Energy cost of physical activities in growing broilers

The time-energy budget method estimates the energy used for physical activity (PA) based on recorded daily patterns and indirect calorimetry. Nevertheless, information about individual energy cost by type of PA are not available and so this study estimated the energy cost of PA for growing broilers.An indirect calorimetry system for single birds was constructed to measure the variation in the rate of O₂ consumption (V˙O₂, L/min) and rate of CO₂ production (V˙CO₂, L/min) produced by these PAs.A total of five birds were used in a replicated trialwhere their body weight (BW) ranged from 1.5 to 2.5 kg to measure the increase in heat production (HP) above resting levels as a result of PA. The procedure in the chamber was divided into five steps: (1) initial baselining, (2) resting metabolic rate, (3) PA such as feeding, drinking and other standing activities, (4) removal of gas exchange produced in step 3 and (5) final baselining. The PA was recorded using a video camera fixed at the chamber's top (and outside).The area under V˙CO₂ and V˙O₂ curves was used to calculate the CO₂ production (vCO₂, L) and O₂ consumption (vO₂, L). Then, the HP (cal/kg^-0.75) was calculated according to the Brouwer equation. Two observers analysed the video records to estimate the time spent for each PA (seconds and frequency).To calculate the energetic coefficients, the HP was regressed with the function of time spent to perform each PA allowing to estimate the energy cost for eating, drinking and stand activities, which were 0.607, 0.352 and 0.938 cal/kg^-0.75/s, respectively.

Many lifetime growth trajectories for a single mammal

Despite their importance in shaping life history tactics and population dynamics, individual growth trajectories have only been rarely explored in the wild because their analysis requires multiple measurements of individuals throughout their lifetime and some knowledge of age, a key timer of body growth. The availability of long-term longitudinal studies of two wild boar populations subjected to contrasting environments (rich vs. poor) provided an opportunity to analyze individual growth trajectories. We quantified wild boar growth trajectories at both the population and the individual levels using standard growth models (i.e., Gompertz, logistic, and monomolecular models) that encompass the expected range of growth shapes in determinate growers. Wild boar is a rather altricial species, with a polygynous mating system and is strongly sexually dimorphic in size. According to current theories of life history evolution, we thus expect wild boar to display a sex-specific Gompertz type growth trajectory and lower sexual size dimorphism in the poorer environment. While wild boar displayed the expected Gompertz type trajectory in the rich site at the population level, we found some evidence for potential differences in growth shapes between populations and individuals. Asymptotic body mass, growth rate and timing of maximum growth rate differed as well, which indicates a high flexibility of growth in wild boar. We also found a cohort effect on asymptotic body mass, which suggests that environmental conditions early in life shape body mass at adulthood in this species. Our findings demonstrate that body growth trajectories in wild boar are highly diverse in relation to differences of environmental context, sex and year of birth. Whether the intermediate ranking of wild boar along the precocial-altricial continuum of development at birth may explain the ability of this species to exhibit this high diversity of growth patterns remains to be investigated.

Multivariate modeling for retained protein and lipid

Energy efficiencies and maintenance parameters have been traditionally estimated with a linear regression model that treated metabolizable energy intake as the dependent variable and protein and lipid depositions as the independent variables. Several studies have described the statistical issues associated with this approach, such as the reverse role of dependent and independent variables and a potential multicollinearity issue due to the high correlation between protein and lipid depositions. Biased regression techniques have been proposed to minimize the harmful effects of multicollinearity on the estimates of energy efficiencies. These approaches, however, only partially addressed the issues described for the linear regression approach. A first multivariate approach was developed by L. J. Koong in the 1970s, who estimated the energy parameters using a set of simultaneous equations. This multivariate approach has been considerably extended in the past two decades with the complete characterization of model’s biological interpretation under different feeding conditions, the simultaneous estimation of maintenance requirements, the extension of the model to a mixed-effects framework, and the implementation of a Bayesian framework for model fitting. The multivariate approach has been successfully applied to model energy deposition and partitioning by mice, pigs, salmon, and rainbow trout. However, multivariate models are, in general, harder to fit than linear regression models due to 1) larger number of parameters, 2) issues with parameter identifiability, and 3) overall lack of algorithm convergence. Therefore, with the recent availability of easy to use and efficient computer packages for model fitting, the use of a Bayesian framework seems to be an attractive approach for fitting multivariate models describing protein and lipid deposition.

