Estimation of indigestible NDF in forages and concentrates from cell wall composition

This study examined the potential of plant cell wall fractions as predictors of indigestible neutral detergent fibre (INDF) in forages with respect to species within plant type, cut number and stage of maturity (harvest time) within primary growth, and for concentrates with respect to species within plant type, where INDF is defined as the portion of plant cell walls not digested after 288 h rumen incubation in Dacron bags with 12 μm pore size. INDF is one of the more important parameters determining the net energy (NE) value of a diet in some recently developed ruminant feed evaluation systems. Effects of maturity and cut number on INDF in three legumes and 18 grasses were determined based on an experiment in which each forage was cut at three times of primary growth and once in each of the following three regrowths. These data were supplemented with data from earlier experiments to develop regression equations for INDF intended for use in practice based on a total of 321 samples. Plant type and species within plant type affected (P<0.001) all cell wall fractions. The INDF/lignin(sa) ratio varied substantially from the 2.4 factor used in the Cornell Net Carbohydrate and Protein System (CNCPS) to predict INDF, averaging 2.6 for legumes, grains and grain byproducts, 2.7 for grasses and 1.0 for oilseeds including byproducts. The INDF/IOM ratio varied less among plant species within plant type than among plant types. Multiple linear regression analysis revealed higher INDF prediction accuracy compared to simple regression equations. Estimation of INDF in DM was more accurate than estimation of INDF in ash-free neutral detergent fibre (aNDFom). Results indicate that universal regression equations among plant types are not possible, and, within plant types, prediction equations are not promising for grasses and legumes if only lignin(sa) is included. Comparing plant types, INDF prediction was best for grains and byproducts in simple linear regression and best for grass/clover in multiple linear regression equations. Variations within and among plant type were related to stage of maturity and cut number.