Articles | Volume 19, issue 14
https://doi.org/10.5194/gmd-19-6687-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-6687-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
23 Jul 2026
Methods for assessment of models |
| 23 Jul 2026
| 23 Jul 2026
A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data
Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Department of Hydro Sciences, TUD Dresden University of Technology, Dresden, Germany
Christoph Müller
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Jonas Jägermeyr
Columbia University, Climate School, New York, NY, USA
NASA Goddard Institute for Space Studies (GISS), New York, NY, USA
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Jakob Zscheischler
Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Department of Hydro Sciences, TUD Dresden University of Technology, Dresden, Germany
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden–Leipzig, Germany
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Soil organic carbon sequestration on cropland has been proposed as a climate change mitigation strategy. We simulate different agricultural management practices under climate change scenarios using a global biophysical model. We find that at the global aggregated level, agricultural management practices are not capable of enhancing total carbon storage in the soil, yet for some climate regions, we find that there is potential to enhance the carbon content in cropland soils.
Cited articles
Anand, M., Bohn, F. J., Camps-Valls, G., Fischer, R., Huth, A., Sweet, L.-b., and Zscheischler, J.: Identifying compound weather drivers of forest biomass loss with generative deep learning, Environ. Data Sci., 3, e4, https://doi.org/10.1017/eds.2024.2, 2024a. a
Anand, M., Hamed, R., Linscheid, N., Silva, P. S., Andre, J., Zscheischler, J., Garry, F. K., and Bastos, A.: Winter Climate Preconditioning of Summer Vegetation Extremes in the Northern Hemisphere, Environ. Res. Lett., 19, 094045, https://doi.org/10.1088/1748-9326/ad627d, 2024b. a
Ben-Ari, T., Boé, J., Ciais, P., Lecerf, R., Van der Velde, M., and Makowski, D.: Causes and implications of the unforeseen 2016 extreme yield loss in the breadbasket of France, Nat. Commun., 9, 1627, https://doi.org/10.1038/s41467-018-04087-x, 2018. a
Bilodeau, B., Jaques, N., Koh, P. W., and Kim, B.: Impossibility theorems for feature attribution, P. Natl. Acad. Sci. USA, 121, e2304406120, https://doi.org/10.1073/pnas.2304406120, 2024. a
Cammarano, D., Rötter, R. P., Asseng, S., Ewert, F., Wallach, D., Martre, P., Hatfield, J. L., Jones, J. W., Rosenzweig, C., Ruane, A. C., Boote, K. J., Thorburn, P. J., Kersebaum, K. C., Aggarwal, P. K., Angulo, C., Basso, B., Bertuzzi, P., Biernath, C., Brisson, N., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Heng, L., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R. C., Müller, C., Kumar, S. N., Nendel, C., O’Leary, G. J., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M. A., Shcherbak, I., Steduto, P., Stöckle, C. O., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., White, J. W., and Wolf, J.: Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO2, Field Crop. Res., 198, 80–92, https://doi.org/10.1016/j.fcr.2016.08.015, 2016. a
Chen, M., Guilpart, N., and Makowski, D.: Comparison of methods to aggregate climate data to predict crop yield: an application to soybean, Environ. Res. Lett., 19, 054049, https://doi.org/10.1088/1748-9326/ad42b5, 2024. a, b, c
Crane-Droesch, A.: Machine learning methods for crop yield prediction and climate change impact assessment in agriculture, Environ. Res. Lett., 13, 114003, https://doi.org/10.1088/1748-9326/aae159, 2018. a
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120, https://doi.org/10.5194/essd-12-2097-2020, 2020. a
