Test-day Yield Research Articles

Genetic Evaluation of Monthly Test-Day Milk Yields of Jersey Crossbred Cattle Under Farmers’ Production System in Tamil Nadu, India

Tamil Nadu, a state in southern India, is bereft of any native dairy or dual-purpose cattle breeds, and the state depends chiefly on crossbred cattle for milk production. Jersey crossbred cattle account for 90% of the state’s milk production. This genetic evaluation study aimed to assess milk production traits, including test-day milk yields (TDMYs), total lactation milk yield (TMY), and 305-day milk yield (305MY), in these cattle reared under farmers’ conditions. Data on monthly TDMYs (TDMY1 to TDMY10) of Jersey crossbred cows (n = 75,627) reared by the farmers over 11 years (2012–2022) were collected for evaluation. The influence of non-genetic factors viz., location (agroclimatic zones), period and season of calving, and parity on TDMYs, TMY and 305MY were assessed, and the variance and covariance components for genetic parameters (heritability and genetic correlation) of the traits were estimated by the method of restricted maximum likelihood (REML) under a multivariate animal model and by a random regression model (RRM). The overall means of various TDMYs ranged from 4.98 (TDMY10) to 9.95 kg (TDMY2), and for TMY and 305MY, the means were 2480.33 and 2393.71 kg, respectively. The heritabilities estimated by the multivariate animal model for TDMYs were moderate and ranged from 0.26 ± 0.02 to 0.37 ± 0.02 and the estimates were less variable throughout lactation, while the estimates obtained by RRM were medium to high and ranged between 0.29 ± 0.01 and 0.67 ± 0.02 for milk yield on day 5 to 305 (DIM 5–DIM 305). The heritabilities estimated for TMY and 305MY were 0.43 ± 0.02 and 0.43 ± 0.02, respectively. The estimates of heritability for mid-lactation yields were comparatively less than those at the beginning and the end of lactation. The moderate to high estimates of heritabilities for TDMYs, TMY, and 305MY obtained in the present study offer ample scope for improving milk production through selective breeding. Genetic correlations between TDMYs estimated by the multivariate animal model were positive and high, with a range between 0.75 ± 0.03 and 0.99 ± 0.00. Considerably higher estimates (0.98–0.99) were observed between adjacent TDMYs showing strong genetic associations. By RRM, genetic correlations estimated between DIMs were positive (except for DIM 5 with DIM 125 to DIM 185 and DIM 125 with DIM 305), and the magnitude of genetic correlation decreased with an increase in the interval between the DIMs. The high genetic correlation observed between the TDMYs (in the early stage of lactation) and total lactation milk yield suggested that these test-day yields could be used favorably for the early selection of cows for milk production, which facilitates reduction in the generation interval and consequently increases the annual genetic gain for the milk production traits.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review.

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.

Test-day and other milk recording options for prediction of lactation milk yield in Jaffarabadi (Bubalus bubalis) buffaloes

Generally, standard lactation milk yield is predicted based on test-day records collected at monthly intervals. Test-day milk production at different time intervals other than monthly intervals can be used to predict lactation milk yield of field bovines in field conditions. With the same possibility, this study was carried out to predict lactation milk yield in Jaffarabadi buffaloes from various test-day milk yield data retrieved for different time intervals. A total of 1,15,339 daily milk yield records in 176 lactations of 1st to 6th parity of 30 Jaffarabadi buffaloes lactating at the Cattle Breeding Farm, Kamdhenu University, Junagadh, Gujarat over a period of 28 years (1991 to 2018) were used for the study. Single monthly test-day milk yield recorded on 125th, 155th or 185th day i.e., 5th, 6th and 7th monthly test day yield alone provided only 50% reliability in determining the standard lactation milk yield. Daily peak yield alone was also found to be a poor predictor for lactation yield. Prediction equations using combination of consecutive two monthly test day yields from 4th to 10th monthly test day were found reliable source for prediction of lactation milk yield providing accuracy up to 82.19% whereas, daily peak yield in combination with single monthly test day yield at mid and late lactation was also predicted lactation milk yield with accuracies up to 72.23%. Milk production recorded at weekly interval could also be used to approximate milk production using the equation 15.35+6.91 × Sum of all weekly test-day yields, with precision of 98.93% or milk production recorded at fortnightly interval by the equation 18.04+14.65 × Sum of fortnightly test-day yields, with precision of 97.14%.

