The Cornell Net Carbohydrate and Protein System (CNCPS) has a submodel that predicts rates of feedstuff degradation in the rumen, the passage of undegraded feed to the lower gut, and the amount of ME and protein that is available to the animal. In the CNCPS, structural carbohydrate (SC) and nonstructural carbohydrate (NSC) are estimated from sequential NDF analyses of the feed. Data from the literature are used to predict fractional rates of SC and NSC degradation. Crude protein is partitioned into five fractions. Fraction A is NPN, which is trichloroacetic (TCA) acid-soluble N. Unavailable or protein bound to cell wall (Fraction C) is derived from acid detergent insoluble nitrogen (ADIP), and slowly degraded true protein (Fraction B3) is neutral detergent insoluble nitrogen (NDIP) minus Fraction C. Rapidly degraded true protein (Fraction B1) is TCA-precipitable protein from the buffer-soluble protein minus NPN. True protein with an intermediate degradation rate (Fraction B2) is the remaining N. Protein degradation rates are estimated by an in vitro procedure that uses Streptomyces griseus protease, and a curve-peeling technique is used to identify rates for each fraction. The amount of carbohydrate or N that is digested in the rumen is determined by the relative rates of degradation and passage. Ruminal passage rates are a function of DMI, particle size, bulk density, and the type of feed that is consumed (e.g., forage vs cereal grain).

The Cornell Net Carbohydrate and Protein System (CNCPS) has a kinetic submodel that predicts ruminal fermentation. The ruminal microbial population is divided into bacteria that ferment structural carbohydrate (SC) and those that ferment nonstructural carbohydrate (NSC). Protozoa are accommodated by a decrease in the theoretical maximum growth yield (.50 vs .40 g of cells per gram of carbohydrate fermented), and the yields are adjusted for maintenance requirements (.05 vs .150 g of cell dry weight per gram of carbohydrate fermented per hour for SC and NSC bacteria, respectively). Bacterial yield is decreased when forage NDF is < 20% (2.5% for every 1% decrease in NDF). The SC bacteria utilize only ammonia as a N source, but the NSC bacteria can utilize either ammonia or peptides. The yield of NSC bacteria is enhanced by as much as 18.7% when proteins or peptides are available. The NSC bacteria produce less ammonia when the carbohydrate fermentation (growth) rate is rapid, but 34% of the ammonia production is insensitive to the rate of carbohydrate fermentation. Ammonia production rates are moderated by the rate of peptide and amino acid uptake (.07 g of peptide per gram of cells per hour), and peptides and amino acids can pass out of the rumen if the rate of proteolysis is faster than the rate of peptide utilization. The protein-sparing effect of ionophores is accommodated by decreasing the rate of peptide uptake by 34%. Validation with published data of microbial flow from the rumen gave a regression with a slope of .94 and an r2 of .88.

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The Cornell Net Carbohydrate and Protein System (CNCPS) has equations for predicting nutrient requirements, feed intake, and feed utilization over wide variations in cattle (frame size, body condition, and stage of growth), feed carbohydrate and protein fractions and their digestion and passage rates, and environmental conditions. Independent data were used to validate the ability of the CNCPS to predict responses compared to National Research Council (NRC) systems. With DMI in steers, the CNCPS had a 12% lower standard error of the Y estimate (Sy.x) and three percentage units less bias than the NRC system. For DMI in heifers, both systems had a similar Sy.x but the NRC had four percentage units less bias. With lactating dairy cows' DMI, the CNCPS had a 12% lower Sy.x. Observed NEm requirement averaged 5% under NRC and 6% under CNCPS predicted values at temperatures above 9 degrees C but were 18% over NRC and 9% under CNCPS at temperatures under 9 degrees C. Energy retained was predicted with an R2 of .80 and .95 and a bias of 8 and 4% for the NRC and CNCPS, respectively. Protein retained was predicted with an R2 of .75 and .85 with a bias of 0 and -1% for NRC and CNCPS, respectively. Biases due to frame size, implant, or NEg were small. Body condition scores predicted body fat percentage in dairy cows with an R2 of .93 and a Sy.x of 2.35% body fat. The CNCPS predicted metabolizable protein allowable ADG with a bias of 1.6% with a Sy.x of .07 kg compared to values of -30% and .10 kg, respectively for the NRC system.

