Publication Abstracts

Sweet et al. 2025, accepted

Sweet, L.-B., I.N. Athanasiadis, R. van Bree, A. Castellano, P. Martre, D. Paudel, A.C. Ruane, and J. Zscheischler, 2025: Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning. One Earth, accepted.

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. To unlock the full potential of machine learning, however, serious pitfalls must first be addressed. We argue that to accelerate progress, transdisciplinary coordination is needed to identify impactful research gaps, curate and maintain benchmark datasets and establish domain-specific best practices.

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BibTeX Citation

@unpublished{sw06000g,
  author={Sweet, L.-B. and Athanasiadis, I. N. and van Bree, R. and Castellano, A. and Martre, P. and Paudel, D. and Ruane, A. C. and Zscheischler, J.},
  title={Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning},
  year={2025},
  journal={One Earth},
}

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RIS Citation

TY  - INPR
ID  - sw06000g
AU  - Sweet, L.-B.
AU  - Athanasiadis, I. N.
AU  - van Bree, R.
AU  - Castellano, A.
AU  - Martre, P.
AU  - Paudel, D.
AU  - Ruane, A. C.
AU  - Zscheischler, J.
PY  - 2025
TI  - Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning
JA  - One Earth
JO  - One Earth
ER  -

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