Single-step genomic predictions for heat tolerance of production yields in US Holsteins and Jerseys

The physiological stress caused by excessive heat affects dairy cattle health and production. This study sought to investigate the impact of heat stress on test-day yields in United States (US) Holstein and Jersey cows and develop single-step genomic predictions to identify heat tolerant animals. Data included 12.8 and 2.1 million test-day records for 923,026 Holstein and 153,710 Jersey cows in 27 US states. From 2015 through 2021, test-day records from the first 5 lactations included milk, fat, and protein yields (kg). Cows' records were included if they had at least 5 test-day records per lactation. Heat stress was quantified by analyzing the impact of a 5-d hourly average temperature-humidity index (THI5d¯) on observed test-day yields. Using a multiple trait repeatability model, a heat threshold (THI threshold) was determined for each breed based on the point that the average adjusted yields started to decrease, which was 69 for Holsteins and 72 for Jerseys. An additive genetic component of general production and heat tolerance production were estimated using a multiple trait reaction norm model and single-step genomic BLUP methodology. Random effects were regressed on a function of THI5d¯ and THI threshold. The proportion of test-day records that occurred on or above the respective heat thresholds was 15% for Holstein and 10% for Jersey. Heritability of milk, fat, and protein yields under heat stress for Holsteins increased, with a small standard error, indicating that the additive genetic component for heat tolerance of these traits was observed. This was not as evident in Jersey traits. For Jersey, the permanent environment explained the same or more of the variation in fat and protein yield under heat stress indicating that non-genetic factors may determine heat tolerance for these Jersey traits. Correlations between the general genetic merit of production (in the absence of heat stress) and heat tolerance genetic merit of production traits were moderate in strength and negative. This indicated that selecting for general genetic merit without consideration of heat tolerance genetic merit of production may result in less favorable performance in hot and humid climates. A general GEBV for genetic merit and a heat tolerance GEBV were calculated for each animal. This study contributes to the investigation of the impact of heat stress on US dairy cattle production yields and offers a basis for the implementation of genomic selection. The results indicate that genomic selection for heat tolerance of production yields is possible for US Holsteins and Jerseys, but a study to validate the genomic predictions should be explored.

A randomized study on the effect of extended voluntary waiting period in primiparous dairy cows on milk yield during first and second lactation

Extending the voluntary waiting period (VWP) for primiparous cows can have a positive impact on fertility without a negative impact on milk production per day in the calving interval (CInt). We investigated the effect of extended VWP during first lactation on milk yield (MY) during 2 consecutive lactations in primiparous cows. The study involved 16 commercial herds in southern Sweden. A total of 533 Holstein and Red dairy cattle (Swedish Red, Danish Red, Ayrshire) dairy cows were randomly assigned to a conventional 25 to 95 d VWP (n = 252) or extended 145 to 215 d VWP (n = 281). Data on calvings, inseminations, and test-day yields were retrieved from the Swedish Milk Recording System. Cows with VWP according to plan and completing 1 or 2 CInt with a second or third calving were included in the data analysis. Whole lactation and 305-d energy-corrected milk (ECM) yield were higher for the extended VWP group than the conventional VWP group in both the first lactation (12,307 vs. 9,587 and 9,653 vs. 9,127 kg ECM) and second lactation (12,817 vs. 11,986 and 11,957 vs. 11,304 kg ECM). We found no difference between the VWP groups in MY per day during the first CInt or during the first and second CInt combined, although MY per day during the second CInt was around 1.5 kg higher for cows with extended VWP than for cows with conventional VWP. Thus extended VWP for primiparous cows can be used as a management tool without compromising MY.

Daily milk yield correction factors: What are they?

