The accuracy of predicting carcass composition of three different pig genetic lines by dual-energy X-ray absorptiometry

Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements benefit from gathering exact measurements of body composition or body nutrient contents. Those are acquired by standardized dissection or by grinding the body followed by wet chemical analysis, respectively. The two methods do not result in the same type of information, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Among imaging techniques, dual-energy X-ray absorptiometry (DXA) is particularly promising. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. Several calibration regressions have been published, but comparative studies of those regression equations and whether they are applicable to different data sets are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (BW range 20-100 kg) as determined by both DXA and chemical analyses, with R² ranging between 0.89 for ash and 0.99 for water and CP. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses, with the prediction error ranging between 4.3% for CP and 12.6% for ash. Finally, we compare existing prediction equations for pigs of a similar range of BWs with the equations derived from our DXA measurements and evaluate their fit with our chemical analysis data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.

Investigating the potential for genetic improvement of nitrogen and phosphorus efficiency in a Swiss large white pig population using chemical analysis

Pig production contributes to environmental pollution through excretion of phosphorus and nitrogenous compounds. European pig production requires annual imports of currently 36 million tons of soya bean, because domestic plant protein sources often do not meet the required protein quality. Most of the mineral phosphate sources are also imported. It is therefore desirable to improve nutrient deposition efficiency through selective breeding, that is to realise similar growth rates and carcass compositions as currently achieved but with a lower intake of dietary crude protein or phosphate. For a preliminary evaluation of the potential of selecting for increased nutrient deposition efficiency, we estimated genetic parameters for nitrogen and phosphorus efficiencies in a Swiss Large White pig population including 294 individuals. Nutrient efficiency phenotypes were obtained from wet‐chemistry analyses of pigs of various live weights. Heritability of nitrogen efficiency was estimated at 41%. Heritability of phosphorus efficiency was very low (0.3%), but positive genetic correlations with nitrogen efficiency suggest that breeding for nitrogen efficiency would positively affect phosphorus efficiency. Further studies are needed to improve the quality of estimates and to obtain accurate high‐throughput measures of nutrient efficiency to be implemented on farms.

Exploration of methods for lamb carcass yield estimation in Canada

Different approaches were evaluated to improve the accuracy of carcass yield predictions of Canadian lamb carcasses using manually obtained measurements and dual-energy X-ray absorptiometry (DEXA). Several linear carcass measurements were obtained from a population of commercial lamb carcasses representative of the variability in Canadian slaughter plants (n = 155). Carcass measures were categorized into four sets according to when each measure could be obtained in the slaughter process. Each set of carcass measurements were subjected to stepwise regression and used to develop models for the estimation of lean meat and saleable yield percentages. Tissue depth measures (at the GR site) explained 44% of variation in lean meat yield in hot carcasses and 53% in cold carcasses. When additional parameters were included with cold GR, the regression model explained 61.9% of the variability in lean meat yield. Saleable yield predictions were less accurate (R2 < 0.40); the greatest degree of variability was predicted when the model included ribeye area (R2 = 0.39). The DEXA scans obtained on carcass sides were able to predict about 78% of variability in carcass lean meat yield and 91% of fat content. This information could be used by the lamb meat industry to establish new carcass classification systems based on more accurate lean meat yield values.

Evaluation of carcass composition of intact boars using linear measurements from performance testing, dissection, dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI)

The objective of this study was to investigate non-invasive imaging methods to update the used regression equation for stationary tested boars. A total of 94 boars were examined. 20 boars were dissected to provide the reference LMP. Performance data (PD) from right carcasses were available from all groups. The left carcasses were studied by MRI & DXA. Based on the reference LMP and the MRI & DXA data, regression equations for LMP were developed. The estimates for LMP based on MRI & DXA data were used to calculate new regression equations for entire male carcass halves based on linear PD. Further 33 PD sets served as independent sample, which was included in a Monte Carlo simulation for imputing the missing reference LMPs (n=74) and discussing the accuracy of the results. The LMP regression equation based on the combined MRI & DXA data is as accurate as the former regression equation, but needs only three instead of seven variables.

In vivo body composition in autochthonous and conventional pig breeding groups by dual-energy X-ray absorptiometry and magnetic resonance imaging under special consideration of Cerdo Ibérico

The improvement of carcass quality is one of the main breeding goals in pig production. To select appropriate breeding animals, it is of major concern to exactly and reliably analyze the body composition in vivo. Therefore, the objective of the study was to examine whether the combination of dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) offers the opportunity to reliably analyze quantitative and qualitative body composition characteristics of different pig breeding groups in vivo. In this study, a total of 77 pigs were studied by DXA and MRI at an average age of 154 days. The pigs originated from different autochthonous or conventional breeds or crossbreeds and were grouped into six breed types: Cerdo Ibérico (Ib); Duroc × Ib (Du_Ib); White Sow Lines (WSL, including German Landrace and German Large White); Hampshire/Pietrain (Pi_Ha, including Hampshire, Pietrain × Hampshire (PiHa) and Pietrain × PiHa); Pietrain/Duroc (Pi_Du, including Pietrain × Duroc (PiDu) and Pietrain × PiDu); crossbred WSL (PiDu_WSL, including Pietrain × WSL and PiDu × WSL). A whole-body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (FM; %FM), lean tissue (LM; %LM) and bone mineral, whereas a Siemens Magnetom Open with a large body coil was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the area of the loin (LA) and the above back fat area (FA) of both body sides. A GLM procedure using SAS 9.2 was used to analyze the data. As expected, the native breed Ib followed by Du_Ib crossbreeds showed the highest %FM (27.2%, 25.0%) combined with the smallest LA (46.2 cm2, 73.6 cm2), whereas Ib had the lowest BW at an average age of 154 days. Pigs with Pi_Ha origin presented the least %FM (12.4%) and largest LA (99.5 cm2). The WSL and PiDu_WSL showed an intermediate body composition. Therefore, it could be concluded that DXA and MRI and especially their combination are very suitable methods to reliably identify differences in body composition and carcass traits among different pig lines in vivo.