Prediction accuracy of direct and indirect approaches, and their relationships with prediction ability of calibration models.

Milk infrared spectra are routinely used for phenotyping traits of interest through links developed between the traits and spectra. Predicted individual traits are then used in genetic analyses for estimated breeding value (EBV) or for phenotypic predictions using a single-trait mixed model; this approach is referred to as indirect prediction (IP). An alternative approach [direct prediction (DP)] is a direct genetic analysis of (a reduced dimension of) the spectra using a multitrait model to predict multivariate EBV of the spectral components and, ultimately, also to predict the univariate EBV or phenotype for the traits of interest. We simulated 3 traits under different genetic (low: 0.10 to high: 0.90) and residual (zero to high: ±0.90) correlation scenarios between the 3 traits and assumed the first trait is a linear combination of the other 2 traits. The aim was to compare the IP and DP approaches for predictions of EBV and phenotypes under the different correlation scenarios. We also evaluated relationships between performances of the 2 approaches and the accuracy of calibration equations. Moreover, the effect of using different regression coefficients estimated from simulated phenotypes (ßp), true breeding values (ßg), and residuals (ßr) on performance of the 2 approaches were evaluated. The simulated data contained 2,100 parents (100 sires and 2,000 cows) and 8,000 offspring (4 offspring per cow). Of the 8,000 observations, 2,000 were randomly selected and used to develop links between the first and the other 2 traits using partial least square (PLS) regression analysis. The different PLS regression coefficients, such as ßp, ßg, and ßr, were used in subsequent predictions following the IP and DP approaches. We used BLUP analyses for the remaining 6,000 observations using the true (co)variance components that had been used for the simulation. Accuracy of prediction (of EBV and phenotype) was calculated as a correlation between predicted and true values from the simulations. The results showed that accuracies of EBV prediction were higher in the DP than in the IP approach. The reverse was true for accuracy of phenotypic prediction when using ßp but not when using ßg and ßr, where accuracy of phenotypic prediction in the DP was slightly higher than in the IP approach. Within the DP approach, accuracies of EBV when using ßg were higher than when using ßp only at the low genetic correlation scenario. However, we found no differences in EBV prediction accuracy between the ßp and ßg in the IP approach. Accuracy of the calibration models increased with an increase in genetic and residual correlations between the traits. Performance of both approaches increased with an increase in accuracy of the calibration models. In conclusion, the DP approach is a good strategy for EBV prediction but not for phenotypic prediction, where the classical PLS regression-based equations or the IP approach provided better results.

An attempt at predicting blood ß-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood ß-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.

Genetic parameters of blood ß-hydroxybutyrate predicted from milk infrared spectra and clinical ketosis, and their associations with milk production traits in Norwegian Red cows.

The aim of this study was to estimate genetic parameters for blood ß-hydroxybutyrate (BHB) predicted from milk spectra and for clinical ketosis (KET), and to examine genetic association of blood BHB with KET and milk production traits (milk, fat, protein, and lactose yields, and milk fat, protein, and lactose contents). Data on milk traits, KET, and milk spectra were obtained from the Norwegian Dairy Herd Recording System with legal permission from TINE SA (Ås, Norway), the Norwegian Dairy Association that manages the central database. Data recorded up to 120 d after calving were considered. Blood BHB was predicted from milk spectra using a calibration model developed based on milk spectra and blood BHB measured in Polish dairy cows. The predicted blood BHB was grouped based on days in milk into 4 groups and each group was considered as a trait. The milk components for test-day milk samples were obtained by Fourier transform mid-infrared spectrometer with previously developed calibration equations from Foss (Hillerød, Denmark). Veterinarian-recorded KET data within 15 d before calving to 120 d after calving were used. Data were analyzed using univariate or bivariate linear animal models. Heritability estimates for predicted blood BHB at different stages of lactation were moderate, ranging from 0.250 to 0.365. Heritability estimate for KET from univariate analysis was 0.078, and the corresponding average estimate from bivariate analysis with BHB or milk production traits was 0.002. Genetic correlations between BHB traits were higher for adjacent lactation intervals and decreased as intervals were further apart. Predicted blood BHB at first test day was moderately genetically correlated with KET (0.469) and milk traits (ranged from -0.367 with protein content to 0.277 with milk yield), except for milk fat content from across lactation stages that had near zero genetic correlation with BHB (0.033). These genetic correlations indicate that a lower BHB is genetically associated with higher milk protein and lactose contents, but with lower yields of milk, fat, protein, and lactose, and with lower frequency of KET. Estimates of genetic correlation of KET with milk production traits were from -0.333 (with protein content) to 0.178 (with milk yield). Blood BHB can routinely be predicted from milk spectra analyzed from test-day milk samples, and thereby provides a practical alternative for selecting cows with lower susceptibility to ketosis, even though the correlations are moderate.

