The role of fixed factors in the variability of milk yield in Irmeni cattle under industrial complex conditions
https://doi.org/10.31677/2072-6724-2021-61-4-137-149
Abstract
The authors evaluated the significance of paratypic factors in fat variability in the article. The study looked at the role of fixed effects such as: “Calving Season”, “Calving Year”, “Starting Season”, “Starting Year” and their interacting factors: “Calving Season: Calving Year”, “Starting Season: Starting Year”, “Calving Season: Starting Season”, “Calving Year: Starting Year”. The authors used data from Irmen’s primary zootechnical census of black-and-white cattle (n = 319210) from 2000 to 2020. The role of genetic and paratypical factors was assessed using linear mixed regression models and appropriate statistical methods and criteria. The following were selected as random effects: father, age of fertile insemination and animal identification data. The influence of the fixed characteristics of the prospective mathematical model was evaluated using an analysis of variance. But beforehand, the authors identified different combinations with adjustment for the proportion of random contribution. The grant levels of the estimated factors to the variability of the dependent trait were determined. The authors note the high conjugate variability between predicted and actual milk yields (r = 0,905; p˂ 0,001). A relatively high coefficient of determination (R2 = 0,819) was observed for the test sample. In this case, only phenotypic data were considered in the example when constructing the model. Application of the resulting model to other subpopulations may require additional correction factors as part of regional or federal breeding value index programs.
About the Authors
A. F. PetrovRussian Federation
Acting Head of the Applied Bioinformatics Laboratory,
Novosibirsk
E. V. Kamaldinov
Russian Federation
Doctor of Biological Sciences,
Novosibirsk
O. V. Bogdanova
Russian Federation
Senior Lecturer,
Novosibirsk
K. S. Shatokhin
Russian Federation
PhD in Biological Sciences, Senior Researcher,
Novosibirsk
O. F. Efremova
Russian Federation
Chief Livestock Breeder,
Verkh-Irmen village
V. A. Rogozin
Russian Federation
Chief Animal technician,
Verkh-Irmen village
References
1. Kamaldinov E.V., Vestnik Krasnojarskogo agrarnogo universiteta, 2012, No. 1, pp. 117–122. (In Russ.)
2. Brito L.F., Bedere N., Douhard F., Oliveira H.R., Arnal M., Peñagaricano F., Schinckel A.P., Baes C.F., Miglior F., Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world, Animal, 2021, pp. 100292, DOI: 10.1016/j.animal.2021.100292.
3. Neethirajan S., The role of sensors, big data and machine learning in modern animal farming, Sensing and Bio-Sensing Research, 2020, Vol. 29, pp. 100367. https://doi.org/10.1016/j.sbsr.2020.100367.
4. Carvalheiro R., Costilla R., Neves, H.H.R., Albuquerque L.G., Moore S., Hayes B.J., Unraveling genetic sensitivity of beef cattle to environmental variation under tropical conditions, Genet Sel Evol, 2019, Vol.51, pp.29, DOI: 10.1186/s12711-019-0470-x.
5. Santos J.C., Malhado C.H.M., Carneiro P.L.S, de Rezende M.P.G., Cobuci J.A., Genotype-environment interaction for age at first calving in Holstein cows in Brazil, Vet Anim Sci., 2020, Vol. 9, pp. 100098, DOI: 10.1016/j.vas.2020.100098.
6. Zhou C., Shen D., Li C., Cai W., Liu S., Yin H., Shi S., Cao M., Zhang S., Comparative Transcriptomic and Proteomic Analyses Identify Key Genes Associated With Milk Fat Traits in Chinese Holstein Cows, Front Genet, 2019, Vol. 10, pp. 672, DOI: 10.3389/fgene.2019.00672.
7. Huang W., Carbone M.A., Lyman, R.F., Anholt R.R.H., Mackay T.F.C., Genotype by environment interaction for gene expression in Drosophila melanogaster, Nat Commun, 2020, Vol. 11, pp. 5451, DOI: 10.1038/s41467-020-19131-y.
8. Zhang Z., Kargo M., Su G., Genotype-by-enviroment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model, Herediti, 2019, Vol. 123, pp. 202–214, DOI: 10.1038/s41437-019-0192-4.
9. Nikitin S.V., Knyazev C.P., Otbor i adaptacija v populjacijah domashnih svinej (Selection and adaptation in domestic pig populations), Lambert Academy Publishing, 2015, 228 p. (In Russ.)
10. Falconer D.S., Mackay T.F.C., Introduction to Quantitative Genetics, Pearson-Longman, Essex, U.K., 1996, 80 p. (In Russ.)
