A new method to estimate residual feed intake in dairy cattle using time series data

In dairy, the usual way to measure feed efficiency is through the residual feed intake (RFI) method. However, this method is, in its classical form, a linear regression, which, by construction, does not take into account the evolution of the RFI components across time, inducing approximations in the results. We present here a new approach that incorporates the dynamic dimension of the data. Using a multitrait random regression model, the correlations between milk, live weight, DM intake (DMI) and body condition score (BCS) were investigated across the lactation. In addition, at each time point, by a matrix regression on the variance–covariance matrix and on the animal effects from the three predictor traits, a predicted animal effect for intake was estimated, which, by difference with the actual animal effect for intake, gave a RFI estimation. This model was tested on historical data from the Aarhus University experimental farm (1 469 lactations out of 740 cows). Correlations between animal effects were positive and high for milk and DMI and for weight and DMI, with a maximum mid-lactation, stable across time at around 0.4 for weight and BCS, and slowly decreasing along the lactation for milk and weight, DMI and BCS, and milk and BCS. At the Legendre polynomial coefficient scale, the correlations were estimated with a high accuracy (averaged SE of 0.04, min = 0.02, max = 0.05). The predicted animal effect for intake was always extremely highly correlated with the milk production and highly correlated with BW for the most part of the lactation, but only slightly correlated with BCS, with the correlation becoming negative in the second half of the lactation. The estimated RFI possessed all the characteristics of a classical RFI, with a mean at zero at each time point and a phenotypic independence from its predictors. The correlation between the averaged RFI over the lactation and RFI at each time point was always positive and above 0.5, and maximum mid-lactation (> 0.9). The model performed reasonably well in the presence of missing data. This approach allows a dynamic estimation of the traits, free from all time-related issues inherent to the traditional RFI methodology, and can easily be adapted and used in a genetic or genomic selection context.     

Genetic and phenotypic correlations among feed efficiency,production and selected conformation traits in dairy cows

The difficulties and costs of measuring individual feed intake in dairy cattle are the primary factors limiting the genetic study of feed intake and utilisation, and hence the potential of their subsequent industry-wide applications. However, indirect selection based on heritable, easily measurable, and genetically correlated traits, such as conformation traits, may be an alternative approach to improve feed efficiency. The aim of this study was to estimate genetic and phenotypic correlations among feed intake, production, and feed efficiency traits (particularly residual feed intake; RFI) with routinely recorded conformation traits. A total of 496 repeated records from 260 Holstein dairy cows in different lactations (260, 159 and 77 from first, second and third lactation, respectively) were considered in this study. Individual daily feed intake and monthly BW and body condition scores of these animals were recorded from 5 to 305 days in milk within each lactation from June 2007 to July 2013. Milk yield and composition data of all animals within each lactation were retrieved, and the first lactation conformation traits for primiparous animals were extracted from databases. Individual RFI over 301 days was estimated using linear regression of total 301 days actual energy intake on a total of 301 days estimated traits of metabolic BW, milk production energy requirement, and empty BW change. Pair-wise bivariate animal models were used to estimate genetic and phenotypic parameters among the studied traits. Estimated heritabilities of total intake and production traits ranged from 0.27±0.07 for lactation actual energy intake to 0.45±0.08 for average body condition score over 301 days of the lactation period. RFI showed a moderate heritability estimate (0.20±0.03) and non-significant phenotypic and genetic correlations with lactation 3.5 % fat-corrected milk and average BW over lactation. Among the conformation traits, dairy strength, stature, rear attachment width, chest width and pin width had significant (P<0.05) moderate to strong genetic correlations with RFI. Combinations of these conformation traits could be used as RFI indicators in the dairy genetic improvement programmes to increase the accuracy of the genetic evaluation of feed intake and utilisation included in the index.     

Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics

A total of 19 376 test day (TD) milk yield records from the first three lactations of 1618 cows daughters of 162 sires were used to estimate genetic and phenotypic parameters and determine the relationship between daily milk yield and lactation milk yield in the Sahiwal cattle in Kenya. Variance components were estimated using animal models based on a derivative free restricted maximum likelihood procedure. Variance components were estimated using various univariate and multi-trait fixed regression test day models (TDM) that defined contemporary groups either based on the year-season of calving (YSCV) or on the year-season of TD milk sampling (YSTD). Variance components were influenced by CG which resulted in differences in heritability and repeatability estimates between TDM. Models considering YSTD resulted in higher additive genetic variances and lower residual variances compared with models in which YSCV was considered. Heritability estimates for daily yield ranged from 0.28 to 0.46, 0.38 to 0.52 and 0.33 to 0.52 in the first, second and third lactation, respectively. In the first and second lactation, the heritability estimates were highest between TD 2 and TD 4. Genetic correlations among daily milk yields ranged from 0.41 to 0.93, 0.50 to 0.83 and 0.43 to 86 in the first, second and third lactation, respectively. The phenotypic correlations were correspondingly lower. Genetic correlations were different from unit when fitting multi-trait TDM. Therefore, a multiple trait model would be more ideal in determining the genetic merit of dairy sires and bulls based on daily yield records. Genetic and phenotypic correlations between daily yield and lactation yields were high and positive. Genetic correlations ranged from 0.84 to 0.99, 0.94 to 1.00 and 0.94 to 0.97 in the first, second and third lactations, respectively. The corresponding phenotypic correlation estimates ranged from 0.50 to 0.85, 0.50 to 0.83 and 0.53 to 0.87. The high genetic correlation between daily yield and lactation yield imply that both traits are influenced by similar genes. Therefore daily yields records could be used in genetic evaluation in the Sahiwal cattle breeding programme.     

Estimation of genetic parameters for test day records of dairy traits in the first three lactations

Application of test-day models for the genetic evaluation of dairy populations requires the solution of large mixed model equations. The size of the (co)variance matrices required with such models can be reduced through the use of its first eigenvectors. Here, the first two eigenvectors of (co)variance matrices estimated for dairy traits in first lactation were used as covariables to jointly estimate genetic parameters of the first three lactations. These eigenvectors appear to be similar across traits and have a biological interpretation, one being related to the level of production and the other to persistency. Furthermore, they explain more than 95% of the total genetic variation. Variances and heritabilities obtained with this model were consistent with previous studies. High correlations were found among production levels in different lactations. Persistency measures were less correlated. Genetic correlations between second and third lactations were close to one, indicating that these can be considered as the same trait. Genetic correlations within lactation were high except between extreme parts of the lactation. This study shows that the use of eigenvectors can reduce the rank of (co)variance matrices for the test-day model and can provide consistent genetic parameters.     

Application of the covariance function approach with an iterative two-stage algorithm to the estimation of parameters of a random regression test day model for dairy production traits

The covariance function approach with an iterative two-stage algorithm of LIU et al. (2000) was applied to estimate parameters for the Polish Black-and-White dairy population based on a sample of 338 808 test day records for milk, fat, and protein yields. A multiple trait sire model was used to estimate covariances of lactation stages. A third-order Legendre polynomial was subsequently fitted to the estimated (co)variances to derive (co)variances of random regression coefficients for both additive genetic and permanent environment effects. Daily and 305-day heritability estimates obtained are consistent with several studies which used both fixed and random regression test day models. Genetic correlations between any two days in milk (DIM) of the same lactation as well as genetic correlations between the same DIM of two lactations were within a biologically acceptable range. It was shown that the applied estimation procedure can utilise very large data sets and give plausible estimates of (co)variance components.     

Effects of stage of pregnancy on variance components,daily milk yields and 305-day milk yield in Holstein cows,as estimated by using a test-day model

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.     

