Articles | Volume 18, issue 6
https://doi.org/10.5194/essd-18-3997-2026
© 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/essd-18-3997-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
11 Jun 2026
Data description article |
| 11 Jun 2026
| 11 Jun 2026
CY-Bench: a comprehensive benchmark dataset for sub-national crop yield forecasting
Michiel Kallenberg
CORRESPONDING AUTHOR
Artificial Intelligence Group, Wageningen University and Research, P.O. Box 16, Wageningen, 6700 AA, the Netherlands
Dilli Paudel
Artificial Intelligence Group, Wageningen University and Research, P.O. Box 16, Wageningen, 6700 AA, the Netherlands
Stella Ofori-Ampofo
Chair of Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany
Hilmy Baja
Artificial Intelligence Group, Wageningen University and Research, P.O. Box 16, Wageningen, 6700 AA, the Netherlands
Ron van Bree
Artificial Intelligence Group, Wageningen University and Research, P.O. Box 16, Wageningen, 6700 AA, the Netherlands
Aike Potze
Artificial Intelligence Group, Wageningen University and Research, P.O. Box 16, Wageningen, 6700 AA, the Netherlands
Pratishtha Poudel
Department of Agronomy, Purdue University, 915 Mitch Daniels Blvd, West Lafayette, IN 47907, United States
Abdelrahman Saleh
Department of Soil Science, University of Manitoba, 13 Freedman Crescent, Winnipeg, MB R3T 2N2, Canada
Weston Anderson
Department of Geographical Sciences, University of Maryland, 7251 Preinkert Drive, Collega Park, MD 20742, United States
Malte von Bloh
Chair of Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany
Andres Castellano
GISS Impacts Group, NASA Goddard Institute for Space Studies, 535 West 116th Street, Mail Code 4312, New York, NY 10027, United States
Oumnia Ennaji
College of Computing, Mohammed VI Polytechnic University, Lot 660, Benguerir, 43150, Morocco
Raed Hamed
Institute for Environmental Studies, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, the Netherlands
Rahel Laudien
Department of Climate Resilience, Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, Potsdam, 4412, Germany
Donghoon Lee
Department of Civil Engineering, University of Manitoba, 15 Gillson Street, Winnipeg, MB R3T 5V6, Canada
Inti Luna
Image Processing Laboratory, Universitat de València, C/Catedràtic Agustín Escardino Benlloch, 9, València, 46980, Spain
Dainius Masiliūnas
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, Wageningen, 6700 AA, the Netherlands
Michele Meroni
Seidor Consulting, Carrer dels Provençals 44, Barcelona, 08019, Spain
Janet Mumo Mutuku
West and Central Africa Region Hub, International Crops Research Institute for the Semi-Arid Tropics, P.O. Box 320, Bamako, Mali
Siyabusa Mkuhlani
Natural Resources Management, International Institute of Tropical Agriculture, P.O. Box 30677, Nairobi, 00100, Kenya
Jonathan Richetti
Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation (CSIRO), 147 Underwood Av, Perth, WA 6014, Australia
Alex C. Ruane
GISS Impacts Group, NASA Goddard Institute for Space Studies, 535 West 116th Street, Mail Code 4312, New York, NY 10027, United States
Ritvik Sahajpal
Department of Geographical Sciences, University of Maryland, 7251 Preinkert Drive, Collega Park, MD 20742, United States
Guanyuan Shuai
Department of Geographical Sciences, University of Maryland, 7251 Preinkert Drive, Collega Park, MD 20742, United States
Vasileios Sitokonstantinou
Artificial Intelligence Group, Wageningen University and Research, P.O. Box 16, Wageningen, 6700 AA, the Netherlands
Rogério de S. Nóia-Júnior
UMR LEPSE, National Research Institute for Agriculture, Food and Environment (INRAE), 2 Pl. Pierre Viala, Montpellier, 34000, France
Amit Kumar Srivastava
Simulation and Data Science- Multiscale modelling and Forecasting, Leibniz Centre for Agricultural Landscape Research, Eberswalder Straße 84, Müncheberg, 15374, Germany
Robert Strong
Agricultural Leadership, Education, and Communications, Texas A&M University, 600 John Kimbrough Blvd, College Station, TX 77843-2116, United States
Lily-belle Sweet
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
Petar Vojnović
Fincons s.p.a, Via Torri Bianche 10, Vimercate, 20871, Italy
Allard de Wit
Earth Observation and Environmental Informatics, Wageningen University and Research, P.O. Box 47, Wageningen, 6700 AA, the Netherlands
Maximilian Zachow
Chair of Digital Agriculture, Technical University of Munich, Liesel-Beckmann-Straße 2, Freising, 85354, Germany
Ioannis N. Athanasiadis
Artificial Intelligence Group, Wageningen University and Research, P.O. Box 16, Wageningen, 6700 AA, the Netherlands
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Cited
2 citations as recorded by crossref.
- Wheat yield forecasts with seasonal climate models and long short-term memory networks M. Zachow et al. https://doi.org/10.1016/j.compag.2025.110965
- A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the US from 2018–2022 A. Höhl et al. https://doi.org/10.1038/s41597-026-06611-x
2 citations as recorded by crossref.
- Wheat yield forecasts with seasonal climate models and long short-term memory networks M. Zachow et al. https://doi.org/10.1016/j.compag.2025.110965
- A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the US from 2018–2022 A. Höhl et al. https://doi.org/10.1038/s41597-026-06611-x
Latest update: 11 Jun 2026
Short summary
Improving crop yield predictions is crucial for food security. Prior research relied on case studies, making it hard to compare methods and track progress. We introduce CY-Bench (Crop Yield Benchmark), a global dataset for forecasting maize and wheat yields across diverse farming systems in over 25 countries. It includes standardized weather, soil, and satellite data, curated by a diverse set of experts. CY-Bench supports the development of tools to help decision-makers plan for food security.
Improving crop yield predictions is crucial for food security. Prior research relied on case...
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