90. Precision livestock farming for reproductive performance optimization: a survey

Lopes, Sérgio Ivan; Bexiga, Ricardo; Araújo, J. P.; Cerqueira, J. O. L.; Abreu, Caíque Menezes de; Paredes, C.; Alonso, Joaquim

doi:10.3920/978-90-8686-834-6_90

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Optimization model Farm type (Lopes et al, 2016) Reproductive performance optimization: survey Cattle (Babić and Perić, 2011) Formulating optimal feed blend multi-criteria based goal programming model pigs (Moraes and Fadel, 2013) Diet optimization linear, dynamic and stochastic programming (Gilkeson et al, 2013) Designing optimal trailers for transporting livestock…”

Section: Optimization Objectivementioning

confidence: 99%

Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

Niloofar

Francis

Lazarova-Molnar

et al. 2021

Computers and Electronics in Agriculture

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Section: Optimization Objectivementioning

confidence: 99%

Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

Niloofar

Francis

Lazarova-Molnar

et al. 2021

Computers and Electronics in Agriculture

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“…According to previous work on PLF development, there are some generally acknowledged ways in which PLF could create added value for farmers and off-farm stakeholders along the pork production value chain (Halachmi et al, 2015;Kamphuis et al, 2015;Lopes et al, 2016). For instance, detailed or long-term data captured by PLF on many individual pigs could generate data on novel phenotypes for pig breeders.…”

Section: Introductionmentioning

confidence: 99%

Precision livestock farming: a qualitative exploration of swine industry stakeholders

Akinyemi,

Vigors,

Turner

et al. 2023

Front. Anim. Sci.

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IntroductionPrecision livestock farming (PLF) technology development has proliferated recently, but on-farm adoption has lagged. Understanding PLF stakeholders’ views, practical applications, limitations, and concerns are necessary to understand the factors influencing the adoption of PLF technology.MethodsUsing semi-structured online interviews, 12 influential stakeholders’ PLF views and lived experiences were investigated. A phenomenological paradigm was used to generate qualitative data that was analyzed using template analysis.ResultsWe identified two unique groups of stakeholders, namely the PLF enthusiast and PLF cautious groups. The majority of the participants were well aware and had firsthand experience with the PLF technologies that are currently being used in the swine industry.DiscussionWe found that PLF technology was perceived to improve specificity in decision-making, better care for pigs, improve animal health and welfare, increase labor efficiency, and improve resource-use efficiency. Poor internet connectivity and the inability to use PLF data for decision-making due to needing to first complete daily on-farm tasks were considered key obstacles to its implementation. To integrate PLF technology into the existing barn environment, it is necessary to modify farm buildings and infrastructure and management protocols. Stakeholders’ main concerns with PLF technology included data privacy issues and the influence of PLF technology on human-animal relationships and farmers’ duty of care to the animals. In conclusion, stakeholders perceived camera systems for monitoring pig health and welfare and ensuring individual pig identification as a high priority in PLF development going forward.

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Precision livestock farming technologies: Novel direction of information flow

Tekin¹,

Dikmen²,

Guatteo³

2021

Ankara Üniversitesi Veteriner Fakültesi Dergisi

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Precision livestock farming (PLF) is a digital management system that continuously measures the production, reproduction, health and welfare of animals and environmental impacts of the herd by using information and communication technologies (ICT) and controls all stages of the production process. In conventional livestock management, decisions are mostly based on the appraisal, judgment, and experience of the farmer, veterinarian, and workers. The increasing demand for production and the number of animals makes it difficult for humans to keep track of animals. It is clear that a person is not able to continuously watch the animals 24 hours a day to receive reliable audio-visual data for management. Recent technologies already changed the information flow from animal to human, which helps people to collect reliable information and transform it into an operational decision-making process (eg reproduction management or calving surveillance). Today, livestock farming must combine requirements for a transparent food supply chain, animal welfare, health, and ethics as a traceable-sustainable model by obtaining and processing reliable data using novel technologies. This review provides preliminary information on the advances in ICT for livestock management.

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90. Precision livestock farming for reproductive performance optimization: a survey

Cited by 3 publications

References 9 publications

Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

Precision livestock farming: a qualitative exploration of swine industry stakeholders

Precision livestock farming technologies: Novel direction of information flow

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