Prediction Of Metabolizable Energy Research Articles

Predicting metabolizable energy of soybean meal and rapeseed meal from chemical composition in broilers of different ages

This study determined metabolizable energy (ME) and developed ME prediction equations for broilers based on chemical composition of soybean meal (SBM) and rapeseed meal (RSM) using a 2 × 10 factorial arrangement of age (11 to 14 or 25 to 28 d of age) and 10 sources of each ingredient. Each treatment contained 6 replicates of 8 broilers. The ME values were determined by total collection of feces and urine. Principal components analysis (PCA) of the chemical composition clearly revealed distinct differences in SBM and RSM based on a principal components (PC) score plot. The nitrogen-corrected apparent metabolizable energy (AMEn) of SBM was higher in broilers from 25 to 28 than 11 to 14 d of age (P = 0.013). Interactions between broiler age and ingredient source affected apparent metabolizable energy (AME) of SBM and ME of RSM (P < 0.05). The ME of SBM in 11 to 14 and 25 to 28-day-old broilers were estimated by crude protein (CP) content (R2≥ 0.782; SEP ≤ 83 kcal/kg DM; P < 0.001). The AME and AMEn of RSM in 11 to 14-day-old broilers were estimated by ether extract (EE), ash and acid detergent fiber (ADF) (R2 = 0.897, SEP = 106 kcal/kg DM; P = 0.002), and by EE and ash (R2 = 0.885, SEP = 98 kcal/kg DM; P = 0.001), respectively. The AME and AMEn of RSM in 25 to 28-day-old broilers were estimated by ash and ADF (R2 = 0.925, SEP = 104 kcal/kg DM; P < 0.001) and by ash and neutral detergent fiber (NDF) (R2 = 0.921, SEP = 91 kcal/kg DM; P < 0.001), respectively. These results indicate that ME of these 2 plant protein ingredients are affected interactively by chemical composition and age of broilers. This study developed robust, age-specific prediction equations of ME for broilers based on chemical composition for SBM and RSM. Overall, ME values can be predicted from CP content for SBM, or EE, ash, ADF, and NDF for RSM.

543 Late-Breaking: Development of a Model to Predict Dietary Metabolizable Energy from Digestible Energy in Beef Cattle

Abstract 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. For this, 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 the 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. The developed equations were evaluated with other published equations. The no-intercept linear equation represented the relationship between DE and ME more appropriately than the equation with a 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). Two linear equations with the dry matter intake and content of several dietary nutrients were developed to predict MDR. 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 (&amp;gt; 0.95) and small root mean square error of prediction (RMSEP), less than 5% of the observed mean. Moreover, a significant portion of the RMSEP was due to random bias (&amp;gt; 93%), without mean or slope bias (P &amp;gt; 0.05). We concluded that dietary ME in beef cattle could be accurately estimated from dietary DE and its conversion factor, MDR, using the two equations developed in this study.

Evaluation of an equation for predicting metabolisable energy concentration in compound feeds for pigs

ABSTRACT It is useful to predict metabolisable energy (ME) concentration based on crude nutrients which can be determined on a laboratory scale to formulate compound feeds for pigs based on ME concentration and to control the declared concentration. In 2008 such an equation was derived premised on 290 balance experiments showing strong associations between ME predicted by digestible crude nutrients and by crude nutrients themselves. Since the suitability of a regression-based prediction equation might be strongly influenced by the number of observations, the current study aimed at 1) checking the suitability of the existing prediction equation by including more datasets and 2) deriving a revised prediction equation. The equations were evaluated by correlation and regression analyses using the energy content calculated on the basis of crude nutrients according to the previously used (MES) and the newly derived (MESnew) equations as well as the energy content calculated on the basis of digestible nutrients (MED). MED was correlated with MES (r s = 0.784; p < 0.001) and MESnew (r s = 0.802; p < 0.001). The root mean square error or the adjusted r2 was 0.332 MJ/kg DM or 0.830 for the regression of MES on MED, and 0.323 MJ/kg DM or 0.839 for the regression of MESnew on MED. Although the regressive evaluation for the prediction of ME revealed satisfying results, the remaining residual variation not explainable by the regression model should be considered. The minimum span of the prediction interval of the regression of MES or MESnew on MED covered a range of 0.65 and 0.64 MJ/kg DM, suggesting the variability of ME estimations to be expected when based on crude nutrients. The quality parameters for the newly derived equation were minimally better and the correlation coefficient between MED and both, MESnew and MES, was strong. Since there is also a non-negligible inaccuracy in the estimation of ME content using the newly derived equation and as the quality parameters were only slightly better, there is at this point no need to introduce the new equation. In future studies, alternative analytical methods for determining the concentration of ME in compound feeds should be considered to improve the accuracy of estimation equations.

