Genetic variation of in vivo muscle glycerol,glycogen, and pigment in Danish purebred pigs

Heritability values of glycerol, glycogen and pigment concentrations measured on muscle biopsy samples from longissimus dorsi obtained from 85?kg boars of Danish purebred pigs (Landrace, Yorkshire, and Duroc) and their genetic correlations to performance and meat quality traits were calculated. The heritability values of glycogen, pigment, and glycerol were 0.38±0.02, 0.17±0.02, and 0.065±0.016, respectively. Glycogen was negatively related to the feed conversion ratio (FCR) (r _g =?0.25±0.06) and ultimate meat pH (r _g =?0.41±0.04), and positively to the carcass meat percentage (r _g =0.35±0.04), L* (lightness of meat, r _g =0.32±0.04) and a* (redness of meat, r _g =0.12±0.03). Pigment was positively related to the FCR (r _g =0.12±0.05), the meat percentage (r _g =0.28±0.05), and a* (r _g =0.59±0.04), and negatively to the L* (r _g =?0.46±0.06). The concentrations of pigment and glycogen (r _g =0.27±0.05) were interrelated. Based on the heritability values and the signs of the genetic correlations the present data suggest that it is possible to select for higher ultimate pH and improved colour by selection for either lower muscle glycogen or higher pigment concentration, respectively. However, the positive genetic correlation between these two traits may restrict the efficiency of simultaneous selection for lower glycogen and higher pigment. All genetic parameter estimates presented are calculated across three purebreds. Values for each breed are warranted for use in pig production. However, this requires more animals per breed.     

Herd diagnosis of low pathogen diarrhoea in growing pigs – a pilot study

Background

The major indication for antibiotic use in Danish pigs is treatment of intestinal diseases post weaning. Clinical decisions on antibiotic batch medication are often based on inspection of diarrhoeic pools on the pen floor. In some of these treated diarrhoea outbreaks, intestinal pathogens can only be demonstrated in a small number of pigs within the treated group (low pathogen diarrhoea). Termination of antibiotic batch medication in herds suffering from such diarrhoea could potentially reduce the consumption of antibiotics in the pig industry. The objective of the present pilot study was to suggest criteria for herd diagnosis of low pathogen diarrhoea in growing pigs.Data previously collected from 20 Danish herds were used to create a case series of clinical diarrhoea outbreaks normally subjected to antibiotic treatment. In the present study, these diarrhoea outbreaks were classified as low pathogen (<15% of the pigs having bacterial intestinal disease) (n =5 outbreaks) or high pathogen (≥15% of the pigs having bacterial intestinal disease) (n =15 outbreaks). Based on the case series, different diagnostic procedures were explored, and criteria for herd diagnosis of low pathogen diarrhoea were suggested. The effect of sampling variation was explored by simulation.

Results

The diagnostic procedure with the highest combined herd-level sensitivity and specificity was qPCR testing of a pooled sample containing 20 randomly selected faecal samples. The criteria for a positive test result (high pathogen diarrhoea outbreak) were an average of 1.5 diarrhoeic faecal pools on the floor of each pen in the room under investigation and a pathogenic bacterial load ≥35,000 per gram in the faecal pool tested by qPCR. The bacterial load was the sum of Lawsonia intracellularis, Brachyspira pilosicoli and Escherichia coli F4 and F18 bacteria per gram faeces. The herd-diagnostic performance was (herd-level) diagnostic sensitivity =0.99, diagnostic specificity =0.80, positive predictive value =0.94 and negative predictive value =0.96.

Conclusions

The pilot study suggests criteria for herd diagnosis of low pathogen diarrhoea in growing pigs. The suggested criteria should now be evaluated, and the effect of terminating antibiotic batch medication in herds identified as suffering from low pathogen diarrhoea should be explored.

Electronic supplementary material

The online version of this article (doi:10.1186/2046-0481-67-24) contains supplementary material, which is available to authorized users.     

