The objective of this study is to provide approaches to determine the mature weight of intact male hair sheep using body composition data. To estimate empty body weight (EBW) at maturity, we used information from eight independent studies comprising a total of 250 intact males in growing and finishing phases. The quantitative data used for each animal were body weight (BW), EBW, water-free EBW protein content, water-free EBW fat content, water-free EBW ash content and EBW water content. The water, protein, fat and ash contents in the EBW and water-free EBW were predicted by non-linear regressions. The best model was chosen considering the achievement of convergence and capacity for biological explanation. The standard deviation of the asymptotic EBW was computed using a simulation method based on a Monte Carlo approach. Among the non-linear evaluations, only the allometric function converged with the parameters within the expected biological limits. To estimate mature EBW, only protein and fat were suitable to predict estimates with the capacity to biological explanation. The water and protein contents in the EBW were closely associated. Animal maturity can be estimated mathematically using body composition. Maturity was reached at an EBW of 47.3 and 57.5 kg when protein and fat were used as a predictor, respectively. We conclude that protein is a good predictor of maturity for intact male hair sheep.

The main objective of the present study was to examine the effects of dehydroepiandrosterone (DHEA) on the digestive efficiency of dietary protein and fat. Second, we analysed the specific changes in muscle composition induced by the hormone. DHEA was given in the diet (0·5 %, w/w) to 75-week-old, high-fat-fed Sprague–Dawley rats (n 11) for 13 weeks; age- and weight-matched rats fed on the same diet without DHEA supplementation were used as controls (n 10). To determine dietary protein and fat apparent digestibility coefficients, 1-week 24 h faecal depositions were collected. In parallel, urine N was assessed. These assays were performed twice, in the short term (2-week treatment) and in the long term (13-week treatment). Body and gastrocnemius muscle compositions were also analysed. The present results show that DHEA decreased energy intake, body weight, body fat, adipocyte size and number (P < 0·001). The feed efficiency ratio indicates that DHEA-treated rats were less efficient in transforming nutrients fed into their own biomass. Also, a short-term reduction in protein digestibility (P < 0·05) and in body-protein degradation (P < 0·01) was found in DHEA-treated rats, resulting in an increased content of body protein (P < 0·05). Gastrocnemius muscles were smaller, as a result of fat (P < 0·05) but not protein reduction. In conclusion, we confirm the slimming effect of DHEA and, for the first time, we demonstrate that DHEA has an effect at the digestive level. The anti-obesity properties of DHEA could be related to a reduction in protein digestibility in the short term and a protective effect on body protein with a selective mass loss from body fat.

The lactation cycle of the dairy cow induces large changes in body fat and protein pools, which can be monitored through loin backfat (BF) and longissimus dorsi (LD) measurements. Data from two experiments (exp) using Holstein-Friesian dairy cows (no. = 40 and 32 respectively) were used to study the association of body weight (BW), BF and LD depth with body condition score (BCS) for the last 6 weeks of the dry period (DP) and the first 8 weeks of lactation. Loin and tail BCS were manually assessed (0 to 5 scale) and BF and LD depth were measured by ultrasound at the fifth lumbar process. The BCS data ranged from 1.3 to 3.0 units in the DP, and from about 1.1 to 3.1 units during early lactation in both experiments. Data were analysed by two models: BW, LD or BF = exp + period (DP or lactation) + BCS + interactions + cow + error (model 1); and BCS = exp + period + LD + BF + LD2 + BF2 + exp × LD + exp × BF + exp × LD2 + exp BF2 + period × LD + period × BF + period × LD2 + period × BF2 BF2 + cow + error (model 2).

A first-order autoregressive (AR(1)) covariance structure was employed for the error terms to account for the correlation among repeated measures within cow. Regressions of BW and LD on BCS (model 1) found pre- to post-calving differences (P < 0.001) in intercept for BW and LD, and slope coefficients of 35 (DP) and 21 (lactation) kg BW, and 5.8 mm LD per BCS unit. Regression of BF on BCS (model 1) showed an exp × period interaction (P < 0.001), with 0.4 mm BF (exp 1; P < 0.05) and 2.0 mm BF (exp 2; P < 0.001) per BCS unit. Regression of BCS on LD and BF (model 2) showed intercepts not equal to 0 (P < 0.06), and differences (P < 0.001) between DP and lactation; BCS increased (P < 0.001) by 0.027 units per mm BF and 0.05 units per mm LD, but LD had a quadratic term −0.0004 (P = 0.02). It is concluded that at BCS lower than 3, LD contributes to BCS following a quadratic function, whereas BF causes BCS to increase linearly. Each unit of BCS equated to about 35 and 20 kg BW for DP and lactation periods respectively, to 5.8 mm LD, and to between 0.4 and 2.0 mm BF.

Body composition in the pig, and its variation, is mostly referred to in terms of body protein and lipid content of the whole body. This study was made to check for animal-intrinsic variation in the partitioning of body protein into protein pools and of body lipid into lipid depots. Results from serial slaughter trials on 316 Danish Landrace and 76 Danish Yorkshire pigs were used to estimate additive genetic and litter-associated variance components for several traits. These traits were total body protein and lipid mass (TOTPROT and TOTLIPD), the proportions of total body protein that are present in the muscles (PROTMUS) or in the (sub-)cutaneous tissue plus bones (connective tissue protein, PROTCON), and the proportions of total body lipid that are present in the (sub-)cutaneous tissue (LIPDSUB), in the muscles (inter- and intramuscular fat, LIPDMUS), or in the bones (LIPDBON). TOTPROT and TOTLIPD were adjusted by regression for body weight; PROTMUS and PROTCON were adjusted for PROTCON, and LIPDSUB, LIPDMUS and LIPDBON were adjusted for TOTLIPD. The pooled estimates (± s.e.) of the degree of genetic determination (the sum of the additive genetic and litter-associated variance components, which approximates the repeatability) of these traits were 0·48 ± 0·19 for TOTPROT, 0·56 ± 0·20 for TOTLIPD, 0·56 ± 0·12 for PROTMUS, 0·57 ± 0·15 for PROTCON, 0·32 ± 0·10 for LIPDMUS, 0·33 ± 0·12 for LIPDSUB, and 0·22 ± 0·10 for LIPDBON. It is concluded that there is animal-intrinsic variation in partitioning of body protein and lipid.

As water is the major component of the pig body its accurate prediction is of importance in pig growth models. It has become conventional to predict the weight of water, WA kg, from the weight of protein, P kg. The purpose of this paper is to find how this can be done across pig genotypes of different mature size. The widely used equation to relate WA to P is of the form: WA = a.Pb. This equation is examined theoretically. It is concluded that the form of the equation is reasonable and, that while the value of the exponent b is likely to be constant across genotypes, the value of the scalar a is not. It is proposed that the value of the scalar a is best estimated as a = WAPRm Pm1·b where WAPRm is the water: protein ratio in the body at maturity and Pm is the weight of protein in the body at maturity. The value of the parameter WAPRm is assumed to be constant across genotypes with a value in the range of 3·04 to 3·20, depending on the methods used for measuring body composition. The general value of b = 0·855, taken from published work, is confirmed. A consequence of the argument quantified in the paper is that the value of a is predicted to vary from a = 4·69 for a pig with Pm = 20 kg to a = 5·36 for a pig with Pm = 50 kg. The general equation is expected to give more accurate predictions of the weight of water and, hence, of body weight, in models intended to predict pig growth, food intake, body composition and efficiency.