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.

Developing Equations for Converting Digestible Energy to Metabolizable Energy for Korean Hanwoo Beef Cattle

Simple SummaryThe available energy in feedstuff represents the largest proportion of the total cost for intensive beef production. Therefore, the energy content of feeds must be known before diet formulation. The determination of digestible energy (DE) and metabolizable energy (ME) values by animal experiments is both time-consuming and costly. Predictive equations to estimate the ME from DE can be useful for feed ingredient evaluations and diet formulations. A range of regression equations were developed in the present study, taking into consideration the gender and body weights of the animals, as well as the feed nutrients, to predict the relationship between the DE and ME. An evaluation of these equations suggested predicting the ME value based on ME = 0.9215 × DE − 0.1434 (R2 = 0.999). The generation of these predictive equations represents a step towards updating the ME:DE default conversion factor value of 0.82 adopted from the National Research Council to meet the ME requirements of beef cattle in Korea. The new recommended predictive equation enables the adjustment of the nutrient requirements, thus enhancing animal productivity and maximising the economic return for beef farmers.This study was performed to update and generate prediction equations for converting digestible energy (DE) to metabolizable energy (ME) for Korean Hanwoo beef cattle, taking into consideration the gender (male and female) and body weights (BW above and below 350 kg) of the animals. The data consisted of 141 measurements from respiratory chambers with a wide range of diets and energy intake levels. A simple linear regression of the overall unadjusted data suggested a strong relationship between the DE and ME (Mcal/kg DM): ME = 0.8722 × DE + 0.0016 (coefficient of determination (R2) = 0.946, root mean square error (RMSE) = 0.107, p < 0.001 for intercept and slope). Mixed-model regression analyses to adjust for the effects of the experiment from which the data were obtained similarly showed a strong linear relationship between the DE and ME (Mcal/kg of DM): ME = 0.9215 × DE − 0.1434 (R2 = 0.999, RMSE = 0.004, p < 0.001 for the intercept and slope). The DE was strongly related to the ME for both genders: ME = 0.8621 × DE + 0.0808 (R2 = 0.9600, RMSE = 0.083, p < 0.001 for the intercept and slope) and ME = 0.7785 × DE + 0.1546 (R2 = 0.971, RMSE = 0.070, p < 0.001 for the intercept and slope) for male and female Hanwoo cattle, respectively. By BW, the simple linear regression similarly showed a strong relationship between the DE and ME for Hanwoo above and below 350 kg BW: ME = 0.9833 × DE − 0.2760 (R2 = 0.991, RMSE = 0.055, p < 0.001 for the intercept and slope) and ME = 0.72975 × DE + 0.38744 (R2 = 0.913, RMSE = 0.100, p < 0.001 for the intercept and slope), respectively. A multiple regression using the DE and dietary factors as independent variables did not improve the accuracy of the ME prediction (ME = 1.149 × DE − 0.045 × crude protein + 0.011 × neutral detergent fibre − 0.027 × acid detergent fibre + 0.683).

Development and Validation of Equations for Predicting the Metabolizable Energy Value of Double-Low Rapeseed Cake for Growing Pigs.

