Genetic effects of heat stress on milk yield of Thai Holstein crossbreds

The threshold for heat stress on milk yield of Holstein crossbreds under climatic conditions in Thailand was investigated, and genetic effects of heat stress on milk yield were estimated. Data included 400,738 test-day milk yield records for the first 3 parities from 25,609 Thai crossbred Holsteins between 1990 and 2008. Mean test-day milk yield ranged from 12.6 kg for cows with <87.5% Holstein genetics to 14.4 kg for cows with ≥93.7% Holstein genetics. Daily temperature and humidity data from 26 provincial weather stations were used to calculate a temperature-humidity index (THI). Test-day milk yield varied little with THI for first parity except above a THI of 82 for cows with ≥93.7% Holstein genetics. For third parity, test-day milk yield started to decline after a THI of 74 for cows with ≥87.5% Holstein genetics and declined more rapidly after a THI of 82. A repeatability test-day model with parities as correlated traits was used to estimate heat stress parameters; fixed effects included herd-test month-test year and breed groups, days in milk, calving age, and parity; random effects included 2 additive genetic effects, regular and heat stress, and 2 permanent environment, regular and heat stress. The threshold for effect of heat stress on test-day milk yield was set to a THI of 80. All variance component estimates increased with parity; the largest increases were found for effects associated with heat stress. In particular, genetic variance associated with heat stress quadrupled from first to third parity, whereas permanent environmental variance only doubled. However, permanent environmental variance for heat stress was at least 10 times larger than genetic variance. Genetic correlations among parities for additive effects without heat stress considered ranged from 0.88 to 0.96. Genetic correlations among parities for additive effects of heat stress ranged from 0.08 to 0.22, and genetic correlations between effects regular and heat stress effects ranged from −0.21 to −0.33 for individual parities. Effect of heat stress on Thai Holstein crossbreds increased greatly with parity and was especially large after a THI of 80 for cows with a high percentage of Holstein genetics (≥93.7%). Individual sensitivity to heat stress was more environmental than genetic for Thai Holstein crossbreds.     

The effects of heat stress in Italian Holstein dairy cattle

The data set for this study comprised 1,488,474 test-day records for milk, fat, and protein yields and fat and protein percentages from 191,012 first-, second-, and third-parity Holstein cows from 484 farms. Data were collected from 2001 through 2007 and merged with meteorological data from 35 weather stations. A linear model (M1) was used to estimate the effects of the temperature-humidity index (THI) on production traits. Least squares means from M1 were used to detect the THI thresholds for milk production in all parities by using a 2-phase linear regression procedure (M2). A multiple-trait repeatability test-model (M3) was used to estimate variance components for all traits and a dummy regression variable (t) was defined to estimate the production decline caused by heat stress. Additionally, the estimated variance components and M3 were used to estimate traditional and heat-tolerance breeding values (estimated breeding values, EBV) for milk yield and protein percentages at parity 1. An analysis of data (M2) indicated that the daily THI at which milk production started to decline for the 3 parities and traits ranged from 65 to 76. These THI values can be achieved with different temperature/humidity combinations with a range of temperatures from 21 to 36°C and relative humidity values from 5 to 95%. The highest negative effect of THI was observed 4 d before test day over the 3 parities for all traits. The negative effect of THI on production traits indicates that first-parity cows are less sensitive to heat stress than multiparous cows. Over the parities, the general additive genetic variance decreased for protein content and increased for milk yield and fat and protein yield. Additive genetic variance for heat tolerance showed an increase from the first to third parity for milk, protein, and fat yield, and for protein percentage. Genetic correlations between general and heat stress effects were all unfavorable (from −0.24 to −0.56). Three EBV per trait were calculated for each cow and bull (traditional EBV, traditional EBV estimated with the inclusion of THI covariate effect, and heat tolerance EBV) and the rankings of EBV for 283 bulls born after 1985 with at least 50 daughters were compared. When THI was included in the model, the ranking for 17 and 32 bulls changed for milk yield and protein percentage, respectively. The heat tolerance genetic component is not negligible, suggesting that heat tolerance selection should be included in the selection objectives.     