Genetic components of milk Fourier-transform infrared spectra used to predict breeding values for milk composition and quality traits in dairy goats.

The usual practice today is that milk component phenotypes are predicted using Fourier-transform infrared (FTIR) spectra and they are then, together with pedigree information, used in BLUP for calculation of individual estimated breeding values. Here, this is referred to as the indirect prediction (IP) approach. An alternative approach-a direct prediction (DP) method-is proposed, where genetic analyses are directly conducted on the milk FTIR spectral variables. Breeding values of all derived milk traits (protein, fat, fatty acid composition, and coagulation properties, among others) can then be predicted as traits correlated only to the genetic information of the spectra. For the DP, no need exists to predict the phenotypes before calculating breeding values for each of the traits-the genetic analysis is done once for the spectra, and is applicable to all traits derived from the spectra. The aim of the study was to compare the effects of DP and IP of milk composition and quality traits on prediction error variance (PEV) and genetic gain. A data set containing 27,927 milk FTIR spectral observations and milk composition phenotypes (fat, lactose, and protein) belonging to 14,869 goats of 271 herds was used for training and evaluating models. Partial least squares regression was used for calibrating prediction models for fat, protein, and lactose percentages. Restricted maximum likelihood was used to estimate variance components of the spectral variables after principal components analysis was applied to reduce the spectral dimension. Estimated breeding values were predicted for fat, lactose, and protein percentages using DP and IP methods. The DP approach reduced the mean PEV by 3.73, 4.07, and 7.04% for fat, lactose, and protein percentages, respectively, compared with the IP method. Given the reduction in PEV, relative genetic gains were 2.99, 2.78, and 4.85% for fat, lactose, and protein percentages, respectively. We concluded that more accurate estimated breeding values could be found using genetic components of milk FTIR spectra compared with single-trait animal model analyses on phenotypes predicted from the spectra separately. The potential and application is not only limited to milk FTIR spectra, but could also be extended to any spectroscopy techniques implemented in other species and for other traits.

Newly identified mutations at the CSN1S1 gene in Ethiopian goats affect casein content and coagulation properties of their milk.

