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Genetic parameters of bovine milk fatty acid profile, yield, composition, total and differential somatic cell count
Full text linkThe growing interest of consumers for milk and dairy products of high nutritional value has pushed researchers to evaluate the feasibility of including fatty acids (FA) in selection programs to modify milk fat profile and improve its nutritional quality. Therefore, the aim of this study was to estimate genetic parameters of FA profile predicted by mid-infrared spectroscopy, milk yield, composition, and total and differential somatic cell count. Edited data included 35,331 test-day records of 25,407 Italian Holstein cows from 652 herds. Variance components and heritability were estimated using single-trait repeatability animal models, whereas bivariate repeatability animal models were used to estimate genetic and phenotypic correlations between traits, including the fixed effects of stage of lactation, parity, and herd-test-date, and the random effects of additive genetic animal, cow permanent environment and the residual. Heritabilities and genetic correlations obtained in the present study reflected both the origins of FA (extracted from the blood or synthesized de novo by the mammary gland) and their grouping according to saturation or chain length. In addition, correlations among FA groups were in line with correlation among individual FA. Moderate negative genetic correlations between FA and milk yield and moderate to strong positive correlations with fat, protein, and casein percentages suggest that actual selection programs are currently affecting all FA groups, not only the desired ones (e.g., polyunsaturated FA). The absence of association with differential somatic cell count and the weak association with somatic cell score indicate that selection on FA profile would not affect selection on resistance to mastitis and vice versa. In conclusion, our findings suggest that genetic selection on FA content is feasible, as FA are variable and moderately heritable. Nevertheless, in the light of correlations with other milk traits estimated in this study, a clear breeding goal should first be established
Short communication: Genetic aspects of milk differential somatic cell count in Holstein cows: A preliminary analysis
No full textThe aim of the present study was to assess genetic variation and heritability of a novel indicator of udder health, milk differential somatic cell count (DSCC), which represents the percentage of neutrophils plus lymphocytes in the total somatic cell count (SCC). Furthermore, we estimated genetic and phenotypic correlations of DSCC with other milk traits routinely measured in Italian Holstein cows. Besides DSCC, test-day data included milk yield, composition traits (i.e., fat, protein, casein, and lactose percentages), pH, milk urea nitrogen, and SCC. After editing, the final data set included 10,709 test-day records of 5,142 cows in 299 herds. Mean of DSCC was 62.07%, which means that macrophages were approximately 38% of total SCC. Comparing our results with the literature offered compelling evidence of the importance of acquiring information about the proportion of the different cell types in milk to better define the udder health status. In addition, our analysis revealed, for the first time, that DSCC is a heritable trait, and heritability (0.08 ± 0.02) was higher than that of traditional somatic cell score (0.04 ± 0.02). Nevertheless, heritability of DSCC is still low compared with milk yield and quality traits. Single-trait analysis within parity showed that DSCC was less heritable in primiparous than in multiparous cows, whereas bivariate analysis confirmed that DSCC and somatic cell score were 2 different traits, as their genetic and phenotypic correlations differed from unity. From a genetic point of view, the DSCC was positively weakly associated with milk yield, lactose percentage, and milk urea nitrogen, and negatively associated with pH. Our findings contributed to the understanding of the genetic background of DSCC and are a precursor to the potential use of DSCC in breeding programs to enhance cow resistance to mastitis. However, further research is needed to determine the weight this novel trait should receive in a selection program aimed to reduce udder health problems
Factors affecting differential somatic cell count, an additional parameter for mastitis screening in dairy cows
No full textAlternative somatic cell count traits exploitable in genetic selection for mastitis resistance in Italian Holstein and Jersey cows
No full textBreeding for mastitis resistance represents an important strategy to decrease the incidence of the disease in the farm. However, as routine disease-recording systems are currently not widely implemented, genetic selection for mastitis resistance is mostly based on test-day (TD) or lactation-mean somatic cell count (SCC) (Martin et al., 2018). Nevertheless, alternative traits derived from SCC and genetically correlated with clinical mastitis have been suggested to better describe SCC variation throughout the lactation and the dynamic of infection (de Haas et al., 2008;
