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The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from −0.22 to −0.59), between milk yield and milk FA (rg from −0.22 to −0.62), and between protein yield and milk FA (rg from −0.11 to −0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.

BackgroundBovine mastitis, an inflammatory disease of the mammary gland, is classified as subclinical or clinical. Circulating neutrophils are recruited to the udder to combat infection. We compared the transcriptomic profiles in circulating leukocytes between healthy cows and those with naturally occurring subclinical or clinical mastitis.Methods and ResultsHolstein Friesian dairy cows from six farms in EU countries were recruited. Based on milk somatic cell count and clinical records, cows were classified as healthy (n = 144), subclinically (n = 48) or clinically mastitic (n = 22). Circulating leukocyte RNA was sequenced with Illumina NextSeq single end reads (30 M). Differentially expressed genes (DEGs) between the groups were identified using a DESeq2 package, followed by GO enrichment analysis. Both subclinical and clinical mastitis caused significant changes in the leukocyte transcriptome, with more intensive changes attributed to clinical mastitis. We detected 491 DEGs between clinical and healthy groups, 350 DEGs between subclinical and healthy groups and 170 DEGs between clinical and subclinical groups. Most DEGs were associated with cell killing and immune processes. Many upregulated DEGs in clinical mastitis encoded antimicrobial peptides (AZU1, CAMP, CATHL1, CATHL2, CATHL4, CATHL5, CATHL6, DEFB1, DEFB10, DEFB4A, DEFB7, LCN2, PGLYRP1, S100A9, TF and LTF) which were not upregulated in subclinical mastitis. This suggests that the leukocytes may have greater killing capacity in cows with clinical mastitis.ConclusionWe have, for the first time, described circulating leukocyte transcriptomic profiles in naturally occurring subclinical mastitis and assessed their differences from healthy cows and those with clinical mastitis.

We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test-day records of milk yield, fat and protein contents, and FAs generated by mid-infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis-9 (oleic acid), LCFAs (long-chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd-year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best-fit models had fifth-order (degree 4) for the p effect and ranged from second- to fifth-order (degree 1-4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis-9 and LCFA). Based on the best-fit models, an effect of overcorrection was observed in early lactation (5-35 days in milk [DIM]). On the contrary, third-order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low-to-high magnitude (0.03-0.56). High Spearman correlations (0.90 for all bulls and0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third-order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows.

Climate change (CC) is expected to increase temperatures and the frequency of extreme weather events, which renewed interest in heat stress (HS) effects on dairy cattle farms. Resilience is a key concept that should be considered to better understand the dairy farms exposure to HS and to combat CC-related risks. Thus, this study aimed to investigate the aspects of HS vulnerability for Tunisian dairy cattle farming systems. Historical milk test-day records from official milk recording were merged with temperature and humidity data provided by public weather stations. Firstly, different models relying in two heat load indices were applied for HS exposure assessment. Secondly, broken line models were used to estimate HS thresholds, milk losses, and rates of decline of milk production associated with temperature-humidity index (THI) across parities. Thirdly, individual cow responses to HS estimated using random regression model were considered as key measures of dairy farming system sensitivity assessment to HS. Dairy farms are annually exposed for 5 months to high THI values above 72 in Tunisia. The tipping points, at which milk yield started to decline over parities with 3-day average THI, ranged between 65 and 67. The largest milk decline per unit of THI above threshold values was 0.135 ± 0.01 kg for multiparous cows. The milk losses estimated due to HS in the Holstein breed during the summer period (June to August) ranged between 110 and 142 kg/cow in north and south, respectively. A high HS sensitivity was proved especially in dairy farms characterized by large herd size and high milk production level. Hence, providing knowledge of dairy farms vulnerability to HS may provide the basis for developing strategies to reduce HS effects and plan for CC adaptation.

The aim of this study was the evaluation of climate sensitivity via genomic reaction norm models [i.e., to infer cow milk production and milk fatty acid (FA) responses on temperature-humidity index (THI) alterations]. Test-day milk traits were recorded between 2010 and 2016 from 5,257 first-lactation genotyped Holstein dairy cows. The cows were kept in 16 large-scale cooperator herds, being daughters of 344 genotyped sires. The longitudinal data consisted of 47,789 test-day records for the production traits milk yield (MY), fat yield (FY), and protein yield (PY), and of 20,742 test-day records for 6 FA including C16:0, C18:0, saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA). After quality control of the genotypic data, 41,057 SNP markers remained for genomic analyses. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI. Genomic reaction norm models were applied to estimate genetic parameters in a single-step approach for production traits and FA in dependency of THI at different lactation stages, and to evaluate the model stability. In a first evaluation strategy (New_sire), all phenotypic records from daughters of genotyped sires born after 2010 were masked, to mimic a validation population. In the second strategy (New_env), only daughter records of the new sires recorded in the most extreme THI classes were masked, aiming at predicting sire genomic estimated breeding values (GEBV) under heat stress conditions. Model stability was the correlation between GEBV of the new sires in the reduced data set with respective GEBV estimated from all phenotypic data. Among all test-day production traits, PY responded as the most sensitive to heat stress. As observed for the remaining production traits, genetic variances were quite stable across THI, but genetic correlations between PY from temperate climates with PY from extreme THI classes dropped to 0.68. Genetic variances in dependency of THI were very similar for C16:0 and SFA, indicating marginal climatic sensitivity. In the early lactation stage, genetic variances for C18:0, MUFA, PUFA, and UFA were significantly larger in the extreme THI classes compared with the estimates under thermoneutral conditions. For C18:0 and MUFA, PUFA, and UFA in the middle THI classes, genetic correlations in same traits from the early and the later lactation stages were lower than 0.50, indicating strong days in milk influence. Interestingly, within lactation stages, genetic correlations for C18:0 and UFA recorded at low and high THI were quite large, indicating similar genetic mechanisms under stress conditions. The model stability was improved when applying the New_env instead of New_sire strategy, especially for FA in the first stage of lactation. Results indicate moderately accurate genomic predictions for milk traits in extreme THI classes when considering phenotypic data from a broad range of remaining THI. Phenotypically, thermal stress conditions contributed to an increase of UFA, suggesting value as a heat stress biomarker. Furthermore, the quite large genetic variances for UFA at high THI suggest the consideration of UFA in selection strategies for improved heat stress resistance.

