Correlations among first and second lactation milk yield and calving interval

Estimates of genetic correlations were .17 between first lactation milk yield and concurrent calving interval, .10 between second lactation milk yield and first calving interval, and .82 between first and second milk yields. Corresponding phenotypic correlations were .27, .16, and .58. Heritability estimates were .27 and .25 for first and second lactations and .15 for calving interval. Estimates were averages of two samples of 15 New York State herds averaging 144 AI-sired Holstein cows and 30 sires. Milk yields were 305-d, mature equivalent. Calving interval was days between first and second freshening. First milk records without a second freshening were included. Multiple-trait animal model included separate herd-year-season effects for first and second milk yields and calving interval. Numerator relationships among animals within herd, except for daughter-dam relationships, were included. The REML with the expectation-maximization algorithm was used to estimate (co)variance matrices among genetic values and environmental effects for the three traits. Results indicate a need to adjust milk records for the phenotypic effects of current and previous calving interval. The genetic association, however, between fertility and milk yield appears small. Genetic improvement of 450 kg of milk yield may result in 2 added d to first calving interval.     

Effect of herd environment on the genetic and phenotypic relationships among milk yield, conception rate, and somatic cell score in Holstein cattle

A total of 248,230 primiparous records of Holstein cows calving from 1987 to 1994 (daughters of 588 sires in 3042 herds) was used to evaluate potential genotype by environment interactions among mature equivalent milk yield, lactation mean somatic cell score, and conception rate at first service. Herds were classified into low and high environmental groups using three different criteria: standard deviation of herd mature equivalent milk yield, a combination of herd mature equivalent milk yield mean and standard deviation, and the herd mean of body weight at first calving divided by age at first calving. Genetic parameters were modeled by using multiple-trait linear mixed models and were fitted using the multiple-trait derivative-free software. Heritabilities for mature equivalent milk yield, lactation mean somatic cell score, and conception rate at first service were 0.221, 0.106, and 0.015 in low environment herds and 0.300, 0.093, and 0.009 in high environment herds, respectively. Genetic (and phenotypic) correlations between mature equivalent milk yield and lactation mean somatic cell score, mature equivalent milk yield and conception rate at first service, and lactation mean somatic cell score and conception rate at first service were 0.277, -0.417, and -0.209, (-0.049, -0.180, and -0.040) and 0.173, -0.318, and -0.144, (-0.087, -0.166, and -0.035) in low and high environment herds, respectively. The genetic correlations between pairs of traits were consistently smaller in high environment herds, suggesting that differences in management between the two environment levels lessened the antagonistic genetic association between the traits studied. A long-range plan for low environment herds should focus on improving the level of management, which would greatly reduce the unfavorable correlated changes in lactation mean somatic cell score and conception rate at first service associated with the genetic improvement of mature equivalent milk yield.     

Characterization of best linear unbiased estimates generated from national genetic evaluations of reproductive performance,survival, and milk yield in dairy cows

Genetic evaluations decompose an observed phenotype into its genetic and nongenetic components; the former are termed BLUP with the solutions for the systematic environmental effects in the statistical model termed best linear unbiased estimates (BLUE). Geneticists predominantly focus on the BLUP and rarely consider the BLUE. The objective of this study, however, was to define and quantify the association between 8 herd-level characteristics and BLUE for 6 traits in dairy herds, namely (1) age at first calving, (2) calving to first service interval (CFS), (3) number of services, (4) calving interval (CIV), (5) survival, and (6) milk yield. Phenotypic data along with the fixed and random effects solutions were generated from the Irish national multi-breed dairy cow fertility genetic evaluations on 3,445,557 cows; BLUE for individual contemporary groups were collapsed into mean herd-year estimates. Data from 5,707 spring-calving herds between the years 2007 and 2016 inclusive were retained; association analyses were undertaken using linear mixed multiple regression models. Pearson coefficient correlations were used to quantify the relationships among individual trait herd-year BLUE, and transition matrices were used to understand the dynamics of mean herd BLUE estimates over years. Based on the mean annual trends in raw, BLUP, and BLUE, it was estimated that BLUE were associated with at least two-thirds of the improvement in CIV and milk production over the past 10 yr. Milk recording herds calved heifers for the first time on average 15 d younger, had an almost 2 d longer CFS but 2.3 d shorter CIV than non-milk-recording herds. Larger herd sizes were associated with worse BLUE for both CFS and CIV. Expanding herds and herds that had the highest proportion of cows born on the farm itself, on average, calved heifers younger and had shorter CIV. By separating the raw performance of a selection of herds into their respective BLUE and BLUP, it was possible to identify herds with inferior management practices that were being compensated by superior genetics; similarly, herds were identified with superior BLUE, but because of their inferior genetic merit, were not reaching their full potential. This suggests that BLUE could have a pivotal role in a tailored decision support tool that would enable producers to focus on the most limiting factor hindering them from achieving their maximum performance.     