Very high casein content and good coagulation properties previously observed in some Ethiopian goat breeds led to investigating the αs1-casein (CSN1S1) gene in these breeds. Selected regions of the CSN1S1 gene were sequenced in 115 goats from 5 breeds (2 indigenous: Arsi-Bale and Somali, 1 exotic: Boer, and 2 crossbreeds: Boer × Arsi-Bale and Boer × Somali). The DNA analysis resulted in 35 new mutations: 3 in exons, 3 in the 5' untranslated region (UTR), and 29 in the introns. The mutations in exons that resulted in an amino acid shift were then picked to evaluate their influence on individual casein content (αs1-, αs2-, ß-, and κ-CN), micellar size, and coagulation properties in the milk from the 5 goat breeds. A mutation at nucleotide 10657 (exon 10) involved a transversion: CAG→CCG, resulting in an amino acid exchange Gln77→Pro77. This mutation was associated with the indigenous breeds only. Two new mutations, at nucleotide 6072 (exon 4) and 12165 (exon 12), revealed synonymous transitions: GTC→GTT in Val15 and AGA→AGG in Arg100 of the mature protein. Transitions G→A and C→T at nucleotides 1374 and 1866, respectively, occurred in the 5' UTR, whereas the third mutation involved a transversion T→G at nucleotide location 1592. The goats were grouped into homozygote new (CC), homozygote reference (AA), and heterozygote (CA) based on the nucleotide that involved the transversion. The content of αs1-CN (15.32g/kg) in milk samples of goats homozygous (CC) for this newly identified mutation, Gln77→Pro77 was significantly higher than in milks of heterozygous (CA; 9.05g/kg) and reference (AA; 7.61g/kg) genotype animals. The αs2-, ß-, and κ-CN contents showed a similar pattern. Milk from goats with a homozygous new mutation had significantly lower micellar size. Milk from both homozygote and heterozygote new-mutation goats had significantly shorter coagulation rate and stronger gel than the reference genotype. Except the transversion, the sequence corresponded to allele A and presumably derived from it. Therefore, this allele is denoted by A3. All goats from the reference genotype (AA) were homozygous for the allele at nucleotide position 1374 and 1866, whereas all mutations in the 5' UTR existed in a heterozygous form in both heterozygous (CA) and the new mutation (CC) genotype. The newly identified mutation (CC) detected in some of the goat breeds is, therefore, important in selection for genetic improvement and high-quality milk for the emerging goat cheese-producing industries. The finding will also benefit farmers raising these goat breeds due to the increased selling price of goats. Further studies should investigate the effect of this amino acid exchange on the secondary and tertiary structure of the αs1-CN molecule and on the susceptibility of peptide hydrolysis by digestive enzymes.

Genetic and environmental information in goat milk Fourier transform infrared spectra.

Fourier transform infrared (FTIR) spectroscopy is often used in prediction of major milk components in genetic evaluation of dairy animals. Until now genetic variability of goat milk FTIR spectra has only been known indirectly through their contribution to the major milk components. In this study, genetic and environmental components of goat milk FTIR spectra were examined directly. A data set containing 83,858 milk FTIR spectral observations belonging to 29,320 Norwegian dairy goats of 271 herds was used for the study. Principal components analysis was applied on both unprocessed and preprocessed spectral data, and new traits (latent traits) were defined because a multitrait analysis of all spectral variables for variance components could not be done. Eight and 7 latent variables, explaining approximately 99% of the total unprocessed and preprocessed spectral variation, respectively, were kept from the principal components analysis for genetic analysis. Genetic and environmental variance components were estimated for the latent traits using restricted maximum likelihood. Genetic-to-total phenotypic variance ratios (heritabilities) of the latent traits were between 0.011 and 0.285 for the unprocessed spectra and between 0.135 and 0.262 for the preprocessed spectra. The estimated variance components for the latent traits were back transformed to the spectral variables. Heritabilities of these spectral variables ranged from 0.018 to 0.408 and variance ratios of the permanent environmental effects of goats were between 0.002 and 0.184 of the phenotypic spectral variation. High-to-moderate heritabilities were observed in particular in spectral regions related to major milk components (fat, lactose, and protein): between 1,030 and 1,300 cm(-1), 1,500 and 1,600 cm(-1), 1,700 and 1,800 cm(-1), and 2,800 and 3,000 cm(-1). Our results confirmed that a substantial amount of genetic variation exists in goat milk FTIR spectra. Not all spectral variations are of genetic origin; some FTIR regions are highly influenced by herd test-day variation. The study also pointed out the possibility of using FTIR spectra as a monitoring tool in herd management.

Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats.

One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.

A method for the prediction of multitrait breeding values for use in stochastic simulation to compare progeny-testing schemes, with large progeny groups for proven sires.

A method of approximating estimated breeding values (EBV) from a multivariate distribution of true breeding values (TBV) and EBV is proposed for use in large-scale stochastic simulation of alternative breeding schemes with a complex breeding goal. The covariance matrix of the multivariate distributions includes the additive genetic (co)variances and approximated prediction error (co)variances at different selection stages in the life of the animal. The prediction error (co)variance matrix is set up for one animal at a time, utilizing information on the selection candidate and its offspring, the parents, as well as paternal and maternal half- sibs. The EBV are a regression on TBV taking individual uncertainty into account, but with additional 'free' variation drawn at random. With the current information included in the calculation of the prediction error variance of a selection candidate, it is concluded that the method can be used to optimize progeny-testing schemes, where the progeny-tested sires are utilized with large progeny groups, e.g. through artificial insemination.