The segregation of the causal mutation () in the muscular hypertrophy gene in dual-purpose Belgian Blue (dpBB) cattle is considered to result in greater calving difficulty (dystocia). Establishing adapted genetic evaluations might overcome this situation through efficient selection. However, the heterogeneity of dpBB populations at the locus implies separating the major gene and other polygenic effects in complex modeling. The use of mixed inheritance models may be an interesting option because they simultaneously assume both influences. A genetic evaluation in dpBB based on a mixed inheritance model was developed for birth and conformation traits: gestation length (GL), calving difficulty (CD), birth weight (BiW), and body conformation score (BC). A total of 27,362 animals having records were used for analyses. The total number of animals in the pedigree used to build the numerator relationship matrix was 62,617. Genotypes at the locus were available for 2,671 animals. Missing records at this locus were replaced with genotype probabilities. A total of 13,221 (48.3%) were registered as dpBB, 1,287 (4.7%) as beef Belgian Blue, and 12,854 (47.0%) were unknown. From those 13,221 dpBB animals, 650, 849, and 534 had double or single copies or no copy, respectively, of the causal mutation () in the muscular hypertrophy gene, whereas 11,188 had missing genotypes. This heterogeneity at the locus may be the reason for high variability in the studied traits, that is, high heritability estimates of 0.33, 0.30, 0.38, and 0.43 for GL, CD, BiW, and BC, respectively. In general, additive ( < 0.05) and dominance ( < 0.001) allele substitution for calves and dams had significant impact for all traits. The moderate coefficient of genetic variation (27.80%) and high direct heritability (0.28) for CD suggested genetic variability in dpBB and possible genetic improvement through selection. This variability has allowed dpBB breeders to successfully apply mass selection in the past. Genetic trend means from 1988 to 2016 showed that sire selection for CD within genotype was progressively applied by breeders. The selection intensity was more important for CD in double-muscled lines than in segregated lines. Our study illustrated the possible confusion caused by the use of major genes in selection and the importance of fitting appropriate models such as mixed inheritance models that combine polygenic and gene content information.

In Benin, family poultry farming has become an important activity in economic and social aspects that contribute to food security, poverty reduction and well-being. However, current information about poultry production and consumption is still limited. This information would be useful to improve the sustainable exploitation of agricultural and commercial genetic resources. We aimed to identify and assess the socio-economic and phenotypic features as well as to investigate phenotypic variability and growth performance of guinea fowls raised under different environments. Growth performance and survival rates of local guinea fowl varieties were recorded in three zones of Benin: Collines, Atacora and Borgou. Seven varieties, Gray, Common, Bonaparte, White, Black, Isabelle and Multicolored, were identified in Benin. The farmers choose a variety to be raised based on breeding system, agro-ecological zone, disease resistance, market price and production purpose. Bonaparte, Common and Gray varieties emerged as the most resistant whereas White, Black and Gray outperformed in growth and may be used for breeding purposes. The semi-confinement system could be recommended for startup as a temporary solution to improve production of local guinea fowls in Benin. The existence of several varieties on farms does not encourage genetic conservation and improvement of these resources. Establishing selection or crossbreeding programs in controlled environments would be more appropriate for guinea fowls raised in Benin. Key words: Survey, agro-ecological zones, confinement systems.

Physiological imbalance is an abnormal physiological condition that cannot be directly observed but is assumed to precede subclinical and clinical diseases in the beginning of lactation. Alert systems to detect the physiological imbalance in a cow using Fourier transform mid-infrared spectroscopy in milk have been developed. The objective of this study was to estimate the value of information provided from such system with different indicator accuracies, herd prevalence and prices. A decision tree was created to model the probabilities of detection and associated costs of test outcome, intervention and occurrence of disease. We assumed that the negative effect of physiological imbalance was the development of subclinical ketosis and that this negative effect was prevented by drenching the cows with propylene glycol for 5 days. We simulated the economic impact of subclinical ketosis mediated through physiological imbalance to be $194 per case. The results showed that if the alert system was highly accurate (Se = 0.99/Sp = 0.99), and the prevalence of physiological imbalance was 30 %, the value of information provided from the system is $19 per cow-year. In case the prevalence is 5 % or 50 %, the value of information is $3 and $13, respectively. These estimates for the value do not cover the capital costs and operational costs of the alert system. This study furthermore clearly demonstrated that in order to estimate the value of information correctly, it is important to consider that drenching all cows and not drenching any of the cows are the two relevant alternative options in the absence of the alert system. In conclusion, the decision tree and sensitivity analysis developed in this study show that final economic results are highly variable to the prevalence of physiological imbalance and highest at an intermediate prevalence. Other relevant factors are the costs associated with drenching and the cost associated with treating false positives and not treating false negatives. In addition, this study highlights the benefits of simulation to pinpoint where additional information is needed to further quantify the economic value and required accuracy of an indication-based intervention system.