Deines, J. M., Swatantran, A., Ye, D., Myers, B., Archontoulis, S., and Lobell, D. B.: Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020, Remote Sens. Environ., 291, 113551, https://doi.org/10.1016/j.rse.2023.113551, 2023. a
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.: GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-87-10-1381, 2006. a
Elliott, J., Kelly, D., Chryssanthacopoulos, J., Glotter, M., Jhunjhnuwala, K., Best, N., Wilde, M., and Foster, I.: The parallel system for integrating impact models and sectors (pSIMS), Environ. Modell. Softw., 62, 509–516, https://doi.org/10.1016/j.envsoft.2014.04.008, 2014. a
Ferraciolli, M. A., Bocca, F. F., and Rodrigues, L. H. A.: Neglecting spatial autocorrelation causes underestimation of the error of sugarcane yield models, Comput. Electron. Agr., 161, 233–240, https://doi.org/10.1016/j.compag.2018.09.003, 2019. a
Filippi, P., Han, S. Y., and Bishop, T. F.: On crop yield modelling, predicting, and forecasting and addressing the common issues in published studies, Precis. Agric., 26, 8, https://doi.org/10.1007/s11119-024-10212-2, 2024. a
Folberth, C., Baklanov, A., Balkovič, J., Skalský, R., Khabarov, N., and Obersteiner, M.: Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning, Agr. Forest Meteorol., 264, 1–15, https://doi.org/10.1016/j.agrformet.2018.09.021, 2019. a
Franch, B., Cintas, J., Becker-Reshef, I., Sanchez-Torres, M. J., Roger, J., Skakun, S., Sobrino, J. A., Van Tricht, K., Degerickx, J., Gilliams, S., Koetz, B., Szantoi, Z., and Whitcraft, A.: Global crop calendars of maize and wheat in the framework of the WorldCereal project, GISci. Remote Sens., 59, 885–913, https://doi.org/10.1080/15481603.2022.2079273, 2022. a
Frieler, K., Schauberger, B., Arneth, A., Balkovič, J., Chryssanthacopoulos, J., Deryng, D., Elliott, J., Folberth, C., Khabarov, N., Müller, C., Olin, S., Pugh, T. A. M., Schaphoff, S., Schewe, J., Schmid, E., Warszawski, L., and Levermann, A.: Understanding the weather signal in national crop-yield variability, Earths Future, 5, 605–616, https://doi.org/10.1002/2016EF000525, 2017. a
Geurts, P., Ernst, D., and Wehenkel, L.: Extremely randomized trees, Mach. Learn., 63, 3–42, https://doi.org/10.1007/s10994-006-6226-1, 2006. a
Han, J., Shi, L., Yang, Q., Yu, J., and Athanasiadis, I. N.: Knowledge-guided machine learning with multivariate sparse data for crop growth modelling, Field Crop. Res., 328, 109912, https://doi.org/10.1016/j.fcr.2025.109912, 2025. a
Haqiqi, I., Grogan, D. S., Hertel, T. W., and Schlenker, W.: Quantifying the impacts of compound extremes on agriculture, Hydrol. Earth Syst. Sci., 25, 551–564, https://doi.org/10.5194/hess-25-551-2021, 2021. a
Heilemann, J., Klassert, C., Samaniego, L., Thober, S., Marx, A., Boeing, F., Klauer, B., and Gawel, E.: Projecting impacts of extreme weather events on crop yields using LASSO regression, Weather and Climate Extremes, 46, 100738, https://doi.org/10.1016/j.wace.2024.100738, 2024. a, b
Heinicke, S., Frieler, K., Jägermeyr, J., and Mengel, M.: Global gridded crop models underestimate yield responses to droughts and heatwaves, Environ. Res. Lett., 17, 044026, https://doi.org/10.1088/1748-9326/ac592e, 2022. a
Hoffman, A. L., Kemanian, A. R., and Forest, C. E.: The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning, Environ. Res. Lett., 15, 094013, https://doi.org/10.1088/1748-9326/ab7b22, 2020. a
Hogan, D. and Schlenker, W.: Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets, Nat. Commun., 15, 4638, https://doi.org/10.1038/s41467-024-48388-w, 2024. a
Hsiao, J., Swann, A. L. S., and Kim, S.-H.: Maize yield under a changing climate: The hidden role of vapor pressure deficit, Agr. Forest Meteorol., 279, 107692, https://doi.org/10.1016/j.agrformet.2019.107692, 2019. a
Hultgren, A., Carleton, T., Delgado, M., Gergel, D. R., Greenstone, M., Houser, T., Hsiang, S., Jina, A., Kopp, R. E., Malevich, S. B., McCusker, K. E., Mayer, T., Nath, I., Rising, J., Rode, A., and Yuan, J.: Impacts of climate change on global agriculture accounting for adaptation, Nature, 642, 644–652, https://doi.org/10.1038/s41586-025-09085-w, 2025. a