Fertility results after vaginal deposition of frozen-thawed buck semen diluted with two different extenders using one- or two-step procedures.

In two field trials (T1 and T2), the effect of two different extenders for buck semen was tested. Semen from six (T1) and seven (T2) bucks of the Norwegian Dairy Goat breed was diluted either in a milk-based extender containing egg yolk (M) or in a commercially available extender without egg yolk [Andromed(®) (A)]. Dilution in M was performed in a two-step procedure including centrifugation of the ejaculates and removal of the supernatant, while dilution in A was performed in one step. During the two trials (T1 and T2) 514 and 714, does, respectively, were artificially inseminated during natural oestrus, and the farmers performed the inseminations themselves after attending an artificial insemination (AI) training course. Vaginal insemination with 200 × 10(6) spermatozoa diluted in M resulted in a 25-day non-return rate (NRR) and kidding rate of 37.3% and 24.5%, respectively, while semen diluted in A resulted in 31.7% NRR and a kidding rate of 19.8% (T1). In T2, NRR and kidding rate for AI performed with semen diluted with M were 42.7% and 28.5%, respectively, while dilution in A gave 37.2% NRR and a kidding rate of 26.8%. There was no significant effect of extender in the two trials [T1:p=0.068 (NRR), p=0.148 (kidding rate), T2:p=0.096 (NRR), p=0.38 (kidding rate)], but farmer had a significant effect on the fertility parameters in both trials. In conclusion, the present studies may indicate that Andromed(®) is suitable for buck semen. However, more research is necessary to confirm the results and to improve the fertility of does after vaginal AI with frozen-thawed semen.

Sheep breeding schemes utilising artificial insemination; large-scale simulation with a complex breeding goal.

Alternative Norwegian sheep breeding schemes were evaluated by stochastic simulation of a breeding population with about 120 000 ewes, considering the gain for an aggregate genotype including nine traits and also the rate of inbreeding. The schemes were: a scheme where both young unproven rams (test rams) and proven rams (elite rams) are used in artificial insemination (AI scheme), a scheme with test rams in natural mating in ram circles and elite rams (from one and a half years of age) in AI across all flocks in the country (NMAI2 scheme), a scheme where, in addition to testing rams, the youngest elite rams (one and a half years of age) are also used in natural mating in ram circles, while older elite rams are used in AI (NMAI1 scheme), and a scheme, acting as a control, where both test and elite rams are used in natural mating (NM scheme). Within the NMAI- and AI-schemes, experimentation was performed for percent ewes inseminated to elite rams v. test rams (EM%), numbers of ewes inseminated per elite ram (EAIn), and numbers of ewes mated per test ram by natural service (TNMn) or by AI (TAIn), respectively. With a restriction on the rate of inbreeding (⩽0.8% per generation), the AI scheme gave similar gain to the NMAI2 scheme (and about 40% more than did the NM scheme). Less gain was generated by the NMAI1 scheme, but it was still considerably more than for the NM scheme (about 25%). In the AI scheme, relatively few ewes (200/300) should be inseminated to each test/elite ram, and a low EM% should be chosen (10%). In the NMAI schemes, TNMn should be relatively high (40 to 50), combined with average and somewhat larger than average EAIn (NMAI2: 700 ewes, NMAI1: 900 ewes), and EM% medium (30%).

Caprine CSN1S1 haplotype effect on gene expression and milk composition measured by Fourier transform infrared spectroscopy.

