长江经济带智慧农场发展的时空特征及驱动因子研究

doi:10.12133/j.smartag.SA202404005

摘要/Abstract

摘要：

［目的/意义］ 为提炼区域智慧农业高质量发展的典范案例，并为国家智慧农业事业持续进步贡献策略，深入探究长江经济带智慧农场时空特征及关键驱动因子。 ［方法］ 基于2014—2023年11省（市）数据，用核密度分析、空间自相关及标准差椭圆方法全面剖析长江经济带智慧农场时空分异特征，并用地理探测器从7个维度考察关键因子及其交互作用。 ［结果和讨论］ 长江经济带智慧农场数量10年间稳步增长，2016年为重要转折点；空间布局呈分散集聚特征，以H-L型和L-H型集聚为主，分布省份略显繁杂；技术创新是时空分布格局形成的首要关键因素，因子解释度为0.311 1，与其他指标交互之后仍保持核心驱动地位。 ［结论］ 长江经济带智慧农场发展势头向好，子区域智慧农场的发展增速与水平呈“下游＞中游＞上游”的差异化特征；整体空间分布均衡，子区域分布均衡程度为“中游（湖北、湖南、江西3省均衡）＞下游（安徽主导）＞上游（四川，独占鳌头）”，智慧农场选址覆盖面持续扩张并形成“东北—西南”的横向扩散格局；时空分异格局形成是多因子综合作用的结果，因子解释力为：技术创新＞城镇化＞农业机械化＞人力资本＞互联网基础＞产业依赖＞专项财政支持，且因子交互作用时影响力会进一步得到强化。

关键词: 智慧农场, 新质生产力, 时空特征, 驱动因子, 高质量发展, 长江经济带

Abstract:

[Objective] In order to summarize exemplary cases of high-quality development in regional smart agriculture and contribute strategies for the sustainable advancement of the national smart agriculture cause, the spatiotemporal characteristics and key driving factors of smart farms in the Yangtze River Economic Belt were studied. [Methods] Based on data from 11 provinces (municipalities) spanning the years 2014 to 2023, a comprehensive analysis was conducted on the spatio-temporal differentiation characteristics of smart farms in the Yangtze River Economic Belt using methods such as kernel density analysis, spatial auto-correlation analysis, and standard deviation ellipse. Including the overall spatial clustering characteristics, high-value or low-value clustering phenomena, centroid characteristics, and dynamic change trends. Subsequently, the geographic detector was employed to identify the key factors driving the spatio-temporal differentiation of smart farms and to discern the interactions between different factors. The analysis was conducted across seven dimensions: special fiscal support, industry dependence, human capital, urbanization, agricultural mechanization, internet infrastructure, and technological innovation. [Results and Discussions] Firstly, in terms of temporal characteristics, the number of smart farms in the Yangtze River Economic Belt steadily increased over the past decade. The year 2016 marked a significant turning point, after which the growth rate of smart farms had accelerated noticeably. The development of the upper, middle, and lower reaches exhibited both commonalities and disparities. Specifically, the lower sub-regions got a higher overall development level of smart farms, with a fluctuating upward growth rate; the middle sub-regions were at a moderate level, showing a fluctuating upward growth rate and relatively even provincial distribution; the upper sub-regions got a low development level, with a stable and slow growth rate, and an unbalanced provincial distribution. Secondly, in terms of spatial distribution, smart farms in the Yangtze River Economic Belt exhibited a dispersed agglomeration pattern. The results of global auto-correlation indicated that smart farms in the Yangtze River Economic Belt tended to be randomly distributed. The results of local auto-correlation showed that the predominant patterns of agglomeration were H-L and L-H types, with the distribution across provinces being somewhat complex; H-H type agglomeration areas were mainly concentrated in Sichuan, Hubei, and Anhui; L-L type agglomeration areas were primarily in Yunnan and Guizhou. The standard deviation ellipse results revealed that the mean center of smart farms in the Yangtze River Economic Belt had shifted from Anqing city in Anhui province in 2014 to Jingzhou city in Hubei province in 2023, with the spatial distribution showing an overall trend of shifting southwestward and a slow expansion toward the northeast and south. Finally, in terms of key driving factors, technological innovation was the primary critical factor driving the formation of the spatio-temporal distribution pattern of smart farms in the Yangtze River Economic Belt, with a factor explanatory degree of 0.311 1. Moreover, after interacting with other indicators, it continued to play a crucial role in the spatio-temporal distribution of smart farms, which aligned with the practical logic of smart farm development. Urbanization and agricultural mechanization levels were the second and third largest key factors, with factor explanatory degrees of 0.292 2 and 0.251 4, respectively. The key driving factors for the spatio-temporal differentiation of smart farms in the upper, middle, and lower sub-regions exhibited both commonalities and differences. Specifically, the top two key factors driver identification in the upper region were technological innovation (0.841 9) and special fiscal support (0.782 3). In the middle region, they were technological innovation (0.619 0) and human capital (0.600 1), while in the lower region, they were urbanization (0.727 6) and technological innovation (0.425 4). The identification of key driving factors and the detection of their interactive effects further confirmed that the spatio-temporal distribution characteristics of smart farms in the Yangtze River Economic Belt were the result of the comprehensive action of multiple factors. [Conclusions] The development of smart farms in the Yangtze River Economic Belt is showing a positive momentum, with both the total number of smart farms and the number of sub-regions experiencing stable growth. The development speed and level of smart farms in the sub-regions exhibit a differentiated characteristic of "lower reaches > middle reaches > upper reaches". At the same time, the overall distribution of smart farms in the Yangtze River Economic Belt is relatively balanced, with the degree of sub-regional distribution balance being "middle reaches (Hubei province, Hunan province, Jiangxi province are balanced) > lower reaches (dominated by Anhui) > upper reaches (Sichuan stands out)". The coverage of smart farm site selection continues to expand, forming a "northeast-southwest" horizontal diffusion pattern. In addition, the spatio-temporal characteristics of smart farms in the Yangtze River Economic Belt are the result of the comprehensive action of multiple factors, with the explanatory power of factors ranked from high to low as follows: Technological innovation > urbanization > agricultural mechanization > human capital > internet infrastructure > industry dependence > special fiscal support. Moreover, the influence of each factor is further strengthened after interaction. Based on these conclusions, suggestions are proposed to promote the high-quality development of smart farms in the Yangtze River Economic Belt. This study not only provides a theoretical basis and reference for the construction of smart farms in the Yangtze River Economic Belt and other regions, but also helps to grasp the current status and future trends of smart farm development.

