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The collective economic and environmental interest of the whole dairy sheep sector is to reduce feed costs and the negative impact of milk production on the environment. Thus, this study focused on the characterisation and genetic selection potential of feed efficiency in the Lacaune breed. Estimates for feed efficiency in dairy ewes are limited, mainly due to a lack of individual feed intake measurements in the sheepfold or in the pasture. We estimated the genetic parameters for two approximated (not entirely based on individual data) feed efficiency traits (lactation feed conversion ratio (LFCR) and residual energy intake (REI)) and daily milk yield (DMY) at different stages of lactation and throughout lactation. The accuracy of the efficiency traits was first evaluated on samples from Lacaune dairy ewes that were monitored individually, especially for their feed intake. Then, feed efficiency estimation methods were applied on eight commercial farms corresponding to 4 680 Lacaune dairy ewes over two milk lactations (30 854 records). Animals were collectively (for a large part of feed intake) or individually (for milk performance and dynamics of body fat reserves) monitored at different lactation stages. The heritabilities of LFCR and REI were estimated over lactations at 0.10 ± 0.01 and 0.11 ± 0.01, respectively. High genetic correlations were observed between the two efficiency traits and milk production traits, with a genetic correlation between LFCR and DMY of 0.74 ± 0.04 and between REI and DMY of -0.79 ± 0.04. A strong influence of environmental factors such as farm, year of milk production and lactation stage affected the genetic link between REI and milk production traits. Efficiency values observed in early lactation when animals were bred in the sheepfold were less genetically correlated with values obtained later in lactation when animals were grass-fed. However, individual characterisation of feed efficiency remains difficult due to the collective feeding context in dairy ewe farms., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

The study was to focus on the relationship between wave motion (mass sperm motility, measured by a mass sperm motility score, manually assessed by artificial insemination (AI) center operators) and fertility in male sheep. A dataset of 711,562 artificial inseminations performed in seven breeds by five French AI centers during the 2001-2005 time period was used for the analysis. Factors influencing the outcome of the insemination, which is a binary response observed at lambing of either success (1) or failure (0), were studied using a joint model within each breed and AI center (eight separate analyses). The joint model is a multivariate model where all information related to the female, the male and the insemination process were included to improve the estimation of the factor effects. Results were consistent for all analyses. The male factors affecting AI results were the age of the ram and the mass motility. After correction for the other factors of variation, the lambing rate increased quasi linearly from three to more than ten points with the mass sperm motility score depending on the breed and the AI center. The consistency of the relationship for all breeds indicated that mass sperm motility is predictive of the fertility resulting when sperm are used from a specific ejaculate. Nonetheless, predictability could be improved if an objective measurement of mass sperm motility were available as a substitute for the subjective scoring currently in use in AI centers., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)

A total of 416,670 lactations for 189,101 ewes from 3,603 sires and distributed across 1,978 herd-year groups were used to estimate genetic and environmental parameters of standardized milk yield (SMY(T)), fertility in ewe lambs (PR(1)), and fertility in adult ewes (PR(A)). Parameters were estimated with a multiple-trait sire linear model. Heritabilities for SMY(T), PR(1), and PR(A) were 0.27 (0.009), 0.04 (0.004), and 0.05 (0.004), respectively. These results were in accordance with the literature. The genetic correlation between PR(1) and PR(A) was 0.55, indicating that fertility is not the same trait in ewe lambs and adult ewes. The genetic correlation between milk yield and lamb fertility was not significantly different from zero. The genetic correlation between milk yield and fertility in adult ewe (-0.23) was in the range of antagonistic correlations reported in dairy cattle. Consequently, these results show that selection for milk yield can induce an indirect decrease in fertility. Nevertheless, no phenotypic decrease in fertility in artificial insemination matings has been observed in this population. This is the first time that correlation between milk yield and fertility is reported in sheep and further investigations are needed to confirm this result.