Jiang, S., Sweet, L.-b., Blougouras, G., Brenning, A., Li, W., Reichstein, M., Denzler, J., Shangguan, W., Yu, G., Huang, F., and Zscheischler, J.: How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences, Earths Future, 12, e2024EF004540, https://doi.org/10.1029/2024EF004540, 2024a. a
Jiang, S., Tarasova, L., Yu, G., and Zscheischler, J.: Compounding effects in flood drivers challenge estimates of extreme river floods, Science Advances, 10, eadl4005, https://doi.org/10.1126/sciadv.adl4005, 2024b. a
Jin, Z., Zhuang, Q., Wang, J., Archontoulis, S. V., Zobel, Z., and Kotamarthi, V. R.: The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2, Glob. Change Biol., 23, 2687–2704, https://doi.org/10.1111/gcb.13617, 2017. a
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., and Ritchie, J. T.: The DSSAT cropping system model, Eur. J. Agron., 18, 235–265, https://doi.org/10.1016/S1161-0301(02)00107-7, 2003. a
Jägermeyr, J., Müller, C., Ruane, A. C., Elliott, J., Balkovic, J., Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J. A., Fuchs, K., Guarin, J. R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A. K., Kelly, D., Khabarov, N., Lange, S., Lin, T.-S., Liu, W., Mialyk, O., Minoli, S., Moyer, E. J., Okada, M., Phillips, M., Porter, C., Rabin, S. S., Scheer, C., Schneider, J. M., Schyns, J. F., Skalsky, R., Smerald, A., Stella, T., Stephens, H., Webber, H., Zabel, F., and Rosenzweig, C.: Climate impacts on global agriculture emerge earlier in new generation of climate and crop models, Nature Food, 2, 873–885, https://doi.org/10.1038/s43016-021-00400-y, 2021. a, b, c
Kim, H.: Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1), version 1, https://doi.org/10.20783/DIAS.501, 2017. a
Kim, Y.-U., Ruane, A. C., Finger, R., and Webber, H.: Robust assessment of climatic risks to crop production, Nature Food, 1–2, https://doi.org/10.1038/s43016-025-01168-1, 2025. a, b
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019. a
Lange, S.: ISIMIP3BASD, Zenodo [code], https://doi.org/10.5281/zenodo.4686991, 2021. a
Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Schmied, H. M., Hersbach, H., Buontempo, C., and Cagnazzo, C.: WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0), version Number: 2.0, ISIMIP Repository [data set], https://doi.org/10.48364/ISIMIP.342217, 2021. a
Lange, S., Mengel, M., Treu, S., and Büchner, M.: ISIMIP3a atmospheric climate input data, version Number: 1.1, ISIMIP Repository [data set], https://doi.org/10.48364/ISIMIP.982724.1, 2022. a, b
Lesk, C., Coffel, E., Winter, J., Ray, D., Zscheischler, J., Seneviratne, S. I., and Horton, R.: Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields, Nature Food, 2, 683–691, https://doi.org/10.1038/s43016-021-00341-6, 2021. a
Lin, T., Zhong, R., Wang, Y., Xu, J., Jiang, H., Xu, J., Ying, Y., Rodriguez, L., Ting, K. C., and Li, H.: DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation, Environ. Res. Lett., 15, 034016, https://doi.org/10.1088/1748-9326/ab66cb, 2020. a
Lischeid, G., Webber, H., Sommer, M., Nendel, C., and Ewert, F.: Machine learning in crop yield modelling: A powerful tool, but no surrogate for science, Agr. Forest Meteorol., 312, 108698, https://doi.org/10.1016/j.agrformet.2021.108698, 2022. a, b
Liu, Q., Yang, M., Mohammadi, K., Song, D., Bi, J., and Wang, G.: Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model, Artificial Intelligence for the Earth Systems, 1, https://doi.org/10.1175/AIES-D-22-0002.1, 2022. a
Liu, W., Ye, T., Müller, C., Jägermeyr, J., Franke, J. A., Stephens, H., and Chen, S.: The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations, Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, 2023. a, b