11. Mrode R.A., Linear models for the prediction of animal breeding values, Wallingford: CAB International Publ., 2014, 360 p.
12. Duque N.P., Casellas J., Quijano J.H., Casals R., Such X., Fitting lactation curves in a Colombian Holstein herd using nonlinear models, Revista Facultad Nacional de Agronomía Medellín, 2018, Vol. 71, No. 2, pp. 8459–8468.
13. Petrov A.F., Kamaldinov E.V., Panferova O.D., Efremova O.V., Rogozin V.A., Sibirskij vestnik sel’skohozjajstvennoj nauki, 2020, No. 50 (6), pp. 106–114. (In Russ.)
14. Bates D., Mächler M., Bolker B., Walker S., Fitting Linear Mixed-Effects Models Using lme4, Journal of Statistical Software, 2015; Vol. 67(1), pp. 1–48, https://doi.org/10.18637/jss.v067.i01.
15. Fazel Y., Fozi M., Esmailizadeh A., Fazel F., Niazi A., Rahmati S., Qasimi M., Use of Random Regression Test-Day Model to Estimate Genetic Parameters of Milk Yield in Holstein Cows, Open Journal of Animal Sciences, 2018, Vol. 8, pp. 27–38, DOI: 10.4236/ojas.2018.81003.
16. Kamaldinov E.V., Panferova O.D., Efremova O.V., Marenkov V.G., Petrov A.F., Ryumkina I.N., Assessment of the variability of reproductive abilities of a black and white cattle using genealogical data and paratypical factors, Data in Brief., 2021, Vol. 35, pp. 106842, DOI:10.1016/j.dib.2021.106842.
17. Piccardi M., Macchiavelli, R., Funes, A., Bó, G., & Balzarini, M., Fitting milk production curves through nonlinear mixed models, Journal of Dairy Research, 2017, Vol. 84, No. 2, pp. 146–153, DOI: 10.1017/S0022029917000085.
18. Wu X.L., Luo X., Xu P., Zhu L., New variable selection for linear mixed-effects models undefined, Ann Inst Stat Math, 2017, Vol. 69, pp. 627–646, DOI: 10.1007/s10463-016-0555-z.
19. Islam M.S., Jensen J., Løvendahl P., Karlskov-Mortensen P., Shirali M., Bayesian estimation of genetic variance and response to selection on linear or ratio traits of feed efficiency in dairy cattle, J Dairy Sci., 2020, Vol. 103, No. 10, pp. 9150–9166, DOI: 10.3168/jds.2019-17137.
20. Pretoriusd A.L., van der Merwe A.J., A nonparametric Bayesian approach for genetic evaluation in animal breeding, South African Journal of Animal Science, 2000, Vol. 30, No. 2, pp. 138–148.
21. Ismael A., Løvendahl P., Fogh A., Lund M.S., Su G., Improving genetic evaluation using a multitrait single-step genomic model for ability to resume cycling after calving, measured by activity tags in Holstein cows, J Dairy Sci., 2017, Vol. 100, No. 10, pp. 8188–8196, DOI: 10.3168/jds.2017-13122.
22. Padilha A.H., Padilha H.Cobuci J.A., dos Santos Daltro D., Neto J.B., Reliability of breeding values between random regression and 305-day lactation models Pesq. agropec. bras., Brasília, 2016, Vol. 51, No. 11, pp. 1848–1856, DOI: 10.1590/S0100-204X2016001100007.
23. Bugakov Yu.F., Labuzova I.M., Schaefer N.A., Irmenskij tip chjorno-pjostrogo skota: slagaemye uspeha (Irmen type of black-and-white cattle: components of success), Novosibirsk - Verh-Irmen: NSTU, 2007, 295 p.
24. Liu S., Trenkler G. Hadamard, Khatri-Rao, Kronecker and other matrix products, International Journal of Information and Systems Sciences, 2008, Vol. 4, No. 1, pp. 160-177.
25. Ludden T.M., Beal S.L., Sheiner L.B., Comparison of the Akaike Information Criterion, the Schwarz criterion and the F test as guides to model selection, J Pharmacokinet Biopharm., 1994, Vol. 22, No. 5, pp. 431–445.
Review
For citations:
Petrov A.F., Kamaldinov E.V., Bogdanova O.V., Shatokhin K.S., Efremova O.F., Rogozin V.A. The role of fixed factors in the variability of milk yield in Irmeni cattle under industrial complex conditions. Bulletin of NSAU (Novosibirsk State Agrarian University). 2021;(4):137-149. (In Russ.) https://doi.org/10.31677/2072-6724-2021-61-4-137-149