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Related Experiment Videos

Predicting forage indigestible NDF from lignin concentration

M J Traxler1, D G Fox, P J Van Soest

  • 1Department of Animal Science, Cornell University, Ithaca, NY 14853, USA.

Journal of Animal Science
|June 11, 1998
PubMed
Summary

New equations accurately predict indigestible neutral detergent fiber (NDF) in forages, improving animal growth predictions. These models offer better accuracy than current methods for estimating animal performance and forage energy value.

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Area of Science:

  • Animal Science
  • Nutrient Analysis
  • Forage Quality

Background:

  • Accurate prediction of forage indigestible neutral detergent fiber (NDF) is crucial for estimating animal performance.
  • Existing models for lignification and indigestible NDF may have limitations in predicting animal growth.

Purpose of the Study:

  • To develop and validate new equations relating forage lignification to indigestible NDF.
  • To compare the predictive accuracy of new equations with existing models, including the Cornell Net Carbohydrate and Protein System (CNCPS).

Main Methods:

  • Utilized in vitro digestibility and chemical composition data from temperate and tropical forages.
  • Developed nonlinear log-log models based on lignin concentration in NDF.
  • Compared predictions from new equations and CNCPS with animal growth data.

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Main Results:

  • Indigestible NDF increased nonlinearly with lignin concentration.
  • Log-log models using permanganate or sulfuric acid lignin provided the best fit and lowest prediction errors.
  • CNCPS consistently underpredicted average daily gain (ADG) in steers, regardless of the indigestible NDF equation used.

Conclusions:

  • New log-log equations offer improved prediction of indigestible NDF and forage energy value.
  • Current CNCPS model may require adjustments for more accurate prediction of steer ADG, especially with varying forage quality.
  • More accurate indigestible NDF prediction leads to revised estimates of forage energy and potential animal performance.