Lobell, D. B. and Di Tommaso, S.: A half-century of climate change in major agricultural regions: Trends, impacts, and surprises, P. Natl. Acad. Sci. USA, 122, e2502789122, https://doi.org/10.1073/pnas.2502789122, 2025. a, b
Lobell, D. B., Hammer, G. L., McLean, G., Messina, C., Roberts, M. J., and Schlenker, W.: The critical role of extreme heat for maize production in the United States, Nat. Clim. Change, 3, 497–501, https://doi.org/10.1038/nclimate1832, 2013. a
Lobell, D. B., Roberts, M. J., Schlenker, W., Braun, N., Little, B. B., Rejesus, R. M., and Hammer, G. L.: Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest, Science, 344, 516–519, https://doi.org/10.1126/science.1251423, 2014. a
Ludwig, M., Moreno-Martinez, A., Hölzel, N., Pebesma, E., and Meyer, H.: Assessing and improving the transferability of current global spatial prediction models, Global Ecol. Biogeogr., 32, 356–368, https://doi.org/10.1111/geb.13635, 2023. a
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J. J., and Müller, C.: Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage), Geosci. Model Dev., 12, 2419–2440, https://doi.org/10.5194/gmd-12-2419-2019, 2019. a
Mamalakis, A., Barnes, E. A., and Ebert-Uphoff, I.: Investigating the Fidelity of Explainable Artificial Intelligence Methods for Applications of Convolutional Neural Networks in Geoscience, Artificial Intelligence for the Earth Systems, 1, https://doi.org/10.1175/AIES-D-22-0012.1, 2022. a
Martínez-Ferrer, L., Piles, M., and Camps-Valls, G.: Crop Yield Estimation and Interpretability With Gaussian Processes, IEEE Geosci. Remote S., 1–5, https://doi.org/10.1109/LGRS.2020.3016140, 2020. a
Matiu, M., Ankerst, D. P., and Menzel, A.: Interactions between temperature and drought in global and regional crop yield variability during 1961–2014, PLOS ONE, 12, e0178339, https://doi.org/10.1371/journal.pone.0178339, 2017. a
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., and Nauss, T.: Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation, Environ. Modell. Softw., 101, 1–9, https://doi.org/10.1016/j.envsoft.2017.12.001, 2018. a, b
Meyer, H., Reudenbach, C., Wöllauer, S., and Nauss, T.: Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction, Ecol. Model., 411, 108815, https://doi.org/10.1016/j.ecolmodel.2019.108815, 2019. a
Mistry, M. N., Wing, I. S., and Cian, E. D.: Simulated vs. empirical weather responsiveness of crop yields: US evidence and implications for the agricultural impacts of climate change, Environ. Res. Lett., 12, 075007, https://doi.org/10.1088/1748-9326/aa788c, 2017. a, b
Moon, T., Kim, D., Kwon, S., and Son, J. E.: Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders, Plant Phenomics, 5, 0035, https://doi.org/10.34133/plantphenomics.0035, 2023. a
Müller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovic, J., Ciais, P., Deryng, D., Folberth, C., Glotter, M., Hoek, S., Iizumi, T., Izaurralde, R. C., Jones, C., Khabarov, N., Lawrence, P., Liu, W., Olin, S., Pugh, T. A. M., Ray, D. K., Reddy, A., Rosenzweig, C., Ruane, A. C., Sakurai, G., Schmid, E., Skalsky, R., Song, C. X., Wang, X., de Wit, A., and Yang, H.: Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403–1422, https://doi.org/10.5194/gmd-10-1403-2017, 2017. a
Müller, C., Jägermeyr, J., Franke, J. A., Ruane, A. C., Balkovic, J., Ciais, P., Dury, M., Falloon, P., Folberth, C., Hank, T., Hoffmann, M., Izaurralde, R. C., Jacquemin, I., Khabarov, N., Liu, W., Olin, S., Pugh, T. A. M., Wang, X., Williams, K., Zabel, F., and Elliott, J. W.: Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality-Based Model Evaluation, Earths Future, 12, e2023EF003773, https://doi.org/10.1029/2023EF003773, 2024. a
Nóia Júnior, R. d. S., Asseng, S., Müller, C., Deswarte, J.-C., Cohan, J.-P., and Martre, P.: Negative impacts of climate change on crop yields are underestimated, Trends Plant Sci., https://doi.org/10.1016/j.tplants.2025.05.002, 2025. a
