National Research Council Equations Research Articles

Effects of neutral detergent fiber digestibility estimation method on calculated energy concentration of canola meals from 12 Canadian processing plants.

Our aim was to determine whether the method used to estimate truly digestible neutral detergent fiber (tdNDF) affects calculated concentrations of total digestible nutrients (TDN1x) and net energy of lactation (NEL3x) of canola meal (CM). Samples were collected from 12 CM processing plants in Canada over 4 yr (2011 to 2014, n = 47) and analyzed for dry matter (DM), crude protein (CP), ether extract (EE), ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), lignin (ADL), and neutral detergent insoluble CP (NDICP). Ruminal in situ incubation of CM samples was performed at 0, 24, 48, 96, and 288 h to determine NDF fractions (A, B, and C), effective ruminal NDF digestibility (ERNDFD), and indigestible NDF (iNDF) of CM. Three tdNDF-estimation methods were evaluated: 1) National Research Council (NRC) = 0.75 × (NDF - NDICP - ADL) × {1- [ADL/ (NDF - NDICP)]0.667}; 2) iNDF = 0.75 × (NDF - NDICP - NDF remaining after 288 h in situ); and 3) ERNDFD estimated from in situ NDF digestion kinetics. Resulting tdNDF values were used for calculation of TDN1x and NEL3x according to NRC (2001) equations. Data were analyzed with MIXED procedure of SAS 9.4 to determine the effect of processing plant on chemical composition, NDF degradation kinetics and NEL3x of CM. Effect of tdNDF estimation method on calculated TDN1x and NEL3x of CM was also evaluated. Model for analysis of processing plant included the fixed effect of plant and the random effect of year (plant) as replication, while analysis of tdNDF methods included the fixed effect of tdNDF estimation method and the random effects of processing plant and of year(plant) as replication. There was an effect of processing plant on DM (P = 0.03), CP (P < 0.01), EE (P < 0.01), and NDF (P < 0.01) of CM. Processing plant also had an effect on NDF fractions A (P < 0.01) and B (P = 0.02) but did not affect fraction C and ERNDFD. The tdNDF estimation method had an effect on tdNDF (P < 0.01), TDN1x (P < 0.01), and NEL3x (P < 0.01) of CM, yielding average NEL3x values of 1.72, 1.87, and 2.07 Mcal/kg for NRC, iNDF, and ERNDFD, respectively. Our results indicate that calculated energy concentration of CM according to NRC (2001) equations varies depending on the method used for estimation of tdNDF. Further research will be needed to determine the most accurate estimation method.

Energy Requirements for Growth in the Norfolk Terrier.

Simple SummaryObesity and related diseases are common problems for dogs and inappropriate feeding during development is a contributor to life-long weight issues. Judging the right amount of food to give a growing puppy is challenging and providing a simple recommendation to owners is essential. However, differences in dog size, activity, and many other factors such as neutering can all have a role in impacting the actual energy requirements for growth. Yet, the current feeding guideline for growth (NRC 2006) does not accommodate these factors. Therefore, this study investigated how much a small breed (Norfolk Terrier puppies) requires to maintain growth and a healthy body condition score (BCS) through their first year of life. We found that they required significantly less than suggested by the NRC. Changes in the assessment of appropriate feeding during development are required and this study supports the need to revise the NRC (2006) equation for small breed dogs.An appropriate energy intake for healthy growth can reduce the risk of obesity and co-morbidities, such as orthopaedic diseases. The 2006 National Research Council (NRC) universal equation calculates the energy requirement of growing dogs based on predicted adult body weight, but evidence suggests a revision may be required. This study investigates the energy requirements of seventeen Norfolk terrier puppies over their first year (10 to 52 weeks). Puppies were individually fed complete and balanced diets in amounts to maintain an optimal body condition score (BCS), recording intake daily and body weight and BCS weekly. To monitor health a veterinary examination, haematology and plasma biochemistry and serum measures of bone turnover were undertaken every 12 weeks. Skeletal development was assessed using dual-energy X-ray absorptiometry (26 and 52 weeks). Puppies were clinically healthy with normal skeletal development and healthy growth throughout. The energy intake to achieve this was significantly lower than that predicted by the NRC (2006) equation at all time points, with largest mean difference of 285 kJ/kg0.75 per day at 10 weeks. If fed according to the NRC 2006 equation, dogs would have been in positive energy balance, possibly leading to obesity. These data support a revision to the NRC (2006) equation.

