| Autor: |
Visentin G; 1Department of Agronomy,Food,Natural Resources,Animals and Environment (DAFNAE),University of Padova,Viale dell'Università 16,35020 Legnaro (PD),Italy., Niero G; 1Department of Agronomy,Food,Natural Resources,Animals and Environment (DAFNAE),University of Padova,Viale dell'Università 16,35020 Legnaro (PD),Italy., Berry DP; 2Animal and Grassland Research and Innovation Center,Teagasc,Moorepark,Fermoy,Co. Cork P61 P302,Ireland., Costa A; 1Department of Agronomy,Food,Natural Resources,Animals and Environment (DAFNAE),University of Padova,Viale dell'Università 16,35020 Legnaro (PD),Italy., Cassandro M; 1Department of Agronomy,Food,Natural Resources,Animals and Environment (DAFNAE),University of Padova,Viale dell'Università 16,35020 Legnaro (PD),Italy., De Marchi M; 1Department of Agronomy,Food,Natural Resources,Animals and Environment (DAFNAE),University of Padova,Viale dell'Università 16,35020 Legnaro (PD),Italy., Penasa M; 1Department of Agronomy,Food,Natural Resources,Animals and Environment (DAFNAE),University of Padova,Viale dell'Università 16,35020 Legnaro (PD),Italy. |
| Abstrakt: |
Milk mineral concentration is important from both the perspective of processing milk into dairy products and its nutritive value for human consumption. Precise estimates of genetic parameters for milk mineral concentration are lacking because of the considerable resources required to collect vast phenotypes quantities. The milk concentration of calcium (Ca), potassium (K), magnesium (Mg), sodium (Na) and phosphorus (P) in the present study was quantified from mid-IR spectroscopy on 12 223 test-day records from 1717 Holstein-Friesian cows. (Co)variance components were estimated using random regressions to model both the additive genetic and within-lactation permanent environmental variances of each trait. The coefficient of genetic variation averaged across days-in-milk (DIM) was 6.93%, 3.46%, 6.55%, 5.20% and 6.68% for Ca, K, Mg, Na and P concentration, respectively; heritability estimates varied across lactation from 0.31±0.05 (5 DIM) to 0.67±0.04 (181 DIM) for Ca, from 0.18±0.03 (60 DIM) to 0.24±0.05 (305 DIM) for K, from 0.08±0.03 (15 DIM) to 0.37±0.03 (223 DIM) for Mg, from 0.16±0.03 (30 DIM) to 0.37±0.04 (305 DIM) for Na and from 0.21±0.04 (12 DIM) to 0.57±0.04 (211 DIM) for P. Genetic correlations within the same trait across different DIM were almost unity between adjacent DIM but weakened as the time interval between pairwise compared DIM lengthened; genetic correlations were weaker than 0.80 only when comparing both peripheries of the lactation. The analysis of the geometry of the additive genetic covariance matrix revealed that almost 90% of the additive genetic variation was accounted by the intercept term of the covariance functions for each trait. Milk protein concentration and mineral concentration were, in general, positively genetically correlated with each other across DIM, whereas milk fat concentration was positively genetically correlated throughout the entire lactation with Ca, K and Mg; the genetic correlation with fat concentration changed from negative to positive with Na and P at 243 DIM and 50 DIM, respectively. Genetic correlations between somatic cell score and Na ranged from 0.38±0.21 (5 DIM) to 0.79±0.18 (305 DIM). Exploitable genetic variation existed for all milk minerals, although many national breeding objectives are probably contributing to an indirect positive response to selection in milk mineral concentration. |
