scholarly journals Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days

2018 ◽  
Vol 31 (10) ◽  
pp. 1542-1549
Author(s):  
Takeshi Yamazaki ◽  
Hisato Takeda ◽  
Koichi Hagiya ◽  
Satoshi Yamaguchi ◽  
Osamu Sasaki
Keyword(s):  
Milk Yield ◽  
Regression Coefficient ◽  
Random Regression ◽  
Coefficient Estimates ◽  
Daily Milk Yield ◽  
Daily Milk

scholarly journals Genetic and environmental parameters estimation for milk traits in Slovenian dairy sheep using random regression model

2013 ◽  
Vol 58 (No. 3) ◽  
pp. 125-135 ◽  
Author(s):  
A. Komprej ◽  
Š. Malovrh ◽  
G. Gorjanc ◽  
D. Kon ◽  
M. Kovač
Keyword(s):  
Regression Model ◽  
Protein Content ◽  
Milk Yield ◽  
Random Regression ◽  
Dairy Sheep ◽  
Milk Traits ◽  
Random Regression Model ◽  
Lactation Curve ◽  
Daily Milk Yield ◽  
Daily Milk

(Co)variance components for daily milk yield, fat, and protein content in Slovenian dairy sheep were estimated with random regression model. Test-day records were collected by the ICAR A4 method. Analysis was done for 38 983 test-day records of 3068 ewes in 36 flocks. Common flock environment, additive genetic effect, permanent environment effect over lactations, and permanent environment effect within lactation were included into the random part of the model and modelled with Legendre polynomials on the standardized time scale of days in lactation. Estimation of (co)variance components was done with REML. The eigenvalues of covariance functions for random regression coefficients were calculated to quantify the sufficient order of Legendre polynomial for the (co)variance component estimation of milk traits. The existing 13 to 24% of additive genetic variability for the individual lactation curve indicated that the use of random regression model is justified for selection on the level and shape of lactation curve in dairy sheep. Four eigenvalues sufficiently explained variability during lactation in all three milk traits. Heritability estimate for daily milk yield was the highest in mid lactation (0.17) and lower in the early (0.11) and late (0.08) lactation. In fat content, the heritability was increasing throughout lactation (0.08–0.13). Values in protein content varied from the beginning toward mid lactation (0.15–0.19), while they rapidly increased at the end of lactation (0.28). Common flock environment explained the highest percentage of phenotypic variability: 27–41% in daily milk yield, 31–41% in fat content, and 41–49% in protein content. Variance ratios for the two permanent environment effects were the highest in daily milk yield (0.10–0.27), and lower in fat (0.04–0.08) and protein (0.01–0.10) contents. Additive genetic correlations during the selected test-days were high between the adjacent ones and they tended to decrease at the extremes of the lactation trajectory.

Population parameter estimation of daily milk yield of the Chios sheep using test-day random regression models and Gibbs sampling

2005 ◽  
Vol 81 (2) ◽  
pp. 233-238 ◽  
Author(s):  
G. Banos ◽  
G. Arsenos ◽  
Z. Abas ◽  
Z. Basdagianni
Keyword(s):  
Gibbs Sampling ◽  
Milk Yield ◽  
Fourth Order ◽  
Regression Models ◽  
Post Partum ◽  
Random Regression ◽  
Population Parameter ◽  
Residual Variance ◽  
Daily Milk Yield ◽  
Daily Milk

AbstractParameters of daily milk yield during the first three lactations of Chios ewes were estimated with random regression models. Data consisted of 42 675 test-day records of 7121 ewes from 75 flocks that had lambed between 1998 and 2000. Models fitted fourth order fixed regressions on Legendre polynomials of the number of days post partum and fourth order random regressions on the individual animal. (Co)variance components were estimated with Gibbs sampling. Lactations were analysed separately. The four eigen values accounted for 0·80 to 0·84, 0·11 to 0·15, 0·04 to 0·05 and about 0·01 of the animal variance, respectively, depending on lactation number. Animal variance estimates, including genetic and, partly, permanent environment effects, were high at the beginning of each lactation and decreased as lactation progressed, suggesting that the animal effect is most important to early daily records. Residual variance was highest in the middle of lactation, suggesting that non-systematic environmental factors play a bigger at that time. Animal correlation estimates between daily yield records ranged from 0·26 to 0·99, were highest for adjacent days and decreased for days further apart. The decline had a different shape in the three lactations and was more evident in the first, suggesting that the three lactations may be biologically distinct traits. Animal correlation estimates between daily and total lactation milk yield ranged from 0·61 to 0·98 and were highest in the middle and lowest towards the end of lactation. Early lactation daily yield had an animal correlation of 0·70 to 0·80 with total lactation milk yield, in all three lactations. Results of this study suggest that daily milk yield records in the early stages of lactation may be useful for selection of ewes with high producing ability and accurate prediction of total lactation milk yield.

