The Journal of Agricultural Using carcass information as a predictor

Science
variable of empty body weight, empty body
cambridge.org/ags weight gain and retained energy of hair sheep
Antonio de Sousa Brito Neto1, Caio Julio Lima Herbster1,
Animal Research Paper Marcos Inacio Marcondes2 , Juana Catarina Cariri Chagas3,
Ronaldo Lopes Oliveira4, Leilson Rocha Bezerra5 , Luciano Pinheiro da Silva1
Cite this article: Brito Neto AdeS, Herbster
CJL, Marcondes MI, Chagas JCC, Oliveira RL, and Elzania Sales Pereira1
Bezerra LR, da Silva LP, Pereira ES (2024).
Using carcass information as a predictor 1
Department of Animal Science, Federal University of Ceara, Fortaleza, Ceara, Brazil; 2Department of Animal
variable of empty body weight, empty body
Science, Washington State University, Pullman, WA, USA; 3Departament of Applied Animal Science and Welfare,
weight gain and retained energy of hair sheep.
The Journal of Agricultural Science 1–10. Swedish University of Agricultural Sciences, Umeå, Sweden; 4School of Veterinary Medicine and Animal Science,
https://doi.org/10.1017/S0021859624000455 Federal University of Bahia, Salvador, Bahia, Brazil and 5Center of Health and Agricultural Technology, Federal
University of Campina Grande, Patos, Paraiba, Brazil
Received: 6 February 2024
Revised: 2 July 2024
Accepted: 8 July 2024
Abstract
The objective was to develop equations to predict carcass weight (CW), use CW to predict
Keywords: empty body weight (EBW); and carcass gain (CG) to predict empty body weight gain
Carcass gain; energy; meta-analysis; prediction
model; sheep
(EBWG) and retained energy (RE) in hair sheep. To generate the prediction models, a data
set was composed of individual measurements from 569 sheep encompassing intact males
Corresponding author: (n = 416), castrated males (n = 51), and females (n = 102). Validation analyses were performed
Elzania Sales Pereira; by using the Model Evaluation System (MES). The prediction equations for CW, EBW, and
Email: elzania@hotmail.com
EBWG were not influenced by sex class (P > 0.05), and the following equations were gener-
ated, respectively: CW (kg) = − 0.234 (±1.1358) + 0.485 (±0.0387) × FBW; EBW (kg) = 1.367
(±0.5472) + 1.681 (±0.0210) × CW and EBWG (kg) = 0.004 (±0.0026) + 1.679 (±0.0758) ×
CG. There was an effect of sex class on the intercept (P = 0.0013) of the relationship between
RE and CG: RE (MJ/day) = 1.448 (±0.0657) × EBW0.75 × CG0.797 (±0.0399); RE (MJ/day) = 1.522
(±0.0699) × EBW0.75 × CG0.797 (±0.0399) and RE (MJ/day) = 1.827 (±0.0739) × EBW0.75 ×
CG0.797 (±0.0399) for intact males, castrated males and females, respectively. This study
highlights the importance of incorporating carcass information into EBW, EBWG, and RE
predictions. Replacing empty body weight gain with carcass gain might be a suitable alterna-
tive to estimate the retained energy of hair sheep. In addition, the generated equations will
provide support for meat production systems in carcass weight prediction.

