Relationships between milk yield and somatic cell score in Canadian Holsteins from simultaneous and recursive random regression models

Multiple-trait random regression animal models with simultaneous and recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test day were fitted to Canadian Holstein data. All models included fixed herd test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Regressions were Legendre polynomials of order 4 on a scale from 5 to 305 d in milk (DIM). Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Heterogeneity of structural coefficients was modeled across (the first 3 lactations) and within (4 DIM intervals) lactation. Model comparisons in terms of Bayes factors indicated the superiority of simultaneous models over the standard multiple-trait model and recursive parameterizations. A moderate heterogeneous (both across- and within-lactation) negative effect of SCS on milk yield (from −0.36 for 116 to 265 DIM in lactation 1 to −0.81 for 5 to 45 DIM in lactation 3) and a smaller positive reciprocal effect of SCS on milk yield (from 0.007 for 5 to 45 DIM in lactation 2 to 0.023 for 46 to 115 DIM in lactation 3) were estimated in the most plausible specification. No noticeable differences among models were detected for genetic and environmental variances and genetic parameters for the first 2 regression coefficients. The curves of genetic and permanent environmental variances, heritabilities, and genetic and phenotypic correlations between milk yield and SCS on a daily basis were different for different models. Rankings of bulls and cows for 305-d milk yield, average daily SCS, and milk lactation persistency remained the same among models. No apparent benefits are expected from fitting causal phenotypic relationships between milk yield and SCS on the same test day in the random regression test-day model for genetic evaluation purposes.     

A structural equation model for describing relationships between somatic cell score and milk yield in first-lactation dairy cows

Relationships between production and diseases may involve recursive or simultaneous effects between traits. Four structural equation models (SEqM) for somatic cell score and milk yield, with varying specifications for the effects relating the 2 traits, were compared. Data consisted of repeated records of milk yield and somatic cell score of 33,453 first-lactation daughters of 245 Norwegian Red sires that had their first progeny test in 1991 and 1992. All models included random effects of the sire and of the cow and were fitted using the LISREL software. The Bayesian information criterion clearly favored a model with a recursive effect from somatic cell score on milk yield over the 3 other models fitted (absence of recursive effects; an effect from milk yield on somatic cell score; simultaneity of effects between the 2 traits). This provides evidence that the negative association between milk yield and somatic cell score is more likely due to an effect of infection (measured indirectly by the somatic cell score) on production than to a dilution effect. Estimates indicated that a mastitis event would reduce milk yield in the following 15 d by about 900 g/d. The estimated genetic (co)variances did not change sizably when the specification of recursive or simultaneous effects was varied. However, estimates of the phenotypic covariance were altered when a recursive effect from somatic cell score on milk yield was included in the model.     

Recursive relationships between milk yield and somatic cell score of Canadian Holsteins from finite mixture random regression models

Finite mixture, multiple-trait, random regression animal models with recursive links between phenotypes for milk yield and somatic cell score (SCS) on the same test-day were applied to first lactation Canadian Holstein data. All models included fixed herd-test-day effects and fixed regressions within region-age at calving-season of calving classes, and animal additive genetic and permanent environmental regressions with random coefficients. Causal links between phenotypes for milk yield and SCS were fitted separately for records from healthy cows and cows with a putative, subclinical form of mastitis. Bayesian methods via Gibbs sampling were used for the estimation of model parameters. Bayes factors indicated superiority of the model with recursive link from milk to SCS over the reciprocal recursive model and the standard multiple-trait model. Differences between models measured by other, single-trait model comparison criteria (i.e., weighted mean squared error, squared bias, and correlation between observed and expected data) were negligible. Approximately 20% of test-day records were classified as originating from cows with mastitis in recursive mixture models. The proportion of records from cows infected with mastitis was largest at the beginning of lactation. Recursive mixture models exhibited different distributions of data from healthy and infected cows in different parts of lactation. A negative effect of milk to SCS (up to −0.15 score points for every kilogram of milk for healthy cows from 5 to 45 d in milk) was estimated for both mixture components (healthy and infected) in all stages of lactation for the most plausible model. The magnitude of this effect was stronger for healthy cows than for cows infected with mastitis. Different patterns of genetic and environmental correlations between milk and SCS for healthy and infected records were revealed, due to heterogeneity of structural coefficients between mixture components. Estimated breeding values for SCS from the best fitting model for sires of infected daughters were more related to estimated breeding values for the same trait from the regular multiple-trait model than evaluations for sires of mastitis-free cows.     