scholarly journals Conditioning Factors of Linearized Wood’s Function Lactation Curve Shape Parameters, Milk Yield, Fat and Protein Content in Murciano-Granadina Primiparous Does

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2115
Author(s):  
Juan Vicente Delgado Bermejo ◽  
Francisco Antonio Limón Pérez ◽  
Francisco Javier Navas González ◽  
Jose Manuel León Jurado ◽  
Javier Fernández Álvarez ◽  
...  
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Random Regression ◽  
Total Production ◽  
Curve Shape ◽  
Conditioning Factors ◽  
Lactation Length ◽  
Lactation Curve ◽  
Daily Milk Yield ◽  
Daily Milk

A total of 137,927 controls of 22,932 Murciano-Granadina first lactation goats (measured between 1996–2016) were evaluated to determine the influence of the number of kids, season, year and farm on total milk yield, daily milk yield, lactation length, total production of fat and protein and percentages of fat and protein. All factors analyzed had a significant effect on the variables studied, except for the influence of the number of kids on the percentages of fat and protein, where the variation was very small. Goats with two offspring produced nearly 15% more milk, fat and protein per lactation compared to goats with simple kids. Kiddings occurring in summer–autumn resulted in average milk, fat and protein yields nearly 14, 19 and 23% higher when compared to winter–spring kiddings. Lactation curves were evaluated to determine the effects of the number of kids and season, using the linearized version of the model of Wood in random regression analyses. Peak Yield increased by about 0.3 kg per additional offspring at kidding, but persistence was higher in goats with single offspring. The kidding season significantly influenced the lactation curve shape. Hence summer-kidding goats were more productive, and peak occurred earlier, while a higher persistence was observed in goats kidding during autumn.

scholarly journals The effect of some environmental factors that affecting daily milk yield of Iraqi buffaloes in Ninewah

2012 ◽  
Vol 36 (2) ◽  
pp. 180-186
Author(s):  
Garabed A. Avadesian
Keyword(s):  
Milk Yield ◽  
Fixed Effects ◽  
Regression Coefficient ◽  
Heritability Estimate ◽  
Prediction Equation ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Stage Of Lactation ◽  
Daily Milk Yield ◽  
Daily Milk

This study was carried out on the local buffalo in Ninewah, data were collected from154 buffalo in two herds during the period 1/7/2010 until 18/7/2010 in which milk collected daily in sequence ( 1st day , 5th day , 9th day , 13th day and 17th day ) and recording maximum and minimum temperature .Data were analyzed using general linear model ( GLM) within SAS program to study the fixed effects ( parity , herd , stage of lactation and test day ) , and regression coefficient with heritability . Overall average daily milk yield was 9.69 ± 0.12 kg and it appeared that parity , herd and stage of lactation has a highly significant effects in daily milk yield , while test day recorded no significant effect in the above . Minimum temperatures ranged (23.5 – 27.5 °C) and maximum was (41.8 – 45.6 °C). Regression coefficient for daily milk yield on maximum temperature was -0.259 kg / °C on (P > 0.01) and for minimum was 0.0325 kg / °C and this was non - significant, while the prediction equation (ŷ) was:Y^ (max) = 21.121 – 0.259 (X1)Y^ (min) = 8.863 + 0.0325 (X2)The heritability estimate for daily milk yield was ranged between (0.17 – 0.21) for the test day (recording day) , it was concluded that from this a number of fixed effects , and yield was decreased significantly with rising in ambient temperature and the estimates of heritability for daily milk yield belonged to test day was rather low.

scholarly journals Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population

Animals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3492
Author(s):  
Yasamin Salimiyekta ◽  
Rasoul Vaez-Torshizi ◽  
Mokhtar Ali Abbasi ◽  
Nasser Emmamjome-Kashan ◽  
Mehdi Amin-Afshar ◽  
...  
Keyword(s):  
Milk Yield ◽  
Cross Validation ◽  
Early Selection ◽  
Random Regression ◽  
Likelihood Method ◽  
Early Lactation ◽  
Breeding Values ◽  
Daily Milk Yield ◽  
Holstein Population ◽  
Daily Milk

The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.

