Explore the vast range of titles available.

The aims of the study described in the Research Communication were to determine the level of influence of some environmental factors on milk yield and quality traits, including lactose, and lactation length in ewes belonging to three different Italian breeds and to estimate the heritability for the same traits. A total of 2138 lactation records obtained from 535 ewes belonging to three different Italian breeds (Comisana, Leccese, and Sarda) were used. Breed significantly affected all of the considered traits. Moreover, year of lambing affected milk yield and lactation length without influence on milk quality traits. Parity affected significantly only the milk yield, whereas type of birth showed its effect on milk yield, fat, protein, and lactose yield. On the whole, the presently reported heritability estimates are within the range of those already obtained in other dairy breeds by other authors, with values for lactation length being very low in all the investigated populations. Considering the heritability estimates for lactose content and yield, to the best of our knowledge, there is a lack of information on these parameters in ovine species and this is the first report on heritability of lactose content and yield in dairy sheep breeds. Our results suggest that genetic variability for milk traits other than lactation length is adequate for selection indicating a good response to selection in these breeds.

This study analyzed the genetic variability, inbreeding and population structure of the Tunisian–North African dairy sheep breed, the Sicilo-Sarde (SS), created by crossing the Sarda and Comisana dairy breeds. The level of variability in the SS, considered as an endangered breed after a dramatic decrease, was assessed using 17 microsatellite markers by analyzing the two breed populations sampled from their respective cradles: SS of Beja (SSB, n = 27) and SS of Mateur (SSM, n = 25). High levels of genetic diversity in SS were revealed, with a total of 212 alleles, a high mean number of alleles (12.47 ± 4.17) and a high average polymorphism information content (PIC) (0.81 ± 0.10). The observed heterozygosity was considerable in SSB and SSM (0.795 and 0.785, respectively). The inbreeding level measured by the population inbreeding coefficient FIS is higher in the SSM population (0.121) than in the SSB population (0.090). The higher genetic diversity level detected in SSB reflected the effect of new Italian Sarda genes introduced by intra-uterine artificial insemination recently practiced in this population. The Wilcoxon test and the mode-shift distribution indicated that the SS breed is a non-bottlenecked population. The structural analysis reflected the historical miscegenation practiced during the breed creation and highlighted further ancient miscegenation, which could date back to the first waves of sheep introduction to the western Mediterranean region. Microsatellite markers were successfully applied in the assessment of the genetic variability of SS and should be used in monitoring this variability during the application of conservation strategies.

In this work, untargeted metabolomics was used to shed light on the impact of different pasture-based diets on the chemical profile of Sarda sheep milk. The study considered 11 dairy sheep farms located in Sardinia, and milk samples were collected in 4 different periods, namely January, March, May, and July 2019, when all sheep had 58, 98, 138, and 178 days in milk, respectively. The animal diet composition was based on the intake of grazed herbage in natural pasture, hay, and concentrate. Overall, the combination of two comprehensive databases on food, namely the Milk Composition Database and Phenol-Explorer, allowed the putative identification of 406 metabolites, with a significant (p < 0.01) enrichment of several metabolite classes, namely amino acids and peptides, monosaccharides, fatty acids, phenylacetic acids, benzoic acids, cinnamic acids, and flavonoids. The multivariate statistical approach based on supervised orthogonal projections to latent structures (OPLS-DA) allowed us to predict the chemical profile of sheep milk samples as a function of the high vs no fresh herbage intake, while the prediction model was not significant when considering both hay and concentrate intake. Among the discriminant markers of the herbage intake, we found five phenolic metabolites (such as hippuric and coumaric acids), together with lutein and cresol (belonging to carotenoids and their metabolites). Additionally, a high discriminant power was outlined for lipid derivatives followed by sugars, amino acids, and peptides. Finally, a pathway analysis revealed that the herbage intake affected mainly five biochemical pathways in milk, namely galactose metabolism, phenylalanine metabolism, alpha-linolenic acid metabolism, linoleic acid metabolism, and aromatic amino acids involved in protein synthesis (namely tyrosine, phenylalanine, and tryptophan).

Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain. This objective was assessed by combining qualitative and quantitative methodologies. In the first stage, with the aim to identify and select the appropriate technologies, a panel of 107 experts in dairy sheep production was used. A questionnaire was applied to all of them with successive rounds using Delphi methodology. Later, these technologies were grouped by principal components analysis (PCA) and cluster analysis (CA). In a second stage the technological results from a random sample of 157 farms in the Center of Spain were collected. The technologies selected were linked to the technological adoption level of the farms in Castilla la Mancha by a multiple regression model. Ten technologies were selected by the 107 experts. Four factors were retained by PCA that explained at 67.11% of variance. The first factor is related to feeding strategies, the second to land use for livestock production, the third to efficient management of land resources or ecoefficiency and the fourth to by-products use. The expert evaluation was grouped in three clusters using the Ward’s method and the squared Euclidean distance measure, where the second showed higher values in the adoption level of each technology. The multiple regression model explained the relationship between the technologies and the technological level of the farms (R2 73.53%). The five technologies selected were: use of unifeed (1), supplemental feeding (5), grazing (6), raw materials production (7) and sustainable use of water and soil (10). These ten technologies identified can be directly extended to small-scale dairy farms from other countries in the Mediterranean basin and Latin America. This technological selection was supported from the broad and diverse panel of experts used. Besides, five technologies identified by the quantitative model will be able to be taken into account for the development of public innovation policies. They are direct technologies and easy to apply on the farm and seeking increased viability through innovation vs. intensification.