Effect of heat stress on nonreturn rate in Holstein cows: genetic analyses

The genetic component in heat tolerance for nonreturn rate in Holsteins was estimated using an animal linear model augmented by a random regression on a temperature-humidity index (THI). Data consisted of 18,059 nonreturn rates at 45,60, and 90 d after insemination and 81,674 first-parity test-day milk yields from 78 herds in Florida. The THI on the day of insemination or test day was added to each record. Only first-insemination records were used. The model for nonreturn rate included the effects of herd-year-season, age, days in milk, milk yield, THI as a covariable, regular additive effect, and random regression on THI for heat-tolerance additive effect. With a single-trait model, heritability estimates for NR45, NR60, and NR90 at THI = 70 for first-lactation cows were 0.006, 0.014, and 0.053, respectively. Genetic correlation between regular NR90 and heat tolerance was -0.95. A bivariate analysis for NR90 and test-day milk production yielded a correlation between regular merit and heat tolerance for NR90 of -0.35, substantially lower than by the univariate model, indicating a bias in the univariate estimates caused by ignored selection. The regular genetic correlation between NR90 and milk yield was -0.41. Genetic correlation between heat tolerance for NR90 and heat tolerance for milk yield was -0.04, indicating the need to separate selection.     

Short communication: Genetic trends of milk yield under heat stress for US Holsteins

Data included 90,242,799 test-day milk records from 5,402,484 Holstein cows in the first 3 parities and 9,326,754 animals in the pedigree. Additionally, daily temperature-humidity indexes from 202 weather stations were available. Analyses were done by a random regression model in which each parity was treated as a separate trait and that accounted for heat stress. The fixed effects included herd test-day, age at calving, milking frequency, and days in milk classes. Random effects included additive genetic, permanent environment, and herd-year effects, all fit as random regressions. Five covariates in the random regressions included linear splines with 4 knots at 5, 50, 200, and 305 DIM and a function of a temperature-humidity index (THI). Mixed model equations were solved by using an iteration on data approach with a preconditioned conjugate gradient algorithm. Genetic trends for daily milk yield in absence of heat stress (intercept) were 0.140 kg/yr, 0.172 kg/yr, and 0.168 kg/yr for the first, second, and third parity, respectively. Genetic trends for decline of milk yield at temperature of 5°C THI over the threshold of sensitivity to heat stress were −0.002 kg/yr, −0.035 kg/yr, and −0.038 kg/yr, for first, second, and third parity, respectively. Genetic profiles were created by contrasting the 100 most and 100 least heat-tolerant bulls for the official proofs. The most heat-tolerant bulls transmitted lower production and dairy form but higher fertility, productive life, and type, especially udder and locomotion traits. In later parities, the type advantages were smaller. Test-day records capture only a fraction of information due to heat stress, and the real trends for heat stress may be stronger. Studies on heat stress for production should include records on later parities.     

Effect of heat stress on production of Mediterranean dairy sheep

A study on heat stress in Mediterranean dairy sheep was undertaken with the objective to examine the relationship between milk production and heat stress, to estimate the additive genetic variances of milk production traits and heat tolerance, and to investigate the possibility of future selection for increased heat tolerance. Production data included 59,661 test-day records belonging to 6624 lactations of 4428 lactating ewes from 17 flocks collected from 1994 through 2003. The traits investigated were daily milk yield, fat and protein percentage, and daily yield of fat-plus-protein. The pedigree file consisted of 5306 animals; in addition to the 4428 animals with records, 188 male and 690 female ancestors were included. Heat stress was modeled by using data from a weather station. Apart from the effects of the weather conditions of the milk recording test-day, the effects of the preceding 1, 2, and 3 d were determined. Because longer periods of heat stress might have a more severe effect than shorter periods, 2-, 3-, and 4-d periods were also considered, by averaging the weather data measurements. Fixed regression analyses were based on models that included effects of flock nested within year of test-day, DIM (days in milk) class x parity class, and several types of weather indicators. The preferred model using the temperature-humidity index (THI) gave a smoother pattern than did the model with temperature x humidity interaction. Both daily milk and fat-plus-protein yield appeared to decrease at THI > or = 23, in all periods considered. Based on the 4-d period, yield decreased for each unit increase of THI above 23 [-62.8 g/unit (-4.2%) for daily milk yield and -8.9 g/unit (-4.9%) for daily fat-plus-protein yield]. Fat and protein percentages appeared to be unaffected by heat stress. A test-day repeatability model was applied for estimation of genetic parameters. The genetic correlations between the general additive effect and the additive effect of heat tolerance were negative (approximately -0.8) for both daily milk and fat-plus-protein yields in all periods considered. Therefore, milk yield is antagonistic with heat tolerance, and selection only for increased milk production will reduce heat tolerance.     

