Evaluation of body measurements of Limousin heifers by backward regression analysis in Western Hungary
DOI:
https://doi.org/10.17205/SZIE.AWETH.2023.1.102Keywords:
body measurements, Limousin breed, heifers, backward regression analysisAbstract
Body measurements of yearling Limousin heifers (height at withers, HW, cm; tail height, HT, cm; back length, LB, cm; width at shoulders, WS, cm; hip bone width, WHB, cm; pin width, WP, cm) were taken in 7 nucleus farms in the Western Hungarian region n=322). The aim was to collect information on body sizes of yearling heifers and to work out regression equations for body measurements and live weight. Backward regression analyses and multifactorial regression analysis were completed using software SPSS 24.0.
Results of backward analysis revealed different R2% values were obtained (49.2, 92.5) for prediction of withers’ height, tail height, length of back, and width of shoulders. Determination coefficients above 90% in cases of withers height and tail height imply that these parameters can be predicted by regression models accurately so one of them can be estimated. Both traits are useful in breeding strategy for planning corrective matings.
For length of back and width at shoulders, precise prediction was not possible by these parameters. More researches are needed to find out better fitting models.
Live weight could not be estimated accurately enough (R2=68.5 – 68.6%) from the available body measurements (withers height, tail height, length of back, width at shoulders, width at hip bones). Since other results imply that chest girth is strongly correlated with live weight, it is considerable for Hungarian Limousine breeders to involve this trait into measured parameters.
References
Abreu, B. A., Magalhães, C. J., Duayer, E., Machado, S. H. M., Silva, D. A. (2015): Variação da medida torácica obtida com a fita métrica tradicional com fator de correção e com a fita de pesagem para bovinos. Acta Biomedica Brasiliensia, 6. 42–48. https://doi.org/10.18571/acbm.08
Afolayan, R. A., Pitchford, W. S., Deland, M. P. B., Mckiernan, W. A. (2007): Breed variation and genetic parameters for growth and body development in diverse beef cattle genotypes. Journal of Animal Genetics, 1. 13–20. https://doi.org/10.1017/S1751731107257933
Arango, J. A., Cundiff, L. V., Van Vleck, L. D. (2002): Genetic parameters for weight, weight adjusted for body condition score, height and body condition score in beef cattle. Journal of Animal Science 80. 3. 3112–3122. https://doi.org/10.2527/2002.80123112x
Ashwin, J. P., Sanjay, P., Amipara, G. J., Lunagariya, P. M., Parmar, D. J., Rank, D. N. (2019): Prediction of Body Weight based on Body Measurementsin Crossbred Cattle. International Journal of Current Microbiology and Applied Sciences, 8. 3. 1597–1611. https://doi.org/10.20546/ijcmas.2019.803.186
Augspurger, N.R., Ellis, M. (2002): Weighing affects short-term feeding patterns of growing-finishing pigs. Canadian Journal of Animal Science, 82. 445–448. https://doi.org/10.4141/A01-046
Bene, Sz., Nagy, B., Nagy, L., Kiss, B., Polgár, J.P., Szabó, F. (2007): Comparison of body measurements of beef cows of different breeds. Archiv für Tierzucht, Dummerstorf, 50. 4. 363–373. https://doi.org/10.5194/aab-50-363-2007
Domokos, Z. (2011): A hazai charolais szarvasmarha állomány típusainak és értékmérő tulajdonságainak elemzése. Doktori PhD értekezés. Állattenyésztés-tudományi Doktori Iskola, Gödöllő.
Francis, J., Sibanda, S., Kristensen, T. (2002): Estimating Body Weight of Cattle Using Linear Body Measurements. Zimbabwe Veterinary Journal, 33. 15–21. https://doi.org/10.4314/zvj.v33i1.5297
Gritsenko, S., Ruchay, A., Kolpakov, V., Lebedev, S., Guo, H., Pezzuolo, A. (2023): On-Barn Forecasting Beef Cattle Production Based on Automated Non-Contact Body Measurement System. Animals, 611. https://doi.org/10.3390/ani13040611
Haq. M. S., Budisatria, I. G. S., Panjono, P., Maharani, D. (2020): Prediction of live body weight using body measurements for Jawa Brebes (Jabres) cattle. The Journal of Animal & Plant Sciences, 30. 3. 552–559. https://doi.org/10.36899/JAPS.2020.3.0065
Hlokoe, V. L., Tyasi, T. L. (2022): Nguni Cattle Body Weight Estimation using Regression Analysis. Journal of Animal Health and Production 10. 3. 375–380. https://doi.org/10.17582/journal.jahp/2022/10.3.375.380
Kashoma I. P. B., Luziga, C., Werema, C. W., Shirima, G. A., Ndossi, D. (2011): Predicting body weight of Tanzania shorthorn zebu cattle using heart girth measurements. Livestock Research Rural Development, 23. 94.