Artificial inseminations (n = 678 168) recorded during 5 years in five French artificial insemination (AI) centres (2 'Lacaune', 1 'Manech tête rousse', 1 'Manech tête noire' and 1 'Basco béarnaise') were analysed to determine environmental and genetic factors affecting the insemination results. Analyses within centre-breed were performed using a linear model, which jointly estimates male and female fertility. This model combined four categories of data: the environmental effects related to the female, those related to the male, the non-sex-specific effects and finally the pedigree data of these males and females. After selection, the environmental female effects considered were age, synchronisation (0/1) on the previous year, total number of synchronisations during the female reproductive life, time interval between previous lambing and insemination, already dry or still lactating (0/1) when inseminated, and milk quantity produced during the previous year expressed as quartiles intra herd * year. The environmental male effects were motility and concentration of the semen. The non-sex-specific effects were the inseminator, the interaction herd * year nested within the inseminator, considered as random effects and the interaction year * season considered as a fixed effect. The main variation factors of AI success were relative to non-sex-specific effects and to female effects. Heritability estimates varied from 0.001 to 0.005 for male fertility and from 0.040 to 0.078 for female fertility. Repeatability estimates varied from 0.007 to 0.015 for male fertility and from 0.104 to 0.136 for female fertility. These parameters indicate that genetic improvement of AI results through a classical polygenic selection would be difficult. Moreover, in spite of the large quantity of variation factors fitted by the joint model, a very large residual variance remained unexplained.

The outcome of an insemination depends on male and female fertility. Nevertheless, few studies have incorporated genetic evaluation of these 2 traits jointly. The aim of this work was to compare genetic parameter estimates of male and female fertility defined as success or failure to artificial insemination (AI), using 8 different models. The first 2 models were simple repeatability models studying fertility of one sex and ignoring any information of the other. Models 3 and 4 took into account the information of the other sex by the inclusion of its random permanent environmental effect, whereas models 5 and 6 included fixed effects of the other sex. Models 7 and 8 were joint genetic evaluation models of male and female fertility ignoring or considering genetic correlation. Data were composed of 147,018 AI of the Manech Tête Rousse breed recorded from 2000 to 2004 corresponding to 79,352 ewes and 963 rams. The pedigree file included 120,989 individuals. Variance component estimates from the different models were quite similar; heritabilities varied from 0.050 to 0.053 for female fertility and were near 0.003 for male fertility. Correlations among estimated breeding values in the same sex using different models were higher than 0.99. The genetic correlation between male and female fertility was not significantly different from 0. These results show that for French dairy sheep with extensive use of AI, estimation of breeding values for male and female fertility might be implemented with quite simple models.

Data from 51,107 and 11,839 ejaculates collected on rams of the "Lacaune" and "Manech tête rousse" breeds, respectively, were analysed to determine environmental and genetic factors affecting semen production traits (ejaculate volume, semen concentration, number of spermatozoa and motility) in young (< or = 1 year) and adult (> or = 2 years) rams. Fixed effects and variance components were estimated using multiple trait animal models within each breed. For all traits, the main environmental effects identified were year, season, number of ejaculations, daily variation, interval from previous to current collection and age. Heritability estimates were moderate for volume, concentration and number of spermatozoa (0.12 to 0.33) and lower for motility (0.02 to 0.14). Genetic correlations between ages differed from 1 for all traits (0.14 to 0.90), indicating that semen characteristics corresponded to different traits in young and adult rams. Genetic and phenotypic correlations among traits within age category were globally similar for the different breeds and categories of animals.

The objective of this study was to build a character process model taking into account serial correlations for the analysis of repeated measurements of semen volume in AI rams. For each ram, measurements were repeated within and across years. Therefore, we considered a model including three environmental effects: the long-term environmental effect, which is a random year(*)subject effect, the short-term environmental effect, which is a random within year subject(*)collection effect, and the classical measurement error. We used a four-step approach to build the model. The first step explored graphically the serial correlations. The second step compared four models with different correlation structures for the short-term environmental effect. We selected fixed effects in the third step. In the fourth step, we compared four correlation structures for the long-term environmental effect. The model, which fitted best the data, used a spatial power correlation structure for the short-term environmental effect and a first order autoregressive process for the long-term environmental effect. The heritability estimate was 0.27 (0.04), the within year repeatability decreased from 0.56 to 0.44 and the repeatability across years decreased from 0.43 to 0.37.

