Assessment of homogeneity vs. heterogeneity of residual variance in random regression test-day models in a Bayesian analysis

Test-day first-lactation milk yields from Holstein cows were analyzed with a set of random regression models based on Legendre polynomials of varying order on additive genetic and permanent environmental effects. Homogeneity and heterogeneity of residual variance, assuming three and 30 arbitrary measurement error classes of different length were considered. Unknown parameters were estimated within a Bayesian framework. Bayes factors and a checking function for the cross-validation predictive densities of the data were the tools chosen for selecting among competing models. Residual variances obtained from 30 arbitrary intervals were nearly constant between d 70 and 300 and tended to increase towards the extremes of the lactation, especially at the onset. In early lactation, the temporary measurement errors were found to be larger and highly variable. A high order of the regression submodels employed for modeling the permanent environmental deviations tended to strongly correct the heterogeneity of the residual variance. Accordingly, the assumption of homogeneity of residual variance was the most plausible specification under both comparison criteria when the number of random regression coefficients was set to five. Otherwise, the heterogeneity assumption, using three or 30 error classes, was better supported, depending on the criterion and on the order of the submodel fitted for the permanent environmental effect.     

Adjustment for heterogeneous covariance due to herd milk yield by transformation of test-day random regressions

A method of accounting for differences in covariance components of test-day milk records was developed based on transformation of regressions for random effects. Preliminary analysis indicated that genetic and nongenetic covariance structures differed by herd milk yield. Differences were found for phenotypic covariances and also for genetic, permanent environmental, and herd-time covariances. Heritabilities for test-day milk yield tended to be lower at the end and especially at the start of lactation; they also were higher (maximum of ∼25%) for high-yield herds and lower (maximum of 15%) for low-yield herds. Permanent environmental variances were on average 10% lower in high-yield herds. Relative herd-time variances were ∼10% at start of lactation and then began to decrease regardless of herd yield; high-yield herds increased in midlactation followed by another decrease, and medium-yield herds increased at the end of lactation. Regressors for random regression effects were transformed to adjust for heterogeneity of test-day yield covariances. Some animal reranking occurred because of this transformation of genetic and permanent environmental effects. When genetic correlations between environments were allowed to differ from 1, some additional animal re-ranking occurred. Correlations of variances of genetic and permanent-environmental regression solutions within herd, test-day, and milking frequency class with class mean milk yields were reduced with adjustment for heterogeneous covariance. The method suggests a number of innovative solutions to issues related to heterogeneous covariance structures, such as adjusted estimates in multibreed evaluation.     

Relationship of test-day somatic cell score with test-day and lactation milk yields

To determine the relationship of test-day (TD) somatic cell score (SCS) to TD and lactation milk yields, 1,320,590 records from Holstein first and second calvings from 1995 through 2002 were examined. All lactations had recorded yield and SCS for at least the first 4 TD. Least square analyses were conducted for yields on TD 2 through 10 within herd and cow. The model included regressions on current TD SCS and mean SCS of all previous TD with separate estimates by parity; effects for parity and calving year were included as well as regression on days in milk on TD 1. Corresponding analyses were conducted without regression on current SCS. An analysis of lactation yield was performed with a similar model and regression on all TD SCS. The SCS was highest most often on TD 1 for parity 1 (22.5%) and on TD 10 for parity 2 (18.5%). Regression of TD milk yield on mean of previous TD SCS was highest during the latter half of lactation (maximum of -0.346 kg/SCS unit on TD 9) for parity 1 and during TD through 7 (maximum of -0.366 kg/SCS unit on TD 4) for parity 2. Regression of TD yield on current TD SCS tended to be larger for later lactation. Regression of lactation yield on TD SCS was negative and important for TD 1 through 6 for parity 1 and for all TD for parity 2. To minimize milk loss, mastitis control is most important immediately pre- and postcalving for parity 1 and throughout lactation for parity 2.     

