<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The aim of the study was to develop new equations for the estimation of meat percentage in the carcasses of young pigs, based on examination of the current pig population. In addition, new regression equations were constructed to predict the weight of meat in primal cuts. In both cases, the estimates were based on performance testing of live animals and post-slaughter evaluation of carcass traits in these animals. The study was conducted on 654 gilts of six breeds. Performance testing of live animals and carcass evaluation were performed at the Pig Testing Station (SKURTCh). Performance records in live animals included body weight (BW [kg]), age (A [days]), daily weight gain (DG [g/day]), and ultrasonic measurements, using a Piglog 105 ultrasound apparatus: backfat thickness at P2 and P4 and loin eye height P4M (at P4). Then the gilts were slaughtered and, after being chilled for 24 hours at 4°C, the right half-carcass was dissected into primal cuts. These cuts were then subjected to detailed dissection, which provided the basis for determination of the weight of meat in the primal cuts E2 [kg] and meat percentage in the carcass E1 [%]. An array of linear models based on multiple regression was constructed to estimate meat percentage in the carcass [%] and weight of meat in primal cuts [kg]. It was found that the developed linear regression equations for all breeds, which estimated meat content in the carcass based on three variables (classical model: P2, P4 and P4M), were characterised by the following values of standard error of the estimate RSE and correlation coefficient R: for E1[%], RSE=3.33; R=0.63; for E2 [kg], RSE=1.65; R=0.58. The addition to the classical model of a variable describing the breed greatly improved the parameters of the equation predictive of E1 (RSE=3.02; R=0.71). The model predictive of E2 was obtained by the addition of two variables: breed and carcass weight on the day of measurement (RSE=1.37; R=0.73). In general, the present studies indicate that equations estimating the weight of meat in a carcass (E2 [kg]) based on performance testing of live animals are characterised by a much lower value of standard error (RSE) than equations estimating meat percentage in the carcass of the same pigs (E1[%]), including those currently used in practice.</p></abstract>

The aim of the study was to develop new equations for the estimation of meat percentage in the carcasses of young pigs, based on examination of the current pig population. In addition, new regression equations were constructed to predict the weight of meat in primal cuts. In both cases, the estimates were based on performance testing of live animals and post-slaughter evaluation of carcass traits in these animals. The study was conducted on 654 gilts of six breeds. Performance testing of live animals and carcass evaluation were performed at the Pig Testing Station (SKURTCh). Performance records in live animals included body weight (BW [kg]), age (A [days]), daily weight gain (DG [g/day]), and ultrasonic measurements, using a Piglog 105 ultrasound apparatus: backfat thickness at P2 and P4 and loin eye height P4M (at P4). Then the gilts were slaughtered and, after being chilled for 24 hours at 4°C, the right half-carcass was dissected into primal cuts. These cuts were then subjected to detailed dissection, which provided the basis for determination of the weight of meat in the primal cuts E2 [kg] and meat percentage in the carcass E1 [%]. An array of linear models based on multiple regression was constructed to estimate meat percentage in the carcass [%] and weight of meat in primal cuts [kg]. It was found that the developed linear regression equations for all breeds, which estimated meat content in the carcass based on three variables (classical model: P2, P4 and P4M), were characterised by the following values of standard error of the estimate RSE and correlation coefficient R: for E1[%], RSE=3.33; R=0.63; for E2 [kg], RSE=1.65; R=0.58. The addition to the classical model of a variable describing the breed greatly improved the parameters of the equation predictive of E1 (RSE=3.02; R=0.71). The model predictive of E2 was obtained by the addition of two variables: breed and carcass weight on the day of measurement (RSE=1.37; R=0.73). In general, the present studies indicate that equations estimating the weight of meat in a carcass (E2 [kg]) based on performance testing of live animals are characterised by a much lower value of standard error (RSE) than equations estimating meat percentage in the carcass of the same pigs (E1[%]), including those currently used in practice.

18. Estimation of Meat Content in the Carcasses of Young Pigs Based on Performance Testing of Live Animals and Carcass Evaluation

Department of Genetics and Animal Breeding, National Research Institute of Animal Production, 32-083 Balice n. Kraków, Poland

Institute of Genetics and Animal Breeding, Polish Academy of Sciences, Postępu 36A, Jastrzębiec, 05-552 Magdalenka, Poland

Annals of Animal Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The aim of the study was to develop new equations for the estimation of meat percentage in the carcasses of young pigs, based on examination of the current pig...

18. Estimation of Meat Content in the Carcasses of Young Pigs Based on Performance Testing of Live Animals and Carcass Evaluation

Article Category: Behavior, Well-Being, Production Technology, and Environment

Data publikacji: 06 maj 2016

Zakres stron: 551 - 564

Otrzymano: 23 lip 2015

Przyjęty: 23 sie 2015

DOI: https://doi.org/10.1515/aoas-2015-0069

Słowa kluczowe
pigs, performance testing in live animals, carcass evaluation, meat percentage in carcasses, weight of meat in cuts, regression equations

© by Magdalena Szyndler-Nędza

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

18. Estimation of Meat Content in the Carcasses of Young Pigs Based on Performance Testing of Live Animals and Carcass Evaluation

Article Category: Behavior, Well-Being, Production Technology, and Environment

Data publikacji: 06 maj 2016

Zakres stron: 551 - 564

Otrzymano: 23 lip 2015

Przyjęty: 23 sie 2015

DOI: https://doi.org/10.1515/aoas-2015-0069

Słowa kluczowepigs, performance testing in live animals, carcass evaluation, meat percentage in carcasses, weight of meat in cuts, regression equations

© by Magdalena Szyndler-Nędza

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
pigs, performance testing in live animals, carcass evaluation, meat percentage in carcasses, weight of meat in cuts, regression equations