Exploration of relationships between claw disorders and milk yield in Holstein cows via recursive linear and threshold models

Relationships between claw disorders and test-day milk yield recorded in 2005 on 5,360 Holstein cows, kept on 11 large-scale dairy farms in eastern Germany, were analyzed in a Bayesian framework with standard linear and threshold models and recursive linear and threshold models. Four different claw disorders, digital dermatitis (DD), sole ulcer (SU), wall disorder (WD), and interdigital hyperplasia (IH), were scored as binary traits within 200 d after calving and analyzed separately. Incidences of disorders were 13.7% for DD, 16.5% for SU, 9.8% for WD, and 6.7% for IH. Heritabilities of disorders were greater when applying threshold or recursive threshold models than with linear or linear recursive models. Posterior means of genetic correlations between test-day milk production and claw disorders ranged from 0.17 to 0.44, suggesting that breeding strategies focusing on increased milk yield will increase incidences of disorders as a correlated response. A progressive path of lagged relationships was postulated for recursive models describing first the influence of test-day milk yield (MY1) on claw disorders and second, the effect of the disorder on milk production level at the following test day (MY2). In recursive models, structural coefficients describe recursive relationships at the phenotypic level. The structural coefficient λ₂₁ was the gradient of disease (trait 2) with respect to MY1 (trait 1) for a model with a recursive effect of trait 1 on trait 2. The increase of disease incidence of the 4 different disorders per 1-kg increase of MY1 ranged from λ₂₁=0.006 to λ₂₁= 0.024 on the visible scale when applying recursive linear models, and from λ₂₁= 0.003 to λ₂₁= 0.016 on the underlying liability scale for recursive threshold models. The rate of change in MY2 (trait 3) with respect to the previous claw disorder is given by λ₃₂ for a model with a recursive effect from trait 2 to trait 3. Structural coefficients λ₃₂ ranged from −0.12 to −0.68 predicting that a 1-unit increase in the incidence of any disorder reduces milk yield at the following test day by up to 0.67 kg. Rank correlations between sire posterior means for the same claw disorders among different models were >0.84, but some changes in rank of sires in distinct top-10 lists were observed. Structural equation models are of increasing importance in genetic evaluations, and this study showed the possible application of recursive systems, even for categorical data.     

Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis

This paper studies whether cows with originally lower somatic cell count (SCC) are more susceptible to clinical mastitis (CM) than cows with higher somatic cell count, and evaluates the correlations between CM, SCC, and milk yield. Data were extracted from the Finnish national milk-recording database and from the health recording system. First and second lactation records of 87,861 Ayrshire cows calving between January 1998 and December 2000 were included. Traits studied were incidence of CM, test-day SCC, and test-day milk yield before and following CM. Genetic parameters were estimated using multitrait REML with a sire model. Results did not indicate that cows with genetically low SCC would be more susceptible to CM. The genetic correlation between CM in the first and second lactation was reasonably high (0.73), suggesting that susceptibility to mastitis remains similar across lactations. The genetic correlation between CM and milk yield traits was positive (from 0.38 to 0.56), confirming the genetic antagonism between production and udder health traits. The genetic correlation between SCC and milk was positive in the first lactation, but negative, or near zero in the second lactation. This indicates that breeding for lower SCC might not affect milk production in later lactations. The results of this study support the use of SCC as an indicator of mastitis and a tool for selection for mastitis resistance.     

