Search

To reduce methane (CH 4) emissions of dairy cows by animal breeding, CH 4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH 4 , differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH 4 and carbon dioxide production recorded by GreenFeed (GF) and for CH 4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH 4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH 4 production (grams/day) by GF units and of CH 4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH 4 records were averaged per week. For daily and weekly mean GF CH 4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH 4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH 4 production and sniffer CH 4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH 4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH 4 production (grams/day) as recorded with GF. [ABSTRACT FROM AUTHOR]

Structural equation models allow causal effects between 2 or more variables to be considered and can postulate unidirectional (recursive models; RM) or bidirectional (simultaneous models) causality between variables. This review evaluated the properties of RM in animal breeding and how to interpret the genetic parameters and the corresponding estimated breeding values. In many cases, RM and mixed multitrait models (MTM) are statistically equivalent, although subject to the assumption of variance-covariance matrices and restrictions imposed for achieving model identification. Inference under RM requires imposing some restrictions on the (co)variance matrix or on the location parameters. The estimates of the variance components and the breeding values can be transformed from RM to MTM, although the biological interpretation differs. In the MTM, the breeding values predict the full influence of the additive genetic effects on the traits and should be used for breeding purposes. In contrast, the RM breeding values express the additive genetic effect while holding the causal traits constant. The differences between the additive genetic effect in RM and MTM can be used to identify the genomic regions that affect the additive genetic variation of traits directly or causally mediated for another trait or traits. Furthermore, we presented some extensions of the RM that are useful for modeling quantitative traits with alternative assumptions. The equivalence of RM and MTM can be used to infer causal effects on sequentially expressed traits by manipulating the residual (co)variance matrix under the MTM. Further, RM can be implemented to analyze causality between traits that might differ among subgroups or within the parametric space of the independent traits. In addition, RM can be expanded to create models that introduce some degree of regularization in the recursive structure that aims to estimate a large number of recursive parameters. Finally, RM can be used in some cases for operational reasons, although there is no causality between traits. [ABSTRACT FROM AUTHOR]

Maintaining genetic variation in a population is important for long-term genetic gain. The existence of subpopulations within a breed helps maintain genetic variation and diversity. The 20,990 genotyped animals, representing the breeding animals in the year 2014, were identified as the sires of animals born after 2010 with at least 25 progenies, and females measured for type traits within the last 2 yr of data. K-means clustering with 5 clusters (C1, C2, C3, C4, and C5) was applied to the genomic relationship matrix based on 58,990 SNP markers to stratify the selected candidates into subpopulations. The general higher inbreeding resulting from within-cluster mating than across-cluster mating suggests the successful stratification into genetically different groups. The largest cluster (C4) contained animals that were less related to each animal within and across clusters. The average fixation index was 0.03, indicating that the populations were differentiated, and allele differences across the subpopulations were not due to drift alone. Starting with the selected candidates within each cluster, a family unit was identified by tracing back through the pedigree, identifying the genotyped ancestors, and assigning them to a pseudogeneration. Each of the 5 families (F1, F2, F3, F4, and F5) was traced back for 10 generations, allowing for changes in frequency of individual SNPs over time to be observed, which we call allele frequencies change. Alternative procedures were used to identify SNPs changing in a parallel or nonparallel way across families. For example, markers that have changed the most in the whole population, markers that have changed differently across families, and genes previously identified as those that have changed in allele frequency. The genomic trajectory taken by each family involves selective sweeps, polygenic changes, hitchhiking, and epistasis. The replicate frequency spectrum was used to measure the similarity of change across families and showed that populations have changed differently. The proportion of markers that reversed direction in allele frequency change varied from 0.00 to 0.02 if the rate of change was greater than 0.02 per generation, or from 0.14 to 0.24 if the rate of change was greater than 0.005 per generation within each family. Cluster-specific SNP effects for stature were estimated using only females and applied to obtain indirect genomic predictions for males. Reranking occurs depending on SNP effects used. Additive genetic correlations between clusters show possible differences in populations. Further research is required to determine how this knowledge can be applied to maintain diversity and optimize selection decisions in the future. [ABSTRACT FROM AUTHOR]

Animal breeding techniques offer potential to reduce enteric emissions of ruminants to lower the environmental impact of dairy farming. The aim of this study was to estimate the heritability and repeatability of methane (CH 4) concentrations, using the largest data set from long-term repeatedly recorded CH 4 on cows to date, and to evaluate (1) the accuracy of breeding values for different CH 4 traits, including using visits or weekly means, and (2) recording strategies (with varying numbers of records and recorded daughters per sire). The data comprised of long-term recording of CH 4 and carbon dioxide (CO 2), from 1,746 Holstein Friesian cows, on 14 commercial dairy farms throughout the Netherlands. Emissions were recorded in 10- to 35-s intervals, between 64 and 436 d, depending on farms. From each robot visit, CH 4 and CO 2 concentrations were summarized into various traits, averaged per visit and per week: mean, median, mean log, and mean CH 4 /CO 2 ratio. Genetic parameters were estimated with animal repeatability models, using a restricted maximum likelihood procedure, and a relationship matrix based on genotypes and pedigree. The heritability was equal for mean and median CH 4 per visit (0.13) but lower for logCH 4 and CH 4 /CO 2 (0.07 and 0.01, respectively). Phenotypic and genetic correlations were high (≥0.78) between the CH 4 traits, apart from the genetic correlations with the CH 4 /CO 2 trait, which were negative. To achieve a minimum reliability of 50% for the estimated breeding value of a bull, 25 records on mean CH 4 , measured on 10 different daughters, were sufficient. Although the heritability and repeatability were higher for weekly (0.32 and 0.68, respectively) than for visit mean CH 4 (0.13 and 0.30, respectively), the reliabilities of estimated breeding values from visit or weekly means were equal; thus, we found no advantage in averaging records to weekly means for genetic evaluations. [ABSTRACT FROM AUTHOR]

Genotype by environment interaction (G×E) may exist for traits that are expressed in different environments. The G×E is often ignored in the genetic evaluation of selection candidates. We hypothesized that genetic gain in 2 environments is always higher when the true value of the genetic correlation (r g) between traits expressed in different environments is considered in the genetic evaluation. We tested this hypothesis by stochastic simulation of dairy cattle breeding programs in a mainstream and a niche environment. The r g was varied from 0 to 1 in steps of 0.1. We simulated the following 3 scenarios: 1Trait_1Index, 2Traits_1Index, and 2Traits_2Indices. The G×E was ignored in the genetic evaluation in the scenario with 1Trait and included in scenarios with 2Traits. Selection was based on the mainstream selection index in both environments in scenarios with 1Index. Selection in the mainstream environment was based on the mainstream selection index and selection in the niche environment was based on the niche selection index in the scenario with 2Indices. With moderate G×E (r g between 0.6 and 0.9), the highest genetic gain was achieved in the niche environment by selecting for the mainstream selection index and ignoring G×E. At lower r g, the highest genetic gain was achieved when considering G×E and selecting for the niche selection index. For the mainstream environment, it was never an advantage to ignore G×E. Therefore, although our hypothesis was confirmed in most cases, there were cases where ignoring G×E was the better option, and using the correct evaluation led to inferior genetic gain. The results of the current study can be used in animal breeding programs that encompass multiple environments. [ABSTRACT FROM AUTHOR]

