Publicação
Simultaneously prediction of sheep and goat carcass composition and body fat depots using in vivo ultrasound measurements and live weight
| Resumo: | The present study established multiple linear regression models using two ultrasound in vivo measurements (at lumbar and sternal regions, with different real-time ultrasonography machines and probes) and live weight, to predict simultaneously carcass composition and body fat depots of different breeds of sheep and goat. This study is important for the small ruminant industry, considering the feasibility of using the ultrasound methodology in field conditions, as well as an online system of the carcass evaluation. The multiple linear regression models were obtained by selecting the best subset of variables between using the in vivo measurements (raw variables), their second degree and interactions, evaluated in terms of prediction performance using cross-validation “K-folds” and validated by a test group. Overall, high accuracy (adj R2) was obtained from the linear relationship between predicted and experimental values of the group test for each of the nine dependent variables, with values varying between adj R2 0.88 and 0.98. |
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| Autores principais: | Dias, L.G. |
| Outros Autores: | Silva, Severiano; Teixeira, Alfredo |
| Assunto: | Body fat depots Carcass composition Goat breeds In vivo ultrasound measurements Predictive models Sheep breeds |
| Ano: | 2020 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | The present study established multiple linear regression models using two ultrasound in vivo measurements (at lumbar and sternal regions, with different real-time ultrasonography machines and probes) and live weight, to predict simultaneously carcass composition and body fat depots of different breeds of sheep and goat. This study is important for the small ruminant industry, considering the feasibility of using the ultrasound methodology in field conditions, as well as an online system of the carcass evaluation. The multiple linear regression models were obtained by selecting the best subset of variables between using the in vivo measurements (raw variables), their second degree and interactions, evaluated in terms of prediction performance using cross-validation “K-folds” and validated by a test group. Overall, high accuracy (adj R2) was obtained from the linear relationship between predicted and experimental values of the group test for each of the nine dependent variables, with values varying between adj R2 0.88 and 0.98. |
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