Body Condition Score Change throughout Lactation Utilizing an Automated BCS System: A Descriptive Study

Carissa M. Truman, Magnus R. Campler, Joao H.C. Costa

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Body condition scoring (BCS) is a traditional visual technique often using a five-point scale to non-invasively assess fat reserves in cattle. However, recent studies have highlighted the potential in automating body condition scoring using imaging technology. Therefore, the objective was to implement a commercially available automated body condition scoring (ABCS) camera system to collect data for developing a predictive equation of body condition dynamics throughout the lactation period. Holstein cows (n = 2343, parity = 2.1 ± 1.1, calving BCS = 3.42 ± 0.24), up to 300 days in milk (DIM), were scored daily using two ABCS cameras mounted on sort-gates at the milk parlor exits. Scores were reported on a 1 to 5 scale in 0.1 increments. Lactation number, DIM, disease status, and 305d-predicted-milk-yield (305PMY) were used to create a multivariate prediction model for body condition scores throughout lactation. The equation derived from the model was: ABCSijk = 1.4838 − 0.00452 × DIMi − 0.03851 × Lactation numberj + 0.5970 × Calving ABCSk + 0.02998 × Disease Status(neg)l − 1.52 × 10−6 × 305PMYm + eijklm . We identified factors which are significant for predicting the BCS curve during lactation. These could be used to monitor deviations or benchmark ABCS in lactating dairy cows. The advantage of BCS automation is that it may provide objective, frequent, and accurate BCS with a higher degree of sensitivity compared with more sporadic and subjective manual BCS. Applying ABCS technology in future studies on commercial dairies may assist in providing improved dairy management protocols based on more available BCS.

Original languageEnglish
Article number601
Issue number5
StatePublished - Mar 1 2022

Bibliographical note

Funding Information:
Funding: This research was funded by DeLaval International AB (Tumba, Sweden), on a research partnership with the University of Kentucky (Grant # 3048113318).

Funding Information:
Acknowledgments: The authors thank the participating herd for their cooperation, time, and enrollment in the study. The authors would like to thank the producers for allowing us to complete the project on their farm. The authors would also like to thank Amanda Lee and Jenna Guinn for assisting in the manual scoring. This project was funded by DeLaval International AB (Tumba, Sweden) company through a research project partnership with the Dairy Science Program at the University of Kentucky.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • 3D camera
  • Automation
  • Precision dairy technology
  • Prediction

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Veterinary (all)


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