employee
Novosibirsk, Novosibirsk, Russian Federation
employee
Novosibirsk, Novosibirsk, Russian Federation
Sibirskiy gosudarstvennyy universitet inzhenerii i biotehnologiy (Applied Bioinformatics Chair, Docent)
employee from 01.01.1925 to 01.01.1926
Novosibirsk, Novosibirsk, Russian Federation
employee
Novosibirsk, Novosibirsk, Russian Federation
employee
Novosibirsk, Novosibirsk, Russian Federation
VAK Russia 4.1.1
VAK Russia 4.1.2
VAK Russia 4.1.3
VAK Russia 4.1.4
VAK Russia 4.1.5
VAK Russia 4.2.1
VAK Russia 4.2.2
VAK Russia 4.2.3
VAK Russia 4.2.5
VAK Russia 4.3.3
VAK Russia 4.3.5
UDC 636.2.034
The objective of the study is to determine the predictive ability of linear traits and their combinations for assessing milk protein yield in first-calf heifers. The study subjects were Holstein first-calf heifers with conformation and milk protein content assessed using 18 linear traits over 305 days of lactation. The subject of the study was the phenotypic correlation of linear traits with milk protein yield. The relationship between the studied traits was assessed in 1,243 first-calf heifers from a high-yielding Holstein herd in the Novosibirsk Region from 2017 to 2023, lactating for 305 days or more, using the Spearman rank correlation coefficient. Preprocessing of the primary data consisted of transforming the linear trait estimates to correctly calculate the correlation coefficient. Grouping of conformation parameters to determine the complex most closely associated with the productive trait was performed using two algorithms. Milk protein content over 305 days of lactation ranged from 156 to 458 kg. A weak correlation was found between conformation and productivity indicators for five udder traits and one body trait, ranging from +0.073 to +0.147 (α < 0.01, α < 0.001). The correlation coefficient between the composite score calculated using the first algorithm and milk protein yield was +0.275 (α < 0.001), while for the second algorithm it was +0.292 (α < 0.001). The approximation coefficients (R2) of the trend functions plotted against the average milk protein level of animal groups, aggregated by the composite score, indicated satisfactory model accuracy in the first case (≈ 0.95) and high accuracy in the second (≈ 0.98). The comparison results suggest that the second method is the preferred algorithm for identifying the best combination of linear features for indirectly assessing and predicting productivity in groups of first-calf heifers. The first method is recommended as a rapid test to determine the feasibility of further application of the second, which is more computationally expensive but more effective.
conjugate variability, Holstein breed, first-calf heifers, productivity, milk protein, correlation, complex of linear traits, complex score
1. Nematollahi AF, Rahiminejad A, Vahidi B. A novel meta-heuristic optimization method based on golden ratio in nature. Journal of Soft Computing. 2020;24(2):1117-1151. DOI:https://doi.org/10.1007/s00500-019-03949-w.
2. Lefler TF, Adushinov DS, Boyarina OA. Linear assessment of Holstein cows exterior under Canadian methodology. Bulletin of KSAU. 2025;8:156-165. (In Russ.). DOI:https://doi.org/10.36718/1819-4036 2025-8-156-165.
3. Alekseeva EA. Index construction to comprehensively assess dairy cows. Bulletin of KSAU. 2023;2:172-179. (In Russ.). DOI:https://doi.org/10.36718/1819-4036-2023-2-172-179.
4. Batanov SD, Baranova IA, Starostina OS. Model for predicting the milk productivity of cows by their exterior features. Vestnik of BashGAU. 2019;1:55-62. (In Rus.). DOI:https://doi.org/10.31563/1684-7628-2019-49-1-55-62.
5. Kartashova AP, Firsova EV. Multipurpose evaluation of the dairy cattle // Agrarian Bulletin of the Urals. 2020;10:50-56. (InRuss.). DOI:https://doi.org/10.32417/1997 4868-2020-201-10-50-56.
6. Trukhachev VI, Zlydnev NZ, Selionova MI. Dairy cattle breeding of northern Europe: Strategies, methods, results (Part 1). Dairy and Breed Cattle Farming. 2016;4:2-5 (in Russ.).
7. Romanova EA, Tulinova OV. Breeding index modeling for airshire dairy cattle with application of exterior parameters. Vestnik of Ulyanovsk State Agricultural Academy. 2021;1:150-155. (In Russ.). DOI:https://doi.org/10.18286/1816 4501-2021-1-150-155.
