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Dayoub, M.; Shnaigat, S.; Tarawneh, R.A.; Al-Yacoub, A.N.; Al-Barakeh, F.; Al-Najjar, K.A. Enhancing Animal Production through Smart Agriculture. Encyclopedia. Available online: https://encyclopedia.pub/entry/54653 (accessed on 19 November 2024).
Dayoub M, Shnaigat S, Tarawneh RA, Al-Yacoub AN, Al-Barakeh F, Al-Najjar KA. Enhancing Animal Production through Smart Agriculture. Encyclopedia. Available at: https://encyclopedia.pub/entry/54653. Accessed November 19, 2024.
Dayoub, Moammar, Saida Shnaigat, Radi A. Tarawneh, Azzam N. Al-Yacoub, Faisal Al-Barakeh, Khaled A. Al-Najjar. "Enhancing Animal Production through Smart Agriculture" Encyclopedia, https://encyclopedia.pub/entry/54653 (accessed November 19, 2024).
Dayoub, M., Shnaigat, S., Tarawneh, R.A., Al-Yacoub, A.N., Al-Barakeh, F., & Al-Najjar, K.A. (2024, February 01). Enhancing Animal Production through Smart Agriculture. In Encyclopedia. https://encyclopedia.pub/entry/54653
Dayoub, Moammar, et al. "Enhancing Animal Production through Smart Agriculture." Encyclopedia. Web. 01 February, 2024.
Enhancing Animal Production through Smart Agriculture
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This entry is adapted from the peer-reviewed paper 10.3390/ruminants4010003

Smart livestock farming utilizes technology to enhance production and meet food demand sustainably. Smart agriculture employs modern technology to improve efficiency, sustainability, and animal welfare in livestock farming. This includes remote monitoring, GPS-based animal care, robotic milking, smart health collars, predictive disease control, and other innovations.

smart livestock farming technology integration sustainable development

1. Introduction

Smart agriculture employs technology to enhance livestock productivity and efficiency. It achieves this by enhancing animal health, productivity, feed quality, traceability, and sustainability in meat and dairy production. Additionally, smart agriculture reduces the consumption of resources, such as water and land, while enhancing environmental quality. As a result, smart agriculture effectively enhances livestock management, leading to higher productivity and efficiency in livestock production, improved animal product quality, and increased environmental sustainability [1]. Smart agriculture, through technological advancements, elevates livestock management, resulting in increased productivity, superior product quality, and improved environmental sustainability. It signifies a transformative shift in the livestock industry towards a more efficient and sustainable future.
Smart agriculture blends cutting-edge technology with traditional farming to increase efficiency and productivity in livestock production. It uses remote sensing, artificial intelligence, and data analysis to optimize farm management and maximize production and quality. Smart agriculture employs technologies such as sensors, robots, drones, and artificial intelligence to achieve these goals [2][3][4]. Smart agriculture integrates technology with traditional farming to optimize livestock production by using sensors, robots, drones, and AI for improved efficiency and product quality.
Specific applications include monitoring animal health, automating tasks like feeding and watering, and collecting data on health and feed consumption and analyzing it to make informed production and management decisions. Smart agriculture greatly boosts farm efficiency, productivity, and sustainability, ensuring a secure and nutritious supply of animal products for the future [5][6][7]. Smart agriculture apps offer animal health monitoring, automated feeding, and data collection, revolutionizing farming for improved efficiency and sustainability.
Smart agriculture brings multiple benefits to livestock farms, including improved animal health and welfare, increased productivity, and reduced environmental impact. Technological advancements ensure that smart agriculture will be crucial in addressing livestock production challenges and enhancing production quality and efficiency [8][9][10]. Smart agriculture is a promising technology with the potential to improve the livestock industry significantly.
Smart agriculture transforms livestock farms, enhancing animal welfare, productivity, and sustainability. Ongoing technological advancements ensure it remains a vital solution for production challenges, paving the way for a more efficient and eco-friendly future in the livestock industry.
Applying smart agriculture to improve animal production is crucial, as it harnesses cutting-edge technologies and data-driven methods to revolutionize animal farming. This topic holds significance in advancing animal welfare, resource efficiency, and agricultural sustainability. It provides potential solutions to meet the increasing global demand for food while mitigating environmental impact. Furthermore, it offers opportunities for farmers and industry stakeholders to elevate their practices and adapt to the ever-changing landscape of modern agriculture [11][12][13]. Smart agriculture is the future of animal farming, with the potential to transform it into a more sustainable, efficient, humane, and productive industry. It harnesses cutting-edge technologies and data-driven methods to make animal farming more precise and optimized while reducing its environmental impact.
Smart animal farming enhances welfare, efficiency, and sustainability through technology and data, addressing global food demand and environmental concerns with innovative solutions for a brighter agricultural future.
The problem statement states that unequal access to and affordability of smart farming tools in livestock production is a barrier to achieving sustainable food production and animal welfare.
Smart agriculture holds great promise for livestock farming by enhancing efficiency, sustainability, and animal welfare. Addressing cost, data management, and connectivity challenges is crucial for its widespread adoption. Implementing the suggested solutions can help the agricultural sector unlock the full potential of smart livestock farming, contributing to the sustainable development goals and improving overall farm performance [14].