Introduction
Hair sheep are commonly used in meat production systems in tropical regions (Araújo et al.,
2017) due to the perception that they are more resistant to harsh environments and heat tol-
erant (Costa et al., 2013; McManus et al., 2020) and therefore offer a valuable genetic resource.
Studies of nutritional requirements are frequently compiled and generate representative and
practical recommendations for animal feeding. This information and learning cycle strongly
depend on the predictive capacity of mathematical equations (Tedeschi, 2023), which, through
the relationship between variables, allow the estimation of parameters with biological mean-
ings. Body weight adjustments represent an indispensable tool for estimating animal perform-
ance in feeding tests, nutritional requirement studies, and production systems (Herbster et al.,
2020).
Empty body weight (EBW) is a basic measurement for nutritional trials, as it accurately repre-
sents the mass of body tissues (BR-CORTE, 2023) and, therefore, the calculation of requirements
is carried out based on EBW (Oliveira et al., 2018). However, as the EBW is determined labori-
ously, we must seek estimation equations that can be easily applied in a practical scenario, facili-
tating the nutritionist’s work in formulating diets. Within this scenario, information about the
carcass can be associated with EBW, empty body weight gain (EBWG), and retained energy
(RE) as it represents tissue deposition during the animal’s growth.
© The Author(s), 2024. Published by Meyer et al. (1960) suggested the use of the term corrected carcass to evaluate the response
Cambridge University Press of beef cattle to various treatments. This measure, which is essentially the carcass weight cor-
rected to a standard caloric value, has the advantage of removing the effect of fill. Furthermore,
with this variable a larger volume of data can be available, generated in non-experimental loca-
tions, such as commercial slaughterhouses (Benedeti et al., 2021), which can provide new data
sets for future estimates or validation of new equations.

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 2 Antonio de Sousa Brito Neto et al.

The EBWG is a measure highly correlated with RE in the body obtained as the FBW minus the GIT, bladder, and gallbladder
and it is therefore useful in many models of energy requirements. contents.
The independent variables input in prediction models must be The carcasses were weighed, refrigerated at 4°C for 24 h, divided
representative of the response variable, easy to obtain, and have in half lengthwise, and then frozen. Subsequently, the non-carcass
a practical relationship with production systems. In this context, components, hides, and right half carcass samples were cut with a
replacing EBWG with carcass gain (CG) in predicting RE is a band saw and ground separately in an industrial meat grinder.
viable alternative, considering that carcass weight (CW) contains After grinding and homogenization, samples were taken for chem-
a significant proportion of body energy retained in the form of ical analysis. For determination of the body composition, the
protein and fat. Furthermore, CW is a measurement routinely ground samples of the right half-carcasses, non-carcass parts, and
performed on the slaughter line and is less susceptible to meas- hides were pre-dried at 55°C to constant weight and after this per-
urement errors. Equations for predicting CW, as well as the use iod, defatted by extraction with ether in a Soxhlet apparatus
of CG as a predictor variable, can be useful tools for the sheep (AOAC, 1990; method number 920.39) for 12 h (Pereira et al.,
meat production system, as well as animal feeding systems. 2017). Subsequently, the fat-free samples were ground in a ball
Our hypothesis is that CG can be used to predict the RE of hair mill and analysed for dry matter (AOAC, 1990; method 967.03)
sheep. Therefore, our objective was to develop equations to predict and crude protein content (AOAC, 1990; method 984.13).
CW and use CW to predict EBW, and CG to predict EBWG and
RE in hair sheep.
Models and estimation of variables
Materials and methods To estimate the initial EBW and CW of the performance animals in