Kongsro, J. (2014): Estimation of pig weight using a Microsoft Kinect prototype imaging system. Computerization and Electronics in Agriculture, 109. 32–35. https://doi.org/10.1016/j.compag.2014.08.008
Li, J., Li, Q., Ma, W., Xue, X., Zhao, C., Tulpan, D., Yang, S.X. (2022): Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds. Agriculture 12. 1012. https://doi.org/10.3390/agriculture12071012
Litwinczuk Z., Szulc, T. (2005): Hodowla i uĪytkowanie bydáa (praca zbiorowa). PWRiL, ISBN: 83-09-01794-4 Warszawa 412.
Mutua, F. K., Dewey, C. E., Arimi, S. M., Schelling, E., Ogara, W. O. (2011): Prediction of live body weight using length and girth measurements for pigs in rural Western Kenya. Journal of Swine Health and Production, 19. 1. 26–33. https://doi.org/10.54846/jshap/651
Nagy, B. (2007): A magyar szürke szarvasmarha néhány értékmérő tulajdonsága. Doktori PhD értekezés. Keszthely.
Ni’am, H. U. M., Purnomoadi, A. -. Dartosukarno, S. (2012): The relationship of body size and body weight of Bali cows in the various age groups. Anim. Agric. Journal, 1. 1. 541–556.
Nogalski, Z. (2003): Relations between the course of parturition, body weights and measurements of Holstein,Friesian calves. Czech Journal of Animal Science 48. 2. 51–59.
Ouédraogo, D., Soudré, A., Ouédraogo-Koné, S., Zoma, B.L., Yougbaré, B., Khayatzadeh, N., Burger, P. A., Mészáros, G., Traoré, A., Mwai, O. A. (2020): Breeding objectives and practices in three local cattle breed production systems in Burkina Faso with implication for the design of breeding programs. Livestock Science, 232. 103910. https://doi.org/10.1016/j.livsci.2019.103910
Ozkaya, S., Neja, W., Krezel- Czopek, S., Oler, A. (2015): Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis. Animal Production Science 56. 12. 2060–2063. https://doi.org/10.1071/AN14943
Paputungan, U., L., Hakim, G., Ciptadi, G., Lapian, H. F. N. (2013): The estimation accuracy of live weight from metric body measurements in Ongole grade cows. J. Indonesian. Trop. Anim. Agric. 38. 3. 149–155. https://doi.org/10.14710/jitaa.38.3.149-155
Petherick, J. C., Doogan, V. J., Venus, B. K., Holroyd, R.G., Olsson, P. (2009): Quality of handling and holding yard environment, and beef cattle temperament: 2. Consequences for stress and productivity. Applied Animal Behaviour Science 120. 28–38. https://doi.org/10.1016/j.applanim.2009.05.009
Przysucha, T., Grodzky, H., Gobiewsky, M., Slósarz, J., Piottrowsky, T. (2012): Analysis of body measurements and pelvis area index of Limousine cows. Annals of Warsaw University of Life Sciences – SGGW, Animal Science 51. 107–112.
Putra, W. P., Sumadi, B. S., Hartatik, T. (2014): The genetic correlation of growth traits in Aceh cattle at Indrapuri district Aceh Province. Journal Agripet. 14. 1. 37–41. https://doi.org/10.17969/agripet.v14i1.1203
Ruchay, A., Kober, V., Dorofeev, K., Kolpakov, V., Dzhulamanov, K., Kalschikov, V., Guo, H. (2022): Comparative analysis of machine learning algorithms for predicting live weight of Hereford cows. Computerization and Electronics in Agriculture, 195. 106837. https://doi.org/10.1016/j.compag.2022.106837
Sales, M. F. L., Paulino, M. F., Valadares Filho, S. C., Paulino, P. V. R., Porto, M. O. – Couto, V. R. M. (2009): Composição corporal e requisitos energéticos de bovinos de corte sob suplementação em pastejo. Revista Brasileira de Zootecnia 38. 1355–1362. https://doi.org/10.1590/S1516-35982009000700027
Shi, C., Teng, G., Li, Z. (2016): An approach of pig weight estimation using binocular stereo system based on LabVIEW. Computerization and Electronics in Agriculture, 129. 37–43. https://doi.org/10.1016/j.compag.2016.08.012
Suliani, S., Pramono, A., Riyanto, J., Prastowo, S. (2017): The relationship between body size and body weight of male Simmental Ongole crossbreed at various age in Jagalan Surakarta Abattoir. Sains Peternakan. 15. 1. 16–21. https://doi.org/10.20961/sainspet.v15i1.4998
Szabó F. (Ed.) (1998): Beef cattle breeding (in Hungarian), Mezőgazda Kiadó, Budapest 376 pp.