Ortiz-Bobea, A., Wang, H., Carrillo, C. M., and Ault, T. R.: Unpacking the climatic drivers of US agricultural yields, Environ. Res. Lett., 14, 064003, https://doi.org/10.1088/1748-9326/ab1e75, 2019. a
Peters, J., Bühlmann, P., and Meinshausen, N.: Causal inference by using invariant prediction: identification and confidence intervals, J. R. Stat. Soc. B, 78, 947–1012, https://doi.org/10.1111/rssb.12167, 2016. a
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000 – Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochem. Cy., 24, https://doi.org/10.1029/2008GB003435, 2010. a
Richens, J. and Everitt, T.: Robust agents learn causal world models, in: The Twelfth International Conference on Learning Representations, https://doi.org/10.48550/arXiv.2402.10877, 2024. a
Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N., and Huybers, P.: Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields, Nature Food, 1, 127–133, https://doi.org/10.1038/s43016-020-0028-7, 2020. a, b
Roberts, M. J., Schlenker, W., and Eyer, J.: Agronomic Weather Measures in Econometric Models of Crop Yield with Implications for Climate Change, Am. J. Agr. Econ., 95, 236–243, https://doi.org/10.1093/ajae/aas047, 2013. a
Ryo, M.: Explainable artificial intelligence and interpretable machine learning for agricultural data analysis, Artificial Intelligence in Agriculture, 6, 257–265, https://doi.org/10.1016/j.aiia.2022.11.003, 2022. a
Sacks, W. J. and Kucharik, C. J.: Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance, Agr. Forest Meteorol., 151, 882–894, https://doi.org/10.1016/j.agrformet.2011.02.010, 2011. a
Schauberger, B., Archontoulis, S., Arneth, A., Balkovic, J., Ciais, P., Deryng, D., Elliott, J., Folberth, C., Khabarov, N., Müller, C., Pugh, T. A. M., Rolinski, S., Schaphoff, S., Schmid, E., Wang, X., Schlenker, W., and Frieler, K.: Consistent negative response of US crops to high temperatures in observations and crop models, Nat. Commun., 8, 13931, https://doi.org/10.1038/ncomms13931, 2017. a
Schlenker, W. and Roberts, M. J.: Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change, P. Natl. Acad. Sci. USA, 106, 15594–15598, https://doi.org/10.1073/pnas.0906865106, 2009. a, b
Shahhosseini, M., Martinez-Feria, R. A., Hu, G., and Archontoulis, S. V.: Maize yield and nitrate loss prediction with machine learning algorithms, Environ. Res. Lett., 14, 124026, https://doi.org/10.1088/1748-9326/ab5268, 2019. a, b
Siebers, M. H., Slattery, R. A., Yendrek, C. R., Locke, A. M., Drag, D., Ainsworth, E. A., Bernacchi, C. J., and Ort, D. R.: Simulated heat waves during maize reproductive stages alter reproductive growth but have no lasting effect when applied during vegetative stages, Agr. Ecosyst. Environ., 240, 162–170, https://doi.org/10.1016/j.agee.2016.11.008, 2017. a
Siebert, S., Webber, H., and Rezaei, E. E.: Weather impacts on crop yields – searching for simple answers to a complex problem, Environ. Res. Lett., 12, 081001, https://doi.org/10.1088/1748-9326/aa7f15, 2017a. a, b, c
Siebert, S., Webber, H., Zhao, G., and Ewert, F.: Heat stress is overestimated in climate impact studies for irrigated agriculture, Environ. Res. Lett., 12, 054023, https://doi.org/10.1088/1748-9326/aa702f, 2017b. a
Silva, J. V., Heerwaarden, J. v., Reidsma, P., Laborte, A. G., Tesfaye, K., and Ittersum, M. K. v.: Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy, Field Crop. Res., 302, 109063, https://doi.org/10.1016/j.fcr.2023.109063, 2023. a
Sweet, L.-b.: A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data, Zenodo [code and data set], https://doi.org/10.5281/zenodo.15725040, 2026. a
Sweet, L.-b., Athanasiadis, I. N., Bree, R. v., Castellano, A., Martre, P., Paudel, D., Ruane, A. C., and Zscheischler, J.: Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning, One Earth, 8, https://doi.org/10.1016/j.oneear.2025.101233, 2025. a
