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This study aimed to assess the environmental impact (cradle-to-farm gate LCA) of milk production of cereal-based dairy farms in northern Italy and to analyse which traits related to farm management (MAN_F), lactating cows’ ration composition (DIET_F), and animal responses (ANI_F) could be identified as main determinants to address specific mitigation strategies. Data originated from 28 dairy farms. The functional unit was 1 kg of fat- and protein-corrected milk (FPCM). Impact categories were global warming (GWP), acidification (AP) and eutrophication (EP) potentials and land occupation (LO). Impact values were analysed with a linear model to test separately the effects of MAN_F, DIET_F and ANI_F variables. One kg FPCM was associated with an average impact of 1.10 kg CO2-eq (GWP), 17.1 g SO2-eq (AP), 6.0 g PO4-eq (EP), and 1.3 m2/y (LO). Regarding MAN_F, GWP and LO linearly decreased with increasing values of stocking rate and feed self-sufficiency (FSS), whereas minimum EP was found for intermediate FSS values (50% DM). Regarding DIET_F, GWP linearly decreased at increasing levels of dietary inclusion of cereal silages, whereas AP and EP linearly decreased at decreasing dietary crude protein (CP) content values. Among ANI_F, GWP and EP were quadratically associated with milk yield (MY), evidencing the lowest values at intermediate MY (9100 ± 477 kg FPCM/cow per 305d-lactation). The GWP, AP and EP decreased with decreasing age at first calving. These results can contribute at drawing good practices for farmers and consultants to promote more environmentally sustainable dairy production while supporting farm functioning and farmers’ income.HIGHLIGHTSFarm and animal determinants of Life Cycle Assessment of cereal-based dairy farms were studied.Mitigation effects can be achieved by operating on farm organisation, cow’s diet, and animal responses.Good practices for farmers and consultants can be proposed for a better dairy production sustainability. Farm and animal determinants of Life Cycle Assessment of cereal-based dairy farms were studied. Mitigation effects can be achieved by operating on farm organisation, cow’s diet, and animal responses. Good practices for farmers and consultants can be proposed for a better dairy production sustainability.

Due to its geographical position and a highly variable orography, Italy is characterized by several climatic areas and thus, by many different dairy cow farming systems. Brown Swiss cattle, in this context, are a very appreciated genetic resource for their adaptability and low metabolic requirement. The significant heterogeneity in farming systems may consist of genotype by environment (G × E) interactions with neglected changes in animals' rank position. The objective of this study was to investigate G × E for heat tolerance in Brown Swiss cattle for several production traits (milk, fat, and protein yield in kilograms; fat, protein, and cheese yield in percentage) and 2 derivate traits (fat-corrected milk and energy-corrected milk). We used the daily maximum temperature-humidity index (THI) range, calculated according to weather stations' data from 2008 to 2018 in Italy, and 202,776 test-day records from 23,396 Brown Swiss cows from 639 herds. Two different methodologies were applied to estimate the effect of the environmental variable (THI) on genetic parameters: (1) the reaction norm model, which uses a continuous random covariate to estimate the animal additive effect, and (2) the multitrait model, which splits each production pattern as a distinct and correlated trait according to the first (a thermal comfort condition), third (a moderate heat stress condition), and fifth (a severe heat stress condition) mean THI value quintile. The results from the reaction norm model showed a descending trend of the additive genetic effect until THI reached the value of 80. Then we recorded an increase with high extreme THI values (THI 90). Permanent environmental variance at increasing THI values revealed an opposite trend: The plot of heritability and the ratio of animal permanent environmental variance to phenotypic variance showed that when the environmental condition worsens, the additive genetic and permanent environmental component for production traits play a growing role. The negative additive genetic correlation between slope and linear random coefficient indicates no linear relationship between the production traits or under heat stress conditions, except for milk yield and protein yield. In tridimensional wireframe plots, the extreme margin decreases until a minimum of ~0.90 of genetic correlation in the ECM trait, showing that the magnitude of G × E interaction is greater than the other traits. Genetic correlation values in Brown Swiss suggest the possibility of moderate changes in animals' estimated breeding value in heat stress conditions. Results indicated a moderate G × E interaction but significant variability in sire response related to their production level.

To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (α

Background Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios. Results The predictive ability of these approaches was measured by the coefficient of determination (R2). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R2 for blood metabolites across the three CV scenarios, especially with the herd-out CV: R2 values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators. Conclusions Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.

Infectious bursal disease virus (IBDV) is an immunosuppressive pathogen with a bi-segmented, double-stranded RNA genome. Two serotypes, named 1 and 2, are recognized, and serotype 1 is traditionally divided into classical, variant and very virulent strains. Despite having great practical relevance, this classification lacks standardization and may therefore generate confusion. The widespread adoption of molecular techniques, which do not allow to direct assess neither pathogenicity nor antigenicity, further complicates this scenario.Recently, two phylogeny-based classification methods have been proposed to obtain informative and standardized results. The first is based on the analysis of the capsid protein VP2 gene, considered as the main determinant of virulence, while the other one considers both VP2 and RNA polymerase VP1, located in different genome segments. The latter approach is therefore better suited to detect reassortment events, which play a significant role in IBDV evolution.This work reports the detection of eight strains in Portuguese broiler farms that diverged from all available IBDV sequences. Subsequent analyses of VP1 and VP2 sequences confirmed that these strains form a new genotype having segment A of a new genogroup and segment B of classical-like genogroup. No recombination events were identified, while several unique amino acid changes were found in key positions within the VP2.This discovery remarks the importance of a steady and attentive monitoring of IBDV epidemiology. Further research efforts will be devoted to characterizing the novel genotype, by isolating new strains, assessing their pathogenicity and determining the level of protection ensured by commonly administered IBDV vaccines.

In recent years, a rising interest has been directed towards the use of nutraceuticals in the zootechnical sector, including probiotics, prebiotics, and synbiotics, as a way to support production efficiency and cope with the increasing limitations to the use of antibiotics. In poultry, however, most studies on these products have been conducted on broilers, while less information is available on their benefits to other productive categories. The present field study aimed to assess the effects of a multi-species synbiotic product (PoultryStar® sol) on the gut health and productive performance of broiler breeders. A total of 24761 day-old Ross 308 parent stock chicks were acquired from a single hatchery and placed on the same farm. Female chicks were divided into three groups and raised in different houses (A, B, and C), in which males were introduced at the age of mating and followed until 40 weeks of age. The synbiotic was provided by drinking water to the flocks in houses A and B, while house C was kept as control. Following the manufacturer’s guidelines, the product was administered intermittently once every two weeks, except in the first and the twenty-first week when it was supplied for three consecutive days. Data on performance parameters, egg quality traits, bacterial enteritis scoring, intestinal morphometry, and histopathology were recorded, and the caecal content was collected at 15, 25, and 40 weeks of age to investigate the intestinal microbiota using high-throughput next-generation sequencing. Synbiotic-treated hens showed significantly higher survivability during production compared to the control group. No clear differences were observed between treated and control chickens in terms of egg production and quality, and the effect of the synbiotic on weight gain also appeared limited. From 25 weeks onwards, synbiotic-treated chickens scored better in terms of macroscopical lesions and had longer intestinal villi. Significant differences in crypt length and histopathological lesions were also found at multiple sampling points. A treatment effect on caecal bacterial composition was detected with a differential abundance of Gastranaerophilales, Lachnospiraceae, Helicobacter, Ruminococcaceae, and Clostridia, among others. Taken together, obtained results support the beneficial effects of the intermittent administration of the synbiotic product PoultryStar® sol on the gut health of broiler breeders. Keywords: Broiler breeder, Gastrointestinal health, Histopathology, Microbiota, Synbiotic

In recent years, the impact of respiratory disease resulting from Avian Metapneumovirus (aMPV) infection has been generally rising in the broiler industry in Europe. In this context, in order to investigate aMPV contribution to the clinical picture and the potential benefits of diversified vaccination strategies compared to nonvaccination policies, a longitudinal monitoring was performed, also evaluating Infectious Bronchitis Virus (IBV) presence. Broiler flocks located in Western France, where aMPV has already proven to be a health and productivity issue, were screened by RT-PCR on rhino-pharyngeal swabs, and the viruses were genetically characterized by sequence analysis. For a more comprehensive picture of aMPV molecular epidemiology and evolution in France, aMPV subtype B strains detected from 1985 to 1998 were sequenced and included in the analysis. The survey confirmed the detection of aMPV subtype B in commercial broiler flocks in France, together with a certain heterogeneity demonstrated by the circulation of more recent and historical French field strains. No IBV field strains were detected. The implementation and evaluation of different management choices and vaccine strategies suggests once again that immunization does not prevent infection but contributes greatly to the containment of the clinical manifestations.

