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

Abstract

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.

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation

“…The h 2 and repeatability for the six traits ranged from 0.10 to 0.14 and 0.40 to 0.67, respectively. These results are similar to our previous findings without using genotypic data (Chen et al, 2022).…”

“…Phenotypic correlations were generally lower than genetic correlations. Overall, the genetic and phenotypic correlations among the six traits are consistent with our previous study (Chen et al, 2022). However, lower genetic correlations were estimated between NINT and MUNY when genotypic data were used.…”

“…Although the mean prediction accuracies (0.52–0.59) of NEI in focal individuals from ssGBLUP are not very high, they are not too far from mean theoretical accuracies (0.58–0.66). The average reliability (square of theoretical accuracy) of the bulls ( n = 736) was 0.68 when establishing NEI without genomic information (Chen et al, 2022), indicating that NEI can still perform genomic predictions when sufficient records are available, especially when combined with genomic information.…”

“…Two scenarios were considered for estimating the (co)variance components: one with both pedigree, phenotypic and genotypic data and the other with only pedigree and phenotypic data. The EBV and GEBV for the six studied traits were estimated using the BLUPF90 program (version 1.71) with the same model used for (co)variance components estimation (Chen et al, 2022). The reliabilities (REL) of EBV and GEBV for the studied traits based upon the whole data are calculated by the formula:where is the reliability of j trait, is the standard error of prediction for j trait retrieved from the inverse of the left‐hand side matrix of the mixed model equations, is the additive genetic variance and f is the mean inbreeding coefficient for all animals included in the pedigree.…”

“…A recent large international collaboration revealed that predicting N excretion in dairy cows requires the inclusion of N intake (NINT) and MUN to more accurately predict N pollution, with the need for region-specific models (Bougouin et al, 2022). We proposed a novel N efficiency index (NEI) that combines the estimated breeding values (EBV) of NINT, milk true protein N (MTPN) and MUN yield (MUNY) through a selection index, considering both NUE and N pollution simultaneously (Chen et al, 2022). MUNY is proportional to urinary N excretion (Wisconsin Alumni Research Foundation, 2015).…”