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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: [email protected]
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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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References

Abdi, H 2003. Factor rotations. In Encyclopedia for social sciences research methods (ed. M Lewis-Beck, A Bryman and T Futing), pp. 978–982. Thousand Oaks, CA, USA.Google Scholar
Aguiar, AD, Tedeschi, LO, Rouquette, FM Jr, McCuistion, K, Ortega-Santos, JA, Anderson, R, DeLaney, D, Moore, S 2011. Determination of nutritive value of forages in south Texas using an in vitro gas production technique. Grass and Forage Science 66, 526–540.Google Scholar
Andueza, D, Picard, F, Jestin, M, Andrieu, J, Baumont, R 2011. NIRS prediction of the feed value of temperate forages: efficacy of four calibration strategies. Animal 5, 1002–1013.CrossRefGoogle ScholarPubMed
Association of Official Analytical Chemists (AOAC) 1995. Official methods of analysis, 15th edition. AOAC, Arlington, VA, USA.Google Scholar
Cerny, BA, Kaiser, HF 1977. A study of a measure of sampling adequacy for factor-analytical correlation matrices. Multivariate Behavioral Research 12, 43–47.CrossRefGoogle Scholar
Cherney, DJR 2000. Characterization of forages by chemical analysis. In Forage evaluation in ruminant nutrition (ed. DI Givens, E Owen, RFE Axford and HM Omed), pp. 281–300. CABI Publishing, New York, USA.Google Scholar
De Boever, JL, Cottyn, BG, De Brabander, DL, Vanacker, JM, Boucqué, CHV 1996. Prediction of the feeding value of grass silages by chemical parameters, in vitro digestibility and near-infrared reflectance spectroscopy. Animal Feed Science and Technology 60, 103–115.Google Scholar
Desnoyers, M, Giger-Reverdin, S, Sauvant, D, Duvaux-Ponter, C 2011. The use of a multivariate analysis to study between-goat variability in feeding behavior and associated rumen pH patterns. Journal of Dairy Science 94, 842–852.Google Scholar
European Community (EC) 1986. Council directive of 24 November 1986 on the approximation of laws, regulations and administrative provisions of the Member States regarding the protection of animals used for experimental and other scientific purposes. Official Journal of the European Communities L358, 1–28.Google Scholar
European Community (EC) 1999. Commission directive of 27 July 1999 amending the third Commission Directive 72/199/EEC of 27 April 1972 establishing Community methods of analysis for the official control of feedingstuffs (text with EEA relevance). Official Journal of the European Communities L209, 23–27.Google Scholar
Ferreira, G, Mertens, DR 2005. Chemical and physical characteristics of corn silages and their effects on in vitro disappearance. Journal of Dairy Science 88, 4414–4425.Google Scholar
Givens, DI, Everington, JM, Adamson, AH 1989. The digestibility and metabolisable energy content of grass silage and their prediction from laboratory measurements. Animal Feed Science and Technology 24, 27–43.Google Scholar
Goeser, JP, Combs, DK 2009. An alternative method to assess 24-h ruminal in vitro neutral detergent fiber digestibility. Journal of Dairy Science 92, 3833–3841.CrossRefGoogle ScholarPubMed
Jayanegara, A, Wina, E, Soliva, CR, Marquardt, S, Kreuzer, M, Leiber, F 2011. Dependence of forage quality and methanogenic potential of tropical plants on their phenolic fractions as determined by principal component analysis. Animal Feed Science and Technology 163, 231–243.Google Scholar
Krämer, M, Weisbjerg, MR, Lund, P 2010. Estimation of indigestible NDF in feedstuffs for ruminants. Proceedings of the 1st Nordic Feed Science Conference, Uppsala, Sweden, pp. 15–19.Google Scholar
Krämer, M, Weisbjerg, MR, Lund, P, Jensen, CS, Pedersen, MG 2012. Estimation of indigestible NDF in forages and concentrates from cell wall composition. Animal Feed Science and Technology 177, 40–51.Google Scholar
Licitra, G, Hernandez, TM, Van Soest, PJ 1996. Standardization of procedures for nitrogen fractionation of ruminant feeds. Animal Feed Science Technology 57, 347–358.Google Scholar
Lundberg, KM, Hoffman, PC, Bauman, LM, Berzaghi, P 2004. Prediction of forage energy content by near infrared reflectance spectroscopy and summative equations. The Professional Animal Scientist 20, 262–269.Google Scholar
