the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Aug 2025
A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data
Abstract. Climate-related impacts, such as agricultural yield failure, often occur in response to a range of specific weather conditions taking place across different time periods, such as during the growing season. Identifying which weather conditions and timings are most strongly associated with a certain impact is difficult because of the overwhelming number of possible predictor combinations from different aggregation periods. Here we address this challenge and introduce a method for identifying a small number of climate drivers of an impact from high-resolution meteorological data. Based on the principle that causal drivers should generalize across different environments, our proposed two-stage approach systematically generates, tests, and discards candidate features using machine learning and then generates a set of robust drivers. We evaluate the method using simulated US maize yield data from two process-based global gridded crop models and rigorous out-of-sample testing (using approximately 30 years of early 20th-century climate and yield data for training and over 70 years of subsequent data for testing). The climate drivers identified align with crop model mechanisms and consistently use only the weather variables that are taken as input by the respective models. Logistic regression models using ten drivers as predictors show strong predictive performance on the held-out test period even under shifting climatic conditions, achieving correlations of 0.70–0.85 between predicted and true annual proportions of grid cells experiencing yield failure. This approach circumvents the limitations of post-hoc interpretability in black-box machine learning models, allowing researchers to use parsimonious statistical models to explore relationships between climate and impacts, while still harnessing the predictive power of high-resolution, multivariate weather data. We demonstrate this method in the context of agricultural yield failure, but it is also applicable for studying other climate-related impacts such as forest die-off, wildfire incidents, landslides, or flooding.
Competing interests: One of the (co-)authors, Christoph Müller, is a member of the editorial board of Geoscientific Model Development.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: closed (peer review stopped)
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CEC1: 'No compliance with the policy of the journal', Juan Antonio Añel, 11 Oct 2025
-
AC1: 'Reply on CEC1', Lily-belle Sweet, 24 Oct 2025
Dear Juan A. Añel,
Thank you for notifying us of this, and I apologise that the data necessary to reproduce our results had not been previously published in a way that complies with the journal's Code and Data policy. This was an unintentional oversight on my part.
I have now included the data that we used, as well as the code for reproducing all results, in a revised Zenodo publication, available at the following URL: https://zenodo.org/records/17426950 and with the following DOI: 10.5281/zenodo.17426950. This information will also be included in the manuscript upon review.
I hope that this resolves the problem and that our manuscript can be considered for publication in Geoscientific Model Development. Please let me know if anything further is needed.
Lily-belle Sweet
Citation: https://doi.org/10.5194/egusphere-2025-3006-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 24 Oct 2025
Dear authors,
Many thanks for your reply. We can consider your manuscript now in compliance with the Code and Data policy of the journal. Please, do not forget to update the "Code and Data Availability" section in any future version of your manuscript.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2025-3006-CEC2
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 24 Oct 2025
-
AC1: 'Reply on CEC1', Lily-belle Sweet, 24 Oct 2025
-
RC1: 'Comment on egusphere-2025-3006', Anonymous Referee #1, 29 Oct 2025
This paper proposes a time-block-based cross-validation framework to identify meteorological drivers causing yield failures in the US, demonstrating the portability and interpretability of the derived metrics. The research topic aligns well with GMD while exhibiting innovation. However, several core methodologies present issues. Therefore, revisions to the article are required.
First of all, the portability claim is not evidenced beyond time. All model setup and evaluation rely on time-blocked splits, while training and test sets share the same geographic units. In the absence of a spatio-temporal block hold-out (simultaneously changing time and space), the results substantiate only temporal generalization within locations, not spatial transferability. As such, the central claim of portability across space remains unsubstantiated. Add a primary evaluation with spatio-temporal blocking, e.g.,leave-one-region-out for multiple consecutive future years.