The Norwegian dairy goat population has a high frequency of a CSN1S1 (alphaS1-casein) haplotype with negative effects on protein and fat content. It is characterized by a single point deletion in exon 12 of CSN1S1, leading to a truncated protein and hence a low content of alphaS1-casein in the milk. This haplotype together with another haplotype with a deletion in exon 9 are called "weak" haplotypes. "Strong" haplotypes, on the other hand, have positive effects on important milk production traits. We show that expression of CSN1S1 in the mammary gland of lactating goats is significantly lower in animals with 2 weak haplotypes. Moreover, the effects of defective alleles were not detected in animals having 1 strong and 1 weak haplotype. Expression levels of other genes in the casein cluster were not affected by the CSN1S1 haplotypes investigated. Milk samples from goats with 2 weak haplotypes could be distinguished from the other milk samples using Fourier transform infrared (FTIR) spectroscopy and partial least squares discriminant analysis (PLS-DA). The PLS-DA components were related to spectra of pure caseins and whey proteins, hence FTIR has a potential for identifying milk samples with low alphaS1-casein content and different protein composition. The results indicate that FTIR-based measurements can be incorporated into breeding plans, or for selection of milk samples with high casein content, which in turn may improve cheese-making properties of the milk.

Vaginal deposition of frozen-thawed semen in Norwegian Dairy goats: comparison of single and double insemination with equal total number of spermatozoa.

In a field trial, a total of 472 Norwegian Dairy goats showing natural estrus were artificially inseminated with frozen-thawed semen. The farmers themselves performed vaginal deposition of 400 x 10(6) spermatozoa; one half of the does received two straws (200 x 10(6) spermatozoa/straw) at the same time (single AI), while the other half received two straws (200 x 10(6) spermatozoa/straw) 12 h apart (double AI). The commercially available extender Andromed was used for dilution. The does were housed at 15 different farms, and on average 31 does were inseminated per farm. Non return rates (NRR) and kidding rates after single insemination were 64.3% and 58.3%, respectively. Double inseminations resulted in a NRR of 62% and a kidding rate of 57%. No significant difference between single and double AI was seen in the study. This study indicates that single or double vaginal insemination with an equal total number of frozen-thawed spermatozoa (400 x 10(6)) can give acceptable fertility results in Norwegian Dairy goats. However, studies on reducing sperm numbers are called for to allow AI donor bucks to be used to their fullest potential.

Effect on field fertility of addition of gelatine, different dilution rates and storage times of cooled ram semen after vaginal insemination.

In a field trial, 633 ewes from 24 farms were inseminated vaginally using liquid semen (150 x 10(6) per dose) collected from 15 rams. The semen was either diluted with a milk-based extender (M), filled in 0.2 ml straws and stored for 12 or 24 h (M12, M24) or diluted with M but with the addition of gelatine, filled in 0.5 ml straws and stored for 12 or 24 h (G12, G24). The hypothesis was that a larger volume and the addition of gelatine would prolong the survival of the spermatozoa. The ewes, aged between 6 months and 5.5 years, were allocated into four groups and inseminated after natural oestrus by the farmers themselves with a dose of 150 x 10(6) spermatozoa. Inseminations in the groups (M12, M24, G12, G24) resulted in lambing rates of 69.6%, 63.6%, 69.4% and 58.3% (overall 65.2%), respectively. Farmer (p < .0002) had a significant effect on the lambing rate, while ram, age of the ewe and dilution rate/addition of gelatine/storage time had not. A pair-wise comparison of the lambing rates between the four groups showed that significant lower results were only achieved for G24 compared with M12. None of the other comparisons showed significant differences. In conclusion, a higher dilution rate of the AI-dose together with the addition of gelatine to the semen extender did not lead to improved fertility results after storage for 24 h when compared with standard AI-doses used in Norway.

Effect of different packages and freezing/thawing protocols on fertility of ram semen.

A field trial was performed in order to evaluate the effect on fertility of different straw types, freezing protocols (one- or two-step) and thawing procedures (35 degrees C and 70 degrees C) using frozen-thawed ram semen. A total of 791 Norwegian Crossbred ewes were artificially inseminated during natural oestrus with semen collected from nine mature and proven Norwegian Crossbred rams. A milk-based extender was used for dilution. The ewes were allocated into one of the following three groups based on the different straw types and thawing temperatures: medium straw (0.5 ml) thawed at 35 degrees C for 20 s (Med35), medium straw thawed at 70 degrees C for 8 s (Med70) and mini straw (0.25 ml) thawed at 35 degrees C for 15 s (Mini35). The semen to be frozen in mini straws was re-concentrated by centrifugation. Sperm number in each insemination dose was approximately 200 x 10(6) spermatozoa. The fertility results [as 25-day non-return rate (NRR)] for Med35, Med70 and Mini35 were 53.1%, 50.8% and 58.3%, respectively, and the lambing rates 49.8%, 46.8% and 53.8%, respectively. No significant main effects were seen for straw type/thawing temperature (p = 0.17), ram (p = 0.06) or age of the ewe (p = 0.18) on NRR or lambing rates (p = 0.19, p = 0.16 and p = 0.27, respectively). Both NRR and lambing rate differed significantly among farms (p < 0.0001).