Genetic relationships between calving interval and body condition score conditional on milk yield

Body condition score (BCS) is a useful tool in assessing the energy status of dairy cattle. Previous research has shown that it is heritable and genetically correlated to reproductive performance. Currently, interest exists in developing selection indexes for fertility that include BCS information. Before such indexes are developed, it is important to assess the genetic covariance between BCS and fertility after fully accounting for the covariance of both traits with milk yield, as indices to predict selection responses require knowledge of these (co)variances. In the present study, calving interval (CI) was used as a measure of reproductive performance. The genetic correlations between BCS and CI before and after genetically adjusting for milk yield were -0.48 and -0.22, respectively. Thus, cows with low BCS have longer CI, which is exacerbated by high levels of milk production. Using selection index theory, we showed that selecting for milk yield alone will result in an increase of 768 kg of milk, an increase of 4.46 d in CI and a reduction of 0.41 BCS units for every standard deviation change in the index. Restricting BCS to no genetic change, whereas still selecting for milk yield will result in an increase of 653.1 kg of milk per standard deviation of the selection index. However, CI will still continue to increase at a rate of 3.20 d per standard deviation of the selection index. The selection indices used here are not optimum, in that they are not economically driven and do not consider all traits that contribute to profitability. However, they demonstrate that, even though restricting BCS may be seen as an attractive way of limiting reliance of body tissue mobilization to fuel milk production, this is unlikely to result in improvements in CI, although the rate of increase in CI will be reduced.     

Mammary pathogens and their relationship to somatic cell count and milk yield losses in dairy ewes

A total of 9592 samples of half udder milk were collected monthly throughout lactation for bacteriological and somatic cell count (SCC) study from 1322 Churra ewe lactations from seven separate flocks enrolled in the recording scheme of the National Association of Spanish Churra Breeders in the Castile-Le6n region of Spain. Statistical analyses were carried out from a mixed model with random factor half udder or ewe for repeated measures. Test of significance of fixed effects of this mixed model showed significant effects of organisms, flock, parity, lactation stage, and birth type on SCC. Special reference must be made to novobiocin-sensitive coagulase-negative staphylococci, which represented more than 50% of the isolates and which elicited SCC geometric means of around 106/ml. In addition, the analysis of 4352 monthly test-day records for milk yield, SCC, and bacteriology showed that the ewes that were uninfected and infected by minor pathogens had the lowest SCC and the highest milk yields, whereas those infected by major pathogens had high SCC and milk yield losses between 8.8 and 10.1% according to the uni- or bilateral character of the infection. Finally, ewes infected by novobiocin-sensitive coagulase-negative staphylococci elicited SCC values similar to those of infections by major pathogens and milk yield losses ranging between those caused by minor and major pathogens. As a result, emphasis should be put on prevention of subclinical mastitis, particularly mastitis caused by novobiocin-sensitive coagulase-negative staphylococci in dairy sheep herds to improve microbiological and hygienic milk quality and to minimize losses in milk yield.     

Relationships between milk culture results and milk yield in Norwegian dairy cattle

Associations between test-day milk yield and positive milk cultures for Staphylococcus aureus, Streptococcus spp., and other mastitis pathogens or a negative milk culture for mastitis pathogens were assessed in quarter milk samples from randomly sampled cows selected without regard to current or previous udder health status. Staphylococcus aureus was dichotomized according to sparse (≤1,500 cfu/mL of milk) or rich (>1,500 cfu/mL of milk) growth of the bacteria. Quarter milk samples were obtained on 1 to 4 occasions from 2,740 cows in 354 Norwegian dairy herds, resulting in a total of 3,430 samplings. Measures of test-day milk yield were obtained monthly and related to 3,547 microbiological diagnoses at the cow level. Mixed model linear regression models incorporating an autoregressive covariance structure accounting for repeated test-day milk yields within cow and random effects at the herd and sample level were used to quantify the effect of positive milk cultures on test-day milk yields. Identical models were run separately for first-parity, second-parity, and third-parity or older cows. Fixed effects were days in milk, the natural logarithm of days in milk, sparse and rich growth of Staph. aureus (1/0), Streptococcus spp. (1/0), other mastitis pathogens (1/0), calving season, time of test-day milk yields relative to time of microbiological diagnosis (test day relative to time of diagnosis), and the interaction terms between microbiological diagnosis and test day relative to time of diagnosis. The models were run with the logarithmically transformed composite milk somatic cell count excluded and included. Rich growth of Staph. aureus was associated with decreased production levels in first-parity cows. An interaction between rich growth of Staph. aureus and test day relative to time of diagnosis also predicted a decline in milk production in third-parity or older cows. Interaction between sparse growth of Staph. aureus and test day relative to time of diagnosis predicted declining test-day milk yields in first-parity cows. Sparse growth of Staph. aureus was associated with high milk yields in third-parity or older cows after including the logarithmically transformed composite milk somatic cell count in the model, which illustrates that lower production levels are related to elevated somatic cell counts in high-producing cows. The same association with test-day milk yield was found among Streptococcus spp.-positive pluriparous cows.     

