Can body volume predict of body weight in Pelibuey ewe lambs?

Vazquez-Martinez, Ignacio; Cruz-Hernandez, Aldenamar; Gomez-Vazquez, Armando; Osorio-Osorio, Rodolfo; Cruz-Lazaro, Efraín de la; Gurgel, Antonio Leandro Chaves; Dias-Silva, Tairon Pannunzio; García-Herrera, Ricardo; Chay-Canul, Alfonso Juventino

doi:10.1590/0103-8478cr20240015

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ANIMAL PRODUCTION • Cienc. Rural 54 (12) • 2024 • https://doi.org/10.1590/0103-8478cr20240015 copy

Can body volume predict of body weight in Pelibuey ewe lambs?

O volume corporal pode predizer o peso corporal em borregas Pelibuey?

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

In traditional sheep production systems in tropical regions, the evaluation and monitoring of animal growth are not commonly practiced. This is primarily attributed to the limited adoption of technology and the high costs associated with acquiring livestock scales. The present study developed equations to predict body weight of Pelibuey ewe lambs using a Body Volume (BV) formula. BW and BV were obtained from 85 ewe lambs. The models evaluated were as follows: 1) linear, 2) quadratic, 3) cubic, 4) allometric, 5) exponential and 6) logarithmic. In addition, the models’ predictive ability for BW was assessed by k-fold (k=10) cross-validation. The BW and BV were 20.60 ± 8.73 kg and 17.54 ± 7.89 dm³, respectively. The quadratic model showed the highest r² and the lowest values of mean square error, root mean square error, and bayesian information criterion, while the k-fold cross-validation technique also had the highest r² and the lowest values of mean squared prediction error and mean absolute error. The quadratic model was the best performing mathematical model for predicting BW of growing Pelibuey ewe lambs using BV.

Key words:
body weight estimation; Pelibuey ewe lambs; mathematical models; regression models

Authorship

Ignacio Vazquez-Martinez

conceptualization

writing-original draft

writing-review and editing

División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, 86280, Colonia Centro Tabasco, México. Universidad Juárez Autónoma de TabascoMéxicoColonia Centro Tabasco, MéxicoDivisión Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, 86280, Colonia Centro Tabasco, México.

https://orcid.org/0000-0001-8895-7277

Aldenamar Cruz-Hernandez

conceptualization

writing-original draft

writing-review and editing

https://orcid.org/0000-0003-0171-4729

Armando Gomez-Vazquez

conceptualization

writing-original draft

writing-review and editing

https://orcid.org/0000-0002-2459-585X

Rodolfo Osorio-Osorio

conceptualization

writing-original draft

writing-review and editing

https://orcid.org/0000-0002-1940-895X

Efraín de la Cruz-Lazaro

conceptualization

writing-original draft

writing-review and editing

https://orcid.org/0000-0002-3353-6229

Antonio Leandro Chaves Gurgel

conceptualization

writing-original draft

writing-review and editing

Universidade Federal do Piauí (UFPI), Bom Jesus, Piauí, Brasil.Universidade Federal do Piauí (UFPI)BrasilBom Jesus, Piauí, BrasilUniversidade Federal do Piauí (UFPI), Bom Jesus, Piauí, Brasil.

https://orcid.org/0000-0001-5911-369X

Tairon Pannunzio Dias-Silva

conceptualization

writing-original draft

writing-review and editing

Universidade Federal do Piauí (UFPI), Bom Jesus, Piauí, Brasil.Universidade Federal do Piauí (UFPI)BrasilBom Jesus, Piauí, BrasilUniversidade Federal do Piauí (UFPI), Bom Jesus, Piauí, Brasil.

https://orcid.org/0000-0002-4786-1399

Ricardo García-Herrera ^*E-mail: ricardo.garcia@ujat.mx. *Corresponding author

conceptualization

writing-original draft

writing-review and editing

https://orcid.org/0000-0003-2456-4727

Alfonso Juventino Chay-Canul

conceptualization

writing-original draft

writing-review and editing

https://orcid.org/0000-0003-4412-4972

E-mail: ricardo.garcia@ujat.mx. ^*Corresponding author

Editor: Rudi Weiblen (0000-0002-1737-9817)

DECLARATION OF CONFLICT OF INTEREST

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

SCIMAGO INSTITUTIONS RANKINGS

Universidade Federal do Piauí (UFPI), Bom Jesus, Piauí, Brasil.Universidade Federal do Piauí (UFPI)BrasilBom Jesus, Piauí, BrasilUniversidade Federal do Piauí (UFPI), Bom Jesus, Piauí, Brasil.

Figures | Tables | Formulas

Figures (1)
Tables (5)
Formulas (12)

Thumbnail

Figure 1
Relationship between live weight and body volume in growing Pelibuey ewe lambs.

Thumbnail

Table 1
Regression models to describe the relationship BW and BV in Pelibuey ewe lambs.

Thumbnail

Table 2
Descriptive statistics of BW and biometric measurements recorded in growing hair lambs.

Thumbnail

Table 3
Body weight prediction equations in Pelibuey ewe lambs through a body volume formula.

Thumbnail

Table 4
Predictive performance of regression models obtained in this study.

Thumbnail

Table 5
Internal k-folds cross-validation of the proposed models.

Thumbnail

Figure 1 Relationship between live weight and body volume in growing Pelibuey ewe lambs.

Table 1 Regression models to describe the relationship BW and BV in Pelibuey ewe lambs.