Simple SummaryDouble-low rapeseed cake could be a cost-effective feed resource for swine, providing amino acids and energy. Accurate evaluation of the energy values of different double-low rapeseed cakes is of great significance to economically and effectively formulate diets when double-low rapeseed cakes are used as an alternative ingredient in diets fed to pigs. Therefore, two experiments were conducted to develop and validate an equation to predict the metabolizable energy of double-low rapeseed cake for growing pigs based on chemical compositions. Results indicated that the best-fit prediction equation for metabolizable energy (MJ/kg) was 9.33 − 0.09 × neutral detergent fiber −0.25 × crude fiber + 0.59 × gross energy (R2 = 0.93). Increasing levels of double-low rapeseed cake linearly reduced apparent total tract digestibility of nutrients, but did not affect growing performance and caloric efficiency of metabolizable energy of growing pigs fed diets balanced for standardized ileal digestibility Lys/metabolizable energy ratio. The best-fit prediction equation in this experiment can be used to accurately estimate the metabolizable energy value of double-low rapeseed cake fed to growing pigs under practical conditions.The study was conducted to develop and validate an equation to predict the metabolizable energy (ME) of double-low rapeseed cakes (DLRSC) for growing pigs based on their chemical compositions. In Experiment 1, 66 growing pigs (initial body weight 36.6 ± 4.1 kg) were allotted randomly to a completely randomized design with 11 diets. The diets included a corn–soybean meal basal diet and 10 test diets containing 19.22% DLRSC supplemented at the expense of corn, soybean meal, and lysine. Neutral detergent fiber (NDF), crude fiber (CF), and gross energy (GE) were the best predictors to determine ME. The best-fit prediction equation of ME (MJ/kg) was ME = 9.33 − 0.09 × NDF − 0.25 × CF + 0.59 × GE (R2 = 0.93). In Experiment 2, a total of 144 growing pigs (initial body weight 29.7 ± 2.7 kg), with six pigs per pen and six pens per treatment, were assigned randomly to four treatments in a completely randomized block design for a 28-day feeding trial. A corn–soybean meal basal diet was prepared, and three additional diets were formulated by adding 7%, 14%, and 21% DLRSC to the basal diet at the expense of soybean meal. All diets were formulated to provide equal standardized ileal digestibility (SID) Lys/ME ratio and SID essential amino acids/SID Lys ratio. Increasing dietary levels of DLRSC had no effect on average daily feed intake, average daily gain, and feed-to-gain ratio. The caloric efficiency of ME (31.83, 32.44, 31.95, and 32.69 MJ/kg, respectively) was not changed by increasing the dietary concentration of DLRSC. Increasing dietary levels of DLRSC linearly reduced (p < 0.05) the concentrations of triiodothyronine and tetraiodothyronine in serum, as well as apparent total tract digestibility of DM, GE, crude protein, acid detergent fiber, and organic matter of the diet. In conclusion, the ME prediction equation obtained in Experiment 1 accurately estimates the ME value of DLRSC fed to growing pigs.

True nutrient and amino acid digestibility of dog foods made with human-grade ingredients using the precision-fed cecectomized rooster assay.

For a pet diet to be labeled as human-grade, every ingredient and the finished food must be stored, handled, processed, and transported according to the current good manufacturing practices for human edible foods. Human-grade dog foods are now available and increasing in popularity, but little research has been conducted to test the digestibility of these foods. For this reason, the objective of this experiment was to determine the true nutrient and amino acid (AA) digestibilities of dog foods formulated with human-grade ingredients using the precision-fed cecectomized rooster assay. Six commercial dog foods were tested, including the Beef & Russet Potato (BRP), Chicken & White Rice (CWR), Fish & Sweet Potato (FSP), Lamb & Brown Rice (LBR), Turkey & Whole Wheat Macaroni (TWM), and Venison & Squash (VSR) formulas provided by Just Food For Dogs LLC (Irvine, CA). Before analysis, all foods were lyophilized and ground. A precision-fed rooster assay using cecectomized roosters was conducted to determine the true nutrient digestibility and standardized AA digestibilities of the foods tested. Conventional roosters were used to determine the nitrogen-corrected true metabolizable energy (TMEn) of the foods. All animal procedures were approved by the University of Illinois Institutional Animal Care and Use Committee prior to experimentation. The substrates and rooster excreta were analyzed for macronutrient and AA composition. All data were analyzed using the Mixed Models procedure of SAS (version 9.4; SAS Institute, Cary, NC). In general, all foods tested were highly digestible. Dry matter digestibility was similar among CWR, LBR, and TWR foods, and greater (P < 0.0001) than that of FSP and VSR foods. Organic matter digestibility was highest (P = 0.0002) for CWR and lowest (P = 0.0002) for VSR. For the majority of indispensable AA, digestibilities were greater than 85%, with some being greater than 90%. TMEn was higher (P < 0.0001) for BRP than the other foods, which were similar to one another. Also, TMEn values were much higher than what would be estimated by using modified Atwater factors and often above the predictive equations for metabolizable energy (ME) recommended by the National Research Council or by using Atwater factors. Although statistical differences were observed among foods, they all performed well and the foods tested had very high AA digestibilities. Additionally, the TMEn data suggest that existing methods and equations for ME prediction underestimate the energy content of the foods tested.

