Bayesian simultaneous equation models for the analysis of energy intake and partitioning in growing pigs

The objective of the current study was to develop Bayesian simultaneous equation models for modelling energy intake and partitioning in growing pigs. A key feature of the Bayesian approach is that parameters are assigned prior distributions, which may reflect the current state of nature. In the models, rates of metabolizable energy (ME) intake, protein deposition (PD) and lipid deposition (LD) were treated as dependent variables accounting for residuals being correlated. Two complementary equation systems were used to model ME intake (MEI), PD and LD. Informative priors were developed, reflecting current knowledge about metabolic scaling and partial efficiencies of PD and LD rates, whereas flat non-informative priors were used for the reminder of the parameters. The experimental data analysed originate from a balance and respiration trial with 17 cross-bred pigs of three genders (barrows, boars and gilts) selected on the basis of similar birth weight. The pigs were fed four diets based on barley, wheat and soybean meal supplemented with crystalline amino acids to meet or exceed Danish nutrient requirement standards. Nutrient balances and gas exchanges were measured at c. 25, 75, 120 and 150 kg body weight (BW) using metabolic cages and open circuit respiration chambers. A total of 56 measurements were performed. The sensitivity analysis showed that only the maintenance component was sensitive to the prior specification, and hence the maintenance estimate of 0·91 MJ ME/kg0·60 per day (0·95 credible interval (CrI): 0·78-1·09) should be interpreted with caution. It was shown that boars' ability to deposit protein was superior to that of barrows and gilts, as these had an estimated maximum PD (PDmax) of 250 g/day (0·95 CrI: 237-263), whereas the barrows and gilts had a PDmax of 210 g/day (0·95 CrI: 198-220). Furthermore, boars reached PDmax at 109 kg BW (0·95 CrI: 93·6-130), whereas barrows and gilts maximized PD at 81·7 kg BW (0·95 CrI: 75·6-89·5). At 25 kg BW, the boars partitioned on average 5-6% more of the ME above maintenance into PD than barrows and gilts, and this was progressively increased to 10-11% more than barrows and gilts at 150 kg BW. The Bayesian modelling framework can be used to further refine the analysis of data from metabolic studies in growing pigs. © Cambridge University Press 2012

Changes in the relative thickness of individual subcutaneous adipose tissue layers in growing pigs

<p>Abstract</p> <p>Background</p> <p>The thickness of the subcutaneous fat layer is an important parameter at all stages of pig production. It is used to inform decisions on dietary requirements to optimize growth, in gilts to promote longevity and finally to assist in the calculation of payments to producers that allow for general adiposity. Currently for reasons of tradition and ease, total adipose thickness measurements are made at one or multiple sites although it has been long recognized that up to three well defined layers (outer (L1), middle (L2), and inner (L3)) may be present to make up the total. Various features and properties of these layers have been described. This paper examines the contribution of each layer to total adipose thickness at three time points and describes the change in thickness of each layer per unit change in body weight in normal growing pigs.</p> <p>Methods</p> <p>A group of nine pigs was examined using 14 MHz linear array transducer on three separate occasions. The average weight was 51, 94 and 124 kg for each successive scan. The time between scanning was approximately 4 weeks. The proportion of each layer to total thickness was modeled statistically with scan session as a variable and the change in absolute thickness of each layer per unit change in body weight was modeled in a random regression model.</p> <p>Results</p> <p>There was a significant change in ratios between scans for the middle and inner layers (<it>P </it>< 0.001). The significant changes were seen between the first and second, and between the first and final, scan sessions. The change in thickness per unit change in body weight was greatest for L2, followed by L1 and L3.</p> <p>Conclusion</p> <p>These results demonstrate that subcutaneous adipose layers grow at different rates relative to each other and to change in body weight and indicate that ultrasound can be used to track these differences.</p

Fine mapping of a QTL affecting levels of skatole on pig chromosome 7

Abstract Background Previous studies in the Norwegian pig breeds Landrace and Duroc have revealed a QTL for levels of skatole located in the region 74.7–80.5 Mb on SSC7. Skatole is one of the main components causing boar taint, which gives an undesirable smell and taste to the pig meat when heated. Surgical castration of boars is a common practice to reduce the risk of boar taint, however, a selection for boars genetically predisposed for low levels of taint would help eliminating the need for castration and be advantageous for both economic and welfare reasons. In order to identify the causal mutation(s) for the QTL and/or identify genetic markers for selection purposes we performed a fine mapping of the SSC7 skatole QTL region. Results A dense set of markers on SSC7 was obtained by whole genome re-sequencing of 24 Norwegian Landrace and 23 Duroc boars. Subsets of 126 and 157 SNPs were used for association analyses in Landrace and Duroc, respectively. Significant single markers associated with skatole spanned a large 4.4 Mb region from 75.9–80.3 Mb in Landrace, with the highest test scores found in a region between the genes NOVA1 and TGM1 (p < 0.001). The same QTL was obtained in Duroc and, although less significant, with associated SNPs spanning a 1.2 Mb region from 78.9–80.1 Mb (p < 0.01). The highest test scores in Duroc were found in genes of the granzyme family (GZMB and GZMH-like) and STXBP6. Haplotypes associated with levels of skatole were identified in Landrace but not in Duroc, and a haplotype block was found to explain 2.3% of the phenotypic variation for skatole. The SNPs in this region were not associated with levels of sex steroids. Conclusions Fine mapping of a QTL for skatole on SSC7 confirmed associations of this region with skatole levels in pigs. The QTL region was narrowed down to 4.4 Mb in Landrace and haplotypes explaining 2.3% of the phenotypic variance for skatole levels were identified. Results confirmed that sex steroids are not affected by this QTL region, making these markers attractive for selection against boar taint

