2024
DOI: 10.3390/plants13071050
|View full text |Cite
|
Sign up to set email alerts
|

Estimating the Amount of the Wild Artemisia annua in China Based on the MaxEnt Model and Spatio-Temporal Kriging Interpolation

Juan Wang,
Tingting Shi,
Hui Wang
et al.

Abstract: In order to determine the distribution area and amount of Artemisia annua Linn. (A. annua) in China, this study estimated the current amount of A. annua specimens based on the field survey sample data obtained from the Fourth National Census of Chinese Medicinal Resources. The amount was calculated using the maximum entropy model (MaxEnt model) and spatio-temporal kriging interpolation. The influencing factors affecting spatial variations in the amount were studied using geographic probes. The results indicate… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 32 publications
0
1
0
Order By: Relevance
“…The potential distribution of H. mutabilis was mapped using MaxEnt version 3.4.4. In the model, 75% of the distribution points were used as training data, while the remaining 25% were used as test data, with other settings kept at their default values [32,33]. The MaxEnt model includes a jackknife test to analyze the contribution and importance of environmental variables.…”
Section: Species Distribution Model Parameter Settingmentioning
confidence: 99%
“…The potential distribution of H. mutabilis was mapped using MaxEnt version 3.4.4. In the model, 75% of the distribution points were used as training data, while the remaining 25% were used as test data, with other settings kept at their default values [32,33]. The MaxEnt model includes a jackknife test to analyze the contribution and importance of environmental variables.…”
Section: Species Distribution Model Parameter Settingmentioning
confidence: 99%