This study reports on the exploration of temporal relationships between milk mid-infrared predicted biomarkers and lameness events. Lameness in dairy cows is an issue that can vary greatly in severity and is of concern for both producers and consumers. Metabolic disorders are often associated with lameness. However, lameness can arise weeks or even months after the metabolic disorder, making the detection of causality difficult. We already use mid-infrared technology to predict major milk components, such as fat or protein, during routine milk recording and for milk payment. It was recently shown that this technology can also be used to predict novel biomarkers linked to metabolic disorders in cows, such as oleic acid (18:1 cis-9), β-hydroxybutyrate, acetone, and citrate in milk. We used these novel biomarkers as proxies for metabolic issues. Other studies have explored the possibility of using mid-infrared spectra to predict metabolic diseases and found it (potentially) usable for indicating classes of metabolic problems. We wanted to explore the possible relationship between mid-infrared-based metabolites and lameness over the course of lactation. In total, data were recorded from 6,292 cows on 161 farms in Austria. Lameness data were recorded between March 2014 and March 2015 and consisted of 37,555 records. Mid-infrared data were recorded between July and December 2014 and consisted of 9,152 records. Our approach consisted of fitting preadjustments to the data using fixed effects, computing pair-wise correlations, and finally applying polynomial smoothing of the correlations for a given biomarker at a certain month in lactation and the lameness events scored on severity scale from sound or non-lame (lameness score of 1) to severely lame (lameness score of 5) throughout the lactation. The final correlations between biomarkers and lameness scores were significant, but not high. However, for the results of the present study, we should not look at the correlations in terms of absolute values, but rather as indicators of a relationship through time. When doing so, we can see that metabolic problems occurring in mo 1 and 3 seem more linked to long-term effects on hoof and leg health than those in mo 2. However, the quantity (only 1 pair-wise correlation exceeded 1,000 observations) and the quality (due to limited data, no separation according to more metabolic-related diseases could be done) of the data should be improved.

Recent publications indicate that single-step models are suitable to estimate breeding values, dominance deviations and total genetic values with acceptable quality. Additive single-step methods implicitly extend known number of allele information from genotyped to non-genotyped animals. This theory is well derived in an additive setting. It was recently shown, at least empirically, that this basic strategy can be extended to dominance with reasonable prediction quality. Our study addressed two additional issues. It illustrated the theoretical basis for extension and validated genomic predictions to dominance based on single-step genomic best linear unbiased prediction theory. This development was then extended to include inbreeding into dominance relationships, which is a currently not yet solved issue. Different parametrizations of dominance relationship matrices were proposed. Five dominance single-step inverse matrices were tested and described as C1 , C2 , C3 , C4 and C5 . Genotypes were simulated for a real pig population (n = 11,943 animals). In order to avoid any confounding issues with additive effects, pseudo-records including only dominance deviations and residuals were simulated. SNP effects of heterozygous genotypes were summed up to generate true dominance deviations. We added random noise to those values and used them as phenotypes. Accuracy was defined as correlation between true and predicted dominance deviations. We conducted five replicates and estimated accuracies in three sets: between all (S1 ), non-genotyped (S2 ) and inbred non-genotyped (S3 ) animals. Potential bias was assessed by regressing true dominance deviations on predicted values. Matrices accounting for inbreeding (C3 , C4 and C5 ) best fit. Accuracies were on average 0.77, 0.40 and 0.46 in S1 , S2 and S3 , respectively. In addition, C3 , C4 and C5 scenarios have shown better accuracies than C1 and C2 , and dominance deviations were less biased. Better matrix compatibility (accuracy and bias) was observed by re-scaling diagonal elements to 1 minus the inbreeding coefficient (C5 ).

The aim of this study was to estimate phenotypic and genetic correlations between methane production (Mp) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation. Calibration equations predicting daily Mp (g/d) and milk fatty acid contents (g/100 dL of milk) were applied on milk mid-infrared spectra related to Walloon milk recording. A total of 241,236 predictions of Mp and milk fatty acids were used. These data were collected between 5 and 305 d in milk in 33,555 first-parity Holstein cows from 626 herds. Pedigree data included 109,975 animals. Bivariate (i.e., Mp and a fatty acid trait) random regression test-day models were developed to estimate phenotypic and genetic parameters of Mp and milk fatty acids. Individual short-chain fatty acids (SCFA) and groups of saturated fatty acids, SCFA, and medium-chain fatty acids showed positive phenotypic and genetic correlations with Mp (from 0.10 to 0.16 and from 0.23 to 0.30 for phenotypic and genetic correlations, respectively), whereas individual long-chain fatty acids (LCFA), and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed null to positive phenotypic and genetic correlations with Mp (from −0.03 to 0.13 and from −0.02 to 0.32 for phenotypic and genetic correlations, respectively). However, these correlations changed throughout lactation. First, de novo individual and group fatty acids (i.e., C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, SCFA group) showed low phenotypic or genetic correlations (or both) in early lactation and higher at the end of lactation. In contrast, phenotypic and genetic correlations between Mp and C16:0, which could be de novo synthetized or derived from blood lipids, were more stable during lactation. This fatty acid is the most abundant fatty acid of the saturated fatty acid and medium-chain fatty acid groups of which correlations with Mp showed the same pattern across lactation. Phenotypic and genetic correlations between Mp and C17:0 and C18:0 were low in early lactation and increased afterward. Phenotypic and genetic correlations between Mp and C18:1 cis-9 originating from the blood lipids were negative in early lactation and increased afterward to become null from 18 wk until the end of lactation. Correlations between Mp and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed a similar or intermediate pattern across lactation compared with fatty acids that compose them. Finally, these results indicate that correlations between Mp and milk fatty acids vary following lactation stage of the cow, a fact still often ignored when trying to predict Mp from milk fatty acid profile.

