Hélène Soyeurt

Genetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle

Many countries have pledged to reduce greenhouse gases. In this context, the dairy sector is one of the identified sectors to adapt production circumstances to address socio-environmental constraints due to its large carbon footprint related to CH4 emission. This study aimed mainly to estimate (1) the genetic parameters of 2 milk mid-infrared-based CH4 proxies [predicted daily CH4 emission (PME, g/d), and log-transformed predicted CH4 intensity (LMI)] and (2) their genetic correlations with milk production traits [milk (MY), fat (FY), and protein (PY) yields] from first- and second-parity Holstein cows. A total of 336,126 and 231,400 mid-infrared CH4 phenotypes were collected from 56,957 and 34,992 first- and second-parity cows, respectively. The PME increased from the first to the second lactation (433 vs. 453 g/d) and the LMI decreased (2.93 vs. 2.86). We used 20 bivariate random regression test-day models to estimate the variance components. Moderate heritability values were observed for both CH4 traits, and those values decreased slightly from the first to the second lactation (0.25 ± 0.01 and 0.22 ± 0.01 for PME; 0.18 ± 0.01 and 0.17 ± 0.02 for LMI). Lactation phenotypic and genetic correlations were negative between PME and MY in both first and second lactations (-0.07 vs. -0.07 and -0.19 vs. -0.24, respectively). More close scrutiny revealed that relative increase of PME was lower with high MY levels even reverting to decrease, and therefore explaining the negative correlations, indicating that higher producing cows could be a mitigation option for CH4 emission. The PME phenotypic correlations were almost equal to 0 with FY and PY for both lactations. However, the genetic correlations between PME and FY were slightly positive (0.11 and 0.12), whereas with PY the correlations were slightly negative (-0.05 and -0.04). Both phenotypic and genetic correlations between LMI and MY or PY or FY were always relatively highly negative (from -0.21 to -0.88). As the genetic correlations between PME and LMI were strong (0.71 and 0.72 in first and second lactation), the selection of one trait would also strongly influence the other trait. However, in animal breeding context, PME, as a direct quantity CH4 proxy, would be preferred to LMI, which is a ratio trait of PME with a trait already in the index. The range of PME sire estimated breeding values were 22.1 and 29.41 kg per lactation in first and second parity, respectively. Further studies must be conducted to evaluate the effect of the introduction of PME in a selection index on the other traits already included in this index, such as, for instance, fertility or longevity.

Feeding sows resistant starch during gestation and lactation impacts their faecal microbiota and milk composition but shows limited effects on their progeny

Background Establishment of a beneficial microbiota profile for piglets as early in life as possible is important as it will impact their future health. In the current study, we hypothesized that resistant starch (RS) provided in the maternal diet during gestation and lactation will be fermented in their hindgut, which would favourably modify their milk and/or gut microbiota composition and that it would in turn affect piglets’ microbiota profile and their absorptive and immune abilities. Methods In this experiment, 33% of pea starch was used in the diet of gestating and lactating sows and compared to control sows. Their faecal microbiota and milk composition were determined and the colonic microbiota, short-chain fatty acids (SCFA) production and gut health related parameters of the piglets were measured two days before weaning. In addition, their overall performances and post-weaning faecal score were also assessed. Results The RS diet modulated the faecal microbiota of the sows during gestation, increasing the Firmicutes:Bacteroidetes ratio and the relative abundance of beneficial genera like Bifidobacterium but these differences disappeared during lactation and maternal diets did not impact the colonic microbiota of their progeny. Milk protein concentration decreased with RS diet and lactose concentration increased within the first weeks of lactation while decreased the week before weaning with the RS diet. No effect of the dietary treatment, on piglets’ bodyweight or diarrhoea frequency post-weaning was observed. Moreover, the intestinal morphology measured as villus height and crypt depths, and the inflammatory cytokines in the intestine of the piglets were not differentially expressed between maternal treatments. Only zonula occludens 1 (ZO-1) was more expressed in the ileum of piglets born from RS sows, suggesting a better closure of the mucosa tight junctions. Conclusion Changes in the microbiota transferred from mother to piglets due to the inclusion of RS in the maternal diet are rather limited even though milk composition was affected.

Estimation of CH4 emissions from milk MIR spectra using respiration chamber as reference technique

