Spatiotemporal pattern of tea industry in Sichuan province and their driving forces based on the geographical detector
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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。可变面域问题会影响地理探测器建模结果, 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三的是, 土地利用强度(0.91)、乡村劳动力(0.87)、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, 本研究认为应重视: 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概念; 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: Tea industry expansion results from the interactions of the natural and social factors. A understanding of the Spatiotemporal pattern change characteristics of tea industry and a reveal of result of the effects of physical and socioeconomic factors are helpful to provide an important basis for the adjustment of tea planting structure. Based on the statistical yearbook data of tea industry in Sichuan province for 40 years from 1980 to 2019, the Spatiotemporal pattern change characteristics of tea industry in Sichuan and their driving forces were studied by using the industrial concentration, exploratory data analysis and industrial gravity model. Natural factors such as elevation, the hydrogen ion concentration(pH) of soil, annual precipitation, accumulated temperature, average temperature of growing season, extreme minimum temperature of overwintering period and extreme maximum temperature of growing season, production factors such as land use intensity, labor, fertilizer, pesticides and irrigation, as well as socioeconomic factors such as per capita disposable, technology and policy were statistically divided by the geographical detector, and the impact of separate driving factors and the interactions between these factors on 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 space in Sichuan province showed an expanding trend, the spatial distribution showed a high degree of concentration and wavelike rose with time (locational Gini index > 0.5). There was a significant geographical agglomeration on the county scale, showing a spatial structure of hot in the southern Sichuan and southern Chengdu Plain (Global Moran’s I > 0). The center of gravity of tea industry space in Sichuan migrated to the west. In the geographical detector, the modifiable areal unit problem (MAUP) is a fundamental issue. To address this issue, both scale effect and zoning effect are tested to examine the MAUP before the geographical detector model was applied in this work. Among the 15 factors selected, land use intensity, labor, fertilizer had higher decisive power. The interactions between these factors mainly manifested dual-factor enhancement type and nonlinear enhancement type, And average the interaction of industry and socioeconomic factors had highest decisive power (0.8870). So the tea industry space in Sichuan Province is mainly driven by factors of industry. Evidence-based hypothetical solutions deriving from these observations focused on three aspects: 1) pay close attention to the influence of lack of water in tea growth period, intense rainfall and freezing damage on tea tree and react effectively. 2) corresponding countermeasures were purposed, including the aspects of 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 which fit to local conditions, as well as set up the system of steady land, labor, fertilizer and pesticides input.
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图 5 不同空间格网下各因素对茶叶空间格局的决定力(q值)及其排名变化
图中15个影响因子分别是: X1 (海拔), X2 (土壤酸碱度), X3 (年降水量), X4 (年活动积温), X5 (生长季日平均气温), X6 (越冬期日极端最低气温), X7 (生长季日极端最高气温), X8 (土地利用强度), X9 (乡村劳动力), X10 (化肥), X11 (农药), X12 (灌溉), X13 (人均可支配收入), X14 (科技), X15 (政策)。There are 15 influence factors as follows: X1 (elevation), X2 (pH), X3 (annual precipitation), X4 (accumulated temperature), X5 (average temperature of growing season), X6 (extreme minimum temperature of overwintering period), X7 (extreme maximum temperature of growing season), X8(land use intensity), X9 (labor), X10 (fertilizer), X11 (pesticides), X12 (irrigation), X13 (per capita disposable), X14 (technology), X15 (policy).