2. Smart Agriculture in Animal Production

Animal healthcare and safety are undergoing a transformative evolution, leveraging technology to enhance the well-being of animals. The integration of smart devices and artificial intelligence contributes significantly to monitoring and improving animal health by identifying patterns and trends. The benefits of smart animal healthcare and safety are evident in early disease detection [15][16].
Biotechnology plays a pivotal role in advancing animal health, with its positive impact extending to the development of new vaccines, drugs, and effective parasite control methods. The integration of smart systems further enhances animal well-being by monitoring their health, administering medications, and overall improving welfare and care [17][18].
In the context of farm animals, smart technologies play a crucial role in ensuring safety and welfare. Sensors, artificial intelligence, virtual fencing, robotic milking, and automatic feeding collectively contribute to improving farm animal safety and welfare. Additionally, smart farm technologies not only increase productivity but also reduce environmental impact, fostering a positive public perception [19][20].
As we look ahead, the role of smart technologies in animal care and health maintenance is expected to grow significantly [21][22][23]. These technologies will continue to advance, further improving animal health and welfare, and contributing to a more sustainable and compassionate approach to animal care.
Livestock feeding is undergoing a revolutionary transformation through smart feeding systems. This innovative technology, powered by sensors, data analysis, and automation, significantly enhances animal feeding precision. The benefits of this high-accuracy approach extend to improved animal health, increased productivity, and enhanced profitability [24][25].
Smart feeding systems leverage sensors and data analysis to elevate animal feeding programs, actively monitoring for any feeding issues and taking corrective actions as needed [26][27]. Notably, these systems contribute to heightened productivity while concurrently reducing labor costs. The integration of data analysis and machine learning plays a pivotal role in refining feed composition, ultimately boosting feed income and mitigating adverse environmental impacts [28][29].
The adoption of needs-based feeding is a key strategy in optimizing animal health, productivity, and environmental impact. Probes and sensors closely monitor feed and water consumption, ensuring a well-maintained nutritional balance. Additionally, feed management, coupled with graphical analysis, improves feed efficiency by optimizing composition, schedules, and water quality, ultimately contributing to heightened resource efficiency [30][31].
The implementation of smart feeding systems, characterized by the utilization of sensors, data analysis, and automation, significantly enhances livestock productivity and profitability.
The field of smart animal monitoring technologies is experiencing significant growth. These technologies gather real-time data to monitor and enhance both animal productivity and the overall environment. Revolutionary sensing and tracking technologies play a key role in animal monitoring, with image analysis and remote sensing allowing for the assessment and tracking of animal conditions on farms without breeder intervention. This, in turn, aids in evaluating animal welfare [32][33].
The integration of intelligent sensing and analysis technologies holds great promise for transforming animal welfare. This has positive implications for both animal farm management and conservation initiatives. The rapid advancement of bio-sensing and analysis technologies is giving rise to innovative applications that can contribute to the improvement of animal production. These applications cover a spectrum of enhancements in animal health and welfare [34][35][36]. In essence, smart animal monitoring technologies are driving a revolution in animal welfare, offering benefits to both animal farm management and conservation efforts.
Technological advancements in smart animal management and reproduction aim to enhance animal productivity, well-being, and promote sustainable farming practices. Sensors and data analytics play a crucial role in refining animal monitoring, breeding, and genomic selection. The integration of robotics and smart technology contributes to increased farm efficiency, productivity, and improved animal welfare [37][38]. Moreover, artificial insemination and selective breeding are employed to enhance animal production, quality, and welfare. The implementation of smart chips and electronic tracking systems further refines animal monitoring and herd management [39][40].
Anticipated growth in animal identification systems within livestock production is expected to amplify productivity and quality. It is essential to consider the ethical and privacy implications associated with animal identification systems to ensure ethical treatment and compliance with local laws [41][42]. Overall, smart animal management and reproduction technologies utilize sensors, data analytics, robotics, and other technologies to advance animal productivity, well-being, and sustainable farming practices.
Smart farming applications play a crucial role in improving livestock marketing and distribution. They achieve this through various features such as tracking, data analysis, remote monitoring, and effective marketing and branding strategies. These applications offer real-time information to both farmers and retailers, ensuring the safety and traceability of produce.
By facilitating instant access to relevant data, smart farming applications empower farmers and retailers to make informed decisions. These decisions extend to crop planning, livestock management, and overall logistical operations. The result is a significant enhancement in efficiency within the animal production industry, ultimately leading to increased sales and profits [43][44][45].
To effectively market smart animal products, it is essential to highlight the benefits for both the animals and their owners, making care more convenient. Employing paid advertisements that focus on product benefits and visuals can be an impactful strategy. Additionally, creating high-quality content that educates and attracts customers is crucial. Staying abreast of industry trends ensures that marketing efforts remain relevant and effective.
Successful marketing of smart animal products plays a pivotal role in raising awareness of their quality [46][47][48]. In summary, smart farming applications contribute to livestock marketing and distribution improvements by providing real-time information and facilitating enhanced decision-making processes.
Smart animal production farms use technology to advance agriculture economically and socially. Smart farms use the internet of things (IoT), data analytics, and automation to improve operations and animal well-being, saving money and boosting efficiency. Smart farms also improve animal welfare, labor conditions, and sustainability, using technology and data to create a more sustainable and socially responsible future for agriculture [49][50][51]. Smart animal production farms use technology to improve agriculture economically, socially, and environmentally.

3. Practical Examples of Smart Animal Production

Smart healthcare solutions for animals, such as remote monitoring and treatments, can improve timely care and reduce the need for physical veterinary visits, making the process more sustainable and efficient [52]. Smart healthcare solutions for animals are in their early stages of development but have the potential to revolutionize animal care by providing the early detection of health problems. Smart healthcare solutions for animals are still in their early stages of development, they have the potential to make a significant positive impact on the lives of pets and their owners.
Utilizing facial recognition and machine vision technologies for observing animal behavior and well-being is crucial for ensuring animal welfare. This approach aids in identifying nutritional requirements and detecting potential productivity issues [53][54]. It is essential to emphasize that these technologies are not intended to substitute human interaction with animals. Humans should remain actively engaged in the care and monitoring of all animals, including those being observed through these technological means.
Radio-frequency identification (RFID) and the Global Positioning System (GPS) play a crucial role in livestock tracking, enhancing traceability, improving food safety, and supporting sustainable practices by facilitating precise resource allocation [55][56]. The utilization of RFID and GPS livestock tracking represents positive advancements for the industry, with the potential to enhance efficiency, promote humane practices, and contribute to overall sustainability.
Milking machines, when used to milk cows at a set time each day, contribute to enhanced efficiency, milk quality, and sustainability, leading to energy savings [57]. These machines serve as valuable tools for dairy farmers, positively impacting efficiency, milk quality, and sustainability.
Smart livestock collars monitor activity levels, body temperature, and signs of illness or distress for animal well-being [58]. Smart livestock collars have the potential to play a significant role in improving animal well-being. By providing farmers and ranchers with real-time data about the health and behavior of their animals, smart livestock collars can help to identify and address potential problems early on, before they become more serious.
Farmers track animal veterinary history, growth rates, and performance metrics to make data-driven decisions for improved animal health and productivity [59]. Tracking and analyzing data can help farmers make better decisions about animal care, leading to healthier and more productive animals, reduced costs, and a more sustainable agricultural system.
The use of data analytics and machine learning algorithms aids farmers in minimizing resource wastage, optimizing breeding practices, and promoting sustainable animal husbandry by predicting diseases and devising effective breeding strategies [60]. This application of data analytics and machine learning has the potential to revolutionize agriculture, fostering a more sustainable, ethical, and profitable environment for farmers.
Smart fencing solutions can control animal movement remotely or independently, enhancing animal management and promoting resource conservation [61][62]. Smart fencing solutions have the potential to revolutionize animal farming, making it more sustainable and profitable.

