The Largest Crop Production Shocks: Magnitude, Causes and Frequency

Jehn, Florian Ulrich; Mulhall, James; Blouin, Simon; Gajewski, Łukasz G.; Wunderling, Nico

doi:10.31223/X55B25

Preprints

https://doi.org/10.31223/X55B25

Preprints

17 Sep 2025

| 17 Sep 2025

The Largest Crop Production Shocks: Magnitude, Causes and Frequency

Florian Ulrich Jehn, James Mulhall, Simon Blouin, Łukasz G. Gajewski, and Nico Wunderling

Abstract. Food is the foundation of our society. We often take it for granted, but stocks are rarely available for longer than a year, and food production can be disrupted by catastrophic events, both locally and globally. To highlight such major risks to the food system, we analyzed FAO crop production data from 1961 to 2023 to find the largest crop production shock for every country and identify its causes. We show that large crop production shocks regularly happen in all countries. This is most often driven by climate (especially droughts), but disruptions by other causes like economic disruptions, environmental hazards (especially storms) and conflict also occur regularly. The global mean of largest country-level shocks averaged -29 %, with African countries experiencing the most extreme collapses (-80 % in Botswana), while Asian and Central European nations faced more moderate largest shocks (-5 to -15 %). While global shocks above 5 % are rare (occurring once in 63 years), continent-level shocks of this magnitude happen every 1.8 years on average. These results show that large disruptions to our food system frequently happen on a local to regional scale and can plausibly happen on a global scale as well. We therefore argue that more preparation and planning are needed to avoid such global disruptions to food production.

Received: 04 Sep 2025 – Discussion started: 17 Sep 2025

Florian Ulrich Jehn, James Mulhall, Simon Blouin, Łukasz G. Gajewski, and Nico Wunderling

Status: closed

RC1:
'Comment on egusphere-2025-4350', Navin Ramankutty, 25 Oct 2025
General comments
This is an impressive and engaging analysis of global food production shocks and their causes. The study makes good use of FAO data to identify and interpret the largest national food production shocks (in total calorie terms) over 1961–2023. I found the paper clear, rich in narrative detail, and well organized. It provides a valuable dataset and synthesis that will be of interest to both researchers and policymakers. My comments below are intended to help the authors strengthen the framing, clarify assumptions, and ensure that key methodological choices are transparent and well justified.

Specific comments
This paper clearly builds on Cottrell et al. (2019). It would help readers if the introduction and discussion more explicitly distinguished this study’s new contributions—for example through expanded data coverage, new cause-attribution methods, or additional insights.

The assumption that all crops could be diverted to human consumption in a crisis is strong and may not hold in practice. Many of the listed crops (for example, seed cotton, maize, soy, and barley) are primarily non-food or feed crops. I encourage the authors to discuss this limitation in more detail or, if feasible, re-analyze using food-only fractions or “delivered calories” that account for feed conversion losses (see Cassidy et al., 2013). Even a short sensitivity check would substantially strengthen the robustness of the findings. Otherwise your findings would be weighted heavily toward feed crops and those countries that produce them (e.g., consider that only about a quarter of produced calories from maize ends up as human calories).

The Savitzky–Golay filter is well motivated, but it would be useful to show that results are not overly sensitive to this choice. I recommend testing one or two alternative detrending methods (e.g., Gaussian or LOESS) and examining whether the identified “largest shocks” or their magnitudes change materially. Similarly, reconsider the “must be below last year” rule or provide a robustness check without it.

In your synchrony analysis, how do you account for the differences in size and production of different countries? Larger producers will naturally drive global totals, so weighting by production share or caloric contribution could yield a more accurate view of which regions most influence global variability. Clarifying or adding this weighting step would help the “buffering” interpretation.

Some of the observed geographic patterns may reflect differences in crop composition rather than exposure or governance. The authors could discuss this possibility, or test whether patterns persist when comparing regions growing similar crop mixes.

A few recent studies might offer useful methodological or interpretive ideas (and very sorry for the self citations—these do not need to be cited if not directly relevant):
On synchrony (sections 2.4, 3.4), see Mehrabi and Ramankutty (2019) and Egli et al. (2021). Drawing on their framework for quantifying and decomposing synchrony could strengthen your analysis.

