The DOI matches its Crossref record; 57 of the 58 references it cites were found in a registry or an index.
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.
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.
Integrity signals
Registry
Cites
Statistics
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
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
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · cited 2×
“Random forests (Breiman, 2001) employ not only bagging (row 295 sub-sampling) but also column sub-sampling, i.”
BackgroundXGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · cited 3×
“Two of the presently most flexible and in recent competitions best performing 131 (Fernández-Delgado, 2014; Chen and Guestrin, 2016) machine learning approaches for numeric 132 predictions, extreme gradient boosting and random forests, are tested and compared against crop 133 model simulations…”
BackgroundMethodInfluentialData Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 24,401 · cited 1×
“386 NSE is a common metric for model performance over time, used especially in hydrology 387 (Nash and Sutcliffe, 1970).”
BackgroundWorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas
Fick, S. E., & Hijmans, R. J. (2017) · International Journal of Climatology · doi:10.1002/joc.5086 · Cited by 16,088 · cited 2×
“with a monthly time step, however, are available at very fine 83 resolutions up to <1 km (e.g. Wang et al., 2016; Fick and Hijmans, 2017).”
Backgroundggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Updated world map of the Köppen-Geiger climate classification
(2007) · doi:10.5194/hess-11-1633-2007 · Cited by 13,640 · cited 1×
“397 Evaluations were partly carried out at the level of major Koeppen-Geiger climate regions 398 (Figure S2) following the rules of Peel et al. (2007).”
InfluentialMERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications
Rienecker, M. M., Suárez, M. J., Gelaro, R., Todling, R., Bacmeister, J. T., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L. L., Kim, G.-K., et al. (2011) · Journal of Climate · doi:10.1175/jcli-d-11-00015.1 · Cited by 4,856 · cited 1×
“AgMERRA covers the period 185 1980-2010 and combines data from the Modern-Era Retrospective Analysis for Research and 186 Applications (MERRA; Rienecker et al., 2011), station data, and remotely sensed datasets and 187 has been bias corrected using stations from agricultural land only.”
MethodSoilGrids250m: Global gridded soil information based on machine learning
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733
Cited by
Sorted by citations
Machine Learning in Agriculture: A Comprehensive Updated Review
Benos, L. T., Tagarakis, A. C., Dolias, G., Berruto, R., Katerıs, D., & Bochtis, D. (2021) · Sensors · doi:10.3390/s21113758 · Cited by 1,048
Sharma, R., Kamble, S., Gunasekaran, A., Kumar, P. V., & Kumar, A., V (2020) · Computers & Operations Research · doi:10.1016/j.cor.2020.104926 · Cited by 837
Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Zhang, J., Han, J., & Xie, J. (2020) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2020.108275 · Cited by 385
Ahmed, K., Sachindra, D. A., Shahid, S., Demirel, M. C., & Chung, . (2019) · Hydrology and earth system sciences · doi:10.5194/hess-23-4803-2019 · Cited by 311
“The RF algorithm is found to perform well with spatial datasets and is less prone to over-fitting (Folberth et al., 2019).”
BackgroundMethodIntegrated phenology and climate in rice yields prediction using machine learning methods
Guo, Y., Fu, Y. H., Hao, F., Zhang, X., Wu, W., Jin, X., Bryant, C. R., & Senthilnath, J. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106935 · Cited by 315
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
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