Genetic and phenotypic statistics of lactation cell counts in different lactations of Holstein cows

Between February 1977 and February 1982, 680,246 monthly test-day observations of somatic cell count were taken for Holstein cows having completed 79,124 lactations in 941 herds on the Quebec Dairy Herd Analysis Service. Data were transformed to natural log scale, and analyses were separate within five parity groups. Two lactational measures of cell count, geometric mean, and weighted (by test-day milk yield) mean of the log of monthly cell counts were calculated for each lactation. Maximum likelihood, iterative Minimum Norm Quadratic Unbiased Estimation, and multivariate Restricted Maximum Likelihood procedures were used for estimation of genetic and phenotypic parameters. Repeatabilities of log test-day cell counts and log of test-day cell counts corrected for milk yield were the same at each parity, .36, .41, .42, .42, and .42 in first, second, third, fourth, and fifth and later parities. Repeatabilities of lactational measures of cell count were .33 between first and second parities, .40 between second and third parities, .13 between first and third parities, and .27 between first, second, and third parities. Heritabilities of measures of lactational cell count were small from .06 to .14 in the five parities. Genetic correlations between measures of lactational cell count in different parities were close to unity, .90 to .97.     

Genetic component of heat stress in dairy cattle, parameter estimation

Our data included 119,205 first-parity, test-day records from 15,002 Holsteins in 134 Georgia farms with temperature and humidity data from 21 weather stations throughout Georgia. The test-day model included the effects of herd test date, days-in-milk (DIM) classes, age, milking frequency, general additive effect, random regression on the heat-humidity index for heat-tolerance additive effect, general permanent environment, and the random regression on the heat-humidity index for a permanent environment. The general effects, which corresponded to effects in the current repeatability models, were assumed to be correlated with the heat-tolerance effects. Variance components were estimated by REML. For heat-humidity indices below 72, heritability for milk was 0.17, and additive variance of heat tolerance was 0. For a heat-humidity index of 86 (which would correspond to temperatures of 36 degrees C at 50% humidity), the additive variance of heat tolerance was as high as for general effect, and the genetic correlation between the two effects was -0.36. Results for fat and protein were similar. Current selection for production reduces heat tolerance. Joint selection for heat tolerance and production is possible.     

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024–0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from −0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from −0.42 to −0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from −0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.     

Genetic correlation patterns between somatic cell score and protein yield in the Italian Holstein-Friesian population

Genetic parameters for somatic cell score (SCS) in the Italian Holstein-Friesian population were estimated addressing the pattern of genetic correlation with protein yield in different parities (first, second, and third) and on different days in milk within each parity. Three approaches for parameter estimation were applied using random samples of herds from the national database of the Italian Holstein Association. Genetic correlations for lactation measures (305-d protein yield and lactation SCS) were positive in the first parity (0.31) and close to zero in the second (0.01) and third (0.09) parities. These results indicated that larger values of SCS were genetically associated with increased production. The second and third sets of estimates were based on random regression test-day models, modeling the shape of lactation curve with the Wilmink function and fourth-order Legendre polynomials, respectively. Genetic correlations from both random regression models showed a specific pattern associated with days in milk within and across parities. Estimates varied from positive to negative in the first and second parity, and from null to negative in the third parity. Patterns were similar for both random regression models. The average overall correlation between SCS and protein yield was zero or slightly positive in the first lactation and ranged from zero to negative in later lactations. Correlation estimates differed by parity and stage of lactation. They also demonstrated the dubiousness of applying a single genetic correlation measure between SCS and protein in setting selection strategies. Differences in magnitude and the sign of genetic correlations between SCS and yields across and within parities should be accounted for in selection schemes.     

