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Fraga, Helder ; Guimarães, Nathalie ; Candiago, Sebastian ; Yang, Chenyao ; Sosa, Eduardo ; Serrano-Notivoli, Roberto ; Dalezios, Nicolas R. ; de Souza Rolim, Glauco ; Santos, João A.:
Agroclimatic drivers and suitability niches of major European crops revealed by explainable machine learning.
In: Ecological Informatics.
Bd. 99
(September 2026)
.
- 104065.
ISSN 1574-9541
DOI: https://doi.org/10.1016/j.ecoinf.2026.104065
Abstract
Anticipating how European agriculture will shift under future climates demands a clear understanding of which climatic conditions govern crops today. Here, we present an explainable machine learning (ML) framework that moves beyond conventional methods to reveal the structure of crop-climate relationships across the continent. By combining ML ensemble classifiers (Random Forest, XGBoost, CatBoost and LightGBM in a VotingClassifier), with SHAP (SHapley Additive exPlanations) dependence analysis and k-Shapley interaction networks, we characterise the agroclimatic niches for 40 crops derived from ∼13 million agricultural grid cells, using bioclimatic indices from state-of-the-art CERRA climate reanalysis data. The framework allowed to accurately assess the agroclimatic niches of all 40 crops, with moderate-to-strong cross-validation performance, ranging from Matthews Correlation Coefficient (MCC) 0.5 (apple) to 0.9 (olive). More importantly, the methodology allowed to identify the most important bioclimatic factors for crop distribution. The Latitude-Temperature Index was found to be the most influential driver for all 40 crops, followed by isothermality, temperature annual range, temperature seasonality, and minimum temperature of the coldest month, demonstrating that the north-south thermal gradient and continentality-oceanicity axis are the primary structuring dimensions of European agriculture. SHAP dependence curves expose crop-specific non-linearities, including long suitability ranges, unimodal optima, and bimodal response patterns. Crucially, interaction networks reveal that cool-season crops exhibit predominantly synergistic driver interactions, while warm-season crops show antagonistic ones, a structural distinction invisible to standard importance methods. The result is a European-wide atlas of crop agroclimatic niches and suitability ranges, offering a quantitative and interpretable foundation for adaptation planning and climate impact assessment.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Keywords: | Ensemble classifier; Machine learning interpretability; AI in agriculture; Agroclimatic indices; Europe |
| Institutionen der Universität: | Fakultäten > Fakultät für Biologie, Chemie und Geowissenschaften > Fachgruppe Geowissenschaften > Professur Ecological Services > Professur Ecological Services - Univ.-Prof. Dr. Thomas Koellner Forschungseinrichtungen > Zentrale wissenschaftliche Einrichtungen > Bayreuther Zentrum für Ökologie und Umweltforschung - BayCEER Graduierteneinrichtungen > Bayreuther Graduiertenschule für Mathematik und Naturwissenschaften - BayNAT > PEER Ökologie und Umweltwissenschaften |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 500 Naturwissenschaften und Mathematik > 550 Geowissenschaften, Geologie |
| Eingestellt am: | 30 Sep 2026 06:55 |
| Letzte Änderung: | 30 Sep 2026 06:55 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/99508 |