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

Published online by Cambridge University Press:  09 January 2013

A. Gallo ,
M. Moschini ,
C. Cerioli  and
F. Masoero
A. Gallo
Affiliation:
Feed & Food Science and Nutrition Institute, Faculty of Agriculture, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29100 Piacenza, Italy
M. Moschini
Affiliation:
Feed & Food Science and Nutrition Institute, Faculty of Agriculture, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29100 Piacenza, Italy
C. Cerioli
Affiliation:
Feed & Food Science and Nutrition Institute, Faculty of Agriculture, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29100 Piacenza, Italy
F. Masoero*
Affiliation:
Feed & Food Science and Nutrition Institute, Faculty of Agriculture, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29100 Piacenza, Italy
*
†E-mail: francesco.masoero@unicatt.it
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Abstract

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 (NEL). The latter was calculated according to the two approaches adopted by the 2001 Nutrient Research Council and based on chemical parameters either alone (NEL3x-Lig) or in combination with 48 h NDF degradability in the rumen (NEL3x-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 NEL 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 NEL3x-Lig (r = −0.84) and NEL3x-48h (r = −0.94). 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, −0.141, 0.227 and 0.170, respectively). The FS showed a high determination coefficient for both NEL3x-Lig (R2 = 0.86) and NEL3x-48h (R2 = 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.

Keywords

nutritive valuenet energy for lactationforagemultivariate principal component analysis
Type
Nutrition
Information
animal , Volume 7 , Issue 6 , June 2013 , pp. 930 - 939
Copyright
Copyright © The Animal Consortium 2013

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