EQUATIONS TO PREDICT THE METABOLIZABLE ENERGY OF MEAT AND BONE MEAL FOR GROWING PIGS

ABSTRACT The prediction of metabolizable energy (ME) of meat and bone meal (MBM) for pigs is an interesting tool, however, used models to predict these values must be validated in order to garantee higher precision. The aim of this study was to determine the chemical and energetic composition of different types of MBM for pigs and to adjust and validate models to better predict the ME based on the chemical composition. Thirty-two barrows, averaging an initial weight of 26.75 ± 1.45 kg, were individually allotted in a randomized block design with eight treatments and four replicates. The treatments consisted of seven types of MBM that replaced 20% of the basal diet. A stepwise procedure was the statistical procedure used to adjust the prediction equations and the ME was the dependent parameter. The validation of the adjusted models was performed using an independent databank of chemical and energetic composition of theBrazilian and international MBM. The metabolizable energy of different meat and bone meals ranged from 1645 to 2645 kcal kg-1. The equations that provide a good prediction of metabolizable energy of meat and bone meal for swine in Brazil are EM1 = -4233.58 + 0.4134GE + 72CP + 89.62ash - 159.06Ca; EM2 = 2087.49 + 0.3446GE + 31.82ash - 189.18Ca; EM3 = 2140.13 + 0.3845GE - 112.33Ca; EM4 = -346.58 + 0.656GE; EM5 = 3221.27 + 178.96fat - 76.55ash; and EM6 = 5356.45 - 84.75ash.

Metabolizable energy intake of client-owned adult cats.

A retrospective analysis of the metabolizable energy (ME) intake of privately owned pet cats from the authors' nutrition consultation practice (years 2007-2011) was carried out to test whether current recommendations are suitable for pet cats. Data of 80 adult cats (median age: 9.0 years, median deviation from ideal weight: +22.5%, majority neutered) at maintenance were available. Six percentage of the cats were healthy and the others were affected by various chronic diseases. A standardized questionnaire was used, cat owners weighed cat and food. For ration calculation, the software Diet Check Munich(™) was used (ME prediction according to National Research Council, 2006: Nutrient Requirements of Dogs and Cats. National Academy Press, Washington, DC). Data were analysed for the factors deviation from ideal weight, breed, age, gender, disease and type of feeding [prepared food (dry, wet) vs. home-made]. Over- or underweight were defined as ≥15% deviation from ideal body weight (BW) according to Kienzle and Moik (British Journal of Nutrition 2011, 106, Suppl 1: S113). Cat owner's estimation of ideal BW was higher than literature data from Kienzle and Moik (2011). Based on literature data, 26.3% of the pet cats were normal weight, 63.7% overweight and 10% underweight. The mean ME intake of all adult cats amounted to 0.40 ± 0.14 MJ/kg actual BW(0.67) (n = 80). When the data were analysed according to normal, over- and underweight, there was a significant effect with normal weight cats eating 0.46 MJ/kg BW(0.67) . Underweight cats ate even more (0.49 MJ/kg BW(0.67) ), whereas overweight cats ate considerably less (0.36 MJ/kg BW(0.67) ). The other factors had no influence on ME intake of adult cats.

Short Communication: An investigation of the use of near infrared reflectance spectroscopy to predict the energy value of meat and bone meal for swine

Olukosi, O. A., Paton, N. D., Van Kempen, T. and Adeola, O. 2011.Short Communication:An investigation of the use of near infrared reflectance spectroscopy to predict the energy value of meat and bone meal for swine. Can. J. Anim. Sci. 91: 405–409. The feasibility of using near infrared reflectance spectroscopy (NIRS) for predicting metabolizable energy of meat and bone meal (MBM) for swine was investigated. Thirty-three MBM samples were analyzed for chemical composition and their metabolizable energy content was determined in metabolism assays. Near infrared reflectance spectroscopy calibrations were developed for gross and metabolizable energy of the samples. Coefficients of determination for calibration and cross-validation were greater for gross energy compared with metabolizable energy. Poorer prediction of metabolizable energy by NIRS may be due to sources of variation unaccounted for by NIRS. It was concluded that NIRS is feasible for predicting gross energy but not metabolizable energy of meat and bone meal.

