Prediction of the feeding value of grass silages by chemical parameters, in vitro digestibility and near-infrared reflectance spectroscopy

Boever, J.L. De; Cottyn, B. G.; Brabander, D.L. De; Vanacker, J.M.; Boucque, C. V.

doi:10.1016/0377-8401(95)00914-0

Cited by 56 publications

(40 citation statements)

References 16 publications

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“…The results obtained through the PCA on the current database confirmed that the use of biological measurement other than chemical parameters could be useful to better discriminate between poor-and high-quality forages (Givens et al, 1989;De Boever et al, 1996;Weiss, 1998). In our experimental condition, the use of iNDF, instead of other biological measurements (i.e.…”

Section: Nutritive and Energy Values Of Foragessupporting

confidence: 70%

“…In the past, linear regression analysis based on both chemical and biological (i.e. in vitro rumen fluid or enzymatic digestibility) parameters was used to predict the energy value of feeds and forages (Givens et al, 1989;De Boever et al, 1996). More recently, some authors used multivariate analysis techniques to extract latent variables linked to the milk yield and lactation persistency of dairy cows (Macciotta et al, 2006), to study the influence of quality parameters on the methanogenic potential of different forages (Jayanegara et al, 2011) or the between-goat variability in feeding behaviour (Desnoyers et al, 2011).…”

Section: Multivariate Analysismentioning

confidence: 99%

“…high soil contamination), whereas the NDF rather than ADF was retained in FS calculation to avoid an additional analytical (i.e. acid detergent treatment) step to obtain a value that is correlated to NDF (De Boever et al, 1996). Therefore, the original parameters retained to calculate FS were ash, starch, NDF and iNDF.…”

Section: Multivariate Analysismentioning

confidence: 99%

“…Therefore, an accurate and rapid estimate of these parameters is critical to formulate diets that meet animal energy requirements (De Boever et al, 1996;Weiss, 1998;Aguiar et al, 2011). Historically, the energy content of forages was estimated using empirical singlecomponent equations based on specific nutrients such as NDF, ADF or crude fibre (Mertens, 1992;Rotz et al, 1999;Ferreira and Mertens, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…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: Nutritive and Energy Values Of Foragessupporting

confidence: 70%

Section: Multivariate Analysismentioning

confidence: 99%

Section: Multivariate Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Use of principal factor analysis to generate a corn silage fermentative quality index to rank well‐ or poorly preserved forages

et al. 2015

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Predicting the chemical composition of fibre and core fraction of hemp (Cannabis sativa L.)

Toonen¹,

Maliepaard²,

Reijmers³

et al. 2004

Euphytica

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Fibre formation in hemp (Cannabis sativa L.) is a dynamic process. In order to follow this process, the chemical composition of the hemp stem was studied during plant development using the acid and neutral detergent fibre and lignin methods. Additionally, near infra-red spectroscopy was carried out. To predict the chemical composition of the stem samples partial least square (PLS) analysis was carried out. The developed PLS models can predict the level of %ADF and %NDF in the hemp bast and core samples. For %ADL, the models are only suited for either core or bast tissues and can not be used for both tissues simultaneously.

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Prediction of the feeding value of grass silages by chemical parameters, in vitro digestibility and near-infrared reflectance spectroscopy

Cited by 56 publications

References 16 publications

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

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

Use of principal factor analysis to generate a corn silage fermentative quality index to rank well‐ or poorly preserved forages

Predicting the chemical composition of fibre and core fraction of hemp (Cannabis sativa L.)

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