Comparison of conjugated linoleic acid (CLA) content in milk of ewes and goats with the same dietary regimen

Milk fat is an important source of potential anticarcinogens named conjugated linoleic acid (CLA). The c9, t11-CLA is the major isomer and it is produced by ruminal hydrogenation of linoleic acid that leads first to vaccenic (11t-18:1) and finally to stearic acid (18:0). An alternative CLA pathway is related to the action of the mammary Δ9-desaturase enzyme on 11t-18:1. Diet is considered the main factor that influence the CLA concentration in milk fat. Differences in CLA content of sheep (Antongiovanni et al., 2002) and goats milk (Nudda et al., 2002) have been hypothesized, but results are difficult to compare due to different experimental condition in which the trials are carried out. The aim of this work is to compare the composition of milk fatty acids and CLA content in goats and sheep maintained in the same environmental condition and dietary regimen

Comparison of parametric, orthogonal, and spline functions to model individual lactation curves for milk yield in Canadian Holsteins

Test day records for milk yield of 57,390 first lactation Canadian Holsteins were analyzed with a linear model that included the fixed effects of herd-test date and days in milk (DIM) interval nested within age and calving season. Residuals from this model were analyzed as a new variable and fitted with a five parameter model, fourth-order Legendre polynomials, with linear, quadratic and cubic spline models with three knots. The fit of the models was rather poor, with about 30%-40% of the curves showing an adjusted R-square lower than 0.20 across all models. Results underline a great difficulty in modelling individual deviations around the mean curve for milk yield. However, the Ali and Schaeffer (5 parameter) model and the fourth-order Legendre polynomials were able to detect two basic shapes of individual deviations among the mean curve. Quadratic and, especially, cubic spline functions had better fitting performances but a poor predictive ability due to their great flexibility that results in an abrupt change of the estimated curve when data are missing. Parametric and orthogonal polynomials seem to be robust and affordable under this standpoint

Effects of lactation stage, parity, beta-lactoglobulin genotype and milk SCC on whey protein composition in Sarda dairy ewes

In 90 Sarda dairy ewes the effects of lactation stage, parity, beta-lactoglobulin genotypes, and somatic cell count (SCC) on the milk content of total protein (TP), casein (CN), whey protein (WP) and its fractions alpha-lactalbumin (ALA), beta-lactoglobulin (BLG), serum albumin (SA), immunoglobulin (IG) and lactoferrin (LF) were analysed using a linear mixed model. Mean values of variables (g/l) were: TP (54.0), CN (43.0), WP (11.0), BLG (4.78), ALA (1.37), SA (0.61), IG (3.83) and LF (0.28). The lactation stage significantly affected all the variables analysed. TP, CN and WP concentrations tended to increase throughout lactation, with the increase of WP being more pronounced than the corresponding variation in CN. There was no definite trend in BLG content, whereas ALA concentration decreased as lactation progressed. The parity affected almost all variables studied. WP concentration differed significantly only between the second and fourth parity (10.45 vs 11.44 g/l). BLG and SA concentrations were significantly lower in the youngest ewes. The BLG genotype affected milk yield, but no effects were observed on the components of the milk. The SCC influenced almost all variables studied. The TP concentration was significantly higher in milk with SCC >1,000,000 (55.0 g/l) than in milk with lower SCC (53.4 g/l). This was mainly due to the increase of WP (12.52 and 10.24 g/l in milk with SCC above and below 1,000,000/ml respectively), especially in those WP fractions originating from blood

Effect of normalisation on detection of differentially expressed genes in cDNA microarray data analysis

Four different normalisation techniques were applied for the corrections of fluorescence data generated by a cDNA microarray experiment. Correction for inaccurate signals and possible bias induced by fluorescence intensity, background intensity and dye effect were used in different combinations. Results of the present study highlight a pronounced role for the normalisation techniques in the absolute number of genes different expressed and a low concordance between different methods. Moreover, a significant effect of the dependent variable used, mean or median fluorescence intensity, was observed

Use of a partial least squares regression model to predict Test Day of milk, fat and protein yields in dairy goats

A model able to predict missing test day data for milk, fat and protein yields on the basis of few recorded tests was proposed, based on the partial least squares (PLS) regression technique, a multivariate method that is able to solve problems related to high collinearity among predictors. A data set of 1731 lactations of Sarda breed dairy Goats was split into two data sets, one for model estimation and the other for the evaluation of PLS prediction capability. Eight scenarios of simplified recording schemes for fat and protein yields were simulated. Correlations among predicted and observed test day yields were quite high (from 050 to 088 and from 053 to 096 for fat and protein yields, respectively, in the different scenarios). Results highlight great flexibility and accuracy of this multivariate technique

