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Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning
Open accessAbstract
Global gridded crop models (GGCMs) are essential tools for estimating agricultural crop yields and externalities at large scales, typically at coarse spatial resolutions. Higher resolution estimates are required for robust agricultural assessments at regional and local scales, where the applicability of GGCMs is often limited by low data availability and high computational demand. An approach to bridge this gap is the application of meta-models trained on GGCM output data to covariates of high spatial resolution. In this study, we explore two machine learning approaches – extreme gradient boosting and random forests - to develop meta-models for the prediction of crop model outputs at fine spatial resolutions. Machine learning algorithms are trained on global scale maize simulations of a GGCM and exemplary applied to the extent of Mexico at a finer spatial resolution. Results show very high accuracy with R²>0.96 for predictions of maize yields as well as the hydrologic externalities evapotranspiration and crop available water with also low mean bias in all cases. While limited sets of covariates such as annual climate data alone provide satisfactory results already, a comprehensive set of predictors covering annual, growing season, and monthly climate data is required to obtain high performance in reproducing climate-driven inter-annual crop yield variability. The findings presented herein provide a first proof of concept that machine learning methods are highly suitable for building crop meta-models for spatio-temporal downscaling and indicate potential for further developments towards scalable crop model emulators.
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“Outputs with high resolution may be aggregated via clustering methods to provide insights [183], while at coarser resolution, statistical downscaling can help to disaggregate data to an appropriate spatial resolution, as seen in applications such as crop yield [249], wind speed [479] or surface temperature [481].”
From Tackling climate change with machine learning (2023) · cites this paper as [249]
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“Emu-lators have been developed for process-based crop models, like APSIM (Shahhosseini et al., 2019), GEPIC (Folberth et al., 2019), GWG (Xu et al., 2021), GAZE (Raimondo et al., 2021), and WOFOST (Tartarini et al., 2021), and used to estimate historical crop yield As an emulator trained by…”
MethodAssessing the impact of climate variability on Australia’s sugarcane yield in 1980–2022
Yao, S., Wang, B., Liu, D. L., Li, S., Ruan, H., & Yu, Q. (2025) · European Journal of Agronomy · doi:10.1016/j.eja.2025.127519 · Cited by 9
“RF ’ s ensemble approach of averaging multiple decision trees provided more stable and consistent results, especially with varied climate data (Folberth et al., 2019).”
Zheng, M., Hu, H., Niu, Y. P., Shen, Q., Jia, F., & Geng, X. (2025) · European Journal of Remote Sensing · doi:10.1080/22797254.2025.2455940 · Cited by 8
“However, the need for detailed soil data and daily climate information often limits the large-scale application of crop growth models (Folberth et al., 2019; Rosenzweig et al., 2018).”
Application of Data Mining in the Evaluation of Enterprise Lean Management Effect
Yang, J., & Liu, Y. (2021) · Scientific Programming · doi:10.1155/2021/4774140 · Cited by 8
“From the perspective of lean management accounting, literature [13] explains the obstacles, such as corporate culture obstacles, financial role transition obstacles, educational background obstacles, and employee personal obstacles, encountered when applying lean accounting theory to implement cost…”
BackgroundMohanasundaram, S., Kasiviswanathan, K. S., Chandra, P., Santikayasa, I. P., & Singh, S. (2022) · International Journal of Plant Production · doi:10.1007/s42106-022-00223-2 · Cited by 8
“These results also strongly support from similar studies in such a way that the average error with respect to the actual range of values was within 15–20% (Folberth et al., 2019; Gaso et al., 2019; Gilardelli et al., 2019).”
ResultInnovative Applications of Computer-Assisted Technology in English Learning under Constructivism
Zhao, D. (2021) · Mobile Information Systems · doi:10.1155/2021/1676197 · Cited by 8
“-e literature [7] conducted more profound research on case-based teaching in AI teaching and classified it into three types: model reference, question reexamination, and game reasoning.”
BackgroundIntelligent analysis of e-government influence factors based on improved machine learning
Wei, L. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06657-5 · Cited by 8
“Moreover, the government can design a government website according to the needs of citizens to provide personalized services [13].”
Backgroundde Souza, D. P., Mendonça, F. C., Bosi, C., Pezzopane, J. R. M., & dos Santos, P. M. (2022) · Grass and Forage Science · doi:10.1111/gfs.12560 · Cited by 7
Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning
Yu, T., Pang, Y., Sun, R., & Niu, X. (2022) · IEEE Access · doi:10.1109/access.2022.3210218 · Cited by 7
“Studies good quality was the base of the machine learning or deep learning models [46], [47], [48].”
BackgroundJafari, M. R., & Keshavarz, A. (2021) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-021-01359-w · Cited by 7
“…1 Kazeroon Agricultural and Natural Resources Research and Education Center, Fars, Iran 2 Water Engineering Department, School of Agricultural Sciences and Natural Resources, Sari University, Mazandaran, Iran machine learning techniques (Folberth, 2019; Kuwata & Shibasaki, 2015; Liakos, 2018).”