Genetic parameters of a biological lactation model: early lactation and secretion rate traits of dairy heifers

Early lactation parameters are difficult to estimate from commercial dairy records due to the small number of records available before the peak of production. A biological model of lactation was used with weekly milk records from a single Holstein herd to estimate these early lactation parameters and the secretion rate of milk from the average cell throughout lactation. A genetic analysis of the lactation curve parameters, calculated curve characteristics and secretion rate traits was undertaken. Early lactation traits were found to have little genetic variation and effectively zero heritability. Secretion rate traits for milk, protein, lactose and water were all moderately heritable and highly genetically correlated (>0.87) but fat secretion rate had lower genetic correlations with the other secretion rates. A similar pattern of correlations was seen between total lactation yield traits for fat, protein, lactose and water. The genetic correlations between the lactation curve traits and the secretion rate traits were calculated. Total milk yield, peak yield and maximum secretion potential were all highly correlated with milk, lactose and water secretion rates but less so with fat and protein secretion rates. In particular, fat secretion rate had a moderate to low genetic correlation with these lactation curve traits. Persistency of lactation was highly correlated with fat and protein secretion rates, more persistent lactations being associated with lower rates of secretion of these milk components. Similar levels of heritability were found, where trait genetic parameters were directly equivalent to those derived from the same dataset by random regression methods. However, by using a biological model of lactation to analyse lactation traits new insights into the biology of lactation are possible and ways to select cows on a range of lactation traits may be achieved.     

Divergence for residual feed intake of Holstein-Friesian cattle during growth did not affect production and reproduction during lactation

Residual feed intake (RFI) is the difference between actual and predicted dry matter intake (DMI) of individual animals. Recent studies with Holstein-Friesian calves have identified an ~20% difference in RFI during growth (calf RFI) and these groups remained divergent in RFI during lactation. The objective of the experiment described here was to determine if cows selected for divergent RFI as calves differed in milk production, reproduction or in the profiles of BW and body condition score (BCS) change during lactation, when grazing pasture. The cows used in the experiment (n=126) had an RFI of −0.88 and +0.75 kg DM intake/day for growth as calves (efficient and inefficient calf RFI groups, respectively) and were intensively grazed at four stocking rates (SR) of 2.2, 2.6, 3.1 and 3.6 cows/ha on self-contained farmlets, over 3 years. Each SR treatment had equal number of cows identified as low and high calf RFI, with 24, 28, 34 and 40/11 ha farmlet. The cows divergent for calf RFI were randomly allocated to each SR. Although SR affected production, calf RFI group (low or high) did not affect milk production, reproduction, BW, BCS or changes in these parameters throughout lactation. The most efficient animals (low calf RFI) lost similar BW and BCS as the least efficient (high calf RFI) immediately post-calving, and regained similar BW and BCS before their next calving. These results indicate that selection for RFI as calves to increase efficiency of feed utilisation did not negatively affect farm productivity variables (milk production, BCS, BW and reproduction) as adults when managed under an intensive pastoral grazing system.     

An analysis of the genetic relationship between udder health and udder conformation traits in South African Jersey cows

A multi-trait animal model was used to estimate genetic parameters among lactation somatic cell score (SCS) and udder-type traits in South African Jersey cattle, through restricted maximum likelihood (REML) procedures. Data comprised records on 18 321 Jersey cows in 470 herds, collected through the National Milk Recording Scheme from 1996 to 2002. Average SCS in the first three lactations (SCS1, SCS2 and SCS3) were considered as different traits and the udder-type traits were fore udder attachment (FUA), rear udder height (RUH), rear udder width (RUW), udder cleft (UC), udder depth (UD), fore teat placement (FTP), rear teat placement (RTP) and fore teat length (FTL). Heritability estimates for the respective lactation SCS were 0.07 ± 0.01, 0.11 ± 0.01 and 0.11 ± 0.02. Udder-type traits had heritability estimates ranging from 0.14 ± 0.01 for UD to 0.30 ± 0.02 for FTL. Genetic correlations between SCS and udder-type traits ranged from -0.003 ± 0.07 between FUA and SCS3 to -0.50 ± 0.07 between UD and SCS3. Slow genetic progress is expected when selection is applied independently on SCS and udder-type traits, due to the generally low heritabilities. Tightly attached shallow udders with narrowly placed rear teats are associated with low SCS in the Jersey population.     