Genetic analysis of herd life in Canadian dairy cattle on a lactation basis using a Weibull proportional hazards model

The objectives of this study were to identify the most important factors that influence functional survival and to estimate the genetic parameters of functional survival for Canadian dairy cattle. Data were obtained from lactation records extracted for the May 2002 genetic evaluation of Holstein, Jersey, and Ayrshire breeds that calved between July 1, 1985 and April 5, 2002. Analysis was performed using a Weibull proportional hazard model, and the baseline hazard function was defined on a lactation basis instead of the traditional analysis of the whole length of life. The statistical model included the effects of stage of lactation; season of production; the annual change in herd size; type of milk recording supervision; age at first calving; effects of milk, fat, and protein yields calculated within herd-year-parity deviations; and the random effects of herd-year-season of calving and sire. All effects fitted in the model had a significant effect on functional survival of cows in all breeds. Milk yield was by far the most important factor influencing survival, and the hazard increased as the milk production of the cows decreased. The hazard also increased as the fat content increased compared with the average group. Heifers that were older at calving were at higher risk of being culled, and expanding herds were at lower risk of being culled compared with stable herds. More culling was found in unsupervised herds than in supervised herds. The heritability values obtained were 0.14, 0.10, and 0.09 for Holstein, Jersey, and Ayrshire, respectively. Rank correlation between estimated breeding values (EBV) obtained from the current national genetic evaluation of direct herd life and the survival kit used in this study ranged from 0.65 to 0.87, depending on the number of daughters per sire. Estimated genetic trend obtained using the survival kit was overestimated.     

Herd-level risk factors associated with cow mortality in Swedish dairy herds

An increase in on-farm mortality (euthanasia and death) in dairy herds has been reported in several countries in the last decade. This does not only imply possible problems with animal welfare, but it also causes economic losses to the farmer. The objective of this study was to evaluate time trends in on-farm dairy cow mortality in Sweden and identify potential herd-level risk factors. Data were retrieved on all Swedish dairy herds enrolled in the milk recording scheme between 2002 and 2010. Herds with a herd size of <20 cows or a mortality rate (MR) of >40 dead or euthanized cows per 100 cow-years were excluded. Two different models were used: 1 multiple-year analysis, which included 6,898 herds during the period 2002 to 2010 and 1 single-year analysis including 4,252 herds for the year 2010, where other variables that were not present during the entire multiple year study were analyzed. The outcome variable was the number of euthanized and dead cows per year and season. A negative binomial regression model, adjusted for clustering within herd, was applied to both models. Fixed effects in the multiple-year analysis were breed, calving interval, herd size, milk yield, region, season, pasture period, and year. The fixed effects in the single-year analysis were breed, calving interval, conventional versus organic farming, herd size, housing system, milk yield, region, and season. The results demonstrated that MR gradually increased from 5.1 to 6.6 events per 100 cow-years during the study period. Swedish MR are consequently on par with, or even greater than, MR among dairy herds in other comparable countries. Higher mortality was associated with larger herd size, longer calving intervals, and herds that had Swedish Holstein as the predominant breed. Lower mortality was observed in herds with a higher herd average milk yield, during the fall and winter, and in organically managed herds. There were regional differences in mortality. An interaction between herd size and season was found in both models. Also, an interaction between housing system and milk yield was found in the single-year analysis. This first assessment of on-farm mortality in Swedish dairy herds confirmed that the MR has increased over the last few years. The study also identified some herd-level risk factors.     