Predicting metabolisable energy in commercial rat diets: physiological fuel values may be misleading

Knowledge about metabolisable energy (ME) intake is crucial for various experimental settings in rodent studies. ME considers faecal and renal energy losses. In particular, faecal energy excretion can vary considerably between differentially composed diets. Thus determination of faecal energy losses, i.e. apparent energy digestibility, is the most important experimental approach to determine ME. Predictive equations for ME such as Atwater factors or an equation for pigs, which are frequently employed for rodent feed, consider an average energy digestibility for nutrients and average renal losses for protein. Both equations, however, were never validated for rat feed. We therefore determined experimentally the digestibility of energy (experimentally determined digestible energy - 5.2 kJ/g digestible protein) and nutrients of eleven natural and five purified rat diets and compared the present results with the predicted values. Compared with natural diets, digestibility of gross energy (GE) and nutrients was higher by about 20 % in the purified diets (P < 0.0001). Mean GE digestibility in natural diets amounted to 71.4 % (range 53.3-83.5 %; n 11). Atwater factors predicted ME with satisfactory accuracy in purified diets. In contrast, for natural diets, only the equation for pig feed gave acceptable estimates of ME. Choosing an inappropriate predictive equation for ME resulted in considerable error. For prediction of ME in mixed rat feed, we propose to use the equation for pig feed for natural diets and Atwater factors for purified diets. If the equation for pig feed cannot be applied we suggest using the lower modified Atwater factors instead of the 'original' Atwater factors to estimate the ME of a diet.

Prediction of metabolisable energy concentrations from nutrient digestibility and chemical composition in grass silages offered to sheep at maintenance

A total of 174 perennial ryegrass silages were evaluated in nine studies for chemical composition, nutrient digestibility and urinary energy output with sheep (four sheep/silage) offered the silage as a sole diet at maintenance energy feeding level. Silage metabolisable energy (ME) concentration was estimated from measured energy intake and outputs from faeces and urine and predicted methane energy. All silage data were expressed on an alcohol corrected toluene dry matter (DM) basis. The objectives were to use these silage data to develop prediction equations for ME concentration and ME/GE (gross energy) and then validate these equations using published grass silage data. There was a large range in ME concentration (7.7–13.6 MJ/kg DM), ME/GE (0.432–0.668) and digestible organic matter in DM (DOMD; 0.530–0.769). The ME concentration and ME/GE were positively related to GE and DE concentration, DOMD and digestibility of DM, organic matter (OM), GE, crude protein (CP) and neutral detergent fibre ( P < 0.05 or less). Prediction of ME concentration using digestible energy (DE) or GE digestibility plus residual GE concentration (GE—mean GE) produced a strong relationship ( R 2 = 0.98), while using DM or OM digestibility or DOMD as a sole predictor reduced R 2 values to approximately 0.73. The latter R 2 values were marginally increased when CP concentration was added as a secondary predictor, and substantially increased to over 0.90 when residual GE was added. Prediction of ME/GE using GE digestibility produced a higher R 2 value (0.97) than those (0.88–0.90) using DM or OM digestibility or DOMD. The R 2 values were marginally increased when adding CP or residual GE concentration as a secondary predictor to the latter relationships. These equations were validated using published grass silage data since 1977 ( n = 21) and the mean-square-prediction error. Prediction of ME using DE or GE digestibility with residual GE had the lowest mean prediction error (MPE), with the predicted ME close to observed ME, and the majority of error derived from random variation. Using DM or OM digestibility or DOMD as a sole predictor for ME concentration produced a relatively large MPE, while adding CP or residual GE generally reduced the MPE and errors derived from both bias (predicted−actual) and line (slope). Similar results also occurred for prediction of ME/GE. DE concentration is the most accurate predictor for ME concentration. Prediction of ME using OM digestibility or DOMD as a sole predictor can result in error, but the prediction accuracy can be improved by adding GE concentration as a secondary predictor.

