Nutritive Value of \u3ci\u3eAlopecurus Pratensis, Festuca Rubra, Arrhenatherum Elatius\u3c/i\u3e and \u3ci\u3eLolium Perenne\u3c/i\u3e Grown in the South of Belgium

In Europe, recent strategies have aimed at encouraging farmers to use production techniques more efficient in preserving the environment and maintaining natural areas. Those strategies have encouraged the use of secondary grass species in forage production systems. However, the nutritive value of those grasses is not well known. Therefore, the aim of the present study was to evaluate the energy and nitrogen values of Alopecurus pratensis (ALPR), Festuca rubra (FERU) and Arrhenatherum elatius (AREL) under moderate rates of nitrogen (N) application (60 kg N/ha per cut) and a hay-cutting regime (2 cuts/year: 25 May and 9 July). Lolium perenne cv. Bastion (LOPE) was used as a control. The first cut of ALPR was a mixture of 18 April and 25 May cuts

Molecular characterisation of a versatile peroxidase from a bjerkandera strain

The cloning and sequencing of the rbpa gene coding for a versatile peroxidase from a novel Bjerkandera strain is hereby reported. The 1777 bp isolated fragment contained a 1698 bp peroxidase-encoding gene, interrupted by 11 introns. The 367 amino acid-deduced sequence includes a 27 amino acid-signal peptide. The molecular model, built via homology modelling with crystal structures of four fungal peroxidases, highlighted the amino acid residues putatively involved in manganese binding and aromatic substrate oxidation. The potential heme pocket residues (R44, F47, H48, E79, N85, H177, F194 and D239) include both distal and proximal histidines (H48 and H177). RBP possesses potential calcium-binding residues (D49, G67, D69, S71, S178, D195, T197, I200 and D202) and eight cysteine residues (C3, C15, C16, C35, C121, C250, C286, C316). In addition, RBP includes residues involved in substrate oxidation: three acidic residues (E37, E41 and D183)—putatively involved in manganese binding and H83 and W172—potentially involved in oxidation of aromatic substrates. Characterisation of nucleotide and amino acid sequences include RBP in versatile peroxidase group sharing catalytic properties of both LiP and MnP. In addition, the RBP enzyme appears to be closely related with the ligninolytic peroxidases from the Trametes versicolor strai

Between and within-herd variation in blood and milk biomarkers in Holstein cows in early lactation

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring

Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total 2manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake

Prediction of key milk biomarkers in dairy cows through milk MIR spectra and international collaborations.

peer reviewedAt the individual cow level, sub-optimum fertility, mastitis, negative energy balance and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were: 1) to assess the potential of milk Mid Infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6P, free glucose), ketosis (BHB and acetone), mastitis (NAGase and LDH), and fertility (progesterone); 2) to test alternative methodologies to partial least square regression (PLS) to better account for the specific asymmetric distribution of the biomarkers; and 3) to create robust models by merging large data sets from 5 international or national projects. Benefiting from this international collaboration, the data set comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents while the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. PLS was used as the reference basis, and compared with a random modification of distribution associated with PLS (Random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS) and Support Vector Machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low vs high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation data set. The remaining 80% of herds were used as the calibration data set. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose and LDH (R2v = 0.48, 0.58, 0.28, and 0.24). For other molecules, PLS-Random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15). Hence, PLS and SVM based on the entire data set provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis and mastitis in dairy cows, which in turn have major influences on their fertility and survival

Genetic parameters for milk urea and its relationship with milk yield and compositions in Holstein dairy cows

peer reviewe

Variations nycthémérales de la concentration en ammoniac dans le liquide du rumen, chez les vaches laitières dans la période péri-partum

International audienc

The vacuum UV photoabsorption spectroscopy of the cis-1,2-dichloroethylene (1,2-ClHC=CHCl) in the 5-20 eV range. An experimental and theoretical investigation

The photoabsorption spectrum of cis-1,2-C2H2Cl2 has been examined in detail in the vacuum UV range between 5 eV and 20 eV photon energy by using synchrotron radiation. Quantum chemical calculations are proposed and applied to the electronic transitions and to the vibrational structures belonging to these transitions. The broad band observed at 6.568 eV includes the X̃1A1→11B1, 31B2 and 21B1 transitions. The two latter excitations correspond to the valence 2b1(π)→π* and to the Rydberg 2b1(π)→3s transitions. The former excitation is described by a more complex 2b1(π)→nCl +σ*CH/Rpσ transition where valence and Rydberg characters are strongly mixed. For these transitions short vibrational progressions are observed, analyzed and tentatively assigned. The abundant structure observed between 7.0 eV and 10.0 eV has been analyzed in terms of vibronic transitions to one ns- (δ¯ = 0.960), two np- (δ¯ = 0.525 and 0.337), and two nd-type (δ¯ = 0.080 and 0.002) Rydberg series, all converging to the X̃2B1 ionic ground state. The vibrational structure analysis of the Rydberg states leads to the following average wave numbers: ω2 ≈ 1420 cm-1 (C=C stretching), ω3 ≈ 1190 cm-1 (symmetric C-H bending), ω4 ≈ 800 cm-1 (symmetric C-Cl stretching) and ω5 ≈ 190 cm-1 (symmetric C-Cl bending). These numbers are compared to previously reported values. Many other transitions are observed between 10 eV and 20 eV and are assigned to transitions to Rydberg states converging to the successive excited states of cis-1,2-C2H2Cl2+. For several of these Rydberg states, a vibrational structure is also observed and interpreted