Our study investigated the inbreeding load for fertility traits in the Italian Brown Swiss dairy cattle breed. Fertility traits included continuous traits (i.e., interval from calving to first service, days open, and calving interval) and categorical traits (i.e., calving rate at first insemination and nonreturn date at d 56). We included only records of the first 3 parities of cows that calved between 2010 and 2018. We traced up the pedigree of the cows with records as far as possible, ending up with a total of 73,246 animals. The final data set consisted of 59,864 records from 34,921 cows. We analyzed all models using a Bayesian approach that included a covariate with total inbreeding in addition to systematic, permanent environment, additive genetic, and inbreeding load effects. We then evaluated the trends in heritabilities and ratios of the inbreeding load using a continuum of partial inbreeding coefficients from 0.001 to 0.100 as reference. Posterior estimates of heritabilities tended to decrease across the continuum, whereas ratios of the inbreeding load tended to increase, more noticeably in categorical traits (calving rate at first insemination and nonreturn date at d 56). From the results obtained, we confirmed the presence of heterogeneity in inbreeding depression. We then predicted the inbreeding load effects, which had a low reliability of prediction, explained by having only 513 ancestors generating inbreeding. However, reliability of prediction was high enough for some of the individuals, obtaining a favorable prediction of inbreeding load for a relevant percentage, which improved the phenotypic performance of their inbred descendants. These results make it feasible to implement breeding and management strategies that select ancestors with a favorable inbreeding load prediction. In addition, it opens the possibility to define a global index for the expected consequences of the inbreeding generated by each individual. [ABSTRACT FROM AUTHOR]

Various studies have validated that genetic divergence in dairy cattle translates to phenotypic differences; nonetheless, many studies that consider the breeding goal, or associated traits, have generally been small scale, often undertaken in controlled environments, and they lack consideration for the entire suite of traits included in the breeding goal. Therefore, the objective of the present study was to fill this void, and in doing so, provide producers with confidence that the estimated breeding values (EBV) included in the breeding goal do (or otherwise) translate to desired changes in performance among commercial cattle; an additional outcome of such an approach is the identification of potential areas for improvements. Performance data on 536,923 Irish dairy cows (and their progeny) from 13,399 commercial spring-calving herds were used. Association analyses between the cow's EBV of each trait included in the Irish total merit index for dairy cows (which was derived before her own performance data accumulated) and her subsequent performance were undertaken using linear mixed models; milk production, fertility, calving, maintenance (i.e., liveweight), beef, health, and management traits were all considered in the analyses. Results confirm that excelling in EBV for individual traits, as well as on the total merit index, generally delivers superior phenotypic performance; examples of the improved performance for genetically elite animals include a greater yield and concentration of both milk fat and milk protein, despite a lower milk volume, superior reproductive performance, better survival, improved udder and hoof health, lighter cows, and fewer calving complications; all these gains were achieved with minimal to no effect on the beef merit of the dairy cow's progeny. The associated phenotypic change in each performance trait per unit change in its respective EBV was largely in line with the direction and magnitude of expectation, the exception being for calving interval. Per unit change in calving interval EBV, the direction of phenotypic response was as anticipated but the magnitude of the response was only half of what was expected. Despite the deviation from expectation between the calving interval EBV and its associated phenotype, a superior total merit index or a superior fertility EBV was indeed associated with an improvement in all detailed fertility performance phenotypes investigated. Results substantiate that breeding is a sustainable strategy of improving phenotypic performance in commercial dairy cattle and, by extension, profit. [ABSTRACT FROM AUTHOR]

A novel technology combining in vitro production and genomic embryo selection is currently under development in dairy cattle breeding. Adoption of this technology will probably accelerate genetic progress toward the main breeding goals of economic interest, as well as allow selection for traits of societal concern such as decreased methane emissions and improved animal welfare. However, dairy farmers, and especially organic farmers, could find the technology morally questionable and reject its use. This cross-sectional study surveyed Danish dairy farmers' general acceptance of the combined technology and their reported likelihood of using semen produced with it. Drawing on diffusion theory, a questionnaire was developed to examine the way farmers discover and communicate about new technological breeding options, and to measure the factors which predict acceptance and likelihood of adopting the technology. The questionnaire was sent to a randomly selected sample of organic and conventional dairy farmers in Denmark, and 85 organic and 71 conventional farmers (41% response rate) completed it. Seventy-six percent of farmers reported that they would be likely to use semen from bulls derived from the technology. A majority (61%) also found the technology acceptable, but many (33%) were unsure or undecided. Most farmers saw the technology as beneficial, but ethical reservations were aired by around a fifth of the farmers. There were no differences between organic and conventional farmers in likelihood of using, perceived utility, and ethical reservations about the technology. Self-reported idealistic organic farmers showed lower acceptance of the technology, but reported similar likelihood of using semen produced by it. Young farmers (20–39 yr) exhibited higher acceptance of the technology. Larger producers (in terms of number of cows) were more likely to report that they will use and accept the technology. We conclude that it is likely that semen from the technology combining in vitro production and genomic selection would be widely used by both organic and conventional farmers provided that costs can be kept low, and that there are advantages in terms of achieving breeding goals. Structural developments, growth in size of dairy farms, acceptance by young farmers, and the fact that economic incentives (and even ethical arguments) seem to favor the technology all point to this conclusion. [ABSTRACT FROM AUTHOR]

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations. [ABSTRACT FROM AUTHOR]

Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. Breeding programs seek to identify genetically superior parents of the next generation, typically as a function of an index that combines information about many economically important traits into a single number. In the United States, the data that drive this system are collected through the national dairy herd improvement program that began more than a century ago. The resulting information about animal performance, pedigree, and genotype is used to compute genomic evaluations for comparing and ranking animals for selection. However, the full expression of genetic potential requires that animals are placed in environments that can support such performance. The Agricultural Research Service of the US Department of Agriculture and the Council on Dairy Cattle Breeding collaborate to deliver state-of-the-art genomic evaluations to the dairy industry. Today, most breeding stock are selected and marketed using the net merit dollars (NM$) selection index, which evolved from 2 traits in 1926 (milk and fat yield) to a combination of 36 individual traits following the last NM$ update in 2018. Updates to NM$ require the estimation of many different values, and it can be difficult to achieve consensus from stakeholders on what should be added to, or removed from, the index at each review, and how those traits should be weighted. Over time, the majority of the emphasis in the index has shifted from yield traits to fertility, health, and fitness traits. Phenotypes for some of these new traits are difficult or expensive to measure, or require changes to on-farm habits that have not been widely adopted. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. There is also a need to capture more detailed data about the environment in which animals perform, including information about feeding, housing, milking systems, and infectious and parasitic load. However, many challenges accompany these new technologies, including a lack of standardization or validation, need for high-speed internet connections, increased computational requirements, and interpretations that are often not backed by direct observations of biological phenomena. This work will describe how US selection objectives are developed, as well as discuss opportunities and challenges associated with new technologies for measuring and recording animal performance. [ABSTRACT FROM AUTHOR]

Red dairy breeds are a valuable cultural and historical asset, and often a source of unique genetic diversity. However, they have difficulties competing with other, more productive, dairy breeds. Improving competitiveness of Red dairy breeds, by accelerating their genetic improvement using genomic selection, may be a promising strategy to secure their long-term future. For many Red dairy breeds, establishing a sufficiently large breed-specific reference population for genomic prediction is often not possible, but may be overcome by adding individuals from another breed. Relatedness between breeds strongly decides the benefit of adding another breed to the reference population. To prioritize among available breeds, the effective number of chromosome segments (M e) can be used as an indicator of relatedness between individuals from different breeds. The M e is also an important parameter in determining the accuracy of genomic prediction. The M e can be estimated both within a population and between 2 populations or breeds, as the reciprocal of the variance of genomic relationships. We investigated relatedness between 6 Dutch Red cattle breeds, Groningen White Headed (GWH), Dutch Friesian (DF), Meuse-Rhine-Yssel (MRY), Dutch Belted (DB), Deep Red (DR), and Improved Red (IR), focusing primarily on the M e , to predict which of those breeds may benefit from including reference animals of the other breeds. All of these breeds, except MRY, are under high risk of extinction. Our results indicated high variability of M e , especially between M e ranging from ∼3,500 to ∼17,400, indicating different levels of relatedness between the breeds. Two clusters are especially important, one formed by MRY, DR, and IR, and the other comprising DF and DB. Although relatedness between breeds within each of these 2 clusters is high, across-breed genomic prediction is still limited by the current number of genotyped individuals, which for many breeds is low. However, adding MRY individuals would increase the reference population of DR substantially. We estimated that between 11 and 133 individuals from other breeds are needed to achieve accuracy of genomic prediction equivalent to using one additional individual from the same breed. Given the variation in size of the breeds in this study, the benefit of a multibreed reference population is expected to be lower for larger breeds than for the smaller ones. [ABSTRACT FROM AUTHOR]