Genetic relationships of clinical mastitis,cystic ovaries,and lameness with milk yield and somatic cell score in first-lactation Canadian Holsteins

The objective of this study was to investigate the genetic relationships of the 3 most frequently reported dairy cattle diseases (clinical mastitis, cystic ovaries, and lameness) with test-day milk yield and somatic cell score (SCS) in first-lactation Canadian Holstein cows using random regression models. Health data recorded by producers were available from the National Dairy Cattle Health System in Canada. Disease traits were defined as binary traits (0 = healthy, 1 = affected) based on whether or not the cow had at least one disease case recorded within 305 d after calving. Mean frequencies of clinical mastitis, cystic ovaries, and lameness were 12.7, 8.2, and 9.1%, respectively. For genetic analyses, a Bayesian approach using Gibbs sampling was applied. Bivariate linear sire random regression model analyses were carried out between each of the 3 disease traits and test-day milk yield or SCS. Random regressions on second-degree Legendre polynomials were used to model the daily sire additive genetic and cow effects on test-day milk yield and SCS, whereas only the intercept term was fitted for disease traits. Estimated heritabilities were 0.03, 0.03, and 0.02 for clinical mastitis, cystic ovaries, and lameness, respectively. Average heritabilities for milk yield were between 0.41 and 0.49. Average heritabilities for SCS ranged from 0.10 to 0.12. The average genetic correlations between daily milk yield and clinical mastitis, cystic ovaries, and lameness were 0.40, 0.26, and 0.23, respectively; however, the last estimate was not statistically different from zero. Cows with a high genetic merit for milk yield during the lactation were more susceptible to clinical mastitis and cystic ovaries. Estimates of genetic correlations between daily milk yield and clinical mastitis were moderate throughout the lactation. The genetic correlations between daily milk yield and cystic ovaries were near zero at the beginning of lactation and were highest at mid and end lactation. The average genetic correlation between daily SCS and clinical mastitis was 0.59 and was consistent throughout the lactation. The average genetic correlation between daily SCS and cystic ovaries was near zero (−0.01), whereas a moderate, but nonsignificant, correlation of 0.27 was observed between SCS and lameness. Unfavorable genetic associations between milk yield and diseases imply that production and health traits should be considered simultaneously in genetic selection.     

Effect of gradual or abrupt cessation of milking at dry off on milk yield and somatic cell score in the subsequent lactation

The objective of this study was to assess the effect of milk cessation method (abrupt or gradual) at dry off on milk yield and somatic cell score (SCS) up to 120 d in milk during the subsequent lactation. Data from 428 cows from 8 dairy herds in Ohio were analyzed. Abrupt cessation cows kept the farm's regular milking schedule (2 or 3 times) through dry off and gradual cessation cows were milked once daily for the final week of lactation. Milk yield and SCS were collected using Dairy Herd Improvement Association test-day records. Aseptic quarter milk samples were collected approximately 1 wk before dry off, at dry off, and within 1 wk after calving for bacterial culture to determine the presence of intramammary infections. Overall, milk cessation method was not significantly associated with either milk yield or SCS in early lactation; however, interaction between the milk cessation method and herd was highly significant. Cows producing greater amounts of milk around dry off had significantly higher SCS in the following lactation. Shorter dry periods were significantly associated with decreased milk yield in the following lactation, especially among abruptly dried off cows. Additionally, as expected, several other factors, such as parity of cows and stage of lactation, were significantly associated with both outcomes. No interactions between the milk cessation method and the other explanatory variables in the final models were significant. The results of the current study suggest that higher milk yield at dry off was associated with higher SCS in the following lactation, even though milk cessation method at the end of lactation had a varying effect on test-day milk yield and SCS in different herds during the first 120 d in milk in the following lactation. The specific herd characteristics influencing this could not be identified within this study, warranting further research.     

