The Influence of Physical Form of Ration on Chewing Activity and Rumen Motility in Lactating Cows

Previously, a linear relationship has been found between net energy intake (NEI) and dietary chewing index (CI) of the diet for different types of cattle. Therefore, we propose to generalize and calibrate this relationship into a new model for direct prediction of NEI by dairy cows from CI values (CI; min/MJ of NE). Furthermore, we studied the forage-to-concentrate substitution rate in this new NEI model. To calibrate the model on a diverse set of situations, we built a database of mean intake from 14 production experiments with a total of 986 primi- and multiparous lactating dairy cows of different breeds fed 136 different diets ad libitum. The NEI were estimated by the Nordic feed evaluation system. The CI value of diets was estimated from the intake of concentrate, intake of forage neutral detergent fiber (NDF), particle length of forage, indigestible NDF/NDF, body weight, NDF/body weight, and the content of NE in DM. We show that the slope values in this regression are proportional to the squared intercepts, giving the nonlinear equation NEI=NEI-k×NEI×CI, where the parameter k represents the decline in NEI with the increasing CI of the diet and a was estimated to have a value of 2, implying a constant maximum daily chewing time. The intercept NEI in the regression of NEI on CI may be interpreted as metabolic net energy intake capacity of the cows fed without physical constraints on intake. Based on experimental data, the maximum chewing time was estimated as 1/(4 × k). The NEI values were parameterized as a linear function of metabolic body size, energy-corrected milk yield (kg/d), days in milk, and days in milk squared. Prediction accuracy was evaluated by mean square prediction error (MSPE) and its decomposition into central tendency, regression, and disturbance, across and within experiments on independent data from 19 experiments including 812 primi- and multiparous lactating dairy cows of different breeds fed 80 different diets ad libitum. The NEI model predicted NEI with an MSPE of 8% of observed, and across the 19 experiments the error central tendency, error regression, and error disturbance were 4.2, 40.6, and 84.9% of MSPE, respectively. The described intake model implies a variable forage-to-concentrate substitution rate as a nonlinear function of NEI, CI of forage, and supplementation of concentrate.

Section: Maximum Chewing Timementioning

confidence: 68%

Description and evaluation of a net energy intake model as a function of dietary chewing index

Jensen

Markussen

Nielsen

et al. 2016

“…A database was constructed to quantify the RR motility and generate the best prediction models. A total of 74 observations in 19 experiments that measured the reticular motility of cattle during eating, ruminating, and resting separately were obtained from 12 scientific journal articles (Balch, 1952;Freer et al, 1962;Freer and Campling, 1965;Campling and Freer, 1966;Bines and Davey, 1970;Dracy et al, 1972;Norgaard, 1989;Okine and Mathison, 1991;Johnson and Combs, 1992;Miaron and Christopherson, 1992;Okine et al, 1993;Okine et al, 1994). Table 1 summarizes the descriptive statistics in the database.…”

Section: Journal Of Dairymentioning

confidence: 99%

Development of a Mechanistic Model to Represent the Dynamics of Liquid Flow Out of the Rumen and to Predict the Rate of Passage of Liquid in Dairy Cattle

