A dynamic model of metabolizable energy utilization in growing and mature cattle. II. Metabolizable energy utilization for gain

Component models were developed to predict the net efficiency of ME utilization for gain in cattle and to predict daily gain using recovered energy as the input. These models were integrated into a single model to predict daily gain from ME available for gain. One component model predicts the net efficiency of ME utilization for gain using constant partial net efficiencies of 0.2 and 0.75 for ME retention as protein and fat, respectively. This model predicts net efficiency of ME utilization for gain as a function of the ratio of the energy recovered in protein to the total energy recovered. The other component model predicts daily gain as a function of recovered energy and is represented by a system of ordinary differential equations that are numerically integrated on a daily basis. This model was developed by reformulating the equations in a published body composition model that uses daily gain to predict composition of gain since recovered energy is a function of gain and composition of gain. The equations in the two component models interact in that net efficiency is used to predict recovered energy from ME for gain, and in turn, recovered energy is used to predict gain in empty BW, which determines net efficiency through composition of gain. The numeric integration procedure provides an iterative solution for net efficiency. Simulated response of net efficiency for Hereford x Angus steers at 400 kg of empty BW decreased from 0.57 to 0.52 on diets with ME densities of 3.1 and 2.6 Mcal/kg of DM, and restricting the lower-quality diet to 75% of ad libitum intake resulted in a simulated net efficiency of 0.47. These responses in net efficiency were shown to be a result of composition of gain, with leaner gains resulting in lower net efficiencies.     

A dynamic model of metabolizable energy utilization in growing and mature cattle. III. Model evaluation

Component models of heat production identified in a proposed system of partitioning ME intake and a dynamic systems model that predicts gain in empty BW in cattle resulting from a known intake of ME were evaluated. Evaluations were done in four main areas: 1) net efficiency of ME utilization for gain, 2) relationship between recovered energy and ME intake, 3) predicting gain in empty BW from recovered energy, and 4) predicting gain in empty BW from ME intake. An analysis of published data showed that the net partial efficiencies of ME utilization for protein and fat gain were approximately 0.2 and 0.75, respectively, and that the net efficiency of ME utilization for gain could be estimated using these net partial efficiencies and the fraction of recovered energy that is contained in protein. Analyses of published sheep and cattle experimental data showed a significant linear relationship between recovered energy and ME intake, with no evidence for a nonlinear relationship. Growth and body composition of Hereford x Angus steers simulated from weaning to slaughter showed that over the finishing period, 20.8% of ME intake was recovered in gain. These results were similar to observed data and comparable to feedlot data of 26.5% for a shorter finishing period with a higher-quality diet. The component model to predict gain in empty BW from recovered energy was evaluated with growth and body composition data of five steer genotypes on two levels of nutrition. Linear regression of observed on predicted values for empty BW resulted in an intercept and slope that were not different (P < 0.05) from 0 and 1, respectively. Evaluations of the dynamic systems model to predict gain in empty BW using ME intake as the input showed close agreement between predicted and observed final empty BW for steers that were finished on high-energy diets, and the model accurately predicted growth patterns for Angus, Charolais, and Simmental reproducing females from 10 mo to 7 yr of age.     

Predicting metabolizable energy from digestible energy for growing and finishing beef cattle and relationships to the prediction of methane

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r² = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH₄, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r² = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.     

