Precision Livestock Farming Research: History
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Precision livestock farming (PLF) utilises information technology to continuously monitor and manage livestock in real-time, which can improve individual animal health, welfare, productivity and the environmental impact of animal husbandry, contributing to the economic, social and environmental sustainability of livestock farming.

  • precision livestock farming
  • animal welfare

1. Introduction

The livestock industry serves as the foundation of the agricultural economy and constitutes the primary source of animal-derived product consumption. The growing demand for these products has led to a significant expansion in the scale of livestock breeding. However, traditional management practices that rely on farmers’ observations, judgment, and experience alone may not fulfill the requirements of modern large-scale livestock farming. Therefore, precision farming, with the support of information technology, has become an unavoidable trend in advancing modern livestock farming.
Since the 1970s, PLF technology has evolved considerably, beginning with individual electronic milk meters for cows and expanding to include behavior-based estrus detection, rumination activity monitoring and other related studies. Scholars have conducted extensive research on intelligent perception and analysis of individual animal information and behavior [1]. In 2003, the European Conference on Precision Livestock Farming (EC-PLF) was launched and held every two years to highlight the latest high-tech research advances in livestock farming. In 2012, The European Union launched the European Precision Livestock Farming Project (EU-PLF), with a mission to translate PLF technology into industrial practice. Participants in the project included influential universities in the field of PLF, such as Wageningen University and Research, Katholieke Universiteit Leuven and the University of Milan, as well as companies dedicated to developing PLF technology, including Facom, Soundtalks NV and GEA Farm Technologies. The project quantified key PLF indicators and guided the use of PLF systems such as video monitoring, sound monitoring, and environmental monitoring on farms, playing a vital role in promoting the research and application of PLF technology [2].
Due to the urgent requirement for integrating information technology, data science, artificial intelligence, and innovative animal husbandry development, China’s animal husbandry industry is rapidly advancing into a new era of integrated fusion innovation [3]. In 2012, the State Council issued the Opinions on Accelerating Agricultural Science and Technology Innovation to Continuously Enhance the Ability to Guarantee the Supply of Agricultural Products, in which it was proposed that a major breakthrough should be made in precision agriculture technology; in 2020, the Opinions on Promoting the High-Quality Development of the Livestock Farming Industry issued by the General Office of the State Council clearly proposed that the application of technologies such as big data, artificial intelligence, cloud computing, Internet of Things and mobile Internet in the livestock industry should be strengthened, and the intelligence level of environmental control of enclosures, precise feeding and animal disease monitoring should be improved. Driven by the policy, the development and application of precision technology in the livestock industry has received widespread attention. In recent years, the research on PLF in China has made great progress in animal respiration frequency detection, individual identification and behavioral analysis [4][5][6]. A lot of improvements and innovations have been made in the automated detection of animal body size and weight [7][8], and equipment and algorithms for environmental monitoring and prediction in livestock housing have been refined [9][10].
PLF is a multidisciplinary concept integrating information technology, data science, and innovative animal husbandry. Five primary research perspectives drive existing PLF research: animal science, veterinary medicine, computer science, agricultural engineering, and environmental science. The animal science perspective aims to optimize animal feeding and management by leveraging sensors and intelligent monitoring systems to real-time monitor and analyze animal behavior, physiological states, and environmental factors [11][12]. The veterinary medicine perspective focuses on intelligent prevention and diagnosis of animal diseases [13][14]. The computer science perspective focuses on the application of sensor networks, artificial intelligence, computer vision and other technologies in animal data monitoring, mining and collection [15][16]. The agricultural engineering perspective focuses on the mechanization of livestock farming and its automation technology to help farmers better manage their farms [17][18]. The environmental science perspective evaluates the environmental impact of using PLF technology as a mitigation strategy for livestock production [19][20]. The existing research on PLF has primarily focused on its technical aspects from a natural science perspective. However, there has been limited discussion on the evolution of basic knowledge and the emergence of new research focal points. In order to address this gap, this study examines PLF-related literature in the Web of Science database from 1973 to 2023. By utilizing the visualization tool CiteSpace, this study creates knowledge graphs to display data such as the research countries, institutions, author collaborations, and keyword networks. Through this analysis, this study objectively reveals the dynamics, developmental processes, and evolution trends of PLF research, while identifying frontiers and hotspots within the field. Ultimately, the aim of this study is to provide a comprehensive overview of PLF research status and scientific references for future research.

2. Precision Livestock Farming

PLF integrates precision agriculture concepts to help farmers manage large-scale livestock farming through the use of sensors and actuators, representing the application of PA in livestock systems [21]. Daniel Berckmans first coined the term PLF and argued that continuous, direct, real-time monitoring or observation of animal status through PLF would enable farmers to rapidly identify and control problems related to animal health and welfare [22]. As scholars have explored PLF, a more unified perception of its concept has gradually emerged. PLF is a method for fine-grained management of modern livestock farming using process engineering principles and techniques [2][23][24], animal science [25] and information technology [2][25][26], and is a set of technologies used to monitor and control animal health, welfare, production, reproduction and environmental impacts in real-time [27][28][29], aiming to provide stakeholders with information as a basis for management decisions [24][30] to improve the management of large-scale livestock and poultry [21][31] to achieve economically, socially and environmentally sustainable farming [19][21][23]. Combining the above concepts, this study concludes that PLF is a series of fine management methods supported by information technology, based on real-time data collection and analysis, with the intelligent sensing and analysis of individual animal information and behavior as the core, aiming to improve animal productivity and animal welfare.
The widespread use of digital technologies has given birth to smart livestock farming (SLF) and digital livestock farming (DLF), whose concepts have some overlap and crossover with PLF. In order to clarify the connotation of PLF, it is necessary to further explore the concepts of SLF and DLF. In much of the literature, SLF is often attributed to PLF [31][32], but recent research proposes that SLF should be considered more of a successor to PLF [33]. PLF focuses on the digital processing of specific information to support stakeholder decision-making, while SLF is a knowledge-based concept that leverages information and communication technology (ICT) to manage cyber-physical livestock farms [33][34]. DLF incorporates the concepts of precision and smart farming that use modern technological tools, advanced equipment and comprehensive data management; it provides important insights, modeling approaches and actionable analytics and automation techniques to provide efficient, accurate and intelligent solutions for livestock farming [33]. The focus of DLF development is no longer on mere accuracy, but on integrating precise data into digital systems, achieving a transcendence of PLF [33]. Both SLF and DLF can be seen as PLF as a natural development based on PLF, reflecting the increasing integration of digital technologies in animal husbandry. The promotion and application of these new models will further improve the productivity and quality of animal husbandry, safeguard animal welfare, and promote the sustainable development of animal husbandry.

This entry is adapted from the peer-reviewed paper 10.3390/ani13132096


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