In recent years, the applicability of prebiotics, probiotics and their mixtures, defined as synbiotics, in poultry production has received considerable attention. Following the increasing regulation of antibiotic use, these nutraceuticals are seen as an alternative way to sustain production efficiency and resistance to pathogens and stressors by modulating birds’ gut health. The aim of this study was to evaluate the benefits provided under field conditions by administering the multi-species synbiotic PoultryStar® sol to broilers in drinking water. To this purpose, three Ross 308 broiler flocks, representing separate progenies of a breeder flock which was treated with the same synbiotic, were housed in separate farms, divided into treatment and control groups, and followed throughout the productive cycle. Synbiotic administration was shown to improve gut health even in absence of a challenge, with limited changes in terms of macroscopic intestinal lesions and more overt differences related to histopathological scores and villi length. Synbiotic-fed chickens performed consistently better in terms of body weight gain, feed conversion ratio and survivability. Lastly, the evaluation of the caecal microbiome through next-generation sequencing highlighted the effects of synbiotic supplementation on the composition of the bacterial population, the implications of which will, however, require further studies to be better comprehended.

Dairy bull fertility is traditionally evaluated using semen production and quality traits; however, these attributes explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using cow field data. The main objective of this study was to investigate bull fertility in the Italian Brown Swiss dairy cattle population using confirmed pregnancy records. The data set included a total of 397,926 breeding records from 1,228 bulls and 129,858 lactating cows between first and fifth lactation from 2000 to 2019. We first evaluated cow pregnancy success, including factors related to the bull under evaluation, such as bull age, bull inbreeding, and AI organization, and factors associated with the cow that receives the dose of semen, including herd-year-season, cow age, parity, and milk yield. We then estimated sire conception rate using only factors related to the bull. Model predictive ability was evaluated using 10-fold cross-validation with 10 replicates. Interestingly, our analyses revealed that there is a substantial variation in conception rate among Brown Swiss bulls, with more than 20% conception rate difference between high-fertility and low-fertility bulls. We also showed that the prediction of bull fertility is feasible as our cross-validation analyses achieved predictive correlations equal to 0.30 for sire conception rate. Improving reproduction performance is one of the major challenges of the dairy industry worldwide, and for this, it is essential to have accurate predictions of service sire fertility. This study represents the foundation for the development of novel tools that will allow dairy producers, breeders, and artificial insemination companies to make enhanced management and selection decisions on Brown Swiss male fertility.

The protein profile of milk includes several caseins, whey proteins, and nonprotein nitrogen compounds, which influence milk's value for human nutrition and its cheesemaking properties for the dairy industry. To fill in the gap in current knowledge of the patterns of these individual nitrogenous compounds throughout lactation, we tested the ability of a parametric nonlinear lactation model to describe the pattern of each N compound expressed qualitatively (as % of total milk N), quantitatively (in g/L milk), and as daily yield (in g/d). The lactation model was tested on a data set of detailed milk nitrogenous compound profiles (15 fractions-12 protein traits and 3 nonproteins-for each expression mode: 45 traits) obtained from 1,342 cows reared in 41 multibreed herds. Our model was a modified version of Wilmink's model, often used for describing milk yield during lactation because of its reliability and ease of parameter interpretation from a biological point of view. We allowed the sign of the persistency coefficient (parameter c) that explained the variation in the long-term milk component (parameter a) to be positive or negative. We also allowed the short-term milk component (parameter b) to be positive or negative, and we estimated a specific speed of adaptation parameter (parameter k) for each trait rather than assumed a value a priori, as in the original model (k = 0.05). These 4 parameters were included in a nonlinear mixed model with cow breed and parity order as fixed effects, and herd-date as random. Combinations of the positive and negative signs of the b and c parameters allowed us to identify 4 differently shaped lactation curves, all found among the patterns exhibited by the nitrogenous fractions as follows: the "zenith" curve (with a maximum peak; for milk yield and 10 other N traits), the "nadir" curve (with a minimum point; for 20 traits, including almost all those expressed in g/L of milk), the "downward" curve (continuously decreasing; for 14 traits, including almost all those in g/d), and the "upward" curve (continuously increasing; only for κ-casein, in % N). Direct estimation of the k parameters specific to each trait showed the large variability in the adaptation speed of fresh cows and greatly increased the model's flexibility. The results indicated that nonlinear parametric mathematical models can effectively describe the different and complex patterns exhibited by individual nitrogenous fractions during lactation; therefore, they could be useful tools for interpreting milk composition variations during lactation.

Thanks to recent developments in molecular methods, many new species have been discovered within the genus Circovirus, which comprises viruses of veterinary relevance found in a broad range of hosts. In particular, several circoviruses are known to infect birds, often causing immunosuppression and feathering disorders. Nonetheless, nothing is known about their circulation in birds of prey. In this study, samples from 61 birds of prey representing ten different species, recovered by a wildlife rescue centre in Southern Italy, were taken at necropsy and analysed by PCR with pan-Circovirus primers. Only one sample, collected from a tawny owl (Strix aluco), tested positive. Its genome, sequenced by primer walking, displays the typical features of circoviruses. Based on demarcation criteria, the detected strain qualifies as a novel species, which was named “tawny owl-associated circovirus” (ToCV). Phylogenetically, ToCV clustered with mammalian rather than avian circoviruses, and its closeness to a rodent circovirus suggests that its host may have been a micromammal eaten by the tawny owl. On the other hand, its detection in the spleen fits with the tropism of other avian circoviruses. Little can be therefore said on its biology and pathogenicity, and further efforts are needed to better characterize its epidemiology.

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.

The aim of this study was to investigate the associations between differential somatic cell count (DSCC) and milk quality and udder health traits, and for the first time, between DSCC and milk coagulation properties and cheesemaking traits in a population of 1,264 Holstein cows reared in northern Italy. Differential somatic cell count represents the combined proportions of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in the total somatic cell count (SCC), with macrophages (MAC) making up the remaining proportion. The milk traits investigated in this study were milk yield (MY), 8 traits related to milk composition and quality (fat, protein, casein, casein index, lactose, urea, pH, and milk conductivity), 9 milk coagulation traits [3 milk coagulation properties (MCP) and 6 curd firming (CF) traits], 7 cheesemaking traits, 3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits. A linear mixed model was fitted to explore the associations between SCS combined with DSCC and the aforementioned milk traits. An additional model was run, which included DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the percentage of PMN-LYM and MAC by SCC in the milk for each cow in the data set. The unfavorable association between SCS and milk quality and technological traits was confirmed. Increased DSCC was instead associated with a linear increase in MY, casein index, and lactose proportion and a linear decrease in milk fat and milk conductivity. Accordingly, DSCC was favorably associated with all MCP and CF traits (with the exception of the time needed to achieve maximum, CF), particularly with rennet coagulation time, and it always displayed linear relationships. Differential somatic cell count was also positively associated with the recovery of milk nutrients in the curd (protein, fat, and energy), which increased linearly with increasing DSCC. The PMN-LYM count was rarely associated with milk traits, even though the pattern observed confirmed the results obtained when both SCS and DSCC were included in the model. The MAC count, however, showed the opposite pattern: MY, casein index, and lactose percentage decreased and milk conductivity increased with an increasing MAC count. No significant association was found between PMN-LYM count and MCP, CF, CY, and REC traits, whereas MAC count was unfavorably associated with MCP, CF traits, some CY traits, and all REC traits. Our results showed that the combined information derived from SCS and DSCC might be useful to monitor milk quality and cheesemaking-related traits.

BackgroundOver the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV).ResultsAddition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV.ConclusionsIntegration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions.

