Classification and comparison of Gliricidia provenances using near infrared reflectance spectroscopy

Awduron Sefydliadau
  • Sue Lister(Awdur)
  • Mewa Singh Dhanoa(Awdur)
  • J. L. Stewart(Awdur)
  • M. Gill(Awdur)
Math Erthygl
Iaith wreiddiolSaesneg
Tudalennau (o-i)221-238
Nifer y tudalennau8
CyfnodolynAnimal Feed Science and Technology
Cyfrol86
Rhif y cyfnodolyn3-4
Dangosyddion eitem ddigidol (DOIs)
StatwsCyhoeddwyd - 30 Awst 2000
Cysylltiadau
Cysylltiad parhaol
Gweld graff cysylltiadau
Fformatau enwi

Crynodeb

There is an ever-increasing need to identify new feed resources in developing countries and the types of forages used tend to be more complex in terms of chemical composition. Near infrared reflectance spectroscopy (NIRS) has the potential to aid the evaluation of forages and in this study was employed to compare and classify Gliricidia spp. provenances. Multivariate statistical techniques, including biplot, principal component analysis (PCA), discrimination, hierarchical cluster and canonical variate analysis (CVA) were used to compare the dried foliage samples of 25 different provenances of Gliricidia spp. which were grown on one site in Honduras to avoid confounding provenance effects with environmental effects or interactions. Marked inter-provenance differences were observed in the 1560–1740, 2060–2170 and 2320–2360 nm spectral regions, particularly for provenance M23 (43/87). This provenance was found to be distinct in graphical plots from biplot, PCA and cluster analysis and is in fact a different species, i.e. Gliricidia maculata. In addition, inter-provenance distances between populations representing provenances G2, G5, H7, M10 and V17 when compared to their intra-provenance variation, were all found to be statistically significant, with the exception of that between G2 and H7. Discriminant analysis showed that of the remaining 20 individual provenances, samples were more similar to the composite (V17) and multiple introduction (H7) populations than the unique populations (G2 and G5). NIRS combined with multivariate techniques therefore shows potential to classify provenances on the basis of their spectral features, which are a comprehensive record of sample chemistry, and aid the selection of alternative forages

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