A dynamic growth model for prediction of nutrient partitioning and manure production in growing–finishing pigs: Model development and evaluation

Nutrient loading and air emissions from swine operations raise environmental concerns. The objective of the study was to describe and evaluate a mathematical model (Davis Swine Model) of nutrient partitioning and predict manure excretion and composition on a daily basis. State variables of the model were AA, fatty acids, and a central pool of metabolites that supplied substrate for lipid synthesis and oxidation. The model traced the fate of ingested nutrients and water through digestion and intermediary metabolism into body protein, fat, water, and ash, where body protein and fat represented the body constituent pools. It was assumed that fluxes of metabolites follow saturation kinetics, depending on metabolite concentrations. The main inputs to the model were diet nutrient composition, feed intake, water-to-feed ratio, and initial BW. First, the model was challenged with nutrient partitioning data and then with excretion data. The data had 48 different feeding regimes with contrasting energy and lysine intakes at 2 different stages of growth. The overall observed and predicted mean were 109 and 112 g/d for protein deposition and 132 and 136 g/d for lipid deposition respectively, suggesting minor mean bias. Root mean square prediction error (RMSPE) was used in evaluation of the model for its predictive power. The overall RMSPE was 2.2 and 4.1 g/d for protein and lipid deposition, respectively. The excretion database used for evaluation of the model was constructed from 150 digestibility trials using growing-finishing pig diets that had a wide range of nutrient chemical composition. Nutrient and water excretion were quantified using the principle of mass conservation. The average daily observed and predicted manure production was 3.79 and 3.99 kg/d, respectively, with a RMSPE of 0.49 kg/d. There was a good agreement between observed and predicted mean fecal N output (9.9 and 9.8 g/d, respectively). Similarly, the overall observed and predicted mean urine N output was 21.7 and 21.3 g/d, respectively, suggesting minor mean bias. The RMSPE was 1.9 and 4.1 g/d for fecal and urinary N, respectively. Evaluation of the model showed that the model predicts manure excretion and N content well and can be used to assess environmental mitigation options from swine operations

Flexible alternatives to the Gompertz equation for describing growth with age in turkey hens

8 páginas.A total of 49 profiles of growing turkey hens from commercial flocks were used in this study. Three flexible growth functions (von Bertalanffy, Richards, and Morgan) were evaluated with regard to their ability to describe the relationship between BW and age and were compared with the Gompertz equation with its fixed point of inflection, which might result in its overestimation. For each function, 4 ways of analysis were implemented. A basic model was fitted first, followed by implementation of a first-order autoregressive correlation structure. A model that considered only mature BW varied with year and another that considered only the rate coefficient varied with different years were applied. The results showed that the fixed point of inflection of the Gompertz equation can be a limitation and that the relationship between BW and age in turkeys was best described using flexible growth functions. However, the Richards equation failed to converge when fitted to the turkey growth data; therefore, it was not considered further. Inclusion of an autoregressive process of the first order rendered a substantially improved fit to data for the 3 growth functions. The Morgan equation provided the best fit to the data set and was used for characterizing mean growth curves for the 7 yr of production. It was estimated that the maximum growth rate occurred at 3.74, 3.65, 3.99, 4.18, 4.05, 4.01, and 3.77 kg BW for production years from 1997 to 2003, respectively. It is recommended that flexible growth functions should be considered as an alternative to the simpler functions (with a fixed point of inflection) for describing the relationship between BW and age in turkeys because they were easier to fit and very often gave a closer fit to data points because of their flexibility, and therefore a smaller residual MS value, than simpler models. It can also be recommended that studies should consider adding a first-order autoregressive process when analyzing repeated measures data with nonlinear models.Canada Research Chairs Program (Ottawa, Ontario, Canada) . Manitoba Rural Adaptation Council (Winnipeg, Manitoba, Canada)Peer reviewe

Energy and protein interactions and their effect on nitrogen excretion in dairy cows

The principal driver of nitrogen (N) losses from the body including excretion and secretion in milk is N intake. However, other covariates may also play a role in modifying the partitioning of N. This study tests the hypothesis that N partitioning in dairy cows is affected by energy and protein interactions. A database containing 470 dairy cow observations was collated from calorimetry experiments. The data include N and energy parameters of the diet and N utilization by the animal. Univariate and multivariate meta-analyses that considered both within and between study effects were conducted to generate prediction equations based on N intake alone or with an energy component. The univariate models showed that there was a strong positive linear relationships between N intake and N excretion in faeces, urine and milk. The slopes were 0.28 faeces N, 0.38 urine N and 0.20 milk N. Multivariate model analysis did not improve the fit. Metabolizable energy intake had a significant positive effect on the amount of milk N in proportion to faeces and urine N, which is also supported by other studies. Another measure of energy considered as a covariate to N intake was diet quality or metabolizability (the concentration of metabolizable energy relative to gross energy of the diet). Diet quality also had a positive linear relationship with the proportion of milk N relative to N excreted in faeces and urine. Metabolizability had the largest effect on faeces N due to lower protein digestibility of low quality diets. Urine N was also affected by diet quality and the magnitude of the effect was higher than for milk N. This research shows that including a measure of diet quality as a covariate with N intake in a model of N execration can enhance our understanding of the effects of diet composition on N losses from dairy cows. The new prediction equations developed in this study could be used to monitor N losses from dairy systems