Prediction of daily milk, fat, and protein production by a random regression test-day model

Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been emphasized. The aim of this paper was to study the ability of a TDM to predict production for the next test day and for the entire lactation. Predictions of future production and detection of outliers are important factors for herd management (e.g., detection of health and management problems and compliance with quota). Because it is not possible to predict the herd-test-day (HTD) effect per se, the fixed HTD effect was split into 3 new effects: a fixed herd-test month-period effect, a fixed herd-year effect, and a random HTD effect. These new effects allow the prediction of future production for improvement of herd management. Predicted test-day yields were compared with observed yields, and the mean prediction error computed across herds was found to be close to zero. Predictions of performance records at the herd level were even more precise. Discarding herds enrolled in milk recording for <1 yr and animals with very few tests in the evaluation file improved correlations between predicted and observed yields at the next test day (correlation of 0.864 for milk in first-lactation cows as compared with a correlation of 0.821 with no records eliminated). Correlations with the observed 305-d production ranged from 0.575 to 1 for predictions based on 0 to 10 test-day records, respectively. Similar results were found for second and third lactation records for milk and milk components. These findings demonstrate the predictive ability of a TDM.     

Genetic parameters for tunisian holsteins using a test-day random regression model

Genetic parameters of milk, fat, and protein yields were estimated in the first 3 lactations for registered Tunisian Holsteins. Data included 140,187; 97,404; and 62,221 test-day production records collected on 22,538; 15,257; and 9,722 first-, second-, and third-parity cows, respectively. Records were of cows calving from 1992 to 2004 in 96 herds. (Co)variance components were estimated by Bayesian methods and a 3-trait-3-lactation random regression model. Gibbs sampling was used to obtain posterior distributions. The model included herd × test date, age × season of calving × stage of lactation [classes of 25 days in milk (DIM)], production sector × stage of lactation (classes of 5 DIM) as fixed effects, and random regression coefficients for additive genetic, permanent environmental, and herd-year of calving effects, which were defined as modified constant, linear, and quadratic Legendre coefficients. Heritability estimates for 305-d milk, fat and protein yields were moderate (0.12 to 0.18) and in the same range of parameters estimated in management systems with low to medium production levels. Heritabilities of test-day milk and protein yields for selected DIM were higher in the middle than at the beginning or the end of lactation. Inversely, heritabilities of fat yield were high at the peripheries of lactation. Genetic correlations among 305-d yield traits ranged from 0.50 to 0.86. The largest genetic correlation was observed between the first and second lactation, potentially due to the limited expression of genetic potential of superior cows in later lactations. Results suggested a lack of adaptation under the local management and climatic conditions. Results should be useful to implement a BLUP evaluation for the Tunisian cow population; however, results also indicated that further research focused on data quality might be needed.     

Use of herd solutions from a random regression test-day model for diagnostic dairy herd management

In a random regression test-day model, environmental effects in addition to individual animal factors can be included and analyzed. Moreover, instead of herd-year classification of the management groups, the herd-test-day classification within the model better accounts for month-to-month short-term environmental variation in production and somatic cell count (SCC) traits. The herd management levels of milk yield (milk deviation from whole-country mean, kilograms/day), protein and fat concentration (protein and fat deviation, %), and SCC (SCC deviation, 1,000 cells/mL) are used in the dairy herd management Web application “Maitoisa” (in English, “Milky”). This management tool helps to recognize several management problems. For recognition of systematic patterns and single unusual test-days, a monthly time-trend analysis was developed to smooth the random fluctuations and display the yearly production pattern. In addition to analyzing single test-day deviations from the mean, modeled herd solutions assist users in identifying repeated phenomena and enable the forecasting of the management pattern for the subsequent year. The solutions are displayed either as tables or graphs plotted by calendar months. In addition to management effects of the farmer's own herd, he or she can request country or region percentiles to be displayed in the graphs. The Web service has been offered to farmers and dairy advisors since 2001, and it has proved to be a powerful tool for herd monitoring and planning.     

Derivation and calculation of approximate reliabilities and daughter yield-deviations of a random regression test-day model for genetic evaluation of dairy cattle

Test-day milk, fat, protein yield, and somatic cell score (SCS) were analyzed separately using data from the first 3 lactations and a random regression model. Data used in the model were from Austria, Germany, and Luxembourg and from Holstein, Red, and Jersey dairy cattle. For reliability approximation, a multiple-trait effective daughter contribution (MTEDC) method was developed under general multiple trait models, including random regression test-day models, by extending the single-trait daughter equivalents concept. The MTEDC was applied to the very large dairy population, with about 15.5 million animals. The calculation of reliabilities required less computer memory than the corresponding iteration program and a significantly lower computing time equivalent to 24 rounds of iteration. A formula for daughter-yield deviations was derived for bulls under multiple-trait models. Reliability associated with daughter-yield deviations was approximated using the MTEDC method. Both the daughter-yield deviation formula and associated reliability method were verified in a simulation study using the random regression test-day model. Correlations of lactation daughter-yield deviations with estimated breeding values calculated from a routine genetic evaluation were 0.996 for all bulls and 0.95 for young bulls having only daughters with short lactations.     

