Defining a nitrogen efficiency index in Holstein cows and assessing its potential effect on the breeding program of bulls

• Published in: Journal of dairy science Vol. 105; no. 9; pp. 7575 - 7587
• Main Authors: Chen, Y., Atashi, H., Grelet, C., Vanderick, S., Hu, H., Gengler, N.
• Format: Journal Article Web Resource
• Language: English
• Published: Elsevier Inc 01.09.2022; American Dairy Science Association; Elsevier
• Subjects: Animal Science and Zoology; genetic correlation; Genetics; Genetics & genetic processes; Génétique & processus génétiques; health; Life sciences; mid-infrared spectrum; N intake; Sciences du vivant; mid-infrared spectrum; health; N intake; genetic correlation
• ISSN: 0022-0302; 1525-3198; 1525-3198
• DOI: 10.3168/jds.2021-21681

The purposes of this study were (1) to explore the relationship between 3 milk mid-infrared predicted features including nitrogen intake (NINT), milk true protein N (MTPN), and milk urea-N yield (MUNY); (2) to integrate these 3 features into an N efficiency index (NEI) and analyses approximate genetic correlations between the NEI and 37 traits (indices) of interest; and (3) to assess the potential effect of including the NEI into breeding programs of bulls. The edited data were 1,043,171 test-day records on 342,847 cows in 1,931 herds and 143,595 test-day records on 53,660 cows in 766 herds used for estimating breeding values (EBV) and variance components, respectively. The used records were within 5 to 50 d in milk. The records were grouped into primiparous and multiparous. The genetic parameters for the included mid-infrared features and EBV of the animals included in the pedigree were estimated using a multiple-trait repeatability animal model. Then, the EBV of the NINT, MTPN, MUNY were integrated into the NEI using a selection index assuming weights based on the N partitioning. The approximate genetic correlations between the NEI and 37 traits of interest were estimated using the EBV of the selected bulls. The bulls born from 2011 to 2014 with NEI were selected and the NEI distribution of these bulls having EBV for the 8 selected traits (indices) was checked. The heritability and repeatability estimates for NINT, MTPN, and MUNY ranged from 0.09 to 0.13, and 0.37 to 0.65, respectively. The genetic and phenotypic correlations between NINT, MTPN, and MUNY ranged from −0.31 to 0.87, and −0.02 to 0.42, respectively. The NEI ranged from −13.13 to 12.55 kg/d. In total, 736 bulls with reliability ≥0.50 for all included traits (NEI and 37 traits) and at least 10 daughters distributed in at least 10 herds were selected to investigate genetic aspects of the NEI. The NEI had positive genetic correlations with production yield traits (0.08–0.46), and negative genetic correlations with the investigated functional traits and indices (−0.71 to −0.07), except for the production economic index and functional type economic index. The daughters of bulls with higher NEI had lower NINT and MUNY, and higher MTPN. Furthermore, 26% of the bulls (n = 50) with NEI born between 2011 to 2014 had higher NEI and global economic index than the average in the selected bulls. Finally, the developed NEI has the advantage of large-scale prediction and therefore has the potential for routine application in dairy cattle breeding in the future.