The use of sexed semen to produce purebred replacement heifers allows a large proportion of dairy cows to be mated to beef sires, and quantitative and qualitative improvements to be made to beef production from dairy herds. The major dairy and beef breeds are undergoing rapid genetic improvement as a result of more efficient selection methods, prompting a need to evaluate the meat production of crossbred beef × dairy cattle produced using current genetics. As part of a large project involving 125 commercial dairy farms, we evaluated the combined use of purebreeding with sexed semen and crossbreeding with semen from beef sires, particularly double-muscled breeds. A survey of 1,530 crossbred calves revealed that, whereas purebred dairy calves are destined almost exclusively for veal production, beef × dairy crossbred calves are also destined for beef production after fattening on either the dairy farm of birth or by specialized fatteners. In veal production, compared with Belgian Blue-sired calves (taken as the reference), double-muscled INRA 95-sired calves had a lighter slaughter weight (303 vs. 346 kg), but a greater dressing percent (62.3 vs. 58.4%). Limousin (also known as Limousine)-sired calves had a smaller average daily gain (1.26 vs. 1.34 kg/d), and lighter slaughter (314 vs. 346 kg) and carcass weights (182 vs. 201 kg). Last, Simmental-sired calves had a similar growth rate, but lighter carcass weight (177 vs. 201 kg), smaller dressing percentage (55.3 vs. 58.4%), and smaller muscularity scores (3.25 vs. 3.72). In the case of young bulls and heifers fattened on the dairy farm of birth, Belgian Blue-, Piemontese (also known as Piedmontese)-, and Limousin-sired calves performed similarly; the only exception was that Piemontese-sired calves had a greater dressing percentage. Belgian Blue- and Limousin-sired calves performed similarly when fattened by specialized beef producers. In both veal and beef production, the effects of dam breed were less important than sire breed. Considering the entire project, we can conclude that the combined use of sexed semen for purebreeding and conventional beef semen for terminal crossbreeding improves meat production from dairy herds, especially when the sires are double-muscled beef breeds.

In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges. Pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks and effects of animal infectious diseases and zoonoses are several topics that have received attention from authorities and the public. The increase in poultry production must be achieved mainly through optimization and increased efficiency. The increasing ability to generate large amounts of data (“big data”) is pervasive in both modern society and the farming industry. Information accessibility—coupled with the availability of tools and computational power to store, share, integrate, and analyze data with automatic and flexible algorithms—offers an unprecedented opportunity to develop tools to maximize farm profitability, reduce socio-environmental impacts, and increase animal and human health and welfare. A detailed description of all topics and applications of big data analysis in poultry farming would be infeasible. Therefore, the present work briefly reviews the application of sensor technologies, such as optical, acoustic, and wearable sensors, as well as infrared thermal imaging and optical flow, to poultry farming. The principles and benefits of advanced statistical techniques, such as machine learning and deep learning, and their use in developing effective and reliable classification and prediction models to benefit the farming system, are also discussed. Finally, recent progress in pathogen genome sequencing and analysis is discussed, highlighting practical applications in epidemiological tracking, and reconstruction of microorganisms’ population dynamics, evolution, and spread. The benefits of the objective evaluation of the effectiveness of applied control strategies are also considered. Although human-artificial intelligence collaborations in the livestock sector can be frightening because they require farmers and employees in the sector to adapt to new roles, challenges, and competencies—and because several unknowns, limitations, and open-ended questions are inevitable—their overall benefits appear to be far greater than their drawbacks. As more farms and companies connect to technology, artificial intelligence (AI) and sensing technologies will begin to play a greater role in identifying patterns and solutions to pressing problems in modern animal farming, thus providing remarkable production-based and commercial advantages. Moreover, the combination of diverse sources and types of data will also become fundamental for the development of predictive models able to anticipate, rather than merely detect, disease occurrence. The increasing availability of sensors, infrastructures, and tools for big data collection, storage, sharing, and analysis—together with the use of open standards and integration with pathogen molecular epidemiology—have the potential to address the major challenge of producing higher-quality, more healthful food on a larger scale in a more sustainable manner, thereby protecting ecosystems, preserving natural resources, and improving animal and human welfare and health.

To promote local food products could be a valuable strategy for preserving autochthonous sheep breeds. A total of 115 lambs and ewes of local sheep breeds (Alpagota, Bogna, Foza and Lamon) of the Veneto region were used to evaluate the effect of different feeding systems on the production and quality of lambs and mutton meat. We carried out three fattening trials involving 24 ewes and their 31 suckling lambs, 24 weaned lambs, and 24 heavy lambs, fed indoor diets with or without a rumen-protected conjugated linoleic acids (rp-CLA) supplement (C18:2c9,t11 and C18:2t10,c12) to improve the nutritional value of the meat. Controls were a group of 12 pasture-fed weaned lambs. The weaned lambs at pasture had good fattening performances (33 kg live-weight at slaughter, 45% dressing percentage), whereas lambs fattened indoors on hay and some concentrates had lower weight (28 kg) and dressing percentages (41%), and require improvements to their feed. Feeding the animals on total mixed diets resulted in good in vivo and post mortem performances of culled ewes (64 kg slaughter weight, 48% dressing percentage), suckling lambs (21 kg, 50%) and heavy lambs (61 kg, 48%), showing that valuable supplies of lamb and mutton meat can be obtained across different seasons. Supplementing indoor diets with rp-CLA does not modify the animals’ performances nor the organoleptic traits of meat.HighlightsIntensive indoor fattening allows to obtain suckling, weaned and heavy lambs and mutton from culled ewes all-year-round beyond the seasonal supplies of traditional feeding system.The supplementation of indoor diets with rp-CLA does not modify the animals’ performances, slaughter traits or meat organoleptic traits. Intensive indoor fattening allows to obtain suckling, weaned and heavy lambs and mutton from culled ewes all-year-round beyond the seasonal supplies of traditional feeding system. The supplementation of indoor diets with rp-CLA does not modify the animals’ performances, slaughter traits or meat organoleptic traits.

In this study, we aimed to investigate differences in the genetics of fertility traits (heritability of traits and correlations between traits in divergent environments) in dairy cows of different production levels defined on the basis of the herd-average daily milk energy output (herd-dMEO). Data were obtained from Holstein-Friesian (n = 37,359 for fertility traits, 381,334 for dMEO), Brown Swiss (n = 79,638 for fertility traits, 665,697 for dMEO), and Simmental cows (n = 63,048 for fertility traits, 448,445 for dMEO) reared in northeastern Italy. Fertility traits under study were interval from calving to first service, interval from first service to conception, days open, calving interval, calving rate, and nonreturn rate at d 56. We classified herds into low and high productivity based on the herd-average dMEO (inferred using mixed effects models). We estimated genetic parameters using Bayesian bivariate animal models, where expressions of a phenotype in the low and high dMEO herds were taken as being different-albeit correlated-traits. Fertility traits were more favorable in Simmental than in Holstein-Friesian cows, whereas for all traits, Holstein-Friesian had the highest estimates of intraherd heritability [ranging from 0.021 (0.006-0.038) to 0.126 (0.10-0.15)] and Simmental the lowest [ranging from 0.008 (0.001-0.017) to 0.101 (0.08-0.12)]. The genetic correlations between fertility traits and dMEO were moderate and unfavorable, ranging, in absolute values, from 0.527 (0.37-0.68) to 0.619 (0.50-0.73) in Holstein-Friesian; from 0.339 (0.20-0.47) to 0.556 (0.45-0.66) in Brown Swiss; and from 0.340 (0.10-0.60) to 0.475 (0.33-0.61) in Simmental cattle. The only exception was the nonreturn rate at d 56, which had weak genetic correlations with dMEO in all 3 breeds. The herd correlations between fertility and dMEO tended to be modest and favorable and the residual correlations modest and variable. The heritability of fertility traits tended to be greater in the low dMEO than in the high dMEO herds in the case of the Holstein-Friesians, but not in the case of the Brown Swiss or Simmentals. The additive genetic correlations between fertility traits in the low and high dMEO herds were always lower than 1 [0.329 (-0.17 to 0.85) to 0.934 (0.86 to 0.99)] for all traits considered in all breeds. The correlation was particularly low for the threshold characters and the interval from first service to conception in Holstein-Friesian, suggesting that the relative performances of genotypes vary significantly between herds of different dMEO levels. Although there was large variability in the estimates, results might support making separate genetic evaluations of fertility in the different herd production groups. Our results also indicate that Simmental, a dual-purpose breed, has higher fertility and lower environmental sensitivity than Holstein-Friesian, with Brown Swiss being intermediate.

