Hostname: page-component-f554764f5-246sw Total loading time: 0 Render date: 2025-04-20T12:24:43.858Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting the body weight of crossbred Holstein × Zebu dairy cows using multivariate adaptive regression splines algorithm

Published online by Cambridge University Press:  14 November 2024

Ignacio Vázquez-Martínez ,
Cem Tirink Open the ORCID record for Cem Tirink [Opens in a new window] ,
Fernando Casanova-Lugo ,
Dixan Pozo-Leyva ,
Daniel Mota-Rojas ,
Murat Baitugelovich Kalmagambetov ,
Rashit Uskenov ,
Ömer Gülboy ,
Ricardo A. Garcia-Herrera  and
Alfonso J. Chay-Canul Open the ORCID record for Alfonso J. Chay-Canul [Opens in a new window]
Ignacio Vázquez-Martínez
Affiliation:
División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villaher-mosa, Tabasco, México Benemérita Universidad Autónoma de Puebla, Complejo Regional Norte, Tetela de Ocampo, Puebla, México
Cem Tirink*
Affiliation:
Faculty of Agriculture, Department of Animal Science, Igdir University, TR76000, Igdir, Türkiye
Fernando Casanova-Lugo
Affiliation:
Tecnológico Nacional de México, Instituto Tecnológico de la Zona Maya, Othón P. Blanco, Quintana Roo, México
Dixan Pozo-Leyva
Affiliation:
Tecnológico Nacional de México, Instituto Tecnológico de la Zona Maya, Othón P. Blanco, Quintana Roo, México
Daniel Mota-Rojas
Affiliation:
Neurophysiology, Behavior and Animal Welfare Assessment, Department of Animal Production and Agriculture (DPAA), Universidad Autónoma Metropolitana Xochimilco Campus, Mexico City 04960, Mexico
Murat Baitugelovich Kalmagambetov
Affiliation:
Aktobe Agricultural Experimental Station, Aktobe, Republic of Kazakhstan
Rashit Uskenov
Affiliation:
Agronomic Faculty, S. Seifullin Kazakh Agrotechnical University, Z10P6B8, 62 Zhenis av., Astana, Kazakhstan
Ömer Gülboy
Affiliation:
Faculty of Agriculture, Department of Animals Science, Ondokuz Mayis University, TR55139, Samsun, Türkiye
Ricardo A. Garcia-Herrera
Affiliation:
División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villaher-mosa, Tabasco, México
Alfonso J. Chay-Canul
Affiliation:
División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villaher-mosa, Tabasco, México
*
Corresponding author: Cem Tirink; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This study aimed to estimate live body weight from body measurements for Holstein × Zebu dairy cows (n = 156) reared under conditions of humid tropics in Mexico using multivariate adaptive regression splines algorithm (MARS) with several train-test proportions. The body measurements included withers height, rump height, hip width, heart girth, body length and diagonal body length. The data were divided into 65:35, 70:30 and 80:20 split data for training and testing sets, respectively. The MARS algorithm was used to construct a prediction model, which predicted the body weight from the body measurements of the test dataset. The results emphasized that the MARS algorithm had an explanation rate for 80:20 train and test set of 0.836 and 0.711, respectively, with minimum Akaike information criterion values. This indicates that it is a reliable way of predicting body weight from body measurements. The results suggest that body weight prediction can be performed with the MARS algorithm in a reliable way, therefore, this algorithm may be a useful tool for animal breeders and researchers in the development of feeding and selection-aimed approaches.