Dynamics of culling for Jersey,Holstein, and Jersey × Holstein crossbred cows in large multibreed dairy herds

The objective of this observational study was to describe and compare the dynamics of reason-specific culling risk for the genetic groups Jerseys (JE), Holsteins (HO), and Jersey × Holstein crossbreds (JH), considering parity, stage of lactation, and milk yield, among other variables, in large multibreed dairy herds in Texas. The secondary objective was to analyze the association between survival and management factors, such as breeding and replacement policies, type of facilities, and use of cooling systems. After edits, available data included 202,384 lactations in 16 herds, ranging from 407 to 8,773 cows calving per year during the study period from 2007 to 2011. The distribution of lactation records by genetic group was 58, 36, and 6% for HO, JE, and JH crosses, respectively. Overall culling rates across breeds were 30.1, 32.1, and 35.0% for JH, JE, and HO, respectively. The dynamics of reason-specific culling were dependent on genetic group, parity, stage of lactation, milk yield, and herd characteristics. Early lactation was a critical period for “died” and “injury-sick” culling. The risk increased with days after calving for “breeding” and, in the case of HO, “low production” culling. Open cows had a 3.5 to 4.6 times greater risk for overall culling compared with pregnant cows. The odds of culling with reason “died” within the first 60 d in milk (DIM) were not significantly associated with genetic group. However, both JE and JH crosses had lower odds of live culling within the first 60 DIM compared with HO cows (OR = 0.72 and 0.82, respectively). Other cow variables significantly associated with the risk of dying within the first 60 DIM were cow relative 305-d mature equivalent (305ME) milk yield, parity, and season of calving. Significant herd-related variables for death included herd size and origin of replacements. In addition to genetic group, the risk of live culling within 60 DIM was associated with cow-relative 305ME milk yield, parity, and season of calving. Significant herd-related variables for live culling included herd-relative 305ME milk yield, herd size, type of facility, origin of replacement, and type of maternity. Overall, reason-specific culling followed similar patterns across DIM in the 3 genetic groups.     

Effect of milk production on reproductive performance in dairy herds

The objective of the present study was to assess the relationship between individual cow milk yield and fertility, accounting for the contextual effect of the herd. A data set including 657,968 lactations from 677 dairy herds in Argentina from 2001 to 2012 was used. The odds of pregnancy by 100 d in milk (DIM) were assessed by a multilevel logistic model (with cow as the first and herd as the second hierarchical level), and time to pregnancy was assessed by a proportional hazards regression model. Multilevel logistic models included the fixed effects of milk yield by 80 DIM, parity, year, and calving season at cow level and quartiles of herd milk yield by 80 DIM as a contextual effect. The proportional hazards model included the effect of daily cow-level milk yield as time-dependent variable, with milk yield at herd level as the stratification variable. Cows producing 1 standard deviation over the mean milk yield of their herd had 1.3 percentage point lower pregnancy by 100 DIM (from 31.4 to 30.1%; odds ratio = 0.942) when in herds in the top quartile of milk yield, whereas they increased 0.5 percentage points (from 27.9 to 28.4%) when in herds in the lowest quartile of milk yield. Only 4% of the observed variation in pregnancy by 100 DIM was explained by the random effect of the herd. Similarly, cows producing 1 standard deviation (8 kg/d) greater than the herd mean daily milk had 1.3% lower hazard of pregnancy (hazard ratio = 0.987) at 63 DIM in herds in the top quartile of milk yield, whereas they had 14.8% higher hazard (hazard ratio = 1.148) in herds in the lowest quartile of milk yield. The magnitude of the negative association between the cow's daily milk yield and the hazard of pregnancy increased with DIM. In conclusion, the relationship between milk yield and reproductive performance is statistically significant, but the effect size is practically small and is modulated by herd production level.     

