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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Authors: AMARILHO-SILVEIRA, F., DE BARBIERI, I., NAVAJAS, E., COBUCI, J. A., CIAPPESONI, G.
Format: article
Status:Published version
Publication Date:2025
Country:Uruguay
Institution:Instituto Nacional de Investigación Agropecuaria
Repository:AINFO
Language:English
OAI Identifier:oai:redi.anii.org.uy:20.500.12381/5123
Online Access:https://ainfo.inia.uy/consulta/busca?b=pc&id=65229&biblioteca=vazio&busca=65229&qFacets=65229
Access Level:Open access
Keyword:K-nearest neighbor
Enteric methane
Carbon dioxide
Random forest
Support vector machines
SISTEMA GANADERO EXTENSIVO - INIA
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spelling 2025-06-23T18:51:57Z2025-06-23T18:51:57Z20252025-06-23T18:51:57Zhttps://ainfo.inia.uy/consulta/busca?b=pc&id=65229&biblioteca=vazio&busca=65229&qFacets=65229ABSTRACT.- 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.https://hdl.handle.net/20.500.12381/5123enenginfo:eu-repo/semantics/openAccessAcceso abiertoK-nearest neighborEnteric methaneCarbon dioxideRandom forestSupport vector machinesSISTEMA GANADERO EXTENSIVO - INIAMachine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).ArticlePublishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:AINFOinstname:Instituto Nacional de Investigación Agropecuariainstacron:Instituto Nacional de Investigación AgropecuariaAMARILHO-SILVEIRA, F.DE BARBIERI, I.NAVAJAS, E.COBUCI, J. A.CIAPPESONI, G.SWORDsword-2025-06-23T15:51:57.original.xmlOriginal SWORD entry documentapplication/octet-stream2405https://redi.anii.org.uy/jspui/bitstream/20.500.12381/5123/1/sword-2025-06-23T15%3a51%3a57.original.xml1ed8f5ef8d111c2ea6905c66456dc01bMD5120.500.12381/51232026-02-10 15:54:04.72oai:redi.anii.org.uy:20.500.12381/5123Institucionalhttps://ainfo.inia.uy/Organismo científico-tecnológicohttp://inia.uyhttps://redi.anii.org.uy/oai/requestlorrego@inia.org.uyUruguayopendoar:2026-02-10T18:54:04AINFO - Instituto Nacional de Investigación Agropecuariafalse
dc.title.none.fl_str_mv Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
title Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
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
title_short 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_sort Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep. (Original research article).
dc.creator.none.fl_str_mv AMARILHO-SILVEIRA, F.
DE BARBIERI, I.
NAVAJAS, E.
COBUCI, J. A.
CIAPPESONI, G.
author AMARILHO-SILVEIRA, F.
author_facet AMARILHO-SILVEIRA, F.
DE BARBIERI, I.
NAVAJAS, E.
COBUCI, J. A.
CIAPPESONI, G.
author_role author
author2 DE BARBIERI, I.
NAVAJAS, E.
COBUCI, J. A.
CIAPPESONI, G.
author2_role author
author
author
author
dc.subject.none.fl_str_mv K-nearest neighbor
Enteric methane
Carbon dioxide
Random forest
Support vector machines
SISTEMA GANADERO EXTENSIVO - INIA
topic K-nearest neighbor
Enteric methane
Carbon dioxide
Random forest
Support vector machines
SISTEMA GANADERO EXTENSIVO - INIA
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.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-06-23T18:51:57Z
dc.date.available.none.fl_str_mv 2025-06-23T18:51:57Z
dc.date.issued.none.fl_str_mv 2025
dc.date.updated.none.fl_str_mv 2025-06-23T18:51:57Z
dc.type.none.fl_str_mv Article
PublishedVersion
info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
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eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.es.fl_str_mv Acceso abierto
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:AINFO
instname:Instituto Nacional de Investigación Agropecuaria
instacron:Instituto Nacional de Investigación Agropecuaria
instname_str Instituto Nacional de Investigación Agropecuaria
instacron_str Instituto Nacional de Investigación Agropecuaria
institution Instituto Nacional de Investigación Agropecuaria
reponame_str AINFO
collection AINFO
bitstream.url.fl_str_mv https://redi.anii.org.uy/jspui/bitstream/20.500.12381/5123/1/sword-2025-06-23T15%3a51%3a57.original.xml
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