the comparative slaughter studies, regression equations of the FBW
Description of the data set against the BW, EBW against the FBW, and CW against the EBW
The data set used to generate the models was composed of were generated from the data baseline animals. Likewise, the initial
individual measurements from 569 sheep derived from sixteen body energy of the performance animals was estimated by regres-
studies (Pereira, 2011; Costa et al., 2013; Oliveira et al., 2014; sion equations of the body energy contents against the EBW
Pereira et al., 2014, 2017, 2018a, 2018b; Rodrigues et al., 2016; from the data baseline animals (Herbster et al., 2024).
Gois et al., 2017; Brito Neto, 2020; Mendes et al., 2021; Silva The CG (kg/day) was obtained by the difference between the
et al., 2021; Rocha, 2022; A. C. Rocha, unpublished data; final and initial carcass weight, divided by the number of experi-
A. S. Brito Neto, unpublished data; C. J. L. Herbster, unpublished mental days, within each study. The daily RE (MJ/day) was
data) with records of three sex classes: intact males (n = 416), obtained by the difference between the final and initial body
castrated males (n = 51) and females (n = 102). The most repre- energy content (BEC). The BEC was obtained for each animal
sentative hair sheep genotypes in the data set were: Santa Ines from the body content of protein and fat and their caloric equiva-
(n = 192); Morada Nova (n = 121), Brazilian Somali (n = 47), lents (ARC, 1980) according to the following equation:
½ Dorper × ½ Santa Ines (n = 63) and crossbred (n = 146).
Within this data set, 429 records were originated from compara- BEC = (5.6405 × BPC) + (9.3929 × BFC) (1)
tive slaughter (Pereira, 2011; Costa et al., 2013; Oliveira et al.,
2014; Pereira et al., 2014, 2017, 2018a; Rodrigues et al., 2016; where BEC is the body energy content (MJ); BPC is body protein
Mendes et al., 2021; A. C. Rocha (unpublished data); A. S. Brito content (kg); and BFC is body fat content (kg).
Neto (unpublished data); C. J. L. Herbster (unpublished data)). The CW, EBW and EBWG were estimated through linear
In these studies, 77 animals were slaughtered initially and regressions using the following equations, respectively:
named as reference or baseline group; 114 animals were fed at
maintenance level, and 238 fed above maintenance. The remain- CW = b0 + b1 × FBW (2)
ing records (n = 140) were originated from feeding trials (Gois
et al., 2017; Pereira et al., 2018b; Brito Neto, 2020; Silva et al.,
2021; Rocha, 2022), studies which did not use the comparative EBW = b0 + b1 × CW (3)
slaughter methodology, with animals fed above maintenance.
Information on level of feeding for each study is described in
the supplementary material (Table S1). The quantitative informa- EBWG = b0 + b1 × CG (4)
tion body weight (BW), fasting body weight (FBW), CW, EBW,
and RE were utilized to generate the models (Table 1). where CW is the carcass weight (kg); FBW is fasting body weight
(kg); EBW is the empty body weight (kg); EBWG is empty body
weight gain; CG is the carcass gain; and β0 and β1 correspond to
Slaughter procedures and chemical analyses the intercept and slope of the linear regression, respectively.
Slaughter procedures were similar in all studies. In summary, To predict the RE, the model suggested by the NRC (1984) was
FBW was obtained before slaughter, after 18 h without feed and used, which describes the relationship between the RE and the
water. Slaughter was carried out by stunning with a captive bolt EBWG for a given EBW, being the EBWG variable replaced by
pistol, causing a cerebral concussion and severing of the jugular the CG variable, as suggested by Benedeti et al. (2021):
vein until the animals completely bled, followed by skinning
and evisceration. The blood, internal organs, visceral fat, head, RE = b0 × EBW 0.75 × CGb1 (5)
hooves, and skin were weighed, collected, and frozen. The gastro-
intestinal tract (GIT), bladder, and gallbladder were weighed full, where RE is the retained energy (MJ/day); EBW0.75 is metabolic
emptied, washed, drained, and weighed empty. The EBW was empty body weight (kg); CG is carcass gain (kg/day); β0 is the

