Skip to main content

Research Repository

Advanced Search

Joint Models to Predict Dairy Cow Survival from Sensor Data Recorded during the First Lactation.

Statham

Joint Models to Predict Dairy Cow Survival from Sensor Data Recorded during the First Lactation. Thumbnail


Authors



Abstract

Early predictions of cows' probability of survival to different lactations would help farmers in making successful management and breeding decisions. For this purpose, this research explored the adoption of joint models for longitudinal and survival data in the dairy field. An algorithm jointly modelled daily first-lactation sensor data (milk yield, body weight, rumination time) and survival data (i.e., time to culling) from 6 Holstein dairy farms. The algorithm was set to predict survival to the beginning of the second and third lactations (i.e., second and third calving) from sensor observations of the first 60, 150, and 240 days in milk of cows' first lactation. Using 3-time-repeated 3-fold cross-validation, the performance was evaluated in terms of Area Under the Curve and expected error of prediction. Across the different scenarios and farms, the former varied between 45% and 76%, while the latter was between 3.5% and 26%. Significant results were obtained in terms of expected error of prediction, meaning that the method provided survival probabilities in line with the observed events in the datasets (i.e., culling). Furthermore, the performances were stable among farms. These features may justify further research on the use of joint models to predict the survival of dairy cattle.

Citation

Statham. (2022). Joint Models to Predict Dairy Cow Survival from Sensor Data Recorded during the First Lactation. Animals, https://doi.org/10.3390/ani12243494

Acceptance Date Dec 8, 2022
Publication Date Dec 10, 2022
Journal Animals
Publisher MDPI
DOI https://doi.org/10.3390/ani12243494
Publisher URL https://www.mdpi.com/2076-2615/12/24/3494
Additional Information © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Files






Downloadable Citations