1,721,067 research outputs found
International genetic evaluation for fertility traits in dairy cattle
No full textThe objective of this study was to review and discuss the results of the first international evaluation for female fertility of Holstein dairy cattle. Fifteen countries, including Italy, provided breeding values of bulls and joined the evaluation. Four trait groups were used to analyze animal's ability to became pregnant and animal's ability to recycle after calving. Italy submitted three traits: days to first service (DTFS), non-return rate at 56 days (NR56) and calving interval (CI). Genetic correlation between Italy and the other countries ranged from 0.72 to 0.94 for DTFS, from 0.25 to 0.90 for NR56 and from 0.67 to 0.87 for CI. Results represent another step forward in the international trade of dairy cattle genetic material
The effect of mislabeled phenotypic status on the identification of mutation-carriers from SNP genotypes in dairy cattle
No full textThe accuracy of test day model evaluation for the Italian Holstein
No full textGenetic evaluation for production traits in the Holstein breed in Italy has been based on a Random Regression Test Day Model (RRTDM) since November 2004. More specifically, the model is a multiple lactation, multiple trait RRTDM, similar to the model used in Canada for official genetic evaluation. Fixed regression curve effect include time, region, age at calving, parity and season of calving. Last changes in the model included a new definition of the proof scale and of the genetic base. The accuracy of the model was assessed by analyzing residuals and testing Mendelian sampling trends. Residuals were normally distributed for all traits and had zero mean. Residual trends for all the effects included in the model were analyzed also for effects not included in the model like number of milkings per day and number of days pregnant at the test date. Mendelian sampling did not show any significant trend over time both for cows and bulls
Crossbreeding affects the production performance of dairy cows exposed to a range of temperature and humidity in a pasture-based system
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Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows
Full text linkBovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow’s milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day
Genetic parameters for body condition score, locomotion, angularity, and production traits in Italian Holstein cattle
No full textThe objectives of this research were to estimate genetic parameters for body condition score (BCS) and locomotion (LOC), and to assess their relationships with angularity (ANG), milk yield, fat and protein content, and fat to protein content ratio (F:P) in the Italian Holstein Friesian breed. The Italian Holstein Friesian Cattle Breeders Association collects type trait data once on all registered first lactation cows. Body condition score and LOC were introduced in the conformation scoring system in 2007 and 2009, respectively. Variance (and covariance) components among traits were estimated with a Bayesian approach via a Gibbs sampling algorithm and an animal model. Heritability estimates were 0.114 and 0.049 for BCS and LOC, respectively. The genetic correlation between BCS and LOC was weak (-0.084) and not different from zero; therefore, the traits seem to be genetically independent, but further investigation on possible departures from linearity of this relationship is needed. Angularity was strongly negatively correlated with BCS (-0.612), and strongly positively correlated with LOC (0.650). The genetic relationship of milk yield with BCS was moderately negative (-0.386), and was moderately positive (0.238) with LOC. These results indicate that high-producing cows tend to be thinner and tend to have better locomotion than low-producing cows. The genetic correlation of BCS with fat content (0.094) and F:P (-0.014) was very weak and not different from zero, and with protein content (0.173) was weak but different from zero. Locomotion was weakly correlated with fat content (0.071), protein content (0.028), and F:P (0.074), and correlations were not different from zero. Phenotypic correlations were generally weaker than their genetic counterparts, ranging from -0.241 (BCS with ANG) to 0.245 (LOC with ANG). Before including BCS and LOC in the selection index of the Italian Holstein breed, the correlations with other traits currently used to improve type and functionality of animals need to be investigated
Short communication: Effect of heat stress on nonreturn rate of Italian Holstein cows
No full textThe data set consisted of 1,016,856 inseminations of 191,012 first, second, and third parity Holstein cows from 484 farms. Data were collected from year 2001 through 2007 and included meteorological data from 35 weather stations. Nonreturn rate at 56 d after first insemination (NR56) was considered. A logit model was used to estimate the effect of temperature-humidity index (THI) on reproduction across parities. Then, least squares means were used to detect the THI breakpoints using a 2-phase linear regression procedure. Finally, a multiple-trait threshold model was used to estimate variance components for NR56 in first and second parity cows. A dummy regression variable (t) was used to estimate NR56 decline due to heat stress. The NR56, both for first and second parity cows, was significantly (unfavorable) affected by THI from 4 d before 5 d after the insemination date. Additive genetic variances for NR56 increased from first to second parity both for general and heat stress effect. Genetic correlations between general and heat stress effects were -0.31 for first parity and -0.45 for second parity cows
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