Predicting dry matter intake in Pelibuey sheep using machine learning methods
This study determined to predict the dry matter intake (DMI) in growing male Pelibuey sheep by using 3 different machine learning methods. Individual data was obtained from 130 animals whose average body weight (ABW) was 23 ± 6 kg and the DMI was 1.04 ± 0.27 kg/d from an experiment conducted under t...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025002932 |
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| author | Enrique Camacho-Perez Cem Tirink Ricardo Garcia-Herrera Ángel T. Piñeiro-Vazquez Fernando Casanova-Lugo Jorge R. Canul-Solis Antonio Leandro Chaves-Gurgel Ceyhun Yücel Einar Vargas-Bello-Pérez Alfonso J. Chay-Canul |
| author_facet | Enrique Camacho-Perez Cem Tirink Ricardo Garcia-Herrera Ángel T. Piñeiro-Vazquez Fernando Casanova-Lugo Jorge R. Canul-Solis Antonio Leandro Chaves-Gurgel Ceyhun Yücel Einar Vargas-Bello-Pérez Alfonso J. Chay-Canul |
| author_sort | Enrique Camacho-Perez |
| collection | DOAJ |
| description | This study determined to predict the dry matter intake (DMI) in growing male Pelibuey sheep by using 3 different machine learning methods. Individual data was obtained from 130 animals whose average body weight (ABW) was 23 ± 6 kg and the DMI was 1.04 ± 0.27 kg/d from an experiment conducted under tropical conditions. To create the database, the following data were recorded: % concentrate in the diet (CON), initial body weight (IBW, kg), final BW (FBW, kg), mean metabolic BW (MBW0.75, kg0.75), daily weight gain (ADG, g/d), crude protein (CP) and neutral detergent fibre (NDF). Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), and Support Vector Regression (SVR) were used for the development of a predictive algorithm. The determination coefficient was determined over 0.90 for the MARS algorithm. Overall, the MARS algorithm was a reliable predictive model for DMI prediction in the Pelibuey sheep. |
| format | Article |
| id | doaj-art-091aadcdabd34681af34183eb674702f |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-091aadcdabd34681af34183eb674702f2025-02-02T05:28:33ZengElsevierHeliyon2405-84402025-01-01112e41913Predicting dry matter intake in Pelibuey sheep using machine learning methodsEnrique Camacho-Perez0Cem Tirink1Ricardo Garcia-Herrera2Ángel T. Piñeiro-Vazquez3Fernando Casanova-Lugo4Jorge R. Canul-Solis5Antonio Leandro Chaves-Gurgel6Ceyhun Yücel7Einar Vargas-Bello-Pérez8Alfonso J. Chay-Canul9Facultad de Ingeniería. Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes s/n, Mérida, Yucatán, MexicoIgdir University, Faculty of Agriculture, Department of Animal Science, TR76000, Igdir, TurkiyeDivisión Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carretera Villahermosa-Teapa, km 25, R/A. La Huasteca 2a Sección, Villahermosa, Tabasco, MexicoTecnológico Nacional de México, MexicoTecnológico Nacional de México, MexicoTecnológico Nacional de México, MexicoFederal University of Piauí, 64900-000, Bom Jesus, Piauí, BrazilDepartment of Animal Science, Faculty of Agriculture, University of Yozgat Bozok, 66900, Yozgat, TurkiyeDepartment of Animal Sciences, School of Agriculture, Policy and Development, University of Reading, P.O. Box 237, Earley Gate, Reading, RG6 6EU, UK; Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Periférico R. Aldama Km 1, 31031, Chihuahua, Mexico; Corresponding author. Department of Animal Sciences, School of Agriculture, Policy and Development, University of Reading, P.O. Box 237, Earley Gate, Reading RG6 6EU, UK.División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carretera Villahermosa-Teapa, km 25, R/A. La Huasteca 2a Sección, Villahermosa, Tabasco, Mexico; Corresponding author.This study determined to predict the dry matter intake (DMI) in growing male Pelibuey sheep by using 3 different machine learning methods. Individual data was obtained from 130 animals whose average body weight (ABW) was 23 ± 6 kg and the DMI was 1.04 ± 0.27 kg/d from an experiment conducted under tropical conditions. To create the database, the following data were recorded: % concentrate in the diet (CON), initial body weight (IBW, kg), final BW (FBW, kg), mean metabolic BW (MBW0.75, kg0.75), daily weight gain (ADG, g/d), crude protein (CP) and neutral detergent fibre (NDF). Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), and Support Vector Regression (SVR) were used for the development of a predictive algorithm. The determination coefficient was determined over 0.90 for the MARS algorithm. Overall, the MARS algorithm was a reliable predictive model for DMI prediction in the Pelibuey sheep.http://www.sciencedirect.com/science/article/pii/S2405844025002932Dry matter intakeMachine learningHair sheep |
| spellingShingle | Enrique Camacho-Perez Cem Tirink Ricardo Garcia-Herrera Ángel T. Piñeiro-Vazquez Fernando Casanova-Lugo Jorge R. Canul-Solis Antonio Leandro Chaves-Gurgel Ceyhun Yücel Einar Vargas-Bello-Pérez Alfonso J. Chay-Canul Predicting dry matter intake in Pelibuey sheep using machine learning methods Heliyon Dry matter intake Machine learning Hair sheep |
| title | Predicting dry matter intake in Pelibuey sheep using machine learning methods |
| title_full | Predicting dry matter intake in Pelibuey sheep using machine learning methods |
| title_fullStr | Predicting dry matter intake in Pelibuey sheep using machine learning methods |
| title_full_unstemmed | Predicting dry matter intake in Pelibuey sheep using machine learning methods |
| title_short | Predicting dry matter intake in Pelibuey sheep using machine learning methods |
| title_sort | predicting dry matter intake in pelibuey sheep using machine learning methods |
| topic | Dry matter intake Machine learning Hair sheep |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025002932 |
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