Digestibility contributes to between-animal variation in feed efficiency in beef cows

Residual feed intake (RFI) is an alternative measure of feed efficiency (FE) and is calculated as the difference between actual and expected feed intake. The biological mechanisms underlying animal-to-animal variation in FE are not well understood. The aim of this study was to investigate the digestive ability of beef cows selected for RFI divergence as heifers, using two contrasted diets. Fifteen 4-year-old beef cows were selected from a total of 69 heifers based on their RFI following the feedlot test. The selected heifers were ranked into high-RFI (+ 1.02 ± 0.28, n = 8) and low-RFI (−0.73 ± 0.28, n = 7), and a digestibility trial was performed after their first lactation. Both RFI groups were offered two different diets: 100% hay or a fattening diet which consisted of a DM basis of 67% whole-plant maize silage and 33% high starch concentrates over four experimental periods (two per diet). A diet effect was observed on feed intake and apparent digestibility, whereas no diet × RFI interaction was detected (P > 0.05). Intake and apparent digestibility were higher in cows fed the fattening diet than in those fed the hay diet (P < 0.0001). DM intake (DMI) and organic matter apparent digestibility (OMd) were repeatable and positively correlated between the two subsequent periods of measurements. For the hay and fattening diets, the repeatability between periods was r = 0.71 and r = 0.73 for DMI and r = 0.87 and r = 0.48 for OMd, respectively. Moreover, both intake (r = 0.55) and OMd (r = 0.54) were positively correlated (P < 0.05) between the hay and fattening diets. Significant differences between beef cows selected for divergence in RFI as heifers were observed for digestive traits (P < 0.05), DM and organic matter (OM) apparent digestibility being higher for low-RFI cows. Overall, this study showed that apparent digestibility contributes to between-animal variation in FE in beef cows.     

Genetic and environmental factors that influence production and quality of milk of Alpine and Saanen goats

Data from 1279 lactations of 783 Alpine and Saanen goats of the herd of our university in Minas Gerais, Brazil, were used to study environmental effects on and to estimate genetic parameters for milk production until 270 days of lactation (MP270) and for production and percentages of fat (PFAT and %FAT), protein (PPROT and %PROT), lactose (PLACT and %LACT), and total dry extract (PEXTR and %EXTR). Environmental effects were estimated by a statistical model that included contemporary group effect, type of kidding, genetic grouping, and kidding order. A multi-trait animal model with animal and permanent environment random effects was used to estimate genetic parameters and the significant environmental effects (fixed). Contemporary group influenced all traits; genetic grouping did not influence %LACT; type of kidding did not influence PFAT, %PROT or %LACT, and kidding order did not influence %FAT or %EXTR. Heritability and repeatability estimates were, respectively, 0.19 and 0.37 (MP270); 0.10 and 0.20 (PFAT); 0.12 and 0.24 (PPROT); 0.15 and 0.27 (PLACT); 0.13 and 0.24 (PEXTR); 0.21 and 0.34 (%FAT); 0.39 and 0.44 (%PROT); 0.17 and 0.29 (%LACT); 0.31 and 0.47 (%EXTR). Estimates of genetic correlations among MP270 and production of milk constituents were positive and high, but correlations between MP270 and %FAT, MP270 and %PROT, MP270 and %ESTR were moderate and negative. These heritability estimates show that satisfactory genetic gains can be obtained by selection, especially for milk constituents.     

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.     

The Effect of Calf Gender on Milk Production in Seasonal Calving Cows and Its Impact on Genetic Evaluations

Gender of the calf whose birth initiates lactation could influence whole lactation milk yield of the dam due to hormonal influences on mammary gland development, or through calf gender effects on gestation length. Fetal gender could influence late lactation yields because cows become pregnant at peak lactation. The effects of calf gender sequences in parities 1–3 were assessed by separately fitting animal models to datasets from New Zealand comprising 274 000 Holstein Friesian and 85 000 Jersey cows, decreasing to 12 000 and 4 000 cows by parity 3. The lactation initiated by the birth of a female rather than a male calf was associated with a 0.33–1.1% (p≤0.05) higher milk yield. Female calf gender had carryover effects associated with higher milk yield in second lactations for Holstein Friesians (0.24%; p = 0.01) and third lactations for Jerseys (1.1%; p = 0.01). Cows giving birth to bull calves have 2 day longer gestations, which reduces lactation length in seasonal calving herds. Adding a covariate for lactation length to the animal model eroded some of these calf gender effects, such that calving a female led to higher milk yield only for second lactation Holstein Friesians (1.6%; p = 0.002). The interval centering method generates lower estimates of whole lactation yield when Wood’s lactation curves are shifted to the right by 2 days for male calves and this explained the higher yield in female calves when differences in lactation length were considered. Correlations of estimated breeding values between models including or excluding calf gender sequence were 1.00 for bulls or cows. Calf gender primarily influences milk yield through increased gestation length of male calves, and bias associated with the interval centering method used to estimate whole lactation milk yields. Including information on calf gender is unlikely to have an effect on selection response in New Zealand dairy cattle.     

