Publication Details |
| Category | Data Publication |
| DOI | 10.5281/zenodo.17279151 |
| Title (Primary) | CY-Bench: a comprehensive benchmark dataset for sub-national crop yield forecasting (Version 1.10) [Dataset] |
| Author | Kallenberg, M.; Paudel, D.; Ofori-Ampofo, S.; Baja, H.; van Bree, R.; Potze, A.; Poudel, P.; Saleh, A.; Anderson, W.; von Bloh, M.; Castellano, A.; Ennaji, O.; Hamed, R.; Laudien, R.; Lee, D.; Luna, I.; Masiliūnas, D.; Meroni, M.; Mutuku, J.M.; Mkuhlani, S.; Richetti, J.; Ruane, A.C.; Sahajpal, R.; Shuai, G.; Sitokonstantinou, V.; de S. Nóia-Júnior, R.; Srivastava, A.K.; Strong, R.; Sweet, L.-B.
; Vojnović, P.; de Wit, A.; Zachow, M.; Athanasiadis, I.N. |
| Source Titel | Zenodo |
| Year | 2025 |
| Department | CER |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Abstract |
In-season, pre-harvest crop yield forecasts
are essential for enhancing transparency in commodity markets and improving
food security. They play a key role in increasing resilience to climate change
and extreme events and thus contribute to the United Nations’ Sustainable
Development Goal 2 of zero hunger. Pre-harvest crop yield forecasting is a
complex task, as several interacting factors contribute to yield formation,
including in-season weather variability, extreme events, long-term climate
change, soil, pests, diseases and farm management decisions. Several modeling
approaches have been employed to capture complex interactions among such
predictors and crop yields. Prior research for in-season, pre-harvest crop
yield forecasting has primarily been case-study based, which makes it difficult
to compare modeling approaches and measure progress systematically. To address
this gap, we introduce CY-Bench (Crop Yield Benchmark), a comprehensive dataset
and benchmark to forecast maize and wheat yields at a global scale. CY-Bench
was conceptualized and developed within the Machine Learning team of the
Agricultural Model Intercomparison and Improvement Project (AgML) in
collaboration with agronomists, climate scientists, and machine learning researchers.
It features publicly available sub-national yield statistics and relevant
predictors, such as weather data, soil characteristics, and remote sensing
indicators, that have been pre-processed, standardized, and harmonized across
spatio-temporal scales. With CY-Bench, we aim to: (i) establish a standardized
framework for developing and evaluating data-driven models across diverse
farming systems in more than 25 countries across six continents; (ii) enable
robust and reproducible model comparisons that address real-world operational
challenges; (iii) provide an openly accessible dataset to the earth system
science and machine learning communities, facilitating research on time series
forecasting, domain adaptation, and online learning. The dataset (https://doi.org/10.5281/zenodo.11502142,
Kallenberg et al., 2025) and accompanying code
(https://doi.org/10.5281/zenodo.20456375, Kallenberg et al., 2026) are openly
available to support the continuous development of advanced data driven models
for crop yield forecasting to enhance decision-making on food security. |
| Kallenberg, M., Paudel, D., Ofori-Ampofo, S., Baja, H., van Bree, R., Potze, A., Poudel, P., Saleh, A., Anderson, W., von Bloh, M., Castellano, A., Ennaji, O., Hamed, R., Laudien, R., Lee, D., Luna, I., Masiliūnas, D., Meroni, M., Mutuku, J.M., Mkuhlani, S., Richetti, J., Ruane, A.C., Sahajpal, R., Shuai, G., Sitokonstantinou, V., de S. Nóia-Júnior, R., Srivastava, A.K., Strong, R., Sweet, L.-B., Vojnović, P., de Wit, A., Zachow, M., Athanasiadis, I.N. (2025): CY-Bench: a comprehensive benchmark dataset for sub-national crop yield forecasting (Version 1.10) [Dataset] Zenodo 10.5281/zenodo.17279151 |
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