Spatial Distribution and Influencing Factors of Modern Characteristic Agricultural Demonstration Zones in Guangxi

Spatial Distribution and Influencing Factors of Modern Characteristic Agricultural Demonstration Zones in Guangxi | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatial Distribution and Influencing Factors of Modern Characteristic Agricultural Demonstration Zones in Guangxi huaqing zhao, jun wen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9016281/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract To address the lack of a spatial perspective in existing research and to clarify the formation mechanisms and optimization pathways for modern characteristic agricultural demonstration zones in Guangxi, this study investigated 650 autonomous region-level core demonstration zones across the region. By integrating methods including the Nearest Neighbor Index, Kernel Density Estimation, and the Geographical Detector, it systematically explored their spatial distribution characteristics and influencing factors. The findings reveal that the spatial distribution of the demonstration zones follows a core pattern characterized by "clustering-dominated, gradient differentiation." A prominent "dual-core, multi-nodal" pattern centered on Nanning and Yulin is evident. Regionally, the zones are concentrated in the two major sectors of southern and eastern Guangxi, while their municipal-scale distribution exhibits marked unevenness. At the sectoral level, crop cultivation forms the dominant sector, with various industries showing differentiated agglomeration features. Regarding the influencing mechanisms, water conservancy and irrigation and total farmland serve as the primary natural basal factors, while leading agricultural enterprises and industrial structure act as core driving elements. Together, they determine the spatial agglomeration pattern of the zones. Furthermore, all interactive combinations of the influencing factors exhibit enhancement effects, with leading agricultural enterprises connecting diverse elements to form a synergistic development chain. This study not only reveals the spatial patterns and underlying driving logic of Guangxi's modern characteristic agricultural demonstration zones but also provides targeted scientific support for their differentiated spatial planning, optimized factor allocation, and high-quality development. Social science/Development studies Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Guangxi modern agricultural demonstration zones spatial distribution influencing factors geographical detector Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Modern Characteristic Agricultural Demonstration Zones are designated areas where agricultural enterprises, specialized cooperatives, family farms, and large-scale growers serve as the primary operating entities. Supported by modern scientific technologies and material facilities, these zones facilitate land consolidation through land transfer, promote appropriate-scale operations, adopt modern management practices, enhance the integration of primary, secondary, and tertiary industries, and effectively achieve sustainable development[ 1 ]. To implement the Central Government's major decisions on comprehensively deepening rural reform and accelerating agricultural modernization, and to promptly enhance the level of agricultural industrialization, marketization, internationalization, and modernization in the autonomous region while strengthening the industrial foundation for the "Beautiful Countryside" initiative and the construction of a new socialist countryside, the autonomous region launched the initiative to establish Modern Characteristic Agricultural (Core) Demonstration Zones. Starting in 2014, a series of policy documents were successively issued, including Interim Measures for the Construction and Management of Guangxi Modern Characteristic Agricultural (Core) Demonstration Zones, Implementation Plan for Establishing Guangxi Modern Characteristic Agricultural (Core) Demonstration Zones, *Three-Year Action Plan for Expanding Coverage, Improving Quality, and Upgrading Guangxi Modern Characteristic Agricultural Demonstration Zones (2018–2020)*, *Notice of the General Office of the People's Government of Guangxi Zhuang Autonomous Region on Issuing the Five-Year Action Plan for High-Quality Construction of Guangxi Modern Characteristic Agricultural Demonstration Zones (2021–2025)*, and *Letter on Carrying Out the Acceptance Inspection of Autonomous Region-Level Modern Characteristic Agricultural Demonstration Zones in 2024*. These policies have systematically facilitated the transformation of Guangxi's agriculture from traditional production to a modern industrial cluster, providing an institutional framework and resource support for rural revitalization. By the end of 2023, a total of 650 autonomous region-level modern characteristic agricultural core demonstration zones had been designated in Guangxi Zhuang Autonomous Region, comprising 123 Five-Star, 445 Four-Star, and 82 Three-Star zones. Their leading industries cover various sectors such as crop cultivation and forestry. The development of demonstration zones has promoted land leasing and transfer, advanced land trusteeship models, improved infrastructure systems, reinforced the advancement of rural ecological civilization, consistently improved the quality of living environments in rural regions, and substantially enhanced the overall appearance and ambiance of villages[ 2 ]. Early academic research on modern characteristic agricultural demonstration zones primarily focused on their development models and analysis of associated challenges. In recent years, as these demonstration zones have matured, their positive impacts on agricultural and rural development have become increasingly prominent, prompting extensive scholarly investigation into their effects and roles. At the level of development models, Zeng Xianghua took Xiaokunshan Town in Shanghai as a case study to explore a "water conservation-oriented" model. Based on the location of a water source protection area, this model focuses on water conservation and integrates agricultural, forestry, and water resource construction, achieving synergistic ecological, economic, and social benefits[ 3 ]. Chen Fumei et al., using the Longmenshi Demonstration Zone in Hefei as an example, constructed an "agriculture-tourism integration" model that promotes agricultural upgrading through business cultivation and five major planning strategies[ 4 ]. Zhang Fenglei et al., taking Liangping District in Chongqing as a case, established a "heavy metal risk prevention and control" model focusing on elements such as As and Hg, providing technical support for ecologically sensitive areas[ 5 ]. Zhou Zhiguo et al. introduced Porter's "Diamond" model and explored an "industrial cluster-driven" model using Yongqing County in Hebei as a case, focusing on farmer cultivation and technological development[ 6 ]. Xu Shangda et al., using Weixian County in Hebei as an example, proposed a "late-development breakthrough" model that relies on policies to achieve point-specific breakthroughs driving regional development[ 7 ]. Li Binglong, taking Dazhu County in Sichuan as an example, elaborated on a "characteristic industry integration" model that extends the industrial chain by leveraging the ramie brand [ 8 ]. Wu Dan, using Tiandeng County in Guangxi as a case, constructed a "multi-dimensional integrated planning" model that formulates strategies at macro, meso, and micro levels [ 9 ]. Guo Shumin et al., taking Fangshan District in Beijing as an example, explored a "gully economy innovation" model to address the challenge of spatial fragmentation in mountainous areas [ 10 ]. Zhang Qiuling et al., using the Lanshan Demonstration Zone in Rizhao, Shandong as a case, constructed a "circular and high-efficiency" model that emphasizes the coordinated advancement of productive, living, and ecological benefits [ 11 ]. Tu Xiaoli, taking the Taizhou Demonstration Zone as an example, analyzed a "brand-led" model focusing on enhancing the value of nationally recognized brands [ 12 ]. Gao Yun et al., based on operational mechanism theory, constructed a long-term development model by addressing institutional shortcomings [ 13 ]. Gao Yali et al., taking Nanchuan District in Chongqing as an example, explored an "eco-agricultural transition" model proposing pathways such as production-marketing integration [ 14 ]. Zeng Lei et al., from the perspective of industrial integration, proposed a "tourism function expansion" planning model that compensates for tourism shortcomings through project innovation [ 15 ]. Zhang Yunpeng et al., using the Bailuyuan Demonstration Zone in Xi'an as a case, constructed a "culture integration" model that incorporates historical and folk culture to create distinctive characteristics [ 16 ]. Lei Suijiang et al., taking a demonstration zone in Lianyungang, Jiangsu as an example, adopted a "problem-oriented" model to provide countermeasure support for long-term operation [ 17 ]. In terms of problem analysis, Zhang Jingyi et al., taking the Pudong New Area of Shanghai as an example, identified challenges faced by new-type agricultural business entities, including a shortage of talent, insufficient ecological subsidies, and restricted access to credit and land use. They proposed countermeasures such as talent introduction, increased subsidies, diversified credit options, and easing land use restrictions [ 18 ]. Meng Zhaodi et al., based on data from 281 demonstration zones, identified issues including the contradiction between food security and farmer income growth, regional development imbalances, and the need for improved management. They recommended enhancing production capacity, promoting inter-regional exchange, refining management systems, and adhering to market-oriented approaches [ 19 ]. Weng Boqi et al., considering the local context of Jian'ou City in Fujian Province, proposed the development of six characteristic functional zones, including mountainous green agriculture and hilly ecological fruit and tea industries [ 20 ]. Qiao Lijuan et al., focusing on the livestock sector in Hebei demonstration zones, addressed issues such as breeding pollution and talent shortages, and proposed strategies including integrated crop-livestock farming, cultivation of professional talent, and enhancing industrial quality and connotation [ 21 ]. Liang Danhui and Jiang Jing, from the perspective of factor endowments, emphasized optimizing allocation through the integrated coordination of land, capital, technology, and other production factors [ 22 ]. Zhang Yuan et al. pointed out deficiencies in standard systems, testing capabilities, and organizational management in the standardization efforts of Hebei demonstration zones, and recommended accelerating land transfer, developing characteristic agriculture, and strengthening technological support [ 23 ]. Su Wuzheng et al. employed SWOT analysis to clarify the objectives, positioning, and formulate targeted development strategies for the demonstration zone in Hutubi County, Xinjiang [ 24 ]. Regarding the effects and impacts, Zhang Qizheng et al., based on county-level data and using the framework of the "three major systems," found that demonstration zones shift the driving force of agricultural growth from labor input to improvements in labor productivity [ 25 ]. Li Shaolin et al., relying on data from 2,649 counties and districts, confirmed that demonstration zones significantly enhance the resilience of grain production, primarily through optimized resource allocation and improved production efficiency [ 26 ]. Zhu Jinxin et al., based on data from 1,565 counties, verified that demonstration zones enhance grain production resilience by increasing mechanization levels and labor productivity, with more pronounced effects in the eastern, western, and non-major grain-producing areas [ 27 ]. Kong Xiangzhi et al., employing a multi-period DID model, found that demonstration zones increase the value-added of agriculture, forestry, animal husbandry, and fishery by 2.8%, with effects dependent on fiscal support for agriculture and technology extension investment [ 28 ]. Min Jisheng et al., using a continuous difference-in-differences approach, demonstrated that modern agricultural mechanization technology can increase household income among part-time farming households by 2.7% [ 29 ]. Zhang Fengbing et al., through a multi-period DID model, found that demonstration zones can sustainably enhance agricultural total factor productivity, primarily achieved through improvements in electricity and farmland water conservancy infrastructure [ 30 ]. Cao Yuliang et al. confirmed that demonstration zones mitigate air pollution by promoting industrial structure upgrading [ 31 ]. Zhao Jianmei et al., employing the PSM-DID method, showed that demonstration zones both promote farmers' output and income growth and reduce fertilizer and pesticide application, primarily relying on the development of tertiary industries and rural e-commerce [ 32 ]. Zeng Changlin et al., using a difference-in-differences approach, found that demonstration zones significantly increase rural labor employment by 5.4%, mainly achieved through infrastructure development and enterprise agglomeration effects [ 33 ]. In summary, modern characteristic agricultural demonstration zones represent a significant approach to promoting sustainable agricultural development. Current research on these zones remains largely focused on descriptive analyzes of their current status, operational models, and existing challenges. There is a notable lack of spatial-perspective investigations into their distribution patterns and underlying mechanisms, as well as insufficient in-depth exploration of the causative factors and specific drivers shaping their development. This gap hinders a comprehensive understanding of the formative mechanisms of modern characteristic agricultural demonstration zones and may impede their sustainable advancement. By identifying the spatial distribution characteristics and influencing factors of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, this study aims to clarify their spatial patterns and formative mechanisms, explore suitable types of demonstration zones for different areas within the region, and provide insights to support the high-quality development of these zones in Guangxi. 