No.	Models	Mathematical equations
1	Linear	$y = β_{0} + β_{1} * x$
2	Quadratic	$y = β_{0} + β_{1} * x + β_{2} * x^{2}$
3	Cubic	$y = β_{0} + β_{1} * x + β_{2} * x^{2} + β_{3} * x^{3}$
4	Power	$y = β_{0} * x β_{1}$
5	Exponential	$y = β_{0} + β_{1} (β_{2,1} + β_{p + 1} x_{i,} 1) + e_{i}$
6	Logirithmic	$y = β_{0} + β_{1} * I n (x)$

β₀ - β3: model parameters; y: body weight; x: body volume.

Table 2 Descriptive statistics of BW and biometric measurements recorded in growing hair lambs.

Variable	N	Mean ± SD	Minimum	Maximum	CV (%)
BW (kg)	85	20.60 ± 8.73	6.22	38.00	42.37
HG (cm)	85	64.17 ± 11.20	43.00	86.00	17.45
BL (cm)	85	33.56 ± 4.96	24.00	55.00	14.77
BV (dm³)	85	17.54 ± 7.69	5.98	35.16	43.84

BW: body weight; HG: heart girth; BL: body length; BV: body volume; N: number of observations; SD: standard deviation; CV: coefficient of variation.

Table 3 Body weight prediction equations in Pelibuey ewe lambs through a body volume formula.

No	Models	Equation
1	Linear	$B W (k g) : 1.67 {(\pm 0.80}^{}) + 1077.40 (\pm {41.63}^{}) \times B V$
2	Quadratic	$B W (k g) : - 1.95 {(\pm 1.70}^{}) + 1560.25 {(\pm 206.53}^{}) \times B V - 13254.54 {(\pm 5560.06}^{*}) \times {B V}^{2}$
3	Cubic	$B W (k g) : 3.58 {(\pm 4.18}^{}) + 484.81 {(\pm 772.18}^{}) \times B V + 46025.30 {(\pm 41405.47}^{}) \times {B V}^{2} - 979518.69 {(\pm 678061.74}^{}) \times {B V}^{3}$
4	Allometric	$B W (k g) : 784.96 {(\pm 123.04}^{}) \times {B V}^{0.90 (\pm 0.04 }$
5	Exponential	$B W (k g) : 8.64 {(\pm 0.52}^{}) \times {E x p}^{46.28 (\pm 2.52 ) \times B V}$
6	Logarithmic	$B W (k g) : 90.65 {(\pm 2.94}^{}) + {(\pm 16.88}^{}) \times L n (B V)$

BW: body weight; BV: body volume; Values in parentheses are the standard errors (SE) of the parameter estimates. The ^* indicates: ^*: P < 0.05.

Table 4 Predictive performance of regression models obtained in this study.

No	r2	MSE	RMSE	AIC	BIC
1	0.89	7.67	2.77	162.90	167.60
2	0.91	7.12	2.67	159.20	166.30
3	0.91	6.93	2.63	159.03	168.45
4	0.89	7.50	2.73	161.17	165.89
5	0.83	12.34	3.51	200.03	204.75
6	0.88	8.79	2.96	173.54	178.25

r²: Coefficient of determination, MSE: mean square error; RMSE: Root MSE; AIC: Akaike information criterion; BIC: Bayesian Information Criterion.

Table 5 Internal k-folds cross-validation of the proposed models.

Model	N	r²	MSPE	MAE
Linear	85	0.85	2.83	2.27
Quadratic	85	0.87	2.71	2.19
Cubic	85	0.87	2.72	2.20
Allometric	85	0.86	2.82	2.24
Exponential	85	0.72	3.46	2.86
Logarithmic	85	0.84	2.99	2.37

MSPE: mean squared prediction error; r²: coefficient of determination; MAE: mean absolute error.

y = β_{0} + β_{1} * x

y = β_{0} + β_{1} * x + β_{2} * x^{2}

y = β_{0} + β_{1} * x + β_{2} * x^{2} + β_{3} * x^{3}

y = β_{0} * x β_{1}

y = β_{0} + β_{1} (β_{2,1} + β_{p + 1} x_{i,} 1) + e_{i}

y = β_{0} + β_{1} * I n (x)

B W (k g) : 1.67 {(\pm 0.80}^{*}) + 1077.40 (\pm {41.63}^{*}) \times B V

B W (k g) : - 1.95 {(\pm 1.70}^{*}) + 1560.25 {(\pm 206.53}^{*}) \times B V - 13254.54 {(\pm 5560.06}^{*}) \times {B V}^{2}

B W (k g) : 3.58 {(\pm 4.18}^{*}) + 484.81 {(\pm 772.18}^{*}) \times B V + 46025.30 {(\pm 41405.47}^{*}) \times {B V}^{2} - 979518.69 {(\pm 678061.74}^{*}) \times {B V}^{3}

B W (k g) : 784.96 {(\pm 123.04}^{*}) \times {B V}^{0.90 (\pm 0.04 *}

B W (k g) : 8.64 {(\pm 0.52}^{*}) \times {E x p}^{46.28 (\pm 2.52 *) \times B V}

B W (k g) : 90.65 {(\pm 2.94}^{*}) + {(\pm 16.88}^{*}) \times L n (B V)

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Universidade Federal de Santa Maria Universidade Federal de Santa Maria, Centro de Ciências Rurais , 97105-900 Santa Maria RS Brazil , Tel.: +55 55 3220-8698 , Fax: +55 55 3220-8695 - Santa Maria - RS - Brazil
E-mail: cienciarural@mail.ufsm.br

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