Key words: smart farm, new-quality productivity, spatial-temporal characteristics, driving factor, high-quality development, Yangtze River Economic Belt

中图分类号:

高群, 王宏杨, 陈诗瑶. 长江经济带智慧农场发展的时空特征及驱动因子研究[J]. 智慧农业(中英文), 2024, 6(6): 168-179.

GAO Qun, WANG Hongyang, CHEN Shiyao. Research on the Spatio-temporal Characteristics and Driving Factors of Smart Farm Development in the Yangtze River Economic Belt[J]. Smart Agriculture, 2024, 6(6): 168-179.

图/表 10

表1

图1

图2

图3

表2

长江经济带智慧农场全局自相关分析结果

指标	长江经济带整体（2014—2023年）										子区域（2023年）
指标	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	上游	中游	下游
Moran $I$ 指数	‒0.035 9	‒0.070 6	‒0.001 1	‒0.043 9	‒0.088 9	‒0.092 4	‒0.109 3	‒0.157 0	‒0.141 5	‒0.171 3	0.133 0	—	‒0.186 5
预期指数	‒0.100 0	‒0.100 0	‒0.100 0	‒0.100 0	‒0.100 0	‒0.100 0	‒0.100 0	‒0.100 0	‒0.100 0	‒0.100 0	‒0.333 3	—	‒0.333 3
方差	0.037 2	0.038 5	0.038 5	0.036 7	0.037 1	0.036 7	0.035 9	0.036 3	0.037 7	0.036 0	0.185 7	0	0.081 8
$Z$ 得分	‒0.332 2	‒0.869 1	‒0.5038	‒0.292 9	‒0.057 8	‒0.039 8	‒0.048 9	‒0.299 2	‒0.214 0	‒0.375 8	1.082 3	—	0.513 4
$P$ 值	0.739 7	0.384 8	0.614 4	0.769 6	0.953 9	0.9683	0.961 0	0.764 8	0.830 5	0.707 0	0.279 1	—	0.607 7
分布模式	随机	随机	随机	随机	随机	随机	随机	随机	随机	随机	随机	随机	随机