Associations of breed and feeding management with milk production curves at herd level using a random regression test-day model

Earlier studies identified large between-herd variation in estimated lactation curve parameters from test-day milk yield and milk composition records collected in Ragusa province, Italy. The objective of this study was to identify sources of variation able to explain these between-herd differences in milk production curves, by estimating associations of animal breed (Holstein Friesian vs. Brown Swiss), feeding system [separate feeding (SF) vs. total mixed ration (TMR)], and TMR chemical composition on milk and milk components herd curves. Data recorded from 1992 through 2007 for test-day (TD) milk, fat, and protein yields from 1,287,019 records of 148,951 lactations of 51,489 cows in 427 herds were processed using a random regression TD model. Random herd curves (HCUR) for milk, fat, and protein yields were estimated from the model per herd, year, and parity (1, 2, and 3+) using 4-order Legendre polynomials. From March 2006 through December 2007, samples of TMR were collected every 3 mo from 37 farms in Ragusa province. Samples were analyzed for dry matter, ash, crude protein, soluble nitrogen, acid detergent lignin, neutral detergent fiber, acid detergent fiber, and starch. Traits used to describe milk production curves were peak, days in milk at peak, persistency, and mean. Association of feeding system and animal breed with HCUR traits was investigated using a general mixed model procedure. Association of TMR chemical composition with HCUR traits was investigated using multivariate analysis with regression and stepwise model selection. Results were consistent for all traits and parities. Feeding system was significantly associated with HCUR peak and mean, with higher values for TMR. Animal breed was significantly associated with HCUR persistency, with higher values for Brown Swiss herds. Furthermore, animal breed influenced HCUR peak and mean, with higher values for Holstein Friesian herds. Crude protein had the largest effect on HCUR peak and mean, whereas the interaction between crude protein and dry matter mainly affected persistency. When provided by a national evaluation system, HCUR can be used as an indicator of herd feeding management.     

Short communication: Effect of preadjusting test-day yields for stage of pregnancy on variance component estimation in Canadian Ayrshires

Preadjustment of phenotypic records is an alternative to accounting for the effect of pregnancy within the genetic evaluation model. Variance components used in the Canadian Test-Day Model may need to be re-estimated after preadjusting for pregnancy. The objective of this study was to assess the effect of preadjusting test-day yields on variance components and estimated breeding values using a random regression test-day model in a random sample of Ayrshire cows. A random sample of 981 Canadian Ayrshire cows from 18 complete herds (average of 54.5 cows/herd) was analyzed. Two data sets were created using the same animals, one with unadjusted milk, fat, and protein yields, and one data set with test-day records adjusted for pregnancy effects. Pregnancy effect estimates from a previous study were used for additive preadjustment of records. Variance components were estimated using both data sets. Results were very similar between the 2 data sets for all estimated genetic parameters (heritabilities, genetic, and permanent environmental correlations). The relative squared differences were very small: 0.05% for heritabilities, 0.20% for genetic correlations, and 0.18% for permanent environmental correlations. Furthermore, paired Student's t-tests showed that the differences between the genetic parameters of data sets adjusted and unadjusted for pregnancy effect were not significantly different from 0. Results from this study show that preadjusting data for pregnancy did not yield changes in covariance component estimates, thus suggesting that preadjusting test-day records could be a feasible solution to account for pregnancy in the Canadian Test-Day Model without changing the current model. Estimated breeding values (EBV) were calculated for both data sets to observe the impact of preadjusting for pregnancy. Overall, the largest changes in EBV seen when preadjusting for pregnancy (compared with unadjusted records) occurred for nonpregnant elite cows, whose EBV declined. Preadjusting for pregnancy before genetic evaluations improves the estimation of breeding values by adding the negative impact of pregnancy back onto pregnant cow test-day records, causing an increase in their production EBV.     

