Evaluation of models for somatic cell score lactation patterns in Holsteins

Reductive acetogens isolated from ruminants and their effect on in vitro methane mitigation and milk performance in Holstein cows

This study was designed to evaluate the in vitro and in vivo effects of reductive acetogens isolated from ruminants on methane mitigation, and milk performance, respectively. Four acetogens, Proteiniphilum acetatigenes DA02, P. acetatigenes GA01, Alkaliphilus crotonatoxidans GA02, and P. acetatigenes GA03 strains were isolated from ruminants and used in in vitro experiment. A control (without acetogen) and a positive group (with Eubacterium limosum ATCC 8486) were also included in in vitro experiment. Based on higher acetate as well as lower methane producing ability in in vitro trial, P. acetatigenes GA03 was used as inoculum for in vivo experiment. Holstein dairy cows (n = 14) were divided into two groups viz. control (without) and GA03 group (diet supplied with P. acetatigenes GA03 at a feed rate of 1% supplementation). Milk performance and blood parameters were checked for both groups. In in vitro, the total volatile fatty acids and acetate production were higher (p < 0.05) in all 4 isolated acetogens than the control and positive treatment. Also, all acetogens significantly lowered (p < 0.05) methane production in comparison to positive and control groups however, GA03 had the lowest (p < 0.05) methane production among 4 isolates. In in vivo, the rate of milk yield reduction was higher (p < 0.05) in the control than GA03 treated group (5.07 vs 2.4 kg). Similarly, the decrease in milk fat was also higher in control (0.14% vs 0.09%) than treatment. The somatic cell counts (SCC; ×10³/mL) was decreased from 128.43 to 107.00 in acetogen treated group however, increased in control from 138.14 to 395.71. In addition, GA03 increased blood glucose and decreased non-esterified fatty acids. Our results suggest that the isolated acetogens have the potential for in vitro methane reduction and P. acetatigenes GA03 strain could be a candidate probiotic strain for improving milk yield and milk fat in lactating cows with lowering SCCs.

Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle

Background

Dual-purpose cattle are more adaptive to environmental challenges than single-purpose dairy or beef cattle. Balance among milk, reproductive, and mastitis resistance traits in breeding programs is therefore more critical for dual-purpose cattle to increase net income and maintain well-being. With dual-purpose Xinjiang Brown cattle adapted to the Xinjiang Region in northwestern China, we conducted genome-wide association studies (GWAS) to dissect the genetic architecture related to milk, reproductive, and mastitis resistance traits. Phenotypic data were collected for 2410 individuals measured during 1995–2017. By adding another 445 ancestors, a total of 2855 related individuals were used to derive estimated breeding values for all individuals, including the 2410 individuals with phenotypes. Among phenotyped individuals, we genotyped 403 cows with the Illumina 150 K Bovine BeadChip.

Results

GWAS were conducted with the FarmCPU (Fixed and random model circulating probability unification) method. We identified 12 markers significantly associated with six of the 10 traits under the threshold of 5% after a Bonferroni multiple test correction. Seven of these SNPs were in QTL regions previously identified to be associated with related traits. One identified SNP, BovineHD1600006691, was significantly associated with both age at first service and age at first calving. This SNP directly overlapped a QTL previously reported to be associated with calving ease. Within 160 Kb upstream and downstream of each significant SNP identified, we speculated candidate genes based on functionality. Four of the SNPs were located within four candidate genes, including CDH2, which is linked to milk fat percentage, and GABRG2, which is associated with milk protein yield.

Conclusions

These findings are beneficial not only for breeding through marker-assisted selection, but also for genome editing underlying the related traits to enhance the overall performance of dual-purpose cattle.

Using quantile regression for fitting lactation curve in dairy cows

The main objective of this study was to compare the performance of different 'nonlinear quantile regression' models evaluated at the τth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali &amp; Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali &amp; Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali &amp; Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.

Effect of mastitis on lactation curves in purebred Jersey cows

Mastitis is a most frequently occurring disease in dairy cattle which causes severe losses in milk production. In our study, we had collected 9960 weekly test day milk yield (WTDMY) records over a period of five years (2010– 2015) of 130 purebred Jersey cows reared at Central Cattle Breeding Farm, Sunabeda, Odisha under Ministry of Agriculture, Government of India. To study the lactation pattern of above milk data, we used six different lactation curve models, viz. Wilmink (WK), Wood (WD), Brody (BRD), Morant and Gnanasakthy (MG), Mitscherlich × Exponential (ME) and Ali and Schaeffer (AS). It was observed that in healthy and cows affected with mastitis, Ali and Schaeffer (AS) model showed best fit giving highest value of adjusted coefficient of determination (R2 adj.= 0.963) and lowest value of root mean square of error (0.303), Akaike’s information criterion (–97.887) and Schwartz Bayesian Information Criterion (–89.081). Testing of residuals was carried out by several tests, viz. the Shapiro- Wilk’s test, the run test and the Durbin-Watson (DW). Summary measures revealed that the loss of milk production due to mastitis with respect to healthy cows was 4.43%. Lactation persistency was estimated by ratio method and Mahadevan method. Higher persistency was observed in healthy 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.

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

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

A structural equation model for describing relationships between somatic cell score and milk yield in dairy goats1

The relationship between milk yield and somatic cell score (log-transformed somatic cell count) in dairy goats may involve complex pathways with recursive or simultaneous effects. Structural equation models were fitted to longitudinal data on milk yield and on somatic cell scores. Data consisted of 4 repeated records of milk production and of somatic cell score from left and right halves of the udder in each of 47 dairy goats; infection status of each of the halves at each test day was also available. Results strongly suggest the existence of a within-half, first-order autoregressive process and of simultaneity of effects between somatic cell scores from the left and right halves of the udder. This indicates that the immune response to an infection is not restricted to the half of the udder in which the infection takes place and that it tends to propagate over time. The existence of a negative effect of somatic cell score on milk yield was also supported by the results; however, evidence in favor of an effect in the opposite direction, a dilution effect, was not strong.

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

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

Detection of quantitative trait loci influencing dairy traits using a model for longitudinal data

A longitudinal-linkage analysis approach was developed and applied to an outbred population. Nonlinear mixed-effects models were used to describe the lactation patterns and were extended to include marker information following single-marker and interval mapping models. Quantitative trait loci (QTL) affecting the shape and scale of lactation curves for production and health traits in dairy cattle were mapped in three U.S. Holstein families (Dairy Bull DNA Repository families one, four, and five) using the granddaughter design. Information on 81 informative markers on six Bos taurus autosomes (BTA) was combined with milk yield, fat, and protein percentage and somatic cell score (SCS) test-day records. Six percent of the single-marker tests surpassed the experiment-wise significance threshold. Marker BL41 on BTA3 was associated with decrease in milk yield during mid-lactation in family one. The scale and shape of the protein percentage lactation curve in family four varied with BMC4203 (BTA6) allele that the son received from the grandsire. Some map locations were associated with variation in the lactation pattern of multiple traits. In family four, the marker HUJI177 (BTA3) was associated with changes in the milk yield and protein percentage curves suggesting a QTL with pleiotropic effects or multiple QTL in the region. The interval mapping model uncovered a QTL on BTA7 associated with variation in milk-yield pattern in family four and a QTL on BTA21 affecting SCS in family five. The developed approach can be extended to random regressions, covariance functions, spline, gametic and variance component models. The results from the longitudinal-QTL approach will help to understand the genetic factors acting at different stages of lactation and will assist in positional candidate gene research. Identified positions can be incorporated into marker-assisted selection decisions to alter the persistency and peak production or the fluctuation of SCS during a lactation.