Genetic Parameters of Several Measures of Milk Flow Rate and Milking Time

Miller, R.H.; Pearson, R.E.; Weinland, B.T.; Fulton, L.A.

doi:10.3168/jds.s0022-0302(76)84304-4

Cited by 28 publications

(17 citation statements)

References 5 publications

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“…These original results in ewes were in agreement with results in cattle where genetic correlations between milk yield and milking traits varied from 0.38 to 0.69 [8,19]. Anatomical and physiological studies on the ewes studied here, revealed that selection on milk solid yield might have also modified the internal teat conformation of ewes.…”

Section: Improvement Of Milking Ability Under Current Selectionsupporting

confidence: 79%

“…V): in first lactation, the more important the genetic level for milk, the higher the average flow (+1.00), the longer the phase of high flows (+0.87), the higher the maximum milk flow (+0.46), the later the time at maximum flow (+0.34) and the shorter the latency time (-0.22). The genetic correlations between milk yield and milking traits were consistent with dairy cattle estimates for maximum milk flow and phase of decreasing flow [8,19,22]. Nevertheless, genetic correlations between milk yield and duration of high flow seemed to be zero or weak in cattle [8,22], while our estimation was high (+0.87).…”

Section: Genetic Correlationssupporting

confidence: 64%

“…The phase of increasing flows, the time at maximum milk flow and the average flow had intermediate heritabilities (0.43, 0.42 and 0.30, respectively), whereas the heritability of the phase of decreasing flows and the phase of high flows was weak (0.16 and 0.07, respectively). In dairy cattle, the heritability of maximum milk flow was slightly smaller than our estimate, with values varying from 0.21 to 0.48 [7,8,19,22]. High heritability estimates could be explained by two main reasons: firstly, we used annual average milking traits while other authors computed either elementary data with a test-day model [7,8] or a single average between evening and morning data [22].…”

Section: Heritabilitymentioning

confidence: 73%

“…For other breeds, Labussière and Bruckmaier et al obtained peak values about 10.0 mL·s −1 for PreAlpe ewes [14] and for Ostfriesian ewes [6], respectively. In other species, FM values were higher than our estimation and very variable: from 30 mL·s −1 to 58 mL·s −1 in cows [7,8,15,19,22], and from 15.2 mL·s −1 to 25.9 mL·s −1 in dairy goats [5,11].…”

Section: Elementary Statisticsmentioning

confidence: 77%

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Genetic analysis of milking ability in Lacaune dairy ewes

Marie-Etancelin¹,

Manfredi²,

Aurel³

et al. 2006

Genet Sel Evol

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-The milking ability of Lacaune ewes was characterised by derived traits of milk flow patterns, in an INRA experimental farm, from a divergent selection experiment in order to estimate the correlated effects of selection for protein and fat yields. The analysis of selected divergent line effects (involving 34 616 data and 1204 ewes) indicated an indirect improvement of milking traits (+17% for maximum milk flow and -10% for latency time) with a 25% increase in milk yield. Genetic parameters were estimated by multi-trait analysis with an animal model, on 751 primiparous ewes. The heritabilities of the traits expressed on an annual basis were high, especially for maximum flow (0.54) and for latency time (0.55). The heritabilities were intermediate for average flow (0.30), time at maximum flow (0.42) and phase of increasing flow (0.43), and low for the phase of decreasing flow (0.16) and the plateau of high flow (0.07). When considering test-day data, the heritabilities of maximum flow and latency time remained intermediate and stable throughout the lactation. Genetic correlations between milk yield and milking traits were all favourable, but latency time was less milk yield dependent (-0.22) than maximum flow (+0.46). It is concluded that the current dairy ewe selection based on milk solid yield is not antagonistic to milking ability. dairy sheep / milk flow / milking ability / milking trait / divergent selection

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Section: Improvement Of Milking Ability Under Current Selectionsupporting

confidence: 79%

Section: Genetic Correlationssupporting

confidence: 64%

Section: Heritabilitymentioning

confidence: 73%

Section: Elementary Statisticsmentioning

confidence: 77%

See 2 more Smart Citations

Genetic analysis of milking ability in Lacaune dairy ewes

Marie-Etancelin¹,

Manfredi²,

Aurel³

et al. 2006

Genet Sel Evol

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“…Milkability measures, either as milking flow or speed, have long been recognized as relevant criteria in animal selection [7,8], due to their impact on management costs of milking cows [9]. An increase in MMF or AVGF, or a reduction in total milking time (TMT), results in a reduction of milking labor requirements and in an increase in the efficiency of automatic milking systems [10].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of genomic prediction on milk flow traits in dairy cattle

et al. 2012

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BackgroundMilkability, primarily evaluated by measurements of milking speed and time, has an economic impact in milk production of dairy cattle. Recently the Italian Brown Swiss Breeders Association has included milking speed in genetic evaluations. The main objective of this study was to investigate the possibility of implementing genomic selection for milk flow traits in the Italian Brown Swiss population and thereby evaluate the potential of genomic selection for novel traits in medium-sized populations. Predicted breeding values and reliabilities based on genomic information were compared with those obtained from traditional breeding values.MethodsMilk flow measures for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow were collected on 37 213 Italian Brown Swiss cows. Breeding values for genotyped sires (n = 1351) were obtained from standard BLUP and genome-enhanced breeding value techniques utilizing two-stage and single-step methods. Reliabilities from a validation dataset were estimated and used to compare accuracies obtained from parental averages with genome-enhanced predictions.ResultsGenome-enhanced breeding values evaluated using two-stage methods had similar reliabilities with values ranging from 0.34 to 0.49 for the different traits. Across two-stage methods, the average increase in reliability from parental average was approximately 0.17 for all traits, with the exception of descending time, for which reliability increased to 0.11. Combining genomic and pedigree information in a single-step produced the largest increases in reliability over parent averages: 0.20, 0.24, 0.21, 0.14, 0.20 and 0.21 for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow, respectively.ConclusionsUsing genomic models increased the accuracy of prediction compared to traditional BLUP methods. Our results show that, among the methods used to predict genome-enhanced breeding values, the single-step method was the most successful at increasing the reliability for most traits. The single-step method takes advantage of all the data available, including phenotypes from non-genotyped animals, and can easily be incorporated into current breeding evaluations.

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Sire evaluation for milkability traits in Swiss Braunvieh

Schneeberger

Hagger²

1985

Livestock Production Science

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Genetic Parameters of Several Measures of Milk Flow Rate and Milking Time

Cited by 28 publications

References 5 publications

Genetic analysis of milking ability in Lacaune dairy ewes

Genetic analysis of milking ability in Lacaune dairy ewes

Effectiveness of genomic prediction on milk flow traits in dairy cattle

Sire evaluation for milkability traits in Swiss Braunvieh

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