Recent developments in forage evaluation with special reference to practical applications

Abstract

The present re-evaluation of a dataset of systematically collected laboratory analyses and in vivo digestibility information for several types of silages gives convincing evidence of the biological weaknesses of feed characterisation based on the proximate feed analysis. The problems include intrinsic failures of the analysis in describing cause-response relationships between forage composition and digestibility, and heavy dependency of the equations on forage specific and environmental factors. It is concluded that proximate analysis is not suitable for characterisation of neither forages nor concentrate feedstuffs. In vitro pepsin-cellulase solubility of organic matter (OMS) and concentration of indigestible neutral detergent fibre (iNDF) predicted forage organic matter digestibility (OMD) with an acceptable accuracy for practical feed evaluation purposes provided that forage type dependent correction equations were employed.The revised detergent system dividing forage dry matter (DM) into almost completely available neutral detergent solubles (NDS), and insoluble residue (neutral detergent fibre, NDF) shows potential for future development. The combined use of long-term in situ ruminal incubation and NDF fractionation can be used to divide forage DM into three biologically meaningful fractions: NDS, iNDF and potentially digestible NDF (pdNDF). The summative models can then be used to predict forage D-value, i.e. apparently digestible organic matter in forage (g kg -1 DM). The models sum digestible NDS, which can be determined by Lucas equation, and digestible NDF (dNDF), which is the amount of pdNDF that is actually digested during any specific fermentation or retention time. Forage type specific summative models were as good as regression equations based on OMS or iNDF in predicting forage D-value and general summative models gave better results than general equations based on iNDF and especially OMS.If the goal is to reduce prediction error of D-value below 15 g kg -1 DM, forage type specific prediction equations should be used regardless of whether they are based on OMS, iNDF or summative models. Another option in the future may be dynamic models, which can incorporate simultaneously the two important dynamic processes constraining feed digestion in ruminants: the rates of NDF passage and degradation (k d ). A G R I C U L T U R A L A N D F O O D S C I E N C E Huhtanen, P. et al. Forage evaluationHowever, a vital prerequisite to employ dynamic models in practical feed evaluation is that iNDF and k d can be easily and reliably determined from on-farm forages. Although a NIRS prediction equation for iNDF will be adopted in practical use in the near future in Finland, the methodology for estimating k d warrants further research.Key words: silage, prediction, cell wall quality, digestibility, near infrared reflectance spectroscopy IntroductionThe main objective of feed evaluation techniques is to predict the availability of nutrients and feeding value of feeds for animal production systems. The methods availabl...

Evaluation of the factors affecting silage intake of dairy cows: a revision of the relative silage dry-matter intake index

“…The regression coefficients of the bivariate model with iNDF and pdNDF as independent variables (20.0186 v. 20.005) may reflect relative differences in the turnover rates of these fibre fractions. Turn-over rate of pdNDF is much faster than that of iNDF Huhtanen et al, 2006), because pdNDF disappears from rumen both by digestion and passage but iNDF can disappear only by passage.…”

“…Energy-corrected milk (ECM) yield was calculated according to Sjaunja et al (1991). For the Finnish data, silage D-values based on in vitro pepsincellulase method were corrected with forage-specific equations (primary growth and regrowth grass, legume and whole-crop silages; Huhtanen et al, 2006). When silage neutral-detergent fibre (NDF) and indigestible NDF (iNDF) concentrations were not reported, estimates derived from regression equations based on the Finnish data sets were used.…”

A comparison of first-, second- and third-cut timothy silages in the diets of finishing beef bulls

“…Crude fat was determined by a Soxcap-Soxtec-analyser (AOAC Official Method 920.39) (AOAC 1990). The silages were analysed for D-value as described by Huhtanen et al (2006). The pepsin-cellulase solubility values were converted to in vivo digestibility using correction equations (different equations for primary growth and regrowth) based on a data set comprised of Finnish in vivo digestibility trials (Huhtanen et al 2006).…”

The development of a model to predict BW gain of growing cattle fed grass silage-based diets

“…Forage D-values (digestible organic matter in DM) based on in vitro pepsin-cellulase method were calculated with forage-specific equations (primary growth and regrowth grass, legume and whole-crop silages) (Huhtanen et al, 2006). When forage NDF concentration was not reported, it was estimated from other forage parameters (crude fibre, ADF, digestibility) using empirical regression equations derived from the data of Huhtanen et al (2006). Fat, starch and NDF concentrations of the concentrates were entered when reported; otherwise tabulated values (MTT, 2014) for each ingredient were used.…”