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The consequences of climate change are becoming a relevant issue also for the livestock industry in temperate countries. Transgenerational studies outlined the effect of the ancestors’ calving season, considered as an indicator of the period during which pregnancy occurs, on their female progeny performance. The aim of this study was to evaluate the effect of great granddam (F0) calving month on their great granddaughters (F3) Test Day (TD) records for milk production. A total of 414,069 TD records of milk, fat, and protein yields from 27,224 Italian Simmental F3 cows was analysed with a mixed linear model with year of calving, parity, age at parity, F0 calving month, days in milk class, the interactions between DIM and F0 calving month and between parity and F0 calving month, F3 calving date and the random effects of herd-test date and animal. The F0 calving month significantly influenced F3 performance for the three considered traits; largest F3 TD records were obtained for F0 calvings occurring in October, smallest for February. The largest milk production of F3 cows were observed when their F1 conceptions occurred in winter. Results were confirmed by a further analysis considering the calving seasons of F0, F1 and F2. The effect of F0 calving month (associated with the conception of F1) on milk production decreased as F3 parity increased. Results of this study suggest that the period of ancestor pregnancy and conception affects milk production traits of further generations also in temperate climates.

Individual methane emissions are a potential breeding goal for selection plans aimed at improving the sustainability of ruminant farming systems. However, a large-scale recording of individual methane emissions is hampered by the high costs of the equipment and the logistics of the experiments. Equations have been developed to predict methane emissions in dairy cattle based on variables such as dry matter intake (DMI), gross energy intake, body weight and milk fatty acids (FAs) profile. No equations are currently available for dairy sheep. The aims of this study were: (i) to estimate the enteric methane yield (eMY, expressed as g/kg of DMI) and enteric methane intensity (eMI, expressed as g/kg of FPCM) of dairy sheep using equations developed for dairy cattle that consider as predictors milk FA; (ii) to evaluate the effect of stage of lactation, parity and month of lambing on eMY and eMI; (iii) to estimate the heritability of eMY and eMI. For this purpose, 964 individual milk samples from Sarda dairy ewes were analysed for milk composition and FA profile. Nine different equations were used to predict eMY and eMI. Values of eMY ranged from 19.4 g/kg to 20.4 g/kg of DMI, whereas values of eMI ranged from 15.1 g/kg to 21.0 g/kg of DMI, respectively. Stage of lactation was an important factor in the variability of eMY (p

The aim of this study was to estimate genetic parameters and investigate the genomic background of scrotal circumference and semen parameters in the Italian Simmental bulls. Scrotal circumference, number of normal spermatozoa, ejaculate volume, spermatozoa motility, and total spermatozoa were measured on 622 bulls, of which 603 had genotypes for 42,141 SNP. Variance components of scrotal circumference were estimated with an animal model that included the fixed effects of birth year, animal age, and measurement method, and the random effects of day of measurement and animal. In the model for the other traits, the scrotal circumference was added as a covariate to account for its influence on the semen parameters. A genome-wide association study was carried out using the ssGBLUP-approach. Heritabilities ranged from 0.07 ± 0.05 (spermatozoa motility) to 0.50 ± 0.14 (scrotal circumference). A total of 13 SNP passed the Bonferroni correction threshold and the number of significantly associated markers ranged from 1 (ejaculate volume and spermatozoa motility) to 5 (total spermatozoa). Genes already associated with reproduction parameters were retrieved close to the significant SNP. Results of the present study gave preliminary insights about the genetic determinism of semen quality in Italian Simmental bulls.Highlights Low to moderate heritabilities were estimated for semen traits. Few markers were associated with the phenotypes, suggesting their polygenic determinism. Candidate genes already associated in literature with sperm traits were found.

The aim of this study was to test the feasibility of genomic selection in the Italian Mediterranean water buffalo, which is farmed mainly in the south Italy for milk, and mozzarella, production. A total of 498 animals were genotyped at 49,164 loci. Test day records (80,417) of milk (MY), fat (FY) and protein (PY) yields from 4127 cows, born between 1975 and 2009, were analysed in a three-trait model. Cows born in 2008 and 2009 with phenotypes and genotypes were selected as validation animals (n = 50). Variance components (VC) were estimated with BLUP and ssGBLUP. Heritabilities for BLUP were 0.25 ± 0.02 (MY), 0.16 ± 0.01 (FY) and 0.25 ± 0.01 (PY). Breeding values were computed using BLUP and ssGBLUP, using VC estimated from BLUP. ssGBLUP was applied in five scenarios, each with a different number of genotypes available: (A) bulls (35); (B) validation cows (50); (C) bulls and validation cows (85); (D) all genotyped cows (463); (E) all genotypes (498). Model validation was performed using the LR method: correlation, accuracy, dispersion, and bias statistics were calculated. Average correlations were 0.71 ± 0.02 and 0.82 ± 0.01 for BLUP and ssGBLUP-E, respectively. Accuracies were also higher in ssGBLUP-E (0.75 ± 0.03) compared to BLUP (0.57 ± 0.03). The best dispersions (i.e. closer to 1) were found for ssGBLUP-C. The use of genotypes only for the 35 bulls did not change the validation values compared to BLUP. Results of the present study, even if based on small number of animals, showed that the inclusion of genotypes of females can improve breeding values accuracy in the Italian Buffalo.Highlights The genotypes of males did not improve the predictions. Genotypes of females improve breeding values accuracy. Slight increase in prediction accuracy with weighted ssGBLUP.