Evaluation of Breeding Value and Selection of Bull Dams in the Danish Dairy Breeds: I. Studies on Realized Efficiency of Bull Dam and Bull Sire Selection

Abstract

The relationship between twice the adjusted daughter yield deviation (DYD) of the son and the average of the estimated breeding values (EBV) of his parents was investigated for milk and fat yield. Deviations from expectations of DYD minus the pedigree index and the regression of DYD on the EBV for each of the parents were estimated. The EBV of each bull dam was calculated in several ways using different combinations of pedigree information and their own records. In all, 1693 bulls (880 Black and White (BW), 418 Red Danish (RD) and 395 Danish Jersey (DJ)) with Direct Updating indices for milk and fat yield were included in the investigation. Mean discrepancies between estimated and observed genetic merit for kg of milk yield were ?348 for BW, ?213 for RD and ?228 for DJ, when all of the bull dams' individual records were utilized in breeding value estimation. The corresponding mean discrepancies for kg of fat yield were ?14.7, ?11.5 and ?11.9, respectively. The discrepancies were positively correlated with the number of lactation records included in the dams' EBVs. Regression coefficients of bull DYDs on bull dam breeding values were significantly lower than expected for RD and BW regardless of the number of individual lactation records included in the dams' indexes for milk and fat yield. For all three breeds, the regression coefficients were lowest for fat yield. The most probable cause of overestimation in bull dam EBVs is preferential treatment, particularly in second and later lactations. To avoid this bias, estimation of a bull dam's breeding value should be based on pedigree information and on records from only her first lactation.     

Functions for estimating belowground and whole tree biomass of birch in Norway

Obtaining accurate estimates of national belowground and whole tree biomass is important to better understand the global carbon cycle and to quantify biomass stocks and changes. However, the availability of individual tree belowground biomass functions is generally low due to the difficulty of extracting roots. Allometric birch (Betula pubescens Ehrh. and Betula pendula Roth) biomass functions were derived from 67 trees for belowground and whole tree biomass using diameter at breast height (dbh) and height as the independent variables. The sampled trees spanned a dbh range from 4.0 to 45.5?cm and the functions provided a good fit to the data (RMSE?=?14.2?kg for BG and 40.7?kg for whole tree with dbh as predictor). Belowground, total stem, live crown, and dead branch biomass comprised 29.2%, 52.2%, 18.1%, and 0.5% of the whole tree biomass, respectively. Observed root-to-shoot ratios were between 0.21 and 0.88 with a mean of 0.42. Comparisons with existing belowground birch biomass functions from Fennoscandia indicated considerable differences in estimates between existing functions. The derived data-set for belowground birch biomass is the largest in Fennoscandia and the developed functions are likely the best available for estimating national birch biomass stock and stock change in Norway.     

Induction of porcine post-weaning multisystemic wasting syndrome (PMWS) in pigs from PMWS unaffected herds following mingling with pigs from PMWS-affected herds

In this paper we present the results from two experimental studies (I and II) investigating whether post-weaning multisystemic wasting syndrome (PMWS) can be induced in pigs from PMWS unaffected herds by mingling with pigs from PMWS-affected herds and to observe whether transportation and/or mingling of healthy pigs from unaffected herds could induce PMWS.The studies comprised pigs from 12 different herds. Eight herds had PMWS while four were unaffected. All 12 herds were found to be infected with PCV2. Pigs from PMWS-affected herds were mingled with pigs from unaffected herds in four separate compartments in both study I and study II. In addition, in study II, four groups of pigs from unaffected herds were included. Two groups with pigs transported and mingled from unaffected herds and two groups with pigs which were only transported. The PMWS diagnoses on the individual pigs were based on lymphoid depletion, histiocytic proliferation and the presence of giant cells or inclusion bodies together with the demonstration of PCV2 in lymphoid tissue.Healthy pigs, in both studies, developed PMWS 4–5 weeks after mingling with pigs clinically affected with PMWS. None of the pigs from unaffected herds which had no contact with pigs from PMWS-affected herds developed clinical signs of PMWS. Transportation and mingling of pigs from PMWS unaffected herds in combination or alone was insufficient to provoke PMWS.     

