Sizes and rates of potentially digestible (B) and undegradable (C) pools of amylase-treated neutral detergent fiber (aNDF) are used to predict ruminal aNDF digestibility (raNDFD%) in widely used dairy cattle diet formulation programs. An exponential 3-pool (3P) model has been suggested for estimating digestion kinetics parameters for this purpose; however, the approach has not been compared with using a simpler exponential 2-pool (2P) model, nor with using commercial laboratory data on which applications would rely, nor on model effect on predictions of raNDFD%, which is the aim of their application. Our objective was to determine whether the 2P or 3P model most accurately and efficiently characterizes aNDF digestion kinetics and whether the models differ in predicted raNDFD%. Dry forages and silages (6 alfalfas, 6 species of grasses) were analyzed by 2 commercial laboratories that each performed 2 in vitro incubation runs with mixed ruminal microbes, with samples and blanks in duplicate at each of 11 time points; residual aNDF (Ut) was measured at each time point. Sampling hours (t) were 0, 3, 6, 12, 18, 24, 30, 48, 72, 120, and 240 h. Outlier Ut values were removed. Pools as proportions of aNDF were B in 2P, B1 rapid and B2 slow in 3P, and C in both; B pools have digestion rates (kd, h-1; denoted as kd"Bpool") and lag (h). Models were fit to data for each forage in each incubation with equations 2P: Ut = B x e(-kdB x z) + C and 3P: Ut = B1 x e(-kdB1 x z) + B2 x e(-kdB2 x z) + C, where z = [-(lag - t - st - lags)/2]. There were 48 curves for each model. Parameters were estimated with the optim function in base R. The Akaike information criterion (AIC) was used to select the model with the best fit for each forage in each incubation: 16 3P and 32 2P curves were selected. Expressed as (difference between runs)/mean, average deviations between runs for laboratories 1 and 2, respectively, were as follows: for 3P, B1 = 0.50, 0.17; B2 = 0.26, 0.33; C = 0.50, 0.06; kdB1 = 0.81, 0.32; and kdB2 = 0.93, 0.54; for 2P, B = 0.04, 0.01; C = 0.07, 0.01; and kdB = 0.17, 0.08. Estimates of raNDFD% for 2P and 3P were calculated with no lag at passage rates (kp) reported for forages of 0.02 through 0.07 h-1. T-tests determined whether differences were not equal 0 for 2P - 3P for raNDFD% at each kp for each feed evaluated. With 2P minus 3P differences in raNDFD% listed sequentially by 0.01 h-1 from kp = 0.02 to 0.07 h-1, for 16 AIC-selected 3P curves, differences were -0.29, -0.45, -0.65, -0.87, -1.04, and -1.20%, and for 32 AIC-selected 2P curves, values were -0.15, -0.13, -0.14, -0.17, -0.19, and -0.22%. Some differences were significant, but all were quite small. With little difference between models, use of the more complex 3P conferred no advantage over 2P for prediction of raNDFD% in this dataset.

Abstract Although lignin has been negatively correlated with neutral-detergent fibre (NDF) digestibility (NDFD) in ruminants and used to predict potential extent of NDF digestion of forages, selection of an analysis, Klason lignin (KL) or acid-detergent lignin (ADL), to describe that the nutritionally relevant lignin has not been resolved. Dismissed as an artifact is the difference between KL and ADL (ΔL). A question is whether ΔL influences NDFD. We evaluated the relationships of ΔL, KL and ADL with NDFD in order to determine the nutritionally homogeneous or heterogeneous nature of KL. Data sets from two laboratories (DS1 and DS2) were used that included ADL, KL and in vitro NDFD at 48 h (NDFD48). DS1 contained seven C3 grasses, seventeen C4 maize forages and nineteen alfalfas, and DS2 had fifteen C3 grasses, eight C4 forages and six alfalfas. Mean ΔL was greater than ADL in C3 and C4 samples and less in alfalfas. Within forage type and laboratory, ΔL was not correlated with NDFD48 (r −0·34–0·49; all P > 0·17). ADL was more consistently correlated with NDFD48 (r −0·47–−0·95; P < 0·01–0·21) than with KL (r 0·03–−0·91; P < 0·01–0·94). ΔL as a proportion of KL was correlated with NDFD48 in C3 and C4 samples (r 0·44–0·76; P < 0·01–0·08). The differing behaviours of ΔL and ADL relative to NDFD48 indicate that KL is a nutritionally heterogeneous fraction, the behaviour of which may vary by forage type and ratios of ADL and ΔL present.