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as β-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations. [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze the impact of incorporating enteric methane into the breeding objective of dairy cattle in Spain, and to evaluate both genetic and economic response of traits in the selection index under 4 scenarios: (1) the current ICO (Spanish total merit index), used as benchmark; (2) a hypothetical penalization of methane emissions through a carbon tax; (3) considering methane as a net energy loss for the animal; and (4) desired genetic response to reduce methane production by 20% in 10 yr. A bio-economic model was developed to derive the economic values for production and methane traits in each scenario. The estimated economic values for methane were estimated at −€1.21/kg and −€0.32/kg for scenarios 2 and 3, respectively. When merged with other traits in the selection index, methane had less economic importance (1–5%) than milk protein yield (39–42%) or milk fat yield (27–28%). Under these scenarios, selection resulted in an unfavorable response in methane emissions when it was included with an economic weight, with an increase in methane estimated from 0.52 to 0.60 kg/cow per year. Small differences in total profit per cow per year were observed between indices. The incorporation of methane production into the breeding objective had a negligible effect on production, with minor reductions in the expected genetic gain for fat and protein yields and in total economic benefits. However, total methane emissions in the dairy industry in Spain were estimated to decrease between 2 and 5% in the next 10 yr due to positive genetic trends for milk yield and an expected decrease in the total number of dairy cows. Additionally, methane intensity per 1 billion liters of milk would decrease in all scenarios. The uncertainty in the genetic parameters of methane and in carbon prices were tested in a sensitivity analysis, resulting in small deviations from the benchmark scenario. A major effect was observed only under the desired genetic response scenario. In this case, it was possible to achieve a 20% reduction of methane production in 10 yr via selective breeding but at the expense of a larger ad hoc weight (33%) of methane in the selection index and decelerating the genetic gain for production traits from 6 to 18%. This study shows the potential of including environmental traits in the selection indices while retaining populations profitable for producers. [ABSTRACT FROM AUTHOR]

Feet and legs disorders influence dairy cattle breeding by their effect on animal welfare, economic losses due to lower production and fertility, costs of treatment, and problems with herd management. In our study, we estimated heritabilities and performed a 2-step GWAS for 3 traits describing hoof health: hoof health status defined by a veterinarian (HSV), hoof health status defined by a claw trimmer (HSC), and the total number of hoof disorders (NHD), scored in 1,998 Fleckvieh and 979 Braunvieh cows. The individuals were genotyped with a high-density (HD) panel consisting of 76,934 SNP. For significant genomic regions, the SNP information was enhanced by SNP imputed from the whole-genome sequence of Fleckvieh and Braunvieh bulls from the 1000 Bulls Genome project. The heritabilities were estimated to be 0.035 for HSV, 0.249 for HSC, and 0.279 for NHD. Based on the first-stage GWAS with SNP from the HD panel, 7 significant genomic regions on 6 chromosomes were defined: (1) 120 SNP spanning 15,522 bp on BTA1, including the TOPBP1 gene; (2) 4,139 SNP spanning 1,426,046 bp on BTA7, including the RIOK2 and RGMB genes; (3) 167 SNP spanning 167,352 bp on BTA13, including the C13H20orf194 gene; (4) 2 regions on BTA14, one harboring 1,071 SNP spanning 380,024 bp, including RRM2B and NCALD , and the other comprising 632 SNP spanning 385,111 bp, including STK3 ; (5) 328 SNP on BTA15, spanning 235,567 bp between FAM168A and PLEKHB1 ; and (6) 1,549 SNP on BTA22, spanning 596,101 bp in the neighborhood of PTPRG. Then, we conducted a second-stage GWAS based on SNP from whole-genome sequences within the significant regions obtained in the first stage of the analysis. For HSV, the highest additive effect was estimated for 23 SNP located within a region on BTA15, close to FAM168A , corresponding to a predicted gene sequence. For HSC, the highest additive effect was attributed to 44 SNP located within a region of BTA22 corresponding to 4 predicted gene sequences, with rs135082893 within a sequence encoding a microRNA. Another potential causal mutation for HSC was rs134142607 on BTA13, within the exon of C13H20orf194. For NHD, 33 SNP with the highest estimated effect were located on BTA7 within a region of a predicted gene positioned between RIOK2 and RGMB. On BTA14, all significant SNP were located in introns of STK3 , which is responsible for the "abnormal gait" phenotype in mice. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the ability of milk infrared spectra to predict cow lameness score (LMS) for use as an indicator of cow health on Australian dairy farms, or as an indicator trait for genetic evaluation purposes. The study involved 3,771 cows from 10 farms in Australia. Milk infrared spectra collected during the monthly herd testing were available in all the farms involved in the study. Lameness score was measured once in each herd, within 72 h from a test day, and merged to the closest spectra records. Lameness score was expressed on a scale from 0 to 3, where 0 is assigned to sound cows and scores 1 to 3 are assigned to cows with increased lameness severity. Partial least squares discriminant analysis was used to develop prediction models for classifying sound (score 0) and not-sound cows (i.e., cows walking unevenly, score greater than 0). Discriminant models were tested in a 10-fold random cross-validation process. Milk infrared spectra correctly classified only 57% of the cows walking unevenly and only 59% of the sound cows. When additional predictors (parity, age at calving, days in milk, and milk yield) were included in the prediction model, the model correctly classified 57% of the cows walking unevenly and 62% of the sound cows. The same model applied only to the cows in the first third of lactation correctly classified 66% of the cows walking unevenly and 57% of the sound cows. When the prediction model was used to identify lame cows (scores 2 and 3), only 49% of them were classified as such. These results are considered to be too poor to envisage a practical application of these models in the near future as on-farm tools to provide an indication of LMS. To investigate whether, at this stage, predictions of the LMS could be useful as large-scale phenotypes for animal breeding purposes, we estimated (co)variance components for actual and predicted LMS using 2,670 and 24,560 records, respectively. As the genetic correlation between actual and predicted LMS was not significantly different from zero, predictions of lameness from milk spectra and additional on-farm variables cannot be used, at this stage, as an indicator trait for actual LMS. More research is needed to find better strategies to predict lameness. [ABSTRACT FROM AUTHOR]

Genomic evaluation has improved both plant and animal breeding by allowing more accurate estimation of an individual's genetic potential. Because often only a small proportion of the population to be evaluated has been genotyped, genomic estimations rely heavily on complete pedigree information. Confirmation, discovery, and correction of parentage and connected relatives allow the creation of more complete pedigrees, which in turn increase the number of usable phenotypic records and prediction accuracy. Previous methods accounted for parent-progeny conflicts using SNP. More recently haplotype methods allowed discovery of distant relationships such as maternal grandsire (MGS) and maternal great-grandsire (MGGS) with improved accuracy. However, discovered MGS and MGGS often were not used, because no dam information was available to link them to the calf. An automated procedure to discover and fill missing maternal identification information was developed, allowing discovered MGS and MGGS to be used in imputation as well as in calculating breeding values for animals in the US dairy cattle database. An MGS was discovered for 295,136 animals with unknown dam, and the MGGS was discovered for 153,909 of these animals. A virtual maternal identification was added for animals with missing information. The effect of pedigree completion on progeny inbreeding, breeding values, and reliabilities was examined. Mean inbreeding of animals with missing maternal pedigree information was 6.69% before and 6.87% after pedigree assignment; expected future inbreeding was 7.24% before and 7.20% after assignment. Reliabilities for traditional breeding values increased from 26.6 to 32.6% for milk yield, 25.9 to 32.0% for fat yield, and 26.9 to 32.9% for protein yield; genomic reliabilities also increased slightly from 76.2 to 77.1% for milk, 76.0 to 76.9% for fat, and 76.3 to 77.3% for protein. The procedure developed for pedigree completion is a useful tool for improving accuracy of national and international evaluations and aiding producers in making better mating decisions. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10-fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-by-herd external validation, using basic on-farm information (model 1) was not sufficient to classify good- and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy. [ABSTRACT FROM AUTHOR]

