Abstract
The role of indigestible NDF is essential in relation to OM digestibility prediction, total tract digestibility, rumen fill, passage rate, and digestion kinetics. Moreover, the truly indigestible NDF (iNDF) represents a core point in dynamic models used for diet formulations. However, despite its wide possible applications, few trials have been conducted to quantify iNDF and even fewer to investigate whether or not it is consistent among different forage sources. The objective of this study was to predict the iNDF by measuring the residual NDF after 240-h in vitro fermentation to determine the unavailable NDF (uNDF) within and among various forage types. Finally, a mathematical approach was investigated for the estimation of the uNDF fraction. In all, 688 forages were analyzed in this study. This pool included 122 alfalfa hays, 282 corn silages, and 284 grass hays. Values of uNDF varied among different forages and within the same type (22.7% ± 4.48%, 20.1% ± 4.23%, and 11.8% ± 3.5% DM for grass hay, alfalfa hay, and corn silages, respectively). The relationship among uNDF and ADL was not constant and, for grass hay and corn silage, was different ( 0.05) from the 2.4 × lignin value applied by the traditional Chandler equation. The observed uNDF:ADL ratio was 3.22 for grass hay and 3.11 for corn silage. Relationships among chemical and biological parameters and uNDF were investigated via simple and multiple regression equations. The greatest correlation with a single variable was obtained by ADL and ADF when applied to the whole data set ( = 0.63). Greater coefficients of determination resulted from a multiple regression equation for the whole data set ( = 0.80) and within each forage type ( = 0.65, 0.77, and 0.54 for grass hay, alfalfa hay, and corn silage, respectively). In conclusion, a regression approach requires specific equations and different regression coefficients for each forage type. The direct measurement of uNDF represented the best approach to obtain an accurate prediction of the iNDF and to optimize its specific purpose in dynamic nutrition models.
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Claims 13
Claim · model-written summary
Grass hay had an uNDF value of 22.7% ± 4.48% DM.
Verbatim
e1“Values of uNDF varied among different forages and within the same type (22.7% ± 4.48%, 20.1% ± 4.23%, and 11.8% ± 3.5% DM for grass hay, alfalfa hay, and corn silages, respectively)”
Claim · model-written summary
Alfalfa hay had an uNDF value of 20.1% ± 4.23% DM.
Verbatim
e1“Values of uNDF varied among different forages and within the same type (22.7% ± 4.48%, 20.1% ± 4.23%, and 11.8% ± 3.5% DM for grass hay, alfalfa hay, and corn silages, respectively)”
Claim · model-written summary
Corn silage had an uNDF value of 11.8% ± 3.5% DM.
Verbatim
e1“Values of uNDF varied among different forages and within the same type (22.7% ± 4.48%, 20.1% ± 4.23%, and 11.8% ± 3.5% DM for grass hay, alfalfa hay, and corn silages, respectively)”
Claim · model-written summary
The observed uNDF:ADL ratio was 3.22 for grass hay.
Verbatim
e1“The observed uNDF:ADL ratio was 3.22 for grass hay and 3.11 for corn silage”
Claim · model-written summary
The observed uNDF:ADL ratio was 3.11 for corn silage.
Verbatim
e1“The observed uNDF:ADL ratio was 3.22 for grass hay and 3.11 for corn silage”
Claim · model-written summary
The relationship between uNDF and ADL was not constant and differed for grass hay and corn silage from the 2.4 × lignin value of the Chandler equation.
Verbatim
e1“The relationship among uNDF and ADL was not constant and, for grass hay and corn silage, was different ( 0.05) from the 2.4 × lignin value applied by the traditional Chandler equation”
Claim · model-written summary
ADL and ADF gave the greatest correlation with uNDF of any single variable in the whole data set.
- Taxon
- forage typest0
- Compound
- ADL and ADFe1
- Outcome
- greatest correlation with a single variablee1
- Value
- 0.63e1
- Qualifier
- whole data sete1
Not reportedDirection
Verbatim
t0“Estimation of the indigestible fiber in different forage types”
e1“The greatest correlation with a single variable was obtained by ADL and ADF when applied to the whole data set ( = 0.63)”
Claim · model-written summary
The multiple regression equation gave a coefficient of determination of 0.80 for the whole data set.
- Taxon
- forage typest0
- Treatment
- multiple regression equatione1
- Outcome
- coefficients of determinatione1
- Value
- 0.80e1
- Qualifier
- whole data sete1
Not reportedDirection
Verbatim
t0“Estimation of the indigestible fiber in different forage types”
e1“Greater coefficients of determination resulted from a multiple regression equation for the whole data set ( = 0.80) and within each forage type ( = 0.65, 0.77, and 0.54 for grass hay, alfalfa hay, and corn silage, respectively)”
Claim · model-written summary
Within grass hay, the multiple regression equation gave a coefficient of determination of 0.65.
- Taxon
- grass haye1
- Treatment
- multiple regression equatione1
- Outcome
- coefficients of determinatione1
- Value
- 0.65e1
Not reportedDirection
Verbatim
e1“Greater coefficients of determination resulted from a multiple regression equation for the whole data set ( = 0.80) and within each forage type ( = 0.65, 0.77, and 0.54 for grass hay, alfalfa hay, and corn silage, respectively)”
Claim · model-written summary
Within alfalfa hay, the multiple regression equation gave a coefficient of determination of 0.77.
- Taxon
- alfalfa haye1
- Treatment
- multiple regression equatione1
- Outcome
- coefficients of determinatione1
- Value
- 0.77e1
Not reportedDirection
Verbatim
e1“Greater coefficients of determination resulted from a multiple regression equation for the whole data set ( = 0.80) and within each forage type ( = 0.65, 0.77, and 0.54 for grass hay, alfalfa hay, and corn silage, respectively)”
Claim · model-written summary
Within corn silage, the multiple regression equation gave a coefficient of determination of 0.54.
- Taxon
- corn silagee1
- Treatment
- multiple regression equatione1
- Outcome
- coefficients of determinatione1
- Value
- 0.54e1
Not reportedDirection
Verbatim
e1“Greater coefficients of determination resulted from a multiple regression equation for the whole data set ( = 0.80) and within each forage type ( = 0.65, 0.77, and 0.54 for grass hay, alfalfa hay, and corn silage, respectively)”
Claim · model-written summary
A regression approach for forage uNDF requires specific equations and different regression coefficients for each forage type.
Verbatim
e1“In conclusion, a regression approach requires specific equations and different regression coefficients for each forage type”
Claim · model-written summary
Direct measurement of uNDF was the best approach for an accurate prediction of iNDF, compared with a regression approach.
Not reportedTaxon
Verbatim
e1“The direct measurement of uNDF represented the best approach to obtain an accurate prediction of the iNDF”
e2“In conclusion, a regression approach requires specific equations and different regression coefficients for each forage type”