Additionally, fixed phenology undermines window-based interpretability. Planting dates in the crop-model experiments vary across space but not across years, whereas real-world phenology adjusts with weather, cultivars, and management. The method’s reliance on calendar time windows makes the selected “critical periods” vulnerable to misalignment with true growth stages, which compromises the mechanistic meaning attributed to the discovered drivers. This concern is amplified by extensive evidence of phenological shortening under warming and by indications of misfit in the county-level results. Labeling and detrending choices compromise internal. The definition of “failure” via a 10% threshold is pivotal yet theoretically unmotivated in the text, and no sensitivity to alternative quantiles is demonstrated. In the non-detrended analyses, the threshold is anchored to the earliest 30% of years and then applied to later decades, conflating long-term shifts with interannual shocks and biasing the failure rate. In addition, detrending windows differ between datasets (7-year for simulated yields versus 5-year for county yields), rendering labels non-comparable and further obscuring inference about drivers.Citation: https://doi.org/10.5194/egusphere-2025-3006-RC1 -
RC2: 'Comment on egusphere-2025-3006', Anonymous Referee #2, 30 Oct 2025
Prediction of crop yield failure is a fundamental to food security but challenging. Both statistical and process based crop models are widely used for crop yield projection and crop failure detection. This article presents a novel approach to identifying the climate drivers of crop yield failure. The authors first developed a machine learning procedure to identify the climate drivers of yield failure and tested it on maize yield data from simulations of two process based crop models (LPJmL and pDSSAT). Since the method well captured the crop failure for the two crop model outputs, the authors further applied it to U.S. county level maize yield data and found four climate drivers for US maize yield failure. I think this approach is very interesting , as it is useful not only for crop yield failure prediction, but also for diagnosing process based models diagnose. It is also has the potential to be used in many other climate related studies. The article is well written, however, there are some non-results descriptions in the results section that could distract from the main point and confuse readers. I suggest moving some of the texts (specified in the following comments) in the results could be moved to the method or discussion sections.
comments:
The authors mentioned several times that it matters a lot whether the climate drivers are the input of the crop models, and they want to avoid using the selected climate drivers if they were not used as inputs of a crop model. I understand that the authors want to guide the machine learning procedure to capture the actual climate factors in each crop model. However, even if a climate driver was not an input of a crop model, some crop models may have internal processes that calculate certain climate variables. For example, VPD is normally not an input climate variable, but it can be calculated using humidity and air temperature. Therefore, I think the authors should also check if a climate driver is calculated or used indirectly in the crop model. If a non-input climate driver is used indirectly in a crop model, then it could be included in the analysis. By the way, I’m a land surface modeler. I’m wondering, if there is no wind speed in PDSSAT, how does this model calculate evapotranspiration? If there is no specific relative humidity in LPJmL, how does it determine near-surface dryness and wetness, the conditions required by radiation and surface energy processes? Do these two crop models not need to calculate plant transpiration for their crop growth simulation?
Line 179-206. These paragraphs in section 3.3 are more like method descriptions rather than results. I suggest reorganizing these texts to only show the model evaluation and sensitivity analysis results, not how to perform these analysis.
Line 326-338. The text of crop-validation could be moved to the discussion section.
Figure 3: In the caption of Figure 3, the authors mention that "solid lines denote
the median daily climate conditions over all years and locations for normal or yield-failure years," I’m still confused about the normal and yield-failure years, as well as the data shown in Figures 3c–h. Based on Figures 3a and 3b, some grid cells experience yield failure each year. Therefore, crop failure occurs in some grid cells every year. Did you calculate the average of a climate variable for grid cells showing yield failure in the U.S. to represent crop failure years? Please clarify this in the figure caption.
Line 96. Why selecting these ten climate variables? And why using different climate variables for the US county level yield failure analysis? Please explain these different selections in the method section.
Line 137. Please specify the multiple pools and candidate features in the text. It is clear in figure 1 but not specified in the text.
Line 41, it is unclear to which reference Sweet et al., 2025 are referring because there are two references by Sweet in 2025.
Line 272. Why are the two most influential climate drivers are based on mean precipitation? Based on which data? The tasmin and tasmax show the largest odds ratio. Doesn’t higher odds ratios imply higher impacts?
Line 276. “both positive and negative associations” How do you tell the positive and negative association? Does it base on the odd ratios?
Line 281-283. “However, when yields are detrended, windspeed and long-wave radiation is not used by any of the climate drivers identified for LPJmL, nor is shortwave radiation for pDSSAT.” How to explain this?