One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (r g). The break-even correlation (r b) refers to the specific r g where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if r g is higher than r b , whereas 2 environment-specific BP have higher genetic gain if r g is lower than r b. Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find r b when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on r b and genetic gain; and (4) to find r b from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The r b was 0.65 and not different from r b with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on r b was not large. A different size in 2 environments decreased r b by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high r g. When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when r g between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP. [ABSTRACT FROM AUTHOR]

Information about genetic parameters is population specific and it is crucial for designing animal breeding programs and predicting response to selection. This study was carried out to estimate the genetic parameters for 23 body conformation traits of 45,517 Chinese Holstein reared in Eastern China from 1995 to 2017 with the Bayesian inference method using a linear animal mixed model. The methods to integrate these traits included (1) using the composite index from the Dairy Association of China and (2) applying principal component analysis and factor analysis to explore the relationship between the conformation traits. Estimates of heritability using the composite index were low (0.04; feet and legs) to moderate (0.23; body capacity). Strong genetic correlations were observed between the individual body conformation traits. Both principal components (1 to 7; eigenvalues ≥ 1) and latent factors (1 to 7; eigenvalues ≥ 1) explained 60.37% of total variability. Principal component 1 and factor 1 accounted for the traits that are usually associated with milk production. Moderate to low heritability were estimated through multi-trait analysis for principal components (from 0.07 to 0.21) and latent factors (from 0.07 to 0.23). Genetic correlations among the 2 multivariate techniques are typically lower compared with the one existing among the measured traits. Results from these analyses suggest the possibility of using both principal component analysis and factor analysis in morphological evaluation, simplifying the information given by the body conformation traits into new variables that could be useful for the genetic improvement of the Chinese Holstein population. This information could also be used to avoid analyzing large number of correlated traits, thereby improving precision and reducing computation burdens to analyze large and complex data. [ABSTRACT FROM AUTHOR]

The objective of this study was to model differences in pedigree accuracy caused by selective genotyping. As genotypes are used to correct pedigree errors, some pedigree relationships are more accurate than others. These accuracy differences can be modeled with uncertain parentage models that distribute the paternal (maternal) contribution across multiple sires (dams). In our case, the parents were the parent on record and an unknown parent group to account for pedigree relationships that were not confirmed through genotypes. Pedigree accuracy was addressed through simulation and through North American Holstein data. Data were simulated to be representative of the dairy industry with heterogeneous pedigree depth, pedigree accuracy, and genotyping. Holstein data were obtained from the official evaluation for milk, fat, and protein. Two models were compared: the traditional approach, assuming accurate pedigrees, and uncertain parentage, assuming variable pedigree accuracy. The uncertain parentage model was used to add pedigree relationships for alternative parents when pedigree relationships were not certain. The uncertain parentage model included 2 possible sires (dams) when the sire (dam) could not be confirmed with genotypes. The 2 sires (dams) were the sire (dam) on record with probability 0.90 (0.95) and the unknown parent group for the birth year of the sire (dam) with probability 0.10 (0.05). An additional set of assumptions was tested in simulation to mimic an extensive dairy production system by using a sire probability of 0.75, a dam probability of 0.85, and the remainder attributed to the unknown parent groups. In the simulation, small bias differences occurred between models based on pedigree accuracy and genotype status. Rank correlations were strong between traditional and uncertain parentage models in simulation (≥0.99) and in Holstein (≥0.99). For Holsteins, the estimated breeding value differences between models were small for most animals. Thus, traditional models can continue to be used for dairy genomic prediction despite using genotypes to improve pedigree accuracy. Those genotypes can also be used to discover maternal parentage, specifically maternal grandsires and great grandsires when the dam is not known. More research is needed to understand how to use discovered maternal pedigrees in genetic prediction. [ABSTRACT FROM AUTHOR]

Cattle breeding programs that strive to reduce the animal-level incidence of lameness are often hindered by the availability of informative phenotypes. As a result, indicator traits of lameness (i.e., hoof health and morphological conformation scores) can be used to improve the accuracy of selection and subsequent genetic gain. Therefore, the objectives of the present study were to estimate the variance components for hoof health traits using various phenotypes collected from a representative sample of Irish dairy cows. Also of interest to the present study was the genetic relationship between both hoof health traits and conformation traits with producer-scored lameness. Producer-recorded lameness events and linear conformation scores from 307,657 and 117,859 Irish dairy cows, respectively, were used. Data on hoof health (i.e., overgrown sole, white line disease, and sole hemorrhage), mobility scores, and body condition scores were also available from a research study on up to 11,282 Irish commercial dairy cows. Linear mixed models were used to quantify variance components for each trait and to estimate genetic correlations among traits. The estimated genetic parameters for hoof health traits in the present study were greater (i.e., heritability range: 0.005 to 0.27) than previously reported in dairy cows. With the exception of analyses that considered hoof health traits in repeatability models, little difference in estimated variance components existed among the various hoof-health phenotypes. Results also suggest that producer-recorded lameness is correlated with both hoof health (i.e., genetic correlation up to 0.48) and cow mobility (i.e., genetic correlation = 0.64). Moreover, cows that genetically tend to have rear feet that appear more parallel when viewed from the rear are also genetically more predisposed to lameness (genetic correlation = 0.39); genetic correlations between lameness and other feet and leg type traits, as well as between lameness and frame type traits, were not different from zero. Results suggest that if the population breeding goal was to reduce lameness incidence, improve hoof health, or improve cow mobility, genetic selection for either of these traits should indirectly benefit the other traits. Results were used to quantify the genetic gains achievable for lameness when alternative phenotypes are available. [ABSTRACT FROM AUTHOR]

National gene bank collections for Holstein Friesian (HF) dairy cattle were set up in the 1990s. In this study, we assessed the value of bulls from the Dutch HF germplasm collection, also known as cryobank bulls, to increase genetic variability and improve genetic merit in the current bull population (bulls born in 2010-2015). Genetic variability was defined as 1 minus the mean genomic similarity (SIMSNP) or as 1 minus the mean pedigree-based kinship (fPED). Genetic merit was defined as the mean estimated breeding value for the total merit index or for 1 of 3 subindices (yield, fertility, and udder health). Using optimal contribution selection, we minimized relatedness (maximized variability) or maximized genetic merit at restricted levels of relatedness. We compared breeding schemes with only bulls from 2010 to 2015 with schemes in which cryobank bulls were also included. When we minimized relatedness, inclusion of genotyped cryobank bulls decreased mean SIMSNP by 0.7% and inclusion of both genotyped and nongenotyped cryobank bulls decreased mean fPED by 2.6% (in absolute terms). When we maximized merit at restricted levels of relatedness, inclusion of cryobank bulls provided additional merit at any level of mean SIMSNP or mean fPED except for the total merit index at high levels of mean SIMSNP. Additional merit from cryobank bulls depended on (1) the relative emphasis on genetic variability and (2) the selection criterion. Additional merit was higher when more emphasis was put on genetic variability. For fertility, for example, it was 1.74 SD at a mean SIMSNP restriction of 64.5% and 0.37 SD at a mean SIMSNP restriction of 67.5%. Additional merit was low to nonexistent for the total merit index and higher for the subindices, especially for fertility. At a mean SIMSNP of 64.5%, for example, it was 0.60 SD for the total merit index and 1.74 SD for fertility. In conclusion, Dutch HF cryobank bulls can be used to increase genetic variability and improve genetic merit in the current population, although their value is very limited when selecting for the current total merit index. Anticipating changes in the breeding goal in the future, the germplasm collection is a valuable resource for commercial breeding populations. [ABSTRACT FROM AUTHOR]