Effect of dietary protein intake on energy utilization and feed efficiency of lactating sows

The objective of the current study was to quantify loss of energy in feces, urine, heat, and milk, to evaluate feed efficiency and to evaluate optimal ratio of dietary CP to energy for lactating sows fed increasing dietary CP. A total of 72 sows were included in the experiment from day 2 after parturition until weaning at day 28. Sows were allocated to 6 dietary treatments formulated to be isocaloric (9.8 MJ NE/kg) and increasing standardized ileal digestible (SID) CP (11.8, 12.8, 13.4, 14.0, 14.7, and 15.6% SID CP). Sows were weighed and back fat scanned within 2 d after farrowing, at days 18 ± 3 and 28 ± 3. Litters were standardized to 14 piglets within 2 d after farrowing and weighed at day 1 or 2 and at days 11, 18, and 28 (within ± 3 d). Feed intake (feed supply minus residue) was registered, and milk, urine, and fecal samples were collected at days 4, 11, and 18 (within ± 3 d). Sow milk yield was estimated from litter gain and litter size, and sow heat production was calculated factorially. On days 4 and 18 (±3 d), sows were enriched with D2O (deuterated water) to estimate body protein and fat pool size. Overall, sow BW loss, back fat loss, fat and protein mobilization, litter size, and piglet performance were not affected by diets, except for sows fed treatment 5, which had lower ADFI and lower milk production, and a tendency to lower piglet ADG compared with the remaining treatment groups (P < 0.01, P = 0.03, P =0.08, respectively). Relative to GE intake, the energy excreted in urine increased from 3.3% to 5.3% (P < 0.001), whereas energy lost as heat increased numerically from 54.5% to 59.0% with increasing dietary CP. The feed efficiency as evaluated by NE corrected for body mobilization peaked when sows were fed at their requirement (treatment 2; 12.8% SID CP; P = 0.01), whereas the feed efficiency was 1% lower for treatment 1, whereas it was 3% to 6% lower for treatments 3 through 6. In conclusion, energy loss in urine and likely also energy lost as heat increase if the dietary protein to energy ratio is unbalanced, and evaluating feed efficiency of lactating sows by correcting for body mobilization seems to be a promising approach to improve sow feeding in the future.

Partitioning the efficiency of utilization of amino acids in growing broilers: Multiple linear regression and multivariate approaches

Determining the efficiency of amino acid (AA) utilization in growing animals is crucial to estimate their requirement accurately. In broiler chickens, the composition of AA in feather is different from feather-free body and the proportion of feathers will change along broiler’s growth, which may impact the efficiency of utilization on AA consumed. Therefore, in order to establish a method that predicts the efficiency of utilization for feather-free body and feather, two approaches were evaluated: a multiple linear regression and a multivariate analysis. Additionally, a new factorial model was proposed to predict AA requirements in broiler chickens. Data from 13 trials that evaluated the requirements for lysine (Lys), sulphur AA (SAA), threonine (Thr), and valine (Val) in male broilers were used for the analyses. Both methods of analysis were consistent in showing that the efficiency of utilization in feather-free body and feather were different. Using multiple linear regression, the values of efficiency of utilization estimated in feather-free body were 0.68, 0.72, 0.81, 0.79 (mg of amino acid deposited / mg of amino acid consumed above maintenance) and in feather were 0.58, 0.77, 0.78, and 1.57 (mg/mg) for Lys, SAA, Thr, and Val, respectively. Applying the multivariate approach, the corresponding predicted values were 0.68, 0.67, 4.23, 0.27 (mg/mg) in feather-free body and 1.16, 0.86, 0.16, and 1.10 (mg/mg) in feather, respectively. According to the results, efficiency of utilization may be related, to some extent, on the concentration determined in each tissue. The uncertainty around the amount of AA consumed for gain directed to feather-free body or feather deposition could be a limitation for multivariate analyses. The results indicated that multiple linear regression predictions may be better estimates of utilization efficiency. However, more studies are needed to elucidate the effect of age on deposition and partitioning of dietary AA in different parts of the broiler.