Residual feed intake (RFI) is the most used measure of feed efficiency. However, considering the importance of concentrates in the ration, a new index, the residual concentrate intake (RCI), was here defined. RCI aims to measure the individual efficiency in converting the concentrate into animal products. Brown Swiss young bulls (N = 736) were genotyped at 41,183 loci. Animals were housed in pens equipped with an automatic feeding system able to recognise the animal and record the concentrate intake. The diet consisted of concentrate and hay (ad libitum). The new RCI index was calculated as the residuals of the linear regression of concentrate intake on metabolic live weight and average daily gain. Animals were ranked according to their corrected RCI and divided into low (LRCI) and high phenotypes (HRCI). A low heritability (0.06 ± 0.03) was estimated using only genomics for this new index. Results from multivariate (M-GWAS) and Bayesian (B-GWAS) approaches were combined to identify SNP associated with RCI. The M-GWAS selected 698 SNPs potentially associated, whereas no significant markers were obtained in B-GWAS. Markers in the last approach were ranked according to their posterior inclusion probability and the first 698 were retained. Only SNPs in common between sorted B-GWAS and M-GWAS (N = 11) were considered associated with RCI. A total of 48 candidate genes were retrieved near these SNPs. Most of them were previously reported to be associated with feed efficiency and RFI. The combined use of multivariate and Bayesian techniques allow to identify SNPs associated with the investigated trait.Highlights RCI could be promising to select animals 48 candidate genes were found associated with RCI Multivariate technique allowed to identify significant SNPs

The world cow milk production will reach between 810 and nearly 1,000 Mt in 2050, implying changes in dairy farm management as well implications in environmental impact, especially as far as greenhouse gases (GHG) emissions, and nitrogen and phosphorus excretions are concerned. The future dairy farms will need to become smarter, profitable, and high yielding to continuously improve the sustainability of milk production. Among western countries, the Italian dairy industry has good performances both for milk yield and quality. Most of its milk is used to produce highly exported PDO cheeses with high added value. It could represent a model to study the impact of accelerated phenotypic trend on technical and environmental challenges. Assuming a constant average increase of milk yield equal to the actual phenotypic trend (+128 kg per cow and per year), the production of the current best cows (20t/head year−1) will become the average herd performance of the intensive dairy farms in 2030. Thus, maintaining the current Italian milk production (equal to 12.1 Mt), the higher milk production per head would cause a reduction of the environmental impact of 11.4% and 60.1% for GHG, 9.1% and 36.0% for N, 15.8% and 52.6% for P considering two scenarios of present phenotypic trend or 20t/head year−1, respectively. To cope with this challenge, technical suggestions for breeding and feeding the 20t dairy cow are given.Highlights Current top Italian cows represent the 2030 average of dairy herds Genomics and management will drive the productivity success Efficiency will reduce dairy environmental impact

The aim of this study was to estimate genetic parameters and investigate the genomic background of scrotal circumference and semen parameters in the Italian Simmental bulls. Scrotal circumference, number of normal spermatozoa, ejaculate volume, spermatozoa motility, and total spermatozoa were measured on 622 bulls, of which 603 had genotypes for 42,141 SNP. Variance components of scrotal circumference were estimated with an animal model that included the fixed effects of birth year, animal age, and measurement method, and the random effects of day of measurement and animal. In the model for the other traits, the scrotal circumference was added as a covariate to account for its influence on the semen parameters. A genome-wide association study was carried out using the ssGBLUP-approach. Heritabilities ranged from 0.07 ± 0.05 (spermatozoa motility) to 0.50 ± 0.14 (scrotal circumference). A total of 13 SNP passed the Bonferroni correction threshold and the number of significantly associated markers ranged from 1 (ejaculate volume and spermatozoa motility) to 5 (total spermatozoa). Genes already associated with reproduction parameters were retrieved close to the significant SNP. Results of the present study gave preliminary insights about the genetic determinism of semen quality in Italian Simmental bulls. Low to moderate heritabilities were estimated for semen traits. Few markers were associated with the phenotypes, suggesting their polygenic determinism. Candidate genes already associated in literature with sperm traits were found. [ABSTRACT FROM AUTHOR]