2. Materials and Methods 2.1 Data Sources and Indicator Selection 2.1.1 Data Source The data for this study were sourced from the annual lists of autonomous region-level modern characteristic agricultural core demonstration zones published by the Department of Agriculture and Rural Affairs of Guangxi Zhuang Autonomous Region, starting from 2014. By the end of 2022, a total of 650 autonomous region-level modern characteristic agricultural core demonstration zones had been announced over 11 batches, which served as the sample for this research. By reviewing the relevant planning and construction documents of each demonstration zone, we identified the township where its core area is located and the specific lead implementation entity to determine its spatial location. Specifically, the geographic coordinates of the core area or the implementing entity were obtained using the Baidu Map Geocoding API. After manual verification, the BD-09 coordinates were converted to the WGS-84 coordinate system via a GIS data converter to vectorize the spatial locations. This process resulted in the construction of a spatial database of Guangxi's modern characteristic agricultural core demonstration zones, containing attribute information such as the city, county, and township of each zone. Based on this database, a spatial distribution map of the modern characteristic agricultural core demonstration zones in Guangxi was generated (Fig. 1 ). 2.1.2 Indicator Selection The formation of modern characteristic agricultural core demonstration zones results from the interaction of multiple factors. Based on data availability, this study selects 14 factors across six dimensions for influence analysis (Table 1 ). Table 1 Factors Influencing the Core Demonstration Areas of Five-Star Modern Characteristic Agriculture in Guangxi Zhuang Autonomous Region Dimension Influencing Factor Specific Indicator Unit Policy Environment Number of OVOP demonstration villages/towns (X1) Number of nationally designated OVOP demonstration villages in the autonomous region number Leading agricultural enterprises (X2) Number of leading agricultural enterprises designated by the autonomous region enterprises Infrastructure Road network density (X3) Total length of roads and railways / regional area km/km² Digital infrastructure (X4) Telecom business revenue / regional GDP % Construction land scale (X5) Proportion of construction land area % Economic Strength Economic development level (X6) GDP per capita CNY Industrial structure (X7) Ratio of secondary and tertiary industries % Market Support Urbanization rate (X8) Urban population / total resident population % Population density (X9) Resident population / regional area persons/km² Innovation Capability R&D investment (X10) R&D intensity of above-scale enterprises % Innovation output (X11) Number of patents granted number Natural Resources Total farmland (X12) Total sown area of crops hm² Water conservancy and irrigation (X13) Effectively irrigated land area hm² Annual precipitation (X14) Average annual precipitation mm The policy environment serves as the institutional foundation. The number of "One Village, One Product" (OVOP) demonstration villages and leading agricultural enterprises reflects the intensity of policy empowerment, laying an industrial foundation for the demonstration zones. Infrastructure acts as the material prerequisite. Road and railway density determines logistics convenience, while the proportion of telecommunications revenue reflects the support of digital infrastructure for smart agriculture. Economic strength provides the resource foundation. The proportion of construction land and GDP per capita offer spatial and capital resources, with the share of secondary and tertiary industries reflecting the supporting capacity of non-agricultural sectors. Market support functions as the demand driver. The proportion of the urban population determines consumption scale, while population density influences circulation efficiency and market responsiveness. Innovation capability serves as the vitality for transformation. R&D intensity of above-scale enterprises and the number of granted patents jointly promote agricultural modernization. Natural resources constitute the production foundation. Total sown area of crops, effectively irrigated area, and precipitation directly affect cultivation potential and stability. Based on this logic, this study conducts empirical analysis at the municipal scale, using indicators from each dimension as independent variables and the number of demonstration zones as the dependent variable. 2.2 Research Methods 2.2.1 Nearest Neighbor Index (NNI) In this study, modern agricultural demonstration zones are treated as point features for analysis. The Nearest Neighbor Index (NNI), which measures the proximity between elements in geographic space, serves to characterize the spatial distribution pattern of point-based features[ 34 ]. The calculation formula is: $$\text{R=}\frac{{\stackrel{\text{̄}}{\text{d}}}_{\text{min}}}{\text{E(}{\stackrel{\text{̄}}{\text{d}}}_{\text{min}}\text{)}}\text{=}\frac{{\stackrel{\text{̄}}{\text{d}}}_{\text{min}}}{\text{1/2}\sqrt{\text{n/A}}}$$ 1 R is the Nearest Neighbor Index; d min is the actual mean observed distance; E(d min ) is the theoretical nearest neighbor distance; n is the number of modern agricultural demonstration zones; A is the area of the study region. If 1R > 1 , the elements exhibit a uniform distribution pattern; if R < 1 , the elements exhibit a clustered distribution pattern; if R = 1 , the elements follow a random distribution pattern. 2.2.2 Kernel Density Estimation (KDE) Kernel Density Estimation (KDE) posits that geographical features can occur at any location in space, albeit with varying likelihoods across different positions[ 35 ]. Consequently, a higher kernel density indicates a greater probability of the geographical features being present. This method is employed in this study to analyze the spatial distribution patterns of modern agricultural demonstration zones. The calculation formula is: $$\text{f(x)=}\frac{\text{1}}{\text{nh}}\sum_{\text{i=1}}^{\text{n}}\text{K}\text{(}{\text{d}}_{\text{is}}\text{/r)}$$ 2 f(x) is the kernel density estimate at location x; h is the bandwidth, i.e., the search radius of the kernel function; n is the number of point features; K (d is /r ) is the kernel function, where dis is the shortest distance between point feature i and point features. 2.2.3Imbalance Index The imbalance index is employed to characterize the spatial distribution equilibrium of research elements within a region, and is generally calculated using the formula for the concentration index derived from the Lorenz curve[ 36 ]. The calculation formula is: $$\text{S=}\frac{\sum_{\text{i}}^{\text{n}}{\text{Y}}_{\text{i}}\text{−50}\text{(n+1)}}{\text{100n−50(n+1)}}$$ 3 S is the imbalance index, n is the number of prefecture-level cities in Guangxi Zhuang Autonomous Region, Y i is the cumulative percentage of the share of modern agricultural demonstration zones in the i-th city after sorting in descending order. The value of S ranges between 0 and 1. When S = 0 , it indicates an even distribution across all cities; when S = 1 , it implies that all demonstration zones are concentrated in a single city. 2.2.4 Geographical Detector The Geographical Detector (Geodetector) is a statistical method designed to identify spatial stratified heterogeneity and examine interaction effects among influencing factors[ 37 ]. Owing to its minimal assumptions, this method has been widely applied. Specifically, the factor detector can identify the influencing factors behind the spatial distribution of modern agricultural demonstration zones and quantify the magnitude of their effects, measured by the *q*-statistic. The interaction detector, on the other hand, assesses the strength and type of interactions between influencing factors. The results can be classified into five interaction types: nonlinear weakening of single factors, nonlinear weakening, independence, two-factor enhancement, and nonlinear enhancement. The calculation formula is: $$\text{q=}\text{1}\text{−}\frac{\sum_{\text{i=1}}^{\text{l}}{\text{N}}_{\text{i}}{\text{σ}}_{\text{i}}^{\text{2}}}{\text{N}{\text{σ}}^{\text{2}}}$$ 4 i denotes the stratification of the independent variable X, with i = 1,2,…,l ; N i is the number of modern agricultural demonstration zones in the i-th stratum; N is the total number of modern agricultural demonstration zones across the number of modern agricultural demonstration zones in the i-th stratum. The value of q ranges between 0 and 1. A higher q-value indicates that the corresponding influencing factor exerts stronger explanatory power over the spatial distribution of modern agricultural demonstration zones. 3. Result 3.1Overall spatial distribution characteristics Using ArcGIS, the average nearest neighbor index for the spatial distribution of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region was computed. As shown in Table 2 , the actual mean observed nearest neighbor distance is 8.058 km, while the theoretical mean nearest neighbor distance is 11.611 km. This yields a nearest neighbor index (R) of 0.693994, which is substantially less than 1. The corresponding Z-score is -14.925118, with a p-value of < 0.000001, passing the significance test at the 5% level. These results indicate that the overall spatial distribution pattern of these demonstration zones in Guangxi is statistically clustered. Distribution statistics across the five major regions of Guangxi reveal the following: the southern region contains 190 zones, accounting for 29.23% of the provincial total; the eastern region has 156 zones (24.00%); the western region includes 136 zones (20.92%); the central region possesses 90 zones (13.85%); and the northern region has 78 zones (12.00%). Consequently, modern characteristic agricultural demonstration zones in Guangxi are predominantly concentrated in the southern and eastern regions. Furthermore, calculation of the nearest neighbor index within each regional scope shows that the R-values for all five regions are below 1. This demonstrates that the spatial distribution of these demonstration zones exhibits a clustered pattern not only at the provincial level but also within each individual region. Table 2 Distribution of Five-Star Modern Characteristic Agricultural Core Demonstration Zones Across the Four Major Regions in Guangxi Zhuang Autonomous Region Region Scope Modern Characteristic Agricultural Demonstration Zones / units Percentage / % Actual Mean Observed Nearest Neighbor Distance / km Theoretical Mean Nearest Neighbor Distance / km R Index Value P Value Spatial Distribution Pattern Guangxi Province-wide 650 100 8.058 11.611 0.693994 < 0.000001 Clustered Eastern Region Hezhou, Guigang, Wuzhou, Yulin 156 24.00 7.757 10.374 0.747746 < 0.000001 Clustered Western Region Hechi, Baise 136 20.92 9.710 13.665 0.710561 < 0.000001 Clustered Southern Region Chongzuo, Nanning, Qinzhou, Beihai, Fangchenggang 190 29.23 7.839 9.577 0.818554 0.000002 Clustered Northern Region Guilin 78 12.00 7.540 9.644 0.781807 0.000227 Clustered Central Region Liuzhou, Laibin 90 13.85 8.137 10.406 0.781924 0.000076 Clustered Using ArcGIS, a kernel density estimation was performed for the distribution of modern characteristic agricultural demonstration zones in the autonomous region, with the results presented in Fig. 2 . The analysis shows that Nanning and Yulin exhibit high kernel density values. Their radiating effect drives the formation of multi-core patchy patterns in surrounding areas, revealing a distinct "core-periphery" structure. These two cities thus serve as the central hubs for the development of such demonstration zones in the region. Concurrently, demonstration zones in multiple other areas, including Liuzhou, Hezhou, and Hechi, show a trend of progressive agglomeration. Together, they form a "dual-core, multi-nodal" contiguous agglomerated spatial pattern centered on Nanning and Yulin. 