Inferring relationships between health and fertility in Norwegian Red cows using recursive models

Health and fertility are complex traits, and the phenotype for one trait may affect the phenotype of one or more other traits. For instance, disease in early lactation may impair a cow's ability to show estrus and to conceive after insemination. The objectives of the present study were to explore phenotypic and genetic relationships among health and fertility traits in Norwegian Red cows using a recursive effects model, which allows disentangling causal effects of phenotypes from the genetic and environmental correlations among traits. Records of interval from calving to first insemination (CFI), nonreturn rate within 56 d after first insemination (NR56), clinical mastitis (CM), ketosis (KET), and retained placenta from 55,568 first-lactation daughters of 1,577 Norwegian Red sires were analyzed. Trivariate recursive Gaussian-threshold models were used to analyze the 2 fertility traits (CFI and NR56) together with 1 disease trait in each analysis. The estimated structural coefficients of the recursive models imply that presence of KET or retained placenta lengthened CFI, whereas causal effects from CM to fertility were negligible. Recursive effects of disease on NR56, and of CFI on NR56, were all close to zero. Genetic correlations between health and fertility traits were low or moderate. The strongest genetic correlation was between KET and CFI (0.29), whereas genetic correlations between CM and NR56 and between CFI and NR56 were nil. In general, selection against disease is expected to slightly improve fertility (shorter CFI and higher NR56) as a correlated response and vice versa. The present results suggest that the use of structural-equation models, such as the one used here, may enhance our understanding of complex relationships among traits.     

Short communication: Genetic relationships between claw disorders,protein yield,and somatic cell score by days in milk

The aim of the present study was to infer daily genetic relationships between the selected claw disorders digital dermatitis, sole ulcer (SU), and interdigital hyperplasia (IH) and protein yield and the udder health indicator somatic cell score (SCS). Data were from 26,651 Holstein cows kept in 15 selected large-scale herds located in the region of Thuringia in the eastern part of Germany. Herds are characterized by organized data recording for novel health traits, and for the present study, claw disorders from the years 2008 to 2012 were used. A longitudinal and binary health data structure was created by assigning claw disorders to adjacent official test days. No entry of a claw disorder within a given interval of approximately 30 d implied a score of 0 (healthy), and otherwise, a score of 1 (diseased). Threshold random regression models (RRM) were applied to binary health data, and linear RRM to Gaussian-distributed protein yield and SCS. Genetic correlations between protein yield and SCS for identical days in milk (DIM) only revealed a tendency for genetic antagonisms between DIM 40 and DIM 180, with a maximal genetic correlation (r_g) of 0.14 at DIM 100. With regard to protein yield and claw disorders, the largest and moderate values of r_g (~0.30), indicating a genetic antagonism between productivity and claw health, were found when correlating protein yield from DIM 300 with SU from DIM 160. Especially for SU and protein yield, time-lagged relationships were more pronounced than genetic relationships from the same test days. Genetic correlations between IH and protein yield were favorable and negative from calving to DIM 300. Generally, on the genetic scale, we found heterogeneous associations between protein yield and claw disorders (i.e., different r_g at identical test days for different claw disorders, and also an alteration of r_g for identical traits at different DIM). The SCS measured at d 20, 160, and 300 was genetically positively correlated with SU over the whole trajectory of 365 d, indicating a common genetic background for claw and udder health. A maximal value of 0.36 was found for the r_g between SCS from d 300 and SU early in lactation. Additionally, a recursive effect was observed (i.e., r_g = 0.26 between SCS from d 20 and SU from d 340). Genetic correlations between SCS and IH, and between SCS and digital dermatitis, were close to zero and partly negative during lactation. Results showed the feasibility of threshold RRM applications to binary claw health data, and a changing genetic background in the course of lactation. From a practical perspective, and with regard to the herds used in this study, continuation of breeding on productivity will have different effects on incidences of different claw disorders, with the highest susceptibility to SU.     

