CoChat
Sign Up
Cited by 78Climate change impacts on agricultureAgricultural risk and resilienceEconomics of Agriculture and Food Markets

The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.

Agricultural input shocks affect crop yields more in the high-yielding areas of the world

Open access
Ahvo, Aino, Heino, Matias, Sandström, Vilma, Chrisendo, Daniel, Jalava, Mika, Kummu, Matti·2023·Nature Food
DOI 10.1038/s43016-023-00873-zPMC PMC10727984

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

The industrialization of agriculture has led to an increasing dependence on non-locally sourced agricultural inputs. Hence, shocks in the availability of agricultural inputs can be devastating to food crop production. There is also a pressure to decrease the use of synthetic fertilizers and pesticides in many areas. However, the combined impact of the agricultural input shocks on crop yields has not yet been systematically assessed globally. Here we modelled the effects of agricultural input shocks using a random forest machine learning algorithm. We show that shocks in fertilizers cause the most drastic yield losses. Under the scenario of 50% shock in all studied agricultural inputs, global maize production could decrease up to 26%, and global wheat production up to 21%, impacting particularly the high-yielding ‘breadbasket’ areas of the world. Our study provides insights into global food system resilience and can be useful for preparing for potential future shocks or agricultural input availability decreases at local and global scales.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s43016-023-00873-z