Cows are typically milked 2 or more times on a test-day, but not all these milkings are sampled and weighed. The initial approach estimated a test-day yield with doubled morning (AM) or evening (PM) yield in the AM-PM milking plans, assuming equal AM and PM milking intervals. However, AM and PM milking intervals can vary, and milk secretion rates may be different between day and night. Statistical methods have been proposed to estimate daily yields in dairy cows, focusing on various yield correction factors in 2 broad categories: additive correction factors (ACF) and multiplicative correction factors (MCF). The ACF are evaluated by the average differences between AM and PM milk yield for various milking interval classes, coupled with other categorical variables. We show that an ACF model is equivalent to a regression model of daily yield on categorical regressor variables, and a continuous variable for AM or PM yield with a fixed regression coefficient of 2.0. Similarly, a linear regression model can be implemented as an ACF model with the regression coefficient for AM or PM yield estimated from the data. The linear regression models improved the accuracy of the estimates compared with the ACF models. The MCF are ratios of daily yield to yield from single milkings, but their statistical interpretations vary. Overall, MCF were more accurate for estimating daily milk yield than ACF. The MCF have biological and statistical challenges. Systematic biases occurred when ACF or MCF were computed on discretized milking interval classes, leading to accuracy loss. An exponential regression model was proposed as an alternative model for estimating daily milk yields, which improved the accuracy. Characterization of ACF and MCF showed how they improved the accuracy compared with doubling AM or PM yield as the daily milk yield. All the methods performed similarly with equal AM and PM milkings. The methods were explicitly described to estimate daily milk yield in AM and PM milking plans. Still, the principles generally apply to cows milked more than 2 times a day and apply similarly to the estimation of daily fat and protein yields with some necessary modifications.

Predicting daily milk yield for primiparous cows using data of within-herd relatives to capture genotype-by-environment interactions

This study develops and illustrates a hybrid k-medoids, random forest, and support vector regression (K-R-S) approach for predicting the lactation curves of individual primiparous cows within a targeted environment using monthly milk production data from their dams and paternal siblings. The model simulation and evaluation were based on historical test-day (TD) milk production data from 2010 to 2016 for 260 Wisconsin dairy farms. Data from older paternal siblings and dams were used to create family units (n = 6,400) of individual calves, from which their future performance was predicted. Test-day milk yield (MY) records from 2010 to 2014 were used for model training, whereas monthly milk production records of Holstein calves born in 2014 were used for model evaluation. The K-R-S hybrid approach was used to generate MY predictions for 5 randomly selected batches of 320 primiparous cows, which were used to evaluate model performance at the individual cow level by cross-validation. Across all 5 batches, the mean absolute error and the root mean square error of the K-R-S predictions were lower (by 24.2 and 23.4%, respectively) than that of the mean daily MY of paternal siblings. The K-R-S predictions of TD MY were closer to actual values 74.2 ± 2.0% of the time, as compared with means of paternal siblings'. The correlation between actual TD MY and K-R-S predictions was greater (0.34 ± 0.01) than the correlation between the actual yield and the mean of paternal siblings (0.08 ± 0.01). The results of this study demonstrate the effectiveness of the K-R-S hybrid approach for predicting future first-lactation MY of dairy calves in management applications, such as milk production forecasting or decision-support simulation, using only monthly TD yields of within-herd relatives and in the absence of detailed genomic data.

Estimation the Genetic Parameters for Milk Yield in Romanian Spotted, Simmental Type Cattle Breed

Abstract The aim of this study was to estimate the genetic parameters for test-day traits milk yield and the breeding value in Romanian Spotted, Simmental type cattle. Random regression test-day animal model was used to estimate the genetic parameters. The number of records were 2062 test-day from 302 cows. The data were obtained from Romanian Breeding Association Romanian Spotted, Simmental type. The heritability estimates values for milk yield ranged between 0.377 and 0.417. The heritability for fat test-day yield in our study was low the values ranged between 0.117 and 0.236 and for protein test-day yield was medium, the values ranged between 0.308 and 0.372. The breeding value for the best ten cows for milk yield ranged from 1946.57 to 3250.38 kg, for fat yield were between 64.92 and 98.86 kg and for protein ranged from 67.26 to 102.21 kg. The correlations between test-day milk yields ranging from 0.28 to 1. Genetic correlations between test day fat and protein yields were high.

Genomic evaluation of milk yield in a smallholder crossbred dairy production system in India