BackgroundMarginal Impact of climate variability on crop yields in Ghana
Gyamerah, S. A., Owusu, V., & Kwakye, S. O. (2024) · Scientific African · doi:10.1016/j.sciaf.2024.e02314 · Cited by 6
Iizumi, T., Iseki, K., Ikazaki, K., Sakai, T., Kobayashi, S., & Batieno, B. J. (2025) · Climate Services · doi:10.1016/j.cliser.2025.100605 · Cited by 6
Patel, J., Chandel, S. T., & Sonker, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20418-1 · Cited by 6
Yu, J., Zhao, Y., Lei, G., & Zeng, W. (2025) · Agronomy Journal · doi:10.1002/agj2.70010 · Cited by 6
Grassland resource evaluation based on improved bp network model and analytic hierarchy process
Jiang, Z., & Wei, Z. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189540 · Cited by 6
Effect of artificial intelligence auxiliary equipment in the process of cognitive learning
Wu, F., Liu, X., & Wang, Y. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06470-0 · Cited by 6
Gopi, P. S. S., & Karthikeyan, M. (2023) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais56108.2023.10073744 · Cited by 6
“…making economic decisions as well as supporting famine prevention activities. crop yield forecasting is a basic research query in plant ecology, that is to realize the ways in which plant phenotype is fixed by genotype (G), management (M), environment (E), and its interactions (G ×× E ×× M) [9, 8].”
BackgroundArtificial Intelligence in Plant and Agricultural Research
Leonelli, S., & Williamson, H. F. (2023) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789811265679_0018 · Cited by 6
Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models.
Ippolito, T., Balkovič, J., Skalský, R., Folberth, C., Krisztin, T., & Neff, J. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.118532 · Cited by 6
Self-citationErmolieva, T. Y., Havlík, P., Augustynczik, A. L. D., Boere, E., Frank, S., Kahil, T., Wang, G., Balkovič, J., Skalský, R., Folberth, C., et al. (2023) · Cybernetics and Systems Analysis · doi:10.1007/s10559-023-00620-z · Cited by 6
Self-citationTracing the life cycle carbon footprint of staple crops in Belt and Road Initiative countries
Liu, S., He, Y., Qi, Z., Liu, Y., & Jiang, Q. (2025) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2025.108382 · Cited by 5
He, Y., Chen, X., Li, H., Li, X., Sun, S., & Wu, M. (2026) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2026.111017 · Cited by 5
Zhu, X., & Tang, S. (2022) · Scientific Programming · doi:10.1155/2022/4131058 · Cited by 5
“With the introduction of artificial intelligence, a multidisciplinary science, it has been used to study, develop, simulate, and extend the theoretical techniques of human intelligence and has led to the creation of probabilistic neural networks [17].”
MethodWeather Index Insurance Can Offset Heat‐Induced Rice Losses Under Global Warming
Zhang, J., Zhang, Z., Wang, C., Wang, X., Zhang, L. L., Ma, X. L., & Tao, F. (2022) · Earth s Future · doi:10.1029/2021ef002534 · Cited by 5
“, 2019), but their large-scale applications are often limited by the complicated parameters, low data availability, and high computational demand (Folberth et al., 2019).”
BackgroundEnhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources
Zou, Y., Kattel, G., & Miao, L. (2024) · Remote Sensing · doi:10.3390/rs16040701 · Cited by 5
“On the other hand, applying gridded crop models at very high resolutions significantly increases computational demands and is often constrained by data availability [21].”
BackgroundInfluential
Metadata
- Authors
- Folberth, C., Baklanov, A., Balkovič, J., Skalský, R., Khabarov, N., & Obersteiner, M.
- Affiliations
- International Institute for Applied Systems Analysis (AT)
- National Research University Higher School of Economics (RU)
- Corresponding author
- Folberth, C.
- Venue
- Agricultural and Forest Meteorology
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 264
- Pages
- 1-15
- ISSN
- 0168-1923 (ISSN-L)
- OpenAlex
- W2895348292
- Cited by
- 161
- References
- 58
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 24 · 2023: 19 · 2022: 29 · 2021: 29 · 2020: 14 · 2019: 12
- FWCI
- 11.60
- Type
- journal article
- Language
- en
- Open access
- green
- ddd.uab.cat
- Licence
- Locations
- Agricultural and Forest Meteorology (publishedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by-nc-nd)
- IIASA PURE (International Institute of Applied Systems Analysis) (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- Funders
- European Research Council (ERC-2013-SynG-610028 Imbalance-P)
- National Research University Higher School of Economics
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- gridded crop models, crop yield prediction, meta-model, maize yield, extreme gradient boosting, random forest, evapotranspiration, Mexico
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