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Implementation of the genetic potential of foreign-bred bulls in breeding farms of Western Siberia

https://doi.org/10.31677/2072-6724-2025-77-4-150-160

Abstract

The large-scale use of breeding material from foreign selection in Russian dairy farming creates biological uncertainty due to the pronounced “genotype-environment” interaction effect. The study aimed to assess the realization of the genetic potential of foreign-bred sires under the conditions of breeding farms in Western Siberia. Based on productivity data from 28,065 first-calf heifers, 21 Holstein bulls were evaluated. The initial data were adjusted for the influence of year and season factors, and the farms were stratified into two categories based on productivity levels. The local breeding value was calculated as the mean of paired differences between the milk yield of daughters and the weighted average milk yield of their herdmates, with outliers iteratively removed. To assess the comparability with the foreign breeding value index, a categorical classification method by sign and a correlation analysis were applied. A strong “genotype-environment” interaction was established, manifesting in the re-ranking of sires across different farm categories. The analysis of sign concordance showed that the agreement between the local estimate and the foreign catalog’s forecast was no more than 50 %, which is equivalent to a random event. A complete sign match across all three estimates, including both local categories and the foreign index, was recorded for only 25 % of the sires. These findings were confirmed by a correlation analysis, which revealed a statistically insignificant relationship between the local breeding value estimate and data from the foreign catalog. The study concludes that the direct use of data from foreign catalogs for making selection decisions is unreliable. A local assessment of breeding value that considers specific production conditions is an indispensable tool for the objective selection of sires and for improving the efficiency of breeding programs.

About the Authors

T. A. Zhigulin
Novosibirsk State Agrarian University
Russian Federation

postgraduate student

Novosibirsk



E. V. Kamaldinov
Novosibirsk State Agrarian University
Russian Federation

D,Sc, (Biology), Assoc, Prof

Novosibirsk



P. ­ N. Palchikov
Novosibirskagroplem
Russian Federation

Director JSC Novosibirskagroplem

Novosibirsk



References

1. Kamaldinov E.V., Petrov A.F., Shatokhin K.S., Narozhnykh K.N., Marenkov V.G., Zhigulin T.A., Bogdanova O.V., Palchikov P.N., Plakhova A.A., Vestnik NGAU (Novosibirskiy gosudarstvennyy agrarnyy universitet), 2022, No. 2 (63), pp. 76–83, DOI: 10.31677/2072-6724-2022-63-2-76-83. (In Russ.).

2. Khromova O.L., Selimyan M.O., Agrarnyy vestnik Verkhnevolzh’ya, 2022, No. 2 (39), pp. 68–78, DOI: 10.35523/2307- 5872-2022-39-2-68-78. (In Russ.).

3. Silva Neto J.B., Mota L.F.M., Londoño-Gil M., Schmidt P.I., Rodrigues G.R.D., Ligori V.A., Arikawa L.M., Magnabosco C.U., Brito L.F., Baldi F., Genotype-by-environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications, Animal Genetics, 2024, DOI: 10.1111/age.13483.

4. Wahinya P.K., Jeyaruban G.M., Swan A.A., van der Werf J.H.J., Optimization of Dairy Cattle Breeding Programs with Genotype by Environment Interaction in Kenya, Agriculture, 2022, Vol. 12, No. 8, pp. 1274, DOI: 10.3390/agriculture12081274.

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, Veterinary and Animal Science, 2020, Vol. 9, pp. 100098, DOI: 10.1016/j.vas.2020.100098.

6. Ducrocq V., Cadet A., Patry C., van der Westhuizen L., van Wyk J.B., Neser F.W.C., Two approaches to account for genotype-by-environment interactions for production traits and age at first calving in South African Holstein cattle, Genetics Selection Evolution, 2022, Vol. 54, pp. 43, DOI: 10.1186/s12711-022-00735-5.

7. Chuma-Alvarez J.L., Montaldo H.H., Lizana C., Olivares M.E., Ruiz-López F.J., Genotype × region and genotype × production level interactions in Holstein cows, Animal, 2021, Vol. 15, No. 9, pp. 100320, DOI: 10.1016/j.animal.2021.100320.

8. Kamaldinov E.V., Petrov A.F., Narozhnykh K.N., Palchikov P.N., Zhivotnovodstvo i kormoproizvodstvo, 2024, Vol. 107, No. 4, pp. 53–67, DOI: 10.33284/2658-3135-107-4-53. (In Russ.).

9. Chechenikhina O.S., Bykova O.A., Loretts O.G., Stepanov A.V., Agrarnyy vestnik Urala, 2021, No. 06 (209), pp. 71–79, DOI: 10.32417/1997-4868-2021-209-06-71-79. (In Russ.).

10. Sheveleva O.M., Svyazhenina M.A., Zhivotnovodstvo i kormoproizvodstvo, 2023, Vol. 106, No. 4, pp. 40–56, DOI: 10.33284/2658-3135-106-4-40. (In Russ.).

11. Alves F., de Souza E.G., Sobjak R., Bazzi C.L., Hachisuca A.M.M., Mercante E., Processamento de dados para remoção de pontos outliers e inliers: Estudo sistemático da literature, Revista Brasileira de Engenharia Agrícola e Ambiental, 2024, Vol. 28, No. 9, e278672. DOI: 10.1590/1807-1929/agriambi.v28n9e278672.

12. Lactanet Canada: Genetic Evaluations Data Files, available at: https://lactanet.ca/en/genetics/genetic-evaluations/ data-files/ (accessed 24.05.2024).

13. Giorgi F.M., Ceraolo C., Mercatelli D., The R Language: An Engine for Bioinformatics and Data Science, Life, 2022, Vol. 12, No. 5, pp. 648, DOI: 10.3390/life12050648.

14. Radomski A., Exploratory analysis and data visualization using the ggplot2 package in R-the basics of a digital cultural and historical researcher’s workshop, Historyka Studia Metodologiczne, 2024, Vol. 54, pp. 175–194, DOI: 10.24425/hsm.2024.153701.

15. Grolemund G., Wickham H., Dates and Times Made Easy with lubridate, Journal of Statistical Software, 2011, Vol. 40, No. 3, pp. 1–25, DOI: 10.18637/jss.v040.i03.

16. Bengtsson H. future: Unified Parallel and Distributed Processing in R for Everyone, CRAN R-project, available at: https://cran.r-project.org/web/packages/future/index.html (accessed: 19.09.2025).

17. Sartori C., Tiezzi F., Guzzo N., Mancin E., Tuliozi B., Mantovani R., Genotype by Environment Interaction and Selection Response for Milk Yield Traits and Conformation in a Local Cattle Breed Using a Reaction Norm Approach, Animals, 2022, Vol. 12, No. 7, Art. No. 839, DOI: 10.3390/ani12070839.


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For citations:


Zhigulin T.A., Kamaldinov E.V., Palchikov P.N. Implementation of the genetic potential of foreign-bred bulls in breeding farms of Western Siberia. Bulletin of NSAU (Novosibirsk State Agrarian University). 2025;(4):150-160. (In Russ.) https://doi.org/10.31677/2072-6724-2025-77-4-150-160

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