This paper was presented at the 2018 Cornell Nutrition Conference. For more information, please visit ansci.cals.cornell.edu/CNC.

Further improvements in ration formulation accuracy will likely come with use of models to account for more of the variation by accurately predicting requirements and feed utilization in each unique production setting. These models must allow inputs from each situation to be adjusted in a logical way until the cattle and feeds are accurately described. The final test is when predicted and observed performance (daily gain, milk amount and composition, and body condition score changes) agree, and observed responses to changes in management and feeds can be explained by predicted effects on rumina! fermentation, intestinal digestion, metabolizability of energy and amino acids, and product amount and composition. Then improved feeding programs can be accurately formulated for that unique situation where nutritional safety factor and nutrient excretion are minimized. This becomes imperative as we attempt to minimize the effects of cattle production on resource use, water quality and other environmental concerns. This requires nutritional accounting systems based on our understanding of the biological responses to variables influencing animal performance, yet driven by inputs available at the farm level, with an acceptable risk of use, considering information available and knowledge of the user.

A dynamic application of the Cornell Net Carbohydrate and Protein System (CNCPS) model was developed to predict annual cycles in animal nutrient requirements and performance of dual-purpose (milk and beef) cows. Interactions from mobilisation and repletion of body tissue reserves and feed biological values are accounted with a time step of one day, which considers physiological status of the animal, variation in dietary composition, and other environmental factors. This outcome was achieved by modifying the input and output structure of the CNCPS version 4.0 to compute body weight and changes in body reserves based on predicted milk production, intake of feed dry matter, and energy balance. The supply of metabolisable energy from dietary intake is supplemented by tissue mobilised to support milk synthesis in early lactation; body tissue is repleted when energy balance is positive. Predicted animal nutrient requirements, milk production, dry matter intake, and changes in body weight and body condition score over the reproductive cycle were consistent with patterns and values in published reports and field observations in the Gulf Coast of Mexico case study region. Our simulations showed that a dynamic application of the CNCPS facilitates more accurate monitoring and management of cyclic changes in energy and protein balances over the calving interval of dual-purpose cows, which can help producers to achieve productivity and profitability goals.

This study evaluated the Cornell Net Carbohydrate and Protein System for dairy cows consuming diets based on pasture, assessed the sensitivity of the model to critical inputs, and demonstrated application opportunities. Data were obtained from four grazing experiments and four indoor pasture feeding experiments (25 dietary treatments) involving dairy cows in New Zealand and the US. The model provided a reasonably good estimate of changes in body condition score (r2 = 0.78; slope not significantly different from 1), estimated energy balance (r2 = 0.76; slope not significantly different from 1), blood urea N (r2 = 0.94; underprediction bias of 0.5%), microbial N flow (r2 = 0.88; slope not significantly different from 1), and milk production. The model underpredicted dry matter intake (r2 = 0.80; 13% bias) and overpredicted ruminal pH (r2 = 0.47; 1.7% bias). Predicted milk production was especially sensitive to changes in pasture lignin content, effective fiber, rate of fiber digestion, and amino acid composition of ruminal microbes. Milk production was first-limited by the supply of metabolizable energy when only high quality pasture was fed, but specific amino acids limited milk production when more than 20% of the diet consisted of a grain supplement. These results indicate that the Cornell Net Carbohydrate and Protein System can be used for dairy cows in a grazing system to make realistic predictions of performance.