Seo

Lanzas

Tedeschi

et al. 2007

A mechanistic and dynamic model was developed to represent the physiological aspects of liquid dynamics in the rumen and to quantitatively predict liquid flow out of the reticulorumen (RR). The model is composed of 2 inflows (water consumption and salivary secretion), one outflow (liquid flow through the reticulo-omasal orifice (ROO), and one in-and-out flow (liquid flux through the rumen wall). We assumed that liquid flow through the ROO was coordinated with the primary reticular contraction, which is characterized by its frequency, duration, and amplitude during eating, ruminating, and resting. A database was developed to predict each component of the model. A random coefficients model was used with studies as a random variable to identify significant variables. Parameters were estimated using the same procedure only if a random study effect was significant. The input variables for the model were dry matter intake, body weight, dietary dry matter, concentrate content in the diet, time spent eating, and time spent ruminating. Total water consumption (kg/d) was estimated as 4.893 x dry matter intake (kg/d), and 20% of the water consumed by drinking was assumed to bypass the RR. The salivary secretion rate was estimated to be 210 g/min during chewing. During ruminating, however, the salivation rate was assumed to be adjusted for the proportion of liquid in the rumen. Resting salivation was exponentially related to dry matter intake. Liquid efflux through the rumen wall was assumed to be the mean value in the database (4.6 kg/h). The liquid outflow rate (kg/h) was assumed to be a product of the frequency of the ROO opening, its duration per opening, and the amount of liquid passed per opening. Simulations of our model suggest that the ROO may open longer for each contraction cycle than had been previously reported (about 3 s) and that it is affected by dry matter intake, body weight, and total digesta in the rumen. When compared with 28 observations in 7 experiments, the model accounted for 40, 70, and 90% of the variation, with root mean square prediction errors of 9.25 kg, 1.84 kg/h, and 0.013 h(-1) for liquid content in the rumen, liquid outflow rate, and fractional rate of liquid passage, respectively. A sensitivity analysis showed that dry matter intake, followed by body weight and time spent eating, were the most important input variables for predicting the dynamics of liquid flow from the rumen. We conclude that this model can be used to understand the factors that affect the dynamics of liquid flow out of the rumen and to predict the fractional rate of liquid passage from the RR in dairy cattle.

“…Dairy cattle are grass and roughage eaters (Hofmann, 1989) and consequently require coarse roughage to maintain normal rumen function and overall health (Cole and Mead, 1943). The positive relationship between particle size and chewing activity has long been proposed as part of a method for assessing the effect of coarse roughage in diets (Balch, 1971;Sudweeks et al, 1981;Nørgaard, 1989). Several investigators have sought to quantify coarseness of roughage so that these measures could be adopted into feeding recommendations (Santini et al, 1983;Mertens, 1997).…”

Section: Introductionmentioning

confidence: 99%

Physically adjusted neutral detergent fiber system for lactating dairy cow rations. I: Deriving equations that identify factors that influence effectiveness of fiber

White

Hall

Firkins

et al. 2017

Physically effective neutral detergent fiber (peNDF) is the fraction of neutral detergent fiber (NDF) that stimulates chewing activity and contributes to the floating mat of large particles in the rumen. Multiplying dietary NDF by particle size has been used as an estimate of peNDF. In re-evaluating the concept of peNDF, we compared the use of peNDF as dietary NDF × particle size with the use of individual NDF and particle size descriptors (physically adjusted NDF; paNDF) when used with other physical and chemical diet descriptors to predict dry matter (DM) intake (DMI), rumination time, and ruminal pH in lactating dairy cows. The purpose is to ultimately use these equations to estimate diet adequacy to maintain ruminal conditions. Each response variable had 8 models in a 2 (peNDF, paNDF) × 2 (diet, diet and ruminal factors) × 2 (DM, as fed basis) factorial arrangement. Particle size descriptors were those determined with the Penn State Particle Separator. Treatment means (n = 241) from 60 publications were used in backward elimination multiple regression to derive models of response variables. When available, peNDF terms entered equations. Models containing peNDF terms had similar or lower unadjusted concordance correlation coefficients (an indicator of similar or lower accuracy and precision) than did models without peNDF terms. The peNDF models for rumen pH did not differ substantially from paNDF models. This suggests that peNDF can account for some variation in ruminal pH; however, overt advantages of peNDF were not apparent. Significant variables that entered the models included estimated mean particle size; as fed or DM proportions retained on 19- and 8-mm sieves of the Penn State Particle Separator; DMI; dietary concentrations of forage; forage NDF; CP; starch; NDF; rumen-degraded starch and rumen-degraded NDF; and the interaction terms of starch × mean particle size, acid detergent fiber/NDF, and rumination time/DMI. Many dietary factors beyond particle size and NDF were identified as influencing the response variables. In conclusion, these results appear to justify the development of a modeling approach to integrate individual physical and chemical factors to predict effects on factors affecting rumen conditions.