Cites

69 of 69 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Impacts of input shocks on wheat yield in a single climate bin. Relationship between modelled baseline and scenario yields for each of the studied shock scenarios for one crop–climate bin combination (wheat bin 10).a–g, Results for each shock scenario: N (nitrogen) shock (a); P (phosphorous) shock (b); K (potassium) shock (c); machinery shock (d); pesticide shock (e); fertilizer shock (f); shock in all inputs (g). The red diagonal line denotes the 1:1 line where scenario yields would be identical to baseline yields. Points below the red line indicate that the agricultural input shock decreased the yields. See plots for all crop–climate bin combinations in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.
Fig. 1 Impacts of input shocks on wheat yield in a single climate bin. Relationship between modelled baseline and scenario yields for each of the studied shock scenarios for one crop–climate bin combination (wheat bin 10).a–g, Results for each shock scenario: N (nitrogen) shock (a); P (phosphorous) shock (b); K (potassium) shock (c); machinery shock (d); pesticide shock (e); fertilizer shock (f); shock in all inputs (g). The red diagonal line denotes the 1:1 line where scenario yields would be identical to baseline yields. Points below the red line indicate that the agricultural input shock decreased the yields. See plots for all crop–climate bin combinations in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.CC BY · PubMed Central
Fig. 2 Wheat yield change after a 50% shock in selected inputs. a–d, Results for each input shock scenario: N (nitrogen) shock (a); P (phosphorous) shock (b); machinery shock (c); pesticide shock (d). Note: here only the decreases in yields are shown, and the agricultural areas where yield might potentially increase following a shock are shown in Extended Data Fig. 8. See all shock scenarios for all crops in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.
Fig. 2 Wheat yield change after a 50% shock in selected inputs. a–d, Results for each input shock scenario: N (nitrogen) shock (a); P (phosphorous) shock (b); machinery shock (c); pesticide shock (d). Note: here only the decreases in yields are shown, and the agricultural areas where yield might potentially increase following a shock are shown in Extended Data Fig. 8. See all shock scenarios for all crops in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.CC BY · PubMed Central
Fig. 3 Crop yield change across all crops after a 50% shock in all inputs. a–l, Results for each crop: barley (a); cassava (b); groundnut (c); maize (d); millet (e); potato (f); rice (g); sorghum (h); soybean (i); sugar beet (j); sugarcane (k); wheat (l). Note: here only the decreases in yields are shown, and the agricultural areas where yield might potentially increase following a shock are shown in Extended Data Fig. 9. See all shock scenarios in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.
Fig. 3 Crop yield change across all crops after a 50% shock in all inputs. a–l, Results for each crop: barley (a); cassava (b); groundnut (c); maize (d); millet (e); potato (f); rice (g); sorghum (h); soybean (i); sugar beet (j); sugarcane (k); wheat (l). Note: here only the decreases in yields are shown, and the agricultural areas where yield might potentially increase following a shock are shown in Extended Data Fig. 9. See all shock scenarios in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.CC BY · PubMed Central
Fig. 4 Extent of maize yield reductions due to a 50% shock across inputs. a–g, Results for each shock: N (nitrogen) shock (a); P (phosphorous) shock (b); K (potassium) shock (c); machinery shock (d); pesticide shock (e); fertilizer shock (f); shock in all inputs (g). Impact is shown as the percentage of a climate bin area where the maize yield, after a 50% shock, was at least 10% lower than the original yield. Results for all crops are shown in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.
Fig. 4 Extent of maize yield reductions due to a 50% shock across inputs. a–g, Results for each shock: N (nitrogen) shock (a); P (phosphorous) shock (b); K (potassium) shock (c); machinery shock (d); pesticide shock (e); fertilizer shock (f); shock in all inputs (g). Impact is shown as the percentage of a climate bin area where the maize yield, after a 50% shock, was at least 10% lower than the original yield. Results for all crops are shown in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.CC BY · PubMed Central
Fig. 5 The effects of the different shock scenarios on global production. a–l, Results for each crop: barley (a); cassava (b); groundnut (c); maize (d); millet (e); potato (f); rice (g); sorghum (h); soybean (i); sugar beet (j); sugarcane (k); wheat (l). Here the production effect is calculated as the product of yield effect and harvested area. The shades indicate the effect of different shock scenario percentages (25%, 50%, 75%).
Fig. 5 The effects of the different shock scenarios on global production. a–l, Results for each crop: barley (a); cassava (b); groundnut (c); maize (d); millet (e); potato (f); rice (g); sorghum (h); soybean (i); sugar beet (j); sugarcane (k); wheat (l). Here the production effect is calculated as the product of yield effect and harvested area. The shades indicate the effect of different shock scenario percentages (25%, 50%, 75%).CC BY · PubMed Central
Fig. 6 Change in total production of the 12 analysed crops after a 50% shock in all inputs. a,b, Results on grid cell level (a) and by country (b). Same colour scale for both maps. Results for all scenarios are shown in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.
Fig. 6 Change in total production of the 12 analysed crops after a 50% shock in all inputs. a,b, Results on grid cell level (a) and by country (b). Same colour scale for both maps. Results for all scenarios are shown in Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.CC BY · PubMed Central
Extended Data Fig. 1 Climate bins for wheat. Each climate bin represents an area with similar Growing Degree Day (GDD) and precipitation characteristics and has roughly similar amount of grid cells. Spatial resolution is 5 arc-min. The GDD and precipitation thresholds are shown in Extended Data Fig. 2. See climate bins for all crops at Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.
Extended Data Fig. 1 Climate bins for wheat. Each climate bin represents an area with similar Growing Degree Day (GDD) and precipitation characteristics and has roughly similar amount of grid cells. Spatial resolution is 5 arc-min. The GDD and precipitation thresholds are shown in Extended Data Fig. 2. See climate bins for all crops at Agri.Input.Shock -explorer: http://193.166.24.46:3838/shock_shiny/.CC BY · PubMed Central
Extended Data Fig. 2 Growing degree day and precipitation thresholds for the climate bins of wheat. See map of the climate bins in Extended Data Fig. 1.
Extended Data Fig. 2 Growing degree day and precipitation thresholds for the climate bins of wheat. See map of the climate bins in Extended Data Fig. 1.CC BY · PubMed Central
Extended Data Fig. 3 Model performance at grid cell level across climate bins. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. Model performance estimated by comparing the model predictions of the testing data to the known original yields using Nash-Sutcliffe model efficiency (NSE). Scores are shown for all crops and climate bins (n = 25). A score above 0.5 denotes a satisfactory model, above 0.65 a good model and above 0.75 a very good model 21. See climate bin for wheat, as an example, in Extended Data Fig. 1.
Extended Data Fig. 3 Model performance at grid cell level across climate bins. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. Model performance estimated by comparing the model predictions of the testing data to the known original yields using Nash-Sutcliffe model efficiency (NSE). Scores are shown for all crops and climate bins (n = 25). A score above 0.5 denotes a satisfactory model, above 0.65 a good model and above 0.75 a very good model 21. See climate bin for wheat, as an example, in Extended Data Fig. 1.CC BY · PubMed Central
Extended Data Fig. 4 Average country level modelled baseline crop yield (t/ha) for the 12 crops included in the study. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat.
Extended Data Fig. 4 Average country level modelled baseline crop yield (t/ha) for the 12 crops included in the study. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat.CC BY · PubMed Central
Extended Data Fig. 5 Model performance at country level. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. Comparison of the modelled mean country level yields (y-axis) with the reported yields from FAOSTAT 11 (x-axis), averaged over years 1997-2013. Bubble size represents the country level modelled production of the crop. See summary statistics in Supplementary Table 1.
Extended Data Fig. 5 Model performance at country level. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. Comparison of the modelled mean country level yields (y-axis) with the reported yields from FAOSTAT 11 (x-axis), averaged over years 1997-2013. Bubble size represents the country level modelled production of the crop. See summary statistics in Supplementary Table 1.CC BY · PubMed Central
Extended Data Fig. 6 Average country level modelled crop yield (t/ha) with zero agricultural input for the 12 crops included in the study. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat.
Extended Data Fig. 6 Average country level modelled crop yield (t/ha) with zero agricultural input for the 12 crops included in the study. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat.CC BY · PubMed Central
Extended Data Fig. 7 Ratio between crop yield from the modelled scenario with zero agricultural input and the modelled baseline scenario. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. The smaller the ratio, the more the yields decreased in zero input scenario compared to the baseline. In case of ration over 100%, the yields increased (see Discussion in the main text).
Extended Data Fig. 7 Ratio between crop yield from the modelled scenario with zero agricultural input and the modelled baseline scenario. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. The smaller the ratio, the more the yields decreased in zero input scenario compared to the baseline. In case of ration over 100%, the yields increased (see Discussion in the main text).CC BY · PubMed Central
Extended Data Fig. 8 Wheat yield change after a 50% shock in selected inputs. Results are shown for each input shock scenario separately: a. N (nitrogen) shock, b. P (phosphorous) shock, c. machinery shock, and d. pesticide shock. Here both decreased and increased yields are shown, otherwise the figure is similar to Fig. 2.
Extended Data Fig. 8 Wheat yield change after a 50% shock in selected inputs. Results are shown for each input shock scenario separately: a. N (nitrogen) shock, b. P (phosphorous) shock, c. machinery shock, and d. pesticide shock. Here both decreased and increased yields are shown, otherwise the figure is similar to Fig. 2.CC BY · PubMed Central
Extended Data Fig. 9 Crop yield changes across all crops after a 50% shock in all inputs. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. Here both decreased and increased yields are shown, otherwise the figure is similar to Fig. 3.
Extended Data Fig. 9 Crop yield changes across all crops after a 50% shock in all inputs. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. Here both decreased and increased yields are shown, otherwise the figure is similar to Fig. 3.CC BY · PubMed Central
Extended Data Fig. 10 Coefficient of variance (c.v.) of the crop yield predictions for the baseline model runs. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. The c.v.s are calculated across the 25 baseline model runs (see Methods) for each location (grid cell) where the crop in question is grown.
Extended Data Fig. 10 Coefficient of variance (c.v.) of the crop yield predictions for the baseline model runs. Results are shown for each crop separately: a. barley, b. cassava, c. groundnut, d. maize, e. millet, f. potato, g. rice, h. sorghum, i. soybean, j. sugar beet, k. sugarcane, and l. wheat. The c.v.s are calculated across the 25 baseline model runs (see Methods) for each location (grid cell) where the crop in question is grown.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 2