BackgroundIndia is the largest milk producer globally, with the largest proportion of cattle milk production coming from smallholder farms with an average herd size of less than two milking cows. These cows are mainly undefined multi-generation crosses between exotic dairy breeds and indigenous Indian cattle, with no performance or pedigree recording. Therefore, implementing genetic improvement based on genetic evaluation has not yet been possible. We present the first results from a large smallholder performance recording program in India, using single nucleotide polymorphism (SNP) genotypes to estimate genetic parameters for monthly test-day (TD) milk records and to obtain and validate genomic estimated breeding values (GEBV).ResultsThe average TD milk yield under the high, medium, and low production environments were 9.64, 6.88, and 4.61 kg, respectively. In the high production environment, the usual profile of a lactation curve was evident, whereas it was less evident in low and medium production environments. There was a clear trend of an increasing milk yield with an increasing Holstein Friesian (HF) proportion in the high production environment, but no increase above intermediate grades in the medium and low production environments. Trends for Jersey were small but yield estimates had a higher standard error than HF. Heritability estimates for TD yield across the lactation ranged from 0.193 to 0.250, with an average of 0.230. The additive genetic correlations between TD yield at different times in lactation were high, ranging from 0.846 to 0.998. The accuracy of phenotypic validation of GEBV from the method that is believed to be the least biased was 0.420, which was very similar to the accuracy obtained from the average prediction error variance of the GEBV.ConclusionsThe results indicate strong potential for genomic selection to improve milk production of smallholder crossbred cows in India. The performance of cows with different breed compositions can be determined in different Indian environments, which makes it possible to provide better advice to smallholder farmers on optimum breed composition for their environment.

The effect of milk urea level on fertility parameters in Holstein–Friesian dairy cows

The objective of this study was to reveal the effect of milk urea levels on the fertility of dairy cows, taking into account their age, herd production level, number of cows, season of first insemination, which were analyzed using data from the SYMLEK national system. The study included data on milk urea levels in 88,745 test-day yields of 55,685 Holstein?Friesian cows, which were milked up to 30 days before first insemination, during the period preceding 180 days of first and second lactation. The GLM procedure of the SAS package was used in the statistical calculations. Considering the observed significant relationships and tendencies, a milk urea level higher than 300 mg/L was found to negatively affect reproductive efficiency in dairy cows, although the effects of the urea level could be masked by the more profound impact of environmental factors. Regardless of the factors, an increased milk urea level was paralleled by decreased cow fertility, with statistically significant differences occurring only for the interactions with herd production level and with herd size. The unfavorable effect of the increase in the level of urea in milk (from ≤150 to >300 mg/L) on cow fertility was particularly evident in the highest producing herds (>9000 kg milk).

Genotype-by-environment (temperature-humidity) interaction of milk production traits in Australian Holstein cattle

Dairying in Australia is practiced in highly diverse climatic conditions and production systems, which means that re-ranking of genotypes could occur across environments that vary in temperature and humidity-that is, genotype-by-environment interactions (G × E) may exist. The objective of this study was to investigate G × E for heat tolerance with respect to milk production traits in Australian Holsteins. A total of 6.7 million test-day milk yield records for first, second, and third lactations from 491,562 cows and 6,410 sires that had progeny in different climatic environments were included in the analysis. The environmental gradient used was the temperature-humidity index (THI) calculated from climate data from 163 Australian public weather stations between 2003 and 2017. Data were analyzed using univariate reaction norm (RM) sire model, and the results were compared with multi-trait model (MT). The MT analysis treated test-day yields at 5th percentile (THI = 61; i.e., thermoneutral conditions), 50th percentile (THI = 67; i.e., moderate heat stress conditions), and 95th percentile (THI = 73; i.e., high heat stress conditions) of the trajectory of THI as correlated traits. A THI series of 61, 67, and 73, for example, is equivalent to average temperature and relative humidity of approximately 20°C and 45%, 25°C and 45%, and 31°C and 50%, respectively. We observed some degree of heterogeneity of additive (AG) and permanent environmental (PE) variance over the trajectory THI from RM analysis, with estimates decreasing at higher THI values more steeply for PE than for AG variance. The genetic correlations of the tests between the 5th and 95th percentiles of THI for milk, protein, and fat yield from RM were 0.88 ± 0.01 (standard error), 0.79 ± 0.01, and 0.86 ± 0.01, respectively, whereas the corresponding estimates from MT were 0.86 ± 0.02, 0.84 ± 0.03, and 0.87 ± 0.03. We observed lower genetic correlations between the 5th and 95th percentiles of THI for milk tests from recent years (i.e., 2009 and 2017) compared with earlier years (i.e., 2003 and 2008), which suggests that the level of G × E is increasing in the studied population and should be monitored especially in anticipation of future expected increase in daily average temperature and frequency of heat events. Overall, our results indicate presence of G × E at the upper extreme of the trajectory of THI, but the current extent of sire re-ranking may not justify providing separate genetic evaluations for different levels of heat stress. However, variations observed in the sire sensitivity to heat stress suggest that dairy herds in high heat load conditions could benefit more from using heat-tolerant or resilient sires.