Figure 5. Scale effects on the q values and the ranks of the factors
表 4 基于先验知识对四川茶产业空间影响因子分类
Table 4. Classify the quantitative variables of tea industry in Sichuan Province by prior knowledge
要素
Variable切割值
Cutting values赋值
Value海拔
Elevation (X1)<200 m 1 200~500 m 2 500~1000 m 3 >1000 m 4 土壤酸碱度
Hydrogen ion concentration of soil (X2)0~5.5 1 5.5~6.5 2 6.5~7.5 3 7.5~8.5 4 >8.5 5 人均可支配收入
Per capita disposable (X13)0~20% 1 20%~40% 2 40%~60% 3 60%~80% 4 80%~100% 5 政策
Policy (X15)1、2、3、4、5 表 1 1980—2019年四川省茶产业空间Moran’s I指数变化
Table 1. Changes of Moran’s I of tea industry space in Sichuan Province from 1980 to 2019
年份 Year 1980 1985 1990 1995 2000 2005 2010 2015 2019 Moran’s I 0.48 0.47 0.45 0.40 0.34 0.38 0.42 0.42 0.42 Z值 11.34 11.48 10.70 9.71 8.45 9.30 10.04 9.90 9.38 P值 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 表 2 1980—2019年四川省茶产业空间重心迁移变化
Table 2. Change of center of gravity of tea industry area in Sichuan Province from 1980 to 2019
年份
Year坐标 Coordinate 重心迁移距离
Moving distance center
of gravity (km)年份
Year坐标 Coordinate 重心迁移距离
Moving distance center
of gravity (km)经度 Longitude 纬度 Latitude 经度 Longitude 纬度 Latitude 1980 104.71 30.08 2006 104.29 30.08 3.71 1985 104.78 30.23 18.04 2007 104.28 30.10 1.59 1990 104.88 30.12 16.00 2008 104.28 30.07 2.73 1995 104.71 30.13 19.05 2009 104.30 30.03 5.03 1996 104.75 30.20 9.27 2010 104.28 30.01 2.93 1997 104.65 30.20 10.57 2011 104.31 29.95 7.47 1998 104.68 30.21 2.96 2012 104.34 29.96 3.40 1999 104.51 30.12 20.99 2013 104.35 29.96 1.04 2000 104.60 30.14 9.94 2014 104.34 29.92 5.24 2001 104.66 30.23 11.99 2015 104.26 29.88 10.05 2002 104.47 30.15 22.43 2016 104.48 29.96 26.29 2003 104.39 30.11 9.54 2017 104.54 29.98 6.66 2004 104.34 30.13 6.12 2018 104.54 29.98 0.89 2005 104.31 30.11 4.15 2019 104.57 29.97 4.20 表 3 四川省茶产业空间地理探测因子
Table 3. Indicators of geographical detector on tea industry space in Sichuan Province
类型
Category影响因子
Influence factors因子意义
The significance of factors自然要素
Physical factors海拔
Elevation (X1)通过温度间接影响茶叶生长
Indirectly affecting tea growth土壤酸碱度
Hydrogen ion concentration of soil(X2)茶叶生长在酸性土壤环境
Acid soil is suitable for tea growth年降水量
Annual precipitation (X3)一年总的水分条件
Moisture conditions年活动积温
Accumulated temperature (X4)积温越多, 年生长期越长
Accumulated heat of tea during its growth生长季日平均气温
Average temperature of growing season (X5)15~23 ℃范围内, 茶梢生长快
The suitable is between 15~23 ℃越冬期日极端最低气温
Extreme minimum temperature of overwintering period (X6)≤−10 ℃, 四川主要栽培的茶树品种不能存活
Tea varieties planted in Sichuan cannot survive at ≤−10 ℃生长季日极端最高气温
Extreme maximum temperature of growing season (X7)受到热害导致生长停滞甚至死亡
Excessive temperature will inhibit tea生产要素
Production
factors土地利用强度
Land use intensity (X8)反映地区茶叶种植的土地投入指标
Directly bearing on tea production乡村劳动力
Labor (X9)茶叶生产需要大量的劳动力
Tea production requires a lot of labor化肥
Fertilizer (X10)化肥投入是提升茶叶生产不断的重要原因
Fertilizer can increase tea yield农药
Pesticides (X11)农药投入能抑制病虫害发生, 提高茶叶产量
Controlling the insects灌溉
Irrigation (X12)衡量农业生产单位和地区水利化程度和农业生产稳定程度
Guaranteeing water supply for tea production社会经济要素
Socioeconomic factors人均可支配收入
Per capita disposable (X13)收入反映出消费能力, 评价市场需求
Reflecting consumption ability科技