Effect of lactation trimester and parity on eating behavior,milk production and efficiency traits of dairy cows

There is absence knowledge about the effects of lactation trimester and parity on eating behavior, production and efficiency of dairy cows. Objective of this study was to identify and characterize in 340 dairy cows, the 20% high efficient (HE), 20% low efficient (LE) and 60% mid efficient (ME) cows according to their individual residual feed intake (RFI) values, within and between lactation trimesters and between 1st and 2nd parities. Efficiency effect within each lactation trimester, was exhibited in daily dry matter intake (DMI), eating rate and meal size, that were the highest in LE cows, moderate in the ME cows and lowest in the HE group. Daily eating time, meal frequency, yields of milk and energy-corrected milk (ECM) and BW were similar in the three efficiency groups within each trimester. The lower efficiency of the LE cows in each trimester attributes to their larger metabolic energy intake, heat production and energy losses. In subgroup of 52 multiparous cows examined along their 1st and 2nd trimesters, milk and ECM production, DMI, eating behavior and efficiency traits were similar with high Pearson’s correlation (r=0.78 to 0.89) between trimesters. In another subgroup of 42 multiparous cows measured at their 2nd and 3rd trimesters, milk and ECM yield, DMI and eating time were reduced (P<0.01) at the 3rd trimester, but eating rate, meal frequency and meal size remained similar with high Pearson’s correlation (r=0.74 to 0.88) between trimesters. In subgroup of 26 cows measured in 1st and 2nd parities, DMI, BW, milk and ECM yield, and ECM/DMI increased in the 2nd lactation, but eating behavior and RFI traits were similar in both parities. These findings encourage accurate prediction of DMI based on a model that includes eating behavior parameters, together with individual measurement of ECM production. This can be further used to identify HE cows in commercial herd, a step necessary for potential genetic selection program aimed to improve herd efficiency.     

Genetic and non-genetic factors associated with milking order in lactating dairy cows

The ability to rapidly identify temporal deviations of an animal from its norm will be important in the management of individual cows in large herds. Furthermore, predictors of genetic merit for especially health traits are useful to augment the accuracy of selection, and thus genetic gain, in breeding programs. The objective of this study was to estimate the repeatability of milking order and to quantify the contribution of differences in additive genetic variation to phenotypic differences (i.e., heritability). The data used in this study included 9813 herd milk recording test-day records with time of milking from 85,532 cows in 1143 herds across an 8-year period. Milking order was available for both morning and evening milking for each cow with, on average, 3.33 milk test-day records (i.e., 6.66 milking events) per lactation, and on average 1.62 lactations per cow. Variance components for milking order were estimated using animal linear mixed models; covariance components between milking order and milk yield, milk composition and somatic cell score (i.e., logarithm₁₀ somatic cell count) were estimated also using animal linear mixed models. The heritability of milking order was 0.20 indicating partial genetic control of milking order. The repeatability of milking order within test-day, within lactation, and across lactations was 0.63, 0.51, and 0.47, respectively. Milking order was positively (P < 0.001), but weakly, phenotypically correlated with milk yield (r = 0.04), and milk fat concentration (r = 0.01) and negatively (P < 0.001), but weakly, correlated with milk protein concentration (r = −0.02) and somatic cell score (r = −0.05). Milking order was positively (P < 0.05), although weakly, genetically correlated with milk yield (r = 0.07) and negatively (P < 0.05), but also weakly, genetically correlated with somatic cell score (r = −0.08). This study is the first to show a contribution of additive genetics to milking order in dairy cattle but the genetic correlation between milking order and somatic cell score was weak.     

