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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Reproduction Research » Research » Research Project #437707

Research Project: Electronic Monitoring of Swine for Precision Livestock Management and Novel Phenotype Development

Location: Reproduction Research

Project Number: 3040-31000-099-04-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Apr 1, 2020
End Date: Mar 31, 2021

Objective:
1. Develop and validate electronic monitoring for animals within production systems. 2. Develop methods to summarize and model data streams for real-time decision making and phenotype development.

Approach:
The swine industry is faced with competing and perennial challenges including improving animal welfare, selecting modern genetic lines, reducing antibiotic use, and employing stock people with appropriate animal husbandry skills. Over recent decades, swine producers have responded to shrinking profit margins through economies of scale. Consequently, current commercial farms house large numbers of pigs managed by few caretakers. Thus, it is difficult for workers to care for individual animals, particularly when the pigs look similar. Current labor and demographic circumstances limit recruitment of skilled employees. Under these conditions, it is difficult for caretakers to identify health, production, and safety concerns of individual animals, thus leading to potential welfare problems and sub-optimal production. Precision Management of Animals (PMA), the application of technology within the animal space, has the potential to simultaneously mitigate these seemingly intractable problems. It is proposed to use technological solutions to capture complex animal behavior on a continuous basis, which will help meet producer goals, improve animal welfare, and can be used to create novel phenotypes. In order to accomplish this, technologies such as Radio Frequency Identification (RFID), digital/depth camera, and other wearable sensors will be employed to capture a real time data stream. These data streams will be used to creating a more complex picture of animals’ responses using innovative modeling techniques.