Evidence map›Paper›PMID 37335911›Full record

ArticleJournal of animal science2023

Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming.

Upinder Kaur, Victor M R Malacco, Huiwen Bai, Tanner P Price, Arunashish Datta, Lei Xin, Shreyas Sen, Robert A Nawrocki, George Chiu, Shreyas Sundaram and 6 more

Abstract read
In one paragraph

Article in Journal of animal science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Navigating AI deployment in precision livestock farming: current trends and future prospects.Animal frontiers : the review magazine of animal agriculture · 2026
    Article
  6. Artificial intelligence for animal science: from applications to integrated knowledge systems.Animal frontiers : the review magazine of animal agriculture · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Internet of Things (IoT): Sensors Application in Dairy Cattle Farming.Animals : an open access journal from MDPI · 2024
    Review
  17. Review
  18. A Framework for Transparency in Precision Livestock Farming.Animals : an open access journal from MDPI · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Upinder KaurSchool of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA.ORCID 0000-0002-5576-1777
Victor M R MalaccoDepartment of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA.
Huiwen BaiSchool of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA.
Tanner P PriceDepartment of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
Arunashish DattaSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Lei XinSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Shreyas SenSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Robert A NawrockiSchool of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA.
George ChiuSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Shreyas SundaramSchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
Byung-Cheol MinDepartment of Computer and Information Technology, West Lafayette, IN, 47907, USA.
Kristy M DanielsDepartment of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
Robin R WhiteDepartment of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, 24061, USA.
Shawn S DonkinDepartment of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA.
Luiz F BritoDepartment of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA.ORCID 0000-0002-5819-0922
Richard M VoylesSchool of Engineering Technology, Purdue University, West Lafayette, IN, 47907, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

Indexed as

Animal HusbandryArtificial IntelligenceAgricultureAnimalsFarmsLivestockTechnologyartificial intelligenceInternet of Thingsnetworkingprecision livestock farmingsensors

Identifiers

PMID37335911
PMCPMC10370899

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.