Digestible and metabolizable energy of crude glycerol for growing pigs

The apparent DE and ME values of crude glycerol for growing pigs were determined in 5 experiments using crude glycerol (86.95% glycerol) from a biodiesel production facility, which used soybean oil as the initial feedstock. Dietary treatments were 0, 5, or 10% glycerol addition to basal diets in Exp. 1; 0, 5, 10, or 20% glycerol addition to basal diets in Exp. 2; and 0 and 10% crude glycerol addition to the basal diets in Exp. 3, 4, and 5. Each diet was fed twice daily to pigs in individual metabolism crates. After a 10-d adjustment period, a 5-d balance trial was conducted. During the collection period, feces and urine were collected separately after each meal and stored at 0 degrees C until analyses. The GE of each dietary treatment and samples of urine and feces from each pig were determined by isoperibol bomb calorimetry. Digestible energy of the diet was calculated by subtracting fecal energy from the GE in the feed, whereas ME was calculated by subtracting the urinary energy from DE. The DE and ME values of crude glycerol were estimated as the slope of the linear relationship between either DE or ME intake from the experimental diet and feed intake. Among all experiments, the crude glycerol (86.95% glycerol) examined in this study was shown to have a DE of 3,344 +/- 8 kcal/kg and an ME of 3,207 +/- 10 kcal/kg, thereby providing a highly available energy source for growing pigs.     

Development of a model to predict dietary metabolizable energy from digestible energy in beef cattle

Understanding the utilization of feed energy is essential for precision feeding in beef cattle production. We aimed to assess whether predicting the metabolizable energy (ME) to digestible energy (DE) ratio (MDR), rather than a prediction of ME with DE, is feasible and to develop a model equation to predict MDR in beef cattle. We constructed a literature database based on published data. A meta-analysis was conducted with 306 means from 69 studies containing both dietary DE and ME concentrations measured by calorimetry to test whether exclusion of the y-intercept is adequate in the linear relationship between DE and ME. A random coefficient model with study as the random variable was used to develop equations to predict MDR in growing and finishing beef cattle. Routinely measured or calculated variables in the field (body weight, age, daily gain, intake, and dietary nutrient components) were chosen as explanatory variables. The developed equations were evaluated with other published equations. The no-intercept linear equation was found to represent the relationship between DE and ME more appropriately than the equation with a y-intercept. The y-intercept (−0.025 ± 0.0525) was not different from 0 (P = 0.638), and Akaike and Bayesian information criteria of the no-intercept model were smaller than those with the y-intercept. Within our growing and finishing cattle data, the animal’s physiological stage was not a significant variable affecting MDR after accounting for the study effect (P = 0.213). The mean (±SE) of MDR was 0.849 (±0.0063). The best equation for predicting MDR (n = 106 from 28 studies) was 0.9410 ( ± 0.02160) +0.0042 ( ± 0.00186) × DMI (kg) – 0.0017 ( ± 0.00024) × NDF(% DM) – 0.0022 ( ± 0.00084) × CP(% DM). We also presented a model with a positive coefficient for the ether extract (n = 80 from 22 studies). When using these equations, the observed ME was predicted with high precision (R² = 0.92). The model accuracy was also high, as shown by the high concordance correlation coefficient (>0.95) and small root mean square error of prediction (RMSEP), <5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (> 93%), without mean or slope bias (P > 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, predicted by the dry matter intake and concentration of several dietary nutrients, using the 2 equations developed in this study.     

Energy and protein utilization in growing cattle

Limited data are available to describe the different phases of dietary protein and energy utilization in growing cattle as compared with those in adult cattle or in growing nonruminants. The European data on this topic are summarized to indicate application in appropriate feeding standards. Net protein requirements are widely variable with breed and sex. They are lower in steers than in bulls and lower in early maturing than in late maturing breeds. They are clearly defined for growing and fattening bulls where they are influenced by breed, live weight and live weight gain. New systems have been proposed to express the protein allowances. They provide a great step towards a concept explaining N supply to ruminants. However, protein degradability in the rumen, efficiency of microbial protein synthesis, intestinal digestibility and metabolic efficiency of amino acid absorption in the intestine need to be described more accurately. Even if body energy retention measured by the slaughter technique is systematically lower than when measured by calorimetric balance, both techniques can correctly describe the effect of breed sex, weight, or daily gain on energy retained, in relative value, and its distribution between protein and fat deposition. But further research is needed to confirm the distribution of metabolizable energy between maintenance and growth and the efficiency of metabolizable energy utilization for growth. Thus, different authors have preferred to calculate the energy allowances, not by a factorial method, but by regression between energy intake and the corresponding weight and daily gain of animals measured during feeding trials.     