8. Mironov EG, Logacheva AYu, Joy IYu, et al. Formation of an expanded linear conformation traits list for the dairy cattle based on international and Russian experience. Genetics and Breeding of Animals. 2024;3:60-76. (In Russ.). DOI:https://doi.org/10.31043/2410-2733-2024-3-60-76.
9. Lapina MYu, Abramova MV. The dynamics of the linear traits and milk production in micropopulation of holstein cattle. Perm agrarian journal. 2020;3:94-102. (In Russ.). DOI:https://doi.org/10.24411/2307-2873-2020 10045.
10. Truhachev VI, Zlydnev NZ, Selionova MI. Dairy cattle breeding of northern Europe: Strategies, methods, results (Part 2). Dairy and Breed Cattle Farming. 2016;5:3-7 (In Russ.).
11. Alimzhanova LV, Bostanova SK, Sheiko YuN, et al. The Level of Milk Production, Depending on the Exterior Traits of Dairy Cows. OnLine Journal of Biological Sciences. 2018;18(1):29-36 DOI:https://doi.org/10.3844/ojbsci.2018.29.36.
12. Tatarkina NI, Svyazhenina MA, Ponomareva EA. The use of exterior assessment in the selection of Holstein cattle. Agrarian Bulletin of the Urals. 2023;23(10).81-90. (In Russ.). DOI:https://doi.org/10.32417/1997-4868-2023-23-10-81-90.
13. Konte AF, Karlikova GG. Parameters of variability of indicators of physique and productivity of Holstein cows depending on the level of milk yield. Agrarian Bulletin of the Urals. 2022;6:37-48. (In Russ.). DOI:https://doi.org/10.32417/1997-4868-2022-221-06-37-48.
14. Tulinova OV, Romanova EA. Impact of classifier qualification on the results of linear assessment of cattle. Animal Husbandry and Fodder Production. 2025;108(3):95-112. (In Russ.). DOI:https://doi.org/10.33284/2658-3135-108-3-95.
15. Prabowo S, Garip M. Using of Conspicuous of Body Angularities Type Traits to Milk Yields as Dairy Cattle Selection Preferences. Basrah Journal of Agricultural Sciences. 2023;36(1):238-253. DOI:https://doi.org/10.37077/25200860.2023.36.1.19.
16. Batanov SD, Amerkhanov KhA, Baranova IA, et al. Milk productivity of cows of different exterior constitutional types. Izvestiya of Timiryazev Agricultural Academy. 2021;2:102-113. (In Russ.). DOI:https://doi.org/10.26897/0021-342X-2021-2-102-113.
17. Gart VV, Kulikova SG, Bogdanova OV, et al. Polynomial conjugate variability of traits of linear assessment of the exterior and milk yield of highly productive Holstein cattle. Izvestiya of Timiryazev Agricultural Academy. 2024;5:86-100. (In Russ.). DOI:https://doi.org/10.26897/0021-342X-2024-5-86-100.
18. Gart VV, Kulikova SG, Narozhnykh KN, et al. Early prediction of milk fat content in Holstein cattle based on correlated variability with linear traits. Bulletin of NSAU. 2024;4:168-176. (In Russ.). DOI:https://doi.org/10.31677/2072-6724-2024-73-4-168-1764.
19. Koterov AN, Ushenkova LN, Zubenkova ES, et al. Strength of Association. Report 2. Graduations of Relative Risk. Medical Radiology and Radiation Safety. 2019;64(6):12-24. (In Russ.). DOI:https://doi.org/10.12737/1024-6177-2019-64-6-12-24.
20. Gart VV, Kulikova SG, Kamaldinov EV, et al. Indirect assessment of the levels of milk productivity traits in first-calf heifers of the Holstein breed. Agrarian Bulletin of the Urals. 2025;25(6):900-914. (In Russ.). DOI:https://doi.org/10.32417/1997-4868-2025-25-06-900-914.
21. Gart VV, Kulikova SG, Kamaldinov EV, et al. Validation of the first method for predicting milk productivity in first-calf heifers based on a composite score of linear type traits. Agrarian Bulletin of the Urals. 2025;25(9):1372-1383. (In Russ.). DOI:https://doi.org/10.32417/1997-4868-2025-25-09-1372-1383.