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 The Journal of Agricultural Science 3

Table 1. Descriptive statistics of the variables used to generate the prediction models

Items FBW (kg) EBW (kg) CW (kg) EBWG (kg/day) CG (kg/day) RE (MJ/kg0.75 EBW/day)

Sex class
Intact males
n 397 300 416 226 226 226
Average ± SD 28 ± 9.1 22 ± 8.9 13 ± 4.8 0.1 ± 0.07 0.05 ± 0.042 0.12 ± 0.095
Minimum 8.60 6.67 3.21 −0.147 −0.091 −0.197
Maximum 53.10 44.19 25.65 0.313 0.216 0.365
Castrated males
n 34 51 51 39 39 39
Average ± SD 19 ± 5.2 16 ± 5.4 9 ± 3.0 0.05 ± 0.068 0.02 ± 0.043 0.1 ± 0.11
Minimum 11.60 7.67 3.45 −0.101 −0.076 −0.155
Maximum 29.5 29.33 14.85 0.171 0.082 0.303
Females
n 84 102 102 87 87 87
Average ± SD 23 ± 7.9 18 ± 7.0 10 ± 4.3 0.06 ± 0.060 0.03 ± 0.037 0.1 ± 0.11
Minimum 11.66 8.17 3.88 −0.062 −0.056 −0.087
Maximum 40.00 35.27 21.65 0.235 0.096 0.391
Genotype
Santa Ines
n 192 180 192 130 130 130
Average ± SD 26 ± 10.0 21 ± 9.2 12 ± 5.4 0.09 ± 0.067 0.05 ± 0.042 0.13 ± 0.093
Minimum 10.08 7.76 3.54 −0.035 −0.029 −0.066
Maximum 53.10 44.19 25.65 0.313 0.216 0.332
Morada Nova
n 121 109 121 86 86 86
Average ± SD 22 ± 8.2 18 ± 7.3 10 ± 4.4 0.07 ± 0.042 0.04 ± 0.025 0.11 ± 0.058
Minimum 8.60 6.67 3.21 0.00002 0.0002 −0.002
Maximum 45.60 40.05 23.65 0.164 0.098 0.217
Brazilian Somali
n 47 47 47 39 39 39
Average ± SD 21 ± 6.3 18 ± 6.0 10 ± 3.3 0.09 ± 0.046 0.05 ± 0.023 0.17 ± 0.084
Minimum 10.24 7.96 4.02 0.0003 0.002 0.021
Maximum 34.96 31.18 17.76 0.183 0.092 0.357
½ Dorper × ½ Santa Ines
n 63 63 63 54 54 54
Average ± SD 32 ± 8.5 27 ± 7.4 16 ± 4.4 0.08 ± 0.062 0.05 ± 0.037 0.13 ± 0.090
Minimum 13.50 9.72 5.00 −0.040 −0.025 −0.050
Maximum 49.30 41.66 24.65 0.209 0.126 0.309
Crossbred
n 92 54 146 43 43 43
Average ± SD 31 ± 5.1 17 ± 6.3 12 ± 4.0 0.03 ± 0.102 0.00 ± 0.055 0.06 ± 0.17
Minimum 21.20 7.67 3.49 −0.147 −0.091 −0.197
Maximum 47.12 30.03 22.20 0.235 0.096 0.391
CW, carcass weight; CG, carcass gain; FBW, fasting body weight; EBW, empty body weight; EBWG, empty body weight gain; RE, retained energy; SD, standard deviation.

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 4 Antonio de Sousa Brito Neto et al.

Table 2. Summary of studies used to validate the generated equations

Study T n Sex class Genotype

Validation of CW and EBWa

Cunha et al. (2008) 4 24 Intact male Santa Ines
Dantas et al. (2008) 3 24 Castrated male Santa Ines
Menezes et al. (2008) b
3 33 Intact male Santa Ines
Medeiros et al. (2009) 4 32 Castrated male Morada Nova
Xenofonte et al. (2009) 4 24 Intact male Crossbred
Araújo Filho et al. (2010) 3 54 Intact male Santa Ines/Morada Nova/½ Dorper × ½ Santa Ines
Pereira et al. (2010) 4 20 Intact male Santa Ines
Vieira et al. (2010) 4 20 Intact male Morada Nova
Costa et al. (2011) 4 20 Intact male Morada Nova
Alvarenga (2013) 2 50 Intact male Crossbred
Fernandes Júnior et al. (2013) 5 30 Intact male Santa Ines
Souza et al. (2013) b
2 20 Intact male ½ Dorper × ½ Santa Ines/½ Dorper × ½ Somalis Brasileira
Bastos et al. (2015) b
5 25 Intact male Santa Ines
Lima Júnior et al. (2015) 2 16 Intact male Morada Nova
Queiroz et al. (2015) 3 24 Intact male Santa Ines
Urbano et al. (2015) c
5 40 Intact male Santa Ines
Silva et al. (2016)b 4 40 Intact male Santa Ines
Oliveira et al. (2017) 4 32 Intact male Santa Ines
Nascimento et al. (2018) 3 24 Intact male ½ Dorper × ½ Santa Ines
Validation of EBWG and REd
Nascimento Júnior (2010) – 16 Castrated male Dorper/Santa Ines
Silva et al. (2010) – 16 Castrated male Santa Ines
Regadas Filho et al. (2013) – 15 Intact male Santa Ines
C. J. L. Herbster (unpublished data) – 16 Female ½ Dorper × ½ Santa Ines
CW, carcass weight; EBW, empty body weight; EBWG, empty body weight gain; n, in the number of observations in the study; RE, retained energy; T, is the number of treatments in the study.
a
Compilation of means of treatments reported by independent publications.
b
Study included only in the validation data set of the CW prediction equations.
c
Study included only in the validation data set of the EBW prediction equations.
d
Compilation of individual information originating from independent study data sets.