Production loss due to new subclinical mastitis in Dutch dairy cows estimated with a test-day model

Milk, fat, and protein loss due to a new subclinical mastitis case may be economically important, and the objective of this study was to estimate this loss. The loss was estimated based on test-day (TD) cow records collected over a 1-yr period from 400 randomly selected Dutch dairy herds. After exclusion of records from cows with clinical mastitis, the data set comprised 251,647 TD records from 43,462 lactations of 39,512 cows. The analysis was carried out using a random regression test-day modeling approach that predicts the cow production at each TD based on the actual production at all previous TD. The definition of new subclinical mastitis was based on the literature and assumed a new subclinical case if somatic cell count (SCC) was >100,000 cells/mL after a TD with SCC <50,000 cells/mL. A second data set was created by applying an adjustment to correct low SCC for the dilution effect when determining if the previous test-day SCC was <50,000 cells/mL. Thereafter, the loss was estimated for records with SCC >100,000 cells/mL. The production (milk, fat, or protein) losses were modeled as the difference between the actual and predicted production (milk, fat, or protein) at the TD of new subclinical mastitis, for 4,382 cow records, and 2,545 cow records after dilution correction. Primiparous cows were predicted to lose 0.31 (0.25-0.37) and 0.28 (0.20-0.35) kg of milk/d at an SCC of 200,000 cells/mL, for unadjusted and adjusted low SCC, respectively. For the same SCC increase, multiparous cows were predicted to lose 0.58 (0.54-0.62) and 0.50 (0.44-0.56) kg of milk/d, respectively. Moreover, it was found that the greater the SCC increase above 100,000 cells/mL, the greater the production losses. The estimated production losses were more precise than previously reported estimates.     

Random herd curves in a test-day model for milk, fat, and protein production of dairy cattle in The Netherlands

With random regression models, genetic parameters of test-day milk production records of dairy cattle can be estimated directly from the data. However, several researchers that used this method have reported unrealistically high variances at the borders of the lactation trajectory and low genetic correlations between beginning and end of lactation. Recently, it has been proposed to include herd-specific regression curves in the random regression model. The objective was to study the effect of including random herd curves on estimated genetic parameters. Genetic parameters were estimated with 2 models; both included random regressions for the additive genetic and permanent environmental effect, whereas the second model also included a random regression effect for herd x 2-yr period of calving. All random regressions were modeled with fourth-order Legendre polynomials. Bayesian techniques with Gibbs sampling were used to estimate all parameters. The data set comprised 857,255 test-day milk, fat, and protein records from lactations 1, 2, and 3 of 43,990 Holstein cows from 544 herds. Genetic variances estimated by the second model were lower in the first 100 d and at the end of the lactation, especially in lactations 2 and 3. Genetic correlations between d 50 and the end of lactation were around 0.25 higher in the second model and were consistent with studies where lactation stages are modeled as different traits. Subsequently, estimated heritabilities for persistency were up to 0.14 lower in the second model. It is suggested to include herd curves in a random regression model when estimating genetic parameters of test-day production traits in dairy cattle.     

Genetic evaluation of dairy cattle with test-day models with autoregressive covariance structures and with a 305-d model

This study compared genetic evaluations from 3 test-day (TD) models with different assumptions about the environmental covariance structure for TD records and genetic evaluations from 305-d lactation records for dairy cows. Estimates of genetic values of 12,071 first-lactation Holstein cows were obtained with the 3 TD models using 106,472 TD records. The compound symmetry (CS) model was a simple test-day repeatability animal model with compound symmetry covariance structure for TD environmental effects. The AR_s and AR_e models also used TD records but with a first-order autoregressive covariance structure among short-term environmental effects or residuals, respectively. Estimates of genetic values with the TD models were also compared with those from a model using 305-d lactation records. Animals were genetically evaluated for milk, fat, and protein yields, and somatic cell score (SCS). The largest average estimates of accuracy of predicted breeding values were obtained with the AR_s model and the smallest were with the 305-d model. The 305-d model resulted in smaller estimates of correlations between average predicted breeding values of the parents and lactation records of their daughters for milk and protein yields and SCS than did the CS and AR_e models. Predicted breeding values with the 3 TD models were highly correlated (0.98 to 1.00). Predicted breeding values with 305-d lactation records were moderately correlated with those with TD models (0.71 to 0.87 for sires and 0.80 to 0.87 for cows). More genetic improvement can be achieved by using TD models to select for animals for higher milk, fat, and protein yields, and lower SCS than by using models with 305-d lactation records.     