Keywords

Body measurementsbody weightcrossbredmultivariate adaptive regression splines (MARS)tropical cows
Type
Research Article
Information
Journal of Dairy Research , Volume 91 , Issue 3 , August 2024 , pp. 267 - 272
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Akin, M, Eyduran, SP, Eyduran, E and Reed, BM (2020) Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Plant Cell, Tissue and Organ Culture 140, 661–670.CrossRefGoogle Scholar
Altay, Y and Delialioğlu, RA (2022) Diagnosing lameness with the random forest classification algorithm using thermal cameras and digital colour parameters. Mediterranean Agricultural Sciences 35, 47–54.CrossRefGoogle Scholar
Arthur, CK, Temeng, VA and Ziggah, YY (2020) Multivariate adaptive regression splines (MARS) approach to blast-induced ground vibration prediction. International Journal of Mining, Reclamation and Environment 34, 198–222.CrossRefGoogle Scholar
Bene, S, Nagy, B, Kiss, B, Polgar, JP and Szabo, F (2007) Comparison of body measurements of beef cows of different breeds. Archiv fur Tierzucht 50, 363–373.Google Scholar
Bretschneider, G, Cuatrin, A, Arias, D and Vottero, D (2014) Estimation of body weight by an indirect measurement method in developing replacement Holstein heifers raised on pasture. Archivos de Medicina Veterinaria 46, 439–443.CrossRefGoogle Scholar
Canga, D (2022) Use of MARS data mining algorithm based on training and test sets in determining carcass weight of cattle in different breeds. Journal of Agricultural Sciences 28, 259–268.Google Scholar
Canga, D and Boğa, M (2022) Detection of correct pregnancy status in lactating dairy cattle using MARS data mining algorithm. Turkish Journal of Veterinary & Animal Sciences 46, 809–819.CrossRefGoogle Scholar
Celik, S (2019) Comparing predictive performances of tree-based data mining algorithms and MARS algorithm in the prediction of live body weight from body traits in Pakistani goats. Pakistan Journal of Zoology 51, 1447–1456.CrossRefGoogle Scholar
Coşkun, G, Şahin, Ö, Delialioğlu, RA, Altay, Y and Aytekin, I (2023a) Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in Brown Swiss cows. Tropical Animal Health and Production 55, 50.CrossRefGoogle ScholarPubMed
Coşkun, G, Şahin, Ö, Altay, Y and Aytekin, I (2023b) Final fattening live weight prediction in Anatolian merinos lambs from somebody characteristics at the initial of fattening by using some data mining algorithms. Black Sea Journal of Agriculture 6, 47–53.CrossRefGoogle Scholar
Cruz-Tamayo, AA, Ramírez-Bautista, MA, Mota-Rojas, D, Escobar-España, JC, García-Herrera, R, Gurgel, ALC, Dias-Silva, TP, de Araújo, MJ, Santana, JCS, Aguiar, IOM, Ítavo, LCV and Chay-Canul, AJ (2024) Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis). Journal of Dairy Research 91, 180–183.CrossRefGoogle Scholar
Dang, CG, Choi, TJ, Lee, SS, Lee, SH, Alam, M, Park, MN, Han, S, Lee, JG and Hoang, DT (2022) Machine learning-based live weight estimation for Hanwoo cow. Sustainability 14, 12661.CrossRefGoogle Scholar
Dingwell, RT, Wallace, MM, McLaren, CJ, Leslie, CF and Leslie, KE (2006) An evaluation of two indirect methods of estimating body weight in Holstein calves and heifers. Journal of Dairy Science 89, 3992–3998.CrossRefGoogle ScholarPubMed
Eyduran, E (2020) ehaGoF: calculates goodness of fit statistics. R package version 0.1.1.CrossRefGoogle Scholar
Eyduran, E, Akin, M and Eyduran, SP (2019) Application of Multivariate Adaptive Regression Splines Through R Software. Ankara. Türkiye: Nobel Academic Publishing, p. 104.Google Scholar