Metabolic utilization of energy and maintenance requirements in growing pigs: effects of sex and genotype.

An experiment was conducted in which the metabolic utilization of energy was measured in individually penned pigs from seven groups that differed in genotype and(or) sex and ranged in body weight between 20 and 107 kg. The animals were fed a diet containing, on a DM basis, 14.7 MJ ME and at least 21% CP. Heat production was measured in an open-circuit calorimeter, and energy, nitrogen, and fat balances were determined at regular intervals over the growing period; a total of 177 measurements were performed. Body composition of the animals was measured by serial slaughter, and these data were used for estimating the body composition of an animal at a given weight through allometric regression. A factorial analysis procedure was used to estimate the utilization of ME by regressing the ME intake on the observed protein and lipid deposition rates. The intercept of this equation is the maintenance energy requirement (MEm) and was represented either as a function of body weight with group-specific parameters (MEm = a(i) BWb) or as a function of the muscle and visceral mass with an additional additive group effect (MEm = aM muscle(b) + a(v) viscera(b) + G(i)). With BW as dependent variable, the exponent b was close to .60 and differed significantly from .75. The regression coefficient (a(i)) averaged 1.02 MJ ME/kg.60 but it was different for most groups, indicating that different groups of animals have different maintenance requirements. Fixing the exponent to .75 consistently underestimated the maintenance requirement. When the exponent b was not fixed to .75 but estimated, the partial efficiencies for protein and lipid deposition were .62 and .84, respectively. Body muscle and visceral mass could explain a large part of the variation in MEm. Viscera contributed three times more to MEm (per kilogram of mass raised to the .70 power) than did muscle. Even though the muscle mass exceeds to a large extent the visceral mass in animals, the contribution of muscle to MEm was lower than that of viscera for most groups.     

Digestible and metabolizable energy in corn grains from different origins for growing pigs

An experiment was conducted to determine the digestible energy (DE) and metabolizable energy (ME) concentrations in nine sources of corn grains fed to growing pigs and to compare the energy values among their countries of origin. A total of nine sources of corn grains including five sources of yellow corn from the United States (USY), two sources of yellow corn from South Africa (SAY), and two sources of white corn from South Africa (SAW) were used. Nine barrows with an initial body weight of 37.1 ± 8.6 kg were allotted to a 9 × 9 Latin square design with nine diets and nine periods. The DE concentration in SAY (3347 kcal/kg) was greater (P < 0.001) than in USY (3269 kcal/kg), but was less (P < 0.001) than in SAW (3436 kcal/kg) on an as‐fed basis. Similarly, the ME concentration in SAY (3291 kcal/kg) was greater (P < 0.001) than in USY (3209 kcal/kg), but was less (P < 0.001) than in SAW (3386 kcal/kg). In conclusion, the DE and ME concentrations in nine sources of corn grains are different among their countries of origin.     

Digestibility and metabolizable energy values of processed cassava chips for growing and finishing pigs