4. Developments in Smart Animal Agriculture

Advancements in ensuring the health and safety of animals have made significant strides, incorporating innovations such as wearable sensors, AI-driven disease diagnosis, predictive analytics, IoT applications for livestock, virtual reality training for veterinarians, satellite tracking, wildlife drones, biometrics for tracking, robotic surgery, and gene editing [63][64]. These remarkable progressions in animal healthcare seamlessly blend technology with compassion, offering a promising outlook for the well-being and conservation of animals, spanning from sensor technologies to gene editing.
Smart feeding systems use sensors, data analysis, and automation to improve animal feeding, benefiting feed efficiency, sustainability, and animal welfare. They employ RFID tagging, sensor analysis, automation, robotic feeders, and precision feeding. Future advances include AI-driven decisions, personalized feeding, and blockchain-based feed tracking [65][66]. Smart feeding systems are revolutionizing animal nutrition by optimizing efficiency and welfare through data-driven automation. The potential integration of AI and blockchain promises even more personalized and sustainable approaches in the future.
Revolutionary smart monitoring and control techniques for animals are reshaping the landscape of animal care. This integration of sensors, AI, robotics, and precision agriculture is not only enhancing welfare and boosting productivity but is also fostering sustainability. The result is a transformative impact on both animal agriculture and animal ownership. This amalgamation of smart technology with animal care is a game-changer, paving the way for a future where animals thrive, and their caregivers excel [67][68]. These advancements hold the key to improved welfare and productivity, from agriculture to animal ownership.
Advanced animal management and breeding techniques are transforming the industry through the incorporation of sensors, robotics, AI, and genomics. These technologies aim to boost animal productivity, welfare, and sustainability by offering real-time data, automating tasks, analyzing patterns, identifying genes, and optimizing breeding practices. This transformation of animal husbandry is heralding a brighter future [69]. The fusion of technology with animal management is redefining the industry, ensuring a more sustainable and welfare-focused future. From instantaneous data to genetic insights, these innovations are paving the way for enhanced animal husbandry practices.
The advent of smart animal production technology has ignited a revolution in product marketing and distribution, enhancing efficiency, traceability, and consumer awareness. Real-time data enables precise feeding and data-driven decision-making. Brand visibility and customer engagement are amplified through social media, direct sales, and partnerships. Crucial pillars in this transformation include sustainability and regulatory compliance. The industry is poised for further advancement with the promise of AI, ML, and VR [70][71]. In essence, smart animal production technology is reshaping the landscape of product marketing and distribution, elevating efficiency, traceability, and consumer engagement. The industry’s ongoing transformation is anticipated to reach new heights with the integration of AI, ML, and VR, all the while emphasizing sustainability and compliance.
Precision livestock farming (PLF) utilizes advanced technology to enhance animal welfare, increase productivity, and foster sustainability in agriculture. This approach yields economic and societal benefits. Precision livestock farming incorporates various tools such as individual animal monitoring, precise feeding systems, disease detection algorithms, robotic milking, and automated feed distribution. These tools collectively reshape animal farming, resulting in greater efficiency and consideration for animal well-being [72][73][74]. The transformative impact of precision livestock farming is evident in its ability to revolutionize agriculture by seamlessly integrating technology with animal welfare and sustainability. This ranges from individual monitoring to the implementation of robotic milking, ultimately molding the future of farming towards increased efficiency and humanity.

5. Technological Devices in Animal Production

Contemporary livestock management seeks to boost productivity, optimize operations, and promote animal welfare. In the domain of animal management, diverse smart technologies cater to specific needs.
The integration of smart agriculture and animal management technologies is transforming the way we manage livestock. As technology continues to evolve, we can expect even more innovative solutions that will further revolutionize the animal management industry and contribute to a more sustainable and efficient agricultural sector.
The use of technological devices in livestock farming offers numerous benefits. First, it contributes to improving productivity by enhancing production efficiency and maximizing growth rates. Secondly, these devices enhance efficiency by reducing the need for labor and increasing production levels. Technological devices also play a vital role in improving animal care by ensuring the provision of proper nutrition and necessary health care [75][76].
In terms of increasing productivity, these technologies contribute to enhancing livestock production by improving feed quality and providing superior health care. Additionally, technological devices play a role in improving efficiency by reducing manual work and making better use of resources. Finally, these technologies enhance animal welfare by creating an improved environment and providing improved health care, contributing to the overall quality of life of livestock [77][78].
The adoption of technological devices in livestock farming presents various challenges. Firstly, the high cost of these devices can be a significant obstacle for some breeders. Moreover, the need for frequent maintenance adds to the already demanding workload of breeders. Another issue is the reliance on energy, especially in areas with an unreliable electricity supply. Additionally, the vulnerability of these devices to breakage or malfunctions raises concerns about their reliability, potentially affecting the overall productivity of livestock farming operations [79][80][81].
In the realm of livestock farming, technological devices present a plethora of advantages, outweighing any potential drawbacks by improving productivity, efficiency, and animal welfare. While these devices bring about significant benefits, it is imperative to thoughtfully evaluate factors such as costs, maintenance, and energy dependence before integrating them into farming practices.

6. Revolutionizing Animal Farming

Smart devices for animal health encompass a variety of tools such as activity trackers, GPS collars, remote monitoring systems, smart feeders, health wearables, pet-access doors, tank regulators, telemedicine services, livestock sensors, and health apps. Collectively, these devices work to guarantee the safety and well-being of animals [82][83].
In the realm of animal farming, smart feeding technology comprises automated feeders, consumption sensors, nutritional analyzers, RFID-tagged systems, precision equipment, monitoring software, custom mixers, and real-time monitoring. These technologies streamline the feeding process, ensuring precise nutrition and efficient feed management for animals [84][85].
Smart animal monitoring and control involve a variety of devices, including tracking collars for location, wearables, remote cameras/sensors, environmental controls, gates/fences for access, automated feeding systems, livestock trackers, behavior sensors, robotic herding, and cloud-based management. Collectively, these technologies enhance animal welfare and streamline care [86][87].
In the realm of intelligent animal breeding and management, a convergence of AI equipment, genetic testing tools, embryo transfer devices, hormone monitors, smart identification systems, health monitors, automated feeding, data analytics platforms, and livestock management software takes place. This integration aims to optimize breeding practices and improve overall animal care. Collectively, these technologies work together to ensure the production of healthier offspring and the efficient management of animal populations [88][89][90].
The smart distribution of animal farm products integrates diverse technologies, such as RFID/barcodes and smart packaging sensors for tracking and quality control during transit, coupled with GPS management and supply chain software for efficient delivery. IoT sensors monitor product quality, while blockchain ensures transparent tracking, complemented by automated packaging and cloud-based inventory for real-time tracking. Together, these technologies ensure reliable, high-quality distribution throughout the supply chain [91][92][93].
The socio-economic impact of smart animal farming revolves around essential technologies, encompassing data analytics and remote monitoring for informed decision-making and animal health. This includes automated feeding, health wearables, management software, genetic testing, and the utilization of supply chain and blockchain technologies for quality enhancement and transparent tracking. Collectively, these technological advancements serve to boost productivity, enhance animal welfare, and improve resource management, thereby fostering positive economic and social outcomes within the farming sector [94][95].
Smart technologies revolutionize animal farming, transforming health, feeding, monitoring, breeding, and distribution. These innovations enhance animal welfare, optimize resource management, boost productivity, and improve economic outcomes, promising a more sustainable, efficient, and humane agricultural future.