Urioste et al., 2010; Koeck et al., 2012). Therefore, the aim of this study was to characterize alternative SCC traits, derived from TD data from routine recording system, and to estimate phenotypic and genetic correlations with milk production traits in Italian Holstein Friesian (HF) and Jersey (JE) cows. Test-day records of 66,407 primiparous HF cows from 404 herds sampled between 1999 and 2014 and TD records of 12,754 primiparous JE cows from 428 herds sampled between 2004 and 2016 were extracted from the databases of the Italian Holstein Association (Cremona, Italy). Along with the traditional lactation-mean somatic cell score (SCS), analyzed traits included average SCS in early lactation (SCS_150), standard deviation of SCS of the whole lactation (SCS_SD), presence or absence of at least one TD SCC above 400,000 cells/mL (Infection) and the ratio of number of TD with SCC above 400,000 cells/mL to total number of TD (Severity). A subset of 22,695 HF and a subset of 8,133 JE cows were randomly extracted from the edited original databases and used for genetic analysis. Multivariate animal models were run to estimate heritability of and genetic correlations between alternative SCC traits, and genetic correlations between alternative SCC traits and milk production traits. Herd-year-season of calving and number of TD were included as fixed effects, and additive genetic animal (n = 62,146 for HF and n = 18,314 for JE cows) as random terms. Holsteins had lower SCS than JE cows, both when averaged over the entire lactation or over the first 150 days in milk: 2.86 vs 3.09 for SCS and 2.66 vs 3.01 for SCS_150, respectively. However, compared to JE, HF showed greater SCS_SD (1.29 vs 1.10), higher Infection (47.4% vs 42.9%) and greater Severity (14% vs 11%). Heritability of novel SCC traits was smaller in comparison to traditional lactation-mean SCS (0.13 for HF and 0.14 for JE), ranging from 0.04 (SCS_SD) to 0.11 (SCS_150) in both breeds. With the only exception of SCS_SD, genetic correlations between SCC traits were strong and comprised between 0.79 and 0.99. Regardless the breed, negative phenotypic correlations were estimated between milk yield and SCC traits, whereas positive genetic relationships were observed, especially with those traits related to variation of SCC (SCS_SD, Infection and Severity). Phenotypic and genetic correlations between SCC traits and milk composition (fat and protein percentage) were mostly close to zero. In conclusion, alternative SCC traits analyzed in the present study showed enough genetic variation to be exploited in breeding programs for mastitis resistance. Moreover, our findings confirmed that high milk SCC is associated with reduced milk production (negative phenotypic correlation) and support the hypothesis that high producing cows could be more susceptible to mastitis (positive genetic correlation). The unfavorable genetic correlation between SCC traits and production highlights the need of improving selection for mastitis resistance. A comparison of alternative SCC traits with clinical mastitis information would be required. de Haas, Y., W. Ouweltjes, J. ten Napel, J. J. Windig, and G. de Jong. 2008. Alternative somatic cell count traits as mastitis indicators for genetic selection. J. Dairy Sci. 91:2501–2511. Koeck, A., F. Miglior, D. F. Kelton, and F. S. Schenkel. 2012. Alternative somatic cell count traits to improve
mastitis resistance in Canadian Holsteins. J. Dairy Sci. 95:432–439. Martin, P., H. W. Barkema, L. F. Brito, S. G. Narayana, and F. Miglior. 2018. Symposium review: Novel strategies to genetically improve mastitis resistance in dairy cattle. J. Dairy Sci. 101:2724-2736. Urioste, J. I., J. Franzén, and E. Strandberg. 2010. Phenotypic and genetic characterization of novel somatic cell count traits from weekly or monthly observations. J. Dairy Sci. 93:5930–5941
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
Short communication: Genetic aspects of milk urea nitrogen and new indicators of nitrogen efficiency in dairy cows
No full textMilk urea nitrogen (MUN), a trait routinely measured in the national milk recording system, is a useful indicator of nitrogen utilization efficiency of dairy cows, and selection for MUN and MUN-derived traits could be a valid strategy to produce better animals with regard to efficiency of nitrogen utilization. Therefore, the aim of the present study was to explore the genetic aspects of MUN and new potential indicators of nitrogen efficiency, namely ratios of protein to MUN, casein to MUN, and whey protein to MUN, in the Italian Brown Swiss population. A total of 153,175 test-day records of 10,827 cows in 500 herds were used for genetic analysis. Variance components and heritability of the investigated traits were estimated using single-trait repeatability animal models, whereas genetic and phenotypic correlations between the traits were estimated through bivariate repeatability animal models, including fixed effects of herd-test-date, stage of lactation, parity, calving year, and calving season, and the random effects of additive genetic animal, cow permanent environment, and the residual. Heritability estimates for MUN (0.20 ± 0.01) and the 3 new indicators of nitrogen utilization efficiency (0.15 ± 0.01 for protein-to-MUN and casein-to-MUN ratios, and 0.12 ± 0.01 for ratio of whey protein to MUN) suggested that additive genetic variation exists for these traits, and thus there is potential to select for greater organic nitrogen and lower inorganic nitrogen in milk. Genetic association between MUN and the 3 ratios was high (−0.87 ± 0.01) but not unity, suggesting that ratios could provide some further information beyond that provided by MUN with regard to efficiency of nitrogen utilization. Genetic trend of the investigated traits by year of birth of Brown Swiss sires showed how the selection applied in the last 30 yr has led to an increase of both quantity and quality of milk, and a decrease of somatic cell score and MUN. The inclusion of MUN in breeding programs could speed up the process of increasing organic nitrogen such as protein, which is useful for cheese-making, and reducing inorganic nitrogen (MUN) in milk