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

Sustainable dairy cow performance relies on coevolution in the development of breeding and management strategies. Tailoring breeding programs to herd performance metrics facilitates improved responses to breeding decisions. Although herd-level raw metrics on performance are useful, implicitly included within such statistics is the mean herd genetic merit. The objective of the present study was to quantify the expected response from selection decisions on additive and nonadditive merit by herd performance metrics independent of herd mean genetic merit. Performance traits considered in the present study were age at first calving, milk yield, calving to first service, number of services, calving interval, and survival. Herd-level best linear unbiased estimates (BLUE) for each performance trait were available on a maximum of 1,059 herds, stratified as best, average, and worst for each performance trait separately. The analyses performed included (1) the estimation of (co)variance for each trait in the 3 BLUE environments and (2) the regression of cow-level phenotypic performance on either the respective estimated breeding value (EBV) or the heterosis coefficient of the cow. A fundamental assumption of genetic evaluations is that 1 unit change in EBV equates to a 1 unit change in the respective phenotype; results from the present study, however, suggest that the realization of the change in phenotypic performance is largely dependent on the herd BLUE for that trait. Herds achieving more yield, on average, than expected from their mean genetic merit, had a 20% greater response to changes in EBV as well as 43% greater genetic standard deviation relative to herds within the worst BLUE for milk yield. Conversely, phenotypic performance in fertility traits (with the exception of calving to first service) tended to have a greater response to selection as well as a greater additive genetic standard deviation within the respective worst herd BLUE environments; this is suggested to be due to animals performing under more challenging environments leading to larger achievable gains. The attempts to exploit nonadditive genetic effects such as heterosis are often the basis of promoting cross-breeding, yet the results from the present study suggest that improvements in phenotypic performance is largely dependent on the environment. The largest gains due to heterotic effects tended to be within the most stressful (i.e., worst) BLUE environment for all traits, thus suggesting the heterosis effects can be beneficial in mitigating against poorer environments.     

Genetic relationships and additive genetic variation of productive and reproductive traits in Guernsey dairy cattle

Data were first lactation production and reproduction records initiated from 1958 to 1981 in two experiment station Guernsey herds. Heritability estimates using paternal half sib groups were .24 +/- .12 for milk yield, .27 +/- .12 for fat yield, and .77 +/- .15 for fat percentage. Heritability estimates for reproductive traits ranged from .01 to .04 for number of services, service period, conception rate, and days open, but were higher for days in milk at first breeding (.12) and age at first calving (.13). Except for age at first calving, coefficients of additive genetic variation were larger for reproductive traits than for productive traits. Genetic correlations between measures of production and reproduction were moderate to large and antagonistic, except that the relationship between production and age at first calving was favorable. Breeding value estimates for milk yield and reproduction were negatively correlated for sires with above average breeding values for milk yield. Huge phenotypic variances for reproductive traits masked substantial additive genetic variation for these traits. When all things are considered it seems unwise to ignore reproductive performance in selection programs for dairy cattle.     

Season of conception is associated with future survival,fertility, and milk yield of Holstein cows

Environmental influences during different stages of pregnancy can induce lifelong changes in the structure, physiology, and metabolism of the offspring. Our hypothesis was that season of conception (when the offspring was conceived), associated with heat stress conditions at conception and during the initial stages of embryonic development, affects the lifetime performance and survival of the female offspring after birth. The objective was to analyze the association between month of conception and subsequent survival, fertility, and milk yield in cows maintained on dairy farms in Florida, where the climate during the summer is hot and humid but winters are mild. Initial data consisted of 667,104 Dairy Herd Improvement lactation records from cows calving between 2000 and 2012 in 152 herds. Dates of conception were estimated as birth date minus 280 d. The magnitude of heat stress in each herd was quantified by comparing milk yield during summer and winter. Wood's lactation curves were fitted to adjust milk yields for effects of days in milk, and residuals were obtained for each calendar month. A sine function was fitted on the 12 residuals per farm. The difference between the highest and lowest points on the sine function was termed the seasonality index, a measure of the direct effect of heat stress on milk production. Herds were categorized in 3 levels of seasonality [low (seasonality index values less than the 25th percentile value; <2.84), medium (values within the interquartile range), and high (values greater than the 75th percentile value; >5.22)]. Cows were grouped by their month of conception: summer (July–September) and winter (December–February), and comparisons were performed by parity using logistic regression, ANOVA, and survival analysis. Two models were developed. Model A included the complete population of cows (n = 337,529 lactation records) conceived in winter or summer. Model B included cows (n = 228,257 lactation records) that had parent-average genetic information available to be able to correct for farmer's use of lower genetic merit of sires in summer. Other variables included in the models were month and year of calving, age at first calving, and herd. Models were run per parity group (1, 2, and ≥3). In both models, age at first calving was lower for cows conceived during winter versus summer. The odds (95% confidence interval) of survival to a second calving for cows conceived in winter were 1.21 and 1.15 times the odds of survival for cows conceived in summer for models A and B, respectively. Numbers of days from calving to first breeding and from calving to conception were consistently smaller for winter versus summer months of conception across all parity categories. Milk yields (305 d and by 70 d in milk) were greater for cows conceived in winter versus summer. In conclusion, cows that were conceived in the winter had better subsequent survival and performance than cows that were conceived in the summer. There is evidence that season of conception may have lifelong consequences for the offspring.     