To validate the accuracy of 2 commercially available activity loggers in determining lying, standing, walking, and number of steps in dairy cows, 30 cows were fitted with the CowScout Leg (GEA Farm Technologies, Bönen, Germany) system and the IceTag (IceRobotics Ltd., Edinburgh, Scotland) system. The CowScout Leg logger reports standing and lying in 15-min periods, whereas the IceTag logger reports standing and lying every second. To make data comparable, the IceTag data were therefore also summarized over 15-min periods corresponding to the paired CowScout Leg sensor. These data from the 2 systems were then analyzed (more than 1,000 cow days in total). Video recordings of a total of 29.5 h were used for labeling the behaviors of the selected cows (n = 10) and these labels were used as a gold standard to determine the accuracy with which these 2 loggers can record behavioral states lying, standing, walking, and the behavioral event number of steps. A concordance correlation coefficient analysis showed that both the standing and lying durations obtained with the 2 systems were almost perfectly correlated with the video labeling (IceTag: ρc = 0.999 and 0.999, respectively; CowScout Leg: ρc = 0.995 and 0.996, respectively). However, both loggers performed poorly regarding number of steps (classified as an event; IceTag: ρc = 0.629; CowScout Leg: ρc = 0.678) and CowScout Leg did not detect walking (classified as a state) very accurately (ρc = 0.860). The IceTag system does not measure walking behavior. When comparing the 2 loggers, the correlation between them for standing and lying was substantial (ρc = 0.953 and ρc = 0.953, respectively). The number of steps poorly correlated between the 2 loggers (ρc = 0.593), which might be due to the CowScout Leg logger being attached to the front leg and the IceTag logger being attached to the hind leg. We conclude that both the IceTag and the CowScout Leg logger are able to record standing and lying almost perfectly, but the step counting by both loggers and the walking recording by the CowScout Leg logger are not very accurate. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate hair cortisol concentration as an indicator for stress caused by chronic lameness in dairy cows. Sixty-eight cows were scored for lameness for 4 consecutive weeks. The hair of the tail switch was clipped at the beginning of the study and regrown hair was clipped after 4 wk. Hair samples were analyzed for cortisol concentration. Animals with 2 consecutive locomotion scores ≥3 or with an overall mean score >1.5 were classified as lame. After pair matching lame and nonlame cows, considering days in milk, lactation number, and milk yield, and excluding cows with less than 20 mg hair sample for analysis, 21 lame and 21 nonlame cows were included in the analysis. The mean hair cortisol concentration in this study was 2.32 ± 0.35 pg/mg (mean ± standard deviation). Cortisol concentration from hair regrown in the study period was 2.38 ± 0.95 and 2.26 ± 1.35 pg/mg for lame and nonlame cows (n = 21), respectively; we found no difference in mean cortisol level of primiparous and multiparous cows. Based on these data, hair cortisol concentration was not a useful indicator to differentiate cows with chronic lameness and healthy cows. [ABSTRACT FROM AUTHOR]

attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training bulls (from 0.004 to 0.09 r points). In this study, as expected, the bull-only reference population resulted in higher accuracy compared with the cow-only reference population of the same size. However, the cow reference population might be an option for countries with a small-scale progeny testing scheme or for minor breeds in large counties, and for traits measured only on a small fraction of the population. The inclusion of preferential treatment to 5 to 20% of the elite cows led to an adverse effect on both accuracy and bias of predictions. When preferential treatment was present, random selection of cows did not reduce the effect of preferential treatment. [ABSTRACT FROM AUTHOR]

Breeding traits are usually combined in a total merit index according to their economic weights to maximize genetic gain based on economic merit. However, this maximization may not always be the aim of the selection decisions by farm managers. A discrete choice experiment was used to evaluate the importance of traits in terms of the selection decisions of farm managers operating in different environments. Six trait complexes, the semen price, the interactions between these traits, and significant characteristics of the farms were included in a conditional logit model to estimate relative economic weights and the marginal willingness to pay for all traits. Milk value, conformation/udder, and fitness were the most important traits for the farmers, and significant interactions indicated that fitness is of greater importance on organically managed farms than on conventional farms. Farm managers with an advanced education placed more weight on the milk value trait than farm managers without advanced education. On conventional farms, managers weighted the traits milk value and conformation/udder highly. The conformation/udder and fitness trait complexes were important on organic farms. A new trait called perinatal sucking behavior of newborn calves should be included in the total merit index. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare genetic trends from single-step genomic BLUP (ssGBLUP) and traditional BLUP models for milk production traits of US Holsteins. Phenotypes were 305-d milk, fat, and protein yields from 21,527,040 cows recorded between January 1990 and August 2015. The pedigree file included 29,651,623 animals and was limited to 3 generations back from recorded or genotyped animals. Genotypes for 764,029 animals were used, and analyses were by a 3-trait repeatability model as used in the US official genetic evaluation. Unknown-parent groups were incorporated into the inverse of a relationship matrix (H−1 in ssGBLUP and A−1 in BLUP) with the QP transformation. For ssGBLUP, 18,359 genotyped animals were randomly chosen as core animals to calculate the inverse of the genomic relationship matrix with the APY algorithm. Computations took 6.5 h and 1.4 GB of memory for BLUP, and 13 h and 115 GB of memory for ssGBLUP. For genotyped sires with at least 10 daughters, the average genetic levels for predicted transmitting ability (PTA) and genomic PTA were similar up to 2008, with a higher level for ssGBLUP later (approximately by 36 kg for milk, 2.1 kg for fat, and 1.1 kg for protein for bulls born in 2010). For genotyped cows, the average genetic levels were similar up to 2006, with a higher level for ssGBLUP (approximately by 91 kg for milk, 3.6 kg for fat, and 2.7 kg for protein for cows born in 2012). For all cows, the average levels were slightly higher for ssGBLUP, with much smaller differences than for genotyped cows. Trends for BLUP indicate bias due to genomic preselection for genotyped sires and cows. For official evaluations released in December 2016, traditional PTA had the same trend as multiple-step genomic PTA for both genotyped bulls and cows except for the youngest bulls, who had traditional PTA slightly lower than genomic PTA. For genotyped bulls born in recent years, genetic gain for official traditional and genomic evaluations was similar in contrast to ssGBLUP and BLUP differences. Official PTA for cows were adjusted so that the Mendelian sampling variance was comparable with that for bulls, and those adjustments likely removed bias due to genomic preselection from traditional PTA, especially for genotyped cows. The ssGBLUP method seems to account partially for that bias and is computationally suitable for national evaluations. [ABSTRACT FROM AUTHOR]

Type traits and mammary health traits are important to dairy ruminant breeding because they influence animal health, milking ability, and longevity, as well as the economic sustainability of farms. The availability of the genomic sequence and a single nucleotide polymorphism chip in goats has opened up new fields of investigation to better understand the genes and mechanisms that underlie such complex traits and to be able to select them. Our objective was to perform a genome-wide association study in dairy goats for 11 type traits and somatic cell count (SCC) as proxies for mastitis resistance. A genome-wide association study was implemented using a daughter design composed of 1,941 Alpine and Saanen goats sired by 20 artificial insemination bucks, genotyped with the Illumina Goat- SNP50 BeadChip (Illumina Inc., San Diego, CA). This association study was based on both linkage analyses and linkage disequilibrium using QTLmap software (http:// dga7 .jouy .inra .fr/ qtlmap/ ) interval mapping was performed with the likelihood ratio test using linear regressions. Breeds were analyzed together and separately. The study highlighted 37 chromosome-wide significant quantitative trait loci (QTL) with linkage analyses and 222 genome-wide significant QTL for linkage disequilibrium, for type and SCC traits in dairy goats. Genomic control of those traits was mostly polygenic and breed-specific, suggesting that within-breed selection would be favored for those traits. Of note, Capra hircus autosome (CHI) 19 appeared to be highly enriched in single nucleotide polymorphisms associated with type and SCC, with 2 highly significant regions in the Saanen breed. One region (33–42 Mb) was significantly associated with SCC and includes candidate genes associated with response to intramammary infections (RARA, STAT3, STAT5A, and STAT5B). Another region of the CHI 19 (24.5–27 Mb) exhibited an adverse pleiotropic effect on milk production (milk, fat yield, and protein yield) and udder traits (udder floor position and rear udder attachment) that agreed with the negative genetic correlations that exist between those 2 groups of traits. These QTL were not found in the Alpine breed. In Alpine, the 2 most significant regions were associated with chest depth on CHI 6 (45.8–46.0 Mb) and CHI 8 (80.7–81.1 Mb). These results will be helpful for goat selection in the future and could lead to identification of causal mutations. [ABSTRACT FROM AUTHOR]