3.2 Spatial Distribution Characteristics of Urban Areas The regional imbalance index (S) for modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, calculated using Formula (3), is 0.242130178 (Table 3 ). This value is substantially less than 1, providing further evidence of their spatially uneven distribution. Data presented in Fig. 3 and Table 4 show that the Lorenz curve at the municipal scale exhibits a distinct convex upward trend, indicating significant spatial inequality in the distribution of these zones across Guangxi. Among them, Nanning accounts for the highest proportion at 13.08%, while Fangchenggang accounts for the lowest at merely 3.08%. Although both Nanning and Wuzhou are located in the central region, the number of demonstration zones in Nanning is three times that in Wuzhou. In terms of ranking, the top six prefecture-level cities are Nanning, Baise, Guilin, Yulin, Hechi, and Liuzhou, which collectively contain 63.08% of the province's total demonstration zones. Among the top ten ranked cities, three are from the eastern region, two from the western region, two from the southern region, one from the northern region, and two from the central region. Notably, all prefecture-level cities from the western, northern, and central regions are represented within the top ten. Compared to the pronounced disparity in the distribution of core demonstration zones among cities within the eastern and southern regions, the gaps between cities in the western, northern, and central regions are relatively smaller, presenting a characteristic of relatively balanced intra-regional distribution. Table 3 Imbalance Index of the Distribution of Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Guangxi Zhuang Autonomous Region Region Imbalance Index S Guangxi Zhuang Autonomous Region 0.242130178 Table 4 Number of Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Prefecture-Level Cities of Guangxi Zhuang Autonomous Region Ranking Prefecture-level City Number of Five-Star Demonstration Zones Percentage (%) Cumulative Percentage (%) 1 Nanning City 85 13.08 13.08 2 Guilin City 78 12.00 25.08 3 Hechi City 74 11.38 36.46 4 Baise City 62 9.54 46.00 5 Yulin City 57 8.77 54.77 6 Liuzhou City 54 8.31 63.08 7 Hezhou City 38 5.85 68.92 8 Chongzuo City 37 5.69 74.62 9 Laibin City 36 5.54 80.15 10 Guigang City 34 5.23 85.38 11 Qinzhou City 28 4.31 89.69 12 Wuzhou City 27 4.15 93.85 13 Beihai City 20 3.08 96.92 14 Fangchenggang City 20 3.08 100.00 3.3 Spatial Distribution Characteristics of the Industry Modern characteristic agricultural demonstration zones are typically established and named based on their leading industries. To explore the sectoral spatial distribution characteristics of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, and with reference to multiple agricultural industry categories and agricultural product classification standards—combined with the leading industry features of these zones in Guangxi—the provincial modern characteristic agricultural demonstration zones were categorized into five major industry types: animal husbandry, forestry, aquaculture, agritourism, and crop cultivation. Subsequently, each major category was further subdivided into 24 specific industry subcategories (Table 5 ). Based on the count and proportion of demonstration zones belonging to each major category, the modern characteristic agricultural demonstration zones in Guangxi are predominantly focused on crop cultivation (42.92%), followed by animal husbandry (23.23%) and forestry (19.69%). Agritourism and aquaculture represent relatively smaller shares, at 6.31% and 7.85%, respectively. In terms of the count and proportion of specific industry subcategories, fruit cultivation constitutes the largest share (19.08%), followed by livestock (14.46%), poultry (8.62%), agritourism (7.85%), and specialized economic forests (6.77%), among others. These findings indicate that the modern characteristic agricultural demonstration zones in Guangxi are primarily established based on crop cultivation, with a significant emphasis on fruit cultivation, while also incorporating the simultaneous development of other industry types. This provides a solid foundation for Guangxi to strengthen its position as a major fruit-producing province. Table 5 Classification of the Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Guangxi Zhuang Autonomous Region Major Category Subcategory Number of Subcategories (units) Percentage of Subcategories (%) Number of Major Categories (units) Percentage of Major Categories (%) Crop Cultivation Fruits 124 19.08 279 42.92 Grains 41 6.31 Vegetables 32 4.92 Tea 30 4.62 Chinese Medicinal Herbs 12 1.85 Sericulture 10 1.54 Sugarcane 10 1.54 Edible Fungi 9 1.38 Others 6 0.92 Seed Industry 3 0.46 Mulberry and Silkworm 2 0.31 Animal Husbandry Livestock 94 14.46 151 23.23 Poultry 56 8.62 Others 1 0.15 Forestry Special Economic Forests 44 6.77 128 19.69 Forest Tourism 29 4.46 Precious Timber Species 21 3.23 Flowers and Seedlings 14 2.15 Forest Product Processing 10 1.54 Under-forest Planting and Breeding 10 1.54 Agritourism Agritourism 51 7.85 51 7.85 Aquaculture Inland Aquaculture 20 3.08 41 6.31 Coastal Aquaculture 13 2.00 Rice-Aquaculture Integrated 8 1.23 To further explore the spatial distribution characteristics of different types of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, kernel density analysis was conducted for the aforementioned five major categories as well as for the top ten subcategories with relatively high proportions among the 24 subcategories. The results (Fig. 4 ) are as follows: Demonstration zones primarily focused on crop cultivation are widely distributed across the province, with the densest concentrations found in the southern and eastern regions, particularly in Hezhou, Yulin, and Nanning, where kernel density values are notably high. Demonstration zones dominated by forestry are mainly located in the central and western regions, followed by the central-eastern regions, and are scarcely found in the northern region. Key distribution areas include Hechi and the border zones involving Nanning, Chongzuo, and Fangchenggang. Demonstration zones centered on animal husbandry show a relatively concentrated distribution, primarily in the eastern and southern regions. High kernel-density areas—indicating strong clustering of this type—are observed in Yulin and Nanning, while other prefecture-level cities, apart from Fangchenggang and Liuzhou, have only very few such zones. Demonstration zones emphasizing agritourism are mainly concentrated in the central, northern, and southern regions, with minor occurrences in the eastern region and the northeastern part of the western region. Major agglomerations are found in Guilin and the border area between Liuzhou and Laibin. Demonstration zones focused on aquaculture are primarily clustered in the border area between Fangchenggang and Qinzhou, with a small number distributed in Beihai and Guigang. Thus, while the core areas of aggregation for different types of modern characteristic agricultural demonstration zones vary, the overall characteristic of "multi-point clustering" remains relatively stable. From the kernel density analysis results of the major subcategories (Fig. 5 ): Demonstration zones for fruit cultivation are distributed in eastern Baise, northwestern Nanning, and Guilin. Livestock zones are located in Yulin, with additional distribution in the border areas between Nanning and Chongzuo, between Baise and Hechi, and scattered presence in other areas. Poultry zones are mainly found in northern Yulin and western Nanning. Agritourism zones are present in all prefecture-level cities except Fangchenggang, with primary distributions in northern Hechi, Guilin, and Nanning, and forming a belt-like pattern across Liuzhou, Laibin, Guigang, and Yulin. Zones for specialized economic forests are primarily located in the border area between Baise and Hechi. Grain crop zones are mainly distributed in the border area between Guigang and Yulin. Vegetable zones are primarily found in Hezhou, with a small number in the border area between Liuzhou and Laibin. Tea cultivation zones are concentrated in northern Liuzhou and the border area between Wuzhou and Hezhou, with sporadic distribution in the border area involving Nanning, Guigang, Yulin, and Qinzhou, as well as the border between Baise and Hechi. Forest tourism zones are mainly distributed in the central region (the border areas involving Guigang, Nanning and Chongzuo, and Fangchenggang) and the northern region (Hechi, Guilin, Hezhou). Zones for precious timber species are primarily concentrated in Liuzhou and Fangchenggang, with a small number in Yulin, Beihai, and northern Guilin. 4. Influencing Factors of Modern Agricultural Industrial Parks in Guangdong Province 4.1 Single-factor impact analysis By ranking the q-values of the significant factors, the strength of each factor's influence can be clarified. Water conservancy and irrigation, total farmland, the number of leading agricultural enterprises, industrial structure, urbanization rate, and annual precipitation are strong dominant factors influencing the spatial distribution, with q-values all above 0.8, substantially higher than those of other factors. This is followed by influencing factors such as total R&D investment, innovation output, population density, economic development level, road network density, and digital infrastructure, with q-values ranging between 0.6 and 0.8. Although their influence is significantly lower than that of the strong dominant factors, they remain important factors affecting the distribution of modern characteristic agricultural demonstration zones in Guangxi. Table 6 Detection Results of Influencing Factors for Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Guangxi Zhuang Autonomous Region Dimension Influencing Factor q-statistic p-value Influence Ranking Policy Environment Number of OVOP demonstration villages/towns (X1) 0.3557 0.6984 — Leading agricultural enterprises (X2) 0.8811 0.0002 3 Infrastructure Road network density (X3) 0.6949 0.0224 11 Digital infrastructure (X4) 0.6661 0.0012 12 Construction land scale (X5) 0.5896 0.0215 13 Economic Strength Economic development level (X6) 0.7615 0.0002 10 Industrial structure (X7) 0.8646 0.0001 4 Market Support Urbanization rate (X8) 0.8480 0.0001 5 Population density (X9) 0.7618 0.0058 9 Innovation Capability R&D investment (X10) 0.7977 0.0067 7 Innovation output (X11) 0.7815 0.0048 8 Natural Resources Total farmland (X12) 0.8874 0.0000 2 Water conservancy and irrigation (X13) 0.9183 0.0000 1 Annual precipitation (X14) 0.3557 0.6984 — In summary, the spatial distribution of modern characteristic agricultural demonstration zones in Guangxi is closely related to local conditions of water conservancy and irrigation, total farmland, the number of leading agricultural enterprises, industrial structure, urbanization rate, and annual precipitation. These findings provide scientific decision-making support for the precise spatial planning, differentiated construction, and high-quality development of Guangxi's modern characteristic agricultural demonstration zones. 4.2 Multifactor impact analysis Based on the geographical detector, an interaction analysis was conducted on 14 factors across six dimensions. The results indicate that all interactive combinations of factors produce enhancement effects on the spatial distribution of the demonstration zones, with two-factor enhancement as the predominant form, and no independent or nonlinear weakening effects observed. Multiple factors collectively shape the distribution pattern through a mechanism of hierarchical synergy. According to the interaction *q*-values, the combinations can be categorized into three tiers. The first tier (strong interactive effects) centers on leading agricultural enterprises (X2). The interaction *q*-values between X2 and factors such as innovation output (X11) and total farmland (X12) generally exceed 0.95, far surpassing their individual effects, making this tier the dominant driving force. Interactions involving natural resource factors (X12, X13) with other factors also predominantly fall into this tier. The second tier (moderate interactive effects) primarily includes interactions among dominant factors such as road network density (X3), digital infrastructure (X4), and R&D investment (X10), as well as combinations of these factors with secondary factors. These serve as important auxiliary synergistic forces. The third tier (weak interactive effects) mainly consists of interactions among secondary factors such as the number of "One Village, One Product" demonstration villages/towns (X1) and population density (X9). Although their interaction values exceed individual factor effects, their influence remains relatively limited. Leading agricultural enterprises (X2) function as the core dominant factor, playing a pivotal role as an "element-activating catalyst." This is manifested in two aspects: first, their interaction with innovation output (X11) forms a dual-core driving model of enterprise carrier + technological innovation" ; second, their interactions with all other factors exhibit significantly enhanced effectiveness, connecting elements such as natural resources and infrastructure to form a synergistic chain of "policy guidance – enterprise leadership – element aggregation." Certain single factors with relatively weak individual influence, such as road network density (X3), digital infrastructure (X4), and R&D investment (X10), achieve substantial enhancement in effectiveness through interaction with core factors, becoming an "invisible supporting force." This demonstrates that the distribution of demonstration zones is the comprehensive result of "core-factor leadership, complemented by weak factors, and multi-element synergy." 5. Discussion This study, employing spatial analysis and the geographical detector method, reveals the distribution patterns and driving mechanisms of Guangxi's modern characteristic agricultural core demonstration zones, addressing the spatial perspective gap in existing research. The findings align with the general logic of agricultural demonstration zone development while highlighting the regional characteristics of Guangxi. Taking 650 autonomous region-level modern characteristic agricultural core demonstration zones in Guangxi as the research object, this study draws the following core conclusions through multi-method empirical analysis: First, the spatial distribution exhibits a characteristic of "predominant clustering with significant differentiation." The overall distribution of the demonstration zones across the province is clustered, forming a "dual-core, multi-nodal" pattern centered on Nanning and Yulin. Regionally, they are concentrated in the southern (29.23%) and eastern (24.00%) regions. Municipal-level distribution is uneven, with the top six prefecture-level cities accounting for 63.08% of the total. At the sectoral level, crop cultivation dominates (42.92%), with fruit cultivation constituting the largest share (19.08%). Different industrial types display distinct spatial differentiation. Second, the distribution is synergistically driven by multi-dimensional factors. Among single factors, water conservancy and irrigation along with total farmland are the primary influencing factors, while leading agricultural enterprises, industrial structure, and urbanization rate are strong dominant factors. Factors such as R&D investment and road network density serve as important supporting elements. All multi-factor interactions exhibit enhancement effects, with leading agricultural enterprises playing a core driving role and forming synergistic chains with other factors to promote element aggregation and the clustering of demonstration zones. Third, optimization pathways must be based on "pattern adaptation and factor synergy." The development of Guangxi's demonstration zones should strengthen the radiating and driving effects of Nanning and Yulin, and implement differentiated planning for types such as forestry and characteristic cultivation demonstration zones in the northern region and peripheral cities based on their resource endowments. Continued cultivation of leading agricultural enterprises should be prioritized, promoting their deep synergy with infrastructure, innovation resources, and market demand. Through factor integration, this approach addresses regional development imbalances, achieving the dual objectives of spatial layout optimization and high-quality industrial development. The findings of this study provide scientific support for optimizing Guangxi's demonstration zones but acknowledge certain limitations. The research focuses on the municipal scale and does not delve into micro-level analyses at the county level or below. It also does not consider the dynamic impact of policy implementation timelines on distribution. Future research could refine the study scale and incorporate panel data to reveal the dynamic evolution patterns of zone distribution, offering more detailed references for targeted policy-making. Declarations Institutional Review Board Statement Studies not involving humans or animals. Conflicts of Interest: The authors declare no conflicts of interest. Funding: Research on Enhancing the Modernization of the Innovation Chain, Industrial Chain and Supply Chain in Guangxi during the "15th Five-Year Plan" under the Background of Global Industrial Transfer (Grant NO: GXZC2024-C3-006083-JDZB) Author Contribution Zhao, H. Q.: Conceptualization; Data curation; Formal analysis; Resources; Software; Validation; Visualization; original draft; Formal analysis; Resources, Review; Editing; Conceptualization; Original draft; Resources. Wen. J.: Funding acquisition; Methodology; Supervision; Validation. All authors have read and agreed to the published version of the manuscript. Data Availability Data are contained within the article. References Lu, A. H. et al. Research on high-quality development of modern characteristic agricultural demonstration zones in Guangxi.Agric. Eng. Technol. 42, 21–24.(2022) (2022). Wang, W. P. 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A quasi-natural experiment based on the ‘National Modern Agricultural Demonstration Zone’.World Agric. 3, 78–90.(2024) (2024). Cao, Y. L., Xing, S. & Shi, R. G. The impact effect of establishing National Modern Agricultural Demonstration Zones on regional air pollution: An empirical study based on 2362 counties and districts in China.World Surv. Res. 11, 68–80.(2023) (2023). Zhao, J. M. & Yu, S. G. Have National Modern Agricultural Demonstration Zones promoted the development of modern agriculture within them? An empirical study based on 2099 counties and cities in China.J. Nanjing Univ. Financ. Econ. 3, 23–31 + 65.(2022) (2022). Zeng, C. L. & Mei, Y. X. National Modern Agricultural Demonstration Zones and labor force: Employment growth driven by policies.Coll. Essays Financ. Econ. 1, 15–24.(2022) (2022). Dai, L. N. & Yang, Q. Spatial layout and influencing factors of leisure agriculture in the Guangdong-Hong Kong-Macao Greater Bay Area.Guangdong Agric. Sci. 50, 72–81.(2023) (2023). 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Introduction","content":"\u003cp\u003eModern Characteristic Agricultural Demonstration Zones are designated areas where agricultural enterprises, specialized cooperatives, family farms, and large-scale growers serve as the primary operating entities. Supported by modern scientific technologies and material facilities, these zones facilitate land consolidation through land transfer, promote appropriate-scale operations, adopt modern management practices, enhance the integration of primary, secondary, and tertiary industries, and effectively achieve sustainable development[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To implement the Central Government's major decisions on comprehensively deepening rural reform and accelerating agricultural modernization, and to promptly enhance the level of agricultural industrialization, marketization, internationalization, and modernization in the autonomous region while strengthening the industrial foundation for the \"Beautiful Countryside\" initiative and the construction of a new socialist countryside, the autonomous region launched the initiative to establish Modern Characteristic Agricultural (Core) Demonstration Zones. Starting in 2014, a series of policy documents were successively issued, including Interim Measures for the Construction and Management of Guangxi Modern Characteristic Agricultural (Core) Demonstration Zones, Implementation Plan for Establishing Guangxi Modern Characteristic Agricultural (Core) Demonstration Zones, *Three-Year Action Plan for Expanding Coverage, Improving Quality, and Upgrading Guangxi Modern Characteristic Agricultural Demonstration Zones (2018\u0026ndash;2020)*, *Notice of the General Office of the People's Government of Guangxi Zhuang Autonomous Region on Issuing the Five-Year Action Plan for High-Quality Construction of Guangxi Modern Characteristic Agricultural Demonstration Zones (2021\u0026ndash;2025)*, and *Letter on Carrying Out the Acceptance Inspection of Autonomous Region-Level Modern Characteristic Agricultural Demonstration Zones in 2024*. These policies have systematically facilitated the transformation of Guangxi's agriculture from traditional production to a modern industrial cluster, providing an institutional framework and resource support for rural revitalization. By the end of 2023, a total of 650 autonomous region-level modern characteristic agricultural core demonstration zones had been designated in Guangxi Zhuang Autonomous Region, comprising 123 Five-Star, 445 Four-Star, and 82 Three-Star zones. Their leading industries cover various sectors such as crop cultivation and forestry. The development of demonstration zones has promoted land leasing and transfer, advanced land trusteeship models, improved infrastructure systems, reinforced the advancement of rural ecological civilization, consistently improved the quality of living environments in rural regions, and substantially enhanced the overall appearance and ambiance of villages[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly academic research on modern characteristic agricultural demonstration zones primarily focused on their development models and analysis of associated challenges. In recent years, as these demonstration zones have matured, their positive impacts on agricultural and rural development have become increasingly prominent, prompting extensive scholarly investigation into their effects and roles.