Associations between herd characteristics and reproductive efficiency in dairy herds

Dairy herds worldwide are experiencing a decline in reproductive efficiency at the same time as management methods are changing. This study aimed to investigate the extent to which herd-level characteristics were associated with reproductive performance. Data from herds using artificial insemination (AI) in the Swedish Official Milk Recording Scheme that had more than 45 cows were included in the study (total of 2,728 herds). Reproductive performance was measured as the average for each herd for the calving interval, calving to first AI interval, calving to last AI interval, number of AI per animal submitted for AI, and culling attributed to reproductive problems. Herds with mainly Swedish Holstein cows had longer calving intervals, calving to first AI, and calving to last AI compared with herds with mainly Swedish Red and White cows. Large herds had shorter calving to first AI but a greater number of AI than small herds, whereas small herds had greater culling attributed to reproductive problems than large herds. Low-yielding herds had longer calving intervals, calving to first AI, and calving to last AI and had greater culling attributed to reproductive problems than high-yielding herds, whereas herds with high milk yields had a greater number of AI than low-yielding herds. Herds with automatic milking systems had shorter calving intervals, calving to first AI, and calving to last AI and had lesser odds for culling attributed to reproductive problems when compared with herds with ordinary pipeline milking systems. Herds that used Advanced Feed Advisory Services had shorter calving to first AI but a greater number of AI and greater culling attributed to reproductive problems. Herds using TMR had longer calving intervals and calving to last AI than herds that did not. Herds with tie stalls had longer calving intervals, calving to first AI, and calving to last AI, and organic herds had shorter calving intervals, calving to first AI, and calving to last AI compared with conventional herds. We found that herds with do-it-yourself inseminations had longer calving intervals and calving to first AI. Our study showed numerous associations between herd characteristics and reproductive performance. When allocating advisory service resources to improve reproductive performance, the focus should be on herd characteristics that are easy to influence, such as TMR and do-it-yourself inseminations.     

Short communication: associations between blood calcium status at calving and milk yield in dairy cows

The purpose of the present study was to estimate the effect of total blood plasma calcium (TBPCC) concentration at calving on milk yield in dairy cows. Data originated from 153 dairy cows in 27 herds from a single veterinary practice. For each cow, data included calcium concentration in a blood sample taken within 12 h postpartum, monthly test-day milk yield until 300 d in milk, calving date, parity, breed, and herd. The TBPCC ranged from 0.69 to 2.73 mmol/L, with a mean value of 1.80 mmol/L. The statistical analysis adjusted for the fixed effects of parity and lactation stage, random effects of herd and cow, and the correlation between repeated measures of test-day milk yield. The results showed that TBPCC at calving was not significantly related to fat- and protein-corrected milk yield at any lactation period. The present study indicates that hypocalcemia (low TBPCC) at calving is not an important risk factor for decreased milk yield.     