Calf mortality and morbidity commonly occurs within the first month of life postpartum. Standard health ranges are invaluable aids in diagnostic veterinary medicine to confirm normal or the degree and nature of abnormal parameters in (sub)clinically ill animals. Extensive research has indicated significant differences between the physiologies of neonate and adult cattle, particularly for blood parameters such as pH, base excess, anion gap, and bicarbonate (HCO3-). The objective of this research was to determine the influence of age, sex, and breed type, in addition to environmental factors, on the normal blood gas profiles of neonatal calves, and thus develop a scientifically validated reference range accounting for any significant factors. The study was conducted on healthy neonatal calves (n = 288), and completed over a 2-yr period. Individual calf blood gas analysis was conducted for parameters of pH, base excess, Na+, K+, Ca2+, Cl-, glucose, total hemoglobin, HCO3-, pCO2, anion gap, strong ion difference, and hematocrit levels. Regression procedures examined the combined effect of year, farm, age, breed type, sex, and hours postfeeding on each variable. Significant effects were observed for age, sex, and breed type on several of the blood gas variables. Furthermore, year, farm, and hours postfeeding appeared to have less of an influence on neonatal bovine blood gas profiles. Consequently, specific ranges based on the neonate's age, sex, and breed type will allow for more detailed and accurate diagnosis of health and ill health in neonatal calves. [ABSTRACT FROM AUTHOR]

Fertility of the dairy cow relies on complex interactions between genetics, physiology, and management. Mathematical modeling can combine a range of information sources to facilitate informed predictions of cow fertility in scenarios that are difficult to evaluate empirically. We have developed a stochastic model that incorporates genetic and physiological data from more than 70 published reports on a wide range of fertility-related traits in dairy cattle. The model simulates pedigree, random mating, genetically correlated traits (in the form of breeding values for traits such as hours in estrus, estrous cycle length, age at puberty, milk yield, and so on), and interacting environmental variables. This model was used to generate a large simulated data set (200,000 cows replicated 100 times) of herd records within a seasonal dairy production system (based on an average New Zealand system). Using these simulated data, we investigated the genetic component of lifetime reproductive success (LRS), which, in reality, would be impractical to assess empirically. We defined LRS as the total number of times, during her lifetime, a cow calved within the first 42 d of the calving season. Sire estimated breeding values for LRS and other traits were calculated using simulated daughter records. Daughter pregnancy rate in the first lactation (PD_1) was the strongest single predictor of a sire's genetic merit for LRS (R² = 0.81). A simple predictive model containing PD_1, calving date for the second season and calving rate in the first season provided a good estimate of sire LRS (R² = 0.97). Daughters from sires with extremely high (n = 99,995 daughters, sire LRS = +0.70) or low (n = 99,635 daughters, sire LRS = -0.73) LRS estimated breeding values were compared over a single generation. Of the 14 underlying component traits of fertility, 12 were divergent between the 2 lines. This suggests that genetic variation in female fertility has a complex and multifactorial genetic basis. When simulated phenotypes were compared, daughters of the high LRS sires (HiFERT) reached puberty 44.5 d younger and calved ~14 d younger at each parity than daughters from low LRS sires (LoFERT). Despite having a much lower genetic potential for milk production (-400 L/lactation) than LoFERT cows, HiFERT cows produced 33% more milk over their lifetime due to additional lactations before culling. In summary, this simulation model suggests that LRS contributes substantially to cow productivity, and novel selection criteria would facilitate a more accurate prediction at a younger age. [ABSTRACT FROM AUTHOR]

Lameness is one of the most prevalent diseases affecting the welfare of cows in modern dairy production. Lameness leads to behavioral changes in severely lame cows, which have been investigated in much detail. For early detection of lameness, knowledge of the effects of moderate lameness on cow behavior is crucial. Therefore, the behavior of nonlame and moderately lame cows was compared on 17 Swiss dairy farms. On each farm, 5 to 11 nonlame (locomotion score 1 of 5) and 2 to 7 moderately lame (locomotion score 3 of 5) cows were selected for data collection in two 48-h periods (A, B) separated by an interval of 6 to 10 wk. Based on visual locomotion scoring, 142 nonlame and 66 moderately lame cows were examined in period A and 128 nonlame and 53 moderately lame cows in period B. Between these 2 periods, the cows underwent corrective hoof trimming. Lying behavior, locomotor activity, and neck activity were recorded by accelerometers (MSR145 data logger, MSR Electronics GmbH, Seuzach, Switzerland), and feeding and rumination behaviors by noseband sensors (RumiWatch halter, ITIN + HOCH GmbH, Liestal, Switzerland). Furthermore, visits to the brush and the concentrate feeder, and the milking order position were recorded. In comparison with nonlame cows, moderately lame cows had a longer lying duration, a longer average lying bout duration, and a greater lateral asymmetry in lying duration. Average locomotor activity, locomotor activity during 1 h after feed delivery or push-ups, and average neck activity were lower in moderately lame cows. Eating time and the number of eating chews (jaw movements) were reduced in moderately lame compared with nonlame cows, whereas no effect of moderate lameness was evident for ruminating time, number of ruminating chews and boluses, and average number of ruminating chews per bolus. Moderately lame cows visited the concentrate feeder and the brush less frequently, and they were further back in the milking order compared with nonlame cows. In conclusion, nonlame and moderately lame cows differed in a biologically relevant way in many of the behavioral variables investigated in this study. Therefore, the use of these behavioral changes seems to be promising to develop a tool for early lameness detection. [ABSTRACT FROM AUTHOR]

A data set including 57,868 records for calf birth weight (CABW) and 9,462 records for weight at first insemination (IBW) were used for the estimation of direct and maternal genetic effects in Holstein Friesian dairy cattle. Furthermore, CABW and IBW were correlated with test-day production records and health traits in first-lactation cows, and with nonreturn rates in heifers. Health traits considered overall disease categories from the International Committee for Animal Recording diagnosis key, including the general disease status, diarrhea, respiratory diseases, mastitis, claw disorders, female fertility disorders, and metabolic disorders. For single-trait measurements of CABW and IBW, animal models with maternal genetic effects were applied. The direct heritability was 0.47 for CABW and 0.20 for IBW, and the direct genetic correlation between CABW and IBW was 0.31. A moderate maternal heritability (0.19) was identified for CABW, but the maternal genetic effect was close to zero for IBW. The correlation between direct and maternal genetic effects was antagonistic for CABW (-0.39) and for IBW (-0.24). In bivariate animal models, only weak genetic and phenotypic correlations were identified between CABW and IBW with either test-day production or health traits in early lactation. Apart from metabolic disorders, there was a general tendency for increasing disease susceptibilities with increasing CABW. The genetic correlation between IBW and nonreturn rates in heifers after 56 d and after 90 d was slightly positive (0.18), but close to zero when correlating nonreturn rates with CABW. For the longitudinal BW structure from birth to the age of 24 mo, random regression models with the timedependent covariate "age in months" were applied. Evaluation criteria (Bayesian information criterion and residual variances) suggested Legendre polynomials of order 3 to modeling the longitudinal body weight (BW) structure. Direct heritabilities around birth and insemination dates were slightly larger than estimates for CABW and IBW from the single-trait models, but maternal heritabilities were exactly the same from both models. Genetic correlations between BW were close to 1 for neighboring age classes, but decreased with increasing time spans. The genetic correlation between BW at d 0 (birth date) and at 24 mo was even negative (-0.20). Random regression model estimates confirmed the antagonistic relationship between direct and maternal genetic effects, especially during calfhood. This study based on a large data set in dairy cattle confirmed genetic parameters and (co)variance components for BW as identified in beef cattle populations. However, BW records from an early stage of life were inappropriate early predictors for dairy cow health and productivity. [ABSTRACT FROM AUTHOR]