Line 295. Please explain the purpose of the 100 bootstrap repeat. Is 100 times enough?
Citation: https://doi.org/10.5194/egusphere-2025-3006-RC2
Status: closed (peer review stopped)
-
CEC1: 'No compliance with the policy of the journal', Juan Antonio Añel, 11 Oct 2025
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlTo get access to the data sources necessary to replicate your work, you have linked webpages that do not comply with our policy, such as the ISIMIP site, the USDA NASS and http://prism.oregonstate.edu. These are not suitable repositories for scientific publication, and because of it your manuscript should not have been accepted for Discussions or peer-review in our journal. Therefore, the current situation with your manuscript is irregular. Please, publish your data in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.
Also, you must include a modified 'Code and Data Availability' section in a potentially reviewed manuscript, containing the information of the new repositories.
I must note that if you do not fix this problem, we cannot accept your manuscript for publication in our journal.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/egusphere-2025-3006-CEC1 -
AC1: 'Reply on CEC1', Lily-belle Sweet, 24 Oct 2025
Dear Juan A. Añel,
Thank you for notifying us of this, and I apologise that the data necessary to reproduce our results had not been previously published in a way that complies with the journal's Code and Data policy. This was an unintentional oversight on my part.
I have now included the data that we used, as well as the code for reproducing all results, in a revised Zenodo publication, available at the following URL: https://zenodo.org/records/17426950 and with the following DOI: 10.5281/zenodo.17426950. This information will also be included in the manuscript upon review.
I hope that this resolves the problem and that our manuscript can be considered for publication in Geoscientific Model Development. Please let me know if anything further is needed.
Lily-belle Sweet
Citation: https://doi.org/10.5194/egusphere-2025-3006-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 24 Oct 2025
Dear authors,
Many thanks for your reply. We can consider your manuscript now in compliance with the Code and Data policy of the journal. Please, do not forget to update the "Code and Data Availability" section in any future version of your manuscript.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2025-3006-CEC2
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 24 Oct 2025
-
AC1: 'Reply on CEC1', Lily-belle Sweet, 24 Oct 2025
-
RC1: 'Comment on egusphere-2025-3006', Anonymous Referee #1, 29 Oct 2025
This paper proposes a time-block-based cross-validation framework to identify meteorological drivers causing yield failures in the US, demonstrating the portability and interpretability of the derived metrics. The research topic aligns well with GMD while exhibiting innovation. However, several core methodologies present issues. Therefore, revisions to the article are required.
First of all, the portability claim is not evidenced beyond time. All model setup and evaluation rely on time-blocked splits, while training and test sets share the same geographic units. In the absence of a spatio-temporal block hold-out (simultaneously changing time and space), the results substantiate only temporal generalization within locations, not spatial transferability. As such, the central claim of portability across space remains unsubstantiated. Add a primary evaluation with spatio-temporal blocking, e.g.,leave-one-region-out for multiple consecutive future years.
Additionally, fixed phenology undermines window-based interpretability. Planting dates in the crop-model experiments vary across space but not across years, whereas real-world phenology adjusts with weather, cultivars, and management. The method’s reliance on calendar time windows makes the selected “critical periods” vulnerable to misalignment with true growth stages, which compromises the mechanistic meaning attributed to the discovered drivers. This concern is amplified by extensive evidence of phenological shortening under warming and by indications of misfit in the county-level results. Labeling and detrending choices compromise internal. The definition of “failure” via a 10% threshold is pivotal yet theoretically unmotivated in the text, and no sensitivity to alternative quantiles is demonstrated. In the non-detrended analyses, the threshold is anchored to the earliest 30% of years and then applied to later decades, conflating long-term shifts with interannual shocks and biasing the failure rate. In addition, detrending windows differ between datasets (7-year for simulated yields versus 5-year for county yields), rendering labels non-comparable and further obscuring inference about drivers.Citation: https://doi.org/10.5194/egusphere-2025-3006-RC1 -
RC2: 'Comment on egusphere-2025-3006', Anonymous Referee #2, 30 Oct 2025
Prediction of crop yield failure is a fundamental to food security but challenging. Both statistical and process based crop models are widely used for crop yield projection and crop failure detection. This article presents a novel approach to identifying the climate drivers of crop yield failure. The authors first developed a machine learning procedure to identify the climate drivers of yield failure and tested it on maize yield data from simulations of two process based crop models (LPJmL and pDSSAT). Since the method well captured the crop failure for the two crop model outputs, the authors further applied it to U.S. county level maize yield data and found four climate drivers for US maize yield failure. I think this approach is very interesting , as it is useful not only for crop yield failure prediction, but also for diagnosing process based models diagnose. It is also has the potential to be used in many other climate related studies. The article is well written, however, there are some non-results descriptions in the results section that could distract from the main point and confuse readers. I suggest moving some of the texts (specified in the following comments) in the results could be moved to the method or discussion sections.