On the role of trade (section 4.3), see Bajaj et al. (2025).

On diversification of trade (section 4.4), see Hertel et al. (2021).

Technical corrections
Consider renaming section 3.2 to something like “Geographic patterns of shock types”, because the geographic patterns of shocks has already been seen in figure 2 of section 3.1.

Lines 221-222: The conclusion that “pests and diseases are not a major factor for the largest shocks” may be too strong given only one observed data point.”

Lines 237-238: The reference to “North America” appears to describe tropical storm impacts in the Caribbean. “Central America” might be more appropriate, and the map (with only one blue country) suggests that this pattern could be toned down in the text.

Lines 250-251: You discuss a decade of mismanagement under Idi Amin – it’s not clear how a decade-long effect would result in a single year shock?

Lines 274-275 & Figure 6: Because the 2020s decade includes only four years of data, the low count of shocks is expected. Normalizing by the number of years per decade (i.e., showing average shocks per year) would provide a fairer comparison.

Lines 338-341: Please add supporting citations for these statements.

Lines 342-347: I had a hard time understanding the argument in this paragraph. How does the Zhang et al. study (which only examined climate driven shocks, so did not compare it to other shocks) support the finding that climate is the main driver of shocks?

References
Bajaj, K., Z. Mehrabi, T. Kastner, J. Jägermeyr, C. Müller, F. Schwarzmüller, T. W. Hertel, and N. Ramankutty, Current food trade helps mitigate future climate change impacts in lower-income nations, PLOS ONE, 20(1), e0314722, 10.1371/journal.pone.0314722, 2025.

Cassidy, E. S., P. C. West, J. S. Gerber, and J. A. Foley, Redefining agricultural yields: from tonnes to people nourished per hectare, Environ. Res. Lett., 8(3), 034015, 2013.

Egli, L., Z. Mehrabi, and R. Seppelt, More farms, less specialized landscapes, and higher crop diversity stabilize food supplies, Environ. Res. Lett., 16(5), 055015, 10.1088/1748-9326/abf529, 2021.

Hertel, T., I. Elouafi, M. Tanticharoen, and F. Ewert, Diversification for enhanced food systems resilience, Nature Food, 2(11), 832-834, 10.1038/s43016-021-00403-9, 2021.

Mehrabi, Z., and N. Ramankutty, Synchronized failure of global crop production, Nature Ecology & Evolution, 3(5), 780-786, 10.1038/s41559-019-0862-x, 2019.
Citation: https://doi.org/10.5194/egusphere-2025-4350-RC1
- AC1: 'Reply on RC1', Florian Ulrich Jehn, 13 Nov 2025
  
  Dear Navin Ramankutty,
  please find our responses in the attached pdf.
  Kind regards,
  Florian Jehn, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-4350-AC1
RC2:
'Comment on egusphere-2025-4350', Anonymous Referee #2, 29 Oct 2025

Nicely done study on crop production shocks, however I'm not sure if it brings so much new information to the table.
The basic methodology of the study is analogous to Cottrell 2019 and Anderson 2023 which the authors already state. The added sophistication and differentiation in my view comes from the use of a LLM to identify possible drivers of crop production shocks, as well as a different filter in the methodology (does the employment of a Gaussian filter change results much? Would be a relatively easy sensitivity test I imagine). The authors use FAOSTAT which provides more countries than Anderson, although less temporal extent; This is more useful for looking at strong shocks in individual countries, while globally synchronous shocks can already be mainly covered by a small number of countries. They also neglect the marine aspect which is already included in Cottrell, which in this paper's case, with its focus on individual countries and large shocks, may be releavant as these are often island countries with low production so indeed marine sources of food could be interesting.
However, as the authors also note, the results are quite similar to what is already in the literature, that climatic factors, also ENSO are strong drivers of production shocks, along with geopolitical factors.
An added element here that could make the paper more interesting, also harnessing its integration of the LLM into the methodology, is to qualitatively trace the biophysical impacts back to human impacts - i.e. in years with production shocks were there reports of price inflation, shifts in global trade patterns, hunger indices, etc. This may be more possible now with the LLM doing the first screening.

