The aim of the present study was to evaluate the protective effects of isoflavone (ISO) against zearalenone (ZEA) residues in the muscle and liver tissues of prepubertal gilts. Seventy 75-day-old, prepubertal, female pigs (Duroc × Landrace × Yorkshire, 26.5 ± 0.60 kg) were allocated randomly to seven diet treatments for 21days as follows: one control group (fed the basal diet) and six groups fed the basal diet with the addition of either 0.5 or 2.0 mg/kg ZEA plus either 0, 300 or 600 mg/kg ISO. The results showed that the diet with 2.0 mg/kg ZEA added caused an increase of ZEA residue level in muscle tissue (P < 0.05), and that the addition of both 0.5 and 2.0 mg/kg ZEA increased the residue level of ZEA in the liver of prepubertal gilts (P < 0.05). Addition of 600 mg/kg ISO to 2.0 mg/kg ZEA-contaminated diet decreased the ZEA residue level in liver tissue (P < 0.05), and the addition of 300 or 600 mg/kg ISO to the 2.0 mg/kg ZEA-contaminated diet decreased the residue levels of ZEA in muscle tissue (P < 0.05). Western blot analysis demonstrated that feeding ZEA to prepubertal gilts increased their protein expression of 3α/3β-hydroxysteroid dehydrogenase (HSD; P < 0.05), and that the addition of 300 or 600 mg/kg ISO to the 2.0 mg/kg ZEA-contaminated diet decreased the protein expression of 3α/3β-HSD (P < 0.05), compared with the addition of 2.0 mg/kg ZEA alone. The results demonstrated that muscle and liver tissues retain residual ZEA when pigs are fed a diet contaminated with high concentrations of ZEA, and that the concentration of ZEA in muscle and liver tissues increased with increased amounts of ZEA in the feed. In diets contaminated with high levels of ZEA, the addition of ISO may accelerate the biotransformation and degradation of ZEA and its metabolites, and reduce the residues of ZEA in liver and muscle tissues of prepubertal gilts.

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible–near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R2CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R2CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 μm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R2CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 μm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.

This study aimed to determine whether there was a difference in skin permeability to methylene blue dye or skin morphology between dairy cows that differed in their susceptibility to digital dermatitis (DD) and to assess the effect of contact with slurry on skin permeability. Twenty nine dairy cows were monitored for DD during the winter housing period and classed as DD+ (previous DD infection, n = 17), or DD− (no recorded infection, n = 12). The animals were culled and a skin sample was taken from above the heel of each hind foot and frozen. Samples were later defrosted and one sample from each cow was tested for permeability, whereas the other was treated with slurry for 24 h before permeability testing. To test permeability, methylene blue dye was applied to the skin surface in a Franz diffusion cell. After 48 h, the amount of dye that had passed through the skin was estimated. The stratum corneum thickness and the density of hair follicles were determined from additional heel skin samples. Skin permeability to methylene blue dye was significantly greater for samples that had been treated with slurry but did not differ between DD+ and DD− animals. No difference was found in the stratum corneum thickness or density of hair follicles between DD+ and DD− animals. These findings imply that individual differences in general skin permeability are not a major factor in determining DD susceptibility and suggest that contact with slurry could contribute to DD infection by increasing the permeability of the skin, which may facilitate pathogen entry. Further work is required to clarify the role played by slurry in the pathogenesis of DD.

Twenty Gascon young bulls that had been reared either in intensive conditions (INT) (n=10) with early weaning at 3 to 4 months, or in a traditional extensive (EXT) system (n=10) with weaning at 7 months, were subjected to the same conditions during the 145-day finishing period. Production system before the finishing period did not affect conformation, dressing percentage or morphology of the carcass; nevertheless, tissue composition differed somewhat between the two groups. Display had a stronger effect on meat colour than did production system. Percentage of myoglobin was highest in INT (P⩽0.001), although meat texture and sensory quality did not differ between rearing conditions. EXT animals had darker, more yellow fat, a higher percentage of n-3 fatty acids (P⩽0.001), a lower percentage of saturated fatty acids (P⩽0.05) and a lower n-6/n-3 index (P⩽0.001) than did the INT-reared animals. Production system before the fattening period might modify some of the characteristics of commercial beef, especially those associated with fat.