Technology (X14)科技与单产有直接关系, 用单产衡量科技
Increasing tea per unit area yield政策
Policy (X15)政策对茶叶生产空间影响
The agricultural policy is an important factor表 5 基于最优离散对四川茶产业空间影响因子分类
Table 5. Classify the quantitative variables of tea industry in Sichuan Province by optimal classification algorithms
要素
Variableq值 The q values 5 6 7 8 9 10 年降水量 Annual precipitation (X3) 0.2643 0.2646 0.2607 0.2648 0.2631 0.2642 年活动积温 Accumulated temperature (X4) 0.1104 0.1098 0.1317 0.1360 0.1387 0.1466 生长季日平均气温 Average temperature of Growing season (X5) 0.1339 0.1639 0.1710 0.2091 0.2302 0.2244 越冬期日极端最低气温 Extreme minimum temperature of overwintering perio (X6) 0.1203 0.1234 0.1212 0.1210 0.1425 0.1570 生长季日极端最高气温 Extreme maximum temperature of growing season (X7) 0.1242 0.1387 0.1369 0.1491 0.1572 0.1628 土地利用强度 Land use intensity (X8) 0.9032 0.9108 0.8860 0.8893 0.8896 0.8897 乡村劳动力 Labor (X9) 0.8613 0.8425 0.8392 0.8483 0.8485 0.8487 化肥 Fertilizer (X10) 0.7886 0.8341 0.8324 0.8584 0.8589 0.8589 农药 Pesticides (X11) 0.7360 0.7411 0.7253 0.7411 0.7553 0.7573 灌溉 Irrigation (X12) 0.0943 0.1569 0.1156 0.1293 0.1476 0.1611 科技 Technology(X14) 0.2805 0.2754 0.2995 0.3120 0.4119 0.3012 表 6 影响因子对四川茶叶空间的交互作用
Table 6. Interactive effects of influence factors on tea industry in Sichuan Province
自然要素 Physical factors 生产要素 Production factors 社会经济要素 Socioeconomic factors X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X1 0.2202 X2 0.2919 0.1149 X3 0.4567 0.4368 0.3125 X4 0.3110 0.2793 0.4548 0.1733 X5 0.2738 0.3027 0.4591 0.3469 0.2236 X6 0.2869 0.2872 0.4626* 0.2695 0.3153 0.1812 X7 0.2592 0.2476 0.4439 0.4308 0.3391 0.3629 0.1607 X8 0.9423 0.9214 0.9258 0.9507* 0.9235 0.9465 0.9283 0.9112 X9 0.9299 0.8909 0.9125 0.9262 0.9066 0.9152 0.9086 0.9324 0.8684 X10 0.9119 0.9166 0.9372 0.9366 0.9095 0.9457 0.9242 0.9497 0.9447 0.8650 X11 0.8829 0.7945 0.8418 0.8814 0.8409 0.8748 0.8789 0.9391 0.9343 0.9309 0.7472 X12 0.3132 0.2943 0.5297 0.3778 0.3401 0.3686 0.3596 0.9644* 0.9527 0.9373 0.9421 0.1729 X13 0.3718 0.2543 0.4610 0.3079 0.3447 0.3182 0.3853 0.9601 0.9178 0.9543 0.8984 0.4706 0.1441 X14 0.5502 0.5616 0.7323 0.5993 0.6567 0.5942 0.6239 0.9752 0.9714 0.9779* 0.9499 0.6631 0.6320 0.4285 X15 0.7803 0.7544 0.8062* 0.7762 0.7660 0.7734 0.7913 0.9425 0.9080 0.9341 0.9112 0.8707 0.7679 0.8493* 0.7365 下划线表示交互作用为非线性增强, 其余交互作用均为双因子增强; "*"表示每类因子交互作用中的最大值; 图中15个影响因子分别是: X1 (海拔), X2 (土壤酸碱度), X3 (年降水量), X4 (年活动积温), X5 (生长季日平均气温), X6 (越冬期日极端最低气温), X7 (生长季日极端最高气温), X8 (土地利用强度), X9 (乡村劳动力), X10 (化肥), X11 (农药), X12 (灌溉), X13 (人均可支配收入), X14 (科技), X15 (政策)。Underline “ ” denotes nonlinear enhancement of factors A and B; The symbol "*"denotes the largest interaction of each type. There are 15 influence factors as follows: X1 (elevation), X2 (pH), X3 (annual precipitation), X4 (accumulated temperature), X5 (average temperature of growing season), X6 (extreme minimum temperature of overwintering period), X7 (extreme maximum temperature of growing season), X8(land use intensity), X9 (labor), X10 (fertilizer), X11 (pesticides), X12 (irrigation), X13 (per capita disposable), X14 (technology), X15 (policy). -
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