Macciotta, NPP, Vicario, D, Cappio-Bonino, A 2006. Use of multivariate analysis to extract latent variables to level of production and lactation persistency in dairy cattle. Journal of Dairy Science 89, 3188–3194.Google Scholar
Mertens, DR 1992. Nonstructural and structural carbohydrates. In Large Dairy Herd Management (ed. HH Van Horn and CJ Wilcox), pp. 219–235. American Dairy Science Association, Champaign, IL, USA.Google Scholar
Mertens, DR 1993a. Kinetics of cell wall digestion and passage in ruminants. In Forage Cell Wall Structure and Digestibility (ed. HG Jung, DR Buxton, RD Hatfield and J Ralph), pp. 535–570. American Society of Agronomy, Madison, WI, USA.Google Scholar
Mertens, DR 1993b. Rate and extent of digestion. In Quantitative Aspects of Ruminant Digestion and Metabolism (ed. JM Forbes and JFrance), pp. 13–52. CAB International, Wallingford, Oxon, UK.Google Scholar
Nutrient Requirements of Dairy Cattle (NRC) 2001 . National Academy Press, Washington, DC, USA.Google Scholar
O'Rourke, N, Hatcher, L, Stepanski, EJ 2005. A Step-by-step approach to using SAS® for univariate and multivariate statistics, 2nd edition. SAS Institute Inc., SAS Campus Drive, Cary, North Carolina, USA.Google Scholar
Oba, M, Allen, MS 1999. Evaluation of the importance of the digestibility of neutral detergent fiber from forage: effects on dry matter intake and milk yield of dairy cows. Journal of Dairy Science 82, 589–596.CrossRefGoogle ScholarPubMed
Robinson, PH, Givens, DI, Getachew, G 2004. Evaluation of NRC, UC Davis and ADAS approaches to estimate the metabolizable energy values of feeds at maintenance energy intake from equations utilizing chemical assays and in vitro determinations. Animal Feed Science and Technology 114, 75–90.CrossRefGoogle Scholar
Rotz, CA, Mertens, DR, Buckmaster, DR, Allen, MS, Harrison, JH 1999. Our industry today: a dairy herd model for use in whole farm simulations. Journal of Dairy Science 82, 2826–2840.Google Scholar
SAS (Statistical Analytical System) 2003. SAS/SAT guide for personal computers, version 9.13. SAS Institute Inc., Cary, NC, USA.Google Scholar
Sniffen, CJ, O'Connor, JD, Van Soest, PJ, Fox, DG, Russell, JB 1992. A net carbohydrate and protein system for evaluating cattle diets: II. Carbohydrate and protein availability. Journal of Animal Science 70, 3562–3577.CrossRefGoogle ScholarPubMed
Spanghero, M, Berzaghi, P, Fortina, R, Masoero, F, Rapetti, L, Zanfi, C, Tassone, S, Gallo, A, Colombini, S, Ferlito, JC 2010. Technical note: precision and accuracy of in vitro digestion of neutral detergent fiber and predicted net energy of lactation content of fibrous feeds. Journal of Dairy Science 93, 4855–4859.CrossRefGoogle ScholarPubMed
Stevens, JP 2009. Applied Multivariate Statistics for the Social Sciences, 5th edition. Routledge, Taylor & Francis Group, New York, USA.Google Scholar
Tagliapietra, F, Cattani, M, Hansen, HH, Hindrichsen, IK, Bailoni, L, Schiavon, S 2011. Metabolizable energy content of feeds based on 24 or 48 h in situ NDF digestibility and on in vitro 24 h gas production methods. Animal Feed Science and Technology 170, 182–191.Google Scholar
Traxler, MJ, Fox, DG, Van Soest, PJ, Pell, AN, Lascano, CE, Lanna, DPD, Moore, JE, Lana, RP, Vèlez, M, Flores, A 1998. Predicting forage indigestible NDF from lignin concentration. Journal of Animal Science 76, 1469–1480.Google Scholar
Van Amburgh, ME, Van Soest, PJ, Robertson, JB, Knaus, WF 2003. Corn silage neutral fiber: refining a mathematical approach for in vitro rates of digestion. Proceedings of the Cornell Nutrition Conference for Feed Manufacturers, Syracuse, NY, USA, pp. 99–108.Google Scholar
Van Soest, PJ, Robertson, JB, Lewis, BA 1991. Methods of dietary fiber, neutral detergent fiber and non-polysaccharides in relation to animal nutrition. Journal of Dairy Science 74, 3583–3597.Google Scholar
Vieira, RAM, Campos, PRDSS, Silva, JFCD, Tedeschi, LO, Tamy, WP 2012. Heterogeneity of the digestible insoluble fiber of selected forages in situ. Animal Feed Science and Technology 171, 154–166.Google Scholar
Weiss, WP 1998. Estimating the available energy content of feeds for dairy cattle. Journal of Dairy Science 81, 830–839.Google Scholar
Weiss, WP, Conrad, HR, St. Pierre, NR 1992. A theoretically-based model for predicting total digestible nutrient values of forages and concentrates. Animal Feed Science and Technology 39, 95–110.CrossRefGoogle Scholar
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