Genetic correlations between milk production traits and somatic cell scores on test day within and across first and second lactations in Holstein cows

Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins

In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, termed ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss (ML) estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of the daily heritability estimates over lactation were 0.086 (0.075-0.095) for SCS, 0.104 (0.073-0.127) for ML_SCS_DSCC, 0.137 (0.014-0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115-0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Optimal value for the exponential term of Wilmink's function according to current Holstein lactation curves in Japan

We compared values of Wilmink's exponential term to describe the lactation curves of Holstein cows in Japan. Data were a total of 100,971,798 test-day records from the first through fifth parities during 1991 through 2018. The lactation curve model used fourth-order Legendre polynomials and Wilmink's exponential term. In total, 810 analyses were executed to compare six values (-0.02, -0.03, …, -0.07) for the exponential term to select the one that yielded the smallest root mean square error. For all parities, daily milk yield and lactation persistency increased consistently and peak lactation days occurred later from year to year. For the years evaluated, the optimal exponential term was -0.05 for first and second parities, -0.04 for third parity, and -0.03 for fourth and fifth parities. The change in the exponential parameter with increasing year was related to delays in peak lactation.

Effect of relationships among clinical mastitis incidence, reproductive performance, and culling rate on the lifetime of dairy cows at Hiroshima University Farm

Farm managers' decision to cull dairy cows is based on the cows' milk production, history of disorder(s), and reproductive performance, each of which affects dairy cows' lifetime (herd life and productive lifespan). We investigated the relationships among the incidence of clinical mastitis (CM), the reproductive performance, and the culling rate. We also assessed the effects of these relationships on the lifetimes of dairy cows, using the records made before and after the introduction of an automatic milking system (AMS) at Hiroshima University Farm. Milk yield, CM incidence density, and culling rate of dairy cows increased after the AMS introduction. The CM incidence was associated with an elongation of the calving interval in cows with the same parity. CM in the 1st parity might have caused the reductions of the cows' lifetime and their parity at culling. A higher age at first calving (AFC) was associated with an increase in culling rate but did not lead to a significant decrease in lifetime. Investigations of the factors mediating CM in the 1st parity or AFC with CM incidence or culling rate in the later stages might contribute to the control of lifetime of dairy cows.

Comparison of three methodologies for the genetic study of lactation persistency in Holstein cattle from Antioquia

Persistency is the rate of decrease after milk production peak, mathematical models such as Wood's can be used to estimate it for describing the lactation curve and its rate of descent; random regression models are also useful, as they describe the genetic lactation curve for each animal. The objective of this study was to compare Best Linear Unbiased Prediction (BLUP), marker-assisted BLUP (MBLUP) model and random regression model (RRM) to estimate genetic parameters and breeding values for the lactation persistency curve. 4,658 test day measurements were available for 733 individuals, from which lactation curves were described to calculate persistency, estimating genetic parameters and values for this trait through BLUP and MBLUP. A similar process was done for RRM, where persistency was estimated from the genetic lactation curve. The heritability obtained using RRM was 0.51, greater than that obtained by BLUP (0.29) and MBLUP (0.21). The reliability of the genetic value for persistency in bulls was greater when RRM was used, but there was no correlation between the genetic values of different models. The highest heritability for persistency and the more reliable genetic values for bulls were achieved under the RRM, it allows positioning this methodology as an important tool for genetic evaluation of persistency.

The use of dijkstra mechanistic model for genetic analysis of the lactation curve characteristics and their relationships with age at first calving and somatic cell score of Iranian dairy cows

The objective of this study was to estimate lactation curve parameters with Dijkstra mechanistic model and to evaluate genetic and phenotypic relationships between the parameters and the average somatic cell count in primiparous cows. The finding indicated that heritability estimates for partial milk yield (PMY1, PMY2 and PMY3), total 305-day milk yield (TMY305), decay parameter (λ2), age at first calving (AFC) and peak yield (PY) were moderate while the heritability of persistency (PS%), average somatic cell score (AVGSCS), time to peak yield (TP), initial milk production (λ0), specific rate of cell proliferation at parturition (λ1), and specific rate of cell death (λ3) were quite low. Genetic correlations between both AFC and PS% traits with average somatic cell scores was negative (-0.047 and -0.060) but low positive genetic correlation were between partial milk yields (PMY1 and PMY3) while negative genetic correlation (-0.06) was obtained between TMY305 and AVGSCS. Differences between TMY305 of cows with less than 100000 cells mL-1 and cows with >1,500,000 cells mL-1 was approximately 708 Kg and is equivalent to 8% loss of milk yield/cow during lactation period and also loss of persistency (11.1 %( was shown for the extreme classes of SCC in this study.

