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

Krämer, M.; Weisbjerg, M.R.; Lund, Peter; Jensen, Casper Bruun; Pedersen, Morten Greve

doi:10.1016/j.anifeedsci.2012.07.027

Cited by 32 publications

(31 citation statements)

References 13 publications

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“…Recently, some authors (Krä mer et al, 2010;Krä mer et al, 2012;Vieira et al, 2012) reported that the use of a constant coefficient could result in an inaccurate estimate of the iNDF among different forages. In particular, Krä mer et al (2010) observed variable iNDF/ADL ratios among forages, ranging from 1.8 to 3.6 in grass hays and red clover, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…However, the methods currently adopted to measure iNDF (i.e. 288 h of rumen incubation) are troublesome and rely on the use of fistulated animals (Krä mer et al, 2012). Other methodological approaches, such as in vitro enzymatic or gas production techniques, could represent valid and attractive alternatives to the in situ determination of iNDF (De Boever et al, 1996;Aguiar et al, 2011).…”

Section: Nutritive and Energy Values Of Foragesmentioning

confidence: 99%

“…Other methodological approaches, such as in vitro enzymatic or gas production techniques, could represent valid and attractive alternatives to the in situ determination of iNDF (De Boever et al, 1996;Aguiar et al, 2011). Alternatively, the development of a specific nearinfrared spectroscopy (NIRS) calibration curve could be used to predict the iNDF of forages (Krä mer et al, 2012). In particular, NIRS measurements have been successfully used to predict fibre digestibility, iNDF, energy content and voluntary intake of forages (De Boever et al, 1996;Lundberg et al, 2004;Andueza et al, 2011;Krä mer et al, 2012).…”

Section: Nutritive and Energy Values Of Foragesmentioning

confidence: 99%

“…Alternatively, the development of a specific nearinfrared spectroscopy (NIRS) calibration curve could be used to predict the iNDF of forages (Krä mer et al, 2012). In particular, NIRS measurements have been successfully used to predict fibre digestibility, iNDF, energy content and voluntary intake of forages (De Boever et al, 1996;Lundberg et al, 2004;Andueza et al, 2011;Krä mer et al, 2012).…”

Section: Nutritive and Energy Values Of Foragesmentioning

confidence: 99%

“…A major disadvantage of complex models is the large number of laboratory parameters, with some being poorly repeatable, timeconsuming and expensive (Cherney, 2000;Spanghero et al, 2010). In addition to chemical parameters, biological parameters such as in situ rumen NDF degradation characteristics have been proposed to properly evaluate forages used in dairy cow diets (Van Amburgh et al, 2003;Robinson et al, 2004;Krä mer et al, 2012). In particular, it has been proved that biological measurements can be useful to improve the prediction of in vivo nutrient digestibility and the energy values of forages (De Boever et al, 1996;Robinson et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Use of principal component analysis to classify forages and predict their calculated energy content

Gallo

Moschini

Cerioli

et al. 2013

Animal

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A set of 180 forages (47 alfalfa hays, 26 grass hays, 52 corn silages, 35 small grain silages and 20 sorghum silages) were randomly collected from different locations of the Po Valley (Northern Italy) from 2009 to 2010. The forages were characterised for chemical composition (11 parameters), NDF digestibility (five parameters) and net energy for lactation (NE L ). The latter was calculated according to the two approaches adopted by the 2001 Nutrient Research Council and based on chemical parameters either alone (NE L3x-Lig ) or in combination with 48 h NDF degradability in the rumen (NE L3x-48h ). Thereafter, a principal component analysis (PCA) was used to define forage populations and limit the number of variables to those useful for obtaining a rapid forage quality evaluation on the basis of the calculated NE L content of forages. The PCA identified three forage populations: corn silage, alfalfa hay and a generic population of so-called 'grasses', consisting of grass hays, small grain and sorghum silages. This differentiation was also confirmed by a cluster analysis. The first three principal components (PC) together explained 79.9% of the total variation. PC1 was mainly associated with protein fractions, ether extract and lignin, PC2 with ash, starch, NDF and indigestible NDF (iNDF) and PC3 with NDF digestibility. Moreover, PC2 was highly correlated to both NE L3x-Lig ( r 5 20.84) and NE L3x-48h ( r 5 20.94). Subsequently, forage-based scores (FS) were calculated by multiplying the original standardised variables of ash, starch, NDF and iNDF with the scoring factors obtained from PCA (0.112, 20.141, 0.227 and 0.170, respectively). The FS showed a high determination coefficient for both NE L3x-Lig ( R 2 5 0.86) and NE L3x-48h ( R 2 5 0.73). These results indicate that PCA enables the distinction of different forage classes and appropriate prediction of the energy value on the basis of a reduced number of parameters. With respect to the rumen in situ parameters, iNDF was found to be more powerful at discriminating forage quality compared with NDF digestibility at different rumen incubation times or rates of NDF digestion.

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Section: Discussionmentioning

confidence: 99%