Finding the appropriate level of complexity for a simulation model: An example with a forest growth model

The topic of model complexity is fundamental to model developers and model users. In this study, we investigate how over- and under-fitting of a driving function in a simulation model influences the predictive ability of the model. Secondly, we investigate whether model selection approaches succeed in selecting driving functions with the best predictive ability. We address these issues through an example with the forest simulator SORTIE-ND. Utilizing maximum likelihood methods and individual tree growth data we parameterize five growth functions of increasing complexity. We then incorporate each growth function into the simulation model SORTIE-ND and test predicted growth against independent data. Compared to the independent data, the simplest and the most complex growth functions had the poorest predictive ability while functions of intermediate complexity had the best predictive ability. The poor predictive ability of the simplest model is caused by poor approximation of the system while the poor predictive ability of the most complex model is caused by biased parameter estimates. A growth function of intermediate complexity was the most parsimonious model where error due to approximation and error due to estimation were simultaneously minimized. The model selection criteria AIC and BIC were found to select complex functions that were over-fitted according to the independent data comparison. BIC was closer to choosing the model that minimized prediction error than AIC. In this example, BIC is the more appropriate model selection criterion. It is important that both model developers and models users remember that more complex models do not always result in better predictive models.     

The effect of lupins as compared with peas and oats on the yield of the subsequent winter barley crop

New high yielding early maturing cultivars of lupins have been introduced in north-west Europe as grain protein crops in crop rotations. This paper reports on a comparative study of lupins with peas and oats, and of their effect on yield of subsequent winter barley crops. These crops were given five levels of N under irrigated and non-irrigated conditions on sand and loam. Under rain fed conditions the grain yield of pea, oat and lupin varied between 24–36, 34–53 and 18–37 hkg DM ha⁻¹, respectively. Supplemental irrigation raised grain yield of oat to 50–60 hkg DM ha⁻¹, while grain yield in pea was not affected and grain yield in lupin in most cases decreased due to gray mould attack and excessive vegetative growth in the indeterminate lupin variety. Under rain fed conditions, the grain nitrogen content of pea, oat and lupin varied between 137–172, 61–80 and 189–226 kg N ha⁻¹, respectively, and was significantly higher in lupin as compared with pea. On sandy soil, similar low-root densities were found for pea, oat and lupin below 30 cm depth. On sand, at final harvest the residual soil-N of lupin and pea, as measured in a subsequent winter barley crop not supplied with N fertilizer, was 15 and 8–10 kg N ha⁻¹ higher than in winter barley following oat, respectively. The nature of the probably more N-root residues of lupin is discussed. On loam, the residual N of lupin and pea was similar, 18–27 kg N ha⁻¹. On sand, under rain fed conditions preceding lupin and pea as compared with oat, increased the barley grain yield at zero N-application 77 and 49%, respectively; the effect of lupin was significantly higher than that of pea until the highest N-level 120 kg N-application ha⁻¹. On loam under rain fed conditions preceding lupin and pea increased the barley grain yield at zero N-application by 36 and 62%, respectively, as compared with oat; at N-application>60 kg N ha⁻¹ the grain yield was similar after all three crops. For both soil types the same level of effect was found under irrigated conditions. Conclusions: Supplemental irrigation might result in lower grain yield in lupin due to gray mould attack and excessive growth if indeterminate lupin varieties are used. Grain nitrogen yield of lupin is significantly higher than that of pea. On sand, the effect of lupin on the subsequent winter barley grain yield is significantly higher than that of pea, probably due to greater N-root nitrogen residues. On loam, lupin and pea have similar effects on the subsequent winter barley crop.