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Precision Livestock Farming Applications (PLF) for Grazing Animals

Author

Listed:
  • Christos Tzanidakis

    (Laboratory of Animal Breeding and Husbandry, Department of Animal Science, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece)

  • Ouranios Tzamaloukas

    (Department of Agricultural Sciences, Biotechnology and Food Science, Cyprus University of Technology, Limassol 3036, Cyprus)

  • Panagiotis Simitzis

    (Laboratory of Animal Breeding and Husbandry, Department of Animal Science, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece)

  • Panagiotis Panagakis

    (Laboratory of Farm Structures, Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece)

Abstract

Over the past four decades the dietary needs of the global population have been elevated, with increased consumption of animal products predominately due to the advancing economies of South America and Asia. As a result, livestock production systems have expanded in size, with considerable changes to the animals’ management. As grazing animals are commonly grown in herds, economic and labour constraints limit the ability of the producer to individually assess every animal. Precision Livestock Farming refers to the real-time continuous monitoring and control systems using sensors and computer algorithms for early problem detection, while simultaneously increasing producer awareness concerning individual animal needs. These technologies include automatic weighing systems, Radio Frequency Identification (RFID) sensors for individual animal detection and behaviour monitoring, body temperature monitoring, geographic information systems (GIS) for pasture evaluation and optimization, unmanned aerial vehicles (UAVs) for herd management, and virtual fencing for herd and grazing management. Although some commercial products are available, mainly for cattle, the adoption of these systems is limited due to economic and cultural constraints and poor technological infrastructure. This review presents and discusses PLF applications and systems for grazing animals and proposes future research and strategies to improve PLF adoption and utilization in today’s extensive livestock systems.

Suggested Citation

  • Christos Tzanidakis & Ouranios Tzamaloukas & Panagiotis Simitzis & Panagiotis Panagakis, 2023. "Precision Livestock Farming Applications (PLF) for Grazing Animals," Agriculture, MDPI, vol. 13(2), pages 1-23, January.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:288-:d:1046670
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    References listed on IDEAS

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    1. Eastwood, C.R. & Chapman, D.F. & Paine, M.S., 2012. "Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia," Agricultural Systems, Elsevier, vol. 108(C), pages 10-18.
    2. Kuehne, Geoff & Llewellyn, Rick & Pannell, David J. & Wilkinson, Roger & Dolling, Perry & Ouzman, Jackie & Ewing, Mike, 2017. "Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy," Agricultural Systems, Elsevier, vol. 156(C), pages 115-125.
    3. Rieple, Alison & Snijders, Sylvia, 2018. "The role of emotions in the choice to adopt, or resist, innovations by Irish dairy farmers," Journal of Business Research, Elsevier, vol. 85(C), pages 23-31.
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    Cited by:

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    3. Verónica Cruz Moriana & Juan Manuel Mancilla-Leytón & Yolanda Mena & Francisco de Asís Ruiz Morales, 2024. "Identification of the Multifunctionality of Andalusian Autochthonous Pastoral Livestock Breeds at the Farm Level," Agriculture, MDPI, vol. 14(4), pages 1-17, April.
    4. Dangguo Shao & Zihan He & Hongbo Fan & Kun Sun, 2023. "Detection of Cattle Key Parts Based on the Improved Yolov5 Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-16, May.
    5. Mengmeng Wang & Meng Lv & Haoting Liu & Qing Li, 2023. "Mid-Infrared Sheep Segmentation in Highland Pastures Using Multi-Level Region Fusion OTSU Algorithm," Agriculture, MDPI, vol. 13(7), pages 1-22, June.

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