Negative influence of high maternal milk production before and after conception on offspring survival and milk production in dairy cattle

There is a paucity of studies on the effect of intrauterine conditions on subsequent progeny performance in dairy cows. Using a large national data set on Irish Holstein-Friesian dairy cows, the objective of this study was to determine if intrauterine conditions, quantified by a maternal genetic variance component, significantly affected milk production, age at first calving, calving interval, somatic cell score (natural logarithm of somatic cell count) and survival in first-, second-, and third-parity female offspring. Maternal genetic variance for each trait in each parity was estimated in a linear mixed model which included, other than fixed effects, direct additive genetic, maternal genetic, cytoplasmic and permanent environmental effect of the dam, and residual component. A covariance was also estimated between the direct additive and maternal genetic components where possible. Because calves in Irish dairy herds are separated from dams at birth, a significant maternal genetic variance (with all other random effects in the model) indicates a prepartum maternal effect. A significant maternal genetic variance was estimated for 305-d milk yield in first and third lactation, somatic cell score in first lactation, and survival to second lactation from 188,144 lactations on 80,881 animals. Where estimated, a negative correlation existed between the direct additive and maternal genetic components. Regression of maternal mixed model solutions on dam milk production at different stages relative to conception revealed that greater milk yield preconception and during gestation was associated with reduced survival and milk yield and greater somatic cell count in the progeny. This study suggests that offspring survival and performance are affected by prepartum conditions that offspring experience as an oocyte, embryo, or fetus, one of which is mediated through milk production (or factors related to milk production) of the dam.     

Effect of a calcium-energy supplement drink at calving on lactation performance: Milk yield and composition,odds to reach a next lactation,and calving interval

Supplementation of Ca products to cows after calving is common in calving protocols. This study evaluated the effect of a Ca-energy drink voluntarily consumed on milk yield and composition, odds to reach a next lactation, and calving interval. This prospective randomized study included a blinded placebo and was conducted in 10 commercial dairy farms that included 504 Holstein dairy cows. Cows were blocked within farm by calving sequence and parity (primiparous or multiparous). Within each block of 2 animals, cows were randomly assigned to 1 of 2 treatments: a Ca-energy supplement drink (CAE, n = 255) providing 45 g of Ca and other components (dextrose, lactose, protein, fat, other minerals and vitamins), a placebo (i.e., 100 g of cellulose and 20 g of dextrose; CON, n = 249), both strictly offered to the animals for voluntary consumption. Treatments were offered mixed in 20 L of water within 3 h after calving. Milk data were analyzed using 2 approaches. The first, most classical, evaluated the effect of the treatments on observed milk data, whereas the second approach evaluated the effect on milk residuals (i.e., the difference between observed milk data and a prediction made by a herd test-day model). Eighty-one percent of the CAE cows fully consumed the treatment, whereas only 50% of CON cows did. No differences were detected for observed milk yield, nor for composition in multiparous cows. The only production effect observed on multiparous cows was a treatment by time interaction for milk fat yield, reflecting greater yield for CAE cows between 100 and 150 d in milk only. However, primiparous cows receiving CAE had increased milk (+0.8 kg/d) and component yields (i.e., +40 g/d of protein) compared with CON cows. These effects were more evident when milk and milk components residuals data were analyzed (i.e., +1.5 kg/d for milk yield and +57 g/d of protein). This was achieved with a herd test-day model that allowed milk and milk components data to be adjusted for environmental and genetic factors (i.e., farm effect, time effect, age at calving, parity, stage of lactation, breeding value). The treatment had no effect on the probability of reaching the next lactation (i.e., 72% of CAE cows had a next calving against 69% in CON). Primiparous cows receiving CAE had a longer calving interval compared with CON cows. At 400 d after the application of the treatment, 65% of CAE primiparous cows had a next calving, whereas 81% of CON primiparous cows had calved already. The supplementation of the tested oral Ca-energy solution at calving did not increase the probability to reach a next lactation for neither primiparous or multiparous, but positively influenced milk yield and milk component yields for primiparous.     

