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Hamilton, New Zealand

Waghorn G.C.,DairyNZ Ltd. | Macdonald K.A.,DairyNZ Ltd. | Williams Y.,Australian Department of Primary Industries and Fisheries | Davis S.R.,Australian Department of Primary Industries and Fisheries | Spelman R.J.,Livestock Improvement Corporation
Journal of Dairy Science

Selection for divergence between individuals for efficiency of feed utilization (residual feed intake, RFI) has widespread application in the beef industry and is usually undertaken when animals are fed diets based on silages with grain. The objective of this research was to develop a feeding system (using Gallagher, Hamilton, New Zealand, electronics) to measure RFI for growth in Holstein-Friesian heifers (aged 5-9 mo), and identify divergent individuals to be tested for RFI during lactation. A dry forage diet (alfalfa cubes) was fed because intakes could be measured accurately, and the New Zealand dairy industry (4.4million milking cows in lactation) relies heavily on forage feeding. The evaluation was undertaken over 3 yr with 1,052 animals fed in a facility for 7 wk, and weighed 3 times weekly. The mean age at the start of measurements was 215 d, body weight (BW) 189kg, and mean daily dry matter intakes averaged 6.7kg. Body weight gain (all animals) averaged 0.88kg/d. The RFI was determined as the residuals from the regression of mean intake on mean BW0.75 and daily BW gain of individuals. Actual and fitted intakes were strongly related (R2=0.82). In terms of gross efficiency (feed intake/BW gain), RFI+year explained 43% of the variation, BW gain+year explained 66%, and RFI+BW gain+year explained 79% of the variation (all P<0.001). Daily BW gains (kg) of the most and least efficient 10% averaged (± standard deviation) 0.88±0.15 and 0.88±0.12 (P=0.568), respectively, and the divergence between mean intakes was 1.46kg of dry matter/d. The most and least efficient animals will be tested for RFI during lactation and genetic markers will be identified for the trait. © 2012 American Dairy Science Association. Source

Lembeye F.,Massey University | Lopez-Villalobos N.,Massey University | Burke J.L.,Massey University | Davis S.R.,Livestock Improvement Corporation
Livestock Science

The objective of the present study was to estimate genetic parameters for milk yields, average somatic cell score (SCS) and milk composition traits in dairy cows milked either once a day (OAD) or twice a day (TAD) in New Zealand. The data set comprised 124,620 and 194,631 lactation records from OAD and TAD populations, respectively, during the period 2008-2012. Overall, estimates of parameters were similar between milking frequencies (MF), although heritabilities of production traits tended to be greater in the TAD cows. Estimates of heritability in OAD and TAD were: 0.33 and 0.36 for milk yield; 0.21 and 0.26 for fat yield; 0.22 and 0.25 for protein yield; and 0.12 and 0.12 for SCS, respectively. Estimates of correlations were similar across MF, in particular the genetic correlation between milk yield and protein yield (0.84 for TAD and 0.85 for OAD). Estimates of genetic correlations between SCS and other traits tended to be close to zero in both populations. The results indicate that genetic progress can be lower in the OAD population due to lower phenotypic and genetic variances compared to the TAD population. However, a potential disadvantage is that evaluating both dairy populations together could lead to systematic inaccuracies and biases in the estimation of breeding values for the population milked OAD as future dams. © 2016 Elsevier B.V. Source

Loker S.,University of Guelph | Loker S.,Livestock Improvement Corporation | Miglior F.,Agriculture and Agri Food Canada | Miglior F.,A+ Network | And 6 more authors.
Journal of Dairy Science

The objective of this research was to estimate daily genetic correlations between longitudinal body condition score (BCS) and health traits by using a random regression animal model in first-lactation Holsteins. The use of indicator traits may increase the rate of genetic progress for functional traits relative to direct selection for functional traits. Indicator traits of interest are those that are easier to record, can be measured early in life, and are strongly genetically correlated with the functional trait of interest. Several BCS records were available per cow, and only 1 record per health trait (1 = affected; 0 = not affected) was permitted per cow over the lactation. Two bivariate analyses were performed, the first between BCS and mastitis and the second between BCS and metabolic disease (displaced abomasum, milk fever, and ketosis). For the first analysis, 217 complete herds were analyzed, which included 28,394 BCS records for 10,715 cows and 6,816 mastitis records for 6,816 cows. For the second analysis, 350 complete herds were analyzed, which included 42,167 BCS records for 16,534 cows and 13,455 metabolic disease records for 13,455 cows. Estimation of variance components by a Bayesian approach via Gibbs sampling was performed using 400,000 samples after a burn-in of 150,000 samples. The average daily heritability (posterior standard deviation) of BCS was 0.260 (0.026) and the heritabilities of mastitis and metabolic disease were 0.020 (0.007) and 0.041 (0.012), respectively. Heritability estimates were similar to literature values. The average daily genetic correlation between BCS and mastitis was -0.730 (0.110). Cows with a low BCS during the lactation are more susceptible to mastitis, and mastitic cows are likely to have low BCS. Daily estimates of genetic correlations between BCS and mastitis were moderate to strong throughout the lactation, becoming stronger as the lactation progressed. The average daily genetic correlation between BCS and metabolic disease was -0.438 (0.125), and was consistent throughout the lactation. A lower BCS during the lactation is genetically associated with the occurrence of mastitis and metabolic disease. © 2012 American Dairy Science Association. Source

Spelman R.J.,Livestock Improvement Corporation | Hayes B.J.,Australian Department of Primary Industries and Fisheries | Berry D.P.,Teagasc
Animal Production Science

The New Zealand, Australian and Irish dairy industries have used genomic information to enhance their genetic evaluations over the last 2-4 years. The improvement in the accuracy obtained from including genomic information on thousands of animals in the national evaluation system has revolutionised the dairy breeding programs in the three countries. The genomically enhanced breeding values (GEBV) of young bulls are more reliable than breeding values based on parent average, thus allowing the young bulls to be reliably selected and used in the national herd. Traditionally, the use of young bulls was limited and bulls were not used extensively until they were 5 years old when the more reliable progeny test results became available. Using young sires, as opposed to progeny-tested sires, in the breeding program dramatically reduces the generation interval, thereby facilitating an increase in the rate of genetic gain by 40-50%. Young sires have been marketed on their GEBV in the three countries over the last 2-4 years. Initial results show that the genomic estimates were overestimated in both New Zealand and Ireland. Adjustments have since been introduced into their respective national evaluations to reduce the bias. A bias adjustment has been included in the Australian evaluation since it began; however, official genomic evaluations have not been in place as long as in New Zealand and Ireland, so there has been less opportunity to validate if the correction accounts for all bias. Sequencing of the dairy cattle population has commenced in an effort to further improve the genomic predictions and also to detect causative mutations that underlie traits of economic performance. © CSIRO 2013. Source

Aguilar I.,University of Georgia | Aguilar I.,Instituto Nacional Of Investigacion Agropecuaria | Misztal I.,University of Georgia | Johnson D.L.,Livestock Improvement Corporation | And 3 more authors.
Journal of Dairy Science

The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2. h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes. © 2010 American Dairy Science Association. Source

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