GIS-Based Study on Ecoclimatic Suitability Regionalization Study of Plum in Sichuan Province
[Objective]The objective of this research is to offer a scholarly resource that focuses on the strategic planning and optimization of the industrial structure and layout within the plum industry in Sich-uan Province.[Method]By utilizing meteorological data obtained from 145 meteorological stations in Si-chuan Province from 2009 to 2018,and considering the climatic conditions prevalent in the primary plum production areas of the province,we have arrived at a comprehensive set of evaluation indicators for regionalizing of plum cultivation based on climatic suitability.These indicators encompass the average an-nual temperature,cumulative temperature exceeding 10℃,winter cold requirements,average tempera-ture in February,precipitation levels,sunshine hours,and altitude.Through the utilization of Geo-graphic Information System(GIS)technology and spatial interpolation,we have analyzed the values of these meteorological elements in each distinct region of Sichuan Province.The analysis categorizes the suitability of each region into four grades:most suitable,suitable,sub-suitable,and unsuitable.[Re-sult]The most suitable locations for plum cultivation in Sichuan Province are primarily concentrated in the central and eastern regions of the basin,as well as certain mountainous areas in the southwestern part of Sichuan.The suitable areas extend to the southeastern portion of the Sichuan Basin and the surround-ing western mountainous regions.The sub-suitable areas are situated in the transitional zone between the northwest Sichuan Plateau and the Sichuan Basin.On the other hand,the unsuitable areas are predomi-nantly located in the northwest Sichuan Plateau.[Conclusion]The results are consistent with the cur-rent status of plum cultivation in Sichuan,offering a scientific foundation for the strategic arrangement of plum cultivation in Sichuan Province.
Sichuan Provinceplumecoclimatesuitability regionalizationgeographic information system
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NSTL
万方数据