Multi-trait Bayesian procedure was used to estimate genetic parameters for reproductive traits in Tunisian Holstein cows. A total of 31 348 lactations of the calving years 2005 to 2012 were analyzed. Fertility traits were the calving interval (CI), days open (DO), days to first insemination (DFI), days from first insemination to conception (FIC), and number of inseminations per conception (NI). Posterior means of heritabilities of CI, DO, DFI, FIC, and NI were 0.047, 0.03, 0.025, 0.024, and 0.069, respectively. Posterior means of repeatabilities of the same respective traits were 0.106, 0.094, 0.051, 0.036, and 0.17. Genetic correlations among female fertility traits were also computed. Calving interval and DO had the highest genetic correlation estimate (0.85) because they have overlapping genetic meanings. The lowest genetic correlation estimate (−0.25) was found between DFI and NI. Genetic parameter estimates are low and are even lower than those reported in most literature, implying that more focus should be put upon improving the management of reproduction in dairy cattle herds in Tunisia.

Heat stress (HS) adversely influences dairy cattle welfare and productivity. This study aimed to investigate the effects of HS on production and physiological parameters of Holstein cows. Two experiments each lasted 6 weeks were conducted in four Tunisian farms, firstly during summer under HS (n = 80, THI = 77) and later during autumn under thermo-neutral (n = 80, THI = 54) conditions. Respiration rate (RR), skin temperature (ST), rectal temperature (RT) and milk yield were measured, and milk samples were collected on 2 days every week during each experimental period. Temperature and relative humidity were measured inside the barn to calculate the temperature-humidity index (THI). Mixed models were used to evaluate the effects of period and the relationships between THI and physiological and production traits. Reaction norm models were applied to quantify the individual responses of cows across the trajectory of THI during the HS period. A clustering methodology was developed to identify tolerant and sensitive cows to HS based on their slope for response of physiological and production traits during HS period. In summer, RR (61 breaths/min) and ST (37.7 °C) were 2.3- and 1.3-fold higher, whereas milk yield per milking was 24% lower compared with thermo-neutral conditions. Linear relationship between THI and RR, ST and RT was observed and showed increases by 2 breaths/min, 0.5 °C and 0.04 °C per increase in one THI unit, respectively. Inversely, milk, fat and protein yields showed a drop of 0.13 kg, 0.4 g and 0.3 g per milking per increase in one THI unit, respectively. Cows qualified to be heat tolerant by our work tended to have higher RR, ST, and RT and lower to almost no decay in milk yield compared to cows qualified to be heat sensitive. Specifically, RR could be used as a reliable indicator for thermotolerance. The results of this study deepen our understanding of different aspects of HS resilience.

Genetic parameters were estimated for first lactation survival defined as a binary trait (alive or dead to second calving) and the curve shape traits of milk yield, fat and protein percentages using information from 25 981 primiparous Tunisian Holsteins. For each trait, shape curves (i.e. peak lactation, persistency), level of production adjusted to 305 days in milk (DIMs) for total milk yield (TMY), and average fat (TF %) and protein (TP %) percentages were defined. Variance components were estimated with a linear random regression model under three bivariate animal models. Production traits were modelled by fixed herd × test-day (TD) interaction effects, fixed classes of 25 DIMs × age of calving × season of calving interaction effects, fixed classes of pregnancy, random environment effects and random additive genetic effects. Survival was modelled by fixed herd × year of calving interaction effects and age of calving × season of calving interaction effects, random permanent environment effects, and random additive genetic effects. Heritability (h2) estimates were 0.03 (±0.01) for survival and 0.23 (±0.01), 0.31 (±0.01) and 0.31 (±0.01) for TMY, TF % and TP %, respectively. Genetic correlations between survival and TMY, TF % and TP % were 0.26 (±0.08), -0.24 (±0.06) and -0.13 (±0.06), respectively. Genetic correlations between survival and persistency for fat and protein percentages were -0.35 (±0.09) and -0.19 (±0.09), respectively. Cows that had higher persistencies for fat and protein percentages were more likely not to survive.

Body weight (BW) of dairy cows can be estimated using linear conformation traits (calculated BW; CBW), which are generally recorded only once during a lactation. However, predicted BW (PBW) throughout the lactation would be useful, e.g., at milk-recording dates allowing feed-intake prediction for advisory purposes. Therefore, a 2-step approach was developed to obtain PBW for each milk-recording date. In the first step, a random-regression test-day model was used with CBW as observations to predict PBW. The second step consisted in changing means and (co)variances of prior distributions for the additive genetic random effects of the test-day model by using priors derived from results of the first step to predict again PBW. A total of 25,061 CBW from 24,919 primiparous Holstein cows were computed using equations from literature. Using CBW as observations, PBW was then predicted over the whole lactation for 232,436 dates corresponding to 207,375milk-recording dates and 25,061 classification dates. Results showed that using both steps (the 2-step approach) provided more accurate predictions than using only the first step (the one-step approach). Based on the results of this preliminary study, BW of dairy cows could be predicted throughout the lactation using this procedure. These predictions could be useful in milk-recording systems to compute traits of interest (e.g., feed-intake prediction). The developed novel method is also flexible because actual direct measurements of BW can also be used together with CBW, the prediction model being able to accommodate different levels of accuracies of used BW phenotypes.

