Indian Journal of Animal Research

  • Chief EditorK.M.L. Pathak

  • Print ISSN 0367-6722

  • Online ISSN 0976-0555

  • NAAS Rating 6.50

  • SJR 0.263

  • Impact Factor 0.4 (2024)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
Science Citation Index Expanded, BIOSIS Preview, ISI Citation Index, Biological Abstracts, Scopus, AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Submit

Research Article

Indian Journal of Animal Research, volume 50 issue 6 (december 2016) : 989-994

Modeling with Gaussian mixture regression for lactation milk yield in Anatolian buffaloes

Abdullah Yesilova1*, Ayhan Yilmaz2, Gazel Ser1, Baris Kaki3
1<p>Department of Animal Science, Faculty of Agriculture,&nbsp;Yuzuncu Yil University, 65080 Van, Turkey.</p>
Cite article:- Yesilova1* Abdullah, Yilmaz2 Ayhan, Ser1 Gazel, Kaki3 Baris (2016). Modeling with Gaussian mixture regression for lactationmilk yield in Anatolian buffaloes . Indian Journal of Animal Research. 50(6): 989-994. doi: 10.18805/ijar.v0iOF.4545.

ABSTRACT

The purpose of this study was to classify Anatolian buffalo using Gaussian mixture regression model according to discrete and continuous environmental effects. Gaussian mixture model performs separately regression analysis both within and between groups. This is an important property of Gaussian mixture models which makes it different from other multivariate statistical methods. The data were obtained from 1455 Anatolian buffalo lactation milk yield records reared in seven different locations in Bitlis province, Turkey. Age of dam, lactation duration and locations were considered as environmental effects on lactation milk yield. Data set was divided into three homogenous subgroups with respect to AIC and BIC in the Gaussian mixture regression, based on environmental effects on lactation milk yield. Estimated mean for lactation milk yields and mixing probabilities for the first, second and third subgroups were determined as 1494.33 kg (16.9%), 540.33 kg (45.2%) and 847.61 (37.9%), respectively. The numbers of buffalo in each subgroup according to mixing probability were obtained as 159, 756, and 540 for the first, second, and third groups, respectively. The effects of lactation period, age of dam and villages were found statistically significant on lactation milk yield in subgroup 1 that was highest mean for lactation milk yield (p<0.01).   In conclusion, results showed that Gaussian mixture regression was an important tool for classifying quantitative traits considering environmental effects in animal breeding.

REFERENCES


  1. Afzal, M., Anwar, M. and Mirza, M.A. (2007). Some factors affecting milk yield and lactation lenght in Nili Ravi buffaloes. Pakistan Vet. J., 27: 113-117.

  2. Berger, Y.M. and Thomas, D.L. (2005). Milk testing, calculation of milk production and adjustment factors. http://    www.ansci.wisc.edu/extensionnew%20copy/sheep/Publications_and_Proceedings/symposium_04/pdf%20of % 20Dairy%20Sheep%20Proceedings/Berger%20Testing%20edited%209-26-4%20Proc.pdf. Accessed: 24.02.2016.

  3. Borghese, A. (2005). Buffalo Production and Research. FAO Ed. REU Technical Series 67: 1-315. 

  4. Borghese, A., Rasmussen, M. and Thomas, C.S. (2007). Milking management of dairy buffalo. Ital. J.Anim.Sci. 6: 39-50.

  5. García, Y., Fraga, L.M., Padrón, E., Rodríguez, N., Alcides, A., Guzmán, G. and Mora, M. (2010). Genetic Studies on Water Buffalo Species (Bubalus bubalis) in Cuba. Revista Veterinaria. 21: 390-392.

  6. Hammack, S.P. (2007). Texas Adapted Genetic Strategies for Beef Cattle I: An Overview. Agri Life Extension. http://    animalscience.tamu.edu/wp-content/uploads/sites/14/2012/04/geneticsE186.pdf . Accessed: 02.04.2016.

  7. Izgi, A.N. and Asker, R. (1988). Mandalarda dogum mevsimi ve ilkine dogurma yasinin laktasyon süresi ve süt verimi üzerine etkileri. Mandacilik Arastirma Enstitüsü, Yayin No: 19, Afyon.

