Ecology and Quality Suitability Regionalization of Sabia parviflora / 中国实验方剂学杂志
Chinese Journal of Experimental Traditional Medical Formulae
; (24): 172-180, 2021.
Article
em Zh
| WPRIM
| ID: wpr-905878
Biblioteca responsável:
WPRO
ABSTRACT
Objective:To explore the potential suitable distribution area and the high-quality distribution area of <italic>Sabia parviflora</italic>. Method:Combined with the distribution information and environmental factors,the maximum entropy (MaxEnt) model and ArcGIS software were used to predict the potential suitable distribution area of <italic>S. parviflora</italic>. Based on the correlation between environmental factors and total saponins,total flavonoids,quercetin-3-<italic>O</italic>-gentiobioside,camellianoside,tsubakioside A,kaempferol-3-<italic>O</italic>-rutinoside and isobariclisin-3-<italic>O</italic>-rutinoside,the quality regionalization was conducted by using spatial interpolation method and fuzzy superposition function in ArcGIS software. Result:<italic>S. parviflora</italic> is mainly distributed in Yunnan,Guizhou,Guangxi province in China. The medium and high suitable areas accounts for about 2.88% of the national area. The precipitation in October and November,the precipitation in the warmest and driest seasons,the standard deviation of seasonal changes in temperature and altitude are the main environmental factors that affect the distribution of <italic>S. parviflora</italic>. Slope,precipitation,solar radiation and temperature change had great influence on the accumulation of secondary metabolites. Based on the results of potential suitable distribution and spatial interpolation of each component,the high-quality areas of <italic>S. parviflora</italic> are mainly concentrated in the southwest of Guizhou,with Qinglong,Guanling,Zhenning,Pu'an,Xingren county and other areas as the core. Conclusion:This study provides a scientific guidance for the site selection of artificial planting and the procurement of medicinal materials for <italic>S. parviflora</italic>.
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Base de dados:
WPRIM
Tipo de estudo:
Prognostic_studies
Idioma:
Zh
Ano de publicação:
2021
Tipo de documento:
Article