The aim of this study was to investigate the accuracy to predict detailed fatty acid (FA) composition of bovine milk by mid-infrared spectrometry, for a cattle population that partly differed in terms of country, breed and methodology used to measure actual FA composition compared with the calibration data set. Calibration equations for predicting FA composition using mid-infrared spectrometry were developed in the European project RobustMilk and based on 1236 milk samples from multiple cattle breeds from Ireland, Scotland and the Walloon Region of Belgium. The validation data set contained 190 milk samples from cows in the Netherlands across four breeds: Dutch Friesian, Meuse-Rhine-Yssel, Groningen White Headed (GWH) and Jersey (JER). The FA measurements were performed using gas–liquid partition chromatography (GC) as the gold standard. Some FAs and groups of FAs were not considered because of differences in definition, as the capillary column of the GC was not the same as used to develop the calibration equations. Differences in performance of the calibration equations between breeds were mainly found by evaluating the standard error of validation and the average prediction error. In general, for the GWH breed the smallest differences were found between predicted and reference GC values and least variation in prediction errors, whereas for JER the largest differences were found between predicted and reference GC values and most variation in prediction errors. For the individual FAs 4:0, 6:0, 8:0, 10:0, 12:0, 14:0 and 16:0 and the groups’ saturated FAs, short-chain FAs and medium-chain FAs, predictions assessed for all breeds together were highly accurate (validation R2 > 0.80) with limited bias. For the individual FAs cis-14:1, cis-16:1 and 18:0, the calibration equations were moderately accurate (R2 in the range of 0.60 to 0.80) and for the individual FA 17:0 predictions were less accurate (R2 < 0.60) with considerable bias. FA concentrations in the validation data set of our study were generally higher than those in the calibration data. This difference in the range of FA concentrations, mainly due to breed differences in our study, can cause lower accuracy. In conclusion, the RobustMilk calibration equations can be used to predict most FAs in milk from the four breeds in the Netherlands with only a minor loss of accuracy.

The targeting of mcrA or 16S rRNA genes by quantitative PCR (qPCR) has become the dominant method for quantifying methanogens in rumen. There are considerable discrepancies between estimates based on different primer sets, and the literature is equivocal about the relationship with methane production. There are a number of problems with qPCR, including low primer specificity, multiple copies of genes and multiple genomes per cell. Accordingly, we have investigated alternative markers for methanogens, on the basis of the distinctive ether lipids of archaeal cell membranes. The membranes of Archaea contain dialkyl glycerol ethers such as 2,3-diphytanayl-O-sn-glycerol (archaeol), and glycerol dialkyl glycerol tetraethers (GDGTs) such as caldarchaeol (GDGT-0) in different proportions. The relationships between estimates of methanogen abundance using qPCR and archaeol measurements varied across primers. Studies in other ecosystems have identified environmental effects on the profile of ether lipids in Archaea. There is a long history of analysing easily accessible samples, such as faeces, urine and milk, to provide information about digestion and metabolism in livestock without the need for intrusive procedures. Purine derivatives in urine and odd-chain fatty acids in milk have been used to study rumen function. The association between volatile fatty acid proportions and methane production is probably the basis for empirical relationships between milk fatty acid profiles and methane production. However, these studies have not yet identified consistent predictors. We have evaluated the relationship between faecal archaeol concentration and methane production across a range of diets in studies on beef and dairy cattle. Faecal archaeol is diagnostic for ruminant faeces being below the limit of detection in faeces from non-ruminant herbivores. The relationship between faecal archaeol and methane production was significant when comparing treatment means across diets, but appears to be subject to considerable between-animal variation. This variation was also evident in the weak relationship between archaeol concentrations in rumen digesta and faeces. We speculate that variation in the distribution and kinetics of methanogens in the rumen may affect the survival and functioning of Archaea in the rumen and therefore contribute to genetic variation in methane production. Indeed, variation in the relationship between the numbers of micro-organisms present in the rumen and those leaving the rumen may explain variation in relationships between methane production and both milk fatty acid profiles and faecal archaeol. As a result, microbial markers in the faeces and milk are unlikely to relate well back to methanogenesis in the rumen. This work has also highlighted the need to describe methanogen abundance in all rumen fractions and this may explain the difficulty interpreting results on the basis of samples taken using stomach tubes or rumenocentesis.

Nitrous oxide (N2O) is a potent greenhouse gas and the dominant anthropogenic stratospheric ozone-depleting emission. The tropospheric concentration of N2O continues to increase, with animal production systems constituting the largest anthropogenic source. Stable isotopes of nitrogen (N) provide tools for constraining emission sources and, following the temporal dynamics of N2O, providing additional insight and unequivocal proof of N2O source, production pathways and consumption. The potential for using stable isotopes of N is underutilised. The intent of this article is to provide an overview of what these tools are and demonstrate where and how these tools could be applied to advance the mitigation of N2O emissions from animal production systems. Nitrogen inputs and outputs are dominated by fertiliser and excreta, respectively, both of which are substrates for N2O production. These substrates can be labelled with 15N to enable the substrate-N to be traced and linked to N2O emissions. Thus, the effects of changes to animal production systems to reduce feed-N wastage by animals and fertiliser wastage, aimed at N2O mitigation and/or improved animal or economic performance, can be traced. Further 15N-tracer studies are required to fully understand the dynamics and N2O fluxes associated with excreta, and the biological contribution to these fluxes. These data are also essential for the new generation of 15N models. Recent technique developments in isotopomer science along with stable isotope probing using multiple isotopes also offer exciting capability for addressing the N2O mitigation quest.