Marek's disease (MD) is an important lymphoproliferative disease of chickens, caused by Gallid alphaherpesvirus 2 (GaHV-2). Outbreaks are commonly reported in commercial flocks, but also in backyard chickens. Whereas the molecular characteristics of GaHV-2 strains from the commercial poultry sector have been reported, no recent data are available for the rural sector. To fill this gap, 19 GaHV-2 strains detected in 19 Italian backyard chicken flocks during suspected MD outbreaks were molecularly characterized through an analysis of the meq gene, the major GaHV-2 oncogene. The number of four consecutive prolines (PPPP) within the proline-rich repeats of the Meq transactivation domain, the proline content, and the presence of amino acid (aa) substitutions were determined. Phylogenetic analysis was performed using the Maximum Likelihood method. Sequence analysis revealed a heterogeneous population of GaHV-2 strains circulating in Italian backyard flocks. Seven strains, detected from birds affected by classical MD, showed a unique meq isoform of 418 aa with a very high number of PPPP motifs. Molecular and clinical features are suggestive of a low oncogenic potential of these strains. The remaining 12 strains, detected from flocks experiencing acute MD, transient paralysis, or sudden death, had shorter Meq protein isoforms (298 or 339 aa) with a lower number of PPPP motifs and point mutations interrupting PPPP. These features allow us to assert the high virulence of these strains. These findings reveal the circulation of low- and high-virulence GaHV-2 strains in the Italian rural sector.

The yield, flavor, and texture of ripened cheese result from numerous interrelated microbiological, biochemical, and physical reactions that take place during ripening. The aims of the present study were to propose a 2-compartment first-order kinetic model of cheese weight loss over the ripening period; to test the variation in new informative phenotypes describing this process; and to assess the effects on these traits of dairy farming system, individual farms within dairy system, animal factors, and milk composition. A total of 1,211 model cheeses were produced in the laboratory using individual 1.5-L milk samples from Brown Swiss cows reared on 83 farms located in Trento Province. During ripening (60 d; temperature 15°C, relative humidity 85%), the weight of all model cheeses was measured, and cheese yield (cheese weight/processed milk weight, %CY) was calculated at 7 intervals from cheese-making (0, 1, 7, 14, 28, 42, and 60 d). Using these measures, a 2-compartment first-order kinetic model (3-parameter equation) was developed for modeling %CY during the ripening period, as follows: [Formula: see text] , where %CYt is the %CY at ripening time t; %CYi and %CYf are the modeled %CY traits at time 0 d (%CYi = initial %CY) and at the end of a ripening period sufficient to reach a constant wheel weight (%CYf = final %CY after 60 d ripening in the case of small model cheeses); kCY is the instant rate constant for cheese weight loss (%/d). Cheese weight and protein and fat losses were calculated as the % difference between the model cheeses at 0 and after 60 d of ripening. The variation in cheese pH was calculated as the % difference between pH at 0 and after 60 d. Dairy system, individual herd within dairy system, and the cow's parity and lactation stage (tested with a linear mixed model) strongly affected almost all the traits collected during model cheese ripening. Milk fat, protein, lactose, pH, and somatic cell score also greatly affected almost all the traits, although kCY was affected only by milk protein. After including milk composition in the linear mixed model, the importance of all the herd and animal sources of variation was greatly reduced for all traits. The proposed model and novel traits could be tested, first, with the aim of establishing new monitoring procedures enabling the dairy industry to improve milk quality-based payment systems at the herd level and, second, with a view to exploring possible genetic improvements to dairy cow populations.

Vaccination is currently one of the most relevant control strategies in poultry production to reduce infectious disease–induced economic losses and decrease antimicrobial use. Besides intrinsic vaccine efficacy, a proper administration is fundamental to achieve an adequate coverage and protection. Hatchery vaccination is becoming the standard approach for routine vaccination because of administration easiness, the possibility to standardize and optimize the overall process, and the lower impact on animal welfare compared with different types of on-farm vaccination. However, a continuous maintenance, refinement, and training of the personnel are the key to success. In the present work, the effect of longitudinal hatchery audits, performed using a standardized, expert-developed questionnaire was evaluated in 169 hatcheries, located in 11 European countries, over a period of more than 4 yr. A dedicated tablet-based application was implemented for data collection, storage, and analysis, and the obtained scores were used in the evaluation, reporting to the hatchery management and improvement of critical points. A positive significant association was demonstrated between the variation in global and process-specific hatchery scores and the number of performed audits. Similarly, when the longitudinal nature of the data (i.e., multiple visits) was accounted for using linear mixed models, including the hatchery and country as random factors, a significant trend in performance improvement was observed visit after visit, although with certain differences based on the specific score and country. The present study demonstrates the benefits of an objective evaluation of hatchery performances through a standardized questionnaire, followed by the discussion on the major required actions. The widespread application of this approach should lead to a significant improvement in vaccine administration performances, with direct consequences on infectious disease occurrence and animal production performances, and indirectly on therapeutic and control-related costs.

The use of sexed semen to produce purebred replacement heifers allows a large proportion of dairy cows to be mated to double-muscled sires and for quantitative and qualitative improvements to be made to beef production from dairy herds. A survey of 1,285 crossbred calves showed that they are destined not only for veal production (like purebred dairy calves) but also for beef production. Crossbred calves obtained from Belgian Blue sires (sold on average at 34 d of age and 64 kg of weight) had a higher market value (€363/calf) than those obtained from double-muscled INRA 95 sires (€297/calf, used for veal production) and from Limousin sires (€216/calf for veal and €271/calf for beef production). As a sire breed, Simmental did not differ significantly from Belgian Blue, but as a dam breed, the crossbred calves fetched a higher price (€5.11/kg) than when Holstein was the dam breed (€4.50/kg). Compared with heifer calves, crossbred bull calves at sale were younger (34.1 vs. 37.2 d) and heavier (64.0 vs. 62.6 kg of live weight), fetched a higher price (€5.13 vs. €4.99/kg), and had a greater value (€328 vs. €312/calf). As the value of purebred dairy calves was about €80 to €100/calf, we are able to confirm that the combined use of sexed semen for pure-breeding and conventional beef semen for terminal crossbreeding can increase the income from dairy farms, especially when the sires are double-muscled beef bulls.

Authentication of dairy systems is of growing interest for the dairy industry and we investigated the potentiality of using volatile fingerprinting of ripened cheeses by proton transfer reaction time-of-flight mass spectrometry. A total of 1,075 individual model cheeses made from milk of individual Brown Swiss cows of 72 farms were analysed. Using a linear discriminant analysis, cows and herds were assigned to 3 or 5 dairy systems differing in management, available facilities, and diets. We obtained variable discrimination abilities (up to 77% of correct classification of cheeses and 70% of farms with cross-validation). We found m/z 61,028 (acetic acid), 109,070 (pyrazine), and m/z 137,132 (terpene) characterising model cheeses from traditional dairy systems and m/z 71,086 (3-methyl-butan-1-ol, 3-methyl-3-buten-1-ol, pentan-1-ol), m/z 101,097 (hexan-2-one, hexanal), m/z 123,117 (nonenal), m/z 129,127 (octan-1-one, octanal), and two unidentified peaks m/z 83,071 and m/z 93,090 characterising model cheeses from the modern farms. In conclusion, it seems possible to discriminate between a range of dairy systems using fast volatile fingerprinting of ripened cheeses but a proper validation of results obtained is needed.Highlights Mass spectrometry technique (PTR-ToF-MS) was able to discriminate between dairy systems. We found m/z 61,028 (acetic acid), 109,070 (pyrazine), and m/z 137,132 (terpene) characterising model cheeses from traditional dairy systems. We found m/z 71,086 (3-methyl-butan-1-ol, 3-methyl-3-buten-1-ol, pentan-1-ol), m/z 101,097 (hexan-2-one, hexanal), m/z 123,117 (nonenal), m/z 129,127 (octan-1-one, octanal), and two unidentified peaks m/z 83,071 and m/z 93,090 characterising model cheeses from the modern farms.

