Comparative Accuracy of In Vitro Rumen Fermentation and Enzymatic Methodologies for Determination of Undigested Neutral Detergent Fiber in Forages and Development of Predictive Equations Using NIRS

Farhad Ahmadi, Yan-Fen Li, Eun-Chan Jeong, Li-Li Wang, Rajaraman Bharanidharan and Jong-Geun Kim ()
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Farhad Ahmadi: Research Institute of Eco-Friendly Livestock Science, Institute of GreenBio Science Technology, Seoul National University, Pyeongchang 25354, Korea
Yan-Fen Li: Graduate School of International Agricultural Technology, Seoul National University, Pyeongchang 25354, Korea
Eun-Chan Jeong: Graduate School of International Agricultural Technology, Seoul National University, Pyeongchang 25354, Korea
Li-Li Wang: Graduate School of International Agricultural Technology, Seoul National University, Pyeongchang 25354, Korea
Rajaraman Bharanidharan: Department of Agricultural Biotechnology, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
Jong-Geun Kim: Research Institute of Eco-Friendly Livestock Science, Institute of GreenBio Science Technology, Seoul National University, Pyeongchang 25354, Korea

Agriculture, 2022, vol. 12, issue 11, 1-16

Abstract: Undigested neutral detergent fiber (uNDF) is becoming more widely recognized as an important fiber fraction in forage quality assessment because it explains a portion of NDF that is inaccessible to digestion in the ruminant digestive system and is, thus, important in modeling the digestion kinetics of the potentially degradable component of NDF. In experiment 1, uNDF was determined in several forage species in order to compare the accuracy of two reference methods: (1) a long-term in vitro ruminal fermentation (240 h) using an Ankom Daisy II incubator and (2) a multi-step enzymatic method without ruminal fluid. The objective of experiment 2 was to construct predictive equations for uNDF estimation using acid detergent lignin (ADL) and near-infrared reflectance spectroscopy (NIRS) in a pool ( n = 264) of alfalfa hay, timothy hay, and tall fescue straw, using the most accurate reference method selected in experiment 1. Partial least squares regression analysis was used to calibrate the reference values against NIRS spectra. Several indicators were used to assess the performance of validation results, including standard error of cross-validation (SE CrV ), coefficient of determination of cross-validation ( R 2 CrV ), and ratio percentage deviation (RPD). The findings of experiment 1 suggested that, relative to the in vitro ruminal methodology, the enzymatic approach overestimated uNDF concentration of forages. Repeatability coefficient was also greater when uNDF was determined using the in vitro versus enzymatic procedure, potentially disqualifying the enzymatic method for the uNDF analysis in forages. In experiment 2, a poor relationship was established between ADL and uNDF ( R 2 < 0.60), suggesting the inadequacy of ADL parameter to represent the uNDF pool size in these forages. The best predictive equation using NIRS was obtained for alfalfa hay ( R 2 CrV = 0.92; SE CrV = 1.16; RPD = 3.57), using the in vitro fermentation as a reference method. The predictive equations were moderately accurate for timothy hay ( R 2 CrV = 0.80; SE CrV = 1.31; RPD = 2.08) and tall fescue straw ( R 2 CrV = 0.79; SE CrV = 1.38; RPD = 2.18). Our findings suggested the inadequacy of the enzymatic procedure in accurately determining uNDF concentration of forages as compared with the in vitro rumen fermentation protocol. Although the NIRS equations developed using the alfalfa hay dataset were more accurate than that of timothy hay and tall fescue straw, the validation results verified applicability of the equations as a fast screening tool for qualitative prediction of uNDF in these forages, which is important in commercial settings.

Keywords: enzymatic method; lignin; near-infrared; rumen fermentation; undigested neutral detergent fiber (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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