A Multivariate measure of lactation persistency for dairy sheep

The persistency of lactation, i.e. the ability of animals to maintain a rconstant level of production after the lactation peak, represents an interesting trait for animal breeding strategies, allowing for the increase of profitability of animal husbandry via the reduction of production costs. Dairy cattle with flatter curves show a higher reproductive efficiency, a better metabolic status and have their nutritional requirements more constantly spread throughout lactation, allowing for the use of cheaper feeds (Dekkers et al., 1998; Solkner and Fucks, 1987). Also in dairy sheep the persistency could represent an interesting trait for breeding purposes. A main problem for the introduction of this trait in an aggregate genotype is represented by the difficulty in finding an objective measure: several measurements of lactation persistency have been proposed but none of them is widely accepted (Gengler, 1996). Recently a new index of the persistency based on multivariate Factor analysis, has been proposed for dairy cattle (Macciotta et al., 2002). Aim of the present work is to check the suitability of this index to discriminate lactation curves with different persistency and to analyse the effect of some environmental factor on this trait

Milk composition and feeding in the Italian dairy sheep

Milk production represents a relevant quota of the energy consumption of the dairy ewe. Studies on relationships among level of production, milk composition and metabolic aspects are the first fundamental step in the development of a feeding system aimed at satisfying nutritive requirements of the animals. This paper reviews the knowledge about the milk composition of main Italian dairy sheep breeds, the relationship among secretion kinetics of milk and protein and productive level of animals, the algorithms used for estimating fat (6.5%) and protein (5.8%) corrected milk yield, the evolution over time of milk production during lactation and the relationships between feeding and milk composition

Modelling extended lactation curves for milk production traits in Italian Holsteins

Test day records of milk production traits (milk yield, fat and protein percentage, and somatic cell score) of 45,132 Italian Holstein cows were analyzed with seven mathematical models in order to assess the main features of lactations of different length. Lactations curves were grouped according to parity (1, 2, and 3) and lactation length (1<350d; 2=from 351 to 450d; 3=from 451 to 650d; 4=651 to 1000d). Models with a larger number of parameters showed better fitting performances for all classes of length for milk yield, whereas poor fitting was observed for fat and protein percentages and SCS in the 651-1000d class. In lactation with length>650d, peak yield was about 31, 37, and 39 kg for first, second, and third parity respectively; peak was predicted at around 60 and 40 days for younger and older animals respectively. The asymptotic level of production was below 10 kg

The Mathematical description of lactation curves in dairy cattle

This review gives an overview of the mathematical modelling of lactation curves in dairy cattle. Over the last ninety years, the development of this field of study has followed the main requirements of the dairy cattle industry. Non-linear parametric functions have represented the preferred tools for modelling average curves of homogeneous groups of animals, with the main aim of predicting yields for management purposes. The increased availability of records per individual lactations and the genetic evaluation based on test day records has shifted the interest of modellers towards more flexible and general linear functions, as polynomials or splines. Thus the main interest of modelling is no longer the reconstruction of the general pattern of the phenomenon but the fitting of individual deviations from an average curve. Other specific approaches based on the modelling of the correlation structure of test day records within lactation, such as mixed linear models or principal component analysis, have been used to test the statistical significance of fixed effects in dairy experiments or to create new variables expressing main lactation curve traits. The adequacy of a model is not an absolute requisite, because it has to be assessed according to the specific purpose it is used for. Occurrence of extended lactations and of new productive and functional traits to be described and the increase of records coming from automatic milking systems likely will represent some of the future challenges for the mathematical modelling of the lactation curve in dairy cattle

Fit of different linear models to the lactation curve of Italian water buffalo

Mathematical modelling of lactation curve by suitable functions of time, widely used in the dairy cattle industry, can represent also for buffaloes a fundamental tool for management and breeding decision, where average curves are considered, and for genetic evaluation by random regression models, where individual patterns are fitted. Average lactation curves of Italian Buffalo cows have been fitted with good results (Catillo et al., 2002) whereas there is a lack of information on individual fitting.Aim of the present work is to check performances of some of the most currently used empirical models in fitting both average and individual lactation curves of Italian water buffaloes