Weight from Body Measurements of Corriedale Sheep using Ridge and Stepwise Regression Models | [Journal of animal health and production • 2024]

Author(s):

1. Roxana Churata-Huacani: Departamento de Estatística, Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Lavras, Código postal 3037, CEP 37200-900 Lavras, MG, Brasil

2. Ali William Canaza-Cayo: Departamento de Estatística, Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Lavras, Código postal 3037, CEP 37200-900 Lavras, MG, Brasil; Facultad de Ciencias Agrarias, Escuela Profesional de Ingeniería Agronómica. Universidad Nacional del Altiplano. Puno. Perú.

3. Tales Jesus Fernandes: Departamento de Estatística, Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Lavras, Código postal 3037, CEP 37200-900 Lavras, MG, Brasil

4. Júlio Sílvio de Sousa Bueno Filho: Departamento de Estatística, Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Lavras, Código postal 3037, CEP 37200-900 Lavras, MG, Brasil

Abstract:

The objective of this research was to estimate body weight of Corriedale sheep from body measurements using ridge (RR) and stepwise regression (SR) models. On hundred (100) records of Corriedale ewes aged 1.5 and 2 years old were obtained in the Illpa Experimental Centre of the National University of Altiplano in Peru. Body weight (BW ) and fourteen body measurements (BM) were taken including body length (BL), abdominal perimeter (AP), rump height (RH), wither height (WH), thoracic perimeter (TP), loin width (LWi), tail perimeter (TPe), hip width (HW ), fore-shank width (FSW ), tail width (TW ), shoulder width (SW ), forelimb length (FL), fore-shank length (FSL) and fore-shank perimeter (FSP). The variance inflation factor (VIF), mean absolute error (MAE), root mean square error (RMSE) and coeficient of determination (R ) were used to determine the best model for predicting body weight. The results revealed that both SR and RR models provided accurate fit to the data, however SR model showed slightly better than RR model to predict body weight of Corriedale ewes from AP, FSL, HW, LWi and BL measurements.

Page(s): 182-188

DOI: 10.17582/journal.jahp/2024/12.2.182.188

Published: Journal: Journal of animal health and production, Volume: 12, Issue: 2, Year: 2024

Keywords:

correlation , body weight , Ridge , Ewes , Stepwise , Body biometrics

References:

[1] Azis R,Ciptadi G,Wahjuningsih S,Hariyono DNH,Tribudi YA,Nurgiartiningsih VMA .2023 .Prediction of body weight from body measurements in bali cattle of indonesia using regression analysis. Adv. Anim. Vet. Sci, 11(9) : 1486-1491.

[2] Çakmakçı C .2022 .tarım gıda bilim teknol. derg, 10(4) : 4670-594.

[3] Canaza-Cayo AW,Beltrán PA,Gallegos E,Mayta J .2017 .. Rev. investig. Altoandin, 19(3) : 313-318.

[4] Canaza-Cayo AW,Mota RR,Amarilho-Silveira F,Duarte DAS,Cobuci JA .2021 .Principal component analysis for body weight prediction of Corriedale ewes from southern Peru. J. Anim. Health Prod, 9(4) : 417-424.

[5] Dakhlan A,Hamdani M,Sulastri S .2020 .Regression models and correlation analysis for predicting body weight of female Ettawa Grade goat using its body measurements. Adv. Anim. Vet. Sci, 8(11) : 1142-1146.

[6] Faraz A,Tırınk C,Önder H,Şen U,Ishaq HM,Tauqir NA,Waheed A,Nabeel MS .2023 .Usage of the XGBoost and MARS algorithms for predicting body weight in Kajli sheep breed. Trop. Anim. Health Prod, 37500805(4) : 276-6.

[7] Gurgel ALC,Difante GS,Emerenciano Neto JV,Santana JCS,Dantas JLS,Roberto FFS,Campos NRF,Costa ABG .2021 .Use of biometrics in the prediction of body weight in crossbred lambs. Arq. Bras. Med. Vet, 73 : 1678-264.

[8] Harrell FE .2001 .Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. , 608 : .

[9] Hastie T,Tibshirani R,Friedman JH,Friedman JH .2009 .The elements of statistical learning: data mining, inference, and prediction. , 2 : 1-758.

[10] Huma ZE,Iqbal F .2019 .Predicting the body weight of Balochi sheep using a machine learning approach. Turkish J. Vet, 43(4) : 23-506.

[11] Kunene NW,Nesamvuni AE,Nsahlai IV .2009 .Determination of prediction equations for estimating body weight of Zulu (Nguni) sheep. Small. Rumin. Res, 05 : 41-46.

[12] Li G,Liu X,Ma Y,Wang B,Zheng L,Wang M .2022 .Body size measurement and live body weight estimation for pigs based on back surface point clouds. Biosyst. Eng, 218 : 10-22.

[13] McDonald GC .2009 .Ridge regression. WIREs Computational Statistics, 1 : .

[14] Miles J .2014 .Tolerance and variance inflation factor. Wiley statsref: statistics reference online., : .

[15] Ministerio de Desarrollo Agrario y Riego - MIDAGRI .2021 .Anuario Estadístico Producción Ganadera y Avícola 2021. , : .

[16] Montgomery DC,Peck EA,Vining GG .2021 .Introduction to linear regression analysis. John Wiley & Sons., : .

[17] Team R Core .2023 .A Language and Environment for Statistical Computing. (version 4.3.1), : .

[18] Rencher AC,Schaalje GB .2008 .Linear models in statistics. , : .

[19] Tırınk C,Piwczyński D,Kolenda M,Önder H. .2023 .Estimation of Body Weight Based on Biometric Measurements by Using Random Forest Regression, Support Vector Regression and CART Algorithms. , 13(5) : 798.

[20] Vázquez-Martínez I,Tırınk C,Salazar-Cuytun R,Mezo-Solis JA,Garcia RA,Orzuna-Orzuna JF,Chay-Canul AJ .2023 .Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms. Trop. Anim. Health Prod, 55(5) : 1-10.

[21] Ye X,Abe S,Zhang S .2020 .Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging. Precis. Agric, 21 : 198-225.

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