Relationship of test-day somatic cell score with test-day and lactation milk yields

To determine the relationship of test-day (TD) somatic cell score (SCS) to TD and lactation milk yields, 1,320,590 records from Holstein first and second calvings from 1995 through 2002 were examined. All lactations had recorded yield and SCS for at least the first 4 TD. Least square analyses were conducted for yields on TD 2 through 10 within herd and cow. The model included regressions on current TD SCS and mean SCS of all previous TD with separate estimates by parity; effects for parity and calving year were included as well as regression on days in milk on TD 1. Corresponding analyses were conducted without regression on current SCS. An analysis of lactation yield was performed with a similar model and regression on all TD SCS. The SCS was highest most often on TD 1 for parity 1 (22.5%) and on TD 10 for parity 2 (18.5%). Regression of TD milk yield on mean of previous TD SCS was highest during the latter half of lactation (maximum of -0.346 kg/SCS unit on TD 9) for parity 1 and during TD through 7 (maximum of -0.366 kg/SCS unit on TD 4) for parity 2. Regression of TD yield on current TD SCS tended to be larger for later lactation. Regression of lactation yield on TD SCS was negative and important for TD 1 through 6 for parity 1 and for all TD for parity 2. To minimize milk loss, mastitis control is most important immediately pre- and postcalving for parity 1 and throughout lactation for parity 2.     

Factors affecting somatic cell counts and their relations with milk and milk constituent yield in buffaloes

Data concerning daily milk yield (MY), percentage of milk fat (%F), protein (%P), lactose (%LT), and total solids (%TS), and somatic cell counts (SCC) for a herd of 222 Murrah buffalo reared in the state of S?o Paulo, Brazil, were collected monthly from 1997 to 2000 in order to study the factors affecting SCC and their relation to milk production and constituents during lactation. SCC decreased in the second month of lactation and increased thereafter, up to the ninth month of lactation. The interaction of month of lactation x order of calving was significant. Mean MY observed during the first month of lactation was 6.87 kg, which increased to 7.65 kg during the second month, and then decreased until the ninth month of lactation (3.83 kg). During the different months of lactation, %F, %P, %LT, and %TS ranged from 6.28 to 8.38%, 4.05 to 4.59%, 4.96 to 5.34%, and 16.94 to 18.55%, respectively. Calving year, calving order, and order of month of lactation significantly affected MY, %F, %P, %LT, and %TS. The regression coefficients of transformed SCC on MY and %LT were negative and significant during all months of lactation, showing that milk and lactose yield decreased with increased transformed SCC, causing losses to buffalo milk producers.     

Effect of pathogen-specific clinical mastitis on milk yield in dairy cows

Our objective was to estimate the effects of the first occurrence of pathogen-specific clinical mastitis (CM) on milk yield in 3071 dairy cows in 2 New York State farms. The pathogens studied were Streptococcus spp.,Staphylococcus aureus, Staphylococcus spp., Escherichia coli, Klebsiella spp., Arcanobacterium pyogenes, other pathogens grouped together, and "no pathogen isolated." Data were collected from October 1999 to July 2001. Milk samples were collected from cows showing signs of CM and were sent to the Quality Milk Production Services laboratory at Cornell University for microbiological culture. The SAS statistical procedure PROC MIXED, with an autoregressive covariance structure, was used to quantify the effect of CM and several other control variables (herd, calving season, parity, month of lactation, J-5 vaccination status, and other diseases) on weekly milk yield. Separate models were fitted for primipara and multipara, because of the different shapes of their lactation curves. To observe effects of mastitis, milk weights were divided into several periods both pre- and postdiagnosis, according to when they were measured in relation to disease occurrence. Another category contained cows without the type of CM being modeled. Because all pathogens were modeled simultaneously, a control cow was one without CM. Among primipara, Staph. aureus, E. coli, Klebsiella spp., and "no pathogen isolated" caused the greatest losses. Milk yield generally began to drop 1 or 2 wk before diagnosis; the greatest loss occurred immediately following diagnosis. Mastitic cows often never recovered their potential yield. Among older cows, Streptococcus spp., Staph. aureus, A. pyogenes, E. coli, and Klebsiella spp. caused the most significant losses. Many multipara that developed CM were actually higher producers before diagnosis than their nonmastitic herd-mates. As in primipara, milk yield in multipara often began to decline shortly before diagnosis; the greatest loss occurred immediately following diagnosis. Milk loss persisted until at least 70 d after diagnosis for Streptococcus spp., Klebsiella spp., and A. pyogenes. The tendency for higher producing cows to contract CM may mask its impact on cow health and production. These findings provide dairy producers with more information on which pathogen-specific CM cases should receive treatment and how to manage these cows, thereby reducing CM impact on cow well being and profitability.     