Given the interest of including dry matter intake (DMI) in the breeding goal, accurate estimated breeding values (EBV) for DMI are needed, preferably for separate lactations. Due to the limited amount of records available on DMI, 2 main approaches have been suggested to compute those EBV: (1) the inclusion of predictor traits, such as fat- and protein-corrected milk (FPCM) and live weight (LW), and (2) the addition of genomic information of animals using what is called genomic prediction. Recently, several methodologies to estimate EBV utilizing genomic information (EBV) have become available. In this study, a new method known as single-step ridge-regression BLUP (SSRR-BLUP) is suggested. The SSRR-BLUP method does not have an imposed limit on the number of genotyped animals, as the commonly used methods do. The objective of this study was to estimate genetic parameters using a relatively large data set with DMI records, as well as compare the accuracies of the EBV for DMI. These accuracies were obtained using 4 different methods: BLUP (using pedigree for all animals with phenotypes), genomic BLUP (GBLUP; only for genotyped animals), single-step GBLUP (SS-GBLUP), and SSRR-BLUP (for genotyped and nongenotyped animals). Records from different lactations, with or without predictor traits (FPCM and LW), were used in the model. Accuracies of EBV for DMI (defined as the correlation between the EBV and pre-adjusted DMI phenotypes divided by the average accuracy of those phenotypes) ranged between 0.21 and 0.38 across methods and scenarios. Accuracies of EBV for DMI using BLUP were the lowest accuracies obtained across methods. Meanwhile, accuracies of EBV for DMI were similar in SS-GBLUP and SSRR-BLUP, and lower for the GBLUP method. Hence, SSRR-BLUP could be used when the number of genotyped animals is large, avoiding the construction of the inverse genomic relationship matrix. Adding information on DMI from different lactations in the reference population gave higher accuracies in comparison when only lactation 1 was included. Finally, no benefit was obtained by adding information on predictor traits to the reference population when DMI was already included. However, in the absence of DMI records, having records on FPCM and LW from different lactations is a good way to obtain EBV with a relatively good accuracy. [ABSTRACT FROM AUTHOR]

This technical note presents the program PaGELL v.1.5 (Parametric Genetic Evaluation of Lifespan in Livestock), a flexible software program to analyze (right-censored) longevity data in livestock populations, with a special emphasis on the genetic evaluation of the breeding stock. This software relies on a parametric generalization of the proportional hazard model; more specifically, the baseline hazard function follows a Weibull process and flexibility is gained by including an additional time-dependent effect with the number of change points defined by the user. The program can accommodate 3 different sources of variation (i.e., systematic, permanent environmental, and additive genetic effects) and both fixed and time-dependent patterns (only for systematic and permanent environmental effects). Analyses are performed within a Bayesian context by sampling from the joint posterior distribution of the model, and model fit can be easily determined by the calculation of the deviance information criterion. Although this software has already been used on field data sets, its performance has been double-checked on simulated data set, and results are presented in this technical note. PaGELL v.1.5 was written in Fortran 95 language and, after compiling with the GNU Fortran Compiler v.4.7 and later, it has been tested in Windows, Linux, and MacOS operating systems (both 32- and 64-bit platforms). [ABSTRACT FROM AUTHOR]

In a 2-step genomic system, genotypes of animals without phenotypes do not influence genomic prediction of other animals, but that might not be the case in single-step systems. We investigated the effects of including genotypes from culled bulls on the reliability of genomic predictions from single-step evaluations. Four scenarios with a constant amount of phenotypic information and increasing numbers of genotypes from culled bulls were simulated and compared with respect to prediction reliability. With increasing numbers of genotyped culled bulls, there was a corresponding increase in prediction reliability. For instance, in our simulation scenario the reliability for selection candidates was twice as large when all culled bulls from the last 4 generations were included in the analysis. Single-step evaluations imply the imputation of all nongenotyped animals in the pedigree. We showed that this imputation was increasingly more accurate as increasingly more genotypic information from the culled bulls was taken into account. This resulted in higher prediction reliabilities. The extent of the benefit from including genotypes from culled bulls might be more relevant for small populations with low levels of reliabilities. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the common method of exploiting infrared spectral data in animal breeding; that is, estimating the breeding values for the traits predicted by infrared spectroscopy, and an alternative approach based on the direct use of spectral information (direct prediction, DP) to predict the estimated breeding values (EBV). Traits were pH, milk coagulation properties, contents of the main casein and whey protein fractions, cheese yield measured by micro-cheese making, lactoferrin, Ca, and fat composition. For the DP method, the number of spectral variables was reduced by principal components analysis to 8 latent traits that explained 99% of the original spectral variation. Restricted maximum likelihood was used to estimate variance components of the latent traits. (Co)variance components of the original spectral traits were obtained by back-transformation and EBV of all derived milk traits were then predicted as traits correlated with the genetic information of the spectra. The rank correlation between the EBV obtained for the infrared-predicted traits and those obtained from the DP method was variable across traits. Rank correlations ranged from 0.07 (for the content of saturated fatty acids expressed as g/100 g of fat) to 0.96 (for dry matter cheese yield, %) and, for most traits, was <0.5. This result can be explained by the nature of the principal components analysis: it does not take into account the covariance between the spectral variables and the reference traits but produces latent traits that maximize the spectral variance explained. Thus, the direct approach is more likely to be effective for traits more related to the main sources of spectral variation (i.e., protein and fat). More research is required to study spectral genetic variation and to determine the best way to choose spectral regions and the type and number of considered latent traits for potential applications. [ABSTRACT FROM AUTHOR]

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides. [ABSTRACT FROM AUTHOR]

Milk flow during the first minute of milking was analyzed using data from 1,132 Murciano-Granadina breed goats belonging to 17 herds. During the individual lactations, 2 test days were scheduled for recording several milk flow traits, total milk, milk composition (fat and protein percentages), and somatic cell count. Average lag time from teatcup attachment to arrival of milk at the milk claw (T0) was 4.9 s and at the milk meter (T1) was 15.8 s. Average milk flow after 30 s (MF0.5) was 0.29 kg/30 s (0 to 1.1 kg/30 s) and milk flow at 60 s or milking speed (MF1) was 0.67 kg/min (0.1 to 2.1 kg/min). Repeatabilities of T0, T1, MF0.5, and MF1 were 0.45, 0.58, 0.62, and 0.68, respectively. The MF1 showed high phenotypic correlation with T1 (-0.63) and MF0.5 (0.90), medium values with T0 (-0.42) and total milk (0.22), and very low values (-0.04 to -0.12) with fat, protein, and somatic cell count. We found no differences between flows during the first 3 lactations, with a reduction as the lactation number increased. Months in milk since parturition affected MF1, being highest in the first 3 mo (0.67-0.71 kg/min) and decreasing until the end of lactation (0.58 kg/min). The effect of herd-test day was significant for all traits. Inclusion of all these effects for the analysis of milk flow traits is considered necessary. [ABSTRACT FROM AUTHOR]

In pasture-based, seasonally calving dairy herds of southern Australia, the mating period usually consists of an initial artificial insemination period followed by a period of natural service using herd bulls. The primary objective of this study was to identify associations between individual bull- and herd-level management factors and bull fertility as measured by a pre- and postmating bull breeding soundness evaluation (BBSE). Multivariable mixed effects logistic regression models were used to identify factors associated with bulls being classified as high risk of reduced fertility at the premating and postmating BBSE. Bulls older than 4 yr of age at the premating BBSE were more likely to be classified high risk compared with bulls less than 4 yr of age. Bulls that were in herds in which concentrates were fed before mating were more likely to be classified as high risk at the postmating BBSE compared with bulls that were in herds where concentrates were not fed. Univariable analyses also identified areas in need of further research, including breed differences between dairy bulls, leg conformation and joint abnormalities, preventative hoof blocking for bulls, and mating ratios. [ABSTRACT FROM AUTHOR]