Determinations of digestibility of dry matter (DM), digestible energy (DE), and metabolizable energy (ME) in cassava chips with different levels of crude fiber (CF) were measured in growing pigs (20 kg) and finishing pigs (60 kg). The treatments were (1) cassava starch (0% CF), (2) peeled cassava chips (2.5% CF), (3) non-peeled washed cassava chips (3.9% CF), and (4) non-peeled and non-washed cassava chips (5.2% CF). In the growing pigs, peeled cassava chips, non-peeled washed cassava chips, and non-peeled and non-washed cassava chips had DM digestibility of 87.51%, 78.63%, and 73.89%, respectively. Their DE was 3.69, 3.49, and 3.32 Mcal/kg DM, respectively (DE of cassava starch is 3.90 Mcal/kg DM). ME was 3.54, 3.35, and 3.19 Mcal/kg DM, respectively (ME of cassava starch is 3.74 Mcal/kg DM). On the other hand, in the finishing pigs, the digestibility of DM was 89.13%, 80.63%, and 76.13%, respectively. Their DE was 3.72, 3.53, and 3.43 Mcal/kg DM, respectively (DE of cassava starch is 3.91 Mcal/kg DM). ME was 3.57, 3.38, and 3.29 Mcal/kg DM, respectively (ME of cassava starch is 3.75 Mcal/kg DM). These values increased with decreasing CF content, and the peeled cassava chips had the highest values (P < 0.01). These suggest that the digestibility values of DM, DE, and ME of cassava chips is inversely related to the CF content in cassava chips. It is recommended that cassava chips be peeled for better nutrition for growing and finishing pigs.     

A simple estimation of ideal profile of essential amino acids and metabolizable energy for growing Japanese quail

An experiment was conducted to determine apparent metabolizable energy (AME) and amino acid requirements of growing Japanese quail based on ideal protein concept using artificial neural network and desirability function (D‐ANN). Seven‐day‐old quail chicks were assigned to nine experimental diets based on central composite design (CCD) containing five levels of AME (2809–3091 kcal/kg) and CP (19–24.8% of diet). The ratio of lysine (Lys) to CP was set at 0.053 among all treatments, and remaining essential amino acids (EAA) were adjusted to Lys. The experimental data of CCD were fitted to D‐ANN model to compute the optimal values for independent variables. The optimal values of inputs including AME, CP, digestible Lys (dLys), methionine (dMet), total sulphur amino acids (dTSAA), threonine (dThr), tryptophan (dTrp), isoleucine (dIle), valine (dVal) and arginine (dArg) for maximizing gain and minimizing feed conversion ratio were estimated at 2865 kcal/kg, 25, 1.32, 0.55, 0.88, 0.84, 0.20, 0.75, 1.04 and 1.45% of diet, respectively, with D (desirability function) = 0.94. The corresponding optimal amounts of amino acids based on total amino acids were 1.42, 0.59, 0.95, 0.90, 0.22, 0.81, 1.12 and 1.56% of diet respectively. The ideal pattern of essential amino acids to Lys was as follows: dMet: dLys = 0.42, dTSAA: dLys = 0.67, dThr: dLys = 0.64, dTrp: dLys = 0.15, dIle: dLys = 0.57, dVal: dLys = 0.79 and dArg: dLys = 1.09. The results of this study showed that amino acid requirements of modern quails might be higher than those reported by NRC.     

Predicting corn digestible and metabolizable energy content from its chemical composition in growing pigs

Background： The nutrient composition of corn is variable. To prevent unforeseen reductions in growth performance, grading and analytical methods are used to minimize nutrient variability between calculated and analyzed values. This experiment was carried out to define the sources of variation in the energy content of corn and to develop a practical method to accurately estimate the digestible energy （DE） and metabolisable energy （ME） content of individual corn samples for growing pigs. Twenty samples were taken from each of five provinces in China （Jilin, Hebei, Shandong, Liaoning, and Henan） to obtain a range of quality. Results： The DE and ME contents of the 100 corn samples were measured in 3.5.3 ±1.92 kg growing pigs （six pigs per corn sample）. Sixty corn samples were used to build the prediction model; the remaining forty samples were used to test the suitability of these models. The chemical composition of each corn sample was determined, and the results were used to establish prediction equations for DE or ME content from chemical characteristics. The mean DE and ME content of the 100 samples were 4,053 and 3,923 kcal/kg （dry matter basis）, respectively. The physical characteristics were determined, as well, and the results indicated that the bulk weight and 1,000-kernel weight were not associated with energy content. The DE and ME values could be accurately predicted from chemical characteristics. The best fit equations were as follows： DE, kcal/kg of DM = 1062.68 ＋ （49.72 ×EE） ＋ （0.54 × GE） ＋ （9.1 ] x starch）, with R^2 = 0.62, residual standard deviation （RSD） = 48 kcal/kg, and P 〈 0.01; ME, kcal/kg of dry matter basis （DM） = 671.54 ＋ （0.89 ×DE） - （5.57 × NDF） - （191.39 ×ash）, with R^2 = 0.87, RSD = 18 kcal/kg, and P〈 0.01. Conclusion： This experiment confirms the large variation in the energy content of corn, describes the factors that influence this variation, and presents equations based on chemical measurements that may be     