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (

This work aimed to study the sources of variation in productive and reproductive traits of the dairy Sicilo-Sarde ewes and to further investigate the interaction between both classes of traits. After edits, a database containing 5935 lactation records collected during 6 successive years in eight dairy flocks in the North of Tunisia was used. Total milked milk (TMM) in the milking-only period was retained as productive trait. The interval from the start of the mating period to the subsequent lambing (IML) and the lambing status (LS) were designed as reproductive traits. Sicilo-Sarde ewes had an average TMM of 60.93 l (±44.12) during 132.8 days (±46.6) after a suckling period of 100.4 days (±24.9). Average IML was 165.7 days. In a first step, the major factors influencing milk production and reproductive traits were determined. The significant sources of variation identified for TMM were: flock, month of lambing, year of lambing, parity, suckling length, litter size and milking-only length. Flock×month of the start of the mating period, parity, year of mating and litter size were identified as significant factors of variation for IML, while flock×month of the start of the mating period, parity and year of mating were identified as significant sources of variation for LS. In a second step, variance components were estimated using a three traits threshold mixed model, which combined LS as categorical trait and TMM and IML as continuous traits. Repeatability estimates were 0.21 (±0.03) for TMM, 0.09 (±0.02) for IML, and 0.10 (±0.05) for LS. Moreover, TMM and IML were found to be favorably associated for the flock× year of lambing effect (−0.45±0.18) but unfavorably associated for the animal effect (0.20±0.09).

This study was aimed to evaluate the degree of thermal stress exhibited by Holsteins under a continental temperate climate. Milk, fat, protein, and somatic cell count test-day records collected between 2000 and 2011 from 23,963 cows in 604 herds were combined with meteorological data from 14 public weather stations in Luxembourg. Daily values of 6 different thermal indices (TI) weighted in term of temperature, relative humidity, solar radiation, and wind speed were calculated by averaging hourly TI over 24h. Heat stress thresholds were first identified by a broken-line regression model. Regression models were thereafter applied to quantify milk production losses due to heat stress. The tipping points at which milk and protein yields declined were effectively identified. For fat yield, no valid threshold was identified for any of the studied TI. Daily fat yields tended to decrease steadily with increasing values of TI. Daily somatic cell score patterns were marked by increased values at both lowest and highest TI ranges, with a more pronounced reaction to cold stress for apparent temperature indices. Thresholds differed between TI and traits. For production traits, they ranged from 62 (TI(1)) to 80 (TI(3)) for temperature-humidity indices (THI) and from 16 (TI(5)) to 20 (TI(6)) for apparent temperature indices. Corresponding somatic cell score thresholds were higher and ranged from 66 (TI(1)) to 82 (TI(3)) and from 20 (TI(5)) to 23 (TI(6)), respectively. The largest milk decline per unit of mild, moderate, and extreme heat stress levels of 0.164, 0.356, and 0.955 kg, respectively, was observed when using the conventional THI (TI(1)). The highest yearly milk, fat, and protein losses of 54, 5.7, and 4.2 kg, respectively, were detected by TI(2), the THI index that is adjusted for wind speed and solar radiation. The latter index could be considered as the best indicator of heat stress to be used for forecast and herd management in a first step in temperate regions under anticipated climate changes.

Changes in milk production traits (i.e., milk yield, fat, and protein contents) with the pregnancy stage are well documented. To our knowledge, the effect of pregnancy on the detailed milk composition has not been studied so far. The mid-infrared (MIR) spectrum reflects the detailed composition of a milk sample and is obtained by a nonexhaustive and widely used method for milk analysis. Therefore, this study aimed to investigate the effect of pregnancy on milk MIR spectrum in addition to milk production traits (milk yield, fat, and protein contents). A model including regression on the number of days pregnant was applied on milk production traits (milk yield, fat, and protein contents) and on 212 spectral points from the MIR spectra of 9,757 primiparous Holstein cows from Walloon herds. Effects of pregnancy stage were expressed on a relative scale (effect divided by the squared root of the phenotypic variance); this allowed comparisons between effects on milk traits and on 212 spectral points. Effect of pregnancy stage on production traits were in line with previous studies indicating that the model accounted well for the pregnancy effect. Trends of the relative effect of the pregnancy stage on the 212 spectral points were consistent with known and observed effect on milk traits. The highest effect of the pregnancy was observed in the MIR spectral region from 968 to 1,577 cm−1. For some specific wavenumbers, the effect was higher than for fat and protein contents in the beginning of the pregnancy (from 30 to 90 or 120 d pregnant). In conclusion, the effect of early pregnancy can be observed in the detailed milk composition through the analysis of the MIR spectrum of bovine milk. Further analyses are warranted to explore deeply the use of MIR spectra of bovine milk for breeding and management of dairy cow pregnancy.

Milk production data of Luxembourg and Tunisian Holstein cows were analyzed using herd management (HM) level. Herds in each country were clustered into high, medium, and low HM levels based on solutions of herd-test-date and herd-year of calving effects from national evaluations. Data from both populations included 730,810 test-day (TD) milk yield records from 87,734 first-lactation cows. A multi-trait, random regression TD model was used to estimate (co)variance components for milk yield within and across country HM levels. Additive genetic and permanent environmental variances of TD milk yields varied with management level in Tunisia and Luxembourg. Additive variances were smaller across HM levels in Tunisia than in Luxembourg, whereas permanent environmental variances were larger in Tunisian HM levels. Highest heritability estimates of 305-d milk yield (0.41 and 0.21) were found in high HM levels, whereas lowest estimates (0.31 and 0.12, respectively) were associated with low HM levels in both countries. Genetic correlations among Luxembourg HM levels were >0.96, whereas those among Tunisian HM levels were below 0.80. Respective rank orders of sires ranged from 0.73 to 0.83 across Luxembourg environments and from 0.33 to 0.42 across Tunisian HM levels indicating high re-ranking of sires in Tunisia and only a scaling effect in Luxembourg. Across-country environment analysis showed that estimates of genetic variance in the high, medium, and low classes of Tunisian environments were 45, 69, and 81% lower, respectively, than the estimate found in the high Luxembourg HM level. Genetic correlations among 305-d milk yields in Tunisian and Luxembourg HM environments ranged from 0.39 to 0.79. The largest estimated genetic correlation was found between the medium Luxembourg and high Tunisian HM levels. Rank correlations for common sires' estimated breeding values among HM environments were low and ranged from 0.19 to 0.39, implying the existence of genotype by environment interaction. These results indicate that daughters of superior sires in Luxembourg have their genetic expression for milk production limited under Tunisian environments. Milk production of cows in the medium and low Luxembourg environments were good predictors of that of their paternal half-sisters in the high Tunisian HM level. Breeding decisions in low-input Tunisian environment should utilize semen from sires with daughters in similar production environments rather than semen of bulls proven in higher management levels.

