Use of biometrics in the prediction of body weight in crossbred lambs

Gurgel, A. L. C.; Difante, Gelson dos Santos; Neto, João Virgínio Emerenciano; Santana, Juliana Caroline Santos; Dantas, Joederson Luiz Santos; Roberto, Francisca Fernanda da Silva; Campos, Nathália Rafaela Fidelis; Costa, Ana Beatriz Graciano da

doi:10.1590/1678-4162-12087

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…Because the WG, TC, and HG variables showed high correction with weight, these variables were included in the equation that showed greater predictive capacity (Equation (3)). Studies with ruminants of different species, breeds and sexes and wide variations in weight and age also showed that heart girth is an efficient measurement to estimate live weight [ 7 , 22 , 23 ].…”

Section: Resultsmentioning

confidence: 99%

Prediction of Carcass Traits of Santa Inês Lambs Finished in Tropical Pastures through Biometric Measurements

Gurgel

Difante

Neto

et al. 2021

Animals

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The aim of this study was to predict carcass traits of Santa Inês lambs finished in tropical pastures by using biometric measurements. Data originated from two experiments involving 56 lambs (32 in experiment I and 24 in experiment II). In both experiments, the sheep were finished in that were finished in pastures of Panicum maximum and Brachiaria brizantha, experiment I being conducted in the rainy season and experiment II in the dry season. The following biometric measurements were recorded before slaughter: body length (BL), withers height (WH), rump height (RH), thorax width (TW), rump width (RW), chest width (CW), heart girth (HG), thigh circumference (TC), rump circumference (RC) and leg length (LL), in addition to live weight at slaughter (SW). After slaughter, hot carcass weight (HCW), cold carcass weight (CCW) and the weights of primal cuts (shoulder, neck, loin, leg and rib) were recorded. In the equations generated to predict SW, HCW and CCW, R2 ranged from 0.58 to 0.91 and the measurements of WH, TC, CW, HG and RW were the most relevant. In the equations developed to predict the weight of primal cuts, in turn, R2 ranged from 0.26 to 0.99. In these models, SW, BL, CW, TC, LL and HG explained most of the variation in the weight of primal cuts. Biometric measurements can be used to accurately and precisely predict HCW, CCW and the weight of primal cuts from the carcass of Santa Inês sheep finished in tropical pastures, since the equations presented R2 and correlation coefficient and agreement above 0.8.

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Section: Resultsmentioning

confidence: 99%

Prediction of Carcass Traits of Santa Inês Lambs Finished in Tropical Pastures through Biometric Measurements

Gurgel

Difante

Neto

et al. 2021

Animals

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“…The estimation of body weight by indirect methods can be an easily adopted, low-cost alternative. In this sense, biometric measurements are a viable option to predict body weight due to the correlation between these traits and the body weight of animals (65,69) . This method consists of developing mathematical models that allow producers to estimate bodyweight using some biometric measurements (Figure 3) from linear and multiple regression analyses.…”

Section: Prediction Of Body Weight and Carcass Traits Of Sheep Throug...mentioning

confidence: 99%

“…In contrast, Canul-Solís et al (66) used RW to estimate the body weight of Pelibuey sheep. However, when more than one measurement is used, the predictive capacity of the equations increases (68,69,73,74) . Chay-Canul et al (65) Pelibuey BW (kg) = -47.97 + 1.07 × HG 0.86…”

Section: Prediction Of Body Weight and Carcass Traits Of Sheep Throug...mentioning

confidence: 99%

Aspectos relacionados con la importancia del uso de modelos predictivos en la producción ovina. Revisión

Gurgel

Difante

Ítavo

et al. 2022

Rev. Mex. Cienc. Pecu.

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Sheep production systems face numerous challenges, which make decision-making a process fraught with risks and uncertainties. Modelling is a helpful tool in this respect, as it allows decision-makers to evaluate the behaviour of variables and their interrelationships, in addition to using previous or related information to predict results and simulate different scenarios. The advent of prediction models has made it possible to monitor the weight of an animal and determine the best time for its sale. Additionally, it allows producers to estimate the weights of the carcass and major marketable cuts before slaughter. All this information is directly associated with the profitability and success of the production activity. Therefore, in view of the different applications of mathematical models in production systems, this literature review examines concepts in modelling studies and the importance of using prediction models in meat sheep production. Furthermore, it addresses the practical application of modelling studies in predicting dry matter intake and carcass traits of meat sheep through correlated variables.

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“…However, Santos et al [ 2 ], noted that such monitoring is infrequent, as many producers lack the necessary technologies or infrastructure, leading to significant challenges. Thus, simple methods like biometric measurements can be adopted to facilitate weight monitoring and address performance issues, proving to be a valuable tool in predicting animal weight [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Weight Development and Growth Curves of Grazing Santa Inês Sheep Supplemented with Concentrate in the Pre-Weaning Phase

da Silva,

Ribeiro,

Silva

et al. 2024

Animals

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Monitoring weight development is essential for decision-making and assessing the effectiveness of management strategies. However, this practice is often hindered by the lack of scales on farms. This study aimed to characterize the weight development and growth curves of male and female Santa Inês lambs from birth to weaning, managed on pasture with creep-fed concentrate supplementation. Data from 212 lambs during the pre-weaning phase were analyzed. The animals were weighed every seven days to evaluate total weight gain and average daily gain. Biometric measurements were taken every 28 days. Mixed models were used to assess the effects of sex and birth type on birth and weaning weights. Simple and multiple linear regression models were employed to estimate live weight using biometric measurements. The non-linear Gompertz model was utilized to describe weight development and formulate growth curves. Results were considered significant at p < 0.05. An interaction effect between birth type and sex (p < 0.05) was noted for birth weight, with the lowest weight observed in twin-birth females (2.96 kg) and the highest in single-birth males (3.73 kg) and females (3.65 kg) (p > 0.05). Birth type significantly influenced average daily gain, total weight gain, and weaning weight (p < 0.05). The Gompertz model accurately depicted the growth curves, effectively describing the weight development. Pearson’s correlation coefficients between biometric measurements and weight were positive and significant (p < 0.05), ranging from 0.599 for hip height to 0.847 for heart girth. Consequently, the simple and multiple regression equations demonstrated high precision in predicting weaning weight. In conclusion, twin-birth lambs receiving concentrate supplementation via creep-feeding and managed on pasture showed different developmental patterns compared to single-birth lambs under the same conditions. The Gompertz model proved effective for monitoring development during the pre-weaning phase. All simple and multiple linear regression models were effective in predicting weaning weight through biometric measurements. However, for practical application, the model incorporating two measurements—body length and abdominal circumference—is recommended.

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Use of biometrics in the prediction of body weight in crossbred lambs

Cited by 12 publications

References 7 publications

Prediction of Carcass Traits of Santa Inês Lambs Finished in Tropical Pastures through Biometric Measurements

Prediction of Carcass Traits of Santa Inês Lambs Finished in Tropical Pastures through Biometric Measurements

Aspectos relacionados con la importancia del uso de modelos predictivos en la producción ovina. Revisión

Weight Development and Growth Curves of Grazing Santa Inês Sheep Supplemented with Concentrate in the Pre-Weaning Phase

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