Dairy cows have high incidences of metabolic disturbances, which often lead to disease, having a subsequent significant impact on productivity and reproductive performance. As the milk fatty acid (FA) profile represents a fingerprint of the cow’s nutritional and metabolic status, it could be a suitable indicator of metabolic status at the cow level. In this study, we obtained milk FA profile and a set of metabolic indicators (body condition score, ultrasound liver measurements, and 29 hematochemical parameters) from 297 Holstein–Friesian cows. First, we applied a multivariate factor analysis to detect latent structure among the milk FAs. We then explored the associations between these new synthetic variables and the morphometric, ultrasonographic and hematic indicators of immune and metabolic status. Significant associations were exhibited by the odd-chain FAs, which were inversely associated with β-hydroxybutyrate and ceruloplasmin, and positively associated with glucose, albumin, and γ-glutamyl transferase. Short-chain FAs were inversely related to predicted triacylglycerol liver content. Rumen biohydrogenation intermediates were associated with glucose, cholesterol, and albumin. These results offer new insights into the potential use of milk FAs as indicators of variations in energy and nutritional metabolism in early lactating dairy cows.

Milk proteins genetic variants have long attracted interest as they are associated with important issues relating to milk composition and technological properties. An important debate has recently opened at an international level on the role of β-casein (β-CN) A1 and A2 polymorphisms, toward human health. For this reason, a lot of efforts has been put into the promotion of A2 milk by companies producing and selling A1-free milk, leading the farmers and breeders to switch toward A2 milk production without paying attention on the potential effect of the processability of milk into cheese. The aim of the present work was to evaluate the effects of β-CN, specifically the A1 and A2 allelic variants, on the detailed milk protein profile and cheese-making traits in individual milk samples of 1,133 Holstein Friesian cows. The protein fractions were measured with reversed-phase (RP)-HPLC (expressed in g/L and % N), and the cheese-making traits, namely milk coagulation properties, cheese yield, and curd nutrient recoveries assessed at the individual level, with a nano-scale cheese-making procedure. The β-CN (CSN2), κ-CN (CSN3), and β-lactoglobulin (LGB) genetic variants were first identified through RP-HPLC and then confirmed through genotyping. Estimates of the effects of protein genotypes were obtained using a mixed inheritance model that considered, besides the standard nuisance variables (i.e., days in milk, parity, and herd-date), the milk protein genes located on chromosome 6 (CSN2, CSN3) and on chromosome 11 (LGB), and the polygenic background of the animals. Milk protein genes (CSN2, CSN3, and LGB) explained an important part of the additive genetic variance in the traits evaluated. The β-CN A1A1 was associated with a significantly lower production of whey proteins, particularly of β-lactoglobulin (-8.2 and -6.8% for g/L and % N, respectively) and α-lactalbumin (-4.7 and -4.4% for g/L and % N, respectively), and a higher production of β-CN (6.8 and 6.1% for g/L and % N, respectively) with respect to the A2A2 genotype. Regarding milk cheese-making ability, the A2A2 genotype showed the worst performance compared with the other genotypes, particularly with respect to the BA1, with a higher rennet coagulation time (7.1 and 28.6% compared with A1A1 and BA1, respectively) and a lower curd firmness at 30 min. Changes in milk protein composition through an increase in the frequency of the A2 allele in the production process could lead to a worsening of the coagulation and curd firming traits.

Avian metapneumovirus (aMPV) economically affects the global poultry industry causing respiratory and reproductive disorders. Considering the paucity of data on aMPV occurrence in European free-ranging avifauna, a molecular survey was conducted on wild birds of 23 species belonging to the orders Anseriformes, Charadriiformes or Passeriformes, captured alive and sampled in Northeast Italy as part of the national avian influenza virus (AIV) surveillance activities. A total of 492 oropharyngeal swabs, collected from 2007���2010, all AIV-negative, were screened from aMPV by subtype-specific qRT-PCR. An aMPV-C strain, named aMPV/C/IT/Wigeon/758/07, was found in a wintering young Eurasian wigeon (Mareca penelope) sampled in November 2007. The matrix, fusion, and attachment glycoprotein genes of the detected strain were subsequently amplified by specific independent RT-PCRs, then sequenced, and compared in a phylogenetic framework with known aMPV homologous sequences retrieved from GenBank. Close genetic relationships were found between the aMPV/C/IT/Wigeon/758/07 strain and subtype C Eurasian lineage strains isolated in the late 1990s in French domestic ducks, suggesting epidemiological links. Eurasian wigeons are medium/long-range migrant dabbling ducks that move along the Black Sea/Mediterranean flyway; our finding might, therefore, be related to migratory bridges between countries. To our knowledge, this is the first molecular evidence of the occurrence of aMPV subtype C in Italy and backdates the aMPV-C circulation to 2007. Moreover, the results suggest the susceptibility of Eurasian wigeons to aMPV. Broader investigations are needed to assess the role of wild ducks and the significance of the wildfowl/poultry interface in aMPV-C epidemiology. RESEARCH HIGHLIGHTSWild birds live-captured in Italy were tested for aMPV detection and characterization.aMPV-C Eurasian lineage was found for the first time in a wintering Eurasian wigeon.Migratory birds could be involved in the aMPV epidemiology. Wild birds live-captured in Italy were tested for aMPV detection and characterization. aMPV-C Eurasian lineage was found for the first time in a wintering Eurasian wigeon. Migratory birds could be involved in the aMPV epidemiology.

Ultrasound (US) imaging has been proposed as a noninvasive tool for monitoring liver dysfunction in dairy cows. This study, carried out on 306 clinically healthy Holstein cows in the first 120 d of lactation kept in 2 herds in northern Italy, aimed at investigating the association between US imaging-derived traits, namely predicted liver triacylglycerol content (pTAG, mg/g), liver depth (LD, mm), portal vein depth (PVD, mm) and area (PVA, mm

In this study, we investigated associations among subclinical intra-mammary infection (IMI) and quarter-level milk composition, udder health indicators, and cheesemaking traits. The dataset included records from 450 Holstein cows belonging to three dairy herds. After an initial screening (T0) to identify animals infected by Streptococcus agalactiae, Streptococcus uberis, Staphylococcus aureus, and Prototheca spp., 613 quarter milk samples for 2 different sampling times (T1 and T2, 1 mo after T1) were used for analysis. Milk traits were analyzed using a hierarchical linear mixed model including the effects of days in milk, parity and herd, and bacteriological and inflammatory category [culture negative with somatic cell count (SCC)200,000 cells/mL; culture negative with SCC ≥200,000 cells/mL; or culture positive]. All udder health indicators were associated with increased SCC and IMI at both sampling times. The largest effects were detected at T2 for milk lactose (-7% and -5%) and milk conductivity (+9% and +8%). In contrast, the increase in differential SCC (DSCC) in samples with elevated SCC was larger at T1 (+17%). Culture-negative samples with SCC ≥200,000 cells/mL had the highest SCC and greatest numbers of polymorphonuclear-neutrophils-lymphocytes and macrophages at both T1 and T2. Regarding milk cheesemaking ability, samples with elevated SCC showed the worst pattern of curd firmness at T1 and T2. At T2, increased SCC and IMI induced large decreases in recoveries of nutrients into the curd, in particular recovered protein (-14% and -16%) and recovered fat (-12% and -14%). Different behaviors were observed between Strep. agalactiae and Prototheca spp., especially at T2. In particular, samples that were positive for Strep. agalactiae had higher proportions of DSCC (+19%) compared with negative samples with low SCC, whereas samples that were positive for Prototheca spp. had lower DSCC (-11%). Intramammary infection with Prototheca spp. increased milk pH compared with culture-negative samples (+3%) and negative samples that had increased SCC (+2%). The greatest impairment in curd firmness at 30 min from rennet addition was observed for samples that were positive for Prototheca spp. (-99% compared with negative samples, and -98% compared with negative samples with high SCC). These results suggest that IMI caused by Prototheca spp. have detrimental effects on milk technological traits that deserve further investigation of the mechanisms underlying animals' responses to infection.

