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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).

ABSTRACT.- Feed intake is a challenging trait to measure due to the high costs associated with labor, feeding, and facilities. Applying machine learning approaches, considering traits as potential predictors, offers a cost-effective alternative to direct feed intake measurement. By leveraging existi...

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Main Author: AMARILHO-SILVEIRA, F. (author)
Other Authors: DE BARBIERI, I. (author), NAVAJAS, E. (author), COBUCI, J. A. (author), CIAPPESONI, G. (author)
Format: article
Language:English
Published: 2025
Subjects:
K-nearest neighbor
Enteric methane
Carbon dioxide
Random forest
Support vector machines
SISTEMA GANADERO EXTENSIVO - INIA
Online Access:https://ainfo.inia.uy/consulta/busca?b=pc&id=65229&biblioteca=vazio&busca=65229&qFacets=65229
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author AMARILHO-SILVEIRA, F.
author2 DE BARBIERI, I.
NAVAJAS, E.
COBUCI, J. A.
CIAPPESONI, G.
author2_role author
author
author
author
author_browse AMARILHO-SILVEIRA, F.
CIAPPESONI, G.
COBUCI, J. A.
DE BARBIERI, I.
NAVAJAS, E.
author_facet AMARILHO-SILVEIRA, F.
DE BARBIERI, I.
NAVAJAS, E.
COBUCI, J. A.
CIAPPESONI, G.
author_role author
collection AINFO
dc.creator.none.fl_str_mv AMARILHO-SILVEIRA, F.
DE BARBIERI, I.
NAVAJAS, E.
COBUCI, J. A.
CIAPPESONI, G.
dc.date.none.fl_str_mv 2025-06-23T18:51:57Z
2025-06-23T18:51:57Z
2025
2025-06-23T18:51:57Z
dc.identifier.none.fl_str_mv https://ainfo.inia.uy/consulta/busca?b=pc&id=65229&biblioteca=vazio&busca=65229&qFacets=65229
dc.language.none.fl_str_mv en
eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Acceso abierto
dc.source.none.fl_str_mv reponame:AINFO
instname:Instituto Nacional de Investigación Agropecuaria
instacron:Instituto Nacional de Investigación Agropecuaria
dc.subject.none.fl_str_mv K-nearest neighbor
Enteric methane
Carbon dioxide
Random forest
Support vector machines
SISTEMA GANADERO EXTENSIVO - INIA
dc.title.none.fl_str_mv Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
dc.type.none.fl_str_mv Article
PublishedVersion
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
description ABSTRACT.- Feed intake is a challenging trait to measure due to the high costs associated with labor, feeding, and facilities. Applying machine learning approaches, considering traits as potential predictors, offers a cost-effective alternative to direct feed intake measurement. By leveraging existing animal data, these models can optimize resources and enable feed intake estimation across a larger population without the need for labor-intensive trials. This research aimed to test combinations offeature selection and prediction models to find the best feed intake (expressed as metabolizable energy intake) prediction approach for a dataset comprising AustralianMerino, Corriedale, and Dohne Merino data. The study dataset with 1,708 observations included 920 Australian Merino, 215 Corriedale, and 337 Dohne Merino sheep from 17 feed intake trials conducted between 2019 and 2022. The dataset was randomly partitioned into two subsets: one for training (80%) the algorithms and the other for direct validation (20%). © 2025 Amarilho-Silveira, De Barbieri, Navajas, Cobuci and Ciappesoni.
eu_rights_str_mv openAccess
format article
id anni_cc4e6843899a67dea8e07919a6d7d72f
instacron_str Instituto Nacional de Investigación Agropecuaria
institution Instituto Nacional de Investigación Agropecuaria
instname_str Instituto Nacional de Investigación Agropecuaria
language eng
language_invalid_str_mv en
network_acronym_str anni
network_name_str oai-lr-anni
oai_identifier_str oai:redi.anii.org.uy:20.500.12381/5123
publishDate 2025
publishDateSort 2025
reponame_str AINFO
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv Acceso abierto
spelling Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).AMARILHO-SILVEIRA, F.DE BARBIERI, I.NAVAJAS, E.COBUCI, J. A.CIAPPESONI, G.K-nearest neighborEnteric methaneCarbon dioxideRandom forestSupport vector machinesSISTEMA GANADERO EXTENSIVO - INIAABSTRACT.- Feed intake is a challenging trait to measure due to the high costs associated with labor, feeding, and facilities. Applying machine learning approaches, considering traits as potential predictors, offers a cost-effective alternative to direct feed intake measurement. By leveraging existing animal data, these models can optimize resources and enable feed intake estimation across a larger population without the need for labor-intensive trials. This research aimed to test combinations offeature selection and prediction models to find the best feed intake (expressed as metabolizable energy intake) prediction approach for a dataset comprising AustralianMerino, Corriedale, and Dohne Merino data. The study dataset with 1,708 observations included 920 Australian Merino, 215 Corriedale, and 337 Dohne Merino sheep from 17 feed intake trials conducted between 2019 and 2022. The dataset was randomly partitioned into two subsets: one for training (80%) the algorithms and the other for direct validation (20%). © 2025 Amarilho-Silveira, De Barbieri, Navajas, Cobuci and Ciappesoni.2025-06-23T18:51:57Z2025-06-23T18:51:57Z20252025-06-23T18:51:57ZArticlePublishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://ainfo.inia.uy/consulta/busca?b=pc&id=65229&biblioteca=vazio&busca=65229&qFacets=65229reponame:AINFOinstname:Instituto Nacional de Investigación Agropecuariainstacron:Instituto Nacional de Investigación Agropecuariaenenginfo:eu-repo/semantics/openAccessAcceso abiertooai:redi.anii.org.uy:20.500.12381/51232026-02-10T17:36:35Z
spellingShingle Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
AMARILHO-SILVEIRA, F.
K-nearest neighbor
Enteric methane
Carbon dioxide
Random forest
Support vector machines
SISTEMA GANADERO EXTENSIVO - INIA
status_str publishedVersion
title Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
title_full Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
title_fullStr Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
title_full_unstemmed Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
title_short Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
title_sort Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
topic K-nearest neighbor
Enteric methane
Carbon dioxide
Random forest
Support vector machines
SISTEMA GANADERO EXTENSIVO - INIA
url https://ainfo.inia.uy/consulta/busca?b=pc&id=65229&biblioteca=vazio&busca=65229&qFacets=65229

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