Energetic efficiency and the first law: the California net energy system revisited

Models of energy utilization used in livestock production predict input:output relationships well, for all the wrong reasons. Predictive accuracy in such models is not due to fidelity to biochemistry and laws of thermodynamics, but because they were developed to predict accurately, often with little regard to biochemical consistency. Relatively static linear statistical models limit thermodynamically relevant descriptions of energy utilization, especially maintenance, in growing beef cattle and are inadequate research tools, in either ordinary least squares (OLS) or Bayesian frameworks. Metabolizable energy intake (MEI) at recovered energy (RE) = 0 (MEm) and efficiencies of ME utilization for maintenance (km) and gain (kg) were estimated for 3 independent data sets using OLS or Bayesian frameworks. Estimates of MEm differed (P < 0.05) between OLS and Bayesian estimates and were not unique, indicating model misspecification. Bayesian estimates of MEm were monotonic, positive, and nonlinear f(MEI); the range was from 6.74 to 14.8 Mcal/d. Estimates of km, the ratio of heat energy (HE) at MEI = 0 to MEm, for the 3 data sets averaged 0.590 for OLS solutions, or 0.616 for the first derivative (km, dHE/dMEI for RE = 0) of a first-order function. The first derivative (dHE/dMEI) of the OLS function was > 1.0 for MEI > 22.1 Mcal/d, counter to the laws of thermodynamics and indicated model misspecification. The Bayesian estimate of km (0.420) differed (P < 0.05) from the OLS estimate and was consistent with the efficiency of ATP synthesis. Efficiency of ME use for gain for RE > 0 (kg, OLS solutions) averaged 0.397, solutions were nonunique and single-variable OLS models were misspecified (P < 0.050) for 2 of the 3 data sets. The OLS estimate of kg differed (P < 0.05) from the estimate of kg (0.676) determined in a Bayesian framework; the latter was calculated as dRE/dMEI for RE > 0. For OLS estimates km > kg; for estimates determined in a Bayesian framework km < kg, the former is inconsistent, while the latter is consistent with the thermodynamic favorability of reactions underlying maintenance and gain. Our results show that the use of relatively fixed coefficients of maintenance in current feeding standards, mathematical descriptions of metabolic processes and concepts regarding efficiencies of energy utilization in those systems need modification to be consistent with animal biology and the laws of thermodynamics.