Predicting corn digestible and metabolizable energy content from its chemical composition in growing pigs

Background

The nutrient composition of corn is variable. To prevent unforeseen reductions in growth performance, grading and analytical methods are used to minimize nutrient variability between calculated and analyzed values. This experiment was carried out to define the sources of variation in the energy content of corn and to develop a practical method to accurately estimate the digestible energy (DE) and metabolisable energy (ME) content of individual corn samples for growing pigs. Twenty samples were taken from each of five provinces in China (Jilin, Hebei, Shandong, Liaoning, and Henan) to obtain a range of quality.

Results

The DE and ME contents of the 100 corn samples were measured in 35.3 ± 1.92 kg growing pigs (six pigs per corn sample). Sixty corn samples were used to build the prediction model; the remaining forty samples were used to test the suitability of these models. The chemical composition of each corn sample was determined, and the results were used to establish prediction equations for DE or ME content from chemical characteristics. The mean DE and ME content of the 100 samples were 4,053 and 3,923 kcal/kg (dry matter basis), respectively. The physical characteristics were determined, as well, and the results indicated that the bulk weight and 1,000-kernel weight were not associated with energy content. The DE and ME values could be accurately predicted from chemical characteristics. The best fit equations were as follows: DE, kcal/kg of DM = 1062.68 + (49.72 × EE) + (0.54 × GE) + (9.11 × starch), with R² = 0.62, residual standard deviation (RSD) = 48 kcal/kg, and P < 0.01; ME, kcal/kg of dry matter basis (DM) = 671.54 + (0.89 × DE) – (5.57 × NDF) – (191.39 × ash), with R² = 0.87, RSD = 18 kcal/kg, and P < 0.01.

Conclusion

This experiment confirms the large variation in the energy content of corn, describes the factors that influence this variation, and presents equations based on chemical measurements that may be used to predict the DE and ME content of individual corn samples.     

A meta-analysis of energy and protein requirements for maintenance and growth of Nellore cattle