Terada, N., Sasanuma, K., Furuta, K., Shinjo, A. (1993): Principal Components Analysis of Body Measurements between the Heifer and the Cow in Japanese Black and Hosltein Cattle. Journal of Veterinary Epidemiology, 31. 1–5. https://doi.org/10.2743/jve1986.1993.31_1
Tőzsér J., Nagy A., Gerszi K., Mézes M., Domokos Z., Kertész I., Fekete T. (1995): A herekörméret, a mellkasszélesség és mélység, valamint az élősúly fenotípusos összefüggésének változása az életkor függvényében charolais fajtájú tenyészbika-jelölteknél. Állattenyésztés és Takarmányozás, 44. 203–210.
Tőzsér, J., Domokos, Z., Alföldi, L. (2000): A proposition to correct some body measurements in Charolais cow. (in Hungarian), Állattenyésztés és Takarmányozás, 49. 1. 13–22.
Tőzsér, J., Sutta, J., Bedő, S. (2000a): Videókép-analízis alkalmazása a szarvasmarhák testméretének értékelésében. Állattenyésztés és Takarmányozás, 49. 385–392.
Tőzsér, J., Ingrand, S., Domokos, Z., Alföldi, L. (2001): Sex effect on body measurements and conformation traits in Charolais weaned calves. (in Hungarian), Állattenyésztés és Takarmányozás, 50. 6. 495–504.
Tőzsér, J., Szűcs, M. (2020): Regression analyzes to determine the selection targets in the central self-performance test in Limousin cattle breed (in Hungarian). Animal Welfare, Ethology and Housing systems, 16. 2. 189–199.
Tyasi, T. L., Makgowo, K. M., Mokoena, K., Rashijane, L. T., Mathapo, M. C., Danguru, L. W., Molabe, K. M., Bopape, P. M., Mathye, N. D., Maluleke, D. (2020): Multivariate adaptive regression splines data mining algorithm for prediction of body weight of Hy-line silver brown commercial layer chicken breed. Adv. Anim. Vet. Sci. 8. 8. 794–799. https://doi.org/10.17582/journal.aavs/2020/8.8.794.799
Udoh, E., Godwin, E., Unah, L. U. (2021): Prediction of body weight from linear body measurement in two breeds of cattle Jessie. Open Access Research Journal of Biology and Pharmacy, 041–046. https://doi.org/10.53022/oarjbp.2021.3.1.0052
Ulutas, Z., Saatci, M., Ozluturk, A. (2001): Prediction of body weights from body measurements in East Anatolian Red calves. Atalürk Üniv. Ziraal Fak. Derg. 32. 1. 61–65.
Weales, D., Moussa, M., Tarry, C. (2021): A robust machine vision system for body measurements of beef calves. Smart Agriculture Technology 1. 100024. https://doi.org/10.1016/j.atech.2021.100024
Weber, V. A. M., Weber, F. L., Gomes, R. C., Oliveira Junior, A. S., Menezes, G. V., Abreu, U. G. P., Belete, N. A. S., Pistori, H. (2020): Prediction of Girolando cattle weight by means of body measurements extracted from images. Revista Brasileira de Zootecnia 49:e20190110. https://doi.org/10.37496/rbz4920190110
Wongsriworaphon, A., Arnonkijpanich, B., Pathumnakul, S. (2015): An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computerization and Electronics in Agriculture, 115. 26–33. https://doi.org/10.1016/j.compag.2015.05.004
Xu, H. X. (2022): Intelligent Measurement Algorithm of Cattle Body dimensions based on Improved CenterNet. Infrared Laser Eng. 2022, 1–7. (In Chinese)
Yakubu, A., Muhammed, M. M., Ari, M. M., Musa-Azara, I. S., Omeje, J. N. (2015): Correlation and path coefficient analysis of body weight and morphometric traits of two exotic genetic groups of ducks in Nigeria. Bangladesh J. Vet. Anim. Sci. 44. 1–9. https://www.doi.org/10.3329/bjas.v44i1.23112
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Tőzsér János, Vertséné Zándoki Rita, Kosztolányiné Szentléleki Andrea, Tarr Bence, Szűcs Márton
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