Genetic evaluation of test-day milk yields from smallholder dairy production systems in Kenya using genomic relationships

Efforts to improve dairy production in smallholder farming systems of East Africa over the past decade have had limited impact because of the lack of records on performance to guide targeted breeding programs. Estimates of genetic parameters in these systems are lacking. Using data generated through a project (“Germplasm for Dairy Development in East Africa”) in Kenya and a genomic relationship matrix from genotypic records, we examined the potential impact of different models handling contemporary groups or herd effects on estimates of genetic parameters using a fixed regression model (FRM) for test-day (TD) milk yields, and the covariance structure for TD milk yield at various stages of lactation for animals using a random regression model (RRM). Models in which herd groups were defined using production levels derived from the data fitted the data better than those in which herds were grouped depending on management practices or were random. Lactation curves obtained for animals under different production categories did not display the typical peak yield characteristic of improved dairy systems in developed countries. Heritability estimates for TD milk yields using the FRM varied greatly with the definition of contemporary herd groups, ranging from 0.05 ± 0.03 to 0.27 ± 0.05 (mean ± standard error). The analysis using the RRM fitted the data better than the FRM. The heritability estimates for specific TD yields obtained by the RRM were higher than those obtained by the FRM. Genetic correlations between TD yields were high and positive for measures within short consecutive intervals but decreased as the intervals between TD increased beyond 60 d and became negative with intervals of more than 5 mo. The magnitude of the genetic correlation estimates among TD records indicates that using TD milk records beyond a 60-d interval as repeated measures of the same trait for genetic evaluation of animals on smallholder farms would not be optimal. Although each individual smallholder farmer retains only a few animals, using the genomic relationship between animals to link the large number of farmers operating under specified environments provides a sufficiently large herd-group for which a breeding program could be developed.

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials.

ObjectiveThe objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model.MethodsA random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler.ResultsAll variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities.ConclusionThese results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

Desempenho produtivo e curva de lactação de animais mestiços holandês x gir na região agreste de Pernambuco

The objective of this study was to evaluate the 305-day cumulative milk yield (MY305) and the lactation curve trajectory of cows from different genetic groups originating from the crossing of Holstein and Dairy Gyr breeds, daughters of proven and unproven sires. Milk yield records of 1,032 animals with a genetic composition of ½, ¾ and ⅞ Holstein x Gyr were used. The analyses to determine the effects of the genetic groups on the shape of the lactation curve were carried out in two steps: 1) pre-adjusting test-day milk yields for fixed effects in a linear model, and 2) using the adjusted yields in a nonlinear model on days in milk to compare the lactation curves of animals from different genetic groups, born to proven and unproven sires. The effects included in the linear model for pre-adjustment of test-day yields were the contemporary group and age class of cow at calving. The data were analyzed by the least squares method. The same model was used for MY305, which included the effects of genetic group of the animal and sire category (proven or unproven). MY305 was influenced by all effects (P<0.01). F1 daughters born to Gyr sires had lower mean MY305 and animals of the ⅞ HG genetic group exhibited higher mean MY305 compared to the other groups (P<0.001). The trajectory of the average lactation curves for all genetic groups was characterized by initial production, followed by an ascending phase until peak lactation and a descending phase thereafter. Only females of the ½ HG genetic group, daughters of Gyr sires, exhibited an atypical curve pattern. The different genetic groups had specific curves characterized by different trajectories and yields, with the observation of higher production levels in cows carrying a higher proportion of Holstein genes. The use of proven sires in dairy cattle crossings should increase genetic merit and consequently the productivity of the dairy herd evaluated.

Reliability of breeding values between random regression and 305-day lactation models

Abstract The objective of this work was to verify the gain in reliability of estimated breeding values (EBVs), when random regression models are applied instead of conventional 305-day lactation models, using fat and protein yield records of Brazilian Holstein cattle for future genetic evaluations. Data set contained 262,426 test-day fat and protein yield records, and 30,228 fat and protein lactation records at 305 days from first lactation. Single trait random regression models using Legendre polynomials and single trait lactation models were applied. Heritability for 305-day yield from lactation models was 0.24 (fat) and 0.17 (protein), and from random regression models was 0.20 (fat) and 0.21 (protein). Spearman correlations of EBVs, between lactation models and random regression models, for 305-day yield, ranged from 0.86 to 0.97 and 0.86 to 0.98 (bulls), and from 0.80 to 0.89 and 0.81 to 0.86 (cows), for fat and protein, respectively. Average increase in reliability of EBVs for 305-day yield of bulls ranged from 2 to 16% (fat) and from 4 to 26% (protein), and average reliability of cows ranged from 24 to 38% (fat and protein), which is higher than in the lactation models. Random regression models using Legendre polynomials will improve genetic evaluations of Brazilian Holstein cattle due to the reliability increase of EBVs, in comparison with 305-day lactation models.

