Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms

Anthony Tedde*, Clément Grelet, Phuong N. Ho, Jennie E. Pryce, Dagnachew Hailemariam, Zhiquan Wang, Graham Plastow, Nicolas Gengler, Yves Brostaux, Eric Froidmont, Frédéric Dehareng, Carlo Bertozzi, Mark Crowe, Isabelle Dufrasne, Hélène Soyeurt, GplusE Consortium Group

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points.

Original languageEnglish
Article number1288
Issue number5
Publication statusPrint publication - 30 Apr 2021
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgments: This work is Supported by the Luxembourg National Research Fund (INTER/FNRS/18/12987586/SimBa) and by the Fonds de la Recherche Scientifique-FNRS under Grant n◦ T.0221.19. The Genotype Plus Environment (GplusE) Project has received funding from the European Union’s Seventh Framework Programme for research, technological development, and demonstration under grant agreement no. 613689. List of authors in the GplusE consortium: Mark Crowe, Alan Fahey, Fiona Carter, Elizabeth Matthews, An-dreia Santoro, Colin Byrne, Pauline Rudd, Roisin O’Flaherty, Sinead Hallinan, Claire Wathes, Mazdak Salavati, Zhangrui Cheng, Ali Fouladi, Geoff Pollott, Dirk Werling, Beatriz Sanz Bernardo, Conrad Ferris, Alistair Wylie, Matt Bell, Mieke Vaneetvelde, Kristof Hermans, Miel Hostens, Geert Opsomer, Sander Moer-man, Jenne De Koster, Hannes Bogaert, Jan Vandepitte, Leila Vandevelde, Bonny Vanranst, Klaus Ingvartsen, Martin Tang Sorensen, Johanna Hoglund, Susanne Dahl, Soren Ostergaard, Janne Rothmann, Mogens Krogh, Else Meyer, Leslie Foldager, Charlotte Gaillard, Jehan Ettema, Tine Rousing, Torben Larsen, Victor H. Silva de Oliveira, Cinzia Marchitelli, Federica Signorelli, Francesco Napolitano, Bianca Moioli, Alessandra Crisà, Luca Buttazzoni, Jennifer McClure, Daragh Matthews, Francis Kearney, Andrew Cromie, Matt McClure, Shujun Zhang, Xing Chen, Huanchun Chen, Junlong Zhao, Liguo Yang, Guohua Hua, Chen Tan, Guiqiang Wang, Michel Bonneau, Marlène Sciarretta, Armin Pearn, Arnold Evertson, Linda Kosten, Anders Fogh, Thomas Andersen, Matthew Lucy, Chris Elsik, Gavin Conant, Jerry Taylor, Deborah Triant, Nicolas Gengler, Michel Georges, Frederic Colinet, Marilou Ramos Pamplona, Hedi Hammami, Catherine Bastin, Haruko Takeda, Aurelie Laine, Anne-Sophie Van Laere, Rodrigo Mota, Saied Naderi Darbagshahi, Frederic Dehareng, Clement Grelet, Amelie Vanlierde, Eric Froidmont, Frank Becker, Martin Schulze, and Sergio Palma Vera. The views expressed in this publication are the authors’ sole responsibility and do not necessarily reflect the views of the European Commission. The authors acknowledge the laboratory “Comité du Lait” (Battice, Belgium) for the Walloon milk mid-infrared spectra. The authors acknowledge the financial support of the National Fund for the Scientific Research (F.R.S-FNRS) for the SimBa research project (T02211). This project is also co-financed by the Luxembourg National Research Fund (FNR). Computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11 and by the Walloon Region.

Funding Information:
Funding: This research was funded by National Fund for the Scientific Research (F.R.S-FNRS) for the SimBa research project, grant number T.0221.19.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Dairy cow bodyweight
  • Dairy cows
  • Dimensionality reduction
  • Feature selection
  • Machine learning
  • Mid infrared spectra
  • Partial least square


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