Genotype by environment interaction due to heat stress in Brown Swiss cattle

Due to its geographical position and a highly variable orography, Italy is characterized by several climatic areas and thus, by many different dairy cow farming systems. Brown Swiss cattle, in this context, are a very appreciated genetic resource for their adaptability and low metabolic requirement. The significant heterogeneity in farming systems may consist of genotype by environment (G × E) interactions with neglected changes in animals' rank position. The objective of this study was to investigate G × E for heat tolerance in Brown Swiss cattle for several production traits (milk, fat, and protein yield in kilograms; fat, protein, and cheese yield in percentage) and 2 derivate traits (fat-corrected milk and energy-corrected milk). We used the daily maximum temperature-humidity index (THI) range, calculated according to weather stations' data from 2008 to 2018 in Italy, and 202,776 test-day records from 23,396 Brown Swiss cows from 639 herds. Two different methodologies were applied to estimate the effect of the environmental variable (THI) on genetic parameters: (1) the reaction norm model, which uses a continuous random covariate to estimate the animal additive effect, and (2) the multitrait model, which splits each production pattern as a distinct and correlated trait according to the first (a thermal comfort condition), third (a moderate heat stress condition), and fifth (a severe heat stress condition) mean THI value quintile. The results from the reaction norm model showed a descending trend of the additive genetic effect until THI reached the value of 80. Then we recorded an increase with high extreme THI values (THI 90). Permanent environmental variance at increasing THI values revealed an opposite trend: The plot of heritability and the ratio of animal permanent environmental variance to phenotypic variance showed that when the environmental condition worsens, the additive genetic and permanent environmental component for production traits play a growing role. The negative additive genetic correlation between slope and linear random coefficient indicates no linear relationship between the production traits or under heat stress conditions, except for milk yield and protein yield. In tridimensional wireframe plots, the extreme margin decreases until a minimum of ～0.90 of genetic correlation in the ECM trait, showing that the magnitude of G × E interaction is greater than the other traits. Genetic correlation values in Brown Swiss suggest the possibility of moderate changes in animals' estimated breeding value in heat stress conditions. Results indicated a moderate G × E interaction but significant variability in sire response related to their production level.     

Genetic parameters for first and second lactation milk yields of Polish black and white cattle with random regression test-day models

Single- and two-trait random regression models were applied to estimate variance components of test-day records of milk, fat, and protein yields in the first and second lactation of Polish Black and White cattle. The model included fixed herd test-day effect, three covariates to describe lactation curve nested within age-season classes, and random regressions for additive genetic and permanent environmental effects. In two-parity models, each parity was treated as a separate trait. For milk and the two-parity model, heritabilities were in the range of 0.14 to 0.19 throughout first lactation and 0.10 to 0.16 throughout second lactation. For fat, heritabilities were within 0.11 to 0.16 and 0.11 to 0.22 throughout first and second lactations, respectively. For protein in the two-parity model, heritabilities were within 0.10 to 0.15 throughout most of first lactation and within 0.06 to 0.15 throughout the most of second lactation. For milk, genetic correlations between the first and second parities were 0.6 at the beginning of the lactation, rising to 0.9 in the middle, and 0.8 at the end of the lactation. For fat, the corresponding correlations were 0.6, 0.8, and 0.7, respectively, and for protein were 0.6, 0.8, and 0.8, respectively. Heritability estimates for all traits were flatter for the two-parity model. Relatively smooth genetic and permanent environmental variances with the two-parity model indicated that large swings of heritabilities could be artifacts of single-trait random regression models. High correlations between most of test day records across lactations suggested that a repeatability model could be considered as an alternative to a multiple-trait model to analyze multiple parities.     

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ～2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from ?0.61 to ?0.41 and from ?0.55 to ?0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.     

Estimating Effects of Permanent Environment, Lactation Stage, Age, and Pregnancy on Test-Day Yield