antilogarithm of the intercept of the linear regression of the loga- Seventeen types of variance-covariance structures were tested,
rithm of RE (MJ/kg0.75 EBW/day) as a function of the logarithm with the choice of structure for defining the most appropriate
of CG (kg/day); and β1 corresponds to the slope of the regression. model based on Akaike’s Information Criteria. Individual obser-
To estimate EBWG and RE, only data from animals fed above vations with Student residuals greater than 2.5 or below −2.5
the maintenance level were used, since the growth pattern of these were considered outliers (Pell, 2000; Tedeschi, 2006) and excluded
animals differs from those feds at the maintenance level. from the data set. When Cook’s distance was greater than 1.0, the
study was considered an outlier and removed from the analysis
(Cook and Weisberg, 1982).
Statistical analysis
The parameters of the linear models were tested using the MIXED
Validation of equations
procedure of SAS (version 9.4, Inst. Inc., Cary, NC), and the sig-
nificance level was set at 0.05. As the data set was composed of An independent validation framework was adopted, which con-
different studies, it was necessary to quantify the variance asso- sisted of searching for studies conducted with hair sheep raised
ciated with the studies using the principles of meta-analysis, in tropical conditions that had the same input information as
described by St-Pierre (2001). The random effect of the study the models generated to predict CW, EBW, EBWG and RE.
was included and tested in the intercept and slope of all models, For CW and EBW we used values of mean of treatments, from
considering the possibility of covariance. The fixed effect of sex published manuscripts, as described in Table 2 and 3. Although,
class on models’ parameters was tested, and when the differences for EBWG and RE we used raw (individual) values extracted from
were significant, an equation was fitted for each sex class. four independent studies.

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 The Journal of Agricultural Science 5