Genetic parameters for somatic cell count (SCC) and milk production traits of Guzerá cows using data normalized by different procedures

This study aimed to estimate the genetic parameters for somatic cell count (SCC) and the genetic association between SCC and milk production traits using two different methods of SCC normalization. The dataset contained information on 8870 lactation records of 6172 Guzerá dairy cows selected for dual-purpose from 95 herds. The lactation means of SCC were normalized in two ways: (a) SCC₁ = log₁₀ (SCC) and (b) SCC₂ = log₂ (SCC/100) + 3. Multivariate analyses were performed considering milk production traits over the course of 305 days of lactation. Estimates of the variance components and genetic parameters were carried out by the Bayesian inference method, applying Gibbs sampling. Single chains of 2,000,000 iterations were used, with sampling discards of the first 5000 chains and a sampling period of every 50 iterations. The deviation of information criteria (DIC) was used to evaluate the best transformation for standardization of the SCC data, comparing analysis 1 (milk production traits over 305 days and SCC₁) with analysis 2 (milk production traits over 305 days and SCC₂). According to the data structure of this study, SCC₁ normalization was the most efficient method and produced better estimates than normalization by the SCC₂ method. The heritability estimates for SCC were low regardless of the transformation method used, indicating a small possibility of expressive genetic gains from the direct selection of these traits. However, the repeatability indicated the potential for increasing heritability estimates if the effects of the permanent environment were reduced. The genetic correlations between the milk yield and SCC traits do not indicate the possibility of a correlated genetic gain from the direct selection of one trait. However, concomitant selection for milk production traits and SCC will likely not affect the individual response either.

Variance components using genomic information for 2 functional traits in Italian Simmental cattle: Calving interval and lactation persistency

Functional traits, such as fertility and lactation persistency, are becoming relevant breeding goals for dairy cattle. Fertility is a key element for herd profitability and animal welfare; in particular, calving interval (CIN) is an indicator of female fertility that can be easily recorded. Lactation persistency (LPE; i.e., the ability of a cow to maintain a high milk yield after the lactation peak) is economically important and is related to several other traits, such as feed efficiency, health, and reproduction. The selection of these functional traits is constrained by their low heritability. In this study, variance components for CIN and LPE in the Italian Simmental cattle breed were estimated using genomic and pedigree information under the single-step genomic framework. A data set of 594,257 CIN records (from 275,399 cows) and 285,213 LPE records (from 1563,389 cows) was considered. Phenotypes were limited up to the third parity. The pedigree contained about 2 million animals, and 7,246 genotypes were available. Lactation persistency was estimated using principal component analysis on test day records, with higher values of the second extracted principal component (PC2) values associated with lower LPE, and lower PC2 values associated with higher LPE. Heritability of CIN and LPE were estimated using single-trait repeatability models. A multiple-trait analysis using CIN and production traits (milk, fat, and protein yields) was performed to estimate genetic correlations among these traits. Heritability for CIN in the single-trait model was low (0.06 ± 0.002). Unfavorable genetic correlations were found between CIN and production traits. A measure of LPE was derived using principal component analysis on test day records. The heritability and repeatability of LPE were 0.11 ± 0.004 and 0.20 ± 0.02, respectively. Genetic correlation between CIN and LPE was weak but had a favorable direction. Despite the low heritability estimates, results of the present work suggest the possibility of including these traits in the Italian Simmental breeding program. The use of a single-step approach may provide better results for young genotyped animals without their own phenotypes.

Different aspects of lactation persistency in dairy cows

Lactation persistency (cow’s ability to maintain milk production after reaching its peak) is a very important economic characteristic in the dairy cattle production system. Different definition and functions for describing and measuring of this trait were proposed by researchers. The random regression model using Legendre polynomial was one of the common and effective methodologies for evaluation of persistency in the last decade. Several factors affecting persistency such as different characteristics of lactation curve, environment factors, reproduction traits and health status of the dairy cow. Based on different studies the heritability of this trait was low to medium and negative or positive amount of genetic correlation between persistency and total milk yield in dairy cattle is attributed to persistency measures and method of data analysis. Persistency is related with low and later peak yield and selecting cows for peak yield will improve persistency and lactation curve traits. Analysis of relationships between persistency and other functional traits show signs that genetic improvement for persistency is possible and favorable. Different aspects and relationships of persistency with various lactation and other functional traits in dairy cows are reviewed in this article.

