Evidence map›Paper›PMID 39158344›Full record

ReviewBriefings in functional genomics2025

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.

Tomas Klingström, Emelie Zonabend König, Avhashoni Agnes Zwane

Abstract readReview
In one paragraph

Review in Briefings in functional genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Artificial intelligence in animal breeding and genetics: applications, opportunities, and challenges.Animal frontiers : the review magazine of animal agriculture · 2026
    Article
  5. Nutrient Utilization, Requirements and Nutrigenomics in Sheep and Goats.Animals : an open access journal from MDPI · 2026
    Review
  6. Review
  7. Review
  8. Big Data and AI-Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  9. Application of Genomic Selection in Beef Cattle Disease Prevention.Animals : an open access journal from MDPI · 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

3 authors.

Tomas KlingströmDepartment of Animal Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.ORCID 0000-0002-9504-1352
Emelie Zonabend KönigSLU Global, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Avhashoni Agnes ZwaneDepartment of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa.

Funding

Livestock Genetics Flagship of the Livestock CGIAR Research Program
6 · The paper itself

Abstract

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

Indexed as

Artificial IntelligenceBig DataInternet of ThingsLivestockWearable Electronic DevicesAnimalsGenomicsPhenotypeartificial intelligencegeneticshigh-throughputinternet of thingslivestockphenotyping

Identifiers

PMID39158344
PMCPMC11735752

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.