Market failures are the main cause of poor acknowledgement of the true impact of functional sheep traits on the management and economic performance of farms, which results in their omission from the breeding goal or the estimation of non-representative economic weights in the breeding goal. Consequently, stated-preference non-market valuation techniques, which recently emerged to mitigate these problems, are necessary to estimate economic weights for functional traits. The purpose of this paper is to present an example of the use of a choice experiment (CE) in the estimation of economic weights for sheep traits for the design of breeding goals. Through a questionnaire survey the preferences of sheep farmers are recorded and their marginal willingness to pay (MWTP) for 10 production and functional traits is estimated. Data are analysed using random parameter logit models. The results reveal unobserved preference heterogeneity for fertility, adaptability to grazing and resistance to disease, thus highlighting that these traits are appreciated differently by farmers, because their needs are diverse. Positive MWTP is found for Greek breeds, high milk production and lambs with low fat deposition, for which there is high demand in Greek markets. On the other hand, MWTP for the cheese-making ability of milk is negative, stemming from the fact that sheep milk prices in Greece are not formulated according to milk composition. In addition, farmers seem to understand differences between udder shapes and attribute different values to various types. This application of the CE method indicates that communication channels among farmers and breeders should be established in order to enhance market performance and to provide orientation to the design of breeding programmes. Non-market valuation can be used complementarily to market valuation techniques, in order to provide accurate estimates for production and functional traits.

One of the main ecological challenges that agricultural and especially livestock production systems face is the adoption of management practices that encourage the mitigation of greenhouse gas (GHG) emissions, while maintaining their production level. According to the relevant literature, the potential for GHG reduction lies mainly in greater efficiency in meat and dairy production, which suggests that the ecological modernization of livestock farms follows the efficiency/substitution pathway. This study aims to investigate the above assumption and explore the link between the technical efficiency (TE) and environmental efficiency (EE) of livestock farms using data envelopment analysis (DEA). The analysis focuses on dairy sheep farming, since the activity is important for the Greek rural economy while at the same time responsible for half of the country’s agricultural methane emissions. Results indicate that the correlation between technical and environmental efficiency of sheep farms is significant. Environmental efficiency is affected by farm size, specialization and production orientation. Feeding practices, like the ratio of concentrates to forage, also appear to have a positive effect on environmental efficiency. On the other hand, experienced farmers tend to have lower environmental efficiency, which may indicate their reluctance to adopt modern farming practices.

Sheep production systems in regions with a Mediterranean climate are important in social, economic and environmental terms. Modeling these systems allows, among others, evaluation of the costs efficiencies which in turn permits assessing the expected effects of changes in production variables. This paper presents a prototype analysis of the economic sustainability of ecological dairy sheep systems of Castilla-La Mancha, Central Spain evaluated through the estimation of costs efficiencies. Costs functions were developed using data from 31 farms. Rate of supplementary feeding, labour use, and flock size were used to measure the cost efficiency. On average, cost efficiency was 61.7±15.5%, with significant differences among typological groups. High efficiency was found in only 29% of the farms. The economic analyses performed suggest that the continued existence of economically unsustainably farms is explained by the available subsidies, lack of amortization of fixed assets leading to progressive decapitalization, and subsistence incomes by family groups (gross family income).

Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tete Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the H matrix (EUPG) and metafounders (MF)].We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information.Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations.The addition of genomic information increases the accuracy of EBV of young rams in Manech Tete Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years.

Background Mastitis is the most prevalent disease in dairy sheep with major economic, hygienic and welfare implications. The disease persists in all dairy sheep production systems despite the implementation of improved management practises. Selective breeding for enhanced mastitis resistance may provide the means to further control the disease. In the present study, we investigated the genetic architecture of four mastitis traits in dairy sheep. Individual animal records for clinical mastitis occurrence and three mastitis indicator traits (milk somatic cell count, total viable bacterial count in milk and the California mastitis test) were collected monthly throughout lactation for 609 ewes of the Greek Chios breed. All animals were genotyped with a custom-made 960-single nucleotide polymorphism (SNP) DNA array based on markers located in quantitative trait loci (QTL) regions for mastitis resistance previously detected in three other distinct dairy sheep populations. Results Heritable variation and strong positive genetic correlations were estimated for clinical mastitis occurrence and the three mastitis indicator traits. SNP markers significantly associated with these mastitis traits were confirmed on chromosomes 2, 3, 5, 16 and 19. We identified pathways, molecular interaction networks and functional gene clusters for mastitis resistance. Candidate genes within the detected regions were identified based upon analysis of an ovine transcriptional atlas and transcriptome data derived from milk somatic cells. Relevant candidate genes implicated in innate immunity included SOCS2, CTLA4, C6, C7, C9, PTGER4, DAB2, CARD6, OSMR, PLXNC1, IDH1, ICOS, FYB, and LYFR . Conclusions The results confirmed the presence of animal genetic variability in mastitis resistance and identified genomic regions associated with specific mastitis traits in the Chios sheep. The conserved genetic architecture of mastitis resistance between distinct dairy sheep breeds suggests that across-breed selection programmes would be feasible.