Genetic parameters for mastitis incidence and its indicators based on somatic cell score for Holsteins in Hokkaido, Japan

The objectives of this study were to estimate the heritability of mastitis incidence and genetic correlations between the mastitis and the somatic cell score (SCS) statistics, and to compare the practicability between different models. We used test-day records with the mastitis incidence and SCS collected from Holstein cows calving from 1988 to 2015 in Hokkaido, Japan. As indicators of mastitis, the average SCS (avSCS), the standard deviation of SCS (sdSCS), and the maximum SCS (maxSCS) were calculated using test-day records up to the first 305 days in milk within a lactation. We compared a four-trait repeatability animal model (MTRP) with a four-trait multiple-lactation animal model (MTML). The heritability for mastitis was equal to or lower than 0.05 in all the models. Genetic correlations between lactations with MTML within the same trait were positive and close to 1. With MTRP, the estimated genetic correlations of the mastitis incidence with avSCS, sdSCS, and maxSCS were 0.66, 0.79, and 0.82, respectively. A joint evaluation with SCS statistics is expected to give an extra reliability for mastitis because of high and positive genetic correlations among the traits.

Genetic trends and genetic correlations between 305-day milk yield, persistency and somatic cell score of Holstein cows in Brazil using random regression model

The objective was to estimate genetic correlations for persistency, milk yield and somatic cell score (SCS) in Holstein cattle in Brazil. A dataset with 190389 records of test-day milk and of test-day SCS from 21824 cows was used. Two-trait random regression model with a fourth order Legendre polynomial was used. Persistency (PS) was defined as the difference between estimated breeding values (EBV) along different days in milk using two formulae: and PS2=(EBV290–EBV90). Larger values for PS2 or lower ones for PS1 indicate higher persistency. Heritability was 0.24 for 305-day milk yield, 0.14 for SCS up to 305 days, 0.15 for PS1 and 0.14 for PS2. Genetic correlation between 305-day milk yield and SCS up to 305 days was –0.47. Genetic correlation of 305-day milk yield with PS1 and PS2 was –0.32 and 0.30, respectively. Genetic correlation of SCS up to 305 days was 0.25 with PS1 and –0.20 with PS2. The additive genetic correlations between milk yield, SCS and persistency showed that selection for higher persistency or for low somatic cell score will increase 305-day milk yield.

Genetic relationship of lactation persistency with milk yield, somatic cell score, reproductive traits, and longevity in Slovak Holstein cattle

Abstract. The objective of this study was to estimate the breeding values (BVs) of lactation persistency, the test day of milk yield, the somatic cell score, reproductive traits (calving interval, days open), longevity in Slovak Holstein dairy cattle. BVs were used for the detection of relationships among the persistency of lactation and other selected traits. Data for the estimation of BVs of milk production and somatic cell score were collected from 855 240 cows. BVs for reproductive traits were estimated for 352 712 cows and for longevity for 528 362 cows. The highest correlations were confirmed between the BV of persistency and the BV of test day milk yield at 100, 200, and 305 days (−0.88, −0.65, and −0.61). Correlations between the BV of lactation persistency and the BV of somatic cell score at day 305 or the BV of somatic cell score persistency were favorable: −0.05 and −0.12, respectively. The relationship between the BV of persistency and the BV of the calving interval or the BV of days open was 0.11 and 0.10 respectively. The selection for the persistency of lactation may not improve longevity because there is no relation between the BV of persistency and the BV of longevity (rg = 0.06).

Effects of heat stress on production, somatic cell score and conception rate in Holsteins

We examined the effects of heat stress (HS) on production traits, somatic cell score (SCS) and conception rate at first insemination (CR) in Holsteins in Japan. We used a total of 228 242 records of milk, fat and protein yields, and SCS for the first three lactations, as well as of CR in heifers and in first- and second-lactation cows that had calved for the first time between 2000 and 2012. Records from 47 prefectural weather stations throughout Japan were used to calculate the temperature-humidity index (THI); areas were categorized into three regional groups: no HS (THI < 72), mild HS (72 ≤ THI < 79), and moderate HS (THI ≥ 79). Trait records from the three HS-region groups were treated as three different traits and trivariate animal models were used. The genetic correlations between milk yields from different HS groups were very high (0.91 to 0.99). Summer calving caused the greatest increase in SCS, and in the first and second lactations this increase became greater as THI increased. In cows, CR was affected by the interaction between HS group and insemination month: with summer and early autumn insemination, there was a reduction in CR, and it was much larger in the mild- and moderate-HS groups than in the no-HS group.