Estimation of genetic parameters of first lactation 305-day and monthly test-day milk yields in Karan Fries cattle

The present investigation was carried out using first lactation 16,184 monthly test-day milk yields (MTDMYs) records of 1,583 Karan Fries cattle that calved from 1983 to 2012 sired by 130 bulls maintained at ICAR-National Dairy Research Institute, Karnal. Mixed model analysis was carried out by least-squares maximum likelihood programme to study the effects of genetic and non-genetic factors on first lactation 305-day milk yield (FL305DMY) and MTDMYs record of Karan Fries cattle. The highest and lowest MTDMYs were observed in TD-2 (12.95 kg) and TD-10 (9.72 kg), respectively. The average FL305DMY was 3123 kg. The effect of season of calving was found to be highly significant on all the MTDMYs while non-significant effect was observed on FL305DMY. Period of calving had non-significant effect on most of the MTDMYs and on FL305DMY, whereas; the effect of age at first calving was highly significant on majority of the MTDMYs and on FL305DMY. The heritability estimates for FL305DMY was 0.35±0.07 and for MTDMYs it ranged from 0.11 (TD-11) to 0.34 (TD-6). The genetic and phenotypic correlations among MTDMYs and with FL305DMY were found to be highly significant. The estimates of phenotypic and genetic correlations among all the MTDMYs ranged from 0.15 (TD-1 and TD-11) to 0.83 (TD- 8 and TD-9) and 0.29 (TD-1 and TD-10) to 0.99 (TD-2 and TD-3), respectively. High phenotypic and genetic correlations of TD-3 (0.69 and 0.91) and TD–4 (0.65 and 0.99) with FL305DMY indicated that these key test-day yields could be used for the early and accurate selection of animals resulting in faster genetic gain.

Genetic parameters of 305 days and monthly test-day milk yields in Murrah buffaloes

The present investigation was carried out using first lactation 9,071 monthly test-day milk yield records of 965 Murrah buffaloes that calved from 1977 to 2012 sired by 98 bulls maintained at ICAR-National Dairy Research Institute, Karnal. Mixed model analysis was carried out by least-squares maximum likelihood programme to study the effects of genetic and non-genetic factors on first lactation 305-day milk yield and monthly test-day (TD) milk yield records of Murrah buffaloes. The highest monthly test-day milk yield was observed in TD3 (7.64 kg) and lowest in TD10 (4.46 kg). The average first lactation 305 days or less milk yield (FL305DMY) was 1806.45±16.99 kg. The effect of season of calving, period of calving and age at first calving was highly significant on FL305DMY and on most of the monthly test-day milk yields. The heritability estimate for FL305DMY was 0.18±0.08 and for monthly test-day milk yields it ranged from 0.09 (TD6) to 0.18 (TD3). The genetic and phenotypic correlations among monthly test-day milk yields and with 305 days milk yield were highly significant. The present investigation revealed that the non-genetic factors such as season of calving in general and year of calving in particular, might be considered when performing an evaluation of Murrah buffaloes based on monthly test-day milk yield records. High genetic and phenotypic correlation among monthly test-day milk yields and with first lactation 305 days milk yield suggested that these test-day yields could be used as the selection criteria, leading to a reduction in generation interval.