Finally, they note that some country-level data appears erroneous or unreliable. Can these be given an initial screen, or some way to account for reliability, especially the earlier FAOSTAT data is often quite dodgy.
Thank you very much.

Citation: https://doi.org/10.5194/egusphere-2025-4350-RC2
- AC2: 'Reply on RC2', Florian Ulrich Jehn, 13 Nov 2025
  
  Dear Reviewer,
  please find our responses in the attached pdf.
  Kind regards,
  Florian Jehn, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-4350-AC2

Status: closed

RC1:
'Comment on egusphere-2025-4350', Navin Ramankutty, 25 Oct 2025
General comments
This is an impressive and engaging analysis of global food production shocks and their causes. The study makes good use of FAO data to identify and interpret the largest national food production shocks (in total calorie terms) over 1961–2023. I found the paper clear, rich in narrative detail, and well organized. It provides a valuable dataset and synthesis that will be of interest to both researchers and policymakers. My comments below are intended to help the authors strengthen the framing, clarify assumptions, and ensure that key methodological choices are transparent and well justified.

Specific comments
This paper clearly builds on Cottrell et al. (2019). It would help readers if the introduction and discussion more explicitly distinguished this study’s new contributions—for example through expanded data coverage, new cause-attribution methods, or additional insights.

The assumption that all crops could be diverted to human consumption in a crisis is strong and may not hold in practice. Many of the listed crops (for example, seed cotton, maize, soy, and barley) are primarily non-food or feed crops. I encourage the authors to discuss this limitation in more detail or, if feasible, re-analyze using food-only fractions or “delivered calories” that account for feed conversion losses (see Cassidy et al., 2013). Even a short sensitivity check would substantially strengthen the robustness of the findings. Otherwise your findings would be weighted heavily toward feed crops and those countries that produce them (e.g., consider that only about a quarter of produced calories from maize ends up as human calories).

The Savitzky–Golay filter is well motivated, but it would be useful to show that results are not overly sensitive to this choice. I recommend testing one or two alternative detrending methods (e.g., Gaussian or LOESS) and examining whether the identified “largest shocks” or their magnitudes change materially. Similarly, reconsider the “must be below last year” rule or provide a robustness check without it.

In your synchrony analysis, how do you account for the differences in size and production of different countries? Larger producers will naturally drive global totals, so weighting by production share or caloric contribution could yield a more accurate view of which regions most influence global variability. Clarifying or adding this weighting step would help the “buffering” interpretation.

Some of the observed geographic patterns may reflect differences in crop composition rather than exposure or governance. The authors could discuss this possibility, or test whether patterns persist when comparing regions growing similar crop mixes.

A few recent studies might offer useful methodological or interpretive ideas (and very sorry for the self citations—these do not need to be cited if not directly relevant):
On synchrony (sections 2.4, 3.4), see Mehrabi and Ramankutty (2019) and Egli et al. (2021). Drawing on their framework for quantifying and decomposing synchrony could strengthen your analysis.

On the role of trade (section 4.3), see Bajaj et al. (2025).

On diversification of trade (section 4.4), see Hertel et al. (2021).

Technical corrections
Consider renaming section 3.2 to something like “Geographic patterns of shock types”, because the geographic patterns of shocks has already been seen in figure 2 of section 3.1.

Lines 221-222: The conclusion that “pests and diseases are not a major factor for the largest shocks” may be too strong given only one observed data point.”

Lines 237-238: The reference to “North America” appears to describe tropical storm impacts in the Caribbean. “Central America” might be more appropriate, and the map (with only one blue country) suggests that this pattern could be toned down in the text.

Lines 250-251: You discuss a decade of mismanagement under Idi Amin – it’s not clear how a decade-long effect would result in a single year shock?

Lines 274-275 & Figure 6: Because the 2020s decade includes only four years of data, the low count of shocks is expected. Normalizing by the number of years per decade (i.e., showing average shocks per year) would provide a fairer comparison.

Lines 338-341: Please add supporting citations for these statements.

Lines 342-347: I had a hard time understanding the argument in this paragraph. How does the Zhang et al. study (which only examined climate driven shocks, so did not compare it to other shocks) support the finding that climate is the main driver of shocks?