Prediction of portal and hepatic blood flow from intake level data in cattle

Interest is growing in developing integrated postabsorptive metabolism models for dairy cattle. An integral part of linking a multi-organ postabsorptive model is the prediction of nutrient fluxes between organs, and thus blood flow. The purpose of this paper was to use a multivariate meta-analysis approach to model portal blood flow (PORBF) and hepatic venous blood flow (HEPBF) simultaneously, with evaluation of hepatic arterial blood flow (ARTBF; ARTBF=HEPBF - PORBF) and PORBF/HEPBF (%) as calculated values. The database used to develop equations consisted of 296 individual animal observations (lactating and dry dairy cows and beef cattle) and 55 treatments from 17 studies, and a separate evaluation database consisted of 34 treatment means (lactating dairy cows and beef cattle) from 9 studies obtained from the literature. Both databases had information on dry matter intake (DMI), metabolizable energy intake (MEI), body weight, and a basic description of the diet including crude protein intake and forage proportion of the diet (FP; %). Blood flow (L/h or L/kg of BW^0.75/h) and either DMI or MEI (g or MJ/d or g or MJ/kg of BW^0.75/d) were examined with linear and quadratic fits. Equations were developed using cow within experiment and experiment as random effects, and blood flow location as a repeated effect. Upon evaluation with the evaluation database, equations based on DMI typically resulted in lower root mean square prediction errors, expressed as a % of the observed mean (rMSPE%) and higher concordance correlation coefficient (CCC) values than equations based on MEI. Quadratic equation terms were frequently nonsignificant, and the quadratic equations did not outperform their linear counterparts. The best performing blood flow equations were PORBF (L/h)=202 (±45.6) + 83.6 (±3.11) × DMI (kg/d) and HEPBF (L/h)=186 (±45.4) + 103.8 (±3.10) × DMI (kg/d), with rMSPE% values of 17.5 and 16.6 and CCC values of 0.93 and 0.94, respectively. The residuals (predicted - observed) for PORBF/HEPBF were significantly related to the forage % of the diet, and thus equations for PORBF and HEPBF based on forage and concentrate DMI were developed: PORBF (L/h)=210 (±51.0) + 82.9 (±6.43) × forage (kg of DM/d) + 82.9 (±6.04) × concentrate (kg of DM/d), and HEPBF (L/h)=184 (±50.6) + 92.6 (±6.28) × forage (kg of DM/d) + 114.2 (±5.88) × concentrate (kg of DM/d), where rMSPE% values were 17.5 and 17.6 and CCC values were 0.93 and 0.94, respectively. Division of DMI into forage and concentrate fractions improved the joint Bayesian information criterion value for PORBF and HEPBF (Bayesian information criterion=6,512 vs. 7,303), as well as slightly improved the rMSPE and CCC for ARTBF and PORBF/HEPBF. This was despite minimal changes in PORBF and HEPBF predictions. Developed equations predicted blood flow well and can easily be used within a postabsorptive model of nutrient metabolism. Results also suggest different sensitivity of PORBF and HEPBF to the composition of DMI, and accounting for this difference resulted in improved ARTBF predictions.

A Bayesian approach to analyze energy balance data from lactating dairy cows

The objective of the present investigation was to develop a Bayesian framework for updating and integrating covariate information into key parameters of metabolizable energy (ME) systems for dairy cows. The study addressed specifically the effects of genetic improvements and feed quality on key parameters in current ME systems. These are net and metabolizable energy for maintenance (NE(M) and ME(M), respectively), efficiency of utilization of ME for milk production (k(L)) and growth (k(G)), and efficiency of utilization of body stores for milk production (k(T)). Data were collated from 38 studies, yielding 701 individual cow observations on milk energy, ME intake, and tissue gain and loss. A function based on a linear relationship between milk energy and ME intake and correcting for tissue energy loss or gain served as the basis of a full Bayesian hierarchical model. The within-study variability was modeled by a Student t-distribution and the between-study variability in the structural parameters was modeled by a multivariate normal distribution. A meaningful relationship between genetic improvements in milk production and the key parameters could not be established. The parameter k(L) was linearly related to feed metabolizability, and the slope predicted a 0.010 (-0.0004; 0.0210) change per 0.1-unit change in metabolizability. The effect of metabolizability on k(L) was smaller than assumed in present feed evaluation systems and its significance was dependent on collection of studies included in the analysis. Three sets of population estimates (with 95% credible interval in parentheses) were generated, reflecting different degrees of prior belief: (1) Noninformative priors yielded 0.28 (0.23; 0.33) MJ/(kg(0.75)d), 0.55 (0.51; 0.58), 0.86 (0.81; 0.93) and 0.66 (0.58; 0.75), for NE(M), k(L), k(G), and k(T), respectively; (2) Introducing an informative prior that was derived from a fasting metabolism study served to combine the most recent information on energy metabolism in modern dairy cows. The new estimates of NE(M), k(L), k(G) and k(T) were 0.34 (0.28; 0.39) MJ/(kg(0.75)d), 0.58 (0.54; 0.62), 0.89 (0.85; 0.95), and 0.69 (0.60; 0.79), respectively; (3) finally, all informative priors were used that were established from literature, yielding estimates for NE(M), k(L), k(G), and k(T) of 0.29 (0.11; 0.46) MJ/(kg(0.75)d), 0.60 (0.54; 0.70), 0.70 (0.50; 0.88), and 0.80 (0.67; 0.97), respectively. Bayesian methods are especially applicable in meta-analytical studies as information can enter at various stages in the hierarchical model.