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Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The objective of this study was to estimate genetic correlations among milk fatty acid (FA) concentrations in New Zealand dairy cattle. Concentrations of each of the most common FA, expressed as a percentage of the total FA, were determined by gas chromatography on a specific cohort of animals. Using this data set, prediction equations were derived using mid-infrared (MIR) spectroscopy data collected from the same samples. These prediction equations were applied to a large data set of MIR measurements in 34,141 milk samples from 3,445 Holstein-Friesian, 2,935 Jersey, and 3,609 crossbred Holstein-Friesian × Jersey cows, sampled an average of 3.42 times during the 2007-2008 season. Data were analyzed using univariate and bivariate repeatability animal models. Heritability of predicted FA concentration in milk fat ranged from 0.21 to 0.42, indicating that genetic selection could be used to change the FA composition of milk. The de novo synthesized FA (C6:0, C8:0, C10:0, C12:0, and C14:0) showed strong positive genetic correlations with each other, ranging from 0.24 to 0.99. Saturated FA were negatively correlated with unsaturated (-0.93) and polyunsaturated (-0.84) FA. The saturated FA were positively correlated with milk fat yield and fat percentage, whereas the unsaturated FA were negatively associated with fat yield and fat percentage. Our results indicate that bovine milk FA composition can be changed through genetic selection using MIR as a phenotypic proxy., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications., (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/4.0/).)

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/).)

Residual feed intake (RFI), as a measure of feed conversion during growth, was estimated for around 2,000 growing Holstein-Friesian heifer calves aged 6 to 9 mo in New Zealand and Australia, and individuals from the most and least efficient deciles (low and high RFI phenotypes) were retained. These animals (78 New Zealand cows, 105 Australian cows) were reevaluated during their first lactation to determine if divergence for RFI observed during growth was maintained during lactation. Mean daily body weight (BW) gain during assessment as calves had been 0.86 and 1.15 kg for the respective countries, and the divergence in RFI between most and least efficient deciles for growth was 21% (1.39 and 1.42 kg of dry matter, for New Zealand and Australia, respectively). At the commencement of evaluation during lactation, the cows were aged 26 to 29 mo. All were fed alfalfa and grass cubes; it was the sole diet in New Zealand, whereas 6 kg of crushed wheat/d was also fed in Australia. Measurements of RFI during lactation occurred for 34 to 37 d with measurements of milk production (daily), milk composition (2 to 3 times per week), BW and BW change (1 to 3 times per week), as well as body condition score (BCS). Daily milk production averaged 13.8 kg for New Zealand cows and 20.0 kg in Australia. No statistically significant differences were observed between calf RFI decile groups for dry matter intake, milk production, BW change, or BCS; however a significant difference was noted between groups for lactating RFI. Residual feed intake was about 3% lower for lactating cows identified as most efficient as growing calves, and no negative effects on production were observed. These results support the hypothesis that calves divergent for RFI during growth are also divergent for RFI when lactating. The causes for this reduced divergence need to be investigated to ensure that genetic selection programs based on low RFI (better efficiency) are robust., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Aim: To estimate genetic and crossbreeding parameters for the incidence of recorded clinical lameness in New Zealand dairy cattle., Methods: Herd records from 76,357 cows, collected during the 2005/06 to 2008/09 milking seasons from 155 herds in the Livestock Improvement Corporation young sire progeny test scheme, were used to estimate genetic parameters and breed effects for incidence of recorded clinical lameness in HolsteinFriesian, Jersey and crossbred dairy cattle. Recorded clinical lameness was coded "1" for cows that presented at least one event of clinical lameness at any day during the season and "0" for unaffected cows. Genetic parameters were estimated using an animal model across breeds considering all and then only first lactation records. Heritability and repeatability of recorded clinical lameness were calculated from the variance component estimates both with and without logit transformation., Results: The mean incidence of recorded clinical lameness per herd was 6.3 (min 2, max 34)%. The incidence of recorded clinical lameness in Holstein Friesian cows (mean 6.8, SE 0.24%) was higher than the incidence of recorded clinical lameness in crossbred (mean 6.1, SE 0.19%) and Jersey cows (mean 6.0, SE 0.28%) (p=0.0002). There was no difference in incidence between crossbred and Jersey cows (p=0.96). Estimates of the heritability of recorded clinical lameness as an untransformed trait were 0.053 (SE 0.014) for first lactation records and 0.016 (SE 0.003) for all lactation records. As a transformed (logit) trait heritabilities were 0.067 (SE 0.024) and 0.044 (SE 0.016) for first and all lactation records, respectively. The repeatability estimates of recorded clinical lameness were 0.071 (SE 0.005) and 0.107 (SE 0.011) for untransformed and logit transformed lactation records, respectively. Sire estimated breeding values for recorded clinical lameness showed the lowest values in Jersey sires, and ranged between -5 and 8%., Conclusions: Despite the low heritability of recorded clinical lameness, this study provided evidence that there is significant exploitable animal genetic variation. Selection of specific sires across and within breeds could be an option for increasing genetic resistance to lameness in New Zealand dairy cattle.

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.)

Recently, DGAT1 was identified as the gene that underlies the QTL for bovine milk production on chromosome 14. This study investigated the effect of the reported polymorphism in three dairy breeds in New Zealand. Statistically significant results were identified for milk fat, milk protein, and volume for Jersey and Holstein-Friesian breeds, and only milk volume for Ayrshires. The average allele substitution effects were 2 to 3 kg of protein and 120 to 130 l milk for both the Jersey and Holstein-Friesian breeds. For milk fat, the average allele substitution effect was 6 kg for Holstein-Friesians and 3 kg for Jerseys. In all breeds, where the polymorphism increased milk fat yield, it decreased milk protein yield and milk volume.

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.

Seven New Zealand Holstein-Friesian families and two Jersey families, a total of 274 sires, were analyzed in a granddaughter design for marker-quantitative trait loci associations. For 17 nonproduction traits (management, size, and conformation traits), an across-family analysis was undertaken using multimarker regression procedures. Threshold levels were set empirically by permuting the data. A quantitative trait locus for stature was identified on chromosome 14, which was significant at the 15% experimentwise level (suggestive linkage). The quantitative trait locus was identified to be segregating in three of the nine families.

Five traits of major economic importance in the New Zealand dairy industry are milk volume, milk fat, milk protein, live weight, and survival. This study evaluated the impact of live weight as a trait in the selection objectives for the New Zealand dairy industry. Live weight of the lactating cow is an important measure because it reflects feeding costs via maintenance feed and salvage values of cows to be culled. In addition, selection responses were evaluated for differing relative economic values for milk protein and milk fat, and selection indexes that included or excluded phenotypic and genotypic correlations between traits were compared. Inclusion of live weight, with a negative economic value in a four-trait selection index with milk, milk fat, and protein resulted in higher economic response. Protein response to selection was not more than 2% when the relative economic value for the ratio of protein to milk fat exceeded 5:1 in a two-trait model; however, milk fat response decreased by over 10%. When a negative relative economic value was assigned to milk fat, economic returns were lower because of lower milk fat responses and the lack of higher protein responses compared with the same ratio for relative economic value but a positive weight for milk fat. Accounting for phenotypic and genetic correlations in deriving selection index weight improved economic response 5%.