Genetics and genomics of reproductive performance in dairy and beef cattle

peer-reviewedExcellent reproductive performance in both males and females is fundamental to profitable dairy and beef production systems. In this review we undertook a meta-analysis of genetic parameters for female reproductive performance across 55 dairy studies or populations and 12 beef studies or populations as well as across 28 different studies or populations for male reproductive performance. A plethora of reproductive phenotypes exist in dairy and beef cattle and a meta-analysis of the literature suggests that most of the female reproductive traits in dairy and beef cattle tend to be lowly heritable (0.02 to 0.04). Reproductive-related phenotypes in male animals (e.g. semen quality) tend to be more heritable than female reproductive phenotypes with mean heritability estimates of between 0.05 and 0.22 for semen-related traits with the exception of scrotal circumference (0.42) and field non-return rate (0.001). The low heritability of reproductive traits, in females in particular, does not however imply that genetic selection cannot alter phenotypic performance as evidenced by the decline until recently in dairy cow reproductive performance attributable in part to aggressive selection for increased milk production. Moreover, the antagonistic genetic correlations among reproductive traits and both milk (dairy cattle) and meat (beef cattle) yield is not unity thereby implying that simultaneous genetic selection for both increased (milk and meat) yield and reproductive performance is indeed possible. The required emphasis on reproductive traits within a breeding goal to halt deterioration will vary based on the underlying assumptions and is discussed using examples for Ireland, the United Kingdom and Australia as well as quantifying the impact on genetic gain for milk production. Advancements in genomic technologies can aid in increasing the accuracy of selection for especially reproductive traits and thus genetic gain. Elucidation of the underlying genomic mechanisms for reproduction could also aid in resolving genetic antagonisms. Past breeding programmes have contributed to the deterioration in reproductive performance of dairy and beef cattle. The tools now exist, however, to reverse the genetic trends in reproductive performance underlying the observed phenotypic trends

Policy-based management for body-sensor networks

Accepted versio

Environmental Clustering of New Zealand Dairy Herds

Previous studies have found that milk yield (a proxy for feeding level) and temperature-humidity index (THI) are important factors in explaining genotype x environment (G x E) interactions, indicating differences between the abilities of genotypes to forage or consume concentrates effectively or to cope with thermal stress (Ravagnolo and Misztal, 2000; Zwald et al., 2003). The objective of this study was to quantify and cluster (CL) herd environments within New Zealand (NZ) based on production levels, a summer heat load index (HLI) and geographical location

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

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, \u3b2-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

Non-Newtonian Mechanics

The classical motion of spinning particles can be described without employing Grassmann variables or Clifford algebras, but simply by generalizing the usual spinless theory. We only assume the invariance with respect to the Poincare' group; and only requiring the conservation of the linear and angular momenta we derive the zitterbewegung: namely the decomposition of the 4-velocity in the newtonian constant term p/m and in a non-newtonian time-oscillating spacelike term. Consequently, free classical particles do not obey, in general, the Principle of Inertia. Superluminal motions are also allowed, without violating Special Relativity, provided that the energy-momentum moves along the worldline of the center-of-mass. Moreover, a non-linear, non-constant relation holds between the time durations measured in different reference frames. Newtonian Mechanics is re-obtained as a particular case of the present theory: namely for spinless systems with no zitterbewegung. Introducing a Lagrangian containing also derivatives of the 4-velocity we get a new equation of the motion, actually a generalization of the Newton Law a=F/m. Requiring the rotational symmetry and the reparametrization invariance we derive the classical spin vector and the conserved scalar Hamiltonian, respectively. We derive also the classical Dirac spin and analyze the general solution of the Eulero-Lagrange equation for Dirac particles. The interesting case of spinning systems with zero intrinsic angular momentum is also studied.Comment: LaTeX; 27 page

Stability and Quasinormal Modes of Black holes in Tensor-Vector-Scalar theory: Scalar Field Perturbations

The imminent detection of gravitational waves will trigger precision tests of gravity through observations of quasinormal ringing of black holes. While General Relativity predicts just two polarizations of gravitational waves, the so-called plus and cross polarizations, numerous alternative theories of gravity predict up to six different polarizations which will potentially be observed in current and future generations of gravitational wave detectors. Bekenstein's Tensor-Vector-Scalar (TeVeS) theory and its generalization fall into one such class of theory that predict the full gamut of six polarizations of gravitational waves. In this paper we begin the study of quasinormal modes (QNMs) in TeVeS by studying perturbations of the scalar field in a spherically symmetric background. We show that, at least in the case where superluminal propagation of perturbations is not present, black holes are generically stable to this kind of perturbation. We also make a unique prediction that, as the limit of the various coupling parameters of the theory tend to zero, the QNM spectrum tends to $1/\sqrt{2}$ times the QNM spectrum induced by scalar perturbations of a Schwarzschild black hole in General Relativity due to the intrinsic presence of the background vector field. We further show that the QNM spectrum does not vary significantly from this value for small values of the theory's coupling parameters, however can vary by as much as a few percent for larger, but still physically relevant parameters.Comment: Published in Physical Review

The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO₂-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO₂-eq emissions per cow over the past decade were made by multiplying predicted CO₂-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO₂-eq/cow.year after 10 years of selection. The same approach was applied to future CO₂-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO₂-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO₂-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO₂-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over the past decade and production has increased; altogether, we estimate that the net impact has been a reduction of CO₂-eq emissions of ~1.0% in total emissions from the dairy industry per year. Using two future scenarios of either keeping the number of cows or amount of product static, we predict that net GHG emissions will reduce by ~0.6%/year of total dairy emissions if milk production remains static, compared with 0.3%/year, if cow numbers remain the same and there is genetic improvement in milk-production traits

Bayes-optimal inverse halftoning and statistical mechanics of the Q-Ising model

On the basis of statistical mechanics of the Q-Ising model, we formulate the Bayesian inference to the problem of inverse halftoning, which is the inverse process of representing gray-scales in images by means of black and white dots. Using Monte Carlo simulations, we investigate statistical properties of the inverse process, especially, we reveal the condition of the Bayes-optimal solution for which the mean-square error takes its minimum. The numerical result is qualitatively confirmed by analysis of the infinite-range model. As demonstrations of our approach, we apply the method to retrieve a grayscale image, such as standard image `Lenna', from the halftoned version. We find that the Bayes-optimal solution gives a fine restored grayscale image which is very close to the original.Comment: 13pages, 12figures, using elsart.cl