The milking ability of Lacaune ewes was characterised by derived traits of milk flow patterns, in an INRA experimental farm, from a divergent selection experiment in order to estimate the correlated effects of selection for protein and fat yields. The analysis of selected divergent line effects (involving 34 616 data and 1204 ewes) indicated an indirect improvement of milking traits (+17% for maximum milk flow and -10% for latency time) with a 25% increase in milk yield. Genetic parameters were estimated by multi-trait analysis with an animal model, on 751 primiparous ewes. The heritabilities of the traits expressed on an annual basis were high, especially for maximum flow (0.54) and for latency time (0.55). The heritabilities were intermediate for average flow (0.30), time at maximum flow (0.42) and phase of increasing flow (0.43), and low for the phase of decreasing flow (0.16) and the plateau of high flow (0.07). When considering test-day data, the heritabilities of maximum flow and latency time remained intermediate and stable throughout the lactation. Genetic correlations between milk yield and milking traits were all favourable, but latency time was less milk yield dependent (-0.22) than maximum flow (+0.46). It is concluded that the current dairy ewe selection based on milk solid yield is not antagonistic to milking ability.

Staphylococci are the main aetiological agents of small ruminants intramammary infections (IMI), the more frequent isolates being S. aureus in clinical cases and coagulase negative species in subclinical IMI. The clinical IMI, whose annual incidence is usually lower than 5%, mainly occur at the beginning of machine milking and during the first third of lactation. These features constitute small ruminant peculiarities compared to dairy cattle. Small ruminant mastitis is generally a chronic and contagious infection: the primary sources are mammary and cutaneous carriages, and spreading mainly occurs during milking. Somatic cell counts (SCC) represent a valuable tool for prevalence assessment and screening, but predictive values are better in ewes than in goats. Prevention is most often based on milking machine management, sanitation and annual control, and milking technique optimisation. Elimination mainly relies on culling animals exhibiting clinical, chronic and recurrent IMI, and on drying-off intramammary antibiotherapy; this treatment allows a good efficacy and may be used selectively by targeting infected udders only. Heritability values for lactation mean SCC scores are between 0.11 and 0.15. Effective inclusion of ewe's mastitis resistance in the breeding goal has recently been implemented in France following experimental and large scale estimations of genetic parameters for SCC scores.

Records from 94,445 and 45,499 French Lacaune dairy ewes in first and second lactations, respectively, were used to estimate genetic parameters for somatic cell scores. Somatic cell count data came from an extensive recording scheme and sample testing that began in 1999 using the flocks enrolled in the official milk recording system. Somatic cell count data were from 2 to 4 test days per lactation. Lactation average and single test-day somatic cell scores were considered in multitrait sire models. The heritability estimate of lactation somatic cell score was close to 0.13 and similar for first and second parity. Heritabilities of somatic cell scores increased from first to fourth test day (from 0.07 to 0.11 in first lactation and from 0.05 to 0.13 in second lactation). Genetic correlations between somatic cell scores were high, usually more than 0.91, but lower between first test day and later test days in first lactation (0.64 to 0.88). The genetic correlations between lactation somatic cell score and milk yield, between lactation somatic cell score and fat content, and between lactation somatic cell score and protein content were 0.18, 0.04, and 0.03 in first lactation, respectively. The genetic antagonism between test day somatic cell score and milk yield measured in first lactation increased from beginning to the end of the lactation (0.05 to 0.23). This antagonism was slightly lower for somatic cell score in second lactation (from 0.09 to 0.14, and 0.08 for lactation mean). Environmental correlations in first lactation between lactation somatic cell score and milk yield, between lactation somatic cell score and fat content, and between lactation somatic cell score and protein content were -0.18, 0.13, and 0.30, respectively.