7. Advantages and Disadvantages of Smart Animal Production

Smart healthcare for animals holds promise with its ability to detect diseases early, enhance nutrition, provide real-time tracking, and improve overall welfare. However, obstacles such as high costs, privacy concerns, data accuracy, user errors, and social acceptance must be addressed [96][97]. While there is exciting potential for advancements in animal health, it is crucial to focus on addressing challenges such as costs, privacy, and social acceptance to ensure widespread success.
Smart feeding systems present a gamut of technological advantages poised to enhance efficiency, productivity, and animal welfare in production farms. Yet, it is crucial to meticulously weigh the potential downsides—think initial investment costs, maintenance demands, and the ever-looming specter of data security concerns—before taking the plunge into adopting these systems [98][99]. Smart feeding systems offer farm benefits, but weighing initial costs, maintenance, and data security is key for informed adoption decisions.
Implementing smart techniques for animal monitoring and control can yield technological advantages that enhance health, welfare, and productivity. Nevertheless, it is essential to weigh the drawbacks, such as initial expenses, technical expertise, data security concerns, potential over-reliance, and the acceptance of these technologies by animals. Achieving success in this endeavor depends on meticulous planning, effective implementation, and thorough training to optimize benefits and mitigate potential drawbacks [100][101]. While smart animal monitoring contributes to well-being and productivity, successful implementation requires thoughtful consideration of costs, security, and the adaptability of animals.
Smart animal management and breeding techniques offer a tech-savvy edge, enhancing efficiency and outcomes in farming. Potential drawbacks like upfront costs, technical challenges, data security risks, and the need for animal adaptation may become hurdles. It is a balancing act between reaping the benefits and managing the potential downsides [102][103]. Smart techniques elevate animal management, but navigating upfront costs, technical challenges, and ensuring animal adaptation is crucial for balanced success.
Smart animal tech offers benefits such as traceability, supply chain, marketing, welfare, and compliance, and disadvantages such as upfront costs, privacy issues, need for tech expertise, errors, and public perception [104][105]. Smart animal tech brings valuable benefits, but upfront costs, privacy concerns, and public perception should be considered carefully for a well-rounded approach.
Smart animal farming presents a promising strategy to boost agricultural productivity, enhance animal welfare, and support environmental sustainability. Nevertheless, a thoughtful examination of potential drawbacks, such as upfront investment costs, data management challenges, animal welfare concerns, and societal implications, is crucial for its successful and responsible implementation [106][107][108]. While smart farming holds significant potential for productivity and sustainability, addressing costs, data challenges, and ethical considerations is vital for its responsible implementation.

8. Technology Alternatives in Smart Animal Production

In smart animal production, alternatives to technology include traditional methods like animal husbandry practices, manual record-keeping, and visual inspections. Practices such as natural grazing management and manual feeding allow farmers to manage livestock without electronic monitoring. Traditional expertise is highlighted through selective breeding and non-digital health monitoring. While these approaches may lack high-tech efficiency, they emphasize the integration of time-tested methods with modern technologies for comprehensive livestock management [109].
Smart animal production farms, with their technological advancements, offer significant benefits. However, ethical and sustainable food production can also be achieved through alternative methods. Enhancing animal welfare, minimizing environmental impact, and fostering community connections are attainable by incorporating genetic selection strategies, implementing effective management practices, exploring alternative feedstuffs, prioritizing local sourcing, and engaging in direct marketing [110][111][112].
While smart farms bring technological benefits, ethical and sustainable food production requires a diverse set of approaches, encompassing genetics, management practices, alternative feedstuffs, and local sourcing for improved welfare and reduced environmental impact.

9. Livestock Accuracy Enhancing Applications

Smart applications have the potential to enhance animal welfare in agriculture through various functionalities. These include monitoring the health and well-being of animals, implementing precision feeding and watering systems, overseeing environmental conditions, automating tasks, and collecting and analyzing data. Notable examples of such applications encompass smart collars, robotic milking systems, precision feeding systems, and environmental monitoring systems [113].
In the realm of livestock management, smart applications play a crucial role by tracking individual animal health, providing personalized feeding and breeding guidance, conducting behavior analysis for early issue detection, managing inventory, regulating environmental conditions, and offering market insights. These tools contribute to the efficient management of livestock, ensuring their health and productivity, and facilitating informed decision-making [114][115].
Smart applications bring a technological boost to animal welfare in farming, utilizing tools such as smart collars and robotic systems to deliver precise care and data-driven insights.
Precision livestock farming (PLF) utilizes data to enhance animal health, welfare, and productivity in large-scale and intensive systems. While PLF holds great potential, challenges like cost, data management, integration, and animal welfare persist. Nevertheless, progressively PLF is employed for automated milking, precision feeding, environmental monitoring, and disease detection. Overall, PLF stands as a promising technology with the potential to revolutionize livestock management [116][117][118]. Precision livestock farming has transformative potential in enhancing animal health and productivity, despite challenges like cost and welfare concerns. Its applications in automated milking, precision feeding, and disease detection mark it as a promising technology for revolutionizing livestock management.
As technology continues to develop, we can expect to see even more innovative livestock accuracy-enhancing applications that will provide even more benefits to farmers, ranchers, and other stakeholders.
Smart technologies for healthcare and safety in the animal domain are advancing swiftly, providing various advantages such as enhanced animal welfare, lowered veterinary expenses, increased productivity, and improved safety. These technologies find application in remote monitoring, precision medicine, behavioral monitoring, and safety monitoring. Noteworthy examples include PetPace, a novel pet-related device or service, Tractive, a GPS animal tracking device, and Cowlar, a smart collar tailored for cows [119][120].
Smart feeding systems (SFSs) employ sensors, artificial intelligence (AI), and machine learning (ML) to automate and enhance livestock feeding processes. These systems have the potential to enhance feed efficiency, lower feed costs, promote animal health and welfare, decrease labor expenses, and elevate the quality of meat and milk. Additionally, SFSs can contribute to the reduction of the environmental footprint associated with animal production and enhance traceability. With the increasing affordability and sophistication of SFSs, a broader adoption in the animal production industry is anticipated [121].
Advanced techniques for the monitoring and controlling of animals are on the rise, employing sensors, actuators, and AI to collect data on behavior, health, and the environment. These methods provide real-time insights into health, behavior, environmental conditions, predator protection, and location tracking. The advantages include improved animal welfare, decreased caretaker workload, and heightened efficiency. However, there are existing challenges such as cost, data privacy, and stakeholder acceptance. Despite these obstacles, these technologies have the potential to bring about significant positive impacts on animals [122][123].
Utilizing advanced technology in animal care enhances welfare, productivity, and sustainability by incorporating AI, data analytics, and sensors. Precision feeding, health monitoring, and managing reproductive and behavioral aspects contribute to the well-being of animals, resulting in increased yields, cost efficiencies, and environmental friendliness. Despite challenges related to costs and standardization, there is the potential to revolutionize animal agriculture, paving the way for a more humane and sustainable future for food. Leading the charge in this technological transformation are key players such as Allflex, Daftras, Delphi Animal Health, Elysis, and Zoetis [124][125].
Smart animal production farms use technology to automate and optimize livestock production processes. This can lead to more sustainable, healthier, and more nutritious animal products. Marketing and distributing smart animal products can be challenging, but producers can overcome these challenges by focusing on the benefits of these products to consumers and educating them about smart farming technologies. Smart farming technologies can be used to market and distribute smart animal products through real-time data monitoring, traceability, e-commerce, social media, content marketing, and partnerships [126].
Smart animal farming is a rapidly evolving field poised to revolutionize the livestock industry by boosting efficiency, reducing costs, improving profitability, enhancing animal welfare, mitigating environmental impact, and ensuring food security. As these technologies become more affordable, they are likely to be widely adopted, fostering a more sustainable, efficient, and humane livestock industry globally [127][128][129].
The use of smart technological applications in animal production has the potential to revolutionize the industry, leading to increased productivity, improved animal welfare, enhanced environmental sustainability, and greater profitability for farmers.

