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The genetic merit of a herd is a key determinant in productivity for dairy farmers. However, making breeding decisions to maximize the rate of genetic gain can be complex because there is no certainty about which cows will become pregnant with a heifer calf. In this study, breeding worth (BrW) was used as a measure of genetic merit, and several mating strategies were evaluated. These strategies included randomly mating whole herds to the entire bull team, excluding low-ranked cows from producing replacement heifers, and nominating high-ranked cows to the most highly ranked bulls. Simulations were undertaken using 4 bull teams generated from bulls currently marketed in New Zealand and a selection of New Zealand dairy herds. Average replacement heifer BrW was calculated for 1,000 iterations of each combination of mating strategy, herd, and bull team (scenario). Variation in resulting average replacement heifer BrW within scenarios was due to random sampling of which cows became pregnant with a heifer calf. Relative to mating the whole herd to an entire bull team, excluding the lowest ranked cows from producing replacements resulted in the greatest increase in average replacement heifer BrW across all herds and bull teams, with a gain of approximately 0.4 BrW point for each 1% of cows excluded. Nominating top-ranking cows to the highest ranking bulls in the team had little effect (0.06-0.13 BrW increase for each 1% of top cows nominated) in improving BrW of replacement heifers. The number of top bulls nominated had a variable effect depending on the BrW spread of the entire bull team. Although excluding cows with the lowest BrW from producing replacement heifers is most effective for improving BrW, it is important to ensure that the number of heifers born is sufficient to replace cows leaving the herd. It is likely that optimal strategies for improving BrW will vary from farm to farm depending not only on the BrW structure of the herd, the bull team available, and the reproduction success on farm but also on farm management practices. This simulation study provides expected outcomes from a variety of mating strategies to allow informed decision making on farm., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

Increasing environmental temperatures are a threat to the sustainability of livestock production and, because of the high metabolic demands of lactation, to dairy production in particular. Summer heat waves in temperate climates reduce feed intake, milk production, and cow comfort. In extreme heat events, there is an increase in cow mortality. In tropical climates, dairy cattle are mostly (zebu) type or zebu crossbred with temperate dairy breeds. Crossbreeding is undertaken to combine the heat tolerance and tick resistance of zebu with the productivity of temperate dairy breeds. In the absence of improved heat tolerance, milk production and fertility of temperate cattle is severely impaired. We have recently identified a key role for the prolactin pathway in regulating heat tolerance. A de novo mutation in prolactin that impairs prolactin activity was discovered in hairy and heat intolerant, New Zealand dairy cattle. The phenotypes produced were remarkably similar to those seen in fescue toxicosis, a syndrome seen in grazing cattle in the U.S. where ingestion of ergovaline, a fungal toxin from infected pasture, inhibits prolactin secretion. Recognition of the role of prolactin in hairy cattle led us to identify a deletion in exon 10 of the long-form of the prolactin receptor in Senepol cattle that causes truncation of the protein and determines the slick coat and heat tolerance traits found in this , beef breed. The short form of the prolactin receptor is predicted to be unaffected by the deletion. Knowledge of this dominant mutation has provided the impetus to begin a crossbreeding program to investigate performance and heat tolerance of temperate dairy cattle carrying the slick, prolactin receptor variant. The perceived opportunity is to introgress this variant into temperate dairy cattle to enable performance and welfare improvement in hot climates. Heat tolerance of cattle with slick coats appears to be mostly associated with coat type although sweating ability may also be enhanced. Further investigation is required of performance traits in cows homozygous for the slick variant because the published data are almost exclusively from heterozygous animals. Combination of the slick mutation with other favorable genes for heat tolerance, especially those for coat color, will be particularly enabled by gene editing technologies, offering opportunities for further improvement in bovine thermotolerance.