\u003c/p\u003e \u003cp\u003eAt the level of development models, Zeng Xianghua took Xiaokunshan Town in Shanghai as a case study to explore a \"water conservation-oriented\" model. Based on the location of a water source protection area, this model focuses on water conservation and integrates agricultural, forestry, and water resource construction, achieving synergistic ecological, economic, and social benefits[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Chen Fumei et al., using the Longmenshi Demonstration Zone in Hefei as an example, constructed an \"agriculture-tourism integration\" model that promotes agricultural upgrading through business cultivation and five major planning strategies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Zhang Fenglei et al., taking Liangping District in Chongqing as a case, established a \"heavy metal risk prevention and control\" model focusing on elements such as As and Hg, providing technical support for ecologically sensitive areas[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Zhou Zhiguo et al. introduced Porter's \"Diamond\" model and explored an \"industrial cluster-driven\" model using Yongqing County in Hebei as a case, focusing on farmer cultivation and technological development[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Xu Shangda et al., using Weixian County in Hebei as an example, proposed a \"late-development breakthrough\" model that relies on policies to achieve point-specific breakthroughs driving regional development[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Li Binglong, taking Dazhu County in Sichuan as an example, elaborated on a \"characteristic industry integration\" model that extends the industrial chain by leveraging the ramie brand [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Wu Dan, using Tiandeng County in Guangxi as a case, constructed a \"multi-dimensional integrated planning\" model that formulates strategies at macro, meso, and micro levels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Guo Shumin et al., taking Fangshan District in Beijing as an example, explored a \"gully economy innovation\" model to address the challenge of spatial fragmentation in mountainous areas [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Zhang Qiuling et al., using the Lanshan Demonstration Zone in Rizhao, Shandong as a case, constructed a \"circular and high-efficiency\" model that emphasizes the coordinated advancement of productive, living, and ecological benefits [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Tu Xiaoli, taking the Taizhou Demonstration Zone as an example, analyzed a \"brand-led\" model focusing on enhancing the value of nationally recognized brands [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Gao Yun et al., based on operational mechanism theory, constructed a long-term development model by addressing institutional shortcomings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Gao Yali et al., taking Nanchuan District in Chongqing as an example, explored an \"eco-agricultural transition\" model proposing pathways such as production-marketing integration [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Zeng Lei et al., from the perspective of industrial integration, proposed a \"tourism function expansion\" planning model that compensates for tourism shortcomings through project innovation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Zhang Yunpeng et al., using the Bailuyuan Demonstration Zone in Xi'an as a case, constructed a \"culture integration\" model that incorporates historical and folk culture to create distinctive characteristics [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Lei Suijiang et al., taking a demonstration zone in Lianyungang, Jiangsu as an example, adopted a \"problem-oriented\" model to provide countermeasure support for long-term operation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of problem analysis, Zhang Jingyi et al., taking the Pudong New Area of Shanghai as an example, identified challenges faced by new-type agricultural business entities, including a shortage of talent, insufficient ecological subsidies, and restricted access to credit and land use. They proposed countermeasures such as talent introduction, increased subsidies, diversified credit options, and easing land use restrictions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Meng Zhaodi et al., based on data from 281 demonstration zones, identified issues including the contradiction between food security and farmer income growth, regional development imbalances, and the need for improved management. They recommended enhancing production capacity, promoting inter-regional exchange, refining management systems, and adhering to market-oriented approaches [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Weng Boqi et al., considering the local context of Jian'ou City in Fujian Province, proposed the development of six characteristic functional zones, including mountainous green agriculture and hilly ecological fruit and tea industries [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Qiao Lijuan et al., focusing on the livestock sector in Hebei demonstration zones, addressed issues such as breeding pollution and talent shortages, and proposed strategies including integrated crop-livestock farming, cultivation of professional talent, and enhancing industrial quality and connotation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Liang Danhui and Jiang Jing, from the perspective of factor endowments, emphasized optimizing allocation through the integrated coordination of land, capital, technology, and other production factors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Zhang Yuan et al. pointed out deficiencies in standard systems, testing capabilities, and organizational management in the standardization efforts of Hebei demonstration zones, and recommended accelerating land transfer, developing characteristic agriculture, and strengthening technological support [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Su Wuzheng et al. employed SWOT analysis to clarify the objectives, positioning, and formulate targeted development strategies for the demonstration zone in Hutubi County, Xinjiang [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the effects and impacts, Zhang Qizheng et al., based on county-level data and using the framework of the \"three major systems,\" found that demonstration zones shift the driving force of agricultural growth from labor input to improvements in labor productivity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Li Shaolin et al., relying on data from 2,649 counties and districts, confirmed that demonstration zones significantly enhance the resilience of grain production, primarily through optimized resource allocation and improved production efficiency [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Zhu Jinxin et al., based on data from 1,565 counties, verified that demonstration zones enhance grain production resilience by increasing mechanization levels and labor productivity, with more pronounced effects in the eastern, western, and non-major grain-producing areas [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Kong Xiangzhi et al., employing a multi-period DID model, found that demonstration zones increase the value-added of agriculture, forestry, animal husbandry, and fishery by 2.8%, with effects dependent on fiscal support for agriculture and technology extension investment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Min Jisheng et al., using a continuous difference-in-differences approach, demonstrated that modern agricultural mechanization technology can increase household income among part-time farming households by 2.7% [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Zhang Fengbing et al., through a multi-period DID model, found that demonstration zones can sustainably enhance agricultural total factor productivity, primarily achieved through improvements in electricity and farmland water conservancy infrastructure [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Cao Yuliang et al. confirmed that demonstration zones mitigate air pollution by promoting industrial structure upgrading [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Zhao Jianmei et al., employing the PSM-DID method, showed that demonstration zones both promote farmers' output and income growth and reduce fertilizer and pesticide application, primarily relying on the development of tertiary industries and rural e-commerce [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Zeng Changlin et al., using a difference-in-differences approach, found that demonstration zones significantly increase rural labor employment by 5.4%, mainly achieved through infrastructure development and enterprise agglomeration effects [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, modern characteristic agricultural demonstration zones represent a significant approach to promoting sustainable agricultural development. Current research on these zones remains largely focused on descriptive analyzes of their current status, operational models, and existing challenges. There is a notable lack of spatial-perspective investigations into their distribution patterns and underlying mechanisms, as well as insufficient in-depth exploration of the causative factors and specific drivers shaping their development. This gap hinders a comprehensive understanding of the formative mechanisms of modern characteristic agricultural demonstration zones and may impede their sustainable advancement. By identifying the spatial distribution characteristics and influencing factors of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, this study aims to clarify their spatial patterns and formative mechanisms, explore suitable types of demonstration zones for different areas within the region, and provide insights to support the high-quality development of these zones in Guangxi.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources and Indicator Selection\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Data Source\u003c/h2\u003e \u003cp\u003eThe data for this study were sourced from the annual lists of autonomous region-level modern characteristic agricultural core demonstration zones published by the Department of Agriculture and Rural Affairs of Guangxi Zhuang Autonomous Region, starting from 2014. By the end of 2022, a total of 650 autonomous region-level modern characteristic agricultural core demonstration zones had been announced over 11 batches, which served as the sample for this research.\u003c/p\u003e \u003cp\u003eBy reviewing the relevant planning and construction documents of each demonstration zone, we identified the township where its core area is located and the specific lead implementation entity to determine its spatial location. Specifically, the geographic coordinates of the core area or the implementing entity were obtained using the Baidu Map Geocoding API. After manual verification, the BD-09 coordinates were converted to the WGS-84 coordinate system via a GIS data converter to vectorize the spatial locations. This process resulted in the construction of a spatial database of Guangxi's modern characteristic agricultural core demonstration zones, containing attribute information such as the city, county, and township of each zone. Based on this database, a spatial distribution map of the modern characteristic agricultural core demonstration zones in Guangxi was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Indicator Selection\u003c/h2\u003e \u003cp\u003eThe formation of modern characteristic agricultural core demonstration zones results from the interaction of multiple factors. Based on data availability, this study selects 14 factors across six dimensions for influence analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors Influencing the Core Demonstration Areas of Five-Star Modern Characteristic Agriculture in Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluencing Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecific Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of OVOP demonstration villages/towns (X1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of nationally designated OVOP demonstration villages in the autonomous region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeading agricultural enterprises (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of leading agricultural enterprises designated by the autonomous region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eenterprises\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad network density (X3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal length of roads and railways / regional area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekm/km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital infrastructure (X4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTelecom business revenue / regional GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruction land scale (X5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of construction land area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic development level (X6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCNY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial structure (X7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRatio of secondary and tertiary industries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanization rate (X8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban population / total resident population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density (X9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResident population / regional area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epersons/km\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInnovation Capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026amp;D investment (X10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026amp;D intensity of above-scale enterprises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInnovation output (X11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of patents granted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal farmland (X12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal sown area of crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater conservancy and irrigation (X13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffectively irrigated land area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual precipitation (X14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe policy environment serves as the institutional foundation. The number of \"One Village, One Product\" (OVOP) demonstration villages and leading agricultural enterprises reflects the intensity of policy empowerment, laying an industrial foundation for the demonstration zones. Infrastructure acts as the material prerequisite. Road and railway density determines logistics convenience, while the proportion of telecommunications revenue reflects the support of digital infrastructure for smart agriculture. Economic strength provides the resource foundation. The proportion of construction land and GDP per capita offer spatial and capital resources, with the share of secondary and tertiary industries reflecting the supporting capacity of non-agricultural sectors. Market support functions as the demand driver. The proportion of the urban population determines consumption scale, while population density influences circulation efficiency and market responsiveness. Innovation capability serves as the vitality for transformation. R\u0026amp;D intensity of above-scale enterprises and the number of granted patents jointly promote agricultural modernization. Natural resources constitute the production foundation. Total sown area of crops, effectively irrigated area, and precipitation directly affect cultivation potential and stability.\u003c/p\u003e \u003cp\u003eBased on this logic, this study conducts empirical analysis at the municipal scale, using indicators from each dimension as independent variables and the number of demonstration zones as the dependent variable.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Methods\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Nearest Neighbor Index (NNI)\u003c/h2\u003e \u003cp\u003eIn this study, modern agricultural demonstration zones are treated as point features for analysis. The Nearest Neighbor Index (NNI), which measures the proximity between elements in geographic space, serves to characterize the spatial distribution pattern of point-based features[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe calculation formula is:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{R=}\\frac{{\\stackrel{\\text{̄}}{\\text{d}}}_{\\text{min}}}{\\text{E(}{\\stackrel{\\text{̄}}{\\text{d}}}_{\\text{min}}\\text{)}}\\text{=}\\frac{{\\stackrel{\\text{̄}}{\\text{d}}}_{\\text{min}}}{\\text{1/2}\\sqrt{\\text{n/A}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eR\u003c/em\u003e is the Nearest Neighbor Index; \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e is the actual mean observed distance; \u003cem\u003eE(d\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e is the theoretical nearest neighbor distance; \u003cem\u003en\u003c/em\u003e is the number of modern agricultural demonstration zones; \u003cem\u003eA\u003c/em\u003e is the area of the study region. \u003cem\u003eIf 1R\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/em\u003e, the elements exhibit a uniform distribution pattern; \u003cem\u003eif R\u0026thinsp;\u0026lt;\u0026thinsp;1\u003c/em\u003e, the elements exhibit a clustered distribution pattern; \u003cem\u003eif R\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e, the elements follow a random distribution pattern.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Kernel Density Estimation (KDE)\u003c/h2\u003e \u003cp\u003eKernel Density Estimation (KDE) posits that geographical features can occur at any location in space, albeit with varying likelihoods across different positions[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consequently, a higher kernel density indicates a greater probability of the geographical features being present. This method is employed in this study to analyze the spatial distribution patterns of modern agricultural demonstration zones.\u003c/p\u003e \u003cp\u003eThe calculation formula is:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\text{f(x)=}\\frac{\\text{1}}{\\text{nh}}\\sum_{\\text{i=1}}^{\\text{n}}\\text{K}\\text{(}{\\text{d}}_{\\text{is}}\\text{/r)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ef(x)\u003c/em\u003e is the kernel density estimate at location x; \u003cem\u003eh\u003c/em\u003e is the bandwidth, i.e., the search radius of the kernel function; \u003cem\u003en\u003c/em\u003e is the number of point features; \u003cem\u003eK (d\u003c/em\u003e\u003csub\u003e\u003cem\u003eis\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e/r )\u003c/em\u003e is the kernel function, where dis is the shortest distance between point feature i and point features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3Imbalance Index\u003c/h2\u003e \u003cp\u003eThe imbalance index is employed to characterize the spatial distribution equilibrium of research elements within a region, and is generally calculated using the formula for the concentration index derived from the Lorenz curve[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe calculation formula is:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{S=}\\frac{\\sum_{\\text{i}}^{\\text{n}}{\\text{Y}}_{\\text{i}}\\text{\u0026minus;50}\\text{(n+1)}}{\\text{100n\u0026minus;50(n+1)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eS\u003c/em\u003e is the imbalance index, \u003cem\u003en\u003c/em\u003e is the number of prefecture-level cities in Guangxi Zhuang Autonomous Region, \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the cumulative percentage of the share of modern agricultural demonstration zones in the \u003cem\u003ei-th\u003c/em\u003e city after sorting in descending order. The value of \u003cem\u003eS\u003c/em\u003e ranges between 0 and 1. When \u003cem\u003eS\u0026thinsp;=\u0026thinsp;0\u003c/em\u003e, it indicates an even distribution across all cities; when \u003cem\u003eS\u0026thinsp;=\u0026thinsp;1\u003c/em\u003e, it implies that all demonstration zones are concentrated in a single city.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Geographical Detector\u003c/h2\u003e \u003cp\u003eThe Geographical Detector (Geodetector) is a statistical method designed to identify spatial stratified heterogeneity and examine interaction effects among influencing factors[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Owing to its minimal assumptions, this method has been widely applied. Specifically, the factor detector can identify the influencing factors behind the spatial distribution of modern agricultural demonstration zones and quantify the magnitude of their effects, measured by the *q*-statistic. The interaction detector, on the other hand, assesses the strength and type of interactions between influencing factors. The results can be classified into five interaction types: nonlinear weakening of single factors, nonlinear weakening, independence, two-factor enhancement, and nonlinear enhancement.\u003c/p\u003e \u003cp\u003eThe calculation formula is:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\text{q=}\\text{1}\\text{\u0026minus;}\\frac{\\sum_{\\text{i=1}}^{\\text{l}}{\\text{N}}_{\\text{i}}{\\text{\u0026sigma;}}_{\\text{i}}^{\\text{2}}}{\\text{N}{\\text{\u0026sigma;}}^{\\text{2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003ei\u003c/em\u003e denotes the stratification of the independent variable X, with \u003cem\u003ei\u0026thinsp;=\u0026thinsp;1,2,\u0026hellip;,l\u003c/em\u003e; \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the number of modern agricultural demonstration zones in the \u003cem\u003ei-th\u003c/em\u003e stratum; \u003cem\u003eN\u003c/em\u003e is the total number of modern agricultural demonstration zones across the number of modern agricultural demonstration zones in the \u003cem\u003ei-th\u003c/em\u003e stratum. The value of \u003cem\u003eq\u003c/em\u003e ranges between 0 and 1. A higher \u003cem\u003eq-value\u003c/em\u003e indicates that the corresponding influencing factor exerts stronger explanatory power over the spatial distribution of modern agricultural demonstration zones.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1Overall spatial distribution characteristics\u003c/h2\u003e \u003cp\u003eUsing ArcGIS, the average nearest neighbor index for the spatial distribution of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region was computed. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the actual mean observed nearest neighbor distance is 8.058 km, while the theoretical mean nearest neighbor distance is 11.611 km. This yields a nearest neighbor index (R) of 0.693994, which is substantially less than 1. The corresponding Z-score is -14.925118, with a p-value of \u0026lt;\u0026thinsp;0.000001, passing the significance test at the 5% level. These results indicate that the overall spatial distribution pattern of these demonstration zones in Guangxi is statistically clustered. Distribution statistics across the five major regions of Guangxi reveal the following: the southern region contains 190 zones, accounting for 29.23% of the provincial total; the eastern region has 156 zones (24.00%); the western region includes 136 zones (20.92%); the central region possesses 90 zones (13.85%); and the northern region has 78 zones (12.00%). Consequently, modern characteristic agricultural demonstration zones in Guangxi are predominantly concentrated in the southern and eastern regions. Furthermore, calculation of the nearest neighbor index within each regional scope shows that the R-values for all five regions are below 1. This demonstrates that the spatial distribution of these demonstration zones exhibits a clustered pattern not only at the provincial level but also within each individual region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Five-Star Modern Characteristic Agricultural Core Demonstration Zones Across the Four Major Regions in Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModern Characteristic Agricultural Demonstration Zones / units\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage / %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eActual Mean Observed Nearest Neighbor Distance / km\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTheoretical Mean Nearest Neighbor Distance / km\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR Index Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSpatial Distribution Pattern\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvince-wide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.693994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHezhou, Guigang, Wuzhou, Yulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.747746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHechi, Baise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.710561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChongzuo, Nanning, Qinzhou, Beihai, Fangchenggang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.818554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.781807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiuzhou, Laibin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.781924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eClustered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing ArcGIS, a kernel density estimation was performed for the distribution of modern characteristic agricultural demonstration zones in the autonomous region, with the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The analysis shows that Nanning and Yulin exhibit high kernel density values. Their radiating effect drives the formation of multi-core patchy patterns in surrounding areas, revealing a distinct \"core-periphery\" structure. These two cities thus serve as the central hubs for the development of such demonstration zones in the region. Concurrently, demonstration zones in multiple other areas, including Liuzhou, Hezhou, and Hechi, show a trend of progressive agglomeration. Together, they form a \"dual-core, multi-nodal\" contiguous agglomerated spatial pattern centered on Nanning and Yulin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Spatial Distribution Characteristics of Urban Areas\u003c/h2\u003e \u003cp\u003eThe regional imbalance index (S) for modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, calculated using Formula (3), is 0.242130178 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This value is substantially less than 1, providing further evidence of their spatially uneven distribution. Data presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that the Lorenz curve at the municipal scale exhibits a distinct convex upward trend, indicating significant spatial inequality in the distribution of these zones across Guangxi. Among them, Nanning accounts for the highest proportion at 13.08%, while Fangchenggang accounts for the lowest at merely 3.08%. Although both Nanning and Wuzhou are located in the central region, the number of demonstration zones in Nanning is three times that in Wuzhou. In terms of ranking, the top six prefecture-level cities are Nanning, Baise, Guilin, Yulin, Hechi, and Liuzhou, which collectively contain 63.08% of the province's total demonstration zones. Among the top ten ranked cities, three are from the eastern region, two from the western region, two from the southern region, one from the northern region, and two from the central region. Notably, all prefecture-level cities from the western, northern, and central regions are represented within the top ten. Compared to the pronounced disparity in the distribution of core demonstration zones among cities within the eastern and southern regions, the gaps between cities in the western, northern, and central regions are relatively smaller, presenting a characteristic of relatively balanced intra-regional distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImbalance Index of the Distribution of Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImbalance Index S\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.242130178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Prefecture-Level Cities of Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrefecture-level City\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Five-Star Demonstration Zones\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCumulative Percentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNanning City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuilin City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHechi City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaise City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYulin City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiuzhou City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHezhou City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChongzuo City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaibin City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuigang City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQinzhou City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWuzhou City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeihai City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFangchenggang City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spatial Distribution Characteristics of the Industry\u003c/h2\u003e \u003cp\u003eModern characteristic agricultural demonstration zones are typically established and named based on their leading industries. To explore the sectoral spatial distribution characteristics of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, and with reference to multiple agricultural industry categories and agricultural product classification standards\u0026mdash;combined with the leading industry features of these zones in Guangxi\u0026mdash;the provincial modern characteristic agricultural demonstration zones were categorized into five major industry types: animal husbandry, forestry, aquaculture, agritourism, and crop cultivation. Subsequently, each major category was further subdivided into 24 specific industry subcategories (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Based on the count and proportion of demonstration zones belonging to each major category, the modern characteristic agricultural demonstration zones in Guangxi are predominantly focused on crop cultivation (42.92%), followed by animal husbandry (23.23%) and forestry (19.69%). Agritourism and aquaculture represent relatively smaller shares, at 6.31% and 7.85%, respectively. In terms of the count and proportion of specific industry subcategories, fruit cultivation constitutes the largest share (19.08%), followed by livestock (14.46%), poultry (8.62%), agritourism (7.85%), and specialized economic forests (6.77%), among others. These findings indicate that the modern characteristic agricultural demonstration zones in Guangxi are primarily established based on crop cultivation, with a significant emphasis on fruit cultivation, while also incorporating the simultaneous development of other industry types. This provides a solid foundation for Guangxi to strengthen its position as a major fruit-producing province.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of the Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubcategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Subcategories (units)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of Subcategories (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of Major Categories (units)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage of Major Categories (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop Cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese Medicinal Herbs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSericulture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSugarcane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdible Fungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeed Industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulberry and Silkworm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal Husbandry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLivestock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoultry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecial Economic Forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest Tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecious Timber Species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlowers and Seedlings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest Product Processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder-forest Planting and Breeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgritourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgritourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAquaculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInland Aquaculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal Aquaculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice-Aquaculture Integrated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further explore the spatial distribution characteristics of different types of modern characteristic agricultural demonstration zones in Guangxi Zhuang Autonomous Region, kernel density analysis was conducted for the aforementioned five major categories as well as for the top ten subcategories with relatively high proportions among the 24 subcategories. The results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) are as follows: Demonstration zones primarily focused on crop cultivation are widely distributed across the province, with the densest concentrations found in the southern and eastern regions, particularly in Hezhou, Yulin, and Nanning, where kernel density values are notably high. Demonstration zones dominated by forestry are mainly located in the central and western regions, followed by the central-eastern regions, and are scarcely found in the northern region. Key distribution areas include Hechi and the border zones involving Nanning, Chongzuo, and Fangchenggang. Demonstration zones centered on animal husbandry show a relatively concentrated distribution, primarily in the eastern and southern regions. High kernel-density areas\u0026mdash;indicating strong clustering of this type\u0026mdash;are observed in Yulin and Nanning, while other prefecture-level cities, apart from Fangchenggang and Liuzhou, have only very few such zones. Demonstration zones emphasizing agritourism are mainly concentrated in the central, northern, and southern regions, with minor occurrences in the eastern region and the northeastern part of the western region. Major agglomerations are found in Guilin and the border area between Liuzhou and Laibin. Demonstration zones focused on aquaculture are primarily clustered in the border area between Fangchenggang and Qinzhou, with a small number distributed in Beihai and Guigang.\u003c/p\u003e \u003cp\u003eThus, while the core areas of aggregation for different types of modern characteristic agricultural demonstration zones vary, the overall characteristic of \"multi-point clustering\" remains relatively stable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom the kernel density analysis results of the major subcategories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e): Demonstration zones for fruit cultivation are distributed in eastern Baise, northwestern Nanning, and Guilin. Livestock zones are located in Yulin, with additional distribution in the border areas between Nanning and Chongzuo, between Baise and Hechi, and scattered presence in other areas. Poultry zones are mainly found in northern Yulin and western Nanning. Agritourism zones are present in all prefecture-level cities except Fangchenggang, with primary distributions in northern Hechi, Guilin, and Nanning, and forming a belt-like pattern across Liuzhou, Laibin, Guigang, and Yulin. Zones for specialized economic forests are primarily located in the border area between Baise and Hechi. Grain crop zones are mainly distributed in the border area between Guigang and Yulin. Vegetable zones are primarily found in Hezhou, with a small number in the border area between Liuzhou and Laibin. Tea cultivation zones are concentrated in northern Liuzhou and the border area between Wuzhou and Hezhou, with sporadic distribution in the border area involving Nanning, Guigang, Yulin, and Qinzhou, as well as the border between Baise and Hechi. Forest tourism zones are mainly distributed in the central region (the border areas involving Guigang, Nanning and Chongzuo, and Fangchenggang) and the northern region (Hechi, Guilin, Hezhou). Zones for precious timber species are primarily concentrated in Liuzhou and Fangchenggang, with a small number in Yulin, Beihai, and northern Guilin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv description=\"\" class=\"Drawing\" id=\"100029\" name=\"\"\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Influencing Factors of Modern Agricultural Industrial Parks in Guangdong Province","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Single-factor impact analysis\u003c/h2\u003e \u003cp\u003eBy ranking the q-values of the significant factors, the strength of each factor's influence can be clarified. Water conservancy and irrigation, total farmland, the number of leading agricultural enterprises, industrial structure, urbanization rate, and annual precipitation are strong dominant factors influencing the spatial distribution, with q-values all above 0.8, substantially higher than those of other factors. This is followed by influencing factors such as total R\u0026amp;D investment, innovation output, population density, economic development level, road network density, and digital infrastructure, with q-values ranging between 0.6 and 0.8. Although their influence is significantly lower than that of the strong dominant factors, they remain important factors affecting the distribution of modern characteristic agricultural demonstration zones in Guangxi.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetection Results of Influencing Factors for Five-Star Modern Characteristic Agricultural Core Demonstration Zones in Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluencing Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eq-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInfluence Ranking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of OVOP demonstration villages/towns (X1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeading agricultural enterprises (X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad network density (X3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital infrastructure (X4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruction land scale (X5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Strength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic development level (X6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial structure (X7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanization rate (X8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density (X9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInnovation Capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026amp;D investment (X10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInnovation output (X11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal farmland (X12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater conservancy and irrigation (X13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual precipitation (X14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn summary, the spatial distribution of modern characteristic agricultural demonstration zones in Guangxi is closely related to local conditions of water conservancy and irrigation, total farmland, the number of leading agricultural enterprises, industrial structure, urbanization rate, and annual precipitation. These findings provide scientific decision-making support for the precise spatial planning, differentiated construction, and high-quality development of Guangxi's modern characteristic agricultural demonstration zones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Multifactor impact analysis\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the geographical detector, an interaction analysis was conducted on 14 factors across six dimensions. The results indicate that all interactive combinations of factors produce enhancement effects on the spatial distribution of the demonstration zones, with two-factor enhancement as the predominant form, and no independent or nonlinear weakening effects observed. Multiple factors collectively shape the distribution pattern through a mechanism of hierarchical synergy.\u003c/p\u003e \u003cp\u003eAccording to the interaction *q*-values, the combinations can be categorized into three tiers. The first tier (strong interactive effects) centers on leading agricultural enterprises (X2). The interaction *q*-values between X2 and factors such as innovation output (X11) and total farmland (X12) generally exceed 0.95, far surpassing their individual effects, making this tier the dominant driving force. Interactions involving natural resource factors (X12, X13) with other factors also predominantly fall into this tier. The second tier (moderate interactive effects) primarily includes interactions among dominant factors such as road network density (X3), digital infrastructure (X4), and R\u0026amp;D investment (X10), as well as combinations of these factors with secondary factors. These serve as important auxiliary synergistic forces. The third tier (weak interactive effects) mainly consists of interactions among secondary factors such as the number of \"One Village, One Product\" demonstration villages/towns (X1) and population density (X9). Although their interaction values exceed individual factor effects, their influence remains relatively limited.\u003c/p\u003e \u003cp\u003eLeading agricultural enterprises (X2) function as the core dominant factor, playing a pivotal role as an \"element-activating catalyst.\" This is manifested in two aspects: first, their interaction with innovation output (X11) forms a dual-core driving model of enterprise carrier\u0026thinsp;+\u0026thinsp;technological innovation\" ; second, their interactions with all other factors exhibit significantly enhanced effectiveness, connecting elements such as natural resources and infrastructure to form a synergistic chain of \"policy guidance \u0026ndash; enterprise leadership \u0026ndash; element aggregation.\"\u003c/p\u003e \u003cp\u003eCertain single factors with relatively weak individual influence, such as road network density (X3), digital infrastructure (X4), and R\u0026amp;D investment (X10), achieve substantial enhancement in effectiveness through interaction with core factors, becoming an \"invisible supporting force.\" This demonstrates that the distribution of demonstration zones is the comprehensive result of \"core-factor leadership, complemented by weak factors, and multi-element synergy.\"\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study, employing spatial analysis and the geographical detector method, reveals the distribution patterns and driving mechanisms of Guangxi's modern characteristic agricultural core demonstration zones, addressing the spatial perspective gap in existing research. The findings align with the general logic of agricultural demonstration zone development while highlighting the regional characteristics of Guangxi.\u003c/p\u003e \u003cp\u003eTaking 650 autonomous region-level modern characteristic agricultural core demonstration zones in Guangxi as the research object, this study draws the following core conclusions through multi-method empirical analysis:\u003c/p\u003e \u003cp\u003eFirst, the spatial distribution exhibits a characteristic of \"predominant clustering with significant differentiation.\" The overall distribution of the demonstration zones across the province is clustered, forming a \"dual-core, multi-nodal\" pattern centered on Nanning and Yulin. Regionally, they are concentrated in the southern (29.23%) and eastern (24.00%) regions. Municipal-level distribution is uneven, with the top six prefecture-level cities accounting for 63.08% of the total. At the sectoral level, crop cultivation dominates (42.92%), with fruit cultivation constituting the largest share (19.08%). Different industrial types display distinct spatial differentiation.\u003c/p\u003e \u003cp\u003eSecond, the distribution is synergistically driven by multi-dimensional factors. Among single factors, water conservancy and irrigation along with total farmland are the primary influencing factors, while leading agricultural enterprises, industrial structure, and urbanization rate are strong dominant factors. Factors such as R\u0026amp;D investment and road network density serve as important supporting elements. All multi-factor interactions exhibit enhancement effects, with leading agricultural enterprises playing a core driving role and forming synergistic chains with other factors to promote element aggregation and the clustering of demonstration zones.\u003c/p\u003e \u003cp\u003eThird, optimization pathways must be based on \"pattern adaptation and factor synergy.\" The development of Guangxi's demonstration zones should strengthen the radiating and driving effects of Nanning and Yulin, and implement differentiated planning for types such as forestry and characteristic cultivation demonstration zones in the northern region and peripheral cities based on their resource endowments. Continued cultivation of leading agricultural enterprises should be prioritized, promoting their deep synergy with infrastructure, innovation resources, and market demand. Through factor integration, this approach addresses regional development imbalances, achieving the dual objectives of spatial layout optimization and high-quality industrial development.\u003c/p\u003e \u003cp\u003eThe findings of this study provide scientific support for optimizing Guangxi's demonstration zones but acknowledge certain limitations. The research focuses on the municipal scale and does not delve into micro-level analyses at the county level or below. It also does not consider the dynamic impact of policy implementation timelines on distribution. Future research could refine the study scale and incorporate panel data to reveal the dynamic evolution patterns of zone distribution, offering more detailed references for targeted policy-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e \u003cp\u003eStudies not involving humans or animals.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eResearch on Enhancing the Modernization of the Innovation Chain, Industrial Chain and Supply Chain in Guangxi during the \"15th Five-Year Plan\" under the Background of Global Industrial Transfer\u003c/p\u003e \u003cp\u003e(Grant NO: GXZC2024-C3-006083-JDZB)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhao, H. Q.: Conceptualization; Data curation; Formal analysis; Resources; Software; Validation; Visualization; original draft; Formal analysis; Resources, Review; Editing; Conceptualization; Original draft; Resources. Wen. J.: Funding acquisition; Methodology; Supervision; Validation. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are contained within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLu, A. H. et al. Research on high-quality development of modern characteristic agricultural demonstration zones in Guangxi.Agric. Eng. Technol. 42, 21\u0026ndash;24.(2022) (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, W. P. Exploration on the current situation and future development of modern characteristic agricultural demonstration zones in Guangxi.South. 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Sin. 72, 116\u0026ndash;134.(2017) (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Guangxi, modern agricultural demonstration zones, spatial distribution, influencing factors, geographical detector","lastPublishedDoi":"10.21203/rs.3.rs-9016281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9016281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the lack of a spatial perspective in existing research and to clarify the formation mechanisms and optimization pathways for modern characteristic agricultural demonstration zones in Guangxi, this study investigated 650 autonomous region-level core demonstration zones across the region. By integrating methods including the Nearest Neighbor Index, Kernel Density Estimation, and the Geographical Detector, it systematically explored their spatial distribution characteristics and influencing factors. The findings reveal that the spatial distribution of the demonstration zones follows a core pattern characterized by \"clustering-dominated, gradient differentiation.\" A prominent \"dual-core, multi-nodal\" pattern centered on Nanning and Yulin is evident. Regionally, the zones are concentrated in the two major sectors of southern and eastern Guangxi, while their municipal-scale distribution exhibits marked unevenness. At the sectoral level, crop cultivation forms the dominant sector, with various industries showing differentiated agglomeration features. Regarding the influencing mechanisms, water conservancy and irrigation and total farmland serve as the primary natural basal factors, while leading agricultural enterprises and industrial structure act as core driving elements. Together, they determine the spatial agglomeration pattern of the zones. Furthermore, all interactive combinations of the influencing factors exhibit enhancement effects, with leading agricultural enterprises connecting diverse elements to form a synergistic development chain. This study not only reveals the spatial patterns and underlying driving logic of Guangxi's modern characteristic agricultural demonstration zones but also provides targeted scientific support for their differentiated spatial planning, optimized factor allocation, and high-quality development.\u003c/p\u003e","manuscriptTitle":"Spatial Distribution and Influencing Factors of Modern Characteristic Agricultural Demonstration Zones in Guangxi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 14:55:42","doi":"10.21203/rs.3.rs-9016281/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-15T13:44:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145651470819106350878247541393264559903","date":"2026-04-06T11:43:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T10:00:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T05:00:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T06:32:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T06:30:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-03T05:46:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36fb41e8-2310-4757-a3d4-ae3dbaad8a15","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65975719,"name":"Social science/Development studies"},{"id":65975720,"name":"Earth and environmental sciences/Environmental sciences"},{"id":65975721,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-04-10T14:55:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 14:55:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9016281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9016281","identity":"rs-9016281","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}