Research on the Spatio-temporal Characteristics and Driving Factors of Smart Farm Development in the Yangtze River Economic Belt

doi:10.12133/j.smartag.SA202404005

Abstract

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

CLC Number:

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.

Figures/Tables 10

Table 1

Fig. 1

Fig. 2

Fig. 3

Table 2

Global auto-correlation analysis results of smart farms in the Yangtze River Economic Belt

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

Table 2

Table 3

Fig. 4

Table 4

Table 5

Key driver identification results of spatio-temporal differentiation of smart farms

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

Table 5

Table 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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