Habitat niche-fitness and radix yield prediction models for Angelica sinensis cultivated in the alpine area of the southeastern region of Gansu Province, China

Dried root of Angelica sinensis has been used for thousands of years in traditional Chinese medicinal prescriptions. Researches on better knowledge of appropriate habitats for cultivation of this species are required to encourage the potential ecological sustainable industry. From 2001 to 2004, tran...

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Veröffentlicht in:Plant production science 2008-01, Vol.11 (1), p.42-58
Hauptverfasser: Lin, H.(Lanzhou Univ. (China)), Zhuang, Q, Fu, H
Format: Artikel
Sprache:eng
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Zusammenfassung:Dried root of Angelica sinensis has been used for thousands of years in traditional Chinese medicinal prescriptions. Researches on better knowledge of appropriate habitats for cultivation of this species are required to encourage the potential ecological sustainable industry. From 2001 to 2004, transplanting trials on the regulation of fertilizers and planting density were conducted for collection of habitat factor data at four sites of four counties in the southeastern region of Gansu Province, China. Introducing the niche theory into the research, habitat niche-fitness (HNF) is defined as the degree of similarity of an actual habitat state to the optimum habitat. A new model of HNF is constructed to evaluate the adaptive extent of A. sinensis. The results showed that the model of HNF notably outperforms the proportional similarity index and the geometric parallelism formula both in mathematical justification and biological principle testing. With HNF as a surrogate for composite environmental factors, a radix yield model was constructed. Evaluation of the present model by the sampled subplots specified for data validation proved that the model could be well used for predicted of radix yield across a wide-spread area. The radix yield prediction model and its uses are recommended within the limitations of the data used in the study area. Beyond this range, validation of the radix yield prediction model will be necessary.
ISSN:1343-943X
1349-1008
DOI:10.1626/pps.11.42