10. Blockchain in Animal Production Farms

Blockchain has the potential to revolutionize the livestock sector, enhancing its efficiency, transparency, and sustainability, providing numerous opportunities and benefits for farmers, consumers, and other stakeholders. Nevertheless, it is crucial to acknowledge the challenges associated with this technology, including high initial investment costs, cybersecurity risks, a shortage of skilled labor, and regulatory hurdles [130]. Investing in research and development is essential to tackle these challenges, and collaboration is key to establishing a supportive environment for the widespread adoption of Blockchain technology. As technology continues to develop and mature, these challenges will likely be addressed, paving the way for Blockchain to play a transformative role in the livestock sector. The potential advantages of Blockchain technology in the livestock sector far surpass the challenges [131][132].
In the future, we anticipate expanded uses of blockchain in animal production, blockchain enhances traceability, prevents disease, and streamlines supply chains, benefiting both producers and consumers. It fosters sustainability, aids disease prevention, and supports financial inclusion for farmers in developing nations. This technology has the potential to significantly improve the efficiency and transparency of the livestock industry, with ongoing innovations expected.

References

  1. Perakis, K.; Lampathaki, F.; Nikas, K.; Georgiou, Y.; Marko, O.; Maselyne, J. CYBELE–Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics. Comput. Netw. 2020, 168, 107035.
  2. Zhang, F.; Zhang, Y.; Lu, W.; Gao, Y.; Gong, Y.; Cao, J. 6G-Enabled Smart Agriculture: A Review and Prospect. Electronics 2022, 11, 2845.
  3. Monteiro, A.; Santos, S.; Gonçalves, P. Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals 2021, 11, 2345.
  4. Farooq, M.S.; Riaz, S.; Abid, A.; Umer, T.; Zikria, Y.B. Role of IoT Technology in Agriculture: A Systematic Literature Review. Electronics 2020, 9, 319.
  5. Alabdali, S.A.; Pileggi, S.F.; Cetindamar, D. Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review. Sustainability 2023, 15, 7908.
  6. Abdelbaki, A.; Udelhoven, T. A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions. Remote Sens. 2022, 14, 3515.
  7. Xu, X.L.; Chen, H.H.; Zhang, R.R. The Impact of Intellectual Capital Efficiency on Corporate Sustainable Growth-Evidence from Smart Agriculture in China. Agriculture 2020, 10, 199.
  8. Rejeb, A.; Rejeb, K.; Abdollahi, A.; Al-Turjman, F.; Treiblmaier, H. The Interplay between the Internet of Things and agriculture: A bibliometric analysis and research agenda. Internet Things 2022, 19, 100580.
  9. De Alwis, S.; Hou, Z.; Zhang, Y.; Na, M.H.; Ofoghi, B.; Sajjanhar, A. A survey on smart farming data, applications and techniques. Comput. Ind. 2022, 138, 103624.
  10. Khan, N.; Ray, R.L.; Kassem, H.S.; Hussain, S.; Zhang, S.; Khayyam, M.; Ihtisham, M.; Asongu, S.A. Potential Role of Technology Innovation in Transformation of Sustainable Food Systems: A Review. Agriculture 2021, 11, 984.
  11. Azadi, H.; Moghaddam, S.M.; Burkart, S.; Mahmoudi, H.; Van Passel, S.; Kurban, A.; Lopez-Carr, D. Rethinking resilient agriculture: From Climate-Smart Agriculture to Vulnerable-Smart Agriculture. J. Clean. Prod. 2021, 319, 128602.
  12. Dawkins, M.S. Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want. Front. Anim. Sci. 2021, 21, 736536.
  13. Held, S.D.E.; Špinka, M. Animal play and animal welfare. Anim. Behav. 2011, 81, 891–899.
  14. Buller, H.; Blokhuis, H.; Lokhorst, K.; Silberberg, M.; Veissier, I. Animal Welfare Management in a Digital World. Animals 2020, 10, 1779.
  15. Alipio, M.; Villena, M.L. Intelligent wearable devices and biosensors for monitoring cattle health conditions: A review and classification. Smart Health 2023, 27, 100369.
  16. Punj, R.; Kumar, R. Technological aspects of WBANs for health monitoring: A comprehensive review. Wireless Netw. 2019, 25, 1125–1157.
  17. Jiang, B.; Tang, W.; Cui, L.; Deng, X. Precision Livestock Farming Research: A Global Scientometric Review. Animals 2023, 13, 2096.
  18. Gehlot, A.; Malik, P.K.; Singh, R.; Akram, S.V.; Alsuwian, T. Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies. Appl. Sci. 2022, 12, 7316.
  19. Stampa, E.; Zander, K.; Hamm, U. Insights into German Consumers’ Perceptions of Virtual Fencing in Grassland-Based Beef and Dairy Systems: Recommendations for Communication. Animals 2020, 10, 2267.
  20. Lomax, S.; Colusso, P.; Clark, C.E. Does Virtual Fencing Work for Grazing Dairy Cattle? Animals 2019, 9, 429.
  21. Curtis, S.E. Animal Well-Being and Animal Care. Vet. Clin. N. Am. Food Anim. Pract. 1987, 3, 369–382.
  22. Wilkie, B.; Mallard, B. Selection for high immune response: An alternative approach to animal health maintenance? Vet. Immunol. Immunopathol. 1999, 72, 231–235.
  23. Yongqiang, C.; Shaofang, L.; Hongmei, L.; Pin, T.; Yilin, C. Application of Intelligent Technology in Animal Husbandry and Aquaculture Industry. In Proceedings of the 14th International Conference on Computer Science & Education (ICCSE), Toronto, ON, Canada, 19–21 August 2019; pp. 335–339.
  24. Place, F.; Niederle, P.; Sinclair, F.; Carmona, N.E.; Guéneau, S.; Gitz, V.; Alpha, A.; Sabourin, E.; Hainzelin, E. Agro Ecologically Conducive Policies: A Review of Recent Advances and Remaining Challenges, 2022; Working Paper 1. Bogor, Indonesia: The Transformative Partnership Platform on Agroecology. Available online: https://www.cifor.org/publications/pdffiles/WPapers/TPP-WP-1.pdf (accessed on 11 March 2023).
  25. Zougmoré, R.; Partey, S.; Ouédraogo, M.; Omitoyin, B.; Thomas, T.; Ayantunde, A.; Ericksen, P.; Said, M.; Jalloh, A. Toward climate-smart agriculture in West Africa: A review of climate change impacts, adaptation strategies and policy developments for the livestock, fishery and crop production sectors. Agric. Food Secur. 2016, 5, 26.
  26. Ruuska, S.; Kajava, S.; Mughal, M.; Zehner, N.; Mononen, J. Validation of a pressure sensor-based system for measuring eating, rumination and drinking behaviour of dairy cattle. Appl. Anim. Behav. Sci. 2016, 174, 19–23.
  27. Herrero, M.; Thornton, P.K.; Notenbaert, A.M.; Wood, S.; Msangi, S.; Freeman, H.A.; Bossio, D.; Dixon, J.; Peters, M.; van de Steeg, J.; et al. Smart Investments in Sustainable Food Production: Revisiting Mixed Crop-Livestock Systems. Science 2010, 327, 822–825.
  28. Hernandez-Patlan, D.; Tellez-Isaias, G.; Hernandez-Velasco, X.; Solis-Cruz, B. Editorial: Technological strategies to improve animal health and production. Front. Vet. Sci. 2023, 10, 1206170.
  29. Mugunieri, G.; Irungu, P.; Omiti, J. Performance of community-based animal health workers in the delivery of livestock health services. Trop. Anim. Health Prod. 2004, 36, 523–535.
  30. Botín-Sanabria, D.M.; Mihaita, A.-S.; Peimbert-García, R.E.; Ramírez-Moreno, M.A.; Ramírez-Mendoza, R.A.; Lozoya-Santos, J.d.J. Digital Twin Technology Challenges and Applications: A Comprehensive Review. Remote Sens. 2022, 14, 1335.
  31. Iwasaki, W.; Morita, N.; Nagata, M.P.B. IoT Sensors for Smart Livestock Management. In Chemical, Gas, and Biosensors for Internet of Things and Related Applications; Mitsubayashi, K., Niwa, O., Ueno, Y., Eds.; Elsevier: Amsterdam, The Netherlands, 2019.
  32. Melnikov, P.; Bobrov, A.; Marfin, Y. On the Use of Polymer-Based Composites for the Creation of Optical Sensors: A Review. Polymers 2022, 14, 4448.
  33. Singh, D.; Singh, R.; Gehlot, A.; Akram, S.V.; Priyadarshi, N.; Twala, B. An Imperative Role of Digitalization in Monitoring Cattle Health for Sustainability. Electronics 2022, 11, 2702.
  34. Dayoub, M.; Nakiyemba, A.; Plosila, J. Applications of Internet of Things (IoT) in Agriculture–The Potential and Challenges in Smart Farm in Uganda. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021), Settat, Morocco, 28–30 June 2021; Hassanien, A.E., Ed.; Springer: Cham, Switzerland, 2021; Volume 1377.
  35. Ullo, S.L.; Sinha, G.R. Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications. Remote Sens. 2021, 13, 2585.
  36. Jukan, A.; Masip-Bruin, X.; Amla, N. Smart Computing and Sensing Technologies for Animal Welfare: A systematic review. ACM Comput. Surv. 2017, 50, 1–27.
  37. Yépez-Ponce, D.F.; Salcedo, J.V.; Rosero-Montalvo, P.D.; Sanchis, J. Mobile robotics in smart farming: Current trends and applications. Front. Artif. Intell. 2023, 6, 1213330.
  38. Hardiki, P.; Namrata, A. Smart Surveillance and Animal Care system using IOT and Deep Learning (8 April 2020). In Proceedings of the 3rd International Conference on Advances in Science & Technology (ICAST), Bahir Dar, Ethiopia, 2–4 October 2020.
  39. Si, L.F.; Li, M.; He, L. Farmland monitoring and livestock management based on internet of things. Internet Things 2022, 19, 100581.
  40. Zhang, M.; Wang, X.; Feng, H.; Huang, Q.; Xiao, X.; Zhang, X. Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. J. Clean. Prod. 2021, 312, 127712.
  41. Stankovski, S.; Ostojic, G.; Senk, I.; Rakic-Skokovic, M.; Trivunovic, S.; Kucevic, D. Dairy cow monitoring by RFID. Sci. Agric. Internet 2012, 69, 75–80.
  42. Sherwin, C.M.; Christiansen, S.B.; Duncan, I.J.; Erhard, H.W.; Lay, D.C.; Mench, J.A.; O’connor, C.E.; Petherick, J. Guidelines for the ethical use of animals in applied ethology studies. Appl. Anim. Behav. Sci. 2003, 81, 291–305.
  43. Dineva, K.; Atanasova, T. Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming. Sensors 2022, 22, 6566.
  44. Mohamed, E.S.; Belal, A.; Abd-Elmabod, S.K.; El-Shirbeny, M.A.; Gad, A.; Zahran, M.B. Smart farming for improving agricultural management. Egypt. J. Remote Sens. Space Sci. 2021, 24, 971–981.
  45. Doshi, J.; Patel, T.; Bharti, S.K. Smart Farming using IoT, a solution for optimally monitoring farming conditions. Procedia Comput. Sci. 2018, 160, 746–751.
  46. Bocken, N.; Morales, L.S.; Lehner, M. Sufficiency Business Strategies in the Food Industry—The Case of Oatly. Sustainability 2020, 12, 824.
  47. Davenport, T.; Guha, A.; Grewal, D.; Bressgott, T. How artificial intelligence will change the future of marketing. J. Acad. Mark. Sci. 2020, 48, 24–42.
  48. Soares, J.A.; Galvão, A.L.B.; de Matos, L.V.S.; Pierucci, J.C.; Bresciani, K.D.S.; Pizzinatto, N.K. Marketing strategies in small animal clinical practice. Int. J. Pharm. Health Mark. 2017, 11, 235–247.
  49. Eastwood, C.; Ayre, M.; Nettle, R.; Rue, B.D. Making sense in the cloud: Farm advisory services in a smart farming future. NJAS Wagening. J. Life Sci. 2019, 90–91, 100298.
  50. Andronie, M.; Lăzăroiu, G.; Iatagan, M.; Hurloiu, I.; Dijmărescu, I. Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review. Sustainability 2021, 13, 751.
  51. Alqahtani, E.; Janbi, N.; Sharaf, S.; Mehmood, R. Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling. Sustainability 2022, 14, 13534.
  52. Mahfuz, S.; Mun, H.-S.; Dilawar, M.A.; Yang, C.-J. Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming. Sustainability 2021, 14, 2607.
  53. Neethirajan, S. The role of sensors, big data and machine learning in modern animal farming. Sens. Bio-Sens. Res. 2020, 29, 100367.
  54. Zuidhof, M.J. Precision livestock feeding: Matching nutrient supply with nutrient requirements of individual animals. J. Appl. Poult. Res. 2020, 29, 11–14.
  55. Neethirajan, S.; Kemp, B. Digital Livestock Farming. Sens. Bio-Sens. Res. 2021, 32, 100408.
  56. Park, J.-H.; Han, M.-H. Enhancing Livestock Management with IoT-based Wireless Sensor Networks: A Comprehensive Approach for Health Monitoring, Location Tracking, Behavior Analysis, and Environmental Optimization . J. Sustain. Urban Futures 2023, 13, 34–46. Available online: https://neuralslate.com/index.php/Journal-of-Sustainable-Urban-Fut/article/view/18 (accessed on 30 September 2023).
  57. Moerkerken, A.; Duijndam, S.; Blasch, J.; van Beukering, P.; Smit, A. Determinants of energy efficiency in the Dutch dairy sector: Dilemmas for sustainability. J. Clean. Prod. 2021, 293, 126095.
  58. Sadeghi, E.; Kappers, C.; Chiumento, A.; Derks, M.; Havinga, P. Improving piglets health and well-being: A review of piglets health indicators and related sensing technologies. Smart Agric. Technol. 2023, 5, 100246.
  59. Neethirajan, S. SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals. Agriculture 2023, 13, 436.
  60. Karunathilake, E.M.B.M.; Le, A.T.; Heo, S.; Chung, Y.S.; Mansoor, S. The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture. Agriculture 2023, 13, 1593.
  61. Herlin, A.; Brunberg, E.; Hultgren, J.; Högberg, N.; Rydberg, A.; Skarin, A. Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture. Animals 2021, 11, 829.
  62. Klaas, E.; Roopaei, M. Immersive Analytics Application in Smart Agriculture and Animal Behavior. In Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 27–30 January 2021; pp. 290–296.
