This research estimated dairy cow feed efficiency genetic characteristics using random regression models. The study collected DMI, ECM, and MBW data from 7,440 first-lactation Holstein cows in six countries from January 2003 to February 2022. A multiple-trait random regression model using a fourth-order Legendre polynomial for all characteristics determined genetic parameters. Genomic residual feed intake (gRFI) was calculated from weekly DMI phenotypes using linear regressions on ECM and MBW to quantify feed efficiency genetically adjusted for these factors. Heritability estimates for DMI, ECM, MBW, and gRFI ranged from 0.15 (0.03) to 0.29 (0.02), 0.24 (0.01) to 0.29 (0.03), 0.55 (0.03) to 0.83 (0.05), and 0.12 (0.03) to 0.22 (0.06)
The findings improve knowledge of genetic parameter change during the first lactation and provide feed efficiency selection techniques for breeding programs. As feed prices rise, dairy cows may be able to improve feed efficiency during ideal lactation phases. The genetic link between feed efficiency indicator features must be understood to determine the best selection program attributes.
This research used random regression to estimate variance components and genetic factors across days in milk (DIM) of first-lactation Holstein cows for DMI, ECM, MBW, and genomic residual feed intake (gRFI). The research employed Resilient Dairy Genome Project data from pre-existing databases. The data on 7,440 first-lactation Holstein cows from six nations included 121,226 DMI, 120,500 ECM, and 98,975 MBW records. The amount of recordings from each source varied throughout a 305-day lactation. The pedigree file for phenotyped cows comprised up to 10 generations, totaling 30,776 animals. WOL daily measurements were averaged to generate weekly phenotypes. Due to biological limits, milk production less than 4kg, fat and protein yield larger than zero, MBW and DMI calculations, and at least 3 WOL records for a characteristic were removed during data editing. The data were adjusted to the Canadian mean and standard deviation owing to country heterogeneity.
The research estimated additive genetic, permanent environmental (PE), and residual variances for dairy milk yield (DMI), early calving months (ECM), and milk yield using a multiple-trait random regression animal model. Average Information Restricted Maximum Likelihood (AIREML) in WOMBAT computed variance components. All studies employed a fourth-order Legendre polynomial since it suited orders one through four best. The data analysis showed significance for all model parameters. All characteristics utilize the generic model:
y = Xb + Za + Wpe + e, where y is the vector of phenotypic records, X, Z, and W are the incidence matrices for fixed (b), additive genetic (a), and PE (pe) effects, and e is the vector of random residuals. Heritability estimates for several WOLs were determined using diagonal elements of G and P, as well as the homogeneous σ of each trait. If traits had correlated residuals, the residual covariance structure for multiple trait analysis was as follows.
The research estimated additive genetic, PE, and phenotypic WOL correlation standard errors using Robertson’s technique. The heritabilities, PE ratio, and phenotypic variance calculated the correlations. Genetic covariance components from multiple-trait random regression computed feed efficiency. ECM and MBW partial regression coefficients for each WOL were determined using the genetic covariance matrix (GCOVj). The equations were:
P.C.E.C.M=(G.C.O.V.12×G.C.O.V.23) – (G.C.O.V.13×G.C.O.V.22) – (12×G.C.O.V.12) – (11×G.C.O.V.22)
P, C, M, B, W = (G, C, O, V, 12 x 13) – (G, C, O, V, 11 x 23).
P(C:O:V:12 × G:C:O:V:12) − (G:C:O:V:11 × G:C:O:V:22) P(C:O:V:12 × G:C:O:V:13) −(G:C:O:V:11 × G:C:O:V:22) P(C:O:V:12 × G:C:O:V:12)
The research examined the heredity of dry matter intake, energy adjusted milk, metabolic body weight, and feed efficiency in first-lactation cows. The partial regression coefficients were utilized to linearly translate DMI phenotypes, which were corrected for energy sinks based on genetic connection. Adjusted DMI (gRFI) accounts for energy sinks’ genetic relationships.
Moderate heritability estimates for DMI, ECM, MBW, and gRFI. Similar DMI heritability estimates were published by Byskov et al., 2017, Li et al., 2018, and Krattenmacher et al., 2019. They evaluated ECM heritability within the range previously predicted. Heritability estimations for MBW were comparable to this research.
The heritability estimates for gRFI were 0.12 (0.02) to 0.23 (0.07). Previous research estimated gRFI heritabilities between 0.10 and 0.25. This research employed change in body weight, body weight, fat-protein adjusted milk, and milk energy, while prior investigations used somewhat different energy sinks.
Trait correlations were examined to find biological trends throughout lactation phases. It has been claimed that feed intake varies genetically during lactation. Changes in genetic and phenotypic correlations within a characteristic were examined through lactation to find these biological patterns.
DMI, ECM, MBW, and gRFI varied throughout early, mid, and late lactaction. Stronger additive genetic connections for DMI were seen when WOL was near together, with correlations ranging from 0.31 (0.17) to 0.99 (<0.01) for weeks far apart. However, correlations diminished with lactaction, notably significantly low DMI correlations between early and late lactation.
Like DMI, gRFI showed association with lactation phases from 0.04 (0.16) to 0.99 (0.01). Early and mid-lactation had the lowest associations, whereas later lactation had substantial relationships. ECM exhibited stronger lactation-wide correlations than DMI, but still showed lactation-specific variance. Timing of trait evaluation throughout lactation may affect selection to increase milk production and gRFI, therefore ECM alterations should be considered.
For all variables, WOL phenotypic correlations varied over lactation, with highest correlations between WOL closest together and lowest between WOL furthest away. Phenotypic associations were highest between WOL near together and lowest between WOL far apart. DMI has low to moderate associations during lactaction, suggesting that it should be regarded a characteristic at different periods. DMI’s dynamic behavior affects gRFI’s phenotype, hence this dynamic behavior may be extended to gRFI.
This research investigates genetic links between cows’ feed intake (DMI) and milk production (ECM) during lactation. DMI and ECM showed minor connections in early lactation but moderate to strong correlations by mid to late lactation. The change to higher favorable connections occurred when lactation intake met output needs. However, an increase in production without an increase in DMI, particularly in early lactaction, may prolong negative energy balance, which may harm health and fertility.
The genetic connection between metabolic body weight (MBW) and ECM changes from low positive to low negative during lactaction. This shows that bigger cows produce more milk early in lactation. The fast shift in correlations to weak negative correlations may indicate that livestock metabolism alters to gain weight as milk output drops and pregnancy progresses.
All characteristics had minimal phenotypic associations across lactation, with DMI and ECM in early lactation and MBW in late lactation. All WOLs had minimal phenotypic correlations between gRFI and MBW and ECM. However, DMI and gRFI displayed high positive correlations in comparable WOLs, indicating DMI’s dynamic phenotypic behavior and effect on gRFI.
In conclusion, breeding for gRFI requires knowing the link between DMI, MBW, ECM, and characteristics like energy balance and body condition score.
