Prediction of carcass characteristics of Nellore cattle managed on tropical pastures through performance measures in the rearing phase
Priscilla Dutra Teixeira
1
,
Luís Carlos Vinhas Ítavo
1, 2
,
Antonio Leandro Chaves Gurgel
3
,
Camila Celeste Brandão Ferreira Ítavo
1
,
Marina De Nadai Bonin Gomes
1
,
Manoel Gustavo Paranhos Da Silva
1
,
Alfonso Juventino Chay-Canul
4
Publication type: Journal Article
Publication date: 2025-02-14
scimago Q2
wos Q2
SJR: 0.511
CiteScore: 3.6
Impact factor: 1.7
ISSN: 00494747, 15737438
Abstract
This work aimed to use the biometric measurements of steers in the rearing phase to predict the carcass characteristics of Nellore cattle managed in tropical pastures. Data from 60 young bulls in the rearing phase supplemented and managed on Brachiaria brizantha pastures during the rainy season and dry-rainy transition and slaughtered at 24 months old after 112 days in feedlot. Descriptive statistical analyses and Pearson's correlation coefficients were performed. The goodness of fit of the developed equations was evaluated by the coefficients of determination (R2) and square root mean error (RMSE). The average body weight (BW) in the rearing phase was 295 kg BW corresponding to 72.8 kg BW0.75. The average of the loin eye area (LEA), subcutaneous fat thickness (SFT), and rump fat thickness (RFT) measured by ultrasound were 43.5 cm2, 3.3 mm, and 3.6 mm, respectively. The correlation between BW and BW0.75, and LEA were positively significant. Total weight gain (TWG) and average daily gain (ADG) showed a correlation of 0.4216 and 0.4235 with SFT. To LEA prediction, the best fitting considered BW, TWG, and average daily gain (ADG) variables. Whereas SFT, considered BW, and ADG, and to RFT prediction, the best fitting considered only BW. The internal validation (k = 10) of the equations for predicting observed random error of 98.74% in LEA equation, 71.35% in SFT equation, and 98.59% in RFT equation. Body weight and weight gain during the rearing period can be used as predictor variables for LEA, SFT, and RFT of Nellore cattle kept in tropical pastures.
Found
Nothing found, try to update filter.
Are you a researcher?
Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
0
Total citations:
0
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
Teixeira P. D. et al. Prediction of carcass characteristics of Nellore cattle managed on tropical pastures through performance measures in the rearing phase // Tropical Animal Health and Production. 2025. Vol. 57. No. 2. 62
GOST all authors (up to 50)
Copy
Teixeira P. D., Ítavo L. C. V., Gurgel A. L. C., Ítavo C. C. B. F., De Nadai Bonin Gomes M., Da Silva M. G. P., Chay-Canul A. J. Prediction of carcass characteristics of Nellore cattle managed on tropical pastures through performance measures in the rearing phase // Tropical Animal Health and Production. 2025. Vol. 57. No. 2. 62
Cite this
RIS
Copy
TY - JOUR
DO - 10.1007/s11250-025-04312-y
UR - https://link.springer.com/10.1007/s11250-025-04312-y
TI - Prediction of carcass characteristics of Nellore cattle managed on tropical pastures through performance measures in the rearing phase
T2 - Tropical Animal Health and Production
AU - Teixeira, Priscilla Dutra
AU - Ítavo, Luís Carlos Vinhas
AU - Gurgel, Antonio Leandro Chaves
AU - Ítavo, Camila Celeste Brandão Ferreira
AU - De Nadai Bonin Gomes, Marina
AU - Da Silva, Manoel Gustavo Paranhos
AU - Chay-Canul, Alfonso Juventino
PY - 2025
DA - 2025/02/14
PB - Springer Nature
IS - 2
VL - 57
SN - 0049-4747
SN - 1573-7438
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2025_Teixeira,
author = {Priscilla Dutra Teixeira and Luís Carlos Vinhas Ítavo and Antonio Leandro Chaves Gurgel and Camila Celeste Brandão Ferreira Ítavo and Marina De Nadai Bonin Gomes and Manoel Gustavo Paranhos Da Silva and Alfonso Juventino Chay-Canul},
title = {Prediction of carcass characteristics of Nellore cattle managed on tropical pastures through performance measures in the rearing phase},
journal = {Tropical Animal Health and Production},
year = {2025},
volume = {57},
publisher = {Springer Nature},
month = {feb},
url = {https://link.springer.com/10.1007/s11250-025-04312-y},
number = {2},
pages = {62},
doi = {10.1007/s11250-025-04312-y}
}