Accurate mathematical models to describe the lactation curve of Lacaune dairy sheep under intensive management

Although the intensive production system of Lacaune dairy sheep is the only profitable method for producers outside of the French Roquefort area, little is known about this type of systems. This study evaluated yield records of 3677 Lacaune sheep under intensive management between 2005 and 2010 in order to describe the lactation curve of this breed and to investigate the suitability of different mathematical functions for modeling this curve. A total of 7873 complete lactations during a 40-week lactation period corresponding to 201 281 pieces of weekly yield data were used. First, five mathematical functions were evaluated on the basis of the residual mean square, determination coefficient, Durbin Watson and Runs Test values. The two better models were found to be Pollott Additive and fractional polynomial (FP). In the second part of the study, the milk yield, peak of milk yield, day of peak and persistency of the lactations were calculated with Pollot Additive and FP models and compared with the real data. The results indicate that both models gave an extremely accurate fit to Lacaune lactation curves in order to predict milk yields (P = 0.871), with the FP model being the best choice to provide a good fit to an extensive amount of real data and applicable on farm without specific statistical software. On the other hand, the interpretation of the parameters of the Pollott Additive function helps to understand the biology of the udder of the Lacaune sheep. The characteristics of the Lacaune lactation curve and milk yield are affected by lactation number and length. The lactation curves obtained in the present study allow the early identification of ewes with low milk yield potential, which will help to optimize farm profitability.     

Estimates of correlations among yield traits and somatic cell score with different models to adjust for bovine somatotropin effects on Holstein dairy cows

Records of Holstein cows from the Dairy Records Processing Center at Raleigh, NC were edited to obtain three data sets: 65,720 first, 50,694 second, and 65,445 later lactations. Correlations among yield traits and somatic cell score were estimated with three different models: 1) bovine somatotropin (bST) administration ignored, 2) bST administration as a fixed effect and 3) administration of bST as part of the contemporary group (herd-year-month-bST). Heritability estimates ranged from 0.13 to 0.17 for milk, 0.12 to 0.20 for fat, 0.14 to 0.16 for protein yields, and 0.08 to 0.09 for somatic cell score. Estimates were less for later than first lactations. Estimates of genetic correlations among yields ranged from 0.35 to 0.85 with no important differences between estimates with the 3 models. Estimates for lactation 2 agreed with estimates for lactation 1. Estimates of genetic correlations for later lactations were generally greater than for lactations 1 and 2 except between milk and protein yields. Estimates of genetic correlations between yields and somatic cell score were mostly negative or small (-0.45 to 0.11). Estimates of environmental correlations among yield traits were similar with all models (0.77 to 0.97). Estimates of environmental correlations between yields and somatic cell score were negative (-0.22 to -0.14). Estimates of phenotypic correlations among yield traits ranged from 0.70 to 0.95. Estimates of phenotypic correlations between yields and somatic cell score were small and negative. For all three data sets and all traits, no important differences in estimates of genetic parameters were found for the two models that adjusted for bST and the model that did not.     

Ruminal Bacterial Community Composition in Dairy Cows Is Dynamic over the Course of Two Lactations and Correlates with Feed Efficiency

Fourteen Holstein cows of similar ages were monitored through their first two lactation cycles, during which ruminal solids and liquids, milk samples, production data, and feed consumption data were collected for each cow during early (76 to 82 days in milk [DIM]), middle (151 to 157 DIM), and late (251 to 257 DIM) lactation periods. The bacterial community of each ruminal sample was determined by sequencing the region from V6 to V8 of the 16S rRNA gene using 454 pyrosequencing. Gross feed efficiency (GFE) for each cow was calculated by dividing her energy-corrected milk by dry matter intake (ECM/DMI) for each period of both lactation cycles. Four pairs of cows were identified that differed in milk production efficiency, as defined by residual feed intake (RFI), at the same level of ECM production. The most abundant phyla detected for all cows were Bacteroidetes (49.42%), Firmicutes (39.32%), Proteobacteria (5.67%), and Tenericutes (2.17%), and the most abundant genera included Prevotella (40.15%), Butyrivibrio (2.38%), Ruminococcus (2.35%), Coprococcus (2.29%), and Succiniclasticum (2.28%). The bacterial microbiota between the first and second lactation cycles were highly similar, but with a significant correlation between total community composition by ruminal phase and specific bacteria whose relative sequence abundances displayed significant positive or negative correlation with GFE or RFI. These data suggest that the ruminal bacterial community is dynamic in terms of membership and diversity and that specific members are associated with high and low milk production efficiency over two lactation cycles.     