Faraz, A, Tirink, C, Eyduran, E, Waheed, A, Taukir, NA, Nabeel, MS and Tariq, MM (2021) Prediction of live body weight based on body measurements in Thalli sheep under tropical conditions of Pakistan using CART and MARS. Tropical Animal Health and Production 53, 301.CrossRefGoogle ScholarPubMed
Fox, J and Weisberg, S (2019) An {R} Companion to Applied Regression, 3rd Edn. Thousand Oaks, CA: Sage.Google Scholar
Franco, MDO, Marcondes, MI, Campos, JMDS, Freitas, DRD, Detmann, E and Valadares, SDC (2017) Evaluation of body weight prediction equations in growing heifers. Acta Scientiarum. Animal Sciences 39, 201–206.CrossRefGoogle Scholar
Friedman, J (1991) Multivariate adaptive regression splines. Annals of Statistics 19, 1–67.Google Scholar
Grzesiak, W and Zaborski, D (2012) Examples of the use of data mining methods in animal breeding. In Karahoca, A (ed.), Data Mining Applications in Engineering and Medicine. London: InTech, pp. 303–324.Google Scholar
Heinrichs, AJ, Rogers, GW and Cooper, JB (1992) Predicting body weight and wither height in Holstein heifers using body measurements. Journal of Dairy Science 75, 3576–3581.CrossRefGoogle ScholarPubMed
Herrera-López, S, García-Herrera, R, Chay-Canul, AJ, González-Ronquillo, M, Macías-Cruz, U, Díaz-Echeverría, VF, Casanova-Lugo, F and Piñeiro-Vázquez, A (2018) Desarrollo y evaluación de una ecuación para predecir el peso vivo en novillas cruzadas usando el ancho de cadera. ITEA-Información Técnica Económica Agraria 114, 368–377.Google Scholar
Kashoma, IPB, Luziga, C, Werema, CW, Shirima, GA and Ndossi, D (2011) Predicting body weight of Tanzania shorthorn zebu cattle using heart girth measurements. Livestock Research for Rural Development 23, 94.Google Scholar
Kuhn, M (2022) caret: Classification and regression training. R package version 6.0–93.Google Scholar
Lesosky, M, Dumas, S, Conradie, I, Handel, IG, Jennings, A, Thumbi, S, Toye, F and de Clare Bronsvoort, BM (2012) A live weight–heart girth relationship for accurate dosing of East African shorthorn zebu cattle. Tropical Animal Health and Production 45, 311–316.CrossRefGoogle ScholarPubMed
Lukuyu, MN, Gibson, JP, Savage, DB, Duncan, AJ, Mujibi, FDN and Okeyo, AM (2016) Use of body linear measurements to estimate live weight of crossbred dairy cattle in smallholder farms in Kenya. SpringerPlus 5, 1–14.CrossRefGoogle Scholar
Magaña, MJG, Ríos, AG and Martínez, GJC (2006) Los sistemas de doble propósito y los desafíos en los climas tropicales de México. Archivos Latinoamericanos de Producción Animal 14, 105–114.Google Scholar
Mota, DA, Berchielli, TT, Canesin, RC, Rosa, BL, Ribeiro, AF and Brandt, HV (2013) Nutrient intake, productive performance and body measurements of dairy heifers fed with different sources of protein. Acta Scientiarum. Animal Sciences 35, 273–279.CrossRefGoogle Scholar
Olfaz, M, Tirink, C and Onder, H (2019) Use of CART and CHAID algorithms in Karayaka sheep breeding. Kafkas Üniversitesi Veteriner Fakültesi Dergisi 25, 105–110.Google Scholar
Oliveira, AS, Abreu, DC, Fonseca, MA and Antoniassi, PMB (2013) Development and evaluation of predictive models of body weight for crossbred Holstein-Zebu dairy heifers. Journal of Dairy Science 96, 6697–6702.CrossRefGoogle ScholarPubMed
Peterson, BG and Carl, P (2020) Performance analytics: econometric tools for performance and risk analysis. R package version 2.0.4.Google Scholar
Putra, WPB (2020) The assessment of body weight of Sumba Ongole cattle (Bos indicus) by body measurements. Manas Journal of Agriculture Veterinary and Life Sciences 10, 52–57.Google Scholar
Putra, WPB, Said, S and Arifin, J (2020) Principal component analysis (PCA) of body measurements and body indices in the Pasundan cows. Black Sea Journal of Agriculture 3, 49–55.Google Scholar