  8. Kreul, W. and Sarican, C. (1993). Türkiye’de manda yetistiriciligi. Hasad Dergisi 95: 8.

  9. Lanza, S.T., Cooper, B.R. and Bray, B .C. (2014). Population heterogeneity in the salience of multiple risk factors for adolescent delinquency. Journal of Adolescent Health 54: 319-325. 

  10. Leisch, F. (2004). A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software. 11: 1-18. 

  11. Lillehammer, M., Meuwissen, T.H.E. and Sonesson, A.K. (2013). A low-marker density implementation of genomic selection in aquaculture using within-family genomic breeding values. Genetics Selection Evolution, 45:39.

  12. Malhado, C.H.M., Malhado, A.C.M., Ramos, A.A., Carneiro, P.L.S., Souza, J.C. and Pala, A. (2013). Genetic parameters for milk yield, lactation length and calving intervals of Murrah buffaloes from Brazil. R. Bras. Zootec. 42: 565–    569. 

  13. Mao, C.X., Yang, N. and Zhong, J. (2013). On population size estimators in the Poisson Mixture model. Biometrics. 69: 758–765.

  14. Morgan, C.J., Lenzenweger, M.F., Rubin, D.B. and Levy, D.L. (2013). A hierarchical finite mixture model that accommodates zero-inflated counts, non-independence, and heterogeneity. Statistics in Medicine. 33: 2238–2250.

  15. Muthén, L.K. and Muthén, B. (2014). Mplus: User’s Guide. Los Angeles, CA: Muthén & Muthén. 

  16. Özenç, E., M.R. Vural., E. seker. and M. Uçar. 2008. An evaluation of subclinical mastitis during lactation in Anatolian buffloes. Türk J Vet Anim Sci., 32: 359-368.

  17. Patil, C.S., Chakravarty, A.K., Kumar, V., Sharma, R.K. and Pankaj, K. (2012). Average Performance of first lactation 305 day and test daymilk yield in Murrah Buffaloes. Indian J. Anim. Res., 46: 310-312.

  18. SAS. (2016). SAS/STAT Software:Hangen and Enhanced. SAS Inst Inc, USA. 

  19. Shepherd, R.K. and Kinghorn, B.P. (1994). A deterministic multi-tier model of assortative mating following selection. Genet. Sel. Evol., 26: 495-516.

  20. Subtil, F., Boussari, O., Bastard, M., Etard, J.F., Ecochard, R. and Genolini, C. (2015). An Alternative Classification to Mixture Modeling for Longitudinal Counts or Binary Measures. Statistical Methods in Medical Research., doi:10.1177/0962280214549040.

  21. sekerden, Ö. (1987). Laktasyon veriminin hesaplanmasinda kullanilan çesitli hesaplama metodlarinin ve degisik süt verim kontrol periyodlarinin karsilastirilmasi. Ondokuz Mayis Üniv. Zir Fakt. Derg. 2: 133-148. 

  22. Tekerli, M., Kücükkebabçi, M., Akakin, N.H. and Kocak, S. (2001). Effects of environmental factors on some milk production traits, persistency and calving interval of Anatolian buffaloes. Livestock Production Science 68: 275-281.

  23. Tekerli, M. (2016). Mandayildizi Hesap Isleme Programi. Afyonkocatepe Üniversitesi, Afyonkarahisar.

  24. Thomas, C.S. (2008). Efficient dairy buffalo production. Handbook, De Laval International AB, Tumba, Sweden.

  25. Vijh, R.K., Tantia, M.S., Mishra, B. and Bharani Kumar, S.T. (2008). Genetic relationship and diversity analysis of Indian water buffalo (Bubalus bubalis). Journal of Animal Sci., 86: 1495–502.

  26. Yesilova, A., Özrenk, K., Kaki, B., Almali, M.N. and Balta, F. (2010). Locational classification of walnut (Juglans Regia L.) genotypes collected from Lake Van basin by using mixture modeling. African Journal of Agricultural Research, 5: 1509-1514. 
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)