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Livestock production has contributed to food security and economic development for thousands of years. Adaptation to wide range of environmental conditions and artificial selection has led to the development of numerous indigenous breeds that are part of the wealth of diversity in agriculture. They are producing a wide range of products for local and domestic consumption as well as for international trade. Livestock are for many local communities, invaluable sources of food and other products and essential sources of income. Maintaining the diversity of breeds in various species of domestic animals enables farmers and breeders to respond to ever changing consumer demands as well as changing production conditions, especially in light of climate change. No one can predict future demands and production conditions, and thus, maintaining the greatest diversity of animal genetic resources provides an insurance policy to enable necessary adaptation. Lack of appreciation for the need to maintain genetic diversity or inadequate farmer and breeder support programmes has resulted in the rapid global erosion of local breeds. Greater effort is required to stem this erosion.

This study is part of the European Regional Focal Point (ERFP) project titled “Development of models assessing the breeds risk status by utilization of population and relevant georeferenced data”. The specific aim of this research is to develop a geographical model to be used to classify the breeds according to their degree of endangerment and to make decisions relevant with the management of animal genetic resources. The geographic distribution of eight local breeds (6 sheep and 2 cattle breeds from United Kingdom, Greece, Slovenia and Italy) was analysed with a GIS approach. We calculated the radius of a buffer including 75% of the population of the investigated breeds; the hypothesis is that 25% of the population of a local breed is the minimum percentage able to guarantee the recovery after an epidemiological outbreak. The results of this study showed that the geographical approach should be used for the definition of the risk status of local breeds.

Study of litter size (LS), birth weight (BW) and litter weight (LW) over successive parities was conducted on diary Alpine goats reared in Croatia. Statistical analysis was performed on records obtained out of 46 722 parturitions from 18 546 goats of different ages and parities. Twining and tripling rates were 27% i.e. 1%. Unadjusted averages for LS, BW and LW were 1.46 lambs, 3.29 kg, and 4.75 kg, respectively. Univariate analysis (repeatability animal model) was used in estimation of variance components and prediction of breeding values. Season (as year- month interaction), age at lambing (as quadratic regression) nested within parity and type of birth were treated as fixed, and herd- year, additive genetic, permanent environmental and residual were treated as random effects, respectively. All investigated effects were statistically significant (P

Chantier qualité GA Liste des auteurs de l'European Cattle Genetic Consortium, classés par pays : Austria: R. Baumung, S. Manatrinon, BOKU University, Vienna; Belgium: G. Mommens, University of Ghent, Merelbeke; Denmark: L.-E. Holm, Aarhus University, Tjele; K.B. Withen, B.V. Pedersen, P. Gravlund, University of Copenhagen, Copenhagen; Estonia: H. Viinalass, Estonian University of Life Sciences, Tartu; Finland: J. Kantanen, I. Tapio, M.H. Li, MTT, Jokioinen; France: K. Moazami-Goudarzi, M. Gautier, Denis Laloë, INRA, Jouy-en-Josas; A. Oulmouden, H. Levéziel, INRA, Limoges; P. Taberlet, Université Joseph Fourier et CNRS, Grenoble; Germany: B. Harlizius, School of Veterinary Medicine, Hannover; H. Simianer, H. Täubert, Georg-August-Universität, Göttingen; G. Erhardt, O. Jann, C. Weimann, E.-M. Prinzenberg, Justus-Liebig Universität, Giessen; I. Medugorac, A. Medugorac, M. Förster, Ludwig-Maximilians Universität, Munich; H.M. Mix, Naturschutz International, Grünheide; C. Looft, E. Kalm, Christian-Albrechts-Universität, Kiel; Ireland: D.G. Bradley, C.J. Edwards, D.E. MacHugh, A.R. Freeman, Trinity College, Dublin; Italy: P. Ajmone Marsan, R. Negrini, Università Cattolica del S. Cuore, Piacenza; M. Longeri, G. Ceriotti, M. Zanotti, Università degli Studi di Milano; D. Marletta, A. Criscione, Universita degli Studi di Catania, Catania; A. Valentini, C. Marchitelli, L. Pariset, M.C. Savarese, Università della Tuscia, Viterbo; F. Pilla, Università del Molise, Campobasso; Latvia: Z. Grislis, Latvia University of Agriculture, Jelgava; Lithuania: I. Miceikienė, Lithuanian Veterinary Academy, Kaunas; Netherlands: I.J. Nijman, Utrecht University; W. van Haeringen, L. van de Goor, Van Haeringen Laboratory, Wageningen; Norway: I. Olsaker, Norwegian School of Veterinary Science, Oslo; Portugal: C. Ginja, L.T.Gama, Instituto Nacional de Recursos Biológicos—INIA, Lisbon University, Lisboa; L.T. Gama, Instituto Nacional de Recursos Biológicos-INIA, I.P., Vale de Santarem; J.C. Mateus, Universidade de Trás-os-Montes e Alto Douro, Vila Real; A. Beja-Pereira, N. Ferrand, Oporto University; Russia: Z. Ivanova, R. Popov, I. Ammosov, Yakutian Research Institute of Agricultural Sciences, Yakutsk, Sakha; T. Kiselyova, All-Russian Research Institute for Farm Animals and Breeding, St. Petersburgh-Pushkin; N. Marzanov, All-Russian Research Institute of Animal Husbandry, Dubrovitsy; Serbia: S. Stojanovic, Ministry of Agriculture and Water Management, Belgrade; Slovenia: M. Simčič, P. Dovč, D.Kompan, University of Ljubljana, Domzale; Spain: S. Dunner, Universidad Complutense de Madrid; C. Rodellar, I. Martín-Burriel, Veterinary Faculty, Zaragoza; A. Sánchez, J. Piedrafita, Universitat Autònoma de Barcelona; P.J. Azor, J.V. Delgado, A. Martínez-Martínez, A. Molina, E. Rodero, University of Córdoba; Switzerland: G. Dolf, University of Berne; UK: J.L. Williams, P. Wiener, Roslin Institute; M.W. Bruford, T.C. Bray, Cardiff University, Cardiff; L. Alderson, Countrywide Livestock Ltd, Shrewsbury; USA: M.C.T. Penedo. University of California, Davis, CA; International audience; Classification of cattle breeds contributes to our understanding of the history of cattle and is essential for an effective conservation of genetic diversity. Here we review the various classifications over the last two centuries and compare the most recent classifications with genetic data. The classifications devised during the 19th to the late 20th century were in line with the Linnaean taxonomy and emphasized cranial or horn morphology. Subsequent classifications were based on coat color, geographic origin or molecular markers. Several theories were developed that linked breed characteristics either to a supposed ancestral aurochs subspecies or to a presumed ethnic origin. Most of the older classifications have now been discarded, but have introduced several Latin terms that are still in use. The most consistent classification was proposed in 1995 by Felius and emphasizes the geographic origin of breeds. This is largely in agreement with the breed clusters indicated by a biochemical and molecular genetic analysis, which reflect either groups of breeds with a common geographic origin or single breeds that have expanded by export and/or crossbreeding. We propose that this information is also relevant for managing the genetic diversity of cattle.