A meta-analysis was conducted to determine NE and net protein requirements of growing bulls, steers, and heifers of Nellore purebred and Nellore x Bos taurus crossbreds. A database of 16 comparative slaughter studies (n = 389 animals) was gathered to provide enough information to develop equations to predict the requirements of NE(m), NE(g), and net protein for maintenance (NP(m)) and growth (NP(g)). The data were analyzed using a random coefficients model, considering studies as random effects, and sex and castrate status (bulls, steers, and heifers; n = 262, 103, and 24, respectively) and breeds as fixed effects. There were no differences in NE(m) requirements among sex and castrate status (P = 0.73) and breeds (P = 0.82). The combined data indicated a NE(m) requirement of 75 kcal/ kg(0.75) of empty BW (EBW) with a partial efficiency of use of ME for NE(m) of 0.67. The NE(g) requirement was different (P = 0.009) among sex and castrate status and tended (P = 0.06) to be different among breeds. The equation for NE(g) requirement for bulls was 0.0514 x EBW(0.75) x EWG(1.070); for steers, it was 0.0700 x EBW(0.75) x EWG(1.070); and for heifers, it was 0.0771 x EBW(0.75) x EWG(1.070), where EWG = EBW gain (kg/d). The partial efficiency of use of ME for NE(g) was not different among sex and castrate status (P = 0.33) and breeds (P = 0.20) and averaged 0.44. There were no differences in NP(m) requirement among sex and castrate status (P = 0.59) and breeds (P = 0.92); the overall NP(m) requirement was 1.74 g of NP.kg(-0.75) of EBW.d(-1). The overall MP requirement for maintenance was 2.59 g of MP.kg(-0.75) EBW.d(-1). The NP(g) requirement (g/d) was not different among sex and castrate status (P = 0.59) and breeds (P = 0.14); the overall equation was EWG x [217 - (12.8 x RE/EWG)], where RE = retained energy (Mcal/d). The percentage of RE deposited as protein (%RE(p)) decreased exponentially as the content of RE in the gain (REc, Mcal/kg of EWG) increased. Because no study effect was observed, we pooled the data across studies and the overall equation to predict %RE(p) was 10.1 + 167e((-0.66 x REc)). Our results do not support the hypothesis that bulls have greater NE(m) requirements than steers and heifers. Likewise, no significant differences in the NP(m) requirements among bulls, steers, and heifers were detected. Nonetheless, the NE(g) requirement of steers was greater than bulls and less than heifers. Even though the %RE(p) was negatively correlated with the concentration of energy in the EWG, our findings indicated no differences in NP(g) requirement among bulls, steers, and heifers.     

The energy requirement of young female cattle. 2. Substance and energy metabolism

The second installment of information on studies over several years of the energy requirement of young female cattle comprises data of the feed intake, the digestibility and metabolizability of the rations used as well as the nitrogen and energy balances of the test animals in six experiments with varying rearing intensities, in which the N, C and energy balances as well as rumen physiologic values were measured monthly using the respiration test technique. Of four experiments, measured values are available over the whole rearing period from calf to calving. The results received from 680 interpretable test periods are-separated for the six experiments--arranged in aggregate form according to live weight range in order to characterize the development of nutrient and energy metabolization processes at various rearing intensities. The results form an essential basis for the derivation of the energy requirement of young female cattle according to factorial criteria.     

The energy metabolism of growing swine in a live weight range of 10-50 kg. 3. Energy maintenance requirement of growing swine]

Investigations into the energy maintenance requirement yielded the following results: For the energy maintenance requirement (EMR) in dependence on live weight (LW) using the relation EMR = aLWb from 13 experiments, an exponent of the live weight of 0.647 +/- 0.054 (0.57 to 0.73) was found out. Increasing the protein content in the feed from approximately 17 to approximately 45% in 6 experiments lowered the energy maintenance requirements about 14, 4, 6, 2, 6 and 12% respectively. The animals' development had no influence on the difference. The amount of the energy maintenance requirement varied greatly between the experiments. Exclusively in the experiments with barrows, a lowest value of 634 and a highest value of 931 kJ metabolizable energy per kg LW0.62.d was measured. On average of 19 comparisons the energy maintenance requirement derived from growth and maintenance periods by means of regression analysis was significant (alpha = 0.05), about 4% higher than the energy maintenance requirement measured on maintenance level directly.     

Energy metabolism of growing broiler chickens kept in groups in relation to the environmental temperature. 3. Moderated model description of the efficiency of the utilization of variable energy for body energy retention

An attempt is made on the basis of extensive studies on the energy metabolism of growing broilers to describe as a model the efficiency of the energetic utilization of the feed. The following parameters are components of the model: metabolizable energy, energy maintenance requirement, thermoneutral temperature, thermoregulatory heat production, heat production from the partial utilization of metabolizable energy for body energy retention, heat production (total), energy retention, utilization of metabolizable energy (total), live weight, environmental temperature. At environmental temperatures of 35, 30, 25, 20 and 15 degrees C resp. the model statement for the total utilization of metabolizable energy amounts to 32.4, 39.4, 39.6, 37.3 and 36.2% resp.     