Ting, M., Lesk, C., Liu, C., Li, C., Horton, R. M., Coffel, E. D., Rogers, C. D. W., and Singh, D.: Contrasting impacts of dry versus humid heat on US corn and soybean yields, Sci. Rep.-UK, 13, 710, https://doi.org/10.1038/s41598-023-27931-7, 2023. a
Troy, T. J., Kipgen, C., and Pal, I.: The impact of climate extremes and irrigation on US crop yields, Environ. Res. Lett., 10, 054013, https://doi.org/10.1088/1748-9326/10/5/054013, 2015. a
U.S. Census Bureau: TIGER/Line Shapefile, 2018, Current State and Equivalent National, https://catalog.data.gov/dataset/tiger-line-shapefile-2018-nation-u-s-current-state-and-equivalent-national (last access: May 2025), 2018. a
Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., Meinshausen, N., and Frieler, K.: The effects of climate extremes on global agricultural yields, Environ. Res. Lett., 14, 054010, https://doi.org/10.1088/1748-9326/ab154b, 2019. a, b
Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C. A., Tschumi, E., van der Wiel, K., Zhang, T., and Zscheischler, J.: Identifying meteorological drivers of extreme impacts: an application to simulated crop yields, Earth Syst. Dynam., 12, 151–172, https://doi.org/10.5194/esd-12-151-2021, 2021. a, b, c
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K., and Zaehle, S.: Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0), Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, 2018. a
Wadoux, A. M. J.-C., Samuel-Rosa, A., Poggio, L., and Mulder, V. L.: A note on knowledge discovery and machine learning in digital soil mapping, Eur. J. Soil Sci., 71, 133–136, https://doi.org/10.1111/ejss.12909, 2020. a
Wang, B., Jägermeyr, J., O’Leary, G. J., Wallach, D., Ruane, A. C., Feng, P., Li, L., Liu, D. L., Waters, C., Yu, Q., Asseng, S., and Rosenzweig, C.: Pathways to identify and reduce uncertainties in agricultural climate impact assessments, Nature Food, 1–7, https://doi.org/10.1038/s43016-024-01014-w, 2024. a
Webber, H., Rezaei, E. E., Ryo, M., and Ewert, F.: Framework to guide modeling single and multiple abiotic stresses in arable crops, Agr. Ecosyst. Environ., 340, 108179, https://doi.org/10.1016/j.agee.2022.108179, 2022. a, b
Wolanin, A., Mateo-García, G., Camps-Valls, G., Gómez-Chova, L., Meroni, M., Duveiller, G., Liangzhi, Y., and Guanter, L.: Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt, Environ. Res. Lett., 15, 024019, https://doi.org/10.1088/1748-9326/ab68ac, 2020. a
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., Huang, M., Yao, Y., Bassu, S., Ciais, P., Durand, J.-L., Elliott, J., Ewert, F., Janssens, I. A., Li, T., Lin, E., Liu, Q., Martre, P., Müller, C., Peng, S., Peñuelas, J., Ruane, A. C., Wallach, D., Wang, T., Wu, D., Liu, Z., Zhu, Y., Zhu, Z., and Asseng, S.: Temperature increase reduces global yields of major crops in four independent estimates, P. Natl. Acad. Sci. USA, 114, 9326–9331, https://doi.org/10.1073/pnas.1701762114, 2017. a
Zhu, P., Zhuang, Q., Archontoulis, S., Bernacchi, C., and Müller, C.: Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration, Glob. Change Biol., 25, https://doi.org/10.1111/gcb.14632, 2019. a
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.: A typology of compound weather and climate events, Nat. Rev. Earth Environ., 1, 333–347, https://doi.org/10.1038/s43017-020-0060-z, 2020. a
Short summary
This study presents a method to identify climate drivers of an impact, such as agricultural yield failure, from high-resolution weather data. The approach systematically generates, selects and combines predictors that generalise across different environments. Tested on crop model simulations, the identified drivers are used to create parsimonious models that achieve high predictive performance over long time horizons, offering a more interpretable alternative to black-box models.
This study presents a method to identify climate drivers of an impact, such as agricultural...
