Validation of an ear tag–based accelerometer system for detecting grazing behavior of dairy cows

peer-reviewedThe objective of the study was to develop a grazing algorithm for an ear tag–based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (RumiWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the University of Minnesota West Central Research and Outreach Center (Morris, MN) and at Teagasc Animal and Grassland Research and Innovation Centre (Moorepark, Fermoy, Co. Cork, Ireland). During May and June 2017, observational data from Minnesota and Ireland were used to develop the grazing algorithm. During September 2018, data were collected by the ear tag and noseband sensor from 12 crossbred cows in Minnesota for a total of 248 h and from 9 Holstein-Friesian cows in Ireland for a total of 248 h. A 2-sided t-test was used to compare the percentage of grazing and nongrazing time recorded by the ear tag and the noseband sensor. Pearson correlations and concordance correlation coefficients (CCC) were used to evaluate associations between the ear tag and noseband sensor. The percentage of total grazing time recorded by the ear tag and by the noseband sensor was 37.0% [95% confidence interval (CI): 32.1 to 42.0] and 40.5% (95% CI: 35.5 to 45.6), respectively, in Minnesota, and 35.4% (95% CI: 30.6 to 40.2) and 36.9% (95% CI: 32.1 to 41.8), respectively, in Ireland. The ear tag and noseband sensor agreed strongly for monitoring grazing in Minnesota (r = 0.96; 95% CI: 0.94 to 0.97, CCC = 0.95) and in Ireland (r = 0.92; 95% CI: 0.90 to 0.94, CCC = 0.92). The results suggest that there is potential for the ear tag to be used on pasture-based dairy farms to support management decision-making

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use

Principal component approach in variance component estimation for international sire evaluation

<p>Abstract</p> <p>Background</p> <p>The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.</p> <p>Methods</p> <p>This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.</p> <p>Results</p> <p>Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.</p> <p>Conclusions</p> <p>In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.</p

Polyethylene glycol as an indigestible marker to estimate fecal output in dairy cows

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Variability of cow-specific organic matter digestibility within lactation and across cows based on NIRS scans from fecal samples

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Cow-specific diet digestibility predictions based on near-infrared reflectance spectroscopy scans of faecal samples

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Developing an indicator for body fat mobilisation using mid-infrared spectrometry of milk samples in dairy cows

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Developing an indicator for body fat mobilisation using mid-infrared spectrometry of milk samples in dairy cows

peer reviewedHigh energy requirement in the initiation of lactation may force dairy cows to mobilize energy from body tissue which leads to negative energy balance (EB). Negative EB can predispose cows to various health and fertility problems, and therefore should be considered in dairy cattle breeding programs especially if feed efficiency traits are included in breeding objectives. However, measuring of EB is difficult and estimates are imprecise. When a cow is in negative EB and mobilizing its fat reserves, concentrations of milk fatty acids and blood non-esterified fatty acids (NEFA) change. Therefore, it is possible to use these changes as biomarkers for energy status. Mid-infrared spectrometry (MIR) is a routinely used tool for milk samples and many milk fatty acids can be predicted with high accuracy by MIR. Our objective was to assess the capability of MIR to predict blood plasma NEFA concentration from milk samples. Milk and corresponding blood samples were collected from Nordic Red dairy cows in three research farms between 2013 and 2016. There were altogether 1585 milk spectral readings and 809 NEFA records from 141 cows in the data set. Partial least squares regression was used to predict NEFA and EB from MIR spectral data. The coefficient of determination of cross validation (R2cv) for NEFA was 0.64 when leave-one-out cross validation was used for the whole data set. Higher R2cv values were found when predicting blood plasma NEFA concentration from evening milk samples (0.67), probably because fatty acid concentrations in milk vary slower than NEFA concentration in blood. The robustness of the developed prediction equation was inspected by calibrating the equations with records from the cows from two research herds and then predicting NEFA for cows from the other herd, so that there were differences in the environment of the calibration data (130 NEFA obs) and test data (647 NEFA obs.) sets. Nevertheless, R2 in the test data was 0.58 and RMSE 0.19 mmol/l, which indicates that the model is robust. Keywords: mid-infrared spectroscopy, non-esterified fatty acids, energy balanc