9326 (ISSN-L)
DOI
10.1088/1748-9326/aa6b0c
OpenAlex
W2411760869
Cited by
185
References
37
Citations by year
2026: 12 · 2025: 17 · 2024: 28 · 2023: 26 · 2022: 35 · 2021: 27 · 2020: 19 · 2019: 7 · 2018: 14
FWCI
9.52
Type
journal article
Language
en
Open access
Licence
Locations
  • Environmental Research Letters (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
Keywords
climate change impacts, cereal yield, wheat yield, barley yield, temperature effects on crop yield, precipitation effects, extreme cold, RCP8.5, agricultural technology trends, France

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