Alternative somatic cell count traits exploitable in genetic selection for mastitis resistance in Italian Holsteins
Full text linkThe aim of the present study was to characterize alternative somatic cell count (SCC) traits that could be exploited in genetic selection for mastitis resistance. Data were from 66,407 first-parity Holsteins in 404 herds. Novel SCC traits included average somatic cell score (SCS, log-transformation of SCC) in early lactation (SCS_150), standard deviation of SCS of the entire lactation (SCS_SD), the presence of at least one test-day (TD) SCC >400,000 cells/mL in the lactation, and the ratio of number of TD SCC >400,000 cells/mL to total number of TD in the lactation. Novel traits and lactation-mean SCS (SCS_LM) were analyzed using linear mixed or logistic regression models, including month of calving, year of calving, number of TD, and milk yield as fixed effects, and herd and residual as random terms. A multitrait linear animal model was applied to a random subset of 152 herds (n = 22,695 cows) to assess heritability of and genetic correlations between SCC traits. Alternative SCC traits were affected by the environmental factors included in the model; in particular, results suggested a seasonal effect and a tendency toward an improvement of the udder health status in the last years. Association was also found between novel SCC traits and milk production. Alternative SCC traits exhibited coefficients of additive genetic variation that were similar to or larger than that of traditional SCS_LM. Heritability of novel SCC traits was smaller than heritability of SCS_LM (0.126 ± 0.014), ranging from 0.044 ± 0.008 (SCS_SD) to 0.087 ± 0.010 (SCS_150). Genetic correlations between SCC traits ranged from 0.217 ± 0.096 (SCS_150 and SCS_SD) to 0.969 ± 0.010 (SCS_LM and SCS_150). Alternative SCC traits exhibited additive genetic variation that is potentially exploitable in breeding programs of Italian Holstein population to improve resistance to mastitis
Combining total and differential somatic cell count to better assess the association of udder health status with milk yield, composition and coagulation properties in cattle
Full text linkThe combined use of somatic cell count (SCC) and differential somatic cell count (DSCC), which is the ratio of neutrophils plus lymphocytes to total milk SCC, represents a novel approach to define cow’s udder health status, as it allows to identify healthy animals (those with low SCC and DSCC), cows susceptible to mastitis (those where an immune response has begun, so that there is an increase of neutrophils, i.e. DSCC, but not yet of total SCC), animals with a mastitic event in progress (those with high SCC and DSCC) and animals with possible chronic inflammation (those with high SCC and low DSCC, as macrophages prevail). We investigated the association of cow’s udder health status with milk yield, composition and coagulation properties in four cattle breeds. Results demonstrated that milk traits vary among cows with different udder health status, especially in terms of fat percentage, lactose percentage and coagulation ability. The most pronounced worsening in milk yield and coagulation ability was observed for animals with chronic inflammation. Our findings support the new approach based on the combined use of SCC and DSCC to screen for cow’s udder health, as it would allow to identify susceptible cows that will probably undergo a mastitic event and chronic cows that would possibly reduce the herd milk production and quality
Genetic relationships of alternative somatic cell count traits with milk yield, composition and udder type traits in Italian Jersey cows
Full text linkThe aim of this study was to estimate genetic associations between alternative somatic cell count (SCC) traits and milk yield, composition and udder type traits in Italian Jersey cows. Alternative SCC traits were test‐day (TD) somatic cell score (SCS) averaged over early lactation (SCS_150), standard deviation of SCS of the entire lactation (SCS_SD), a binary trait indicating absence or presence of at least one TD SCC >400,000 cells/ml in the lactation (Infection) and the ratio of the number of TD SCC >400,000 cells/ml to total number of TD in the lactation (Severity). Heritabilities of SCC traits, including lactation‐mean SCS (SCS_LM), ranged from 0.038 to 0.136. Genetic correlations between SCC traits were moderate to strong, with very few exceptions. Unfavourable genetic associations between milk yield and SCS_SD and Infection indicated that high‐producing cows were more susceptible to variation in SCC than low‐producing animals. Cows with deep udders, loose attachments, weak ligaments and long teats were more susceptible to an increase of SCC in milk. Overall, results suggest that alternative SCC traits can be exploited to improve cow's resistance to mastitis in Italian Jersey breed
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