Outbreaks of inclusion body hepatitis (IBH) and adenoviral gizzard erosion have been anecdotally reported in Greece since approximately 2011. However, a relevant increase in clinical outbreaks compatible with IBH has been described since 2014. Unfortunately, with limited exceptions, only serological assays were performed, and involved strains were not properly characterized. In the present study, 35 outbreaks were investigated in the period between July 2017 and February 2018 in Greece. In addition to clinical and histopathological diagnosis, fowl adenovirus (FAdV) presence was investigated by PCR and sequencing. Thirty-four out of 35 samples tested FAdV positive. Twenty-nine (85.29%) and 5 (14.71%) strains were classified as FAdV-E and FAdV-D, respectively. Fowl adenovirus-E strains were genetically homogeneous and formed an independent cluster of Greek-only sequences, including the sole previously available sequence, suggesting the prolonged circulation of this species in Greece. On the contrary, FAdV-D strains were more heterogeneous and closely related to strains sampled in other European countries, testifying the occurrence of multiple introduction events. The evaluation of phylogenetic relationships, geographic clustering, age of infection, and origin of the broiler breeder flocks suggests that both vertical and horizontal transmission are important in FAdV epidemiology in Greece and highlights the limited efficacy of currently implemented control measures. Of note, a significantly higher mortality was observed in precociously infected flocks, likely because of the higher susceptibility of younger animals. This evidence stresses the need of preventing vertical and/or early infection to limit the economic impact of adenovirus-induced diseases.

In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual-purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum β-hydroxybutyrate (BHB), and kappa casein (k-CN) in the major cattle breed, i.e., Holstein-Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual-purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross-validation (CV) strategies: a within-breed scenario using 10-fold cross-validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10-fold_HO), an across-breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set, a specialized multi-breed scenario (BS+HO_10-fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi-breed scenario (Multi-breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual-purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi-breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi-breed. Within-Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k-CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for κ-CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k-CN. Using multiple specialized and dual-purpose animals in the training set outperforms the 10-fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k-CN. When the Multi-breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual-purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased

In a recent study, an emerging infectious bursal disease virus (IBDV) genotype (ITA) was detected in IBDV-live vaccinated broilers without clinical signs of infectious bursal disease (IBD). VP2 sequence analysis showed that strains of the ITA genotype clustered separately from vaccine strains and from other IBDV reference strains, either classic or very virulent. In order to obtain a more exhaustive molecular characterization of the IBDV ITA genotype and speculate on its origin, genome sequencing of the field isolate IBDV/Italy/1829/2011, previously assigned to the ITA genotype, was performed, and the sequences obtained were compared to the currently available corresponding sequences. In addition, phylogenetic and recombination analyses were performed. Interestingly, multiple amino acid (AA) sequence alignments revealed that the IBDV/Italy/1829/2011 strain shared several AA residues with very virulent IBDV strains as well as some virulence markers, especially in the VP1 protein. Nevertheless, sequence analysis demonstrated the presence of several residues typical of IBDV strains at a low degree of virulence in the IBDV/Italy/1829/2011 strain. Although homologous recombination and reassortant phenomena may occur naturally among different IBDV strains, no evidence of those events was found in the genome of the IBDV/Italy/1829/2011 strain, which was confirmed to be a genetically distinctive IBDV genotype.

The aim of this study was to quantify the major sources of variation in the levels of 15 minerals in individual milk samples collected from cows raised in multibreed dairy herds. The herds (n = 27) were classified into 2 categories, according to milk productivity. Milk productivity was based on the net energy of lactating cows' average daily milk yield. Milk samples were collected from 240 cows of 6 different breeds: 3 specialized dairy (Holstein-Friesian, Brown Swiss, and Jersey) and 3 dual-purpose (Simmental, Rendena, and Alpine Grey). The samples were analyzed for macro-elements (Na, Mg, P, S, K, and Ca), essential micro-elements (Mn, Fe, Cu, Zn, and Se), and environmental micro-elements (B, Si, Sr, and Sn), using inductively coupled plasma-optical emission spectrometry. Data were analyzed using a linear mixed model that included fixed effects of days in milk (DIM), parity, breed, and herd productivity, and a random effect of herd-date within productivity level. Results showed that the effect of herd-date varied across minerals. It was especially large for environmental minerals (ranging from 47 to 91% of total variance) and ranged from 11 to 61% for macrominerals and essential microminerals. Milk samples collected from farms with a high level of herd productivity had a richer mineral profile than samples from low-productivity herds. Parity only influenced macrominerals, with the exception of S and Ca, while DIM influenced almost all minerals, with a few exceptions among the environmental elements. Differences in mineral profile were small between specialized and dual-purpose breeds, but they were large within the group of the specialized cows. These breed differences were reduced after adjusting for milk quality and yield, particularly in the case of milk Mg, S, Ca, Mn, and Zn levels. Milk samples from the Jersey and Brown Swiss cows had higher mineral levels (Sn excluded) than milk from the Holstein-Friesian cows; the other breeds of Alpine origin produced milk of intermediate quality. Our findings suggest that breed has a stronger effect on macrominerals and some of the essential microminerals than herd productivity, parity, and DIM. The modification of the mineral profile in milk seems possible for many minerals, but it likely depends on genetics (e.g., breed, selection) and on environmental and management factors in variable proportions according to the mineral considered.

Fourier-transform infrared (FTIR) spectra collected during milk recording schemes at population level can be used for predicting novel traits of interest for farm management, cows' genetic improvement, and milk payment systems. The aims of this study were as follows. (1) To predict cheese yield traits using FTIR spectra from routine milk recordings exploiting previously developed calibration equations. (2) To compare the predicted cheese-making abilities of different dairy and dual-purpose breeds. (3) To analyze the effects of herds' level of intensiveness (HL) and of the cow's level of productivity (CL). (4) To compare the patterns of predicted cheese yields with the patterns of milk composition in different breeds to discern the drivers of cheese-making efficiency. The major sources of variation of FTIR predictions of cheese yield ability (fresh cheese or cheese solids produced per unit milk) of individual milk samples were studied on 115,819 cows of 4 breeds (2 specialized dairy breeds, Holstein and Brown Swiss, and 2 dual-purpose breeds, Simmental and Alpine Grey) from 6,430 herds and exploiting 1,759,706 FTIR test-day spectra collected over 7 yr of milk sampling. Calibration equations used were previously developed on 1,264 individual laboratory model cheese procedures (cross-validation R2 0.85 and 0.95 for fresh and solids cheese yields, respectively). The linear model used for statistical analysis included the effects of parity, lactation stage, year of calving, month of sampling, HL, CL, breed of cow, and the interactions breed × HL and breed × CL. The HL and CL stratifications (5 classes each) were based on average daily secretion of milk net energy per cow. All effects were highly significant. The major conclusions were as follows. (1) The FTIR-based prediction of cheese yield of milk goes beyond the knowledge of fat and protein content, partially explaining differences in cheese-making ability in different cows, breeds and herds. (2) Differences in cheese yields of different breeds are only partially explained by milk fat and protein composition, and less productive breeds are characterized by a higher milk nutrient content as well as a higher recovery of nutrients in the cheese. (3) High-intensive herds not only produce much more milk, but the milk has a higher nutrient content and a higher cheese yield, whereas within herds, compared with less productive cows, the more productive cows have a much greater milk yield, milk with a greater content of fat but not of protein, and a moderate improvement in cheese yield, differing little from expectations based on milk composition. Finally, (4) the effects of HL and CL on milk quality and cheese-making ability are similar but not identical in different breeds, the less productive ones having some advantage in terms of cheese-making ability. We can obtain FTIR-based prediction of cheese yield from individual milk samples retrospectively at population level, which seems to go beyond the simple knowledge of milk composition, incorporating information on nutrient retention ability in cheese, with possible advantages for management of farms, genetic improvement of dairy cows, and milk payment systems.

Background The possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions. Results The prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R2 from 0.52 to 0.80), low for pH and purge losses (R2 around 0.30), and very poor for cooking losses and tenderness (R2 below 0.20). Except for lightness and purge losses, the heritability estimates of most of the predicted traits were lower than those of the measured traits while the genetic correlations between measured and predicted traits were high (average value 0.81). Conclusions Results showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.

