Bayesian and average information restricted maximum likelihood (AI-REML) approaches were used to estimate variance components and genetic parameters of Wood's lactation curve parameters of milk ...production traits in three lactations of Iranian Holstein dairy cows. Wood's function parameters (a, b, and c) of each lactation were initially estimated for individual cows using the NLIN procedure of the SAS programme, separately. The variance components of the parameters of each lactation were estimated by the AI-REML method using a single-trait animal model inairemlf90 programme. To do this with the Bayesian method by using the Gibbs sampling technique, the same animal model was used in the gibbs3f90 programme. The estimated heritability values by the AI-REML were 0.018, 0.00, and 0.019 for a parameter; 0.015, 0.007, and 0.02 for b parameter; and 0.23, 0.625, and 0.049 for c parameter in the 1st, 2nd, and 3rd lactations, respectively. The corresponding results from the Bayesian procedure (a posterior means) were 0.019, 0.01, and 0.035 for a parameter; 0.018, 0.019, and 0.043 for b parameter; and 0.024, 0.1, and 0.058 for c parameter, respectively. These obtained results reveal that the differences between the Bayesian and AI-REML methods in terms of estimation of heritability and variance components were small.
•Milk yield improved by 20.3 % with yeast supplementation at 10 g/ewe/day.•Dry yeast changed the lactation curve profile over the 15-week lactation period.•Increased total protein, albumin, glucose, ...urea and AST concentrations with yeast addition.•Dietary yeast improved the average daily weight gain of lambs by 18.4 %.
Thirty-nine pregnant Sohagi ewes were divided into three equal groups to investigate the effects of an active dry yeast (Saccharomyces cerevisiae- SC, 2.44 × 1011 CFU/g yeast product) on lactation curve, milk composition, blood components and growth performance of newborn lambs. Ewes in the control group were fed a basal diet without yeast supplementation (SC0), the second (SC5) and third (SC10) groups were fed the basal diet with 5 or 10 g per head/day of SC, respectively. Dry yeast supplementation increased (P < 0.05) daily milk yield, total milk yield, milk fat, protein, solids not fat contents but did not affect (P> 0.05) lactose and ash contents. Compared with the control treatment, total milk yield was improved in SC10 and SC5 by 20.3 and 14.3 %, respectively. Dry yeast supplementation resulted in significant changes (P < 0.05) in the lactation curve with greater average daily milk yield noted for SC5 and SC10 over a 12-week period. Greater (P < 0.05) total protein, albumin, glucose, urea and AST concentrations were noted with dry yeast inclusion. There were no differences in globulin, cholesterol, creatinine and alanine aminotransferase concentrations. Lambs from SC10 ewes had higher (P < 0.05) birth weight than lambs from the control group. Moreover, SC10 and SC5 lambs grew faster (18.4 and 13.8 %) and were heavier (16.6 and 11.9 %) than lambs from control ewes. Data suggested that dry yeast supplementation improved milk yield and composition of Sohagi ewes, modified the lactation curve and enhance growth performance of lambs.
Feed behavior and milk productivity in cows of different fattening Polishchuk, T. V.; Bondarenko, V. V.
Naukovij vìsnik Lʹvìvsʹkogo nacìonalʹnogo unìversitetu veterinarnoï medicini ta bìotehnologìj ìmenì S.Z. Gžicʹkogo. Serìâ: Sìlʹsʹkogospodarsʹkì nauki,
12/2021, Letnik:
23, Številka:
95
Journal Article
Recenzirano
Odprti dostop
The results of feed behavior and milk productivity in the cows of Ukrainian black-speckled dairy breed of different fattening of the first and second lactations from the first month to the end of ...lactation are given. The analysis of the herd showed that the highest milk productivity in cows of the first lactation was found in the group of fattening from 3 to 4 points, which was by 9.2 % (P ≥ 0.99) higher, compared to the group of cows with fattening 4 points and more. The milk yield in cows of the second lactation with fattening from 3 to 4 points exceeded the milk yield of cows with fattening 4 points and more by 12.9 % (P ≥ 0.95). The content of fat and protein in milk was higher in the cows with higher fattening, compared to the cows with lower fattening. The highest average daily milk yield was found in cows of the first lactation with fattening from 3 to 4 points. Depending on the month, it was by 5.5–11.7 % (P ≥ 0.95 – P ≥ 0.999) higher, compared to the cows with fattening 4 points and more, while it was by 6.9–10.0 % (P ≥ 0.95 – P ≥ 0.999) higher in cows of the second lactation. The gestation period of cows has a significant effect on the reduction of milk yield. The milk yield of cows in connection with the term of their gestation are reduced by 0.1 kg per day during the second month after insemination, by 0.2 kg per day during the third month, by 0.3 kg per day during the fourth month, by 0.6 kg per day during the fifth month, by 1.0 kg per day during the sixth month, by 1.7 kg per day during the seventh month and by 2.8 kg per day during the eighth month. The analysis of the dynamics of monthly milk yield shows that lactation curves of cows increase from the first day of lactation to its peak, which occurs in the 2nd – 4th month after calving. The lactation curve in the cows with fattening from 3 to 4 points has a higher peak in the 2nd –3d month of lactation, compared to the cows with higher (more than 4 points) fattening. The animals of all groups, except for cows of the second lactation (with fattening 4 and more points) showed the maximum productivity in the 2nd –3d month of lactation; then the lactation curve decreased with different intensity. The index of constancy of lactation and the index of falling milk yield are the important indicators that characterize the stability of lactation curves. The constancy of lactation curves having been determined by I. Johansson-Hansson index was higher in the cows with fattening 4 and more points, compared to the indicator of the cows with fattening from 3 to 4 points. The studies of behavioral reactions have shown that the animals of the first lactation with an average (from 3 to 4 points) fattening consume feed during 208.5 minutes, which is by 17 minutes (P ≥ 0.999) longer than the animals with higher average fattening, and by 14.5 minutes (P ≥ 0.99) longer than the animals of the second lactation.
Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the ...foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.
A study was conducted to find the most appropriate mathematical model that describes the first lactation milk yield of Frieswal cattle. Data on 42,368 individual first lactation test day yields of ...1,072 Frieswal cows calved during 2005 to 2014 in Ambala and Meerut Military dairy farms were utilized for the study. The first test day milk yield was recorded on 6thday after calving while the subsequent records were collected at seven days interval and so the average 43 test day yields were taken for fitting the lactation curve models. Five different mathematical models, viz. Exponential decline function (EDF), Parabolic exponential function (PEF), Inverse polynomial function (IPF), Gamma function (GF) and Mixed log function (MLF) were fitted.The accuracy of fitting (R2 value) the models revealed that the MLF (96.14) was more appropriate followed by IPF (95.57), GF (93.85), PEF (83.68) and EDF (69.09). The RMSE estimate of MLF was lowest (0.3483) as expected while the EDF had the highest RMSE value of 0.9858.The AIC criterion was lowest for IPF (5.7175) and highest for GF (8.0212). The BIC values of five functions ranged between –83.6262 for MLF to 3.1809 for EDF. All the DW estimates were positive and ranged between 0.3656 for EDF to 0.7106 for GF indicating positive autocorrelation between the residuals. Based on the results obtained in the present study, it may be inferred that the first lactation yield was explained accurately by the mixed log function (MLF) in Frieswal cattle. As the inverse polynomial (IPF) and gamma function (GF) also had satisfactory results, these two functions can also be used for fitting the lactation curve models in Frieswal cattle. On the other hand, exponential decline function and parabolic exponential functions least explain the first lactation curve in Frieswal cattle.
The single-step genomic model has become the golden standard for routine evaluation in livestock species, such as Holstein dairy cattle. The single-step genomic model with direct estimation of marker ...effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic, and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multilactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat, and protein yields, and somatic cell scores (SCS). Approximately one million genotyped Holstein animals were considered in the single-step genomic evaluations including ∼21 million animals in pedigree. Deregressed multiple across-country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 yr were deleted, and the integrated bulls born in the last 4 yr were truncated from the complete phenotypic dataset. Estimates of the marker effects were shown to be highly correlated, with correlations ∼0.9, between the full and truncated evaluations. Regression slope values of the marker-effect estimates from the full on the truncated evaluations were all close to their expected value, being ∼1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of the genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and SCS. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained ∼0 correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second or third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and SCS (e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations).
A high level of production at the peak of lactation may be associated with animal health disorders, high feeding costs, and reduced milk supply throughout the year. The objective of this study was to ...typologize the lactation curves in French dairy goats and analyze the influence of environmental and genetic factors on these curves. The data set consisted of 2,231,720 monthly test-day records of 213,534 French Saanen and Alpine goats recorded between September 2008 and June 2012. First, principal component analysis classified the shape of the lactation curves into 3 principal components: the first component accounted for milk yield level throughout lactation, the second component accounted for lactation persistency, and the third component accounted for milk yield in mid-lactation. Then, from the principal component scores, the lactations were clustered into 5 different groups. Most lactations had a similar shape to the mean curve, except 30% of the lactations that fell into 3 clusters that had a high production level at the peak and then a different persistency according to cluster. Estimated breeding value for milk yield and home region of breeding were the factors most related to lactation production level. Month of kidding, breed, and gestation stage had the biggest effect on persistency. Month of kidding was the factor most strongly linked to mid-lactation production. A herd effect was observed on all 3 principal components.
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.
Although dairy control is a widely used tool in herds with numerous animals (mainly cows), it is often neglected in small farms. The aim of the present work was to select the most suitable animals ...for milk production using dairy control and multivariate statistics. Results indicate a great dispersion of the data as a consequence of milk production variation from each goat. These results demonstrate the need to identify and select the most productive animals in order to have a selected and controlled flock. The analysis of hierarchical conglomerates showed that the herd can be divided into three groups: Cluster 1 is integrated by 31 goats with the highest productive parameters, above the herd average and together produce 59.1% of the accumulative milk yield; Cluster 2 consists of 24 animals whose production parameters are close to the current average of the herd and accumulates 27.7% of milk production and Cluster 3 groups animals with the lowest production. From the results obtained, it was possible to make the selection of the 55 most productive animals for milk production. This represents 64.70% of the herd and they are responsible for 86.8% of the total milk production.
The aim of the paper was to study the evolution of the chemical composition and somatic cell count during lactation in Romanian Black and White cows and effect of calving season on the shape of the ...lactation curve. Lactations form 125 multiparous cows were studied. Milk yield and sampling were carried out using the official performance control method A4. Milk was analyzed for composition in infrared spectrometry and for SCC using a viscosimeter. Results were modeled using Wood’s incomplete gamma function y=abx e (-cx), and season effect was assessed using ANOVA/MANOVA. A discussion was carried out regarding the shape of the lactation curves for milk yield, each milk component and SCC. The calving season had a significant effect (p<0.005) on the shape of the lactation curve for milk yield, milk chemical composition and milk somatic cell count. Summer calving cows had flatter lactation curves for milk yield and composition compared to winter calving cows. For somatic cell count spring calving cows had the flattest lactation curve while autumn calving cows has the steepest lactation curve.
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