Progress with genetic selection for low methane traits in dairy cows

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Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia

peer-reviewedFinancial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697).With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in Holstein-Friesian dairy cattle, data from 10 research herds in Europe, North America, and Australasia were combined. The DMI records were available on 10,701 parity 1 to 5 records from 6,953 cows, as well as on 1,784 growing heifers. Predicted DMI at 70 d in milk was used as the phenotype for the lactating animals, and the average DMI measured during a 60- to 70-d test period at approximately 200 d of age was used as the phenotype for the growing heifers. After editing, there were 583,375 genetic markers obtained from either actual high-density single nucleotide polymorphism (SNP) genotypes or imputed from 54,001 marker SNP genotypes. Genetic correlations between the populations were estimated using genomic REML. The accuracy of genomic prediction was evaluated for the following scenarios: (1) within-country only, by fixing the correlations among populations to zero, (2) using near-unity correlations among populations and assuming the same trait in each population, and (3) a sharing data scenario using estimated genetic correlations among populations. For these 3 scenarios, the data set was divided into 10 sub-populations stratified by progeny group of sires; 9 of these sub-populations were used (in turn) for the genomic prediction and the tenth was used for calculation of the accuracy (correlation adjusted for heritability). A fourth scenario to quantify the benefit for countries that do not record DMI was investigated (i.e., having an entire country as the validation population and excluding this country in the development of the genomic predictions). The optimal scenario, which was sharing data, resulted in a mean prediction accuracy of 0.44, ranging from 0.37 (Denmark) to 0.54 (the Netherlands). Assuming near-unity among-country genetic correlations, the mean accuracy of prediction dropped to 0.40, and the mean within-country accuracy was 0.30. If no records were available in a country, the accuracy based on the other populations ranged from 0.23 to 0.53 for the milking cows, but were only 0.03 and 0.19 for Australian and New Zealand heifers, respectively; the overall mean prediction accuracy was 0.37. Therefore, there is a benefit in collaboration, because phenotypic information for DMI from other countries can be used to augment the accuracy of genomic evaluations of individual countries.financial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697).financial support for gDMI from CRV (Arnhem, the Netherlands), ICBF (Cork, Ireland), CONAFE (Madrid, Spain), DairyCo (Warwickshire, UK) directly to the gDMI consortium, and The Natural Science and Engineering Research Council of Canada and DairyGen Council of Canadian Dairy Network (Guelph, ON, Canada) is gratefully appreciated, as well as the EU FP7 IRSES SEQSEL (Grant no. 317697)

Covariance among milking frequency, milk yield, and milk composition from automatically milked cows

AbstractAutomatic milking systems allow cows voluntary access to milking and concentrates within set limits. This leads to large variation in milking intervals, both within and between cows, which further affects yield per milking and composition of milk. This study aimed to describe the degree to which differences in milking interval were attributable to individual cows, and how this correlated to individual differences in yield and composition of milk throughout lactation. Data from 288,366 milkings from 664 cow-lactations were used, of which 229,020 milkings had milk composition results. Cows were Holsteins, Red Danes, and Jerseys in parities 1, 2, and 3. Data were analyzed using a linear mixed model, with cow-lactation as a random effect and assuming heterogeneous residual variance over the lactation. Cow-lactation variance was fitted using linear spline functions with 5 knot-points. Residual variance was generally greatest in early lactation and declined thereafter. Accordingly, animal-related variance tended to increase with progression of lactation. Milking frequency (the reverse of milking interval) was found to be moderately repeatable throughout lactation. Daily milk yield expressed per milking was found to be highly repeatable in all breeds, with the highest values occurring by the end of lactation. Fat percentage had only moderate repeatability in early to mid lactation but increased toward the end of lactation. Individual level correlations showed that cows with higher milking frequency also had greater yields, but had lower fat percentage. Correlations were slightly weaker in very early lactation than in the remaining parts of lactation. We concluded that individual differences exist among cows milked automatically. Cows with higher yields are milked more often and have lower fat content in their milk

On the use of physical activity monitoring for estrus detection in dairy cows

AbstractDetection of estrus in dairy cattle is effectively aided by electronic activity tags or pedometers. Characterization of estrus intensity and duration is also possible from activity data. This study aimed to develop an algorithm to detect and characterize behavioral estrus from hourly recorded activity data and to apply the algorithm to activity data from an experimental herd. The herd comprised of Holstein (n=211), Jersey (n=126), and Red Dane (n=178) cattle, with virgin heifers (n=132) and lactating cows in the first 4 parities; n=895 cow-parities, with a total of 3,674 activity episodes. The algorithm was based on deviations from exponentially smoothed hourly activity counts and was used to identify onset, duration, and intensity of estrus. Learning data included 461 successful inseminations with activity records over a 2-wk period before and after the artificial insemination. Rates of estrus detection and error rate depended on the chosen threshold level. At a threshold giving 74.6% detection rate, daily error rate was 1.3%. When applied to a subset of the complete data where milk progesterone was also available, concordance of days to first activity-detected estrus with the similar trait based on progesterone was also dependent on the chosen threshold so that, with stricter thresholds, the agreement was closer. A single-trait mixed model was used to determine the effects of systematic factors on the estrus activity traits. In general, an activity episode lasted 9.24h in heifers and 8.12h in cows, with the average strength of 1.03 ln units (equivalent to a 2.8-fold increase) in both age groups. Red Danes had significantly fewer days to first episode of high activity than Holsteins and Jerseys (29.4, 33.1, and 33.9 d, respectively). However, Jerseys had significantly shorter duration and less strength of estrus than both Red Danes and Holsteins of comparable age. The random effect of cow affected days to first episode of high activity and strength as well as estrus duration. Days from calving to first episode of high activity correlated negatively with body condition scores in early lactation. The results suggest that data from activity monitors could supply valuable information about fertility traits and could thereby be helpful in management of herd fertility. To establish the complementarities or interdependence between progesterone and activity measurements, further studies with more information from different sources of measuring estrus are needed