The aim of this study was to develop an adapted random regression test-day model for milk urea (MU) and to study the possibility of using predictions and solutions given by the model for management purposes. Data included 607,416 MU test-day records of first-lactation cows from 632 dairy herds in the Walloon Region of Belgium. Several advanced features were used. First, to detect the herd influence, the classical herd x test-day effect was split into 3 new effects: a fixed herd x year effect, a fixed herd x month-period effect, and a random herd test-day effect. A fixed time period regression was added in the model to take into account the yearly oscillations of MU on a population scale. Moreover, first autoregressive processes were introduced and allowed us to consider the link between successive test-day records. The variance component estimation indicated that large variance was associated with the random herd x test-day effect (48% of the total variance), suggesting the strong influence of herd management on the MU level. The heritability estimate was 0.13. By comparing observed and predicted MU levels at both the individual and herd levels, target ranges for MU concentrations were defined to take into account features of each cow and each herd. At the cow level, an MU record was considered as deviant if it was200 or400 mg/L (target range used in the field) and if the prediction error was50 mg/L (indicating a significant deviation from the expected level). Approximately 7.5% of the MU records collected between June 2007 and May 2008 were beyond these thresholds. This combination allowed for the detection of potentially suspicious cows. At the herd level, the expected MU level was considered as the sum of the solutions for specific herd effects. A herd was considered as deviant from its target range when the prediction error was greater than the standard deviation of MU averaged by herd test day. Results showed that 6.7% of the herd test-day MU levels between June 2007 and May 2008 were considered deviant. These deviations seemed to occur more often during the grazing period. Although theoretical considerations developed in this study should be validated in the field, this research showed the potential use of a test-day model for analyzing functional traits to advise dairy farmers.

Genetic parameters of milk, fat, and protein yields were estimated in the first 3 lactations for registered Tunisian Holsteins. Data included 140,187; 97,404; and 62,221 test-day production records collected on 22,538; 15,257; and 9,722 first-, second-, and third-parity cows, respectively. Records were of cows calving from 1992 to 2004 in 96 herds. (Co)variance components were estimated by Bayesian methods and a 3-trait-3-lactation random regression model. Gibbs sampling was used to obtain posterior distributions. The model included herd x test date, age x season of calving x stage of lactation [classes of 25 days in milk (DIM)], production sector x stage of lactation (classes of 5 DIM) as fixed effects, and random regression coefficients for additive genetic, permanent environmental, and herd-year of calving effects, which were defined as modified constant, linear, and quadratic Legendre coefficients. Heritability estimates for 305-d milk, fat and protein yields were moderate (0.12 to 0.18) and in the same range of parameters estimated in management systems with low to medium production levels. Heritabilities of test-day milk and protein yields for selected DIM were higher in the middle than at the beginning or the end of lactation. Inversely, heritabilities of fat yield were high at the peripheries of lactation. Genetic correlations among 305-d yield traits ranged from 0.50 to 0.86. The largest genetic correlation was observed between the first and second lactation, potentially due to the limited expression of genetic potential of superior cows in later lactations. Results suggested a lack of adaptation under the local management and climatic conditions. Results should be useful to implement a BLUP evaluation for the Tunisian cow population; however, results also indicated that further research focused on data quality might be needed.

Genetic diversity and relatedness between 2 geographically distant Holstein populations (in Luxembourg and Tunisia) were studied by pedigree analysis. These 2 populations have similar sizes and structures and are essentially importing populations. Edited pedigrees included 140,392 and 151,381 animals for Tunisia and Luxembourg, respectively. To partially account for pedigree completeness levels, a modified algorithm was used to compute inbreeding. The effective numbers of ancestors were derived from probabilities of gene origin for the 2 populations of cows born between 1990 and 2000. The 10 ancestors with the highest contributions to genetic diversity in the cow populations accounted for more than 32% of the genes. Eight of these 10 ancestors were the same in both populations. The rates of inbreeding were different in the 2 populations but were generally comparable to those found in the literature for the Holstein breed. Average inbreeding coefficients per year, estimated from the data, ranged from 0.91 and 0.50 in 1990 to 3.10 and 2.12 in 2000 for the Tunisian and Luxembourg populations, respectively. Genetic links have also strengthened with time. Average additive relationships between the 2 populations were as high as 2.2% in 2000. Results suggest that it would be possible to investigate genotype by environment interactions for milk traits using the Tunisian and Luxembourg dairy populations.

