AHP-GIS and MaxEnt for delineation of potential distribution of Arabica coffee plantation under future climate in Yunnan, China

Coffee, as one of three major beverages all over the world, is featured by many functions mainly including refreshment, diuresis, invigorating stomach and stimulating appetite. However, there still lack of studies regarding the potential distribution of suitable planting regions and the important en...

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Bibliographic Details
Main Authors: Cheng, J. (Author), Kong, H. (Author), Li, R. (Author), Liu, X. (Author), Wang, X. (Author), Yang, Q. (Author), Zhang, S. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
GIS
Online Access:View Fulltext in Publisher
LEADER 04136nam a2200601Ia 4500
001 10.1016-j.ecolind.2021.108339
008 220427s2021 CNT 000 0 und d
020 |a 1470160X (ISSN) 
245 1 0 |a AHP-GIS and MaxEnt for delineation of potential distribution of Arabica coffee plantation under future climate in Yunnan, China 
260 0 |b Elsevier B.V.  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.ecolind.2021.108339 
520 3 |a Coffee, as one of three major beverages all over the world, is featured by many functions mainly including refreshment, diuresis, invigorating stomach and stimulating appetite. However, there still lack of studies regarding the potential distribution of suitable planting regions and the important environmental factors that affect the growth and development of Arabica coffee. Based on climate, terrain and soil data, MaxEnt model and AHP-GIS technology were used to evaluate and analyze the increase and decrease range of Arabica coffee ecological suitability planting areas and most suitable areas in different periods, determine the most significant environmental factors affecting ecological suitability, and determine the distribution transformation under climate change scenario (SSPs370). Results showed that the ecological suitability of Arabica coffee plantation was mainly subjected to the climate factors, among which the maximum temperature in the warmest month, minimum temperature in the coldest month and the annual precipitation were particularly important, followed by terrain and soil factors. The model accuracies of MaxEnt and AHP-GIS were 0.925 and 0.833 respectively, indicating that the two methods were reliable in the ecological suitability evaluation of Arabica coffee. MaxEnt predicted that the most and moderately suitable area accounted for 16.26% and 22.59%, and AHP-GIS predicted that they accounted for 13.35% and 41.32% of the total area respectively. The most and moderately suitable areas were mainly concentrated in west, southeast, south and southwest Yunnan. Under the future climate scenario model, MaxEnt and AHP-GIS predicted that the area of the most suitable area would increase by 2.62 × 104–4.42 × 104 km2 and 3.53 × 104–5.44 × 104 km2 respectively from 2021 to 2100, which were mainly concentrated in west, southwest and southeast Yunnan. At the same time, the distribution of the most suitable area migrated northward generally to higher altitude and higher latitude, and the migration distance was 27.44–92.22 km. The predicted potential distribution of Arabica coffee based on MaxEnt model and AHP-GIS model could provide reference for the implementation of long-term planning and development programs so as to alleviate the effects of climate change on the distribution of Arabica coffee. © 2021 
650 0 4 |a AHP-GIS 
650 0 4 |a AHP-GIS 
650 0 4 |a analytical hierarchy process 
650 0 4 |a Arabica coffee 
650 0 4 |a Arabicum coffee 
650 0 4 |a China 
650 0 4 |a climate change 
650 0 4 |a Climate change 
650 0 4 |a Climate change 
650 0 4 |a Climate models 
650 0 4 |a Coffea arabica 
650 0 4 |a coffee 
650 0 4 |a Coffee plantation 
650 0 4 |a Ecological suitability 
650 0 4 |a Ecology 
650 0 4 |a Environmental factors 
650 0 4 |a Environmental technology 
650 0 4 |a Future climate 
650 0 4 |a Geographic information systems 
650 0 4 |a Geographical distribution 
650 0 4 |a GIS 
650 0 4 |a growth 
650 0 4 |a Hierarchical systems 
650 0 4 |a MaxEnt model 
650 0 4 |a MaxEnt models 
650 0 4 |a Metadata 
650 0 4 |a plantation 
650 0 4 |a Plantings 
650 0 4 |a Potential distributions 
650 0 4 |a Potential geographical distribution 
650 0 4 |a Potential geographical distribution 
650 0 4 |a temperature effect 
650 0 4 |a Yunnan 
700 1 |a Cheng, J.  |e author 
700 1 |a Kong, H.  |e author 
700 1 |a Li, R.  |e author 
700 1 |a Liu, X.  |e author 
700 1 |a Wang, X.  |e author 
700 1 |a Yang, Q.  |e author 
700 1 |a Zhang, S.  |e author 
773 |t Ecological Indicators