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ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Cell Wall Biology and Utilization Research » Research » Publications at this Location » Publication #340306
Title: Physically adjusted NDF (paNDF) system for lactating dairy cow rations. I: Deriving equations that identify factors that influence effectiveness of fiber

Author
item WHITE, ROBIN - Virginia Tech
item Hall, Mary Beth
item FIRKINS, JEFFREY - The Ohio State University
item KONONOFF, PAUL - University Of Nebraska

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/28/2017
Publication Date: 12/1/2017
Citation: White, R.R., Hall, M., Firkins, J.L., Kononoff, P.J. 2017. Physically adjusted NDF (paNDF) system for lactating dairy cow rations. I: Deriving equations that identify factors that influence effectiveness of fiber. Journal of Dairy Science. 100:9551-9568. doi.org/10.3168/jds.2017-12765.
DOI: https://doi.org/10.3168/jds.2017-12765

Interpretive Summary: Maintaining a healthy ruminal pH is essential for keeping dairy cows healthy and productive. However, many different dietary physical and chemical characteristics, as well as animal behavior, affect ruminal pH. Using these many factors, the goal of this work was to develop equations to predict rumen pH, feed intake, and rumination time in order to use the equations to create workable diet formulation guidelines that would help nutritionists maintain ruminal health. In this work, we used data from research with lactating dairy cows. Dietary particle size, forage amount, fiber, starch, and other dietary chemical and digestibility characteristics were used in the equations. This first step provided viable equations that will be used to build the diet formulation system.

Technical Abstract: Physically effective neutral detergent fiber (peNDF) is defined as the fraction of NDF that stimulates chewing activity and contributes to the floating mat of large particles in the rumen. The objective of this work was to re-evaluate the concept of peNDF by quantitatively relating physical and chemical characteristics of diets fed, dry matter intake (DMI), ruminating behavior, and ruminal pH in lactating dairy cows. A total of 619 treatment means from 58 trials were assembled. Backward elimination multiple regression was used to derive models of response variables. Studies were limited to those that used the Penn State Particle Separator (PSPS). Notable continuous variables include mean particle size (MPS) the proportion of material (as fed [AF] or dry matter [DM] basis) retained on the 19- and 8-mm sieves of the PSPS, the proportion of forage, proportion of forage NDF, the diet concentration of starch, diet concentration of NDF, rumen degraded starch (dStarch) and rumen degraded NDF (dNDF). Effective fiber representations of multiplying some measure of particle size by diet NDF, resulting in a peNDF representation or keeping these factors separate in a physically adjusted NDF (paNDF) system was also evaluated. Dry matter intake was consistently and negatively associated with MPS. In models that included TMR particle size on an AF basis, the inclusion of dStarch and dNDF improved fit. Models with peNDF representations had reduced accuracy and precision compared with paNDF system models. The prediction of total time spent ruminating was similar between models that included particle size data on either an AF or DM basis. Models predicting rumen pH with peNDF representations did not differ substantially from models with individual factor representations. Despite not improving model fit for the prediction of ruminal pH, the peNDF representations for MPS × NDF and the proportion of particles greater than 8 mm × NDF were significant predictors in two models. This suggests that such an index does account for some variation in ruminal pH; however, based on the current data, advantages of any peNDF index are not immediately apparent. As expected, a large number of dietary chemical, and physical factors were identified as influencing mean ruminal pH. In several cases, prediction was improved when also accounting for rumination times and dNDF or dStarch. In conclusion, these results appeared to justify the development of a modeling approach to integrate physical and chemical factors to predict impacts on ruminal pH.