Adsa Logo White Adsa Title White

Assessing animal welfare: Deriving individual welfare phenotypes from existing milk recording data.

S. Franceschini

Events

06-23-2020

Join S. Franceschini on this page for a live text chat!
6:00 PM - 8:00 PM GMT

Abstract:

T14
Assessing animal welfare: Deriving individual welfare phenotypes from existing milk recording data.
S. Franceschini*1, J. Leblois2, F. Lepot2, C. Bertozzi2, N. Gengler1. 1ULi�ge - Gembloux Agro-Bio Tech Gembloux, Belgium, 2Association Wallonne de l'Elevage Ciney, Belgium.

Animal welfare is an increasing concern in dairy production. Consumers want an ethical production while farmers want to ensure the health of the animals. Animal welfare measurements at the herd level such as the Welfare Quality (WQ) Protocol already exist but are time-consuming and costly. Moreover, assessing the overall well-being at the animal level becomes a challenge as herd measures for welfare cannot be directly translated to the animal level. Two projects, active in the Walloon Region of Belgium, HappyMoo (Interreg NWE) and ScorWelCow, are trying to define individual welfare scores (IWS) and their prediction from routinely measured milk recording data, including mid-infrared spectral data representing fine milk composition. Data from WQ Protocol and routine milk recording was collected during the same timeframe in 18 dairy farms with 1386 cows, the majority being genotyped. Two approaches to assess and to predict individual animal welfare were developed. The first approach consisted of 2 steps: translating the WQ principles into IWS and predicting these from milk recording data. The variation observed in the first step while regressing WQ animal measures on WQ principles was considered representative of the biological variation between cows. IWS prediction Partial Least Square regression for the 4 principles of the welfare quality scores have R2 between 0.65 and 0.77. Moreover, results from this first approach showed a significant welfare assessor effect suggesting that welfare measurements were strongly human interpretation-dependent. This suggested the need for an alternative approach. The second approach directly used milk recording data such as spectral data to cluster cows in different groups, bypassing a priori definition of welfare by WQ. Those groups were compared with results from the first approach and showed possible discrimination for herds with enhanced WQ score (specificity = 1.00 but sensitivity = 0.10) thus suggesting further unsupervised analysis. Based on this research, novel individual welfare traits could be developed allowing future genomic selection for improved welfare.

Keywords: animal welfare, mid-infrared spectra, machine learning.