  1. Dall’Erba, S., Chen, Z. & Nava, N. J. U.S. Interstate trade will mitigate the negative impact of climate change on crop profit. Am. J. Agric. Econ. 103, 1721–1741 (2021).
  2. O’Hara, J. K., Mulik, K. & Gurian-Sherman, D. Agricultural production impacts of higher phosphate fertilizer prices. J Int. Agric. Trade Dev. 9, 233–253 (2015).

Cited by

Sorted by citations

See all 78

Related

See all

Metadata

Authors
Ahvo, A., Heino, M., Sandström, V., Chrisendo, D., Jalava, M., & Kummu, M.
Affiliations
Aalto University (FI)
Corresponding author
Kummu, Matti
ORCID
Venue
Nature Food
Publisher
Springer Science and Business Media LLC
Published
2023-11-09
Volume
4
Issue
12
Pages
1037-1046
ISSN
2662-1355 (ISSN-L)
DOI
10.1038/s43016-023-00873-z
PMC
PMC10727984
OpenAlex
W4388523335
Cited by
78
References
69
Citations by year
2026: 34 · 2025: 33 · 2024: 10 · 2023: 1
FWCI
19.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Food (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
  • Aaltodoc (Aalto University) (submittedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
Funders
  • European Commission (HORIZON2020, 819202)
  • Academy of Finland (339834)
  • Aalto-Yliopisto
  • China Scholarship Council
  • Maa- ja Vesitekniikan Tuki Ry
Topics
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Agricultural risk and resilience
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economics of Agriculture and Food Markets
Keywords
crop yield, fertilizer availability, pesticide availability, maize production, wheat production, food system resilience, global food security, mineral fertilizers
MeSH terms
Crop Production, Agriculture, Fertilizers, Pesticides, Crops, Agricultural

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review