Short communication: Modification of genetic evaluation of herd life from a three-trait to a five-trait model in Canadian dairy cattle

The national genetic evaluation of herd life for Canadian dairy breeds was modified from a 3-trait to a 5-trait animal model. The genetic evaluation incorporates information from daughter survival (direct herd life) and information from conformation, fertility, and udder health traits that are related to longevity (indirect herd life). Genetic evaluations for direct herd life were based on cows’ survival from first calving to 120 days in milk (DIM), from 120 to 240 DIM, from 240 DIM to second calving, survival to third calving, and survival to fourth calving, which were analyzed using a multiple-trait animal model. Sire evaluations obtained for each of the 5 survival traits were combined into an overall sire evaluation for direct herd life. Sire evaluations for indirect herd life were based on an index of sire evaluations for dairy strength, feet and legs, overall mammary, rump angle, somatic cell score, milking speed, nonreturn rate in cows, and interval from calving to first service. A multiple-trait sire model based on multiple-trait across-country evaluation methodology was used to combine direct and indirect genetic evaluations for herd life into an overall genetic evaluation for herd life. Sire evaluations for herd life were expressed as an estimated transmitting ability for the number of lactations. The transmitting ability represents expected differences among daughters for herd life; and the average herd life was set to 3 lactations.     

Association of total mixed ration particle fractions retained on the Penn State Particle Separator with milk,fat, and protein yield lactation curves at the cow level

As part of a larger project aiming to develop management evaluation tools based on results from test-day (TD) models, the objective of this study was to examine the effect of physical composition of total mixed rations (TMR) tested quarterly from March 2006 through December 2008 on milk, fat, and protein yield curves for 25 herds in Ragusa, Sicily. A random regression sire-maternal grandsire model was used to estimate variance components for milk, fat, and protein yields fitted on a full data set, including 241,153 TD records from 9,809 animals in 42 herds recorded from 1995 through 2008. The model included parity, age at calving, year at calving, and stage of pregnancy as fixed effects. Random effects were herd × test date, sire and maternal grandsire additive genetic effect, and permanent environmental effect modeled using third-order Legendre polynomials. Model fitting was carried out using ASREML. Afterward, for the 25 herds involved in the study, 9 particle size classes were defined based on the proportions of TMR particles on the top (19-mm) and middle (8-mm) screen of the Penn State Particle Separator. Subsequently, the model with estimated variance components was used to examine the influence of TMR particle size class on milk, fat, and protein yield curves. An interaction was included with the particle size class and days in milk. The effect of the TMR particle size class was modeled using a ninth-order Legendre polynomial. Lactation curves were predicted from the model while controlling for TMR chemical composition (crude protein content of 15.5%, neutral detergent fiber of 40.7%, and starch of 19.7% for all classes), to have pure estimates of particle distribution not confounded by nutrient content of TMR. We found little effect of class of particle proportions on milk yield and fat yield curves. Protein yield was greater for sieve classes with 10.4 to 17.4% of TMR particles retained on the top (19-mm) sieve. Optimal distributions different from those recommended may reflect regional differences based on climate and types and quality of forages fed.     

Genetics of growth, feed intake, and milk yield in Holstein cattle.

Heritabilities, genetic and phenotypic correlations among growth, forage consumption, and BW changes of heifers and feed consumption, BW changes, and yields of first lactation cows were estimated. Data were from 1266 Holstein progeny of 74 sires born from 1972 to 1985 at three Agriculture Canada research herds. Heavier heifers at 26 wk consumed more feed from 26 to 34 wk than smaller heifers but gained the same BW. The BW gain and feed consumption heritabilities were .17 and .23, respectively; genetic correlation was .44, and phenotypic correlation was .27. During first lactation, feed intake from 8 to 16 wk and measures of milk yield are very tightly intercorrelated both phenotypically and genetically (.78 to .98). Precalving BW gain and BW at calving were genetically uncorrelated with measures of milk yield (-.09 to +.05). Loss of BW during the first 8 wk of first lactation was moderately heritable (.29) and correlated genetically and phenotypically with measures of milk yield in early lactation (.32 to .39) and feed consumption (.26). From 8 to 16 wk, average BW changes were small and had low heritability and weak phenotypic correlations with measures of milk yield or feed intake. The BW at 26 wk and BW gain from 26 to 34 wk were very poor indicators of early first lactation milk yield. Heifer feed intake was weakly correlated phenotypically (-.07 to .16) but moderately genetically correlated (.17 to .23) with early first lactation milk yield and feed consumption.