Random regression animal models for genetic evaluation of test-day milk yield and milking duration in Saudi dairy goats

2018 ◽  
Author(s):  
A.A. Amin
Keyword(s):  
Milk Yield ◽  
Genetic Correlations ◽  
Random Regression ◽  
Dairy Goats ◽  
Data Set ◽  
Breeding Values ◽  
Sample Test ◽  
Early Production ◽  
Daily Milk Yield ◽  
Daily Milk

Random regression animal model was applied for analyzing the relationships between daily milk yield (MK) and milking duration (DR) in dairy goats comparing with reviewed estimates in dairy cows. The current analyzed data involved 17345 sample test-day records from multiparous Saudi dairy goats. A cubic random regression was applied for representing additive genetic variances in all studied traits across all different days in milk (12 groups). Based on multi-lactation random regression data-set analysis, the role of inheritance was greatest during the later stages of lactation. Heritability estimates of daily milk yield (h2MK) ranged from 0.15 to 0.54. While estimates of heritability for milking duration (h2DR) were very low during the first 60 days of lactation, being not more than 0.04. During the 2nd half of lactation the estimates of h2DR ranged from 0.35 to 0.39. Results of genetic variations for lactation records during early production life showed that highest milk harvest with intermediate milking rate could be achieved. Estimates of expected breeding values for milk yield and milking duration increased in different rates with progressing days in milk groups. These results indicated that individual selection results would be favorably achieved during the late part of lactation. Additive genetic correlations between measures of all traits at different lactation months continuously decreased as the interval between test days increased. Additive genetic correlations between milking duration and milk yield were positive and considerably high. Correlations between expected breeding values of both traits ranged from 0.41 to 0.83 (mean = 0.69) across different lactation months. More details on estimates of breeding values, estimates of permanent environmental and additive genetic correlations for all traits were tabulated.

scholarly journals Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka

2020 ◽  
Vol 33 (11) ◽  
pp. 1741-1754
Author(s):  
Amali Malshani Samaraweera ◽  
Vinzent Boerner ◽  
Hewa Waduge Cyril ◽  
Julius van der Werf ◽  
Susanne Hermesch
Keyword(s):  
Sri Lanka ◽  
Animal Models ◽  
Milk Yield ◽  
Genetic Parameters ◽  
Random Regression ◽  
Breeding Values ◽  
Daily Milk Yield ◽  
Jersey Cows ◽  
Daily Milk ◽  
Friesian Cows

Objective: This study was conducted to estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka.Methods: Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30-day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model.Results: For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08±0.03) was higher than that of Jersey-Friesian cows (0.02±0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28±0.01 and the heritability ranged from 0.002±0.05 to 0.19±0.02 depending on day of milk. Pearson product-moment correlations between the bull estimated breeding values (EBVs) in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and –0.35 in Jersey-Friesian cows.Conclusion: The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

Genetic modelling of daily milk yield using orthogonal polynomials and parametric curves

2000 ◽  
Vol 70 (3) ◽  
pp. 407-415 ◽  
Author(s):  
S. Brotherstone ◽  
I. M. S. White ◽  
K. Meyer
Keyword(s):  
Orthogonal Polynomials ◽  
Milk Yield ◽  
Measurement Errors ◽  
Regression Models ◽  
Genetic Correlations ◽  
Random Regression ◽  
Parametric Curve ◽  
Parametric Curves ◽  
Daily Milk Yield ◽  
Daily Milk

AbstractRandom regression models have been advocated for the analysis of test day records in dairy cattle. The effectiveness of a random regression analysis depends on the function used to model the data. To investigate functions suitable for the analysis of daily milk yield, test day milk yields of 7860 first lactation Holstein Friesian cows were analysed using random regression models involving three types of curves. Each analysis fitted the same curve to model overall trends through a fixed regression and random deviations due to animals. Curves included orthogonal polynomials, fitted to order 3 (quadratic), 4 (cubic) and 5 (quartic), respectively, a three-parameter parametric curve and a five-parameter parametric curve. Sets of random regression coefficients were fitted to model both animals’ genetic effects and permanent environmental effects. Temporary measurement errors were assumed independently but heterogeneously distributed, and assigned to one of 12 classes. Results showed that the measurement error variances were generally lowest around peak lactation, and higher at the beginning and end of lactation. Parametric curves yielded the highest likelihoods, but produced negative genetic associations between yield in early lactation and later lactation yields, while positive genetic correlations across the entire lactation were estimated with all models involving orthogonal polynomials. The fit of models using orthogonal polynomials to model test day yield was improved by including higher order fixed regressions.

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