2021
DOI: 10.3390/agriculture11111122
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Predicting Possible Distribution of Tea (Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China

Abstract: Climate change has dramatic impacts on the growth and the geographical distribution of tea (Camellia sinensis L.). Assessing the potential distribution of tea will help decision makers to formulate appropriate adaptation measures to use the altered climatic resources and avoid the damage from climate hazards. The objective in this study is to model the current and future distribution of tea species based on the four SSPs scenarios using the MaxEnt model in China. For the modeling procedure, tea growth records … Show more

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Cited by 30 publications
(17 citation statements)
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“…It can be seen that this part of the region is mainly located in the northern temperate zone. This part of the region has a warm climate and fertile soil, ideal for agricultural development [ 12 ]. Located in the interior of the Eurasian continent, the area is closed, less subject to external intrusion, and cultural traditions can be continued.…”
Section: Introductionmentioning
confidence: 99%
“…It can be seen that this part of the region is mainly located in the northern temperate zone. This part of the region has a warm climate and fertile soil, ideal for agricultural development [ 12 ]. Located in the interior of the Eurasian continent, the area is closed, less subject to external intrusion, and cultural traditions can be continued.…”
Section: Introductionmentioning
confidence: 99%
“…MaxEnt models can predict the suitable distribution of species under future climate change scenarios; it is thus an important tool for simulating the distribution of suitable habitats of species ( Zhao et al, 2021 ). Although MaxEnt models have high accuracy, this does not mean that the predicted suitable area is always completely consistent with the actual distribution of species ( Gebrewahid et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…AUC is widely used to evaluate the performance of a variety of models and is not affected by the threshold setting. Usually, its value is taken as an indicator of the prediction effect of the model, and its range is usually 0.5–1 (Zhao et al., 2021 ). In the case of 0.5 ≤ AUC < 0.6, model performance is unqualified; 0.6 ≤ AUC < 0.7 means the model performance is poor; 0.7 ≤ AUC < 0.8 means the model performance is average; 0.8 ≤ AUC < 0.9 means the model performance is good; and 0.9 ≤ AUC < 1 means the model performance is excellent (Aidoo et al., 2022 ; Zhang et al., 2019 ).…”
Section: Methodsmentioning
confidence: 99%