Deconstructing milk yield and composition during lactation using biologically based lactation models

A recently developed biological model of lactation described changes in daily milk yield throughout lactation as the result of 3 processes, secretory cell differentiation, cell death, and secretion rate per cell. This paper extends the model to describe the production of milk components (fat, protein, lactose, and water) throughout lactation by replacing milk secretion rate of the original model with the secretion rates of the four components. The milk component model approach was used to examine the relationship between milk yield and the major determinants of its production, using the secretion of milk components throughout lactation. Newly derived models were tested on 461 lactations from a single Holstein herd and used to estimate variability of secretion rates throughout lactation. Because the pattern of cell numbers throughout lactation is not precisely known, an alternative pattern of cell numbers was modeled and the concomitant change in secretion rates outlined. Fat secretion rate was the most variable, as measured by its weekly coefficient of variation throughout lactation. Secretion rates of lactose and water were nearly constant throughout lactation and highly correlated (0.94). Fat and protein secretion rates also were well correlated (0.53). The known biochemistry of milk component production related well to the secretion rate observations derived from the model. Lactose secretion rate and numbers of active secretory cells primarily determined daily milk yield.     

Genetic parameters of udder traits, somatic cell score, and milk yield in Latxa sheep

Genetic parameters have been estimated in the Black-Face ecotype of the Latxa breed for udder type traits (udder depth and attachment and teat placement and size) at first or later lactations (considered as different traits), as well as for udder type traits, milk yield, and lactational somatic cell score, including all lactations. Genetic correlations between udder type traits at first or later lactations ranged from 0.85 and 0.95 suggesting that they are nearly identical traits. Udder type traits had moderate heritabilities. Milk yield was estimated to have a genetic correlation of 0.43 with udder depth, 0.10 with udder attachment, −0.25 with teat placement, and −0.10 with teat size, which were unfavorable in general. Genetic correlations of lactational somatic cell score were 0.10 with udder depth, −0.27 with udder attachment, −0.01 with teat placement, and 0.29 with teat size. Genetic correlations between lactational somatic cell score and udder type traits show that udders with good shape are less prone to subclinical mastitis.     

Pathogen-specific effects on milk yield in repeated clinical mastitis episodes in Holstein dairy cows

The objective of this study was to estimate the effects of clinical mastitis (CM) cases due to different pathogens on milk yield in Holstein cows. The first 3 CM cases in a cow’s lactation were modeled. Eight categories of pathogens were included: Streptococcus spp.; Staphylococcus aureus; coagulase-negative staphylococci (CNS); Escherichia coli; Klebsiella spp.; cases with CM signs but no bacterial growth (above the level detectable by our microbiological procedures) observed in the culture sample, and cases with contamination (≥3 pathogens in the sample); other pathogens that may be treated with antibiotics (included Citrobacter, Corynebacterium bovis, Enterobacter, Enterococcus, Pasteurella, Pseudomonas; “other treatable”); and other pathogens not successfully treated with antibiotics (Trueperella pyogenes, Mycoplasma, Prototheca, yeasts; “other not treatable”). Data from 38,276 lactations in cows from 5 New York State dairy herds, collected from 2003–2004 until 2011, were analyzed. Mixed models with an autoregressive correlation structure (to account for correlation among the repeated measures of milk yield within a lactation) were estimated. Primiparous (lactation 1) and multiparous (lactations 2 and 3) cows were analyzed separately, as the shapes of their lactation curves differed. Primiparas were followed for up to 48 wk of lactation and multiparas for up to 44 wk. Fixed effects included parity, calving season, week of lactation, CM (type, case number, and timing of CM in relation to milk production cycle), and other diseases (milk fever, retained placenta, metritis, ketosis, displaced abomasum). Herd was modeled as a random effect. Clinical mastitis was more common in multiparas than in primiparas. In primiparas, Streptococcus spp. occurred most frequently as the first case. In multiparas, E. coli was most common as the first case. In subsequent cases, CM cases with no specific growth or contamination were most common in both parity groups. The hazard of CM increased with case number. Mastitic cows were generally higher producers before the CM episode than their nonmastitic herdmates. Milk loss varied with pathogen and case number. In primiparas, the greatest losses were associated with E. coli and “other not treatable” organisms. In multiparas, the greatest losses were associated with Klebsiella spp. and “other not treatable” organisms. Milk loss was not associated with occurrence of CNS. The findings may help farmers to make optimal management decisions for their cows.     