Analysis of milk production traits in early lactation using a reaction norm model with unknown covariates

The reaction norm model is becoming a popular approach to study genotype × environment interaction (G×E), especially when there is a continuum of environmental effects. These effects are typically unknown, and an approximation that is used in the literature is to replace them by the phenotypic means of each environment. It has been shown that this method results in poor inferences and that a more satisfactory alternative is to infer environmental effects jointly with the other parameters of the model. Such a reaction norm model with unknown covariates and heterogeneous residual variances across herds was fitted to milk, protein, and fat yield of first-lactation Danish Holstein cows to investigate the presence of G×E. Data included 188,502 first test-day records from 299 herds and 3,775 herd-years in a time period ranging from 1991 to 2003. Variance components and breeding values were estimated with a Bayesian approach implemented using Markov chain Monte Carlo. The posterior distribution of the variance of genetic slopes was markedly shifted away from zero for all traits under study, supporting the presence of G×E. The ratio of the genetic slope variance to the genetic level variance was highest for fat yield, followed by protein and milk yields. Genetic correlations between environments that differ by plus and minus 1 standard deviation from the mean environmental effect were 0.93, 0.91, and 0.89 for milk, protein, and fat yield, respectively. Genetic variances and heritabilities increased with increasing level of environmental effects. The rank correlations between predicted breeding values at the 5th and 95th percentiles of the distribution of environmental effects were, respectively, equal to 0.91, 0.90, and 0.76, for milk, protein, and fat yield. Thus in this study, although G×E was detected, it has a small effect on reranking of candidates for selection.     

香菇多糖的提取数学模型研究

运用统计分析方法,采用五元二次回归正交旋转组合设计进行香菇多糖提取试验,选择酸的浓度(mol/L)x1、料液比x2、浸提时间(t)x3、浸提温度(℃)x4、加醇比x5为调控因子,以香菇多糖的提取率y为目标函数,建立数学模型,经微机仿真寻优,决策出对香菇多糖提取有影响的5个因素的最佳范围是酸的浓度0.16￣0.23mol/L、料液比1∶36～1∶49、浸提时间2.7￣3.8h、浸提温度50￣67.8℃、加醇比1∶2￣1∶3组合方案,并研究了各项措施的交互作用效应,为香菇多糖的提取提供科学依据。     

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.     

Stillbirth (co)variance components for a sire-maternal grandsire threshold model and development of a calving ability index for sire selection

(Co)variance components for stillbirth in US Holsteins were estimated under a sire-maternal grandsire threshold model using subsets of data from the national calving ease database, which includes over 6 million calving records with associated stillbirth scores. Stillbirth was coded as a binomial trait indicating whether the calf was alive 48 h postpartum. Records were selected for calves whose sire and maternal grandsire (MGS) were among the 2,600 most frequently appearing bulls (2,578 sires and 2,586 MGS). Herd-years were required to contain at least 20 records and only single births were used. After editing, the data set included 2,083,979 calving records from 5,765 herds and 33,304 herd-years. Six sample datasets of approximately 250,000 records each were created by randomly selecting herd codes. Quasi-REML and Bayesian approaches were used to estimate (co)variance components from each sample. The model included fixed year-season, parity-sex, birth year group of sire, and birth year group of MGS effects and random herd-year, sire, MGS, and residual effects. Quasi-REML and Bayesian analyses produced similar results, although the Bayesian estimates were slightly larger. Marginal posterior means (and standard deviations) from the Bayesian analysis averaged 0.0085 (0.0015), 0.0181 (0.0020), 0.0872 (0.0538), and 0.00410 (0.0001) for sire, MGS, and herd-year variances and the sire-MGS covariance, respectively. Mean direct and maternal heritabilities were 0.030 (0.003) and 0.058 (0.005), respectively, and the mean genetic correlation between the 2 effects was −0.02 (0.16). A calving ability index combining stillbirth (SB) and calving ease (CE) was developed for inclusion in the Lifetime Net Merit index. The index was calculated as −4(sire CE)−3(daughter CE)−4(sire SB) −8(daughter SB).     

The effect of high-sugar grass on predicted nitrogen excretion and milk yield simulated using a dynamic model