  63. Muthu, B.; Sivaparthipan, C.B.; Manogaran, G.; Sundarasekar, R.; Kadry, S.; Shanthini, A.; Dasel, A. IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector. Peer-to-Peer Netw. Appl. 2020, 13, 2123–2134.
  64. Huang, J.; Wang, J.; Ramsey, E.; Leavey, G.; Chico, T.J.; Condell, J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. Sensors 2021, 22, 8002.
  65. Alshehri, D.M. Blockchain-assisted internet of things framework in smart livestock farming. Internet Things 2023, 22, 100739.
  66. Khan, P.W.; Byun, Y.-C.; Park, N. IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning. Sensors 2020, 20, 2990.
  67. Mekonnen, Y.; Namuduri, S.; Burton, L.; Sarwat, A.; Bhansali, S. Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture. J. Electrochem. Soc. 2019, 167, 037522.
  68. Bhat, S.A.; Huang, N.-F. Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE Access 2021, 9, 110209–110222.
  69. Pérez-Enciso, M.; Steibel, J.P. Phenomes: The current frontier in animal breeding. Genet. Sel. Evol. 2021, 53, 22.
  70. Dwivedi, Y.K.; Ismagilova, E.; Hughes, D.L.; Carlson, J.; Filieri, R.; Jacobson, J.; Jain, V.; Karjaluoto, H.; Kefi, H.; Krishen, A.S.; et al. Setting the future of digital and social media marketing research: Perspectives and research propositions. Int. J. Inf. Manag. 2020, 59, 102168.
  71. Grewal, D.; Hulland, J.; Kopalle, P.K.; Karahanna, E. The future of technology and marketing: A multidisciplinary perspective. J. Acad. Mark. Sci. 2020, 48, 1–8.
  72. Pretty, J. Agricultural sustainability: Concepts, principles and evidence. Philos. Trans. R. Soc. B 2008, 363, 447–465.
  73. Kremsa, V.Š. Sustainable management of agricultural resources (agricultural crops and animals). Sustain. Resour. Manag. 2021, 25, 99–145.
  74. Pretty, J.; Bharucha, Z.P. Sustainable intensification in agricultural systems. Ann. Bot. 2014, 114, 1571–1596.
  75. Nimbalkar, V.; Kumar Verma, H.; Singh, J. Dairy Farming Innovations for Productivity Enhancement; IntechOpen: London, UK, 2022.
  76. Nakamura, K.; Kaihatsu, S.; Yagi, T. Productivity improvement and economic growth: Lessons from Japan. Econ. Anal. Policy 2018, 62, 57–79.
  77. Shuvo, A.A.S.; Rahman, S.; Al-Mamum, M.; Islam, K.M.S. Cholesterol reduction and feed efficiency enhancement in broiler through the inclusion of nutritionally improved fermented rice bran. J. Appl. Poult. Res. 2022, 31, 100226.
  78. Flint, J.F.; Garner, M.R. Feeding beneficial bacteria: A natural solution for increasing efficiency and decreasing pathogens in animal agriculture. J. Appl. Poult. Res. 2009, 18, 367–378.
  79. Zio, E. Reliability engineering: Old problems and new challenges. Reliab. Eng. Syst. Saf. 2009, 94, 125–141.
  80. Eriksen, S.; Utne, I.B.; Lützen, M. An RCM approach for assessing reliability challenges and maintenance needs of unmanned cargo ships. Reliab. Eng. Syst. Saf. 2021, 210, 107550.
  81. Mikdashi, J.; Nived, O. Measuring disease activity in adults with systemic lupus erythematosus: The challenges of administrative burden and responsiveness to patient concerns in clinical research. Arthritis Res. Ther. 2015, 17, 183.
  82. Pandey, S.; Kalwa, U.; Kong, T.; Guo, B.; Gauger, P.C.; Peters, D.J.; Yoon, K. Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap. Animals 2021, 11, 2665.
  83. Benis, A.; Haghi, M.; Deserno, T.M.; Tamburis, O. One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case. JMIR Med. Inform. 2023, 11, e43871.
  84. González, L.A.; Kyriazakis, I.; Tedeschi, L.O. Review: Precision nutrition of ruminants: Approaches, challenges and potential gains. Animal 2018, 12 (Suppl. S2), S246–S261.
  85. Gaillard, C.; Brossard, L.; Dourmad, J.-Y. Improvement of feed and nutrient efficiency in pig production through precision feeding. Anim. Feed. Sci. Technol. 2020, 268, 114611.
  86. Whitham, J.C.; Miller, L.J. Using technology to monitor and improve zoo animal welfare. Anim. Welf. 2016, 25, 395–409.
  87. Ali, W.; Ali, M.; Ahmad, M.; Dilawar, S.; Firdous, A.; Afzal, A. Application of Modern Techniques in Animal Production Sector for Human and Animal Welfare. Turk. J. Agric.-Food Sci. Technol. 2020, 8, 457–463.
  88. Brito, L.F.; Oliveira, H.R.; McConn, B.R.; Schinckel, A.P.; Arrazola, A.; Marchant-Forde, J.N.; Johnson, J.S. Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding. Front. Genet. 2020, 11, 793.
  89. Flint, A.P.F.; Woolliams, J.A. Precision animal breeding. Phil. Trans. R. Soc. 2008, 363, 573–590.
  90. Suravajhala, P.; Kogelman, L.J.A.; Kadarmideen, H.N. Multi-omic data integration and analysis using systems genomics approaches: Methods and applications in animal production, health and welfare. Genet. Sel. Evol. 2016, 48, 38.
  91. Abdel-Basset, M.; Manogaran, G.; Mohamed, M. Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Gener. Comput. Syst. 2018, 86, 614–628.
  92. Wang, S.; Li, D.; Zhang, Y.; Chen, J. Smart Contract-Based Product Traceability System in the Supply Chain Scenario. IEEE Access 2019, 7, 115122–115133.
  93. Cerullo, G.; Guizzi, G.; Massei, C.; Sgaglione, L. Efficient Supply Chain Management: Traceability and Transparency. In Proceedings of the 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, Italy, 28 November–1 December 2016; pp. 750–757.
  94. Song, B.; Robinson, G.M.; Bardsley, D.K.; Xue, Y.; Wang, B. Multifunctional agriculture in a peri-urban fringe: Chinese farmers’ responses to shifts in policy and changing socio-economic conditions. Land Use Policy 2023, 133, 106869.
  95. Swain, S.; Patoju, S.K.S.; Manjushree, P. Entrepreneurial Value Creation: Study on Small Agri-Tech Enterprise. South Asian J. Bus. Manag. Cases 2022, 11, 167–177.
  96. Schillings, J.; Bennett, R.; Rose, D.C. Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare. Front. Anim. Sci. 2021, 2, 639678.
  97. Benjamin, M.; Yik, S. Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners. Animals 2019, 9, 133.
  98. Tuyttens, F.A.M.; Molento, C.F.M.; Benaissa, S. Twelve Threats of Precision Livestock Farming (PLF) for Animal Welfare. Front. Vet. Sci. 2022, 9, 889623.
  99. Tullo, E.; Finzi, A.; Guarino, M. Review: Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy. Sci. Total. Environ. 2019, 650, 2751–2760.
  100. Menendez, H.M.; Brennan, J.R.; Gaillard, C.; Ehlert, K.; Quintana, J.; Neethirajan, S.; Remus, A.; Jacobs, M.; Teixeira, I.A.; Turner, B.L.; et al. ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production. J. Anim. Sci. 2022, 100, skac160.