Feed makes up a large proportion of variable costs in dairying. For this reason, selection for traits associated with feed conversion efficiency should lead to greater profitability of dairying. Residual feed intake (RFI) is the difference between actual and predicted feed intakes and is a useful selection criterion for greater feed efficiency. However, measuring individual feed intakes on a large scale is prohibitively expensive. A panel of DNA markers explaining genetic variation in this trait would enable cost-effective genomic selection for this trait. With the aim of enabling genomic selection for RFI, we used data from almost 2,000 heifers measured for growth rate and feed intake in Australia (AU) and New Zealand (NZ) genotyped for 625,000 single nucleotide polymorphism (SNP) markers. Substantial variation in RFI and 250-d body weight (BW250) was demonstrated. Heritabilities of RFI and BW250 estimated using genomic relationships among the heifers were 0.22 and 0.28 in AU heifers and 0.38 and 0.44 in NZ heifers, respectively. Genomic breeding values for RFI and BW250 were derived using genomic BLUP and 2 bayesian methods (BayesA, BayesMulti). The accuracies of genomic breeding values for RFI were evaluated using cross-validation. When 624,930 SNP were used to derive the prediction equation, the accuracies averaged 0.37 and 0.31 for RFI in AU and NZ validation data sets, respectively, and 0.40 and 0.25 for BW250 in AU and NZ, respectively. The greatest advantage of using the full 624,930 SNP over a reduced panel of 36,673 SNP (the widely used BovineSNP50 array) was when the reference population included only animals from either the AU or the NZ experiment. Finally, the bayesian methods were also used for quantitative trait loci detection. On chromosome 14 at around 25 Mb, several SNP closest to PLAG1 (a gene believed to affect stature in humans and cattle) had an effect on BW250 in both AU and NZ populations. In addition, 8 SNP with large effects on RFI were located on chromosome 14 at around 35.7 Mb. These SNP may be associated with the gene NCOA2, which has a role in controlling energy metabolism., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to evaluate the profitability of dairy herds under three mating systems involving the Holstein-Friesian, Jersey, and Ayrshire breeds. Mating systems were straight breeding and rotational cross-breeding using two or three breeds. A deterministic model was developed to simulate the nutritional, biological, and economic performance of dairy herds under New Zealand conditions. Expected performances per cow were obtained using estimates of breed group and heterosis effects, age effects, and age distribution in the herd. Requirements for dry matter in feed were estimated per cow for maintenance, lactation, pregnancy, and growth of the replacements. Stocking rate was calculated by assuming 12,000 kg of dry matter utilized annually per hectare. Productivity per hectare was calculated as performance per cow multiplied by stocking rate. Profitability was the difference between income (sale of milk and salvage value of animals) and costs (related to the number of cows in the herd and the land area farmed). Under current market values for milk and meat, all of the rotational crossbred herds showed superior profitability to the straightbred herds (Holstein-Friesian x Jersey, NZ$505/ha; Holstein-Friesian x Jersey x Ayrshire NZ$493/ha; Jersey x Ayrshire, NZ$466/ha; Holstein-Friesian x Ayrshire, NZ$430/ha; Jersey, NZ$430/ha; Holstein-Friesian, NZ$398/ha; and Ayrshire, NZ$338/ha). Changes in the value for fat relative to protein affected profitability more significantly in herds using the Jersey breed, and changes in the value for meat affected profitabiity more significantly in herds using the Holstein-Friesian and Ayrshire breeds. Results suggested that, under New Zealand conditions, the use of rotational crossbreeding systems could increase profitability of dairy herds under the conceivable market conditions.

The effects of selection and crossbreeding on the New Zealand dairy industry net income were evaluated with a deterministic model over a 25 yr planning horizon. Several mating strategies involving Holstein-Friesian, Jersey and Ayrshire cattle were evaluated. These strategies were straight breeding, upgrading to Holstein-Friesian, upgrading to Jersey, upgrading to Ayrshire, use of the best bulls irrespective of breed and two- and three-breed rotational crossbreeding. Industry productions of milk, fat, protein, and lactose were calculated assuming that 12,000 kg of dry matter per hectare was utilized from 1,224,911 hectares of pasture. Profitability was the difference between income (international sale of whole milk powder, casein, butter, and beef from salvage animals) and costs (farm expenses, milk collection, manufacture, and marketing). Casein and whole milk powder were valued at NZ$8.345 and NZ$3.306/ per kilogram, respectively, over 25 yr. Butter was valued at NZ$2.995/kilogram for base year production levels and NZ$0.45/kilogram for marginal increases in production. Upgrading to Holstein-Friesian resulted in the highest industry net income (NZ$1119 million) followed by straight breeding (NZ$1086 million) and two-breed rotational Holstein-Friesian x Jersey (NZ$1076 million). However, if the marginal value of extra butter production was assumed equal to the average base value, then upgrading to Jersey resulted in the highest industry net income (NZ$1185 million) followed by two-breed rotational Holstein-Friesian x Jersey (NZ$1177 million) and use of the best bulls (NZ$1173 million). Future costs and prices of dairy products have major impact on mating strategies.