High-sugar grass varieties have received considerable attention for their potential to reduce nitrogen (N) excretion and increase milk yield in cattle. However, considerable variation exists in the magnitude of response in published results. The purpose of this study is to explain the variation in response using a dynamic mechanistic model to predict observed N and milk yield results from the literature, and from simulated data. Examined effects were (1) water-soluble carbohydrate [WSC; g/kg of dry matter (DM)] increase; (2) change in crude protein (CP) and neutral detergent fiber (NDF) content of the plant with WSC increase; and (3) the level of N fertilization. The database for evaluation of model N and milk yield predictions consisted of 4 published studies with 28 treatment means for which high-sugar grasses were being evaluated. Water-soluble carbohydrate content of the diets ranged from 95 to 248 g/kg of DM, CP content ranged from 115 to 263 g/kg of DM, and the NDF content ranged from 400 to 568 g/kg of DM. Urine N, milk N, and total N excretion were predicted well by the model and followed the directional pattern of observed values within each study. Simulation results showed that the N utilization ratio increased as the WSC content of the diet increased, but to varying degrees depending on the grass scenario examined. The greatest benefit in terms of N utilization ratio and urine N levels were seen when the WSC content of grass increased at the expense of CP, followed by a 50:50 CP and NDF mix, followed by a trade for NDF. Simulated milk yield decreased slightly when WSC increased at the expense of CP, increased slightly when it increased at the expense of a CP and NDF mix, and increased most when WSC increased at the expense of NDF. Results were amplified slightly under conditions of low-N fertilization and in the absence of grain feeding. Overall, modeling is useful as an explanatory tool. The variation from results in the literature with high-WSC grass feeding may be, at least in part, the result of the level of WSC (g/kg of DM) increase, concurrent changes occurring within the CP and NDF fractions of the plant, and the plane of nutrition of the diet (grain feeding and N fertilization levels).     

Bivariate analysis of conception rates and test-day milk yields in Holsteins using a threshold-linear model with random regressions

The objective of this study was to estimate genetic correlations between conception rates (CR) and test-day (TD) milk yields in Holsteins for different days in milk (DIM) in small and large herds. The data included 217,213 first-parity service records of 94,984 cows in New York State between 1999 and 2003. The CR was defined as the outcome of a single insemination. Conception rate and TD milk were analyzed using a series of threshold-linear models with fixed effects that included herd-test-date for TD milk and herd-year for CR, age, service month, cubic regressions on DIM using Legendre polynomials and with random effects that included herd × sire interaction, sire additive genetic and permanent environments with quadratic random regressions on DIM, service sire for CR, and residual. Variance components were estimated using a Bayesian method via Gibbs sampling. Herds were categorized into small (≤80 cows) and large operations. Large herds produced a higher TD milk (34.0 vs. 30.8 kg), had lower CR (29.5 vs. 34.4%), began breeding earlier (75 vs. 92 d to first service), and had fewer days open (138 vs. 145 d). The average CR was 20% at 50 DIM, increased to about 38% at DIM 100, and then leveled off. Estimated genetic correlations between CR and TD milk stayed around −0.15 for small herds but changed from positive (0.3) at 60 DIM to negative (−0.3) at 120 DIM for large herds. Genetic correlations for CR between small and large herds were highest at 80 DIM and lowest at 140 DIM. The chi-square test showed that the frequency of service records was significantly different during a given week for 71% of large herds and for 15% of small herds, suggesting more timed artificial insemination services in large herds. For the top 15% of cows for milk, fertility peaked around 100 DIM in large herds and at around 100 and 170 DIM in small herds. It seems that optimum breeding practices in large herds of breeding cows earlier are already followed.     

Impact of seasonal changes in ovine milk on composition and yield of a hard-pressed cheese

A hard-pressed, brined cheese was produced from frozen ovine milk collected in February, May, and August. Solids in the milk decreased as the season progressed. This was a result of high solids in early-lactation milk and low solids in August milk because of hot weather and poorer quality pastures. Casein as a percentage of true protein and the casein to fat ratio were higher in May and August milk. Fat in the cheese from February milk was higher and total protein was lower than in May and August. Milk, whey, and press whey composition were influenced by season and followed the trends of milk composition. Fat recovery in the cheeses ranged from 83.2 to 84.2%. Protein recovery in the cheeses was not affected by season. Cheese yield from February milk was higher than from May and August milk and was a result of higher casein and fat in the milk.     

响应面法优化复合食品添加剂对猪肉蒸煮出品率工艺研究

为了提高猪肉的蒸煮出品率,首先将三聚磷酸钠∶焦磷酸钠∶六偏磷酸钠按4∶3∶2比例复配成复合磷酸盐添加剂。以猪肉蒸煮出品率为评价指标,在单因素工艺试验的基础上,应用响应面分析法对复合磷酸盐、谷氨酰胺转胺酶、大豆分离蛋白、卡拉胶添加量进行优化,结果表明,最佳添加量为复合磷酸盐0.36%、TG酶0.60%、卡拉胶0.20%、大豆分离蛋白0.41%,在此条件下,猪肉蒸煮出品率可达到89.5311%。验证试验猪肉蒸煮出品率为(89.1±0.4)%,表明实验结果与软件优化结果相符。