A kinetic model of phosphorus metabolism in growing goats

The effect of increasing phosphorus (P) intake on P utilization was investigated in balance experiments using 12 Saanen goats, 4 to 5 mo of age and weighing 20 to 30 kg. The goats were given similar diets with various concentrations of P, and 32P was injected to trace the movement of P in the body. A P metabolism model with four pools was developed to compute P exchanges in the system. The results showed that P absorption, bone resorption, and excretion of urinary P and endogenous and fecal P all play a part in the homeostatic control of P. Endogenous fecal output was positively correlated to P intake (P < .01). Bone resorption of P was not influenced by intake of P, and P recycling from tissues to the blood pool was lesser for low P intake. Endogenous P loss occurred even in animals fed an inadequate P diet, resulting in a negative P balance. The extrapolated minimum endogenous loss in feces was .067 g of P/d. The minimum P intake for maintenance in Saanen goats was calculated to be .61 g of P/d or .055 g of P/(kg(.75) x d) at 25 kg BW. Model outputs indicate greater P flow from the blood pool to the gut and vice versa as P intake increased. Intake of P did not significantly affect P flow from bone and soft tissue to blood. The kinetic model and regressions could be used to estimate P requirement and the fate of P in goats and could also be extrapolated to both sheep and cattle.     

Digestible and metabolizable energy concentrations and amino acid digestibility of dried yeast and soybean meal for growing pigs

Energy values and amino acid (AA) digestibility of dried yeast (DY) and soybean meal (SBM) were determined in 2 experiments with growing pigs. Experiment 1 was conducted to determine the digestible energy (DE) and metabolizable energy (ME) in DY and SBM. Thirty barrows with a mean initial body weight (BW) of 20.6 kg (SD = 1.04) were assigned to 5 dietary treatments in a randomized complete block design with period and BW as blocking factors. A reference diet was prepared with corn, canola meal, and soybean oil as energy-contributing ingredients. Four additional diets were prepared by adding 5% and 10% DY or SBM at the expense of energy-contributing ingredients in the reference diet. The ratio of corn, canola meal, and soybean oil was kept consistent across the experimental diets. Each experimental period consisted of 5-d adaptation and 5-d quantitative collection of feces and urine. Test ingredient-associated DE or ME intake (kcal/d) was regressed against test ingredient intake [kg dry matter (DM)/d] to estimate the DE or ME in test ingredients as the slope of linear regression model. The DE in DY was estimated at 3,933 kcal/kg DM, which was not different from the estimated DE in SBM at 4,020 kcal/kg DM. Similarly, there was no difference between DY and SBM in the estimated ME (3,431 and 3,756 kcal/kg DM, respectively). Experiment 2 was conducted to determine the standardized ileal digestibility (SID) of AA in DY and SBM. Twenty-one barrows with a mean initial BW of 20.0 kg (SD = 1.31) were surgically fitted with T-cannulas at the distal ileum and assigned to 3 dietary treatments in a randomized complete block design with BW as a blocking factor. Two semi-purified diets containing DY or SBM as the sole nitrogen source and one nitrogen-free diet (NFD) were prepared. The NFD was used to estimate the basal ileal endogenous losses of CP and AA. Pigs were fed the 3 diets for 5 d as adaptation, followed by 2 d of feeding with ileal digesta collection. The SID of AA, except Gly and Pro, in DY was less (P < 0.05) than in SBM. The SID of indispensable AA in DY ranged from 64.1% for Thr to 85.2% for Arg, and those in SBM ranged from 83.9% for Thr to 91.8% for Arg. In conclusion, energy values of DY are not different from those of SBM, whereas AA in DY is less digestible than in SBM. The estimated DE and ME as well as the SID of AA in DY and SBM can be used in diet formulation for growing pigs using these ingredients.