GENETIC AND PHENOTYPIC TRENDS FOR TEST DAY MILK, FAT AND PROTIN YIELDS APPLYING RANDOM REGRESSION MODEL IN EGYPTIAN BUFFALOES

SUMMARY The aim of this study was to detect genetic and phenotypic trends for test day milk, fat and protein yields in Egyptian buffalo applying the random regression model (RRM) and determining the genetic and phenotypic trend. Data of 4971 test days (TD) milk yield traits were recorded for 691 Egyptian buffalo cows, daughters of 120 sires and 532 dams from four herds belonging to the Animal Production Research Institute, Egypt. Ten-month classes of lactation days were considered for the test-day yields. The model included the random effects of direct additive genetic, permanent environment and error, while the fixed effects were herd-test day, year and season of calving and parity as well as days in milk as a covariable, which was modeled by orthogonal Legendre polynomials. The additive genetic variance estimates at first test day for milk, fat and protein yields were respectively 0.035kg, 2.26g, 0.80g, increased until the fourth (0.807kg, 30.52g, 12.52g), decreased thereafter, reaching the lowest value at the ninth test day for milk and protein yields (0.238kg, 0.97g) and at the tenth test day for fat yield (7.28g).Heritability estimated at first test day was 0.05, increased until the fourth test day (0.30), and decreased thereafter and reached the lowest value at the tenth test day (0.06). The highest heritabilities were found to be 0.29 and 0.31 for fourth test day in fat and protein yields, respectively. The range in phenotypic values change decreased from 7.99 to 5.66kg, 53.37 to 35.07g and 30.86 to 21.54g, while the respective genetic values change increased from -0.22 to 0.17kg, -1.41 to 1.36g and 0.82 to 0.70g for milk, fat and protein yields, respectively. The genetic trends were slightly positive for all traits indicating that the selection program performs correctly. For all traits, the phenotypic trends showing deteriorating trends indicating the presence of some environmental inadequacies especially for nutritional level.

Random regression models for milk, fat and protein in Colombian Buffaloes

Objective. Covariance functions for additive genetic and permanent environmental effects and, subsequently, genetic parameters for test-day milk (MY), fat (FY) protein (PY) yields and mozzarella cheese (MP) in buffaloes from Colombia were estimate by using Random regression models (RRM) with Legendre polynomials (LP). Materials and Methods. Test-day records of MY, FY, PY and MP from 1884 first lactations of buffalo cows from 228 sires were analyzed. The animals belonged to 14 herds in Colombia between 1995 and 2011. Ten monthly classes of days in milk were considered for test-day yields. The contemporary groups were defined as herd-year-month of milk test-day. Random additive genetic, permanent environmental and residual effects were included in the model. Fixed effects included the contemporary group, linear and quadratic effects of age at calving, and the average lactation curve of the population, which was modeled by third-order LP. Random additive genetic and permanent environmental effects were estimated by RRM using third- to- sixth-order LP. Residual variances were modeled using homogeneous and heterogeneous structures. Results. The heritabilities for MY, FY, PY and MP ranged from 0.38 to 0.05, 0.67 to 0.11, 0.50 to 0.07 and 0.50 to 0.11, respectively. Conclusions. In general, the RRM are adequate to describe the genetic variation in test-day of MY, FY, PY and MP in Colombian buffaloes.Key words: Cattle, genetics, zootechnics (Source: EuroVoc).

Genetic Parameters of Fatty Acids in Italian Brown Swiss and Holstein Cows

The aim of this study was to estimate the genetic parameters and to predict experimental breeding values (EBVs) for saturated (SFA), unsaturated (UFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids, the ratio of fatty acids, and the productive traits in Italian Brown Swiss (BSW) and Holstein Friesian (HOL) cattle. Test-day yields from 235,658 HOL and 21,723 BSW cows were extracted from the Italian HOL and BSW Associations databases from November 2009 to October 2012 out of 3310 herds. The milk samples collected within the routine milk recording scheme were processed with the Milkoscan™ FT 6500 Plus (Foss, Hillerød, Denmark) for the identification of SFA, UFA, MUFA and PUFA composition in milk. Genetic parameters for fatty acids and productive traits were estimated on 1,765,552 records in HOL and 255,592 records in BSW. Heritability values estimated for SFA, UFA, MUFA and PUFA ranged from 0.06 to 0.18 for the BSW breed and from 0.10 to 0.29 for HOL. The genetic trends for the fatty acids were consistent between traits and breeds. Pearson’s and Spearman’s correlations among EBVs for SFA, UFA, MUFA and PUFA and official EBVs for fat percentage were in the range 0.32 to 0.54 for BSW and 0.44 to 0.64 for HOL. The prediction of specific EBVs for milk fatty acids and for the ratio among them may be useful to identify the best bulls to be selected with the aim to improve milk quality in terms of fat content and fatty acid ratios, achieving healthier dairy productions for consumers.