The availability of high-density single nucleotide polymorphism (SNPs) panels for humans and, recently, for several livestock species has given a great impulse to genome-wide association studies towards the identification of genes associated with complex traits and diseases. The frequentist and the Bayesian approach are commonly used to investigate marker associations with traits of interest. Briefly, the former is the most widely used method, being intuitive and easily to apply, whereas the latter requires deeper statistical knowledge, but has the advantage to include prior information to obtain a posterior probability of association. Both methods, however, require parameters or distributions to be set a priori by the researcher. In this work, we suggest a new empirical method for genome-wide studies (GWAS), which verifies marker-trait associations using the bootstrap resampling and Chebyshev’s inequality. This method, called Maximum Difference Analysis (MDA), was tested on a real dataset of 2093 Italian Holstein bulls with the objective of finding associations between SNPs and milk, fat and protein yield and fat and protein percentage. Results of the MDA method were compared with those obtained to a genome-wide association analysis performed using the R package GenABEL. In addition, we assessed the bovine annotated genes related to the traits under study. The MDA method was able to locate known important loci for milk productive traits, such as the DGAT1, PRLR, GHR and SCD. Moreover, some new putative candidate genes were detected. The python script of MDA procedure is available at www.animalbreeding.uniss.it.

In 90 Sarda dairy ewes the effects of lactation stage, parity, β-lactoglobulin genotypes, and somatic cell count (SCC) onthe milk content of total protein (TP), casein (CN), whey protein (WP) and its fractions α-lactalbumin (ALA), β-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) andLF (0.28). The lactation stage significantly affected all the variables analysed. TP, CN and WP concentrations tended toincrease 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 parityaffected 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 affectedmilk 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 below1,000,000/ml respectively), especially in those WP fractions originating from blood.

The present review consists of two parts. In the first part, the authors briefly describe the state of the art of breedingprogrammes for Italian dairy sheep; then they report new models for genetic evaluation and consider the problem ofgenotype x environment interaction and the impact of farming systems on the genetic merit of animals. In the secondpart new breeding goals regarding the evolution of milk quality concept and the increasing importance of functional traitsare reported. Regarding milk quality, the authors especially focus on the traits related to cheese-making ability and onthe nutraceutical aspects of milk. Among functional traits, resistance to diseases (mastitis and Scrapie) has been highlightedfor its great importance in livestock species. Finally, the perspectives of marker-assisted selection have also beenreported.

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 feed- ing 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 pro- ductive 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.

Milk production is largely related to the shape of the lactation curve. Key elements of the lactation pattern are peak yield, which is the maximum daily yield reached during lactation, and lactation persistency, which is the medium rate of milk yield decrease after the lactation peak. The ideal lactation curve should have a reasonably high peak and a flat trend afterwards. A more persistent lactation is desirable because it is related to better animal health and reduction of feeding costs. Effective strategies to improve lactation persistency require a deep understanding of the main factors that affect this trait, including genetics, hormonal status and administration, udder morphology, seasonal changes, management, animal health (e.g. mastitis), stress and nutrition. This review covers the effects of non-nutritional factors on lactation persistency in 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)..........

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...

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.

The shape of individual deviations of milk yield for dairy cattle from the fixed part of a random regression test day model (RRTDM) was investigated. Data were 53,217 TD records for milk yield of 6,229 first lactation Canadian Holsteins in Ontario. Data were fitted with a model that included the fixed effects of herd-testdate, DIM interval nested within age and season of calving. Residuals of the model were then fitted with the following functions: Ali and Schaeffer 5 parameter model, fourth-order Legendre Polynomials, and cubic spline with three, four or five knots. Result confirm the great variability of shape that can be found when individual lactation are modeled. Cubic splines gave better fitting pe4rformances although together with a marked tendency to yield aberrant estimates at the edge of the lactation trajectory.

A meta-analysis approach was used in order to synthesize findings of six trials comparing 177 individual data of CLA, vaccenic acid (VA) and total trans fatty acids (TFA) of lamb’s meat: “slaughter weight” (0-14kg, 14-18kg, 18-24kg and >24kg), “muscle” (longissimus dorsi, and other muscles); “feeding regimen”: milk (pre-weaned animals fed maternal milk); pasture (weaned grazing animals) and “confined” (weaned animals reared indoor and fed hay and concentrate). The fixed effects included in the model. Results showed that the meat from lambs slaughtered at 14-18kg and from grazing animals maximized the content of CLA and VA and minimized the content of others TFA.

- 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 (1650d, 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.

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.

In Random Regression models (RRM), the most updated version of Test Day (TD) models, the lactation curve is split into a fixed average curve and a random animal specific part (deviation from the average curve) (Schaeffer, 2004). The variance component of the RR coefficients determines the (co) variance function of each pair of days in milk (DIM) (Pool and Meuwissen, 2000).

On a simulated population of 2,500 individuals, Principal Component Analysis and Factor Analysis were used to reduce the number of independent variables for the prediction of GEBVs. A genome of 100 cM with 300 bialleic SNPs and 20 multiallelic QTLs was considered. Two heritabilities (0.2 and 0.5) were tested. Multivariate reduction methods performed better than the traditional BLUP with all the SNPs, either on generations with phenotypes available or on those without phenotypes, especially in the low heritability scenario (about 0.70 vs. 0.45 in generations without phenotypes). The use of multivariate reduction techniques on the considered data set resulted in a simplification of calculations (reduction of about 90% of predictors) and in an improvement of GEBV accuracies.

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.

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.