R Core Team (2018) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Ramos-Zapata, R, Dominguez-Madrigal, C, García-Herrera, R-A, Camacho-Perez, E, Lugo-Quintal, JM, Tyasi, TL, Gurgel, ALC, Ítavo, LCV and Chay-Canul, AJ (2023) Predicting live weight using body volume formula in lactating water buffalo. Journal of Dairy Research 90, 138–141.CrossRefGoogle ScholarPubMed
Reis, GL, Albuquerque, FHMAR, Valente, BD, Martins, GA, Teodoro, RL, Ferreira, MBD, Monteiro, JBN, de Almeida, M and Madalena, FE (2008) Predição do peso vivo a partir de medidas corporais em animais mestiços Holandês/Gir. Ciência Rural 38, 778–783.CrossRefGoogle Scholar
Revelle, WR (2017) Psych: procedures for personality and psychological research.Google Scholar
Rojo-Rubio, R, Vázquez-Armijo, JF, Pérez-Hernández, P, Mendoza-Martínez, GD, Salem, AZM, Albarrán-Portillo, B, González-Reyna, A, Hernández-Martínez, J, Rebollar-Rebollar, S, Cardoso-Jiménez, D, Dotantes-Coronado, EJ and Gutierrez-Cedillo, JG (2009) Dual purpose cattle production in Mexico. Tropical Animal Health and Production 41, 715–721.CrossRefGoogle Scholar
Román-Ponce, SI, Ruiz-López, FDJ, Montaldo, HH, Rizzi, R and Román-Ponce, H (2013) Efectos de cruzamiento para producción de leche y características de crecimiento en bovinos de doble propósito en el trópico húmedo. Revista Mexicana de Ciencias Pecuarias 4, 405–416.Google Scholar
Ruchay, AN, Kolpakov, VI, Kalschikov, VV, Dzhulamanov, KM and Dorofeev, KA (2021) Predicting the body weight of Hereford cows using machine learning. IOP Conference Series: Earth and Environmental Science 624, 1–5.Google Scholar
Stajnko, D, Brus, M and Hočevar, M (2008) Estimation of bull live weight through thermographically measured body dimensions. Computers and Electronics in Agriculture 61, 233–240.CrossRefGoogle Scholar
Tatliyer, A (2020) The effects of raising type on performances of some data mining algorithms in lambs. Journal of Agriculture and Nature 23, 772–780.Google Scholar
Tebug, SF, Missohou, A, Sourokou Sabi, S, Juga, J, Poole, EJ, Tapio, M and Marshall, K (2018) Using body measurements to estimate live weight of dairy cattle in low-input systems in Senegal. Journal of Applied Animal Research 46, 87–93.CrossRefGoogle Scholar
Tedde, A, Grelet, C, Ho, PN, Pryce, JE, Hailemariam, D, Wang, Z, Plastow, G, Gengler, N, Brostaux, Y, Froidmont, E, Dehareng, F, Bertozzi, C, Crowe, MA, Dufrasne, I, GplusE Consortium Group and Soyeurt, H (2021) Validation of dairy cow bodyweight prediction using traits easily recorded by dairy herd improvement organizations and its potential improvement using feature selection algorithms. Animals 11, 1288.CrossRefGoogle ScholarPubMed
Tırınk, C, Önder, H, Francois, D, Marcon, D, Şen, U, Shaikenova, K, Omarova, K and Tyasi, TL (2023a) Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed. PLoS ONE 18, e0289348.CrossRefGoogle ScholarPubMed
Tırınk, C, Piwczyński, D, Kolenda, M and Önder, H (2023b) Estimation of body weight based on biometric measurements by using random forest regression, support vector regression and CART algorithms. Animals 13, 798.CrossRefGoogle ScholarPubMed
Wangchuk, K, Wangdi, J and Mindu, M (2018) Comparison and reliability of techniques to estimate live cattle body weight. Journal of Applied Animal Research 46, 349–352.CrossRefGoogle Scholar
Zaborski, D, Ali, M, Eyduran, E, Grzesiak, W, Tariq, MM, Abbas, F, Waheed, A and Tirink, C (2019) Prediction of selected re-productive traits of indigenous Harnai sheep under the farm management system via various data mining algorithms. Pakistan Journal of Zoology 51, 421–431.CrossRefGoogle Scholar