Effects over time of different types of sounds on gestating sows’ behavior

Gaillard, Charlotte; Deroiné, Clémentine; Misrach, Malinka; Durand, Maëva

doi:10.1016/j.applanim.2023.106012

Cited by 1 publication

(1 citation statement)

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“…The competitive situation for feed was created by closing one of the two available feeders in the gestation room for 5 days and nights 28 . The sound event was induced by the random emission of 40 sounds of 30 s, under 85 decibels, every 10 min, twice a day (from 23:00 h to 04:00 h and from 13:30 h to 18:30 h) for 3 days 29 . These two moments were chosen to correspond to a moment of high activity level (the night, as new feeding day started at midnight) and a moment of low activity level (the afternoon with a resting period).…”

Section: Methodsmentioning

confidence: 99%

Estimation of gestating sows’ welfare status based on machine learning methods and behavioral data

Durand,

Largouët,

de Beaufort

et al. 2023

Sci Rep

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Estimating the welfare status at an individual level on the farm is a current issue to improve livestock animal monitoring. New technologies showed opportunities to analyze livestock behavior with machine learning and sensors. The aim of the study was to estimate some components of the welfare status of gestating sows based on machine learning methods and behavioral data. The dataset used was a combination of individual and group measures of behavior (activity, social and feeding behaviors). A clustering method was used to estimate the welfare status of 69 sows (housed in four groups) during different periods (sum of 2 days per week) of gestation (between 6 and 10 periods, depending on the group). Three clusters were identified and labelled (scapegoat, gentle and aggressive). Environmental conditions and the sows’ health influenced the proportion of sows in each cluster, contrary to the characteristics of the sow (age, body weight or body condition). The results also confirmed the importance of group behavior on the welfare of each individual. A decision tree was learned and used to classify the sows into the three categories of welfare issued from the clustering step. This classification relied on data obtained from an automatic feeder and automated video analysis, achieving an accuracy rate exceeding 72%. This study showed the potential of an automatic decision support system to categorize welfare based on the behavior of each gestating sow and the group of sows.

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Section: Methodsmentioning

confidence: 99%