Avaliação de equações de predição de exigências energéticas na alimentação de frangas de postura

O objetivo do presente estudo foi testar a validade de equações de predição de exigências energéticas para frangas de postura, as quais foram determinadas em estudos anteriores realizados na Faculdade de Ciências Agrárias e Veterinárias da UNESP- Jaboticabal. O delineamento experimental utilizado foi o inteiramente casualizado com três tratamentos e seis repetições compostas por 18 aves no período de 3 a 8 semanas, 15 aves de 9 a 12 semanas e 12 aves de 13 a 18 semanas de idade. Os tratamentos consistiram em comparar três diferentes formas de se alimentar as aves: alimentação à vontade, alimentação de acordo com as recomendações para a linhagem e alimentação de acordo com as equações de predição das exigências de energia metabolizável (EM). Os tratamentos foram avaliados por intermédio do desempenho das aves durante o período de crescimento e na fase de produção. No período de 3 a 8 semanas de idade, as aves alimentadas de acordo com as equações de predição de EM apresentaram menor consumo de ração e de energia, o que determinou menor peso corporal e uniformidade insatisfatória. Em função dos resultados da fase de 3 a 8 semanas de idade, foi adotada uma correção na equação de predição das exigências de EM para as fases seguintes, acrescentando-se uma porcentagem de 37% sobre as exigências de EM para mantença, valor relativo às atividades das aves. Às 18 semanas de idade, a ingestão de EM foi maior para o tratamento à vontade, sendo que as aves de todos os tratamentos apresentaram peso corporal superior ao proposto pelo manual e boa uniformidade. O experimento teve continuidade durante a fase de produção, sendo que os tratamentos aplicados na fase de crescimento não afetaram o desempenho produtivo das aves.

Prediction of metabolizable energy and protein requirements for maintenance, gain and fiber growth of Angora goats

A database was constructed for growing and mature Angora goats to estimate metabolizable energy (ME) and protein (MP) requirements for maintenance, whole BW gain (ADG), non-fiber tissue gain (TG) and fiber growth (clean fiber growth rate; CFGR). MP intake (MPI) was calculated from CP degradability properties and dietary proportions of feedstuffs. Variables, scaled by BW 0.75, consisted of mean BW (kg), ME intake (MEI; kJ per day), MPI (g per day), ADG (g per day), TG (g per day; i.e., ADG corrected for grease fiber) and CFGR (g per day). For ME, the final simple regression equation for mature goats was MEI=533 ( SE=18.8)+(43.2 ( SE=4.77)× ADG ) ( n=77; R 2=0.52). The final multiple regression equation for mature goats was MEI=473 ( SE=49.85)+(37.2 ( SE=6.97)× TG)+(157 ( SE=52.5)× CFGR) ( n=48; R 2=0.53). For MP, the final simple regression equation with a development data set for growing and mature goats was MPI=4.30 ( SE=0.286)+(0.318 ( SE=0.0471)× ADG) ( n=68; R 2=0.41), which resulted in unbiased prediction of MPI in an evaluation data set. The final multiple regression equation for growing and mature goats was MPI=3.35 ( SE=0.440)+(0.281 ( SE=0.0486)× TG)+(1.65 ( SE=0.394)× CFGR) ( n=83; R 2=0.46). In conclusion, ME requirements of mature Angora goats for maintenance, TG and CFGR were 473 kJ/kg BW 0.75, 37.2 and 157 kJ/g (113 kcal/kg BW 0.75, 8.89 and 37.5 kcal/g), respectively. MP requirements of growing and mature Angora goats for maintenance, TG and CFGR were 3.35 g/kg BW 0.75, 0.281 and 1.65 g/g, respectively.

Further Developments in the Prediction of Metabolizable Energy (ME) in Pet Food

Further Developments in the Prediction of Metabolizable Energy (ME) in Pet Food

Prediction of the metabolizable energy value of whole-crop wheat from laboratory-based measurements