References
Bajaj, K., Z. Mehrabi, T. Kastner, J. Jägermeyr, C. Müller, F. Schwarzmüller, T. W. Hertel, and N. Ramankutty, Current food trade helps mitigate future climate change impacts in lower-income nations, PLOS ONE, 20(1), e0314722, 10.1371/journal.pone.0314722, 2025.

Cassidy, E. S., P. C. West, J. S. Gerber, and J. A. Foley, Redefining agricultural yields: from tonnes to people nourished per hectare, Environ. Res. Lett., 8(3), 034015, 2013.

Egli, L., Z. Mehrabi, and R. Seppelt, More farms, less specialized landscapes, and higher crop diversity stabilize food supplies, Environ. Res. Lett., 16(5), 055015, 10.1088/1748-9326/abf529, 2021.

Hertel, T., I. Elouafi, M. Tanticharoen, and F. Ewert, Diversification for enhanced food systems resilience, Nature Food, 2(11), 832-834, 10.1038/s43016-021-00403-9, 2021.

Mehrabi, Z., and N. Ramankutty, Synchronized failure of global crop production, Nature Ecology & Evolution, 3(5), 780-786, 10.1038/s41559-019-0862-x, 2019.
Citation: https://doi.org/10.5194/egusphere-2025-4350-RC1
- AC1: 'Reply on RC1', Florian Ulrich Jehn, 13 Nov 2025
  
  Dear Navin Ramankutty,
  please find our responses in the attached pdf.
  Kind regards,
  Florian Jehn, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-4350-AC1
RC2:
'Comment on egusphere-2025-4350', Anonymous Referee #2, 29 Oct 2025

Nicely done study on crop production shocks, however I'm not sure if it brings so much new information to the table.
The basic methodology of the study is analogous to Cottrell 2019 and Anderson 2023 which the authors already state. The added sophistication and differentiation in my view comes from the use of a LLM to identify possible drivers of crop production shocks, as well as a different filter in the methodology (does the employment of a Gaussian filter change results much? Would be a relatively easy sensitivity test I imagine). The authors use FAOSTAT which provides more countries than Anderson, although less temporal extent; This is more useful for looking at strong shocks in individual countries, while globally synchronous shocks can already be mainly covered by a small number of countries. They also neglect the marine aspect which is already included in Cottrell, which in this paper's case, with its focus on individual countries and large shocks, may be releavant as these are often island countries with low production so indeed marine sources of food could be interesting.
However, as the authors also note, the results are quite similar to what is already in the literature, that climatic factors, also ENSO are strong drivers of production shocks, along with geopolitical factors.
An added element here that could make the paper more interesting, also harnessing its integration of the LLM into the methodology, is to qualitatively trace the biophysical impacts back to human impacts - i.e. in years with production shocks were there reports of price inflation, shifts in global trade patterns, hunger indices, etc. This may be more possible now with the LLM doing the first screening.

Finally, they note that some country-level data appears erroneous or unreliable. Can these be given an initial screen, or some way to account for reliability, especially the earlier FAOSTAT data is often quite dodgy.
Thank you very much.

Citation: https://doi.org/10.5194/egusphere-2025-4350-RC2
- AC2: 'Reply on RC2', Florian Ulrich Jehn, 13 Nov 2025
  
  Dear Reviewer,
  please find our responses in the attached pdf.
  Kind regards,
  Florian Jehn, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2025-4350-AC2

Florian Ulrich Jehn, James Mulhall, Simon Blouin, Łukasz G. Gajewski, and Nico Wunderling

Data sets

Code and Data Repository Florian Ulrich Jehn and James Mulhall https://github.com/allfed/Historical-Food-Shocks

Model code and software

Code and Data Repository Florian Ulrich Jehn and James Mulhall https://github.com/allfed/Historical-Food-Shocks

Florian Ulrich Jehn, James Mulhall, Simon Blouin, Łukasz G. Gajewski, and Nico Wunderling

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Short summary

Large crop failures happen regularly around the world, threatening food security. We analyzed sixty years of global crop production data and found that every country has experienced major crop losses. Climate events like droughts cause most severe disruptions, with some African nations losing up to eighty percent of production. While global crop shocks above five percent are rare, regional disruptions occur frequently. These findings show our food system faces regular large-scale threats.


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