A dataset to study group-housed sows’ individual behaviours and production responses to different short-term events

Durand, Maëva; Largouët, C.; Beaufort, Louis Bonneau de; Dourmad, Jean-Yves; Gaillard, Charlotte

doi:10.1016/j.anopes.2023.100039

Cited by 2 publications

(2 citation statements)

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“…Manual analysis of videos was carried out in continuous by trained observers to monitor individual behavior (social and activity) for two moments (23:00 h–04:00 h and 13:30 h–18:30 h) of 2 days per period. The detailed ethogram is available in Durand et al 28 , and raw data are available in a datapaper 32 . From the video analysis, 3 indexes were calculated to obtain a limited number of variables gathering the intensity and the valence of the level of activity and social interactions (Table 1 ).…”

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%

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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“…25883-2020070711528084). The data used in this study is available for public access and described in the data paper ( Durand et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Durand,

Largouët,

de Beaufort

et al. 2023

Journal of Animal Science

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Precision feeding is a strategy for supplying an amount and composition of feed as close that are as possible to each animal’s nutrient requirements, with the aim of reducing feed costs and environmental losses. Usually, the nutrient requirements of gestating sows are provided by a nutrition model that requires input data such as sow and herd characteristics, but also an estimation of future farrowing performances. New sensors and automatons, such as automatic feeders and drinkers, have been developed on pig farms over the last decade, and have produced large amounts of data. This study evaluated machine-learning methods for predicting the daily nutrient requirements of gestating sows, based only on sensor data, according to various configurations of digital farms. The data of 73 gestating sows was recorded using sensors such as electronic feeders and drinker stations, connected weight scales, accelerometers, and cameras. Nine machine-learning algorithms were trained on various dataset scenarios according to different digital farm configurations (one or two sensors), in order to predict the daily metabolizable energy and standardized ileal digestible lysine requirements for each sow. The prediction results were compared to those predicted by the InraPorc model, a mechanistic model for the precision feeding of gestating sows. The scenario predictions were also evaluated with or without the housing conditions and sow characteristics at artificial insemination usually integrated into the InraPorc model. Adding housing and sow characteristics to sensor data improved the mean average percentage error by 5.58% for lysine and by 2.22% for energy. The higher correlation coefficient values for lysine (0.99) and for energy (0.95) were obtained for scenarios involving an automatic feeder system (daily duration and number of visits with or without consumption) only. The scenarios including an automatic feeder combined with another sensor gave good performance results. For the scenarios using sow and housing characteristics and automatic feeder only, the root mean square error was lower with Gradient Tree Boosting (0.91 MJ/d for energy and 0.08 g/d for lysine) compared with those obtained using linear regression (2.75 MJ/d and 1.07 g/d). The results of this study show that the daily nutrient requirements of gestating sows can be predicted accurately using data provided by sensors and machine-learning methods. It paves the way to simpler solutions for precision feeding.

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A dataset to study group-housed sows’ individual behaviours and production responses to different short-term events

Cited by 2 publications

References 1 publication

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

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

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

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