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Asian-Australas J Anim Sci > Volume 4(3); 1991 > Article
Ruminant Nutrition and Forage Utilization
Asian-Australasian Journal of Animal Sciences 1991;4(3): 245-250.
DOI: https://doi.org/10.5713/ajas.1991.245    Published online September 1, 1991.
The analysis and diagnosis of sown pasture vegetation 2.Grouping and characterization the sown and weed species by means of principal component analysis
S. Kawanabe
Abstract
Analysis of the characteristics and the grouping of the species of sown and weeks in artificial pastures was studied applying the principal component analysis method. Presency and coverage of six sown species and fifteen weed species which occurred in pastures of under-grazing and optimum-grazing were subject to analysis. From field survey, species were divided into three groups: the group A included five species such as Festuca arundinacea, Lolium perenne and Dactylis glomerata, etc., the group B included eleven species such as Polygonum longisetum, Agrostis alba and Rumex obtusifolius, ets., and the group C included five species such as Miscanthus sinensis, Rubus palmatus and Artemisia princes, etc. The group A species corresponded to good pasture conditions and management. On the contrary, the group C species occurred in poor pasture conditions with inadequate management. The group B species corresponded to intermediate pasture conditions and management. Interrelated pair species co-existing and species non-co-existing were discovered. Factor loading as negative for the group A species, positive for the group C species and positive but lower than the group C species for the group B species. From these results it is concluded that the principal component analysis seems to one of the useful tools for the analysis of characteristics of species and the diagnosis of sown pasture vegetation, although further studies are required to get more general information about species characteristics.
Keywords: Characterization of Pasture Species; Diagnosis of Pasture Condition; Grouping Species in Pasture; Principal Component Analysis
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