AbstractThe accuracy with which several laboratory-based measurements predict the metabolizable energy (ME) value of whole-crop wheat (WCW) was determined. Twenty-six WCW forages differing in variety (cv. Slepjner, Hussar and Cadenza), maturity at harvest (milk, cheese and dough stages) and treatment applied (urea-treated, untreated or acid-based additive treated) were harvested in 2 years and conserved anaerobically in 200·1 barrels. The forages were then scanned using near infrared reflectance spectroscopy (NIRS) and analysed for chemical composition,in vitrorumen fluid-pepsin digestibility,in vitroneutral detergent-cellulase plus gamannase digestibility,in vitrofermentation gas production and in situ rumen degradability. ME was calculated using measured energy losses in faeces and urine and predicted energy losses as methane. The relationships between ME and the laboratory-based measurements were determined by regression. Gross energy was consistently the best predictor of ME (R2= 0.53 and 0.86 in years 1 and 2 respectively). However the autocorrelation involved, militates against the prediction of ME from gross energy. None of the chemical constituents or biological techniques gave a good, robust prediction of ME. However, an NIRS calibration developed using the WCW samples was highly correlated (R2 = 0.68) with ME. This work therefore suggests that traditional laboratory-based, food evaluation techniques are unsuitable for predicting the ME content of WCW but that NIRS holds promise.

The use of NIRS to predict the chemical composition and the energy value of compound feeds for cattle

By means of a scanning monochromator and partial least-squares analysis, near-IR reflectance spectroscopy (NIRS) calibrations were developed to predict the chemical composition and the energy value of two different sets of compound feeds for cattle. The first set contained 179 compounds of heterogeneous nature, for which the energy value was calculated from in vivo digestibility. The second set consisted of 163 commercial dairy compounds for which the energy value was predicted from enzymatic digestibility. Spectral and reference data were highly correlated for all parameters, except for ash. For the first set, the root mean square error of prediction (RMSEP) amounted to 0.5% (percentage units) for moisture, 1.4% on dry matter basis (DM) for crude protein, 1.4% on DM for crude fibre, 0.7% on DM for crude fat, 2.1% for in vivo digestibility, 0.37 MJ kg −1 DM for metabolizable energy (ME) and 0.27 MJ kg −1 DM for net energy lactation (NEL). Prediction of ME and NEL with regression equations based on enzymatic digestibility was somewhat more accurate, with an RMSEP of 0.30 MJ kg −1 DM and 0.22 MJ kg −1 DM, respectively. For the more homogeneous second set, the RMSEP was lower for all parameters: 0.3% for moisture, 1.1% on DM for crude protein, 0.9% on DM for crude fibre, 0.3% on DM for crude fat, 1.3% for enzymatic digestibility, 0.30 MJ kg −1 DM for ME and 0.18 MJ kg −1 DM for NEL. It was proved that calibrations derived from energy values predicted from enzymatic digestibility were hardly less accurate than those derived from in vivo data. Calibrations based on the currently used NIRS instrument with 19 filters gave similar results to those based on the full spectrum for moisture, crude protein and crude fat, were less accurate for crude fibre, and clearly failed for energy evaluation. It was concluded that NIRS can be used as a screening method for the control of the chemical composition and the energy value of compound feeds for cattle.

The digestibility and metabolisable energy content of grass silage and their prediction from laboratory measurements

The results of a study into the prediction of digestible organic matter content (DOMD) and energy values involving a total of 124 grass silages are reported. The silages were examined in vivo using 4 wether sheep per sample fed at maintenance. The most powerful single predictor of DOMD was the rumen fluid in vitro method. The rumen fluid method accounted for 74.1% of the variability in DOMD compared to 55.8% when using modified acid detergent fibre. The rumen fluid and cellulase methods were, in some cases, additive to one another in prediction power, suggesting that they did not measure identical feed characteristics. In addition, the inclusion of total ash and certain other feed fractions such as hemicellulose in multivariate relationships with the major predictors allowed further variability in DOMD to be explained. Equations were derived for prediction of the proportional gross energy (GE) losses in faeces and urine. The prediction of faecal energy losses was always poorer than for DOMD content and urinary losses were more closely related to silage digestibility than crude protein content. In all cases, the relationships between metabolisable energy (ME) and laboratory measurements primarily related to digestibility were much poorer than for DOMD. For example, the rumen fluid in vitro method accounted for only 24% of the variability in ME, although this was increased to 77.4% by the inclusion of GE in a bivariate relationship. The inclusion of GE as a prediction term always substantially improved the prediction of ME and whilst ether extract content appeared to influence GE content the most, GE was not closely related to the organic components measured. It is concluded that the inclusion of GE or terms related to GE is essential if silage ME content is to be predicted with an acceptable error. The implications of this in extension work are discussed.

