Evidence map›Paper›PMID 37443894›Full record

ReviewAnimals : an open access journal from MDPI2023

Precision Livestock Farming Research: A Global Scientometric Review.

Bing Jiang, Wenjie Tang, Lihang Cui, Xiaoshang Deng

Open access · goldAbstract readReview
In one paragraph

Review in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
19.2field-weighted citation impact, top 1% of its field
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

24 citing papers in PubMed, 63 citations in OpenAlex.

  1. Review
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  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. A Supervised Deep Learning Model Was Developed to Classify Nelore Cattle (Animals : an open access journal from MDPI · 2026
    Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
  20. Research trends in livestock facial identification: a review.Journal of animal science and technology · 2025
    Review
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

4 authors at 1 institution in 1 country.

Bing JiangCollege of Economics and Management, Northeast Agricultural University, Harbin 150030, China.ORCID 0009-0000-1893-5902
Wenjie TangCollege of Economics and Management, Northeast Agricultural University, Harbin 150030, China.ORCID 0009-0002-6039-9722
Lihang CuiCollege of Economics and Management, Northeast Agricultural University, Harbin 150030, China.
Xiaoshang DengCollege of Economics and Management, Northeast Agricultural University, Harbin 150030, China.ORCID 0000-0003-3462-6127
Northeast Agricultural University · CN

Funding

Emergency Project Fund of National Natural Science Foundation of China 71640017General Program of National Natural Science Foundation of China 71773134General Program of National Natural Science Foundation of China 72072125National Natural Science Foundation of China for Young Scholars 72203034Natural Science Foundation of Heilongjiang Province LH2021G002
6 · The paper itself

Abstract

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. PLF has emerged as a pivotal area of multidisciplinary interest. In order to clarify the knowledge evolution and hotspot replacement of PLF research, based on the relevant data from the Web of Science database from 1973 to 2023, this study analyzed the main characteristics, research cores and hot topics of PLF research via CiteSpace. The results point to a significant increase in studies on PLF, with countries having advanced livestock farming systems in Europe and America publishing frequently and collaborating closely across borders. Universities in various countries have been leading the research, with Daniel Berckmans serving as the academic leader. Research primarily focuses on animal science, veterinary science, computer science, agricultural engineering, and environmental science. Current research hotspots center around precision dairy and cattle technology, intelligent systems, and animal behavior, with deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification being the main research directions, and deep learning and machine learning represent the forefront of current research. Research hot topics mainly include social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. Future research in PLF should prioritize inter-institutional and inter-scholar communication and cooperation, integration of multidisciplinary and multimethod research approaches, and utilization of deep learning and machine learning. Furthermore, social science issues should be given due attention in PLF, and the integration of intelligent technologies in animal management should be strengthened, with a focus on animal welfare and the environmental impact of animal husbandry, to promote its sustainable development.

Indexed as

animal welfarebibliometricsCiteSpaceprecision livestock farming

Identifiers

PMID37443894
PMCPMC10340063
OpenAlexW4382064735

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.