Symposium review: Re-evaluation of National Research Council energy estimates in calf starters

Provision of nutrients in appropriate amounts to meet nutrient requirements for growth, production, and reproduction is the basis for modern animal nutrition. Ration formulation systems predict nutrient requirements based on numerous inputs and then predict nutrient supply based on predicted intake and nutrient content of feeds. Energy systems are used to predict energy supply based on gross caloric content of feeds followed by adjustments for digestion and metabolism of ingested energy. Many models of energy supply use static coefficients of digestibility based on nutrient composition of feed. Other models partition digestion dynamically between ruminal and postruminal digestion but use static estimates of intestinal digestibility to predict energy supplied to the animal. In young calves, both ruminal fermentation and intestinal digestion are underdeveloped; therefore, existing models of energy supply might overestimate the energy available before complete gastrointestinal maturation. In a series of experiments, we reported that total-tract digestion of nutrients changes with advancing age and nutrient intake. Total-tract digestion was measured in calves from 3 to 16 wk of age when fed different amounts and types of milk replacers. Calves were also fed different types of calf starter for ad libitum consumption. Total-tract digestibility of protein, fat, neutral detergent fiber, and nonfiber carbohydrate (NFC) was used to calculate the metabolizable energy (ME) in starter. We used nonlinear regression to estimate the contribution of protein and fat from starter and milk replacer before weaning. Early in life, calculated ME of starter was low and increased with increasing intake of NFC. Cumulative intake of NFC was more highly correlated with changing ME values than other indices, including age, intake of milk replacer, or intake of other nutrients in starter. When calves consumed at least of 15 kg of NFC, ME calculated from digestibility measurements was similar to the ME calculated using National Research Council equations and indicated maturation of gastrointestinal digestion. Our data suggest that intake of NFC is critical to gastrointestinal maturation and the calf's ability to extract energy from calf starter.

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirements series is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition.

Energy requirements for growth in the Yorkshire terrier.

The 2006 National Research Council (NRC) equation calculating puppy energy requirements does not account for reported breed differences in growth pattern. Energy requirements of toy breed puppies are unknown and it is unclear whether feeding guidelines should differ between breeds. Energy requirements of Yorkshire terrier (YT) puppies were observed over their first year of life and compared with those predicted by the NRC and those previously observed in large (Labrador retriever) and medium (miniature Schnauzer; MS) breed puppies. Twenty-two puppies (from eight litters) were offered complete and balanced diets to maintain ideal body condition score (BCS). Energy intake, body weight and BCS were recorded from 10 to 52 weeks of age. Every 12 weeks, health was monitored by veterinary examination, routine haematology and plasma biochemistry. Puppies remained clinically healthy with normal skeletal development throughout. After analysis by linear mixed models it was observed that the NRC equation overestimates YT energy requirements between 10 and 20 weeks of age by up to 324·3 (95% CI 390·4, 258·2) kJ/kg0·75. Energy intake was lower (P<0·05) in YT than Labradors until 29 weeks by up to 376·6 (95% CI 477·4, 275·3) kJ/kg0·75 and lower than MS between 16 and 25 weeks by up to 216·3 (95% CI 313·0, 119·7) kJ/kg0·75 (P<0·05). Data indicate differences in toy, medium and large breed energy requirements for growth. The NRC equation for puppy energy requirements overestimated the requirements of this YT population, suggesting the need for breed-specific feeding guides for growth to avoid overfeeding.