Test-day variances for permanent environmental effects within and across parities were estimated along with lactation stage, age, and pregnancy effects for use with a test-day model. Data were test-day records for calvings since 1990 for Jerseys and for Holsteins from California, Pennsylvania, Texas, and Wisconsin. Single-trait repeatability models were fitted for milk, fat, and protein test-day yields. Method R and a preconditioned conjugate gradient equation solver were used for variance component estimation because of large data sets. Test-day yields were adjusted for environmental effects of calving age, calving season, and milking frequency and for estimated breeding value (EBV) expressed on a daily basis. To assess the effect of adjustments, test-day yields also were analyzed without adjustment. For adjusted data, permanent environmental variances across parities relative to phenotypic variance ranged from 8.3 to 15.2% for milk, 4.4 to 8.3% for fat, and 6.9 to 11.0% for protein across regions and breeds; relative permanent environmental variances within parity ranged from 31.4 to 34.7% for milk, 18.2 to 22.3% for fat, and 28.3 to 29.1% for protein and were similar across regions and breeds. Adjustment for EBV reduced permanent environmental variance across parities and removed cow genetic variance. Relative permanent environmental variances within parity from unadjusted test-day yields were nearly identical to those from adjusted test-day yields. For unadjusted test-day yields, heritabilities ranged from 0.19 to 0.30 for milk, 0.13 to 0.15 for fat, and 0.17 to 0.23 for protein. Adjustments for lactation stage, age at milking, previous days open, and days pregnant were estimated from adjusted test-day yields using the same single-trait repeatability models and variance ratios estimated for permanent environment within and across parities. Those adjustments can be applied additively to test-day yields before evaluation analysis. Variance components and solutions for the various effects can be used to calculate test-day deviations in an analysis within herd that contributes to an analysis across herds.     

Genetic determination of the onset of heat stress on daily milk production in the US Holstein cattle

Existence of individual variation in the onset of heat stress for daily milk yield of dairy cows was assessed. Data included 353,376 test-day records of 38,383 first-parity Holsteins from a random sample of US herds. Three hierarchical models were investigated. Model 1 inferred the value of a temperature-humidity index (THI) at which mean yield began to decline as well as the extent of that decline. Model 2 assumed individual variation in yield decline beyond a common THI threshold. Model 3 additionally assumed individual variation for the onset of heat stress. Deviance information criteria indicated the superiority of model 3 over model 2. For model 2, genetic correlation between milk yield in the absence of heat stress and the THI threshold for heat stress was −0.4 (0.11) [marginal posterior mean (marginal posterior standard deviation)]. For model 3, genetic correlations were −0.53 (0.05) between milk yield and THI threshold and −0.62 (0.08) between milk yield and yield decay beyond the THI threshold. Total standard deviation (sum of additive genetic and permanent environmental standard deviations) for the THI threshold was 3.95 (0.06), and more than half of that variation had an additive genetic origin [56% (5%)]. Because of the high genetic correlation [0.95 (0.03)] between yield decay and THI threshold with model 3, using only one of them as a selection criterion for heat tolerance would modify the other in the desired direction.     

Genetic analysis of milk urea concentration and its genetic relationship with selected traits of interest in dairy cows

The aim of this study was to estimate genetic parameters of milk urea concentration (MU) and its genetic correlations with milk production traits, longevity, and functional traits in the first 3 parities in dairy cows. The edited data set consisted in 9,107,349 MU test-day records from the first 3 parities of 560,739 cows in 2,356 herds collected during the years 1994 to 2020. To estimate the genetic parameters of MU, data of 109 randomly selected herds, with a total of 770,016 MU test-day records, were used. Genetic parameters and estimated breeding values were estimated using a multiple-trait (parity) random regression model. Herd-test-day, age-year-season of calving, and days in milk classes (every 5 d as a class) were used as fixed effects, whereas effects of herd-year of calving, permanent environment, and animal were modeled using random regressions and Legendre polynomials of order 2. The average daily heritability and repeatability of MU during days in milk 5 to 365 in the first 3 parities were 0.19, 0.22, 0.20, and 0.48, 0.48, 0.47, respectively. The mean genetic correlation estimated among MU in the first 3 parities ranged from 0.96 to 0.97. The average daily estimated breeding values for MU of the selected bulls (n = 1,900) ranged from −9.09 to 7.37 mg/dL. In the last 10 yr, the genetic trend of MU has gradually increased. The genetic correlation between MU and 11 traits of interest ranged from –0.28 (milk yield) to 0.28 (somatic cell score). The findings of this study can be used as the first step for development of a routine genetic evaluation for MU and its inclusion into the genetic selection program in the Walloon Region of Belgium.     

