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Améliorer les performances et le bien être des truies gravides par la mobilisation de nouvelles technologies pour une alimentation de précision et la détection de signaux comportementaux.

Abstract : Since the new welfare regulation, farmers have to breed pregnant sows penned in the group. Thus, breeders observed more heterogeneity in the backfat thickness of sows when they are entering the farrowing units, implying more losses of piglets. It is also more difficult to observe lameness issues in large groups of sows. The objectives of this project are (i) to develop an activity sensor to feed each sow according to the energy it spends and (ii) to create an early detection system for lameness problems. The first step of this project was to develop a sensor able to record the individual activity level of sows penned in a group. As a result, Acti’Sow has been created. It is an ear tag accelerometer offering to know the daily time spent lying, standing and walking by a sow with a global accuracy close to 85 %. This project offers a better knowledge about sow behavior thanks to automatic feeders, connected drinkers, weighing scale and activity sensors. On average, the daily water consumption is 8.2 l/day/sow, but this result hides a huge variability close to 50 % when comparing a sow to another and 38 % for the same sow from a day to the next one. About their activity, an average sow spends 67 % of its time lying down, a bit more than 28 % standing up without moving and less than 5% walking. According to that, between the laziest one and the more active one, energy expenditure represent more than 500 g of feed. It means, with the same fixed objective of backfat thickness with these two sows, a farmer will need to give 500 g more feed/day for the very active sow. Early warning system for lameness issues was the other main result of this study. Through the use of watering and feeding behavior (number of visits per day, time of each visit, quantity of water/feed consumed, access rank to the feeder), individual weight and activity level, we built a first model able to individually predict lameness issue 24 hours before the farmer can observe it. The accuracy is close to 77 %. It means, that a cell phone app can alert farmers when a sow needs to be checked.
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https://hal.inrae.fr/hal-03109316
Contributor : Roselyne Tâche <>
Submitted on : Wednesday, January 13, 2021 - 4:40:22 PM
Last modification on : Friday, March 19, 2021 - 12:26:02 PM
Long-term archiving on: : Wednesday, April 14, 2021 - 7:01:46 PM

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Michel Marcon, N. Quiniou, C. Courboulay, Y. Rousselière, G. Melot, et al.. Améliorer les performances et le bien être des truies gravides par la mobilisation de nouvelles technologies pour une alimentation de précision et la détection de signaux comportementaux.. Innovations Agronomiques, INRAE, 2020, 79, pp.245-256. ⟨10.15454/vk07-4b16⟩. ⟨hal-03109316⟩

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