Short communication: Prediction of intake in dairy cows under tropical conditions

A meta-analysis was conducted to develop a model for predicting dry matter intake (DMI) in dairy cows under the tropical conditions of Brazil and to assess its adequacy compared with 5 currently available DMI prediction models: Agricultural and Food Research Council (AFRC); National Research Council (NRC); Cornell Net Carbohydrate and Protein System (CNCPS; version 6); and 2 other Brazilian models. The data set was created using 457 observations (n=1,655 cows) from 100 studies, and it was randomly divided into 2 subsets for statistical analysis. The first subset was used to develop a DMI prediction equation (60 studies; 309 treatment means) and the second subset was used to assess the adequacy of DMI predictive models (40 studies; 148 treatment means). The DMI prediction model proposed in the current study was developed using a nonlinear mixed model analysis after reparameterizing the NRC equation but including study as a random effect in the model. Body weight (mean=540±57.6kg), 4% fat-corrected milk (mean=21.3±7.7kg/d), and days in milk (mean=110±62d) were used as independent variables in the model. The adequacy of the DMI prediction models was evaluated based on coefficient of determination, mean square prediction error (MSPE), root MSPE (RMSPE), and concordance correlation coefficient (CCC). The observed DMI obtained from the data set used to evaluate the prediction models averaged 17.6±3.2kg/d. The following model was proposed: DMI (kg/d)=[0.4762 (±0.0358) × 4% fat-corrected milk + 0.07219 (±0.00605) × body weight0.75] × (1 – e−0.03202 (±0.00615) × [days in milk + 24.9576 (±5.909)]). This model explained 93.0% of the variation in DMI, predicting it with the lowest mean bias (0.11kg/d) and RMSPE (4.9% of the observed DMI) and the highest precision [correlation coefficient estimate (ρ)=0.97] and accuracy [bias correction factor (Cb)=0.99]. The NRC model prediction equation explained 92.0% of the variation in DMI and had the second lowest mean bias (0.42kg/d) and RMSPE (5.8% of the observed DMI), as well as the second highest precision (ρ=0.94) and accuracy (Cb=0.98). The CNCPS and AFRC DMI prediction models explained 93.0 and 85.0% of the variation in DMI but underpredicted DMI by 1.8 and 1.4kg/d, respectively. These 2 models (CNCPS and AFRC) resulted, respectively, in RMSPE of 11.3 and 10.7% of the observed DMI, with moderate to high precision (ρ=0.81 and 0.82) and accuracy (Cb=0.84 and 0.89). The remaining 2 models resulted in the poorest results, underpredicting DMI by 2.3 and 1.9kg/d, with RMSPE of 22.8 and 14.9% of the observed DMI and moderate to low precision (ρ=0.49 and 0.76) and accuracy (Cb=0.81 and 0.86). The new model derived from the current meta-analytical approach provided the best accuracy and precision for predicting DMI in lactating dairy cows under Brazilian conditions.

Using Gross Energy Improves Metabolizable Energy Predictive Equations for Pet Foods Whereas Undigested Protein and Fiber Content Predict Stool Quality

Because animal studies are labor intensive, predictive equations are used extensively for calculating metabolizable energy (ME) concentrations of dog and cat pet foods. The objective of this retrospective review of digestibility studies, which were conducted over a 7-year period and based upon Association of American Feed Control Officials (AAFCO) feeding protocols, was to compare the accuracy and precision of equations developed from these animal feeding studies to commonly used predictive equations. Feeding studies in dogs and cats (331 and 227 studies, respectively) showed that equations using modified Atwater factors accurately predict ME concentrations in dog and cat pet foods (r2 = 0.97 and 0.98, respectively). The National Research Council (NRC) equations also accurately predicted ME concentrations in pet foods (r2 = 0.97 for dog and cat foods). For dogs, these equations resulted in an average estimate of ME within 0.16% and 2.24% of the actual ME measured (equations using modified Atwater factors and NRC equations, respectively); for cats these equations resulted in an average estimate of ME within 1.57% and 1.80% of the actual ME measured. However, better predictions of dietary ME in dog and cat pet foods were achieved using equations based on analysis of gross energy (GE) and new factors for moisture, protein, fat and fiber. When this was done there was less than 0.01% difference between the measured ME and the average predicted ME (r2 = 0.99 and 1.00 in dogs and cats, respectively) whereas the absolute value of the difference between measured and predicted was reduced by approximately 50% in dogs and 60% in cats. Stool quality, which was measured by stool score, was influenced positively when dietary protein digestibility was high and fiber digestibility was low. In conclusion, using GE improves predictive equations for ME content of dog and cat pet foods. Nondigestible protein and fiber content of diets predicts stool quality.