AIM Identify signals of fat deposition and adaptation through genome-wide scan of the Barbaresca fat-tail sheep. ANIMALS Barbaresca in an ancient Sicilian fat-tail sheep, highly endangered at present. Of the 35 000 heads of 1980, abour 1 300 are left nowadays in 20 flocks. The breed originated from crosses between Barbary sheep from North Africa and the Pinzirita breed at times of the Arab settling in Sicily (9th century). The breed is reared in a very restricted area in central Sicily on smalland medium-sized farms under a semi-extensive farming system. It is a dual-purpose breed: milk for cheese and meat. Barbaresca is one of the only two fat-tail sheep of Italy. METHODS Genotypic data were obtained with the OvineSNP50K array. Fst values of differentiation for 43072 markers were calculated in pairwise comparisons of Barbaresca with each of 13 Italian thin tail breeds. Fat-tail sheep still represent twenty-five percent of the world sheep population; they are predominant in pastoral, transhumant and low input systems. In Western countries and in high input systems they are generally endangered. Fat-tail sheep preserved genetic variability for functional adaptation. The identification of the genes with a role in the fat-tail phenotype contributes to the understanding of the physiology of fat deposition as well as the mechanisms of adaptation and is essential for maintaining future breeding options.uf071 Heritability estimates for the 1st litter size, pregnancy rate and whelping success were low (0.05-0.14) uf071 Grading size and quality had moderate heritability estimates 0.27 and 0.21, respectively uf071 Genetic correlations between animal grading size and fertility traits were unfavourable (from -0.15 to -0.53) uf071 Grading quality and guard hair coverage had antagonistic relationships with all the studied fertility traits (from -0.21 to -0.54) Genetic parameters of fertility and grading traits in Finnish blue foxTrabajo presentado al: 68th Annual Meeting of the European Federation of Animal Science (EAAP). (Tallin, Estonia. 28 agosto - 2 septiembre).As genomic selection has been used already for several years, it has become evident that the validation of genomic evaluations relying on traditional animal models is becoming unsuitable. The GEBV validation test recommended by Interbull is cross-validation based on the forward prediction. It was designed at the time when the multi-step genomic evaluation was the standard method.xa0 The aim of this study was to take a closer look on accuracy and stability of (G)EBVs. Validations for GEBVs were done using yield deviations (YD) or daughter yield deviations (DYD) calculated with single-step GBLUP instead of EBV model. Moreover, we studied the stability of (G)EBV estimations in consecutive evaluations. We used Nordic Holstein 305 days production data containing ca. 7.3 million cows with 15.6 million observations.xa0 Genotypes were available for 30056 animals which had either records or offspring in the full 305d data. The test setup consisted of four data sets: the full data, called data 0 , included calvings up to March 2016. Three reduced data sets were data -1 , data -2 , and data -3 , from which one year of calvings was deleted at a time.xa0 This allowed studying the accuracy of predictions by production years, and also the stability of (G)EBV estimates across lactations. The bull validation was a regression of DYD EBV on PA data-3 or, for GEBV data-3 , regression of DYD GEBV on GEBV data-3 .xa0 The results suggested that after use of genomic selection the DYD from EBV model become biased and that GEBVs validated using DYDs from the BLUP model might receive too low reliability. The validation reliability for protein GEBV (r 2 ) was 0.34 using DYD from EBV model and 0.36 using DYD from ssGBLUP. Similarly, when making cow validations, it might be advisable to use YDs calculated from ssGBLUP for the validation.xa0 The r 2 in GEBV validations using YD from ssGBLUP were on average 5 % units higher compared to validations using YDs from the EBV model.Trabajo presentado al: 68th Annual Meeting of the European Federation of Animal Science (EAAP). (Tallin, Estonia. 28 agosto - 2 septiembre).

Consequences of genetic selection for environmental impact traits on economically important traits in dairy cows

Methane (CH4) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH4 emissions require the estimation of genetic correlations with other economically important traits and the prediction of their selection response. In this study, test-day CH4 emissions were predicted from milk mid-infrared spectra of Holstein cows. Predicted CH4 emissions (PME) and log-transformed CH4 intensity (LMI) computed as the natural logarithm of PME divided by milk yield (MY). Genetic correlations of PME and LMI with traits used currently were approximated from correlations between estimated breeding values of sires. Values were for PME with MY 0.06, fat yield (FY) 0.09, protein yield (PY) 0.13, fertility 0.17; body condition score (BCS) –0.02; udder health (UDH) 0.22; and longevity 0.22. As expected by its definition, values were negative for LMI with production traits (MY –0.61; FY –0.15 and PY –0.40) and positive with fertility (0.36); BCS (0.20); UDH (0.08) and longevity (0.06). The genetic correlations of 33 type traits with PME ranged from –0.12 to 0.25 and for LMI ranged from –0.22 to 0.18. Without selecting PME and LMI (status quo) the relative genetic change through correlated responses of other traits were in PME by 2% and in LMI by –15%, but only due to the correlated response to MY. Results showed for PME that direct selection of this environmental trait would reduce milk carbon foot print but would also affect negatively fertility. Therefore, more profound changes in current indexes will be required than simply adding environmental traits as these traits also affect the expected progress of other traits.

The feeding system impacts relationships between calving interval and economic results of dairy farms

The calving interval (CI) can potentially impact the economic results of dairy farms. This study highlighted the most profitable CI and innovated by describing this optimum as a function of the feeding system of the farm. On-farm data were used to represent real farm conditions. A total of 1832 accounts of farms recorded from 2007 to 2014 provided economic, technical and feeding information per herd and per year. A multiple correspondence analysis created four feeding groups: extensive, low intensive, intensive and very intensive herds. The gross margin and some of its components were corrected to account for the effect of factors external to the farm, such as the market, biological status, etc. Then the corrected gross margin (cGMc) and its components were modelled by CI parameters in each feeding system by use of GLM. The relationship between cGMc and the proportion of cows with CI<380 days in each feeding group showed that keeping most of the cows in the herd with CI near to 1 year was not profitable for most farms (for the very intensive farms there was no effect of the proportion). Moreover, a low proportion of cows (0% to 20%) with a near-to-1-year CI was not profitable for the extensive and low intensive farms. Extending the proportion of cows with CI beyond 459 days until 635 days (i.e. data limitation) caused no significant economic loss for the extensive and low intensive farms, but was not profitable for the intensive and very intensive farms. Variations of the milk and feeding components explained mainly these significant differences of gross margin. A link between the feeding system and persistency, perceptible in the milk production and CI shown by the herd, could explain the different relationships observed between the extent of CI and the economic results in the feeding groups. This herd-level study tended to show different economic optima of CI as a function of the feeding system. A cow-level study would specify these tendencies to give CI objectives to dairy breeders as a function of their farm characteristics.