Effect of mobility score on milk yield and activity in dairy cattle

Previous studies have indicated that lame cows have reduced milk yield both before and after they are treated for lameness. One explanation for the reduction in yield before treatment is delay to treatment; that is, cows have impaired mobility for some time before they are treated. The aim of this study was to test this hypothesis by investigating temporal associations between change in milk yield and change in mobility score. Mobility score (MS, on a scale from 0 to 3), milk yield, treatments for lameness, and cow activity were recorded on 312 cows in a dairy herd in Somerset, UK, for 1 yr. The MS was scored every 2 wk and compared with daily yield and activity (mean log steps/h) averaged over the previous 16 d. Approximately 52% of MS changed within 14 d, usually by 1 unit. Overall, milk yields of cows with MS 1 were greater than those of cows with other scores. Cows with MS 2 and 3 produced 0.7 (95% confidence interval: 0.35–0.97) and 1.6 (0.98–2.23) kg less milk/d, respectively, compared with cows with MS 1. In addition, cows with MS 1 were significantly more active than cows with MS 0, 2, or 3. Cows with MS 2 and 3 were 0.02 (0.01–0.03) and 0.03 (0.01–0.05) mean log steps less active than cows with MS 1. Six to 8 wk before nonlame cows became MS 2 or 3, their daily milk yield decreased by a mean (95% CI) of 0.5 kg (0.12–0.47) and 0.9 kg (0.16–1.65) respectively. Daily yield remained lower by 0.42 kg (0.09–0.75) for 4 wk after cows with MS 2 had recovered. The activity of cows was significantly less (0.01 mean log steps) with increasing MS; the associations between activity and parity (means 0.03–0.11) and month of lactation (means 0.03–0.36) were quantitatively larger. Results from a multistate model indicated that once cows were lame they remained lame or became lame again despite treatment. In conclusion, cows’ milk production started to decline before their mobility was visibly impaired.     

Genetic analysis of somatic cell score in danish holsteins using a liability-normal mixture model

Mixture models are appealing for identifying hidden structures affecting somatic cell score (SCS) data, such as unrecorded cases of subclinical mastitis. Thus, liability-normal mixture (LNM) models were used for genetic analysis of SCS data, with the aim of predicting breeding values for such cases of mastitis. Here, putative mastitis statuses and breeding values for liability to putative mastitis were inferred solely from SCS observations. In total, there were 395,906 test-day records for SCS from 50,607 Danish Holstein cows. Four different statistical models were fitted: A) a classical (nonmixture) random regression model for test-day SCS; B1) an LNM test-day model assuming homogeneous (co)variance components for SCS from healthy (IMI-) and infected (IMI+) udders; B2) an LNM model identical to B1, but assuming heterogeneous residual variances for SCS from IMI- and IMI+ udders; and C) an LNM model assuming fully heterogeneous (co)variance components of SCS from IMI- and IMI+ udders. For the LNM models, parameters were estimated with Gibbs sampling. For model C, variance components for SCS were lower, and the corresponding heritabilities and repeatabilities were substantially greater for SCS from IMI- udders relative to SCS from IMI+ udders. Further, the genetic correlation between SCS of IMI- and SCS of IMI+ was 0.61, and heritability for liability to putative mastitis was 0.07. Models B2 and C allocated approximately 30% of SCS records to IMI+, but for model B1 this fraction was only 10%. The correlation between estimated breeding values for liability to putative mastitis based on the model (SCS for model A) and estimated breeding values for liability to clinical mastitis from the national evaluation was greatest for model B1, followed by models A, C, and B2. This may be explained by model B1 categorizing only the most extreme SCS observations as mastitic, and such cases of subclinical infections may be the most closely related to clinical (treated) mastitis.