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Articles | Volume 69, issue 3
Articles | Volume 69, issue 3
https://doi.org/10.5194/aab-69-469-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/aab-69-469-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 69, issue 3
Original study
 | 
28 Aug 2026
Original study |  | 28 Aug 2026

Assessment of reproductive performance in dairy cows using explainable machine learning

Elif Çelik Gürbulak, Uğur Kara, Esra Canooğlu, Hazal Aysın Yüceel, Ece Çetin, Mehmet Demirel, and Kutlay Gürbulak

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Short summary

This study demonstrates the applicability of an explainable machine learning framework (XGBoost + SHAP) for investigating reproductive performance in dairy cows. Although the predictive performance of the independent test dataset was limited (R² = 0.01), SHAP provided transparent global- and individual-level explanations of model predictions, highlighting the methodological potential of explainable artificial intelligence for veterinary data analysis.

 
 
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