The nonlinear effects of inbreeding were studied by comparing linear and curvilinear regression models of phenotypic performances on inbreeding coefficients for production traits (milk, fat, and protein yields) of Holstein cows in their first lactation. Three different regression models (linear, quadratic, and cubic) were introduced separately into a single-trait, single-lactation, random regression test-day model. The significance of the different regression coefficients was studied based on a t-test after estimation of error variances and covariances associated with the different regression coefficients. All of the tested regression coefficients were significantly different from 0. The traditional regression coefficients of milk, fat, and protein yields on inbreeding were, respectively, -22.10, -1.10, and -0.72 kg for Holstein cows in their first lactation. However, the estimates of 305-d production losses for various classes of animals based on inbreeding coefficients showed that the effect of inbreeding was not a linear function of the percentage of inbreeding. The 305-d milk yield loss profiles attributable to inbreeding, obtained by the various regression models, were different. However, for inbreeding coefficients between 0 and 10%, these differences were small.

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest.

The incomplete gamma function was used to fit lactation curves of Holstein–Friesian cows in four types of herds in Tunisia. A total of 8640 records were used in the analysis. These included 1269, 637, 239, and 498 first lactation and 2986, 1441, 650, and 920 second and later lactation records in four herd groups namely investors, state, cooperative, and farmers’ herds, respectively. The effects of environmental variables, production sector, herd, parity, first test-day date, calving year, and calving season on the main lactation curve traits were analysed. The factors associated with milk yield at the beginning of lactation and the decreasing phase of the curve, persistency, and peak yield varied significantly (P

Genetic parameters that considered tolerance for heat stress were estimated for production, udder health, and milk composition traits. Data included 202,733 test-day records for milk, fat, and protein yields, fat and protein percentages, somatic cell score (SCS), 10 individual milk fatty acids (FA) predicted by mid-infrared spectrometry, and 7 FA groups. Data were from 34,468 first-lactation Holstein cows in 862 herds in the Walloon region of Belgium and were collected between 2007 and 2010. Test-day records were merged with daily temperature-humidity index (THI) values based on meteorological records from public weather stations. The maximum distance between each farm and its corresponding weather station was 21km. Linear reaction norm models were used to estimate the intercept and slope responses of 23 traits to increasing THI values. Most yield and FA traits had phenotypic and genetic declines as THI increased, whereas SCS, C18:0, C18:1 cis-9, and 4 FA groups (unsaturated FA, monounsaturated FA, polyunsaturated FA, and long-chain FA) increased with THI. Moreover, the latter traits had the largest slope-to-intercept genetic variance ratios, which indicate that they are more affected by heat stress at high THI levels. Estimates of genetic correlations within trait between cold and hot environments were generally high (>0.80). However, lower estimates (

Michael addition of chiral β-enamino esters to MVK and acrylates promoted by Lewis acids (ZnCl 2 MgBr 2 ) or high pressure (11–14 Kbar) afforded, after hydrolytic work-up, α,α-disubstituted β-keto esters in good enantiomeric excesses.

β - enamino esters derived from β - keto esters and (S)-1- phenylethytamine, react with acrylonitrile in the presence of a Lewis acid or under high pressure activation to give, after hydrolytic work up, α,α- disubstituted β - keto esters 4 (EWG=CN) in enantiomeric excesses close lo 90%.

Random regression test-day models using Legendre polynomials are commonly used for the estimation of genetic parameters and genetic evaluation for test-day milk production traits. However, some researchers have reported that these models present some undesirable properties such as the overestimation of variances at the edges of lactation. Describing genetic variation of saturated fatty acids expressed in milk fat might require the testing of different models. Therefore, 3 different functions were used and compared to take into account the lactation curve: (1) Legendre polynomials with the same order as currently applied for genetic model for production traits; 2) linear splines with 10 knots; and 3) linear splines with the same 10 knots reduced to 3 parameters. The criteria used were Akaike's information and Bayesian information criteria, percentage square biases, and log-likelihood function. These criteria indentified Legendre polynomials and linear splines with 10 knots reduced to 3 parameters models as the most useful. Reducing more complex models using eigenvalues seemed appealing because the resulting models are less time demanding and can reduce convergence difficulties, because convergence properties also seemed to be improved. Finally, the results showed that the reduced spline model was very similar to the Legendre polynomials model.

First-lactation test-day (TD) milk records of Luxembourg and Tunisian Holsteins were analysed for evidence of genotype by environment interaction (G x E). The joint data included 730 810 TD records of 87 734 cows and 231 common sires. Random regression TD sire models with fourth-order Legendre polynomials were used to estimate genetic parameters via within- and across-country analyses. Daily heritability estimates of milk yield from within-country analysis were between 0.11 and 0.32, and 0.03 and 0.13 in Luxembourg and Tunisia, respectively. Heritability estimates for 305-day milk yield and persistency (defined as the breeding value for milk yield on DIM 280 minus the breeding value on DIM 80) were lower for Tunisian Holsteins compared with the Luxembourg population. Specifically, heritability for 305-day milk yield was 0.16 for within- and 0.11 for across-country analyses for Tunisian Holsteins and 0.38 for within- and 0.40 for across-country analyses for Luxembourg Holsteins. Heritability for apparent persistency was 0.02 for both within- and across-country analyses for Tunisian Holsteins and 0.08 for within- and 0.09 for across-country analyses for Luxembourg Holsteins. Genetic correlations between the two countries were 0.50 for 305-day milk yield and 0.43 for apparent persistency. Moreover, rank correlations between the estimated breeding values of common sires for 305-day milk yield and persistency, estimated separately in each country, were low. Low genetic correlations are evidence for G x E for milk yield production while low rank correlations suggest different rankings of sires in both environments. Results from this study indicate that milk production of daughters of the same sires depends greatly on the production environment and that importing high merit semen for limited input systems might not be an effective strategy to improve milk production.