ABSTRACT:The Cornell Net Carbohydrate and Protein System (CNCPS), a mechanistic model that predicts nutrient requirements and biological values of feeds for cattle, was modified for use with sheep. Published equations were added for predicting the energy and protein requirements of sheep, with a special emphasis on dairy sheep, whose specific needs are not considered by most sheep-feeding systems. The CNCPS for cattle equations that are used to predict the supply of nutrients from each feed were modified to include new solid and liquid ruminal passage rates for sheep, and revised equations were inserted to predict metabolic fecal N. Equations were added to predict fluxes in body energy and protein reserves from BW and condition score. When evaluated with data from seven published studies (19 treatments), for which the CNCPS for sheep predicted positive ruminal N balance, the CNCPS for sheep predicted OM digestibility, which is used to predict feed ME values, with no mean bias (1.1 g/100 g of OM; P > 0.10) and a low root mean squared prediction error (RMSPE; 3.6 g/100 g of OM). Crude protein digestibility, which is used to predict N excretion, was evaluated with eight published studies (23

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A concern of the USEPA is the volatilization of NH3 from animal manure and CH4 produced from ruminal fermentation. Excess N in the environment has been associated with adverse effects on human health, and CH4 and N2O emissions are sources of greenhouse gases. The objectives of this paper are to summarize and quantify the benefits of ionophores, principally monensin, in decreasing NH3 and CH4 emissions to the environment and reducing resource utilization in cattle (Bos spp.) production. The data indicate that monensin in the diets of ruminants may decrease protein degradation in the rumen and may increase feed protein utilization by an average of 3.5 percentage units. These changes would have an effect in reducing N losses and decreasing fecal N and the amount of protein that must be fed to meet animal requirements. Additionally, CH4 is produced by enteric fermentation in ruminants, which is responsible for about 33 to 39% of CH4 emissions from agriculture. Ionophores can reduce CH4 production by 25% and decrease feed intake by 4% without affecting animal performance. The inclusion of monensin in beef and dairy cattle diets may benefit air quality by reducing CH4 and N emissions and water quality by reducing N in manure, which can potentially leave the farm through leaching into ground water and through runoff into surface water.

The effects of body weight (BW) gain, different sources of protein during the prepubertal period (90 to 320 kg of BW), and the performance of Holstein heifers during their first lactation were studied. Heifers (n = 273) were assigned to one of three dietary energy treatments that were designed to achieve average daily gains of 0.6, 0.8, and 1.0 kg/d. Within each energy treatment, different protein sources (plant protein and urea or both plant and animal proteins) were imposed. Actual average daily gains by heifers on each energy treatment were 0.68, 0.83, and 0.94 kg/d for heifers that were fed diets formulated for average daily gains of 0.6, 0.8, and 1.0 kg/d, respectively, which allowed the following ages at first calving: 24.5, 22.0, and 21.3 mo. Breeding was initiated when heifers weighed approximately 340 kg. Protein sources did not affect average daily gain or milk yield. Analysis of the preplanned comparisons of actual 305-d and 4% fat-corrected milk yields indicated that yield was significantly reduced for heifers grown at 0.94 kg/d (9387 and 8558 kg, respectively) compared with that of heifers grown at 0.68 kg/d (9873 and 9008 kg, respectively). However, further regression analysis of fat-corrected milk and residual milk from a test day model on prepubertal BW gain only explained 8 and 2% of the variation in milk yield, respectively. Postcalving BW and body condition score were different among treatments. Posttreatment factors, such as postcalving BW, accounted for more of the variation in milk yield than did prepubertal BW gain. Prepubertal BW gains, when evaluated on a continuum from 0.5 to 1.1 kg/d, explained little of the variation in milk yield; therefore, BW gain during the prepubertal period did not significantly affect milk yield during first lactation.