The sustainable use of farm animal genetic resources is connected with the recognition of their contribution to the society and the environment and the assessment of the threats they are facing. The category of the heritage breeds, which are genetically distinct, geographically concentrated, adapted to their environment, commercially farmed to contribute to the local economy were considered in the frame of the HERITAGESHEEP project. The aim of this project was to deliver the potential of the heritage sheep breeds for a sustainable future for medium to low input production systems, which support local rural communities throughout Europe. This was achieved by addressing the conservation of these breeds, defining the current and future threats and developing new uses and markets for products.

Co)variance components for daily milk yield, fat, and protein content in Slovenian dairy sheep were estimated with random regression model. Test-day records were collected by the ICAR A4 method. Analysis was done for 38 983 test-day records of 3068 ewes in 36 flocks. Common flock environment, addi- tive genetic effect, permanent environment effect over lactations, and permanent environment effect within lactation were included into the random part of the model and modelled with Legendre polynomials on the standardized time scale of days in lactation. Estimation of (co)variance components was done with REML. The eigenvalues of covariance functions for random regression coefficients were calculated to quantify the sufficient order of Legendre polynomial for the (co)variance component estimation of milk traits. The existing 13 to 24% of additive genetic variability for the individual lactation curve indicated that the use of random regression model is justified for selection on the level and shape of lactation curve in dairy sheep. Four eigenvalues suf - ficiently explained variability during lactation in all three milk traits. Heritability estimate for daily milk yield was the highest in mid lactation (0.17) and lower in the early (0.11) and late (0.08) lactation. In fat content, the heritability was increasing throughout lactation (0.08-0.13). Values in protein content varied from the begin- ning toward mid lactation (0.15-0.19), while they rapidly increased at the end of lactation (0.28). Common flock environment explained the highest percentage of phenotypic variability: 27-41% in daily milk yield, 31-41% in fat content, and 41-49% in protein content. Variance ratios for the two permanent environment effects were the highest in daily milk yield (0.10-0.27), and lower in fat (0.04-0.08) and protein (0.01-0.10) contents. Additive genetic correlations during the selected test-days were high between the adjacent ones and they tended to decrease at the extremes of the lactation trajectory.

Genetic diversity and genetic differentiation were analysed, in a total of 103 sheep from four different populations from Croatia and Slovenia using 24 microsatellite loci. The aim of the study was to provide an initial understanding on the genetic diversity and structure of Istrian dairy sheep by analysing individuals sampled from reproductively isolated populations of Croatia (IST) and Slovenia (ISTs), while Krk island sheep (KRK) and Lika pramenka (LIK) were used as outgroups. Results revealed considerable levels of genetic diversity in the studied samples, similar to results reported in other indigenous sheep breeds related to low production systems. The genetic parameters estimated showed the highest diversity in KRK, and the lowest in LIK sheep population. Istrian breed populations were in between with expected and observed heterozygosity, and the number of private alleles identified, being higher in IST than in ISTs. In the four populations analysed, 67 private alleles were identified. KRK had the highest number of loci with population-specific alleles (12). On the contrary, LIK and ISTs showed the lowest number of private alleles (8). The observed and expected heterozygosity ranged from 0.648 and 0.634 (in LIK), respectively, to 0.723 and 0.732 (KRK), respectively. KRK had the lowest Fis value (0.034), while ISTs showed the highest Fis estimate (0.052). In conclusion, the results presented here show high level of conserved genetic diversity in the Istrian dairy sheep breed. The smaller and reproductively isolated Istrian sheep population from Slovenia shows less diversity and a higher inbreeding level.