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.

Evaluation of different lactation curve models fitted for milk viscosity recorded by an automated on-line California Mastitis Test

Laboratory somatic cell count (LSCC) records are usually recorded monthly and provide an important information source for breeding and herd management. Daily milk viscosity detection in composite milking (expressed as drain time) with an automated on-line California Mastitis Test (CMT) could serve immediately as an early predictor of udder diseases and might be used as a selection criterion to improve udder health. The aim of the present study was to clarify the relationship between the well-established LSCS and the new trait,'drain time', and to estimate their correlations to important production traits. Data were recorded on the dairy research farm Karkendamm in Germany. Viscosity sensors were installed on every fourth milking stall in the rotary parlour to measure daily drain time records. Weekly LSCC and milk composition data were available. Two data sets were created containing records of 187,692 milkings from 320 cows (D1) and 25,887 drain time records from 311 cows (D2). Different fixed effect models, describing the log-transformed drain time (logDT), were fitted to achieve applicable models for further analysis. Lactation curves were modelled with standard parametric functions (Ali and Schaeffer, Legendre polynomials of second and third degree) of days in milk (DIM). Random regression models were further applied to estimate the correlations between cow effects between logDT and LSCS with further important production traits. LogDT and LSCS were strongest correlated in mid-lactation (r = 0.78). Correlations between logDT and production traits were low to medium. Highest correlations were reached in late lactation between logDT and milk yield (r = -0.31), between logDT and protein content (r = 0.30) and in early as well as in late lactation between logDT and lactose content (r = -0.28). The results of the present study show that the drain time could be used as a new trait for daily mastitis control.

Genetic correlations between production and disease traits during first lactation in Holstein cows

The aim of this study was to estimate genetic correlations between milk yield, somatic cell score (SCS), mastitis, and claw and leg disorders (CLDs) during first lactation in Holstein cows by using a threshold-linear random regression test-day model. We used daily records of milk, fat and protein yields; somatic cell count (SCC); and mastitis and CLD incidences from 46 771 first-lactation Holstein cows in Hokkaido, Japan, that calved between 2000 and 2009. A threshold animal model for binary records (mastitis and CLDs) and linear animal model for yield traits were applied in our multiple trait analysis. For both liabilities and yield traits, additive genetic effects were used as random regression on cubic Legendre polynomials of days on milk. The highest positive genetic correlations between yields and disease incidences (0.36 for milk and mastitis, 0.56 for fat and mastitis, 0.24 for protein and mastitis, 0.32 for milk and CLD, 0.44 for fat and CLD and 0.31 for protein and CLD) were estimated at about the time of peak milk yield (36 to 65 days in milk). Selection focused on early lactation yield may therefore increase the risk of mastitis and CLDs. The positive genetic correlations of SCS with mastitis or CLD incidence imply that selection to reduce SCS in the early stages of lactation would decrease the incidence of both mastitis and CLD.

Relationships between conception rate in Holstein heifers and cows and milk yield at various stages of lactation

We investigated the relationships between conception rates (CRs) at first service in Japanese Holstein heifers (i.e. animals that had not yet had their first calf) and cows and their test-day (TD) milk yields. Data included records of artificial insemination (AI) for heifers and cows that had calved for the first time between 2000 and 2008 and their TD milk yields at 6 through 305 days in milk (DIM) from first through third lactations. CR was defined as a binary trait for which first AI was a failure or success. A threshold-linear animal model was applied to estimate genetic correlations between CRs of heifers or cows and TD milk yield at various lactation stages. Two-trait genetic analyses were performed for every combination of CR and TD milk yield by using the Bayesian method with Gibbs sampling. The posterior means of the heritabilities of CR were 0.031 for heifers, 0.034 for first-lactation cows and 0.028 for second-lactation cows. Heritabilities for TD milk yield increased from 0.324 to 0.433 with increasing DIM but decreased slightly after 210 DIM during first lactation. These heritabilities from the second and third lactations were higher during late stages of lactation than during early stages. Posterior means of the genetic correlations between heifer CR and all TD yields were positive (range, 0.082 to 0.287), but those between CR of cows and milk yields during first or second lactation were negative (range, -0.121 to -0.250). Therefore, during every stage of lactation, selection in the direction of increasing milk yield may reduce CR in cows. The genetic relationships between CR and lactation curve shape were quite weak, because the genetic correlations between CR and TD milk yield were constant during the lactation period.