Test-day (TD) milk yield records of first-lactation Holstein cows in Luxembourg and Tunisia were analyzed using within-and between-country random regression TD models. Edited data used for within-country analysis included 661,453 and 281,913 TD records in Luxembourg and Tunisia, respectively. The joint data included 730,810 TD records of 87,734 cows and 231 common sires. Both data sets covered calving years 1995 to 2006. Fourth-order Legendre polynomials for random effects and a Gibbs sampling method were used to estimate variance components of lactation curve parameters in separate and joint analyses. Genetic variances of the first 3 coefficients from Luxembourg data were 46 to 69% larger than corresponding estimates from the Tunisian data. Inversely, the Tunisian permanent environment variances for the same coefficients were 52 to 65% larger than the Luxembourg ones. Posterior mean heritabilities of 305-d milk yield and persistency, defined as estimated breeding values (EBV) at 280 days in milk-EBV at 80 days in milk, from between-country analysis were 0.42 and 0.12 and 0.19 and 0.08 in Luxembourg and Tunisia, respectively. Heritability estimates for the same traits from within-country analyses, mainly from the Tunisian data, were lower than those from the joint analysis. Genetic correlations for 305-d milk yield and persistency between countries were 0.60 and 0.36. Product moment and rank correlations between EBV of common sires for 305-d milk yield and persistency from within-country analyses were 0.38 and 0.41 and 0.27 and 0.26, respectively. Differences between genetic variances found in both countries reflect different milk production levels. Moreover, low genetic and rank correlations suggest different ranking of sires in the 2 environments, which implies the existence of a genotype × environment interaction for milk yield in Holsteins.

Development of a new crossbreed based evaluation for carcass quality of Pietrain boars in the Walloon Region of Belgium M Dufrasne, H Hammami, V Jaspart, J Wavreille, N Gengler University of Liege Gembloux Agro-Bio Tech, Gembloux, Belgium, Walloon Pig Breeders Association, Ciney, Belgium, Walloon Agricultural Research Centre, Gembloux, Belgium, National Fund for Scientific Research, Brussels, Belgium Email: Marie.Dufrasne@ulg.ac.be

Bluetongue (BT) is an arboviral disease that affects ruminants. BT virus is transmitted by biting midges of the genus Culicoides (Diptera: Ceratopogonidae). Since 1999, BTV outbreaks have occurred in Tunisia and two serotypes (BTV-2 and BTV-1) have been involved in 2000 and 2006, respectively. Entomological surveillance has been implemented in Tunisia with the aim to study population dynamics of C. imicola and other potential vectors. Two-night catches of midges per site were performed monthly from June 2006 through July 2008 on 14 sites through­out the country. A total of 11,582 Culicoides specimens, col­lected from 336 light traps, comprised 25 species of which seven were identified for the first time in Tunisia. The seasonal occur­rence of these species is described.

Afin de mesurer les performances reproductives et de productivité (poids total de la portée) de la brebis Noire de Thibar, et de déterminer l’influence de facteurs non génétiques sur ces paramètres, une base de données initiale, constituée par 101 000 agneaux contrôlés pour leur croissance dans le cadre du programme national d’amélioration des ovins à viande, a été utilisée. Une base de données indexée par brebis a ensuite été générée avec un total de 64 137 agnelages représentant 12 années (1991-2002), 25 fermes et 48 troupeaux. Le taux de mise bas moyen dans les troupeaux de la race Noire de Thibar a été de 80,1 ± 12,9 p. 100 avec des effets importants (p < 0,001) de la ferme et de l’année de mise bas. A la naissance, la taille de portée moyenne par brebis mettant bas a atteint 1,32 ± 0,47 ; elle a baissé à 1,23 ± 0,55 à 70 jours après l’agnelage. Toutes les principales sources de variation retenues dans le modèle (ferme, année de mise bas, mois de mise bas et âge de la brebis), ainsi que leurs interactions ont significativement affecté la taille de la portée aux différents stades après la mise bas. La survie des agneaux par portée entre la naissance et 70 jours a été observée pour 89,9 p. 100 des brebis ; seulement 5,8 p. 100 des brebis ont perdu la totalité des agneaux de leur portée. Une analyse de la variance a révélé que les facteurs année de mise bas, mois de mise bas, âge de la brebis, sexe de la portée et taille de la portée ont eu un effet très significatif sur les proportions de brebis présentant des niveaux différents de survie des agneaux au sein de la portée. En moyenne, la productivité des brebis Noires de Thibar à 10, 30 et 70 jours après l’agnelage a été respectivement de 6,6 ± 2,2, 10,3 ± 3,5 et 18,6 ± 6,6 kg. Les principales sources de variations (ferme, année de mise bas, mois de mise bas, âge de la brebis et taille de la portée), ainsi que certaines interactions de deuxième niveau ont eu des effets significatifs (p < 0,001) sur les caractères de productivité étudiés. La catégorie des brebis âgées de quatre ans ayant eu des portées de triplets tous mâles a été associée aux niveaux de productivité les plus élevés à 70 jours. Ce travail préliminaire sur les caractères reproductifs et de productivité des brebis Noires de Thibar est essentiel à l’élaboration d’un index de sélection femelle qui pourra être incorporé dans un éventuel schéma d’amélioration génétique de cette race.

β - enamino esters derived from β - keto esters and (S)-1- phenylethytamine, react with acrylonitrile in the presence of a Lewis acid or under high pressure activation to give, after hydrolytic work up, α,α- disubstituted β - keto esters 4 (EWG=CN) in enantiomeric excesses close lo 90%.