Natural variations in milk minerals, their relationships, and their associations with the coagulation process and cheese-making traits present an opportunity for the differentiation of milk destined for high-quality natural products, such as traditional specialties or Protected Designation of Origin (PDO) cheeses. The aim of this study was to quantify the effects of the native contents of Ca, P, Na, K, and Mg on 18 traits describing traditional milk coagulation properties (MCP), curd firming over time (CFt) equation parameters, cheese yield (CY) measures, and nutrient recoveries in the curd (REC) using models that either included or omitted the simultaneous effects of milk fat and casein contents. The results showed that, by including milk fat and casein and the minerals in the statistical model, we were able to determine the specific effects of each mineral on coagulation and cheese-making efficiency. In general, about two-thirds of the apparent effects of the minerals on MCP and the CFt equation parameters are actually mediated by their association with milk composition, especially casein content, whereas only one-third of the effects are direct and independent of milk composition. In the case of cheese-making traits, the effects of the minerals were mediated only negligibly by their association with milk composition. High Ca content had a positive effect on the coagulation pattern and cheese-making traits, favoring water retention in the curd in particular. Phosphorus positively affected the cheese-making traits in that it was associated with an increase in CY in terms of curd solids, and in all the nutrient recovery traits. However, a very high P content in milk was associated with lower fat recovery in the curd. The variation in the Na content in milk only mildly affected coagulation, whereas with regard to cheese-making, protein recovery was negatively associated with high concentrations of this mineral. Potassium seemed not to be actively involved in coagulation and the cheese-making process. Magnesium content tended to slow coagulation and reduce CY measures. Further studies on the relationships of minerals with casein and protein fractions could deepen our knowledge of the role of all minerals in coagulation and the cheese-making process.

Several local sheep breeds of the Alpine regions are in danger of extinction as a result of mountain farms being abandoned. Three fattening trials were carried out on 4 breeds of the Veneto region (Foza, Lamon, Alpagota, and Brogna) on a total of 115 ewes and suckling lambs, weaned lambs and heavy lambs slaughtered at 1356 ± 267, 104 ± 24, 219 ± 26, and 342 ± 14 d of age, respectively. The results confirmed that the adoption of total mixed diets resulted in good in vivo and post mortem performances of culled ewes (64 kg slaughter weight, 48% dressing percentage), suckling lambs (21 kg, 50%), weaned lambs (30 kg, 42%) and heavy lambs (61 kg, 48%). Carcass compactness and fatness increased with the age of the lambs, whereas the proportions of lean meat and bone decreased. Meat lightness and cooking losses decreased with age, whereas redness and chroma increased. The meat quality of culled ewes was like that of heavy lambs. Carcass conformation and composition (aside from size traits), and the quality of the meat differed little among the four Alpine breeds. These results show that local breeds are potentially valuable sources of lamb and mutton meat across different seasons, paving the way for strategies to develop and promote the meat with the involvement of high-quality restaurants and meat retailers.Highlights Compared to outdoor grazing, the adoption of indoor diets resulted in good in vivo and post-mortem performances of ewes, suckling lambs and heavy lambs. Autochthonous Alpine breeds are potentially valuable sources of high-quality lamb and mutton across different seasons of the year. This potential could be exploited in developing a prestigious product aimed at high quality restaurants and butchers. The subsequent demand for the product would, in turn, contribute to the survival of the local sheep breeds of Alpine regions.

The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, β-, αS1-, and αS2-CN, and 2 whey proteins, namely β-lactoglobulin (β-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between β-CN and αS1-CN (-0.706) and between β-CN and κ-CN (-0.735). The application of the MMHC algorithm revealed that κ-CN and β-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for β-CN, 30 SNP for αS1-CN, and 14 SNP for αS2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for β-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.

Mastitis is one of the most prevalent diseases in dairy cattle and is the cause of considerable economic losses. Alongside somatic cell count (SCC), differential somatic cell count (DSCC) has been recently introduced as a new indicator of intramammary infection. The DSCC is expressed as a count or a proportion (%) of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in milk somatic cells. These numbers are complemented to total somatic cell count or to 100 by macrophages (MAC). The aim of this study was to investigate the genetic variation and heritability of DSCC, and its correlation with milk composition, udder health indicators, milk composition, and technological traits in Holstein cattle. Data used in the analysis consisted in single test-day records from 2,488 Holstein cows reared in 36 herds located in northern Italy. Fourier-transform infrared (FTIR) spectroscopy was used to predict missing information for some milk coagulation and cheese-making traits, to increase sample size and improve estimation of the genetic parameters. Bayesian animal models were implemented via Gibbs sampling. Marginal posterior means of the heritability estimates were 0.13 for somatic cell score (SCS); 0.11 for DSCC, MAC proportion, and MAC count; and 0.10 for PMN-LYM count. Posterior means of additive genetic correlations between SCS and milk composition and udder health were low to moderate and unfavorable. All the relevant genetic correlations between the SCC traits considered and the milk traits (composition, coagulation, cheese yield and nutrients recovery) were unfavorable. The SCS showed genetic correlations of -0.30 with the milk protein proportion, -0.56 with the lactose proportion and -0.52 with the casein index. In the case of milk technological traits, SCS showed genetic correlations of 0.38 with curd firming rate (k20), 0.45 with rennet coagulation time estimated using the curd firming over time equation (RCTeq), -0.39 with asymptotic potential curd firmness, -0.26 with maximum curd firmness (CFmax), and of -0.31 with protein recovery in the curd. Differential somatic cell count expressed as proportion was correlated with SCS (0.60) but had only 2 moderate genetic correlations with milk traits: with lactose (-0.32) and CFmax (-0.33). The SCS was highly correlated with the log PMN-LYM count (0.79) and with the log MAC count (0.69). The 2 latter traits were correlated with several milk traits: fat (-0.38 and -0.43 with PMN-LYM and MAC counts, respectively), lactose percentage (-0.40 and -0.46), RCTeq (0.53 and 0.41), tmax (0.38 and 0.48). Log MAC count was correlated with k20 (+0.34), and log PMN-LYM count was correlated with CFmax (-0.26) and weight of water curd as percentage of weight of milk processed (-0.26). The results obtained offer new insights into the relationships between the indicators of udder health and the milk technological traits in Holstein cows.

Despite the efforts to achieve a consistent classification scheme based on the complete S1 gene, the genetic characterization of infectious bronchitis virus (IBV) is often performed on partial S1 regions due to economic and time constraints in the diagnostic routine. Sanger sequencing remains the most common and cost-effective option even if the analysis of samples where multiple field and vaccine strain populations coexist can lead to partial or misleading results. The present study aimed to evaluate the different diagnostic outcomes of three commonly used RT-PCR methods targeting two regions of the S1 gene. A possible bias in IBV detection and characterization was investigated in relation to the adopted method, the strain concentration as well as their ratio in mixed samples. Thirty samples were prepared by artificially mixing two vaccine strains, combined at different ratios and selected among four different IBV lineages, i.e. GI-1 (Mass), GI-13 (793/B), GI-19 (QX), GI-23 (Israeli Variant 2). Sequence analysis was conducted both manually and with bioinformatic methods. The result agreement among methods, replicates and analysis approaches was statistically evaluated. Consistent results emerged among the three assays, with a few discrepancies likely caused by primer affinity and target amount. This study confirms the complexity of IBV strain identification and highlights the importance of evaluating and updating the available diagnostic assays for a reliable detection of all circulating IBV strains.

Our study investigated the inbreeding load for fertility traits in the Italian Brown Swiss dairy cattle breed. Fertility traits included continuous traits (i.e., interval from calving to first service, days open, and calving interval) and categorical traits (i.e., calving rate at first insemination and nonreturn date at d 56). We included only records of the first 3 parities of cows that calved between 2010 and 2018. We traced up the pedigree of the cows with records as far as possible, ending up with a total of 73,246 animals. The final data set consisted of 59,864 records from 34,921 cows. We analyzed all models using a Bayesian approach that included a covariate with total inbreeding in addition to systematic, permanent environment, additive genetic, and inbreeding load effects. We then evaluated the trends in heritabilities and ratios of the inbreeding load using a continuum of partial inbreeding coefficients from 0.001 to 0.100 as reference. Posterior estimates of heritabilities tended to decrease across the continuum, whereas ratios of the inbreeding load tended to increase, more noticeably in categorical traits (calving rate at first insemination and nonreturn date at d 56). From the results obtained, we confirmed the presence of heterogeneity in inbreeding depression. We then predicted the inbreeding load effects, which had a low reliability of prediction, explained by having only 513 ancestors generating inbreeding. However, reliability of prediction was high enough for some of the individuals, obtaining a favorable prediction of inbreeding load for a relevant percentage, which improved the phenotypic performance of their inbred descendants. These results make it feasible to implement breeding and management strategies that select ancestors with a favorable inbreeding load prediction. In addition, it opens the possibility to define a global index for the expected consequences of the inbreeding generated by each individual.