Climate sensitivity of milk production traits and milk fatty acids in genotyped Holstein dairy cows

The aim of this study was the evaluation of climate sensitivity via genomic reaction norm models [i.e., to infer cow milk production and milk fatty acid (FA) responses on temperature-humidity index (THI) alterations]. Test-day milk traits were recorded between 2010 and 2016 from 5,257 first-lactation genotyped Holstein dairy cows. The cows were kept in 16 large-scale cooperator herds, being daughters of 344 genotyped sires. The longitudinal data consisted of 47,789 test-day records for the production traits milk yield (MY), fat yield (FY), and protein yield (PY), and of 20,742 test-day records for 6 FA including C16:0, C18:0, saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA). After quality control of the genotypic data, 41,057 SNP markers remained for genomic analyses. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI. Genomic reaction norm models were applied to estimate genetic parameters in a single-step approach for production traits and FA in dependency of THI at different lactation stages, and to evaluate the model stability. In a first evaluation strategy (New_sire), all phenotypic records from daughters of genotyped sires born after 2010 were masked, to mimic a validation population. In the second strategy (New_env), only daughter records of the new sires recorded in the most extreme THI classes were masked, aiming at predicting sire genomic estimated breeding values (GEBV) under heat stress conditions. Model stability was the correlation between GEBV of the new sires in the reduced data set with respective GEBV estimated from all phenotypic data. Among all test-day production traits, PY responded as the most sensitive to heat stress. As observed for the remaining production traits, genetic variances were quite stable across THI, but genetic correlations between PY from temperate climates with PY from extreme THI classes dropped to 0.68. Genetic variances in dependency of THI were very similar for C16:0 and SFA, indicating marginal climatic sensitivity. In the early lactation stage, genetic variances for C18:0, MUFA, PUFA, and UFA were significantly larger in the extreme THI classes compared with the estimates under thermoneutral conditions. For C18:0 and MUFA, PUFA, and UFA in the middle THI classes, genetic correlations in same traits from the early and the later lactation stages were lower than 0.50, indicating strong days in milk influence. Interestingly, within lactation stages, genetic correlations for C18:0 and UFA recorded at low and high THI were quite large, indicating similar genetic mechanisms under stress conditions. The model stability was improved when applying the New_env instead of New_sire strategy, especially for FA in the first stage of lactation. Results indicate moderately accurate genomic predictions for milk traits in extreme THI classes when considering phenotypic data from a broad range of remaining THI. Phenotypically, thermal stress conditions contributed to an increase of UFA, suggesting value as a heat stress biomarker. Furthermore, the quite large genetic variances for UFA at high THI suggest the consideration of UFA in selection strategies for improved heat stress resistance.     

Reproductive performance in genetic lines selected for high or average milk yield.

Genetic lines were created by selection of service sires differing by approximately 450 kg of milk for estimated transmitting ability. High line sires were selected from the best available proven sires. Selection continued over 24 yr with up to eight generations of selection. Records from 708 nulliparous, 575 first parity, and 437 second parity animals were analyzed. High milk yield was associated with longer days open and calving intervals in both first and second parities. A 1000-kg increase in 305-d milk production was associated with average increases in both days open and calving interval of around 7 d in first parity and 13 d in second parity and with average increases in days to first detected estrus of 4.5 d in first parity. Difference between genetic lines for milk yield was 804 kg in first parity and 772 kg in second parity. Days open and calving interval were less for the average line in both parities and differed by 10 d in second parity. Other reproductive differences were small or insignificant. Selection for yield has affected reproductive fitness modestly.     

Genetic analysis of the Israeli Holstein dairy cattle population for production and nonproduction traits with a multitrait animal model

Milk, fat, and protein production, somatic cell score (SCS), and female fertility in the Israeli Holstein dairy cattle population were analyzed using a multitrait animal model (AM) with parities 1 through 5 as separate traits. Female fertility was measured as the inverse of the number of inseminations to conception in percent. Variance components were estimated using both the repeatability AM and multitrait AM. The multitrait heritabilities for individual parities were greater than the heritabilities from the repeatability AM, and heritabilities decreased with an increase in parity number. Heritabilities were higher for production traits, lower for SCS, and lowest for female fertility. The genetic correlations were higher than the environmental correlations. Genetic correlations between parities decreased with an increase in the difference in parity number, but all were greater than 0.5. The environmental correlations were higher for production traits, lower for SCS, and close to zero for female fertility. In the analysis of the complete milk recorded population, genetic trends from the repeatability and multitrait models were very similar. The genetic trend for SCS was economically unfavorable until 1993, and favorable since then. The genetic trend for female fertility was close to zero, but the annual environmental trend was -0.2%. The multitrait lactation model is an attractive compromise between repeatability lactation models, which do not account for maturing trends across parities, and test-day models, which are much more demanding computationally.     