A steady-state model of the production, absorption, passage, and concentration of ruminal VFA and pH is developed from published literature data and is structured to use the feed descriptions and inputs from the net carbohydrate and protein system. Included are the effects of pH on growth rate and yield of structural and non-structural carbohydrate-fermenting bacteria; production of acetate, propionate, butyrate, lactate, and methane; conversion of lactate to VFA; ruminal absorption of acids; and prediction of ruminal pH from dietary measures and from ruminal buffering and acidity. The root mean square error of predicted total VFA concentration was 12 mM. Individual VFA fractions were inadequately predicted. In a review of literature data, effective NDF (eNDF) provided a better correlation with ruminal pH than forage or NDF. Digestion rate of NDF remained at normal levels above pH 6.2, which corresponds to a minimum eNDF of 20% of dietary DM. Further research is needed to determine the individual VFA produced from carbohydrate fractions at various pH, the appropriateness of partitioning the starch and pectin carbohydrate pool into slowly and rapidly degraded fractions, and the effect on microbial yield, total tract digestibility, and predicted energy values of feeds.

Accurate prediction of forage biological values and performance with animals fed forages requires accurately accounting for factors that influence animal requirements and feedstuff utilization. The Cornell Net Carbohydrate and Protein System (CNCPS) is an application model that uses a combination of mechanistic and empirical approaches to account for the effects of variation in animal factors and feed carbohydrate and protein fractions on animal performance. Thus, accurate animal and environmental descriptions, DMI, feed carbohydrate, and protein fractions and their digestion rates are required inputs. In 25 growth periods with calves fed high-forage diets, the CNCPS accounted for 74, 81, and 83%, respectively, of the variation in ADG predicted to be supported by the ME, metabolizable protein, and essential amino acid intake, the first-limiting of all three accounting for 81% of the variation with a -1% bias. Thus, the CNCPS can be used to accurately describe forage quality and the effects of changes in forage composition on animal performance. The model was sensitive to variations in NDF, CP, protein solubility, NDF and starch digestion rates, feed and microbial amino acid composition, maintenance protein requirement, body protein amino acid content, and the coefficient of efficiency of use of absorbed protein. Analysis of several trials indicates an improved efficiency of ME use with improved amino acid balances. Uses of the CNCPS discussed include interpreting, planning and applying research, teaching, developing tables of requirements and biological values for feeds, complex nutritional accounting, and predicting performance and profits.

The Cornell Net Carbohydrate and Protein System (CNCPS) and NRC (1985) models were evaluated for accuracy in predicting metabolizable protein (MP) and essential amino acid (EAA) allowable ADG, using chemical body and feed composition data from feeding trials with Holstein steers. Nine Holstein steers (113 to 200 kg) were slaughtered and determined to have the following whole-body essential amino acid composition of (grams/100 grams of protein): arginine, 5.94; histidine, 2.07; isoleucine, 2.28; leucine, 5.72; lysine, 5.81; methionine, 1.99; phenylalanine, 3.04; threonine, 3.52; tryptophan, .57; and valine, 3.32. The NRC and CNCPS were then tested against data from 25 feeding periods, each representing the 56-d growth of 10 Holstein steers (mean BW of 162 kg), to determine their ability to predict the gain allowed by the supply of MP and the first-limiting EAA. The NRC (1985) system accounted for 46% of the variation in MP allowable gain, with an average bias of -30%. The CNCPS accounted for 87 and 73% of the variation in MP and EAA allowable gain, with a bias of 8 and 5%, respectively. The bias was reduced to 3% (R2 of .82) when ADG was predicted by the factor (ME, MP, or EAA) first-limiting ADG.

A microcomputer-based system, Compares (Computerized Milking Parlor Evaluation System), was developed by the authors to evaluate the efficiency of milking parlor operations. The system utilizes a hand-held microcomputer to collect on-site milking parlor operation information, which is down-loaded to an IBM-compatible microcomputer to generate an analysis and summary report. The Compares system is capable of monitoring the activities of multiple operators in a milking parlor using a single hand-held microcomputer. Reports can be generated within minutes after the information is recorded, thus providing an immediate analysis of the milking system being examined. The Compares system excludes the human error that is possible in other manual recording procedures that can occur from transferring or calculating data, thereby reducing time and. effort required to collect parlor operation information and enabling extensive data collection. Thus, this system provides a convenient tool for studying milking parlor operations.