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

曹杰; 林正雨; 陈春燕; 刘远利; 高文波; 邵周玲

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

doi: 10.12357/cjea.20220278

四川省农业科学院农业信息与农村经济研究所　成都　610066

基金项目: 国家重点研究计划课题(2020YFD1100601)、四川省杰出青年科技人才项目(2020JDJQ0073)、四川省重点研发计划项目(2021YFYZ0028)和四川省财政自主创新专项项目(2022ZZCX036)资助

详细信息

作者简介:
曹杰, 主要研究方向为农业资源利用与区域农业发展。E-mail: 2435711812@qq.com

通讯作者:
林正雨, 主要研究方向为农业资源利用与区域农业发展。E-mail: 1456875524@qq.com

中图分类号: F323.1; F304.5
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出版历程
- 收稿日期: 2022-04-14
- 录用日期: 2022-09-30
- 修回日期: 2022-09-30
- 网络出版日期: 2022-11-25
- 刊出日期: 2023-04-10

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

Institute of Agricultural Information and Rural Economy, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China

Funds: This study was supported by the National Key Research and Development Project of China (2020YFD1100601), the Program for Distinguished Young Technology Talents of Sichuan Province (2020JDJQ0073), the Provincial Key Research and Development Project of Sichuan Province (2021YFYZ0028), and Sichuan Provincial Financial Independent Innovation Project (2022ZZCX036).

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Corresponding author: E-mail: 1456875524@qq.com

摘要

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。
- 茶产业 /
- 时空格局 /
- 驱动因子 /
- 可变面域问题 /
- 地理探测
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.
- Tea industry /
- Spatiotemporal pattern /
- Driving force /
- Modifiable areal unit problem /
- Geographical detector

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图 1 四川省地理分区

Figure 1. Geographical regions map of Sichuan Province

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图 2 1980—2019年四川省茶产业面积时序变化

Figure 2. Temporal changes of tea industry area in Sichuan Province from 1980 to 2019

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图 3 1980—2019年四川省茶产业空间集聚特征

Figure 3. Spatial agglomeration characteristics of tea industry in Sichuan Province from 1980 to 2019

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图 4 1980—2019年四川省茶产业空间热点分布

Figure 4. Spatial hot spot distribution of tea industry in Sichuan Province from 1980 to 2019

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图 5 不同空间格网尺度下各影响因素对茶产业空间格局的决定力(影响因子的q值)及其排名变化

图中15个影响因子分别是: 海拔(X1)、土壤酸碱度(X2)、年降水量(X3)、年活动积温(X4)、生长季日平均气温(X5)、越冬期日极端最低气温(X6)、生长季日极端最高气温(X7)、土地利用强度(X8)、乡村劳动力(X9)、化肥(X10)、农药(X11)、灌溉(X12)、人均可支配收入(X13)、科技(X14)和政策 (X15)。There are 15 influence factors as follows: elevation (X1), pH (X2), annual precipitation (X3), accumulated temperature (X4), average temperature of growing season (X5), extreme minimum temperature of overwintering period (X6), extreme maximum temperature of growing season (X7), land use intensity (X8), labor (X9), fertilizer (X10), pesticides (X11), irrigation (X12), per capita disposable income (X13), technology (X14), policy (X15).

Figure 5. Deciding power (q value) and its rank of each influencing factor on spatial patterns of tea industry under different spatial grid scales in Sichuan Province

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图 6 影响因子对四川茶产业空间格局的驱动分析结果

Figure 6. Driving analysis of influence factors on tea industry in Sichuan Province

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表 1 1980—2019年四川省茶产业空间Moran’s I指数变化

Table 1. Changes of space Moran’s I of tea industry 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	11.34	11.48	10.70	9.71	8.45	9.30	10.04	9.90	9.38
P	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01

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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 of gravity center (km)	年份 Year	坐标 Coordinate		重心迁移距离 Moving distance of gravity center (km)
年份 Year	经度 Longitude (°)	纬度 Latitude (°)	重心迁移距离 Moving distance of gravity center (km)	年份 Year	经度 Longitude (°)	纬度 Latitude (°)	重心迁移距离 Moving distance of gravity center (km)
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

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表 3 四川省茶产业空间格局的地理探测因子

Table 3. Indicators of space geographical detector of tea industry in Sichuan Province

类型 Category	影响因子 Influence factor	因子意义 Significance of factor
自然要素 Physical factor	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 during tea 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 rang is 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 growth
生产要素 Production factor	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 land resource input 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提高的重要原因 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 diseases and insect pests of tea
	灌溉 Irrigation (X12)	茶叶生产需要灌溉保障 Guaranteeing water supply for tea production
社会经济要素 Socioeconomic factor	人均可支配收入 Per capita disposable income (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 yield
	政策 Policy (X15)	政策对茶叶生产空间影响 The agricultural policy is an important factor

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表 4 基于先验知识对四川省茶产业空间格局影响因子分类

Table 4. Classify the quantitative variables of tea industry in Sichuan Province by prior knowledge

要素 Variable	切割值 Cutting value		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 income (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

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表 5 基于最优离散的四川茶产业空间格局影响因子分类

Table 5. Classify the quantitative variables of tea industry in Sichuan Province by optimal classification algorithms

要素 Variable	q值 q value
要素 Variable	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 period (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

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表 6 影响因子对四川省茶产业空间格局的交互作用

Table 6. Interactive effects of influence factors on tea industry in Sichuan Province

	自然要素 Physical factor							生产要素 Production factor					社会经济要素 Socioeconomic factor
	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; 表中15个影响因子分别是: 海拔(X1)、土壤酸碱度(X2)、年降水量(X3)、年活动积温(X4)、生长季日平均气温(X5)、越冬期日极端最低气温(X6)、生长季日极端最高气温(X7)、土地利用强度(X8)、乡村劳动力(X9)、化肥(X10)、农药(X11)、灌溉(X12)、人均可支配收入(X13)、科技(X14)和政策(X15)。“_” denotes nonlinear enhancement of factors A and B. The symbol “” denotes the largest interaction of each type. There are 15 influence factors as follows: elevation (X1), pH (X2), annual precipitation (X3), accumulated temperature (X4), average temperature of growing season (X5), extreme minimum temperature of overwintering period (X6), extreme maximum temperature of growing season (X7), land use intensity (X8), labor (X9), fertilizer (X10), pesticides (X11), irrigation (X12), per capita disposable income (X13), technology (X14), policy (X15).

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