Review: Precision Livestock Farming technologies in pasture-based livestock systems.


Journal

Animal : an international journal of animal bioscience
ISSN: 1751-732X
Titre abrégé: Animal
Pays: England
ID NLM: 101303270

Informations de publication

Date de publication:
Jan 2022
Historique:
received: 04 12 2020
revised: 09 11 2021
accepted: 19 11 2021
pubmed: 26 12 2021
medline: 2 2 2022
entrez: 25 12 2021
Statut: ppublish

Résumé

Precision Livestock Farming (PLF) encompasses the combined application of single technologies or multiple tools in integrated systems for real-time and individual monitoring of livestock. In grazing systems, some PLF applications could substantially improve farmers' control of livestock by overcoming issues related to pasture utilisation and management, and animal monitoring and control. A focused literature review was carried out to identify technologies already applied or at an advanced stage of development for livestock management in pastures, specifically cattle, sheep, goats, pigs, poultry. Applications of PLF in pasture-based systems were examined for cattle, sheep, goats, pigs, and poultry. The earliest technology applied to livestock was the radio frequency identification tag, allowing the identification of individuals, but also for retrieving important information such as maternal pedigree. Walk-over-weigh platforms were used to record individual and flock weights. Coupled with automatic drafting systems, they were tested to divide the animals according to their needs. Few studies have dealt with remote body temperature assessment, although the use of thermography is spreading to monitor both intensively reared and wild animals. Global positioning system and accelerometers are among the most applied technologies, with several solutions available on the market. These tools are used for several purposes, such as animal location, theft prevention, assessment of activity budget, behaviour, and feed intake of grazing animals, as well as for reproduction monitoring (i.e., oestrus, calving, or lambing). Remote sensing by satellite images or unmanned aerial vehicles (UAVs) seems promising for biomass assessment and herd management based on pasture availability, and some attempts to use UAVs to monitor, track, or even muster animals have been reported recently. Virtual fencing is among the upcoming technologies aimed at grazing management. This system allows the management of animals at pasture without physical fences but relies on associative learning between audio cues and an electric shock delivered if the animal does not change direction after the acoustic warning. Regardless of the different technologies applied, some common constraints have been reported on the application of PLF in grazing systems, especially when compared with indoor or confined livestock systems. Battery lifespan, transmission range, service coverage, storage capacity, and economic affordability were the main factors. However, even if the awareness of the existence and the potential of these upcoming tools are still limited, farmers' and researchers' demands are increasing, and positive outcomes in terms of rangeland conservation, animal welfare, and labour optimisation are expected from the spread of PLF in grazing systems.

Identifiants

pubmed: 34953277
pii: S1751-7311(21)00275-5
doi: 10.1016/j.animal.2021.100429
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

100429

Informations de copyright

Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.

Auteurs

C Aquilani (C)

Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy. Electronic address: chiara.aquilani@unifi.it.

A Confessore (A)

Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy.

R Bozzi (R)

Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy.

F Sirtori (F)

Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy.

C Pugliese (C)

Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università di Firenze, Scuola di Agraria, Via delle Cascine 5, 50144 Florence, Italy.

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Classifications MeSH