Table 3. Characteristics of the data sets used to validate the generated SB; systematic bias, MaF; and random errors, MoF; Bibby and
equations Toutenburg, 1977). The root mean square error of prediction
Validation data sets (RMSEP) was used to evaluate model accuracy, and the lower the
RMSEP, the better the model accuracy.
EBWG and
Items CWa EBWa REb
Results
Study (N ) 18 15 4
Sex class did not influence the intercept (P = 0.831) and slope (P
Means of treatments (n) 63 54 – = 0.247) of the linear regression of CW as a function of FBW. The
Animals (n) 512 434 63 lack of effect of sex class was also verified on the intercept (P =
0.807) and slope (P = 0.251) of the linear relationship between
Sex class (n)
EBW and CW, and on the intercept (P = 0.253) and slope (P =
Intact males 456 378 15 0.250) of the linear relationship between EBWG and CG.
Castrated males 56 56 32 Therefore, general equations were adjusted to predict CW (Eqn
(7)), EBW (Eqn (8)), and EBWG (Eqn (9)). For the CW and
Females – – 16
EBWG models, the variance-covariance structure selected was
Genotype (n) Antedependence and for the EBW model the variance-covariance
Santa Ines 270 212 39 structure selected was Autoregressive Heterogeneous.
Morada Nova 106 106 –
CW = −0.234 (+1.1358) + 0.485 (+0.0387) × FBW
Dorper – – 8 (7)
R2 = 0.983; MSE = 0.386; AIC = 1106.5
Crossbred 74 74 –
½ Dorper × ½ Santa 52 42 16
Ines EBW = 1.367 (+0.5472) + 1.681 (+0.0210) × CW
– –
(8)
½ Dorper × ½ 10 R2 = 0.992; MSE = 0.569; AIC = 1075.7
Brazilian Somali
CW (kg) 7.66–21.90 7.66–21.90 –
FBW (kg) 18.64–44.88 – – EBWG = 0.004 (+0.0026) + 1.679 (+0.0758) × CG
(9)
EBW (kg) – 14.38–39.36 – R2 = 0.940; MSE = 0.0001; AIC = −1283
EBWG (kg/day) – – 0.056–0.182
The validation analysis demonstrated that Eqns (7)–(9) can
CG (kg/day) – – 0.043–0.104
adequately predict CW, EBW and EBWG (Table 4), respectively,
RE (MJ/day) – – 0.492–3.182 as the intercept was not different from 0 (P > 0.05) and the slope
CW, carcass weight; CG, carcass gain; FBW, fasting body weight; EBW, empty body weight; was not different from 1 (P > 0.05) in none of the three equations
EBWG, empty body weight gain; RE, retained energy. (Fig. 1), which presented R2 of 0.912, 0.958 and 0.820, respect-
a
Compilation of means of treatments reported by independent publications. ively. For Eqn (7), a CCC of 0.933 and MSEP of 0.859 were
b
Compilation of individual information originating from independent study data sets.
obtained; for Eqn (8), a CCC of 0.972 and MSEP of 1.101; and
Eqn (9), CCC of 0.889 and MSEP of 0.0002.
Model validation analyses were carried out using the Model There was a significant effect of sex class on the intercept (P =
Evaluation System (MES) software, version 3.1.13 (Tedeschi, 2006), 0.0013) of the linear relationship between RE and CG. Therefore,
and the significance established was 0.05. The predicted and observed Eqns (10)–(12) were adjusted for intact males, castrated males,
values were compared using the following regression model: and females, respectively. For the RE models, the variance-
covariance structure selected was Toeplitz.
Y = b0 + b1 × X (6) Intact males:RE = 1.448 (+0.0657) × EBW 0.75
× CG0.797 (+0.0399) (10)
where Y represents the observed values; X represents the predicted
values; and β0 and β1 correspond to the intercept and slope of the
regression, respectively. The regression was evaluated according to
the following statistical hypotheses (Neter et al., 1996): H0: β0 = 0 Castrated males:RE = 1.522(+0.0699) × EBW 0.75
and β1 = 1; Ha: rejection of H0. If the null hypothesis is not rejected, × CG0.797 (+0.0399) (11)
it is concluded that the tested equation estimates precisely and accur-
ately. The coefficient of determination (R2) was used as an indicator
of precision, with values closer to 1 being better. The correlation and Females:RE = 1.827 (+0.0739) × EBW 0.75 × CG0.797 (+0.0399)
concordance coefficient (CCC) or reproducibility index, was used to
evaluate the model in terms of prediction efficiency (Deyo et al., R2 = 0.784; MSE = 0.005; AIC = −411.7
1991; Nickerson, 1997; Liao, 2003) and varies from –1 to +1, with (12)
values closer to +1 being better. The models’ prediction errors
were evaluated using the estimated mean squared error of prediction where RE is the retained energy (MJ/day); EBW0.75 is metabolic
(MSEP; the closer to 0 the better), and its components (squared bias, empty body weight (kg); and CG is the carcass gain (kg/day).

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 6 Antonio de Sousa Brito Neto et al.

