Determining the Factors Affecting 305-Day Milk Yield of Dairy Cows with Regression Tree

Serdar Genç, Mehmet Mendes

Abstract


The purpose of this study was to determine the factors affecting the 305-day milk yield of dairy cattle by using Regression Tree Analysis (RTA). The data set of this study consisted of 8 different cattle breeds grown in Turkey. Breed (B), Province (P), Lactation Length (LL), Service Period (SP), Dry Period (DP), Parity (PR), Calving Year (CY), Calving Age (CA) and Calving Month (CM) were used to predict the 305-day milk yield. Results of RTM showed that the usage of this method might be appropriate for determining the important factors that would be able to affect the 305-day milk yield (R2=71.3%). It was seen that the most important factors affecting the 305-day milk yield were the Breed, Lactation Length, Province, and Parity. Therefore, those selected factors were more efficient than the others in predicting the 305-day milk yield. RTA results also indicated that the lowest milk yield was estimated for Jersey, Jersey Crossbred, and Yerli Kara. Among the highest 305-day milk yield cows, the milk yield estimates of the cows in the second, third, fourth, fifth, and the sixth parities were found significantly higher than that of the cows in the first and seventh parities.

Keywords


305-day milk yield; regression tree; prediction; dairy cows; breed

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References


Ashmawy AA, Khattab AS, Hamed MK. 1985. Ratio and regression factors for predicting 305-day production from part-lactation milk records in Friesian cattle in Egypt. Bulletin of Faculty of Agriculture, Cairo University, Egypt.

Berry DP, Buckley F, Dillon P. 2007. Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows. Animal, 1(9), 1351–1359.

Bevilacqua M, Braglia M, Montanari R. 2003. The classification and regression tree approach to pump failure rate analysis. Reliab Eng. Syst. Safe. 79, 59-67.

Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984). Classification and regression trees. Chapman and Hall, Wadsworth Inc., New York, NY, USA.

Camdeviren H, Mendeş M, Özkan, MM, Toros F, Şaşmaz T, Öner S. 2005. Determination of depression risk factors in children and adolescents by regression tree methodology. Acta Med. Okayama, 59(1), 19-26.

David RL, Paul LS. 2004. Multivariate regression trees for analysis of abundance data. Biometrics, 60, 543–549.

De’ath G, Katharina EF. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81(11), 3178–3192.

Genc S, Soysal MI. 2018. Milk yield and reproductive traits of Holstein cattle population in Turkey. Journal of Tekirdag Agriculture Faculty, 15 (1), 76-85.

Genc S, Soysal MI. 2019. Estimation of genetic parameters and genetic trend of Holstein Friesian cattle population in Turkey. Fresenius Environmental Bulletin, 28 (4), 2617-2624.

Karabag K, Mendes M, Alkan S, Balcioglu MS. 2010. An assessment of embryonic mortality stages in Chukar partridge (Alectoris chukar) by means of classification tree method. Archiv Fur Geflugelkunde, 74, 269-273.

Khalid J, Masroor EB, Muhammad A. 2007. Within herd phenotypic and genetic trend lines for milk yield in Holstein Friesian dairy cows. J. Anim. Biol., 1, 66-70.

Kocak S, Tekerli M, Ozbeyaz C, Yuceer B. 2007. Environmental and genetic effects on birth weight and survival rate in Holstein calves. Turk. J. Vet. Anim. Sci., 31(4), 241-246.

Kuthu ZH, Javed K, Ahmad N. 2007. Reproductive performance of indigenous cows of Azad Kashmir. J. Anim. Plants Sci., 17, 47-51.

Lacroix R, Wade KM, Kok R, Hayes JF. 1995. Prediction of cow performance with a connectionist model. Transactions of the American Society of Agricultural Engineers, 38 (5), 1573–1579.

Mendes M, Akkartal E. 2009. Regression tree analysis for predicting slaughter weight in broilers. Italian Journal of Animal Science, 8 (4), 615-624.

Mirtagioglu H, Keskin S, Bakir G. 2008. Regression tree analysis for 305 day milk yield in Holstein cows. Indian Vet. J., 85, 943-945.

Olori VE, Hill WG, McGuirk BJ, Brotherstone S. 1999. Estimating variance components for test day milk records by restricted maximum likelihood with a random regression animal model. Livest. Prod. Sci., 61, 53-63.

Sahin A, Ulutas Z, Adkinson YA, Adkinson RW. 2014. Genetic parameters of first lactation milk yield and fertility traits in Brown Swiss Cattle. Annals of Animal Science, 14(3), 545-557.

Soydan E, Sahin A. 2016. Estimates of genetic parameters for direct and maternal effects with six different models on birth weight of brown Swiss calves. Journal of Animal and Plant Sciences, 26 (3), 577-582.

SPSS, 2008. Statistical Package Social Science: SPSS for windows release 17.0, SPSS Inc., 2008.

Topal M, Aksakal V, Bayram B, Yağanoğlu AM. 2010. An analysis of the factors affecting birth weight and actual milk yield in swedish red cattle using regression tree analysis. The Journal of Animal and Plant Sciences, 20 (2), 63-69.

Ulutas Z. 2002. Estimation of genetic and phenotypic trends of 305-day milk yield for Holsteins reared at Gelemen State Farm in Turkey. Indian Journal of Animal Sciences, 72 (10), 875-877.

Van Vleck, L.D., Henderson, C.R. (1961). Estimates of genetic parameters of some functions of part lactation milk records. Journal of Dairy Science, 44, 1073-1084.

Zheng H, Chen L, Han X, Zhao X, Ma Y. 2009. Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions. Agriculture, Ecosystems and Environment, 132, 98–105.




DOI: https://doi.org/10.24925/turjaf.v9i6.1154-1158.4384

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ISSN: 2148-127X

Turkish JAF Sci.Tech.

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