Infectious bronchitis (IB), caused by infectious bronchitis virus (IBV), account for severe economic losses in the poultry industry. The continuous emergence of a multitude of IBV variants poses many challenges for its diagnosis and control, and live attenuated vaccines, despite their routine use, still plays a significant role in driving IBV evolution, further complicating the epidemiological scenario. Unfortunately, the impact of different vaccination strategies on IB control, epidemiology, and diagnosis has rarely been investigated. This work presents the results of a large-scale diagnostic survey performed in Poland to study IBV molecular epidemiology and how vaccination may affect the viral circulation in the field. To this purpose, 589 samples were collected between May 2017 and January 2019, tested by reverse transcription-PCR for IBV and sequenced. Vaccine and field strains were discriminated based on genetic and anamnestic information. The most commonly detected lineages were 793B (79%) and variant 2 (17.4%), with sporadic detections of QX, Mass, and D274-like strains. Most of the detected strains had a vaccine origin: 46.3% matched one of the applied vaccines, while 36.5% were genetically related to vaccines not implemented in the respective protocol. Besides their practical value for the proper planning of vaccination protocols in Poland, these results suggest that only a fraction (17.2%) of the circulating strains are field ones, imposing a careful assessment of the actual IBV field menaces. Moreover, phenomena like vaccine spreading and persistence seem to occur commonly, stressing the need to further study the epidemiological consequences of the extensive use of live vaccines.

This study aimed to investigate the genetic and putative causal relationships between fertility traits [i.e., days open (DO) and calving rate (CR)] and milk quality, composition, and fatty acid contents (milk composition traits) in Holstein-Friesian, Brown Swiss, and Simmental cattle, using recursive models within a Bayesian framework. Trivariate animal models were run, each including one fertility trait, one milk composition trait, and, in all models, milk yield. The DO and CR data were merged with the test days closest to the insemination date for milk composition traits. After editing, 16,468 to 23,424 records for Holstein-Friesian, 23,424 to 46,660 for Brown Swiss, and 26,105 to 35,574 for Simmental were available for the analyses. Recursive animal models were applied to investigate the possible causal influences of milk composition traits on fertility and the genetic relationships among these traits. The results suggested a potential cause-and-effect relationship between milk composition traits and fertility traits, with the first trait influencing the latter. We also found greater recursive effects of milk composition traits on DO than on CR, the latter with some putative differences among breeds in terms of sensitivity. For instance, the putative causal effects of somatic cell score on CR (on the observed scale, %) varied from -0.96 to -1.39%, depending on the breed. Concerning fatty acids, we found relevant putative effects of C18:0 on CR, with estimates varying from -7.8 to -9.9%. Protein and casein percentages, and short-chain fatty acid showed larger recursive effects on CR, whereas fat, protein, and casein percentages, C16:0, C18:0, and long-chain fatty acid had larger effects on DO. The results obtained suggested that these milk traits could be considered as effective indicators of the effects of animal metabolic and physiological status on fertility.

This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH4/DM: 0.232&ndash, 0.317) and methane intensity per kg of corrected milk (CH4/CM: 0.177&ndash, 0.279), smaller per kg cheese solids (CH4/SO: 0.093&ndash, 0.165), but greater per kg fresh cheese (CH4/CU: 0.203&ndash, 0.267) and for methane production (dCH4: 0.195&ndash, 0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH4/DM (0.45&ndash, 0.60), and very variable for dCH4 according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH4/CM, CH4/CU and dCH4, could be considered in breeding programs aimed at the improvement of milk ecological footprint.

Simple Summary Improving day-old chick quality is essential for the overall profitability of the broiler productive cycle and has been associated with a decreased feed conversion rate, increased growth performance, resistance to infectious diseases, and welfare parameters. The hatchery practices are fundamental, since adequate hygiene and handling are crucial in reducing egg contamination and cross-contamination. Particularly, the efficient removal of rotten eggs has been suggested to reduce the overall bacterial burden contaminating the needle used for in-ovo vaccination, the nearby eggs, and the whole incubator/hatching room when broken. In the present multicentric study, including 11 European countries, a remarkable impact of the rotten egg percentage on hatchery productive parameters, such as the hatchability, embryo mortality, and level of contamination, was demonstrated. Efficient rotten egg removal and the application of new generation technologies for appropriate detection and removal tools should thus provide remarkable benefits for hatchery performances and indirectly for downstream poultry production. Abstract Day-old chick quality is an essential element for the overall profitability of the broiler productive cycle and has been associated with the growth performance and feed conversion rate. An effect on the development of the immune system was also reported, which could likely account for reduced susceptibility to infectious diseases and improved animal welfare parameters. Besides direct cost reduction, lower antimicrobial use and improved animal welfare are crucial in the directive of European Union legislation and are at the forefront of customer choices. Several factors contribute to determining the chick quality. Breeder flocks genetics, health, and management affect the egg features, quality, and bacterial load. However, hatchery practices play a pivotal role, since adequate hygiene and handling are fundamental in reducing egg contamination and cross-contamination. The presence of rotten eggs is often regarded as a major risk, since the internal bacterial load can contaminate the needle used for in-ovo vaccination, the nearby eggs, and the whole incubator/hatching room when broken. In the present multicentric study, representative of 40 hatcheries located in 11 European countries, a remarkable impact of the rotten egg percentage on the hatchery productive parameters, representative of the hatchability, embryo mortality, and level of contamination, was demonstrated. Efficient rotten egg removal and the application of appropriate detection and removal tools should thus provide remarkable benefits for hatchery performance and indirectly for downstream poultry production.

Maedi–Visna virus (MVV) and Mycobacterium avium ssp. paratuberculosis (MAP) are two pathogens that cause chronic, production-limiting diseases in dairy sheep. Although they are present worldwide, there are no detailed reports on their actual effects on milk traits in the literature. This study was designed to investigate the effects of test positivity to MVV and MAP on ovine milk yield, composition and coagulation properties, and curd-firming over time (CFt) variables in clinically healthy animals at the field level. The additive genetic variation and heritabilities of MVV and MAP positivity were also estimated. Milk samples were collected from 1,079 Sarda sheep kept on 23 farms, and pedigree information was obtained from the flock book. Milk yield was also recorded on the sampling date. Positivity for MVV and MAP was determined from milk samples using indirect ELISA test kits. Milk composition traits were measured by spectroscopy, milk coagulation properties were measured with a Formagraph (Foss Italia, Padua, Italy), and CFt traits were calculated using the data from the Formagraph diagram. The effects of MVV and MAP positivity on milk traits were determined through a set of mixed linear models, which took into account various sources of variation, such as days in milk, parity, and flock effects, and included the effects (positive or negative) of the 2 pathogens. A Bayesian threshold sire model with sire relationship was used to estimate genetic variation and heritability. The overall animal prevalence of MVV-positive ewes was 43.6%; on only 1 farm of the 23 tested were all sampled ewes negative. An overall animal prevalence of 10.6% was recorded for MAP, with 4 farms at 0%. Positivity for MVV significantly affected the logarithmic score of the bacterial count, curd firmness after 30 min and 45 min, and the curd-firming instant rate constant. We found significant effects of MAP infection on milk composition, pH, and rennet coagulation time. The mean of the posterior distributions of heritability estimates on the liability scale was 0.15 for MAP and 0.07 for MVV. Our results demonstrate that only a few traits are negatively affected by MVV and MAP positivity, and that there is exploitable genetic variation in MVV and MAP susceptibility in dairy sheep.