Current Formulas for Water Requirements Produce Different Estimates

Weight-based and energy-based formulas are used to estimate the water requirements of patients receiving nutrition support, yet these formulas have not been validated nor quantitatively compared with one another. The objective of this study was to determine if there was agreement among commonly used formulas for estimating water requirements. This cross-sectional survey design was from the continuous National Health and Nutrition Examination Survey 1999-2004. To determine estimated water requirements for each participant, 2 weight-based formulas were used (30 mL/kg body weight and an adjusted body weight formula) and 3 energy-based formulas based on 1 mL/kcal (energy needs estimated by the Harris Benedict, Mifflin St Jeor, and National Research Council equations). Simple linear regression was performed between each pair of formulas to determine correlations. The regression line was then compared to the line of equality to determine agreement between pairs. Although the 5 formulas were strongly correlated to each other (|r| > 0.7), they demonstrated poor agreement with one another with significant differences when the regression line was compared to the line of equality. Current formulas that estimate the water requirements of patients receiving enteral nutrition and parenteral nutrition all compute a dissimilar quantity of water. The consequences of these estimates could result in major differences in water intake recommendations and prescriptions between patients or in the same patient, thus suggesting a need for improved standardization of practice for estimating water requirements in clinical practice.

Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns

The objectives of this study were to determine the feasibility of measuring feed intake in commercial tie-stall dairies and infer genetic parameters of feed intake, yield, somatic cell score, milk urea nitrogen, body weight (BW), body condition score (BCS), and linear type traits of Holstein cows. Feed intake, BW, and BCS were measured on 970 cows in 11 Pennsylvania tie-stall herds. Historical test-day data from these cows and 739 herdmates who were contemporaries during earlier lactations were also included. Feed intake was measured by researchers once per month over a 24-h period within 7 d of 6 consecutive Dairy Herd Information test days. Feed samples from each farm were collected monthly on the same day that feed intake was measured and were used to calculate intakes of dry matter, crude protein, and net energy of lactation. Test-day records were analyzed with multiple-trait animal models, and 305-d fat-corrected milk yield, dry matter intake, crude protein intake, net energy of lactation intake, average BW, and average BCS were derived from the test-day models. The 305-d traits were also analyzed with multiple-trait animal models that included a prediction of 40-wk dry matter intake derived from National Research Council equations. Heritability estimates for 305-d intake of dry matter, crude protein, and net energy of lactation ranged from 0.15 to 0.18. Genetic correlations of predicted dry matter intake with 305-d dry matter, crude protein, and net energy of lactation intake were 0.84, 0.90, and 0.94, respectively. Genetic correlations among the 3 intake traits and fat-corrected milk yield, BW, and stature were moderate to high (0.52 to 0.63). Results indicate that feed intake measured in commercial tie-stalls once per month has sufficient accuracy to enable genetic research. High-producing and larger cows were genetically inclined to have higher feed intake. The genetic correlation between observed and predicted intakes was less than unity, indicating potential variation in feed efficiency.

Comparison of Thoroughbred Growth Data to Body Weights Predicted by the NRC

The National Research Council (NRC) recently published a method for calculating expected body weight (BW) in growing horses. The objective of this study was to compare foal BW data obtained from commercial Thoroughbred (TB) farms with BW estimates obtained using the 2007 NRC equation. A total of 9,589 observations on 885 growing TB horses were obtained. Data were sorted by age, and a subset was used for statistical analysis. Comparisons between actual BW and 2007 NRC-predicted BW at each month (0–18 months of age) were made using paired t-tests. The 2007 NRC closely predicted BW of growing TB horses at birth and between 6 and 9 months of age (within 3 kg). However, the equation underestimated BW between 1 and 5 months of age (by as much as 10 kg) and overestimated BW between 10 and 13 months of age (by as much as 9 kg). The 2007 NRC also underestimated BW by as much as 9 kg between 15 and 16 months of age. In the current study, the 2007 NRC equation did not precisely predict BW of horses at all ages. The 2007 NRC equation predicts that daily gain will decrease over time, whereas foals in the current study had an increase in daily gain between 12 and 14 months of age. Foals in the current study were raised in pasture-based management systems in which variations in forage availability and quality could have resulted in fluctuations in growth rate.