comments:
The authors mentioned several times that it matters a lot whether the climate drivers are the input of the crop models, and they want to avoid using the selected climate drivers if they were not used as inputs of a crop model. I understand that the authors want to guide the machine learning procedure to capture the actual climate factors in each crop model. However, even if a climate driver was not an input of a crop model, some crop models may have internal processes that calculate certain climate variables. For example, VPD is normally not an input climate variable, but it can be calculated using humidity and air temperature. Therefore, I think the authors should also check if a climate driver is calculated or used indirectly in the crop model. If a non-input climate driver is used indirectly in a crop model, then it could be included in the analysis. By the way, I’m a land surface modeler. I’m wondering, if there is no wind speed in PDSSAT, how does this model calculate evapotranspiration? If there is no specific relative humidity in LPJmL, how does it determine near-surface dryness and wetness, the conditions required by radiation and surface energy processes? Do these two crop models not need to calculate plant transpiration for their crop growth simulation?
Line 179-206. These paragraphs in section 3.3 are more like method descriptions rather than results. I suggest reorganizing these texts to only show the model evaluation and sensitivity analysis results, not how to perform these analysis.
Line 326-338. The text of crop-validation could be moved to the discussion section.
Figure 3: In the caption of Figure 3, the authors mention that "solid lines denote
the median daily climate conditions over all years and locations for normal or yield-failure years," I’m still confused about the normal and yield-failure years, as well as the data shown in Figures 3c–h. Based on Figures 3a and 3b, some grid cells experience yield failure each year. Therefore, crop failure occurs in some grid cells every year. Did you calculate the average of a climate variable for grid cells showing yield failure in the U.S. to represent crop failure years? Please clarify this in the figure caption.
Line 96. Why selecting these ten climate variables? And why using different climate variables for the US county level yield failure analysis? Please explain these different selections in the method section.
Line 137. Please specify the multiple pools and candidate features in the text. It is clear in figure 1 but not specified in the text.
Line 41, it is unclear to which reference Sweet et al., 2025 are referring because there are two references by Sweet in 2025.
Line 272. Why are the two most influential climate drivers are based on mean precipitation? Based on which data? The tasmin and tasmax show the largest odds ratio. Doesn’t higher odds ratios imply higher impacts?
Line 276. “both positive and negative associations” How do you tell the positive and negative association? Does it base on the odd ratios?
Line 281-283. “However, when yields are detrended, windspeed and long-wave radiation is not used by any of the climate drivers identified for LPJmL, nor is shortwave radiation for pDSSAT.” How to explain this?
Line 295. Please explain the purpose of the 100 bootstrap repeat. Is 100 times enough?
Citation: https://doi.org/10.5194/egusphere-2025-3006-RC2
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
To get access to the data sources necessary to replicate your work, you have linked webpages that do not comply with our policy, such as the ISIMIP site, the USDA NASS and http://prism.oregonstate.edu. These are not suitable repositories for scientific publication, and because of it your manuscript should not have been accepted for Discussions or peer-review in our journal. Therefore, the current situation with your manuscript is irregular. Please, publish your data in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.
Also, you must include a modified 'Code and Data Availability' section in a potentially reviewed manuscript, containing the information of the new repositories.
I must note that if you do not fix this problem, we cannot accept your manuscript for publication in our journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor