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Assessing the suitable cultivation areas for Scutellaria baicalensis in China using the Maxent model and multiple linear regression
Affiliation:1. Centro Conservazione Biodiversità (CCB), Dipartimento di Scienze della Vita e dell''Ambiente, Università degli Studi di Cagliari, Viale S. Ignazio da Laconi 13, 09123 Cagliari, Italy;2. Department of Botany, Faculty of Science, Mansoura University, 35516 Mansoura, Egypt;3. Hortus Botanicus Karalitanus (HBK), Università degli Studi di Cagliari, Viale S. Ignazio da Laconi 9-11, 09123 Cagliari, Italy
Abstract:Cultivating medical plants is an efficient way not only to meet the increasingly high demand for plant-based drugs but also to protect wild populations from overexploitation. The environments of cultivation areas should be suitable for both plant growth and accumulation of bioactive constituents. Scutellaria baicalensis Georgi (Huang-qin or Chinese skullcap) is a widely used herb that is suffering rapid population decline in China. To promote better cultivation of this herb, this paper reports a new approach for predicting potentially suitable cultivation areas and for building a mathematical relationship between environmental factors and the active ingredient content in S. baicalensis using the Maxent model and multiple linear regression. The results showed that extreme temperatures and precipitation had considerable impacts on the potential distribution of S. baicalensis. Higher annual mean temperature, precipitation seasonality, and lower isotherms contributed to higher baicalin content. The potential cultivation areas for S. baicalensis were mainly distributed in northeast China. Northeastern Inner Mongolia, part of Hebei and the regions in southwestern Liaoning Province were found to be highly suitable for cultivating S. baicalensis in China. The results of this study can allow growers and pharmaceutical companies to identify suitable areas for planting herbs, which could prevent the blind cultivation of this species in unsuitable habitats while ensuring the quality of S. baicalensis.
Keywords:Chemical constituents  Maxent modelling  Potential cultivation distribution
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