Comparison of casein haplotypes between two geographically distant European dairy goat breeds.

The aim of this paper was to characterize the diversity among haplotypes based on 22 single nucleotide polymorphisms (SNPs) and one deletion within four casein genes in two geographically distant goat populations, the Sicilian Girgentana breed and the Norwegian goat breed. Forty Girgentana goats were genotyped for the aforementioned polymorphisms and the resulting data set was compared with 436 goats from the Norwegian population previously genotyped for these markers. Several casein gene polymorphisms were not in Hardy-Weinberg equilibrium either in Girgentana, or in the Norwegian breed. The SNP haplotype frequencies for the four casein genes were calculated and despite the large geographical distance and phenotypic divergence between these breeds, a proportion of casein loci haplotypes were found to be identical between both Norwegian and Girgentana goats. However, for the CSN2 gene there were no common haplotypes between the two populations. The level of linkage disequilibrium between the casein genes was less in the Girgentana population than in the Norwegian population.

Genotype by environment interaction for lamb weaning weight in two Norwegian sheep breeds.

Genotype x environment interaction (G x E) effects on live weaning weights of lambs were studied by using the 2 breeds Norwegian White sheep (NWS; heavy, long-tailed) and Spel sheep (Spel; lighter, short-tailed) as genetic groups (G). A total of 37,338 NWS lambs and 30,075 Spel lambs born from 1989 to 1999 on 40 farms that kept both breeds together were included in the analyses. Environment was characterized by farm x year (E). In a mixed linear model framework, significance of the random G x E effect and breed-specific environmental variances were tested by using a log-likelihood approach. Directions and magnitudes of the effect were described through variance component estimates. An across-genotype environmental correlation was also used. There was a significant G x E effect on lamb BW; significant breed differences were found for variance of flock x year effects, indicating different phenotypic plasticities with changing flock x year environments, with the NWS being more sensitive to environmental change. Further, the breed-specific residual variance was greater for NWS, indicating that the effects of environmental variation were larger for the weaning weights of the NWS breed within flock and year. Further, the correlation between flock x year effects for the 2 breeds was significantly different from unity (0.82 +/- 0.02), indicating that the common environment is "perceived" differently in the 2 breeds. The best environment for one breed is not necessarily best for the other breed, and vice versa. Solutions of flock x year effects may be used to describe how environmental characteristics such as climate and topography affect the production of different genotypes, and for clustering of environments, thus facilitating improvement of breeding programs and management schemes for domestic and wild ungulate populations.

The Norwegian sheep breeding scheme: description, genetic and phenotypic change.

The developments in Norwegian sheep breeding since the early 1990s are reviewed. For the largest breeding population, the Norwegian White Sheep, results are presented for both genetic and phenotypic changes. Of the nine traits that make up the aggregate genotype, the largest gain per year, in per cent of the corresponding phenotypic average, was found for carcass grade (1.66%) and carcass weight (0.99%), number of lambs born at 1, 2 and 3 years of age (0.32% to 0.60%) and the maternal effect on weaning weight (0.26%). For fat grade, a genetic deterioration was estimated. This may be due to the too small weighting of this trait in the aggregate genotype and the true genetic parameters being somewhat different from the estimates in the prediction of breeding values. For lamb as well as ewe fleece weight, genetic change was close to zero - interpreted as mainly a correlated response to other traits in the aggregate genotype. Data for the two traits of fleece weight were, respectively, selected and few. Thus, phenotypic change was calculated for all traits except for fleece weight, and in addition for number of lambs at weaning, being indirectly selected for through number of lambs born. For all traits, with the exception of fat grade, advantageous phenotypic change was estimated. For weaning and carcass weight, the phenotypic change was less than the genetic change, while the opposite was observed for carcass and fat grade and number of lambs born. The latter traits can be more easily controlled by environmental actions, and the results thus exemplify the interdependency between environmental and genetic change.