Genotype × region and genotype × production level interactions in Holstein cows

The identification of the presence of genotype by environment interaction effects on important traits in Holstein cattle allows for the use of international genetic evaluations and a more efficient design of regional genetic evaluation programmes. The aim of this study was to determine the genotype × environment interaction effects in Chilean Holstein dairy cattle through the analysis of records corresponding to calvings between 1998 and 2015. Herds were classified in the central and southern regions of Chile based on herd location as well as by high and low levels of production environments based on the fat plus protein yield averages per herd within each region. The central region has a Mediterranean climate and a confined production system while the southern region has a humid temperate climate and a production system based on grazing with supplementation. Traits studied were milk yield (MY), fat yield (FY), protein yield (PY), fat content (FC) and protein content (PC) by lactation, age at first calving (AFC) and calving interval (CI). Several four-trait mixed animal models were applied to environmental category data as different traits, which included herd-year-calving season (herd-year-birth season for AFC) and lactation number as fixed effects, and animal additive genetic, sire-herd, permanent environment and residual effects as random effects. Genetic correlations (r_g) for MY, FY, FC, PC and CI were found to decrease as differences between environmental categories increased. The r_g between the most extreme environmental categories considered in this study for AFC (0.26) was the only one found statistically lower than 0.60. Genetic correlation values statistically lower than 0.80 (P < 0.05) were observed for AFC, CI, MY, FY and PY between some environmental categories. If separate genetic evaluations are adopted as practical criteria when the value of r_g is lower than 0.60, the consequence of improving a multi-trait economic breeding objective in this population is likely to be small unless extreme environmental categories are considered. However, a moderate decrease in selection response and re-ranking of selection candidates is expected for AFC, CI and yield traits when selection is performed in different environmental conditions. Genotype × environment interaction effects involving production systems in a Mediterranean climate and confinement vs. Temperate Oceanic climate and grazing with supplementation, and between two fat plus protein yield level categories within each environment, were at most moderate for the studied traits.     

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Test-day milk yield and somatic cell count data over extended lactation (lactation to 540-600 days) were analysed considering part lactations as different traits and fitting random regression (RR) models. Data on Australian Jersey and Holstein Friesian (HF) were used to demonstrate the shape of the lactation curve and data on HF were used for genetic study. Test-day data from about 100 000 cows that calved between 1998 and 2005 were used for this study. In all analyses, a sire model was used.When part lactations were considered as different traits, protein yield early in the lactation (e.g. first 2 months) had a genetic correlation of about 0.8 with protein yield produced after 300 days of lactation. Genetic correlations between lactation stages that are adjacent to each other were high (0.9 or more) within parity. Across parities, genetic correlations were high for both protein and milk yield if they are within the same stage of lactation. Phenotypic correlations were lower than genetic correlations. Heritability of milk-yield traits estimated from the RR model varied from 0.15 at the beginning of the lactation to as high as 0.37 by the 4th month of lactation. All genetic correlations between different days in milk were positive, with the highest correlations between adjacent days in milk and decreasing correlations with increasing time-span. The pattern of genetic correlations between milk yield in the second 300 days (301 to 600 days of lactation) do not markedly differ from the pattern in the first 300 days of lactation. The lowest estimated genetic correlation was 0.15 between milk yield on days 45 and 525 of lactation. The result from this study shows that progeny of bulls with high estimated breeding values for yield traits and those that produce at a relatively high level in the first few months are the most likely candidates for use in herds favouring extended lactations.