曹杰, 林正雨, 陈春燕, 刘远利, 高文波, 邵周玲. 基于地理探测器的四川省茶产业时空格局变化及驱动因素研究[J]. 中国生态农业学报 (中英文), 2023, 31(4): 619−631. DOI: 10.12357/cjea.20220278
引用本文: 曹杰, 林正雨, 陈春燕, 刘远利, 高文波, 邵周玲. 基于地理探测器的四川省茶产业时空格局变化及驱动因素研究[J]. 中国生态农业学报 (中英文), 2023, 31(4): 619−631. DOI: 10.12357/cjea.20220278
CAO J, LIN Z Y, CHEN C Y, LIU Y L, GAO W B, SHAO Z L. Spatiotemporal pattern of the tea industry in Sichuan Province and its driving forces based on the geographical detector[J]. Chinese Journal of Eco-Agriculture, 2023, 31(4): 619−631. DOI: 10.12357/cjea.20220278
Citation: CAO J, LIN Z Y, CHEN C Y, LIU Y L, GAO W B, SHAO Z L. Spatiotemporal pattern of the tea industry in Sichuan Province and its driving forces based on the geographical detector[J]. Chinese Journal of Eco-Agriculture, 2023, 31(4): 619−631. DOI: 10.12357/cjea.20220278

基于地理探测器的四川省茶产业时空格局变化及驱动因素研究

Spatiotemporal pattern of the tea industry in Sichuan Province and its driving forces based on the geographical detector

  • 摘要: 茶产业时空格局形成和演变是自然因素和人类活动共同作用的结果, 理解茶产业时空格局变化过程, 揭示不同自然-社会-经济驱动因子对茶产业时空格局演变的作用机制, 对区域茶叶种植结构调整具有重要意义。本文基于1980—2019年四川省县区尺度茶叶生产统计年鉴数据, 运用产业集中度、探索性空间数据分析和产业重心模型分析了四川省茶产业时空格局演化过程, 对研究区内海拔、土壤酸碱度、年降水量、年活动积温、生长季日平均气温、越冬期日极端最低气温、生长季日极端最高气温等自然因素, 土地利用强度、乡村劳动力、化肥、农药、灌溉等生产要素以及人均可支配收入、科技、政策等社会经济要素进行离散分层并确定最优尺度单元, 基于地理探测器探讨了各驱动因子对四川省茶产业分布的解释力以及交互作用。结果表明: 从时间上看, 四川省茶产业规模总体呈上升趋势, 区位基尼系数均大于0.5, 空间特征呈现出高度集聚, 且集聚程度随时间波动上升。从空间上看, 全局莫兰指数均大于0, 县域尺度上表现出明显的空间集聚, 且相邻县域之间相互影响, 热点区主要分布在川南地区和成都平原区南部, 茶产业重心整体上向西迁移。忽视可变面域问题会影响地理探测器建模结果, 因此对连续型因子离散化和空间单元尺度优化, 得到最优参数。单个因子对茶产业空间影响程度排前3的是土地利用强度(0.91)、乡村劳动力(0.87)和化肥(0.86); 影响因子相互作用主要表现为非线性增强和双因子增强, 生产与社会经济因子平均交互作用最大(0.8870), 四川省茶产业表现出生产要素驱动为主的空间格局。基于此, 本研究认为四川省茶产业应: 1)关注生长季缺水、突发性强降水以及低温冻害对茶树的影响; 2)加强“宜机采”茶园建设, 树立绿色茶园绿色发展理念; 3)提升良种普及率以及推广新技术, 保障用地、劳动力、化肥、农药等生产要素的稳定投入。

     

    Abstract: The growth of tea industry is the result of interactions between natural and social factors. An understanding of the spatiotemporal pattern of the tea industry and the effects of natural and socioeconomic factors provides an important basis for the adjustment of tea planting structures. Based on the statistical yearbook data of the tea industry in Sichuan Province over the last 40 years, from 1980 to 2019, the spatiotemporal pattern of tea industry in Sichuan Province and its driving forces were studied using industrial concentration, exploratory data analysis, and an industrial gravity model. Natural factors, such as elevation, soil pH, annual precipitation, accumulated temperature, average temperature of tea growing season, extreme minimum temperature of the overwintering period, and extreme maximum temperature of tea growing season; production factors, such as land use intensity, labor, fertilizer, pesticides, and irrigation; as well as socioeconomic factors, such as per capita disposable income, technology, and policy were statistically divided by the geographical detector. The impact of separate driving factors and the interactions between these factors on the spatial pattern of tea industry in Sichuan Province were systematically discussed. The results of this study were as follows: in the past 40 years, the tea industry in Sichuan Province had shown an expanding trend; the spatial distribution showed a high degree of concentration; and a wavelike increase with time (locational Gini index > 0.5). There was a significant geographical agglomeration on the county scale, showing a hot spatial structure in southern Sichuan and the southern Chengdu Plain (global Moran’s I > 0). The center of gravity of the tea industry in Sichuan migrated to the west. The modifiable areal unit problem (MAUP) is a fundamental issue in geographical detectors. To address this issue, both scale and zoning effects were tested to examine the MAUP before applying the geographical detector model in this study. Among the 15 influencing factors selected, land use intensity, labor, and fertilizer had the highest deciding power. The interactions between these factors mainly manifested as dual-factor enhancement and nonlinear enhancement types, and the average interaction of production factors and socioeconomic factors had the highest decisive power (0.8870). Thus, the tea industry in Sichuan Province was mainly driven by production factors. Evidence-based hypothetical solutions derived from these observations focused on three aspects: 1) Pay close attention to the influence of water shortage in the tea growth period, intense rainfall, and freezing damage on tea trees and react effectively. 2) Implement corresponding countermeasures, including strengthening the construction of machine-plucking tea gardens “suitable for mechanization” and establishing the concept of green development. 3) Accelerate the promotion and application of modern agricultural technology; breed new tea varieties that fit local conditions; and set up a system of steady land, labor, fertilizer, and pesticide input.

     

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