Precision livestock farming for pigs

Vranken, Erik; Berckmans, D.

doi:10.2527/af.2017.0106

Cited by 90 publications

(68 citation statements)

References 13 publications

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“…Continuous monitoring of behaviour enables early identification of subtle changes that would otherwise be impossible for a human observer with intermittent monitoring on a commercial scale. The potential impact of early corrective action stands to improve the effectiveness of treatment, enhance animal welfare 21 , support profitable and sustainable production systems 22 , and may even have an impact on antimicrobial inputs 23 . A practical automated monitoring system was developed with a single type of sensor specifically for measuring multiple behaviours that change and have diagnostic validity.…”

Section: Discussionmentioning

confidence: 99%

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring

Matthews

Miller

Plötz

et al. 2017

Sci Rep

124

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Since animals express their internal state through behaviour, changes in said behaviour may be used to detect early signs of problems, such as in animal health. Continuous observation of livestock by farm staff is impractical in a commercial setting to the degree required to detect behavioural changes relevant for early intervention. An automated monitoring system is developed; it automatically tracks pig movement with depth video cameras, and automatically measures standing, feeding, drinking, and locomotor activities from 3D trajectories. Predictions of standing, feeding, and drinking were validated, but not locomotor activities. An artificial, disruptive challenge; i.e., introduction of a novel object, is used to cause reproducible behavioural changes to enable development of a system to detect the changes automatically. Validation of the automated monitoring system with the controlled challenge study provides a reproducible framework for further development of robust early warning systems for pigs. The automated system is practical in commercial settings because it provides continuous monitoring of multiple behaviours, with metrics of behaviours that may be considered more intuitive and have diagnostic validity. The method has the potential to transform how livestock are monitored, directly impact their health and welfare, and address issues in livestock farming, such as antimicrobial use.

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Section: Discussionmentioning

confidence: 99%

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring

Matthews

Miller

Plötz

et al. 2017

Sci Rep

124

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“…Previous reviews have focused on the use of precision technologies for a variety of purposes, especially on-farm management (Neethirajan, 2017;Neethirajan et al, 2017;Vranken and Berckmans, 2017;Croney et al, 2018b;Benjamin and Yik, 2019;Halachmi et al, 2019). The current review, expands this scope by focusing on the use of precision technologies for selective breeding to enhance animal welfare in commercial livestock production, with a focus on terrestrial species.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

et al. 2020

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Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and

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“…Research on animal welfare often use sensor data (e.g., video camera or microphones) paired with data processing for monitoring and forecast solutions [4][5][6]. Because animal welfare is often animal-specific, these approaches capture and process animal-specific data [7].…”

Section: Introductionmentioning

confidence: 99%

Designing a Smart Farming Platform for Sustainable Decision Making

Zimpel¹,

Riekert²,

Wild³

2020

WI2020 Community Tracks

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Smart farming platforms (SFP's) for pig livestock farming are of increasingly relevance to increase sustainable decision making and enhancement of animal welfare. SFPs involve the whole supply chain and integrate various types of data measured, thus enable data-driven solutions using artificial intelligence. While there exists research about SFPs, issues concerning data governance of SFPs are still lacking. Against this backdrop, we develop a SFP for sustainable decision making with respect to data privacy and data security. Our SFP integrates 4 sensor data sources (e.g., temperature control system, and feeding stations), considers farmer characteristics (e.g., projects with pigs), and provides data-driven solutions (e.g., prediction of animal welfare indicators). We report on the current process situation in pig livestock farming as well as on our concept of SFPs for sustainable decision making. We also report on the evaluation of our SFP by validation against defined requirements during the deployment phase.

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Precision livestock farming for pigs

Cited by 90 publications

References 13 publications

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring

Automated tracking to measure behavioural changes in pigs for health and welfare monitoring

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Designing a Smart Farming Platform for Sustainable Decision Making

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