  101. Racewicz, P.; Ludwiczak, A.; Skrzypczak, E.; Biesiada, H.; Nowak, T.; Nowaczewski, S.; Zaborowicz, M.; Stanisz, M.; Ślósarz, P. Welfare Health and Productivity in Commercial Pig Herds. Animals 2021, 11, 1176.
  102. Hedley, C. The role of precision agriculture for improved nutrient management on farms. J. Sci. Food Agric. 2015, 95, 12–19.
  103. Barnes, A.; Soto, I.; Eory, V.; Beck, B.; Balafoutis, A.; Sánchez, B.; Vangeyte, J.; Fountas, S.; van der Wal, T.; Gómez-Barbero, M. Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy 2019, 80, 163–174.
  104. Lezoche, M.; Hernandez, J.E.; Díaz, M.d.M.E.A.; Panetto, H.; Kacprzyk, J. Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Comput. Ind. 2020, 117, 103187.
  105. Costa, C.; Antonucci, F.; Pallottino, F.; Aguzzi, J.; Sarriá, D.; Menesatti, P. A Review on Agri-Food Supply Chain Traceability by Means of RFID Technology. Food Bioprocess Technol. 2013, 6, 353–366.
  106. Eastwood, C.; Edwards, J.; Turner, J. Review: Anticipating alternative trajectories for responsible Agriculture 4.0 innovation in livestock systems. Animal 2021, 15, 100296.
  107. Molnár, M. Transforming Intensive Animal Production: Challenges and Opportunities for Farm Animal Welfare in the European Union. Animals 2021, 12, 2086.
  108. Pfeiffer, J.; Gabriel, A.; Gandorfer, M. Understanding the public attitudinal acceptance of digital farming technologies: A nationwide survey in Germany. Agric. Hum. Values 2021, 38, 107–128.
  109. Mu, R.; Mu, R.; Wang, H.; Wang, H. A systematic literature review of open innovation in the public sector: Comparing barriers and governance strategies of digital and non-digital open innovation. Public Manag. Rev. 2022, 24, 489–511.
  110. Olesen, I.; Groen, A.F.; Gjerde, B. Definition of animal breeding goals for sustainable production systems. J. Anim. Sci. 2000, 78, 570–582.
  111. McEachern, M.G.; McClean, P. Organic purchasing motivations and attitudes: Are they ethical? Int. J. Consum. Stud. 2002, 26, 85–92.
  112. Buller, H.; Morris, C. Farm Animal Welfare: A New Repertoire of Nature-Society Relations or Modernism Re-Embedded ? Sociol. Rural. 2003, 43, 216–237.
  113. Paunova-Hubenova, E.; Trichkova-Kashamova, E. Smart Solutions for Control and Management in Livestock Farms. In Proceedings of the 2021 Big Data, Knowledge and Control Systems Engineering (BdKCSE), Sofia, Bulgaria, 28–29 October 2021; pp. 1–7.
  114. Matthews, S.G.; Miller, A.L.; Clapp, J.; Plötz, T.; Kyriazakis, I. Early detection of health and welfare compromises through automated detection of behavioural changes in pigs. Vet. J. 2016, 217, 43–51.
  115. Shu, H.; Wang, W.; Guo, L.; Bindelle, J. Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review. Animals 2021, 11, 980.
  116. Vaintrub, M.O.; Levit, H.; Chincarini, M.; Fusaro, I.; Giammarco, M.; Vignola, G. Review: Precision livestock farming, automats and new technologies: Possible applications in extensive dairy sheep farming. Animal 2020, 15, 100143.
  117. Morrone, S.; Dimauro, C.; Gambella, F.; Cappai, M.G. Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions. Sensors 2021, 22, 4319.
  118. Tzanidakis, C.; Tzamaloukas, O.; Simitzis, P.; Panagakis, P. Precision Livestock Farming Applications (PLF) for Grazing Animals. Agriculture 2023, 13, 288.
  119. Kleen, J.L.; Guatteo, R. Precision Livestock Farming: What Does It Contain and What Are the Perspectives? Animals 2022, 13, 779.
  120. Bortoluzzi, E.M.; Goering, M.J.; Ochoa, S.J.; Holliday, A.J.; Mumm, J.M.; Nelson, C.E.; Wu, H.; Mote, B.E.; Psota, E.T.; Schmidt, T.B.; et al. Evaluation of Precision Livestock Technology and Human Scoring of Nursery Pigs in a Controlled Immune Challenge Experiment. Animals 2022, 13, 246.
  121. Lovarelli, D.; Bacenetti, J.; Guarino, M. A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production? J. Clean. Prod. 2020, 262, 121409.
  122. Egon, K.; Oloyede, J.O. Advancements in Sensor Technologies for Precision Livestock Farming. 31 October 2023. Available online: https://osf.io/preprints/osf/av68m (accessed on 10 November 2023).
  123. Ahmad, M.; Abbas, S.; Fatima, A.; Ghazal, T.M.; Alharbi, M.; Khan, M.A.; Elmitwally, N.S. AI-Driven livestock identification and insurance management system. Egypt. Inform. J. 2023, 24, 100390.
  124. Wathes, C.; Kristensen, H.; Aerts, J.-M.; Berckmans, D. Is precision livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe, and a farmer’s panacea or pitfall? Comput. Electron. Agric. 2008, 64, 2–10.
  125. Neethirajan, S.; Kemp, B. Digital Twins in Livestock Farming. Animals 2021, 11, 1008.
  126. Davies, F.T.; Garrett, B. Technology for Sustainable Urban Food Ecosystems in the Developing World: Strengthening the Nexus of Food–Water–Energy–Nutrition. Front. Sustain. Food Syst. 2018, 2, 420758.
  127. Yahya, N. Agricultural 4.0: It’s Implementation Toward Future Sustainability. In Green Urea: Green Energy and Technology; Springer: Singapore, 2018.
  128. De Rosa, M.; Di Pasquale, J.; Adinolfi, F. The Root towards More Circularized Animal Production Systems: From Animal to Territorial Metabolism. Animals 2021, 11, 1540.
  129. Sassenrath, G.; Heilman, P.; Luschei, E.; Bennett, G.; Fitzgerald, G.; Klesius, P.; Tracy, W.; Williford, J.; Zimba, P. Technology, complexity and change in agricultural production systems. Renew. Agric. Food Syst. 2008, 23, 285–295.
  130. Sendros, A.; Drosatos, G.; Efraimidis, P.S.; Tsirliganis, N.C. Blockchain Applications in Agriculture: A Scoping Review. Appl. Sci. 2022, 12, 8061.
  131. Shen, L.; Zhang, Z.; Zhou, Y.; Xu, Y. Applying Blockchain Technology and the Internet of Things to Improve the Data Reliability for Livestock Insurance. Sensors 2023, 23, 6290.
  132. Kampan, K.; Tsusaka, T.W.; Anal, A.K. Adoption of Blockchain Technology for Enhanced Traceability of Livestock-Based Products. Sustainability 2021, 14, 13148.
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Moammar Dayoub
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Saida Shnaigat
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Radi A. Tarawneh
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Azzam N. Al-Yacoub
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Faisal Al-Barakeh
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Khaled A. Al-Najjar
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