Table 4. Statistical validation of the relationship between predicted and observed values of carcass weight, empty body weight, empty body weight gain, and
retained energy

CW equation EBW equation EBWG equation RE equations

Items Observed Predicted Observed Predicted Observed Predicted Observed Predicted

Average (kg) 13.75 14.28 24.62 24.11 0.118 0.122 1.596 1.649
Standard deviation (kg) 2.59 2.43 4.52 4.39 0.031 0.027 0.573 0.497
Maximum (kg) 21.90 21.60 39.40 38.20 0.182 0.179 3.182 3.179
Minimum (kg) 7.70 8.80 14.40 14.20 0.056 0.077 0.492 0.828
R 2
– 0.912 – 0.958 – 0.820 – 0.600
CCC – 0.933 – 0.972 – 0.889 – 0.763
Intercept
Estimate – −0.737 – 0.332 – −0.008 – 0.125
Standard deviation – 0.585 – 0.715 – 0.008 – 0.161
P-value (H0: β0 = 0) – 0.213 – 0.644 – 0.281 – 0.440
Slope
Estimate – 1.01 – 1.01 – 1.04 – 0.893
Standard deviation – 0.040 – 0.029 – 0.062 – 0.093
P-value (H0: β1 = 1) – 0.717 – 0.803 – 0.550 – 0.254
MSEP – 0.859 – 1.10 – 0.00018 – 0.135
SB – 0.277 – 0.259 – 0.00002 – 0.003
MaF – 0.001 – 0.001 – 0.00000 – 0.003
MoF – 0.581 – 0.840 – 0.00017 – 0.129
RMSEP – 0.927 – 1.05 – 0.013 – 0.367
2
CW, carcass weight; EBW, empty body weight; EBWG, empty body weight gain; RE, retained energy; R , determination coefficient; CCC, correlation and concordance coefficient; MSEP, mean
squared error of prediction; SB, square bias; MaF, systematic bias; MoF, random errors; RMSEP, root mean squared error of prediction.

The validation analysis of Eqns (10)–(12) showed that RE can to representing the tissues accumulated during growth (Benedeti
be accurately estimated from CG (Table 4), as the intercept (P = et al., 2021). Chay-Canul et al. (2014) proposed an equation to
0.440) and slope (P = 0.254) were not different from 0 and 1, predict EBW as a function of CW for Pelibuey sheep. However,
respectively (Fig. 1). In validating the RE prediction equations, for hair sheep, equations have not yet been generated from CW
an R2 of 0.600, CCC of 0.763 and MSEP of 0.135 were observed. data, based on studies with different breeds and sex classes raised
in tropical conditions. Much information related to BW, EBW,
FBW and CW of hair sheep has been generated in recent years.
Discussion
Our study highlights the advantage of using CW or CG as vari-
Several feeding systems use models to predict the performance ables to predict EBW, EBWG, and RE, because CW and/or CG
and nutritional needs of ruminants based on information esti- are practical variables, due to the simplicity of quantifying and
mated based on EBW (Cannas et al., 2004; Oliveira et al., 2018; calculating.
Herbster et al., 2024). The EBW exactly represents the animal The adjusted equation for predicting CW from FBW was not
mass (Salazar-Cuytun et al., 2022), and corresponds to the most affected by sex class. Greater carcass yields were reported for
precise measurement to express nutritional requirements females due to early fat deposition compared to males (Osório
(Owens et al., 1995; Costa et al., 2013). The empty BW has et al., 1999). However, heavier carcasses are expected for intact
been estimated as a function of BW (ARC, 1980), FBW males due to the greater potential for muscle growth (Hegarty
(Herbster et al., 2020; Salazar-Cuytun et al., 2022), and CW et al., 2006). The effect of sex on carcass weight and composition
(Owens et al., 1995). However, the difficulty of obtaining EBW is well reported, but Prache et al. (2022) explained that this effect
has been reported (Owens et al., 1995; Chay-Canul et al., 2014; depends on age, which justifies obtaining a general equation for
Herbster et al., 2020; Benedeti et al., 2021), due to the laborious predicting CW. The Eqn (7) has been validated, which indicated
procedures of evisceration, emptying of gastric compartments, that it adequately predicts the CW of hair sheep. The high values
washing, draining and weighing. Thus, it is necessary to evaluate of R2 (0.912) and CCC (0.933), and the low MSEP (0.859) verified
alternative measures to predict this variable, since EBW cannot be in the validation indicate good reproducibility and precision of
used for much longer. the proposed equation. Furthermore, the MSEP partitioning
The CW was used to predict EBW in cattle (Garrett and demonstrated low proportion of SB and MaF, which indicates a
Hinman, 1969; Fox et al., 1976), due to the advantage of being lower of prediction error associated with the model. Using Eqn.
a variable commonly measured in the slaughter line, in addition (7) and considering sheep with FBW of 10, 20, 30 and 40 kg,

https://doi.org/10.1017/S0021859624000455 Published online by Cambridge University Press

