Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems

Hannon, Fergus P.; Green, Martin J.; O’Grady, Luke; Hudson, Chris; Gouw, Anneke; Randall, Laura V.

Abstract

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1–3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1–30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (</= −15% YD), AMBER Group (−14% ̶ 0% YD), GREEN Group (>0% YD). In Step 3, yield, rumination, and physical activity data from days 1–3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1–3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Citation

Hannon, F. P., Green, M. J., O’Grady, L., Hudson, C., Gouw, A., & Randall, L. V. (2024). Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems. Preventive Veterinary Medicine, 225, Article 106160. https://doi.org/10.1016/j.prevetmed.2024.106160

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2024
• Online Publication Date: Mar 6, 2024
• Publication Date: 2024-04
• Deposit Date: Mar 6, 2024
• Publicly Available Date: Mar 7, 2024
• Journal: Preventive Veterinary Medicine
• Print ISSN: 0167-5877
• Electronic ISSN: 1873-1716
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 225
• Article Number: 106160
• DOI: https://doi.org/10.1016/j.prevetmed.2024.106160
• Keywords: Machine learning; Transition cow; Automatic milking; Precision technology
• Public URL: https://nottingham-repository.worktribe.com/output/32159027
• Additional Information: This article is maintained by: Elsevier; Article Title: Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems; Journal Title: Preventive Veterinary Medicine; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.prevetmed.2024.106160; Content Type: article; Copyright: © 2024 The Author(s). Published by Elsevier B.V.