dc.contributor.author |
Klingström, Tomas
|
|
dc.contributor.author |
König, Emelie Zonabend
|
|
dc.contributor.author |
Zwane, Avhashoni Agnes
|
|
dc.date.accessioned |
2024-11-29T05:09:12Z |
|
dc.date.available |
2024-11-29T05:09:12Z |
|
dc.date.issued |
2024 |
|
dc.description.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 largescale 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. |
en_US |
dc.description.department |
Biochemistry, Genetics and Microbiology (BGM) |
en_US |
dc.description.sdg |
SDG-02:Zero Hunger |
en_US |
dc.description.sdg |
SDG-09: Industry, innovation and infrastructure |
en_US |
dc.description.sponsorship |
The Livestock Genetics Flagship
of the Livestock CGIAR Research Program. Work was performed
as part of a South Africa (NRF)/Sweden (STINT) science and
technology research collaboration (SA2018-7728) with additional
funding from Kunskapsnavet för jordbrukets digitalisering which
is partially funded by the European Agricultural Fund for Rural
Development. |
en_US |
dc.description.uri |
https://academic.oup.com/bfg |
en_US |
dc.identifier.citation |
Tomas Klingström, Emelie Zonabend König, Avhashoni Agnes Zwane, Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping, Briefings in Functional Genomics, 2024;, elae032, https://doi.org/10.1093/bfgp/elae032. |
en_US |
dc.identifier.issn |
2041-2649 (print) |
|
dc.identifier.issn |
2041-2657 (online) |
|
dc.identifier.other |
10.1093/bfgp/elae032 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/99681 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
Oxford University Press |
en_US |
dc.rights |
© The Author(s) 2024. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). |
en_US |
dc.subject |
Phenotyping |
en_US |
dc.subject |
Livestock |
en_US |
dc.subject |
High-throughput |
en_US |
dc.subject |
Artificial intelligence |
en_US |
dc.subject |
Genetics |
en_US |
dc.subject |
SDG-02: Zero hunger |
en_US |
dc.subject |
SDG-09: Industry, innovation and infrastructure |
en_US |
dc.subject |
Internet of Things (IoT) |
en_US |
dc.title |
Beyond the hype : using AI, big data, wearable devices, and the Internet of Things for high-throughput livestock phenotyping |
en_US |
dc.type |
Article |
en_US |