Predicting Metabolizable Energy in the Diet of Ruffed Grouse

The percent metabolizable energy (ME) in 13 of 14 diets fed to captive ruffed grouse (Bonasa umbellus) in metabolism trials was highly related (P = 0.0001, R2 = 0.88) to the percent neutral detergent solubles (NDS) minus total phenol content of the diets. A regression equation based on these data provided good prediction of the ME of 8 diets fed to grouse in a 2nd set of metabolism trials. The ME of diets containing acorn meat was underestimated by the prediction equation; therefore, a multiple regression equation based on NDS minus phenols and percent acorn meat was developed to predict the ME of diets containing acorn meat. The effects of storage and drying treatments of forages on measured NDS, total phenols, and predicted ME levels were determined. Oven-drying fresh, leafy forages and oven-drying after frozen storage consistently resulted in lower levels of NDS and total phenols than freeze-drying fresh forages. Effects on predicted ME were small because changes in NDS and total phenol levels are offsetting to a large extent in the prediction equation. J. WILDL. MANAGE. 51(3):560-567 Ruffed grouse food habits are well documented (Brown 1946, Bump et al. 1947, Korschgen 1966, Phillips 1967, Woehr and Chambers 1975, Stafford and Dimmick 1979, Seehorn et al. 1981), but relationships between food habits and diet quality are largely unknown. Little information is available on the ME of grouse forages or diets. This is due in part to a lack of accurate and efficient methods for measuring the ME in the natural diet of ruffed grouse. Studying all forages individually in conventional metabolism trials is impractical because of the varied diet of grouse. In addition, 1-time measurements of ME from metabolism trials does not account for variation among seasons, years, or locations. Predicting the ME of forages of ruffed grouse from forage chemical composition is a promising alternative method because large numbers of forages could be studied much more efficiently and crop contents could be chemically analyzed, thereby eliminating the bias between hand-picked forages and forages selected by wild birds. The Van Soest method of forage chemical analysis (Goering and Van Soest 1970) has proven to be reliable for predicting forage digestion in both ruminant and monogastric mammals (Van Soest 1967, Mould and Robbins 1982, Servello et al. 1983, MacPherson et al. 1985) and may be useful for avian species also. However, high levels of phenolic compounds in forages may complicate prediction of forage digestion when using the Van Soest analysis. Phenols are extracted as part of the theoretically highly digestible neutral detergent soluble fraction; i.e., primarily soluble carbohydrate, protein, and fat. However, they have little or no nutritional value and also may interfere with digestion and absorption (McLeod 1974; Mould and Robbins 1981a,b, 1982). Also, storage and drying treatments can influence measured chemical constituents of forages, especially highly reactive phenols (Mould and Robbins 1981a). The objectives of the present study were: (1) to determine the relationship between forage chemical composition (as measured by the Van Soest method) and forage ME for ruffed grouse, (2) to develop a predictive equation for estimating ME based upon this relationship, (3) to determine the influence of phenolic compounds on prediction of forage ME, and (4) to determine the effects of a number of commonly used storage and drying treatments on NDS and phenol levels and therefore on prediction of forage ME. We thank W. B. Morehead, S. L. MacPherson, and K. B. Preli for assistance with metabolism trials and laboratory analyses. This research was supported in part by the Natl. Rifle Assoc. and the Dep. Fish. and Wildl. Sci., Virginia Polytechnic Inst. and State Univ. The senior author was supported by a John Lee Pratt Anim. Nutr. Fellowship.

Recent developments in predicting the nutritive value grass silage for ruminants

Nutritive value 1n the context of this paper refers to metabolisable energy (ME) content. Silage composition and hence ME content 1s determined essentially by the composition of the ensiled crop, pre-ensiling treatments such as wilting or additive use and the conditions within the silo. Consequently the ME content of silage 1s very variable and means of predicting it are required for routine purposes.Until 1985 the prediction of ME was based on a series of digestibility trials carried out on MAFF EHFs between 1967 and 1972. A relationship between DOMD content and modified add detergent fibre (MADF) content (Clancey and Wilson, 1966) was calculated and the assumption that ME: (MJ/kg DM)= DOMD (%) X 0.16 was made. This did not take account of the energy contribution from volatile components.