Effect of heat stress on nonreturn rate in Holsteins: fixed-model analyses

The objective of this study was to examine the relationship between reproductive traits and heat stress. Nonreturn rate at 45 d (NR45) was analyzed in a fixed-effect model that included the temperature-humidity index (THI) from a nearby weather station as a measurement of heat stress. Data consisted of 150,200 first inseminations at first and later parities of 110,860 Holstein cows from 550 herds in Georgia, Tennessee, and Florida with weather information from 16 weather stations. THI on the day of the insemination, 2 d prior, 5 d prior, 5, 10, 20, and 30 d after insemination were studied as independent variables. The THI on the day of insemination showed the highest effect on NR45, followed by 2 d prior, 5 d prior, and 5 d after insemination, but no relationship was found with THI at 10, 20, and 30 d after insemination. NR45 showed a decrease of 0.005 per unit increase in THI on the day of insemination for THI >68. First and later parities presented similar thresholds but responded differently to an increase in THI, with NR45 being significantly lower and more susceptible to increases of THI in cows in their first parity than in later parities (0.008 vs. 0.005 decrease). Threshold for sensitivity to heat stress changed with the states, with Florida, Georgia, and Tennessee having thresholds of 70, 70, and 66, respectively. The decrease in NR45 per unit increase of THI was 0.007, 0.005, and 0.006 for Florida, Georgia, and Tennessee, respectively. With respect to only the Florida data, the final fixed-effect model used was NR45 = herd(year) + month(year) + month(year) + age(parity) + days in milk + 100d milk + THI + error. Animals with more than 150 d in milk (DIM) had a 0.16 lower NR45 than animals with less than 60 DIM at insemination. Lower milk-producing animals showed 0.08 higher NR45 than higher-producing animals. A difference of 0.10 in NR45 was observed between THI lower than 70 and THI 84. This variation in NR45 caused by THI changes is sufficient to merit further studies to examine genetic components of heat tolerance for this trait.     

Exploiting genetic variability in the trajectory of lactation yield and somatic cell score with each progressing parity

The inclusion of reproductive performance in dairy cow breeding schemes has resulted in a cumulative improvement in genetic merit for reproductive performance; this improvement should manifest in longer productive lives through a reduced requirement for involuntary culling. Nonetheless, the average length of dairy cow productive life has not changed in most populations, suggesting that risk factors for culling, especially in older cows, are possibly more associated with lower yield or high somatic cell score (SCS) than compromised reproductive performance. The objective of the present study was to understand the dynamics of lactation yields and SCS in dairy cows across parities and, in doing so, quantify the potential to alter this trajectory through breeding. After edits, 3,470,520 305-d milk, fat, and protein yields, as well as milk fat and protein percentage and somatic cell count records from 1,162,473 dairy cows were available for analysis. Random regression animal models were used to identify the parity in which individual cows reached their maximum lactation yields, and highest average milk composition and SCS; also estimated from these models were the (co)variance components for yield, composition, and SCS per parity across parities. Estimated breeding values for all traits per parity were calculated for cows reaching ≥fifth parity. Of the cows included in the analyses, 91.0%, 92.2%, and 83.4% reached maximum milk, fat, and protein yield in fifth parity, respectively. Conversely, 95.9% of cows reached their highest average fat percentage in first parity and 62.9% of cows reached their highest average protein percentage in third parity. In contrast to both milk yield and composition traits, 98.4% of cows reached their highest average SCS in eighth parity. Individual parity estimates of heritability for milk yield traits, milk composition, and SCS ranged from 0.28 to 0.44, 0.47 to 0.69, and 0.13 to 0.23, respectively. The strength of the genetic correlations per trait among parities was inversely related to the interval between the parities compared; the weakest genetic correlation was 0.67 (standard error = 0.02) between milk yield in parities 1 and 8. Eigenvalues and eigenfunctions of the additive genetic covariance matrices for all investigated traits revealed potential to alter the trajectory of parity profiles for milk yield, milk composition, and SCS. This was further demonstrated when evaluating the trajectories of animal estimated breeding values per parity.