In the pasture-based, seasonally calving dairy herds of southern Australia, the mating period usually consists of an initial artificial insemination period followed by a period of natural service using herd bulls. Bull breeding soundness evaluations (BBSE) were performed on 256 bulls from 32 dairy herds in southwest Victoria, using guidelines produced by the Australian Cattle Veterinarians, before and immediately after a single natural mating period. At the same time, herd managers were questioned regarding the management of the bulls. The objectives of this study were to describe the management practices of dairy herd bulls; to describe the causes of increased risk of reduced fertility in dairy herd bulls, as measured by a standard BBSE; and to describe the reasons for bull removal by herd managers during mating. At the premating BBSE, 19.5% of bulls were classified as high risk of reduced fertility, mostly due to physical abnormalities and reduced semen quality. At the postmating BBSE, 36.5% of bulls were classified as high risk of reduced fertility, mostly due to physical abnormalities, primarily lameness. Of the bulls used, 15.9% were removed from normal mating use by the herd manager, predominantly due to lameness and injuries. A premating BBSE is recommended in dairy herd bulls to identify bulls at risk of reduced fertility. Lameness is the most common problem in dairy herd bulls during the natural mating period, and risk factors associated with lameness in these bulls should be identified to better manage herd bulls. [ABSTRACT FROM AUTHOR]

Multiple embryos can be produced from a heifer or cow donors using an in vivo or an in vitro technique. Comparisons of the number of embryos produced by the same donors as heifers and cows and using different techniques are limited. The main objectives of this study were to assess the genetic correlation between the number of embryos produced by Holstein donors using an in vivo and in vitro technique as a heifer and as a cow. The data set used was recorded by Holstein Canada and included all successful superovulations or ovum pickup and in vitro fertilization procedures performed on Holstein donors for more than 20 yr. The type of technique used was known for all records and the status of the donor at recovery was retrieved from calving records. Bivariate repeatability animal model analyses were performed for both the total number of embryos (NE) and the number of viable embryos (VE) recovered per procedure. Logarithmic transformation was performed on the traits to normalize the data. Heritability estimates for the donor varied between 0.14 (0.02) and 0.19 (0.03) over all analyses, indicating that the number of embryos produced by a donor is influenced by the genetic potential of the donor. Genetic correlations between records produced in vivo and in vitro were moderately high and positive (NE = 0.85 × 0.07; VE = 0.63 × 0.09), suggesting that donors with high genetic potential for in vivo superovulation tend also to have high potential to produce multiple embryos in vitro. Similarly, the moderately high genetic correlations (NE = 0.79 × 0.05; VE = 0.72 × 0.05) found between heifer and cow records indicate that a donor tends to produce a comparable number of embryos as a heifer or as a cow. The estimated repeatabilities (0.23 to 0.35) indicated that the number of embryos recovered should be somewhat repeatable in the same donor over time. On the other hand, the service sires seem not to play an important role on the total number of embryos produced by a donor no matter the technique used or the status of the donor at recovery. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the ability of mid-infrared predictions of fine milk composition and technological traits to serve as a tool for large-scale phenotyping of the Italian Simmental population. Calibration equations accurately predicted the fatty acid profile of the milk, but we obtained moderate or poor accuracy for detailed protein composition, coagulation properties, curd yield and composition, lactoferrin, and concentration of major minerals. To evaluate the role of infrared predictions as indicator traits of fine milk composition in indirect selective breeding programs, the genetic parameters of the traits predicted using mid-infrared spectra need to be estimated. [ABSTRACT FROM AUTHOR]

This study comprises an update of the economic values for dairy traits for the Australian industry and the formulation of updated selection indices. An economic model, which calculates partial economic values for each trait individually, was developed to determine the economic implications of selective dairy breeding, based on the effect of trait changes on the profit of commercial dairy farms in Australia. Selection indices were developed from economic values, which were transformed into base economic weights by including the discounted genetic expressions coefficients. Economic weights (in Australian dollars) were 1.79, 6.92, -0.10, -5.44, 8.84, 7.68, 1.07, 4.86, 1.91, 3.51, 4.90, 0.31, 2.03, 2.00, and 0.59, for milk fat (kg), milk protein (kg), milk volume (L), body weight (kg), survival (%), residual survival (%), somatic cell count (cells/mL), fertility (%), mammary system [Australian Breeding Value (ABV) unit], temperament (ABV unit), milking speed (ABV unit), udder depth (%), overall type (%), fore udder attachment (%), and pin set (%), respectively. The updated economic weights presented in this study constituted the basis of the definition for 3 new indices. These indices were developed from combination of bioeconomic principles, patterns of farmer preferences for trait improvements, and desired gains approaches. The 3 indices, Balanced Performance Index, Health Weighted Index, and Type Weighted Index, have been released to the industry. [ABSTRACT FROM AUTHOR]

The economic values (EV) of production traits, rennet coagulation time (RCT, min), and curd firmness (a30, mm) were derived for Italian Holstein-Friesian dairy cattle, based on the Grana Padano cheese industry. Three different sets of EV for RCT and a30 were estimated, assuming +2.5% (scenario 1), +5% (scenario 2), and +10% (scenario 3) increment in cheese yield due to the effect of milk coagulation properties (MCP). A model was developed to simulate the transformation of milk into Grana Padano cheese. The EV of RCT and a30 were -€2.213, -€4.426, and -€8.852/min, and €0.877, €1.755, and €3.509/mm for scenarios 1, 2, and 3, respectively. Relative emphasis of traits in the breeding objectives of the Italian Holstein-Friesian dairy cattle population should account for the effect of MCP on cheese yield. Economic values for milk components and MCP were affected by changes of dairy products, whereas variations of feed prices did not influence EV of RCT and a30. [ABSTRACT FROM AUTHOR]

Concentrations of multiple elements and ratios of stable isotopes of carbon and nitrogen were measured and combined to create a chemical fingerprint of production batches of goat whole milk powder (WMP) produced by different manufacturers. Our objectives were to determine whether or not differences exist in the chemical fingerprint among samples of goat WMP produced at different sites, and assess temporal changes in the chemical fingerprint in product manufactured at one site. In total, 58 samples of goat WMP were analyzed by inductively coupled plasma-mass spectrometry as well as isotope ratio mass spectrometry and a suite of 13 elements (Li, Na, Mg, K, Ca, Mn, Cu, Zn, Rb, Sr, Mo, Cs, and Ba), δ13C, and δ15N selected to create the chemical fingerprint. Differences in the chemical fingerprint of samples between sites and over time were assessed using principal components analysis and canonical analysis of principal coordinates. Differences in the chemical fingerprints of samples between production sites provided a classification success rate (leave-one-out classification) of 98.1%, providing a basis for using the approach to test the authenticity of product manufactured at a site. Within one site, the chemical fingerprint of samples produced at the beginning of the production season differed from those produced in the middle and late season, driven predominantly by lower concentrations of Na, Mg, K, Mn, and Rb, and higher concentrations of Ba and Cu. This observed temporal variability highlights the importance of obtaining samples from throughout the season to ensure a representative chemical fingerprint is obtained for goat WMP from a single manufacturing site. The reconstitution and spray drying of samples from one manufacturer by the other manufacturer enabled the relative influence of the manufacturing process on the chemical fingerprint to be examined. It was found that such reprocessing altered the chemical fingerprint, although the degree of alteration varied among samples and individual elements. The findings of this study support the use of trace elements and stable isotope ratios to test the authenticity of goat WMP, which can likely be applied to other dairy goat products. This approach could be used test to the factory of origin (and potentially batch of origin) of products in the supply chain, thus providing the ability to audit the supply chain and monitor for fraudulent activity. [ABSTRACT FROM AUTHOR]