 The Journal of Agricultural Science 7

Figure 1. Relationships of observed and residual (observed – predicted) values with predicted values of carcass weight (CW; A), empty body weight (EBW; B), empty
body weight gain (EBWG; C), and retained energy (RE; D). The points in the graphs originate from the validation data set, with the points in A and B corresponding
to the compilation of treatment averages reported by independent publications; and in C and D, corresponding to individual information originating from inde-
pendent studies data sets.

the CW is estimated at 4.62, 9.48, 14.34 and 19.20 kg, with a car- The EBWG is generally predicted to be a function of ADG
cass yield equivalent to 46.2; 47.4; 47.8 and 48.0%, respectively. (Cannas et al., 2004; Herbster et al., 2024). For hair sheep,
These values are consistent with the yields commonly found for Herbster et al. (2020) proposed that the EBWG is equivalent to
hair sheep (Souza et al., 2013; Queiroz et al., 2015; Nascimento 91% of the ADG. The use of carcass information to predict
et al., 2018), which range from 44 to 50%. EBW and EBWG is an interesting alternative since obtaining
In our study, sex class did not affect the EBW estimate as a func- EBW is a laborious measure in the experimental trials. In add-
tion of CW. This can be explained by the young age of the sheep in ition, CG is composed only of retained tissues and can be calcu-
this study. In addition, greater differences in body composition lated from a variable routinely measured in slaughterhouses. The
generally occur closer to maturity, due to the effects of the hor- accretion of carcass protein and fat as the sheep grows depends on
mone’s testosterone and oestrogen, which modulate tissue depos- adult BW which varies with genotype, sex class and birth weight
ition (Herbster et al., 2023). The validation analysis demonstrated (Prache et al., 2022).
that the CW predictor variable adequately estimates EBW (Eqn In our study, the intercept of the RE prediction equation as a
(8); R2 = 0.958). The CCC was close to 1 (0.972), which indicated function of CG was influenced by sex class. The RE is equivalent
good reproducibility, and the MSEP was 1.10, with 76% associated to the heat of combustion of the protein and fat deposited in the
with the MoF, that is, error associated with random fluctuation of gain (ARC, 1980). The composition of the gain is markedly
the data, which suggests that Eqn (8) has good predictive ability. affected by sex class (Pereira et al., 2018a; Herbster et al., 2024)
For example, considering CW of sheep with 5, 10, 15 and 20 kg, so females have more fat in the gain and less protein than
we obtain a EBW of 9.77, 18.18, 26.59 and 34.99 kg, respectively. castrated males, which deposit more fat and less protein in the

https://doi.org/10.1017/S0021859624000455 Published online by Cambridge University Press

 8 Antonio de Sousa Brito Neto et al.

gain compared to intact males (Greenhalgh, 1986). The anabolic committee on animal use was not necessary for this study because the data
effect of testosterone in intact males reduces protein catabolism in were collected from previously published sources.
muscles and intensifies the proliferation of satellite cells (Paulino
et al., 2009), which causes them to deposit more protein in the
gain compared to castrated males. In this way, castrated males References
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EP, MM, JC, RO, LB and LS; writing – original draft preparation, EP, AB and and net energy and protein requirements of Morada Nova lambs. Small
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Características quantitativas de carcaça de ovinos Santa Inês confinados ali-
mentados com rações contendo diferentes níveis de caroço de algodão inte-
Funding statement. This research received no specific grant from any fund-
gral. Revista Brasileira de Zootecnia 37, 1112–1120.
ing agency, commercial or not-for-profit sectors.
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of Ceara (protocol 1502202201). For other studies, the approval of the ethics Características de carcaça e qualidade da carne de cordeiros Santa Inês

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