Selective genotyping (i.e., genotyping of individuals that are extreme for a quantitative trait) has been undertaken in many studies but has usually been limited to one trait of interest. This paper outlines the implications of selective genotyping when applied to a daughter design. Formulas are presented that enable unbiased estimation of QTL effects for the selectively genotyped and correlated traits. Formulas were verified using simulation. Further, formulas are presented to enable power calculations to be undertaken for the selectively genotyped trait and correlated traits. These algorithms are demonstrated with a numerical example.

Marker-assisted selection schemes that utilize information about quantitative trait loci to preselect progeny test bulls within a family are the most practical application of quantitative trait loci results in the short-term. Technical difficulties exist for across-family marker-assisted selection using BLUP procedures. Two within-family marker-assisted selection schemes were evaluated genetically and economically using stochastic simulation for a locus that explained 5% of phenotypic variance. The genetic and economic impacts of variation in the number of offspring per bull-dam were evaluated. The top down marker-assisted selection scheme identifies sires that are heterozygous for the locus based on the granddaughter design and uses the quantitative trait locus information in the preselection of grandsons entering progeny testing. The bottom up marker-assisted selection scheme identifies sires heterozygous for a quantitative trait locus based on the daughter design and uses the information in the preselection of sons entering progeny testing. The top down scheme with one progeny per bull-dam reduced the rate of genetic gain compared with that from a breeding scheme that ignored knowledge of the quantitative trait locus. The top down scheme with reproductive performance of 3 or 40 progeny per bull-dam increased genetic gain by 1 to 2%. The bottom up scheme increased the rate of genetic gain by 1.5, 3.5, and 5% for 1, 3, and 40 progeny per bull-dam, respectively. When the top down scheme was used on the maternal path and the bottom up scheme on the paternal path, increases were 9% with 40 progeny per bull-dam. The use of reproductive technologies on bull-dams is imperative to prevent gains from marker-assisted selection being eroded by the loss in polygenic selection differential that results when more bull-dams are required to enable preselection of sons using markers.

Results from quantitative trait loci studies cannot be readily implemented into breeding schemes through marker assisted selection because of uncertainty about whether the quantitative trait loci identified are real and whether the identified quantitative trait loci are segregating in the breeding population. The present paper outlines and discusses strategies to reduce uncertainty in the results from quantitative trait loci studies. One strategy to confirm results from quantitative trait loci studies is to combine P-values from many quantitative trait loci experiments, while another is to establish a confirmation study. The power of a confirmation study must be high to ensure that the postulated quantitative trait loci can be verified. In the calculation of the experimental power, there are many issues that have to be addressed: size of the quantitative trait loci to be detected, significance level required, experimental design and expected heterozygosity for the design. To ensure marker assisted selection can be quickly implemented once quantitative trait loci are confirmed, DNA samples should be retained from daughters, and the sires and dams of elite sires.

The effect of inaccurate estimates of variance and of the location of the quantitative trait locus on the genetic response to marker-assisted selection was studied by simulation of an adult multiple ovulation and embryo transfer nucleus breeding scheme. Two genetic models were simulated for the quantitative trait locus: a total of 10 alleles or 2 distinct alleles per base parent. For both models, the locus explained either 5 or 10% of phenotypic variance. A polygenic component was simulated, and the two genetic components were summed to 35% heritability for a trait measured on females. Overestimation of variance of the quantitative trait locus had minimal effect on genetic gain for marker-assisted selection over the short term, but decreased long-term response. The long-term loss was reduced when variance of the quantitative trait locus was reestimated after four generations of marker-assisted selection. Selection for favorable alleles at a nonexistent quantitative trait locus resulted in first generation losses of 3 and 7% for postulated quantitative trait loci, explaining 5 and 10% of variance, respectively. The larger the degree of error in location, the larger was the genetic loss compared with the correct location scenario. For the largest simulated location error of 15 cM, genetic superiority of marker-assisted selection was reduced by 80% in the first generation. We concluded that studies should be undertaken to verify estimates of quantitative trait locus and location to make optimal use of marker-assisted selection.