Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.oneear.2025.101233 |
Licence  |
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| Title (Primary) | Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning |
| Author | Sweet, L.-B.
; Athanasiadis, I.N.; van Bree, R.; Castellano, A.; Martre, P.; Paudel, D.; Ruane, A.C.; Zscheischler, J.
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| Source Titel | One Earth |
| Year | 2025 |
| Department | CER |
| Volume | 8 |
| Issue | 4 |
| Page From | art. 101233 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Keywords | machine learning; crop models; agriculture; food security; model development; AgMIP; climate impacts |
| Abstract | Crop models play a key role in the study of climate change impacts on food production as well as improving food systems resilience and analyzing the effect of potential adaptation interventions. Here, we illustrate opportunities that machine learning offers for tackling key challenges of agricultural modeling. However, to unlock the full potential of machine learning, and thereby accelerate progress toward a more secure and sustainable global food system, serious pitfalls must first be addressed. We argue that transdisciplinary coordination is needed to identify impactful research gaps, curate and maintain benchmark datasets, and establish domain-specific best practices. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30718 |
| Sweet, L.-B., Athanasiadis, I.N., van Bree, R., Castellano, A., Martre, P., Paudel, D., Ruane, A.C., Zscheischler, J. (2025): Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning One Earth 8 (4), art. 101233 10.1016/j.oneear.2025.101233 |
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