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.

Path analysis and logistic regression were used to model direct and indirect relationships among clinical periparturient (within 30 d after calving) retained placenta, metritis, veterinary-assisted dystocia, uncomplicated and complicated ketosis, left displaced abomasum, parturient paresis, mastitis, and estimated nutrient intakes (protein, calcium, phosphorus, energy; coded into terciles) in the last 3 wk of the dry period. Data were from 1,374 multiparous Holstein lactations for calvings from March 1981 through February 1982 in 31 commercial herds in central New York. Periparturient disorders occurred as a complex. Odds ratios for the multiplicative effects of parturient paresis on incidence of veterinary-assisted dystocia, retained placenta, complicated ketosis, and clinical mastitis were 7.2, 4.0, 23.6, and 5.4, respectively. Reproductive disorders were interrelated. Retained placenta, left displaced abomasum, and parturient paresis directly increased risk of complicated ketosis (odds ratios were 16.4, 53.5, and 23.6, respectively). Higher terciles of estimated energy intake in the last 3 wk of the dry period decreased risk of veterinary-assisted dystocia and left displaced abomasum, while higher terciles of estimated protein intake decreased risk of retained placenta and uncomplicated ketosis. Estimated nutrient intakes were directly related to subsequent metabolic disorders and directly and indirectly related (mediated by metabolic disorders) to reproductive disorders. The study suggests that feeding higher intakes (relative to National Research Council recommendations) of protein and energy in the last 3 week of the dry period may reduce the incidence of metabolic and reproductive disorders. Exact recommendations as to the amounts and types of feed cannot be made from our results.

The relationship of body condition score with disease occurrence was examined in 561 cows in nine herds. Cows were body condition scored on a five-point scale (1 = thin, 5 = obese) every 2 wk from drying off until 150 d in milk. Cows scored between 3- and 3+ were considered to be in average or good condition. Cows scored less than or equal to 2+ were considered to be underconditioned, whereas those scored greater than or equal to 4- were considered to be overconditioned. Relationships of health and condition score were examined using multiple logistic regression for dichotomous outcomes (e.g., diseased or healthy). Cows that developed dystocia or were culled lost more condition during the dry period than those that did not develop dystocia or were not culled. Cows overconditioned at drying off were more likely to develop cystic ovarian disease and reproductive problems. Cows underconditioned or overconditioned at drying off were more prone to foot problems after calving. Cows overconditioned at 30 d postpartum were more likely to have metritis.

1. Twelve grain mixtures, one lucerne (Medicago sativa) hay and one maize silage which had been used in mixed diets for which dietary nitrogen undegraded in the rumen (UDN) had been estimated with duodenally-cannulated cows, were studied. Total N in the feeds was fractionated into pool A (N soluble in borate-phosphate buffer), pool B (total N-(pool A+pool C)) and pool C (acid-detergent-insoluble N or residual N after 24 h incubation in protease solution).2. N solubilization in protease solution containing 6.6 units/ml (substrate-saturating enzyme concentration) indicated the presence of subfractions in pool B, with different rates of solubilization. Such subfractions were not detectable from in situ, Dacron bag, estimates of N solubilization.3. UDN was estimated using a dynamic mathematical model and rate-constants obtained from N solubilization in protease solution or in situ. For three grain mixtures tested using the protease technique the model predicted UDN values of 7, 10 and 12% compared with values of 47, 66 and 59% estimated in vivo. The full range of experimental feeds was tested using the in situ technique and UDN values predicted by the model were used to derive UDN values for twelve mixed diets. The latter values were significantly but not closely correlated with those determined in vivo (rz 0.41, P < 0.05).4. An attempt was made to simulate rumen proteolysis in vitro by choosing a protease enzyme concentration (0.066 units/ml) providing a proteolytic activity similar to that of whole rumen fluid. The experimental samples of feed were subjected to simulated rumen proteolysis for 18 or 48 h to resemble the mean retention times in the rumen for grain mixtures and roughages respectively. The residual N at the end of incubation was considered as an estimate of UDN. The UDN values estimated from simulated rumen proteolysis and those determined in vivo for twelve mixed diets were in close agreement (re 0.61, P < 0.01). 5.Simulated rumen proteolysis can serve as a simple, rapid and sensitive method to estimate UDN in a varief y of feedstuffs.