表2

表3

图4

表4

表5

智慧农场时空分异关键驱动因子判别结果

维度	调查指标	整体		上游		中游		下游
维度	调查指标	$q$ 统计量	$P$ 值	$q$ 统计量	$P$ 值	$q$ 统计量	$P$ 值	$q$ 统计量	$P$ 值
专项财政支持	农业生产设备财政支出专项	0.119 1	0.016 4	0.782 3	0.000 0	0.128 5	0.393 2	0.404 2	0.008 8
产业依赖	农业增加值	0.161 6	0.002 2	0.625 5	0.000 0	0.256 5	0.302 8	0.396 7	0.006 0
人力资本	人力资本水平	0.231 9	0.000 0	0.336 0	0.017 4	0.600 1	0.000 0	0.187 2	0.173 6
城镇化	城镇化水平	0.292 2	0.000 0	0.401 6	0.008 9	0.570 6	0.003 3	0.727 6	0.000 0
农业机械化	农业机械动力	0.251 4	0.000 0	0.280 6	0.044 7	0.300 5	0.103 0	0.503 4	0.004 6
互联网基础	互联网覆盖度	0.196 4	0.000 0	0.380 9	0.011 1	0.520 2	0.009 8	0.061 5	0.721 7
技术创新	专利授权量	0.311 1	0.000 0	0.841 9	0.000 0	0.619 0	0.003 5	0.425 4	0.014 0

表5

表6

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维度	调查指标	指标说明
专项财政支持	农业生产设备财政支出专项/%	农业生产设备购置补贴占农业财政总支出的比重
产业依赖	农业增加值/%	第一产业增加值占地区生产总值的比重
人力资本	人力资本水平/%	受高等教育人数占地区6岁以上人口比重
城镇化	城镇化水平/%	城镇人口占地区总人口的比重
农业机械化	农业机械动力/（kW/hm²）	农业机械总动力/农作物总播种面积
互联网基础	互联网覆盖度/（个/人）	互联网宽带接入端口数与地区总人数的比值
技术创新	专利授权量/项	国内专利授权总量

年份	集聚类型
年份	H-H型	L-L型	H-L型	L-H型
2014	皖2；鄂1	湘3；黔2	赣1；鄂1；黔1；滇2	皖4；鄂2
2015	皖*2	湘2；黔2	赣1；鄂1；黔1；滇2	苏1；皖5；鄂*2
2016	皖2；鄂1	湘2；黔2	赣1；鄂1；黔1；滇2	苏1；皖5；鄂*2
2017	苏1；皖4	沪1；黔1；滇*1	鄂1；黔1；滇*2	皖5；赣3；鄂*2
2018	苏1；皖4	滇*5	鄂1；湘1；黔1；滇2	浙江2；皖5；赣2；鄂1
2019	皖4；川1	渝1；滇7	鄂1；湘1；黔1；滇1	皖5；赣2；鄂1；四川1
2020	皖6；川3	渝1；黔1；滇*7	鄂1；湘1；黔2；滇2	皖3；鄂1；川*2
2021	皖6；川1	渝1；川2；黔2；滇11	鄂1；黔1；滇*1	皖3；鄂1；川*1
2022	苏1；皖5；川*4	渝1；川2；黔1；滇11	鄂1；黔3；滇*2	皖3；鄂1；川*2
2023	皖8；川3	渝1；川2；黔4；滇10	苏1；滇1	苏1；皖3；鄂1；川1

年份	中心坐标	覆盖面积/km²	方位角/（°）	扁率
2014	116°16'26.4"E，30°16'29.3"N（安徽省安庆市）	491 517.475 8	89.118 7	0.553 3
2015	115°44'34.5"E，30°26'56.0"N（湖北省黄冈市）	563 071.910 9	85.192 8	0.637 5
2016	114°02'51.1"E，29°56'09.8"N（湖北省咸宁市）	820 424.294 1	84.304 1	0.644 4
2017	114°02'02.3"E，29°49'35.7"N（湖北省咸宁市）	764 479.620 8	83.076 5	0.612 6
2018	114°07'59.1"E，29°57'00.8"N（湖北省咸宁市）	771 297.113 9	83.127 8	0.627 4
2023	112°58'25.1"E，30°07'15.6"N（湖北省荆州市）	890 615.358 1	79.796 8	0.604 7

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