Using biometric analysis to estimate body weight in Creole goats.
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| Názov: | Using biometric analysis to estimate body weight in Creole goats. |
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| Autori: | Trillo-Zárate, Fritz1,2 ftrillo@lamolina.edu.pe, Paredes-Chocce, Miguel Enrique1, Salinas, Jorge1, Temoche-Socola, Víctor Alexander1, Gutiérrez, Lucinda Tafur1, Sessarego, Emmanuel Alexander2, Acosta, Irene1, Palomino-Guerrera, Walter2, Cruz-Luis, Juancarlos Alejandro1, Ruiz-Chamorro, Jose Antonio1 |
| Zdroj: | Open Veterinary Journal. 2025, Vol. 15 Issue 9, p4496-4504. 9p. |
| Druh dokumentu: | Article |
| Predmet: | Body weight, Prediction models, Biometry, Livestock farms, Data mining, Goats, Support vector machines, Random forest algorithms |
| Geografický termín: | Peru (Viceroyalty) |
| Author-Supplied Keywords: | Algorithms Creole Machine learning Morphometrics goats Predictive models |
| Abstrakt: | Background: Creole goat husbandry for milk and meat improves food security in rural areas in Perú. Body weight (BW) is a key trait for selecting breeding stock, and it is estimated to be using algorithms. Likewise, BW is common in livestock farming. Aim: This study aimed to compare BW prediction models using a data mining algorithm in Creole goats, considering their biometric measurements. Methods: Data from 1,075 females aged between 1 and 4 years were used. Measurements of chest width, thoracic perimeter, wither height, sacrum height, rump width and length, body length, cannon bone perimeter, age, and region of the herd were recorded. The regression trees (classification and regression tree), support vector regression (SVR), and random forest regression (RFR) algorithms were used. Results: The SVR was better at predicting BWs in Creole goat herds. Similarly, the results were stable during training (R² = 0.765) and testing (R² = 0.707). However, it should be noted that RFR performed better with training data (R² = 0.942). Conclusion: The proposed predictive models have demonstrated significant potential for accurately predicting BW based on biometric data. Finally, it contributes to better selection, feeding, and sanitary management of Creole goats. [ABSTRACT FROM AUTHOR] |
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| Author Affiliations: | 1Dirección de Servicios Estratégicos Agrarios, Instituto Nacional de Innovación Agraria (INIA), Lima, Perú. 2Departamento Académico de Producción Animal, Facultad de Zootecnia, Universidad Nacional Agraria La Molina, (UNALM), Lima, Perú. |
| ISSN: | 2226-4485 |
| DOI: | 10.5455/OVJ.2025.v15.i9.55 |
| Prístupové číslo: | 190365168 |
| Databáza: | Veterinary Source |
| Abstrakt: | Background: Creole goat husbandry for milk and meat improves food security in rural areas in Perú. Body weight (BW) is a key trait for selecting breeding stock, and it is estimated to be using algorithms. Likewise, BW is common in livestock farming. Aim: This study aimed to compare BW prediction models using a data mining algorithm in Creole goats, considering their biometric measurements. Methods: Data from 1,075 females aged between 1 and 4 years were used. Measurements of chest width, thoracic perimeter, wither height, sacrum height, rump width and length, body length, cannon bone perimeter, age, and region of the herd were recorded. The regression trees (classification and regression tree), support vector regression (SVR), and random forest regression (RFR) algorithms were used. Results: The SVR was better at predicting BWs in Creole goat herds. Similarly, the results were stable during training (R² = 0.765) and testing (R² = 0.707). However, it should be noted that RFR performed better with training data (R² = 0.942). Conclusion: The proposed predictive models have demonstrated significant potential for accurately predicting BW based on biometric data. Finally, it contributes to better selection, feeding, and sanitary management of Creole goats. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 22264485 |
| DOI: | 10.5455/OVJ.2025.v15.i9.55 |