Validation of test-day models for genetic evaluation of dairy goats in Norway.

Test-day data for daily milk yield and fat, protein, and lactose content were sampled from the years 1988 to 2003 in 17 flocks belonging to 2 genetically well-tied buck circles. In total, records from 2,111 to 2,215 goats for content traits and 2,371 goats for daily milk yield were included in the analysis, averaging 2.6 and 4.8 observations per goat for the 2 groups of traits, respectively. The data were analyzed by using 4 test-day models with different modeling of fixed effects. Model [0] (the reference model) contained a fixed effect of year-season of kidding with regression on Ali-Schaeffer polynomials nested within the year-season classes, and a random effect of flock test-day. In model [1], the lactation curve effect from model [0] was replaced by a fixed effect of days in milk (in 3-d periods), the same for all year-seasons of kidding. Models [2] and [3] were obtained from model [1] by removing the fixed year-season of kidding effect and considering the flock test-day effect as either fixed or random, respectively. The models were compared by using 2 criteria: mean-squared error of prediction and a test of bias affecting the genetic trend. The first criterion indicated a preference for model [3], whereas the second criterion preferred model [1]. Mean-squared error of prediction is based on model fit, whereas the second criterion tests the ability of the model to produce unbiased genetic evaluation (i.e., its capability of separating environmental and genetic time trends). Thus, a fixed structure with year (year, year-season, or possibly flock-year) was indicated to appropriately separate time trends. Heritability estimates for daily milk yield and milk content were 0.26 and 0.24 to 0.27, respectively.

Effect of volatile compounds in grass silage on voluntary intake by growing cattle.

Twenty-four low dry matter (DM) silages differing in fermentation quality were harvested at the same time from a crop that consisted mainly of timothy (Phleum pratense), and meadow fescue (Festuca pratensis). The silage samples were analysed by gas chromatography (GC) - mass spectrometry and gas chromatography - flame ionisation detection in order to determine and quantify volatiles present in silage. The voluntary intake of the 24 silages had been measured in a previous feeding trial with growing steers of Norwegian Red. Thirteen esters, five aldehydes, three alcohols, and one sulphide were identified and quantified. A total of 51 variables describing the chemical composition of the silages were included in a partial least-squares regression, and the relationship of silage fermentation quality to voluntary intake was elucidated. The importance of variables describing silage fermentation quality in relation to intake was judged from a best combination procedure, jack-knifing, and empirical correlations of the variables to intake. The GC-analysed compounds were mainly present in poorly fermented silages. However, compared with other explanatory chemical variables none of these compounds was of importance for the voluntary intake as evaluated by partial least-squares regression. A validated variance of 71% in silage DM intake was explained with the selected variables: total acids (TA), total volatile fatty acids (TVFA), lactic acid/total acid ratio and propionic acid. In this study extent (by the variable TA) and type of silage fermentation (by TVFA) influenced intake. Further, it is suggested that by restricting the fermentation in low DM grass silages the potential intake of silage DM is maximised.

Effect of vaginal and cervical deposition of semen on the fertility of sheep inseminated with frozen-thawed semen.

The effect of vaginal and cervical deposition of frozen-thawed semen on the fertility of sheep was tested in a field trial in which 543 Norwegian crossbred ewes aged between six months and five-and-a-half years from 10 farms were inseminated after natural oestrus. Cervical insemination with 200 x 10(6) spermatozoa resulted in 25-day non-return and lambing rates of 75.4 and 72.7 per cent, respectively, and vaginal insemination gave rates of 71.3 and 67.4 per cent; the cervical inseminations produced significantly higher lambing rates (P=0.04). There were significant differences between the lambing rates for different rams (P=0.006) and different farmers (P=0.003), and there was a significant interaction between farmer and deposition site (P=0.03). After vaginal insemination fertility was encouragingly high, but the results varied with the farmer, and different flock and management conditions.