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Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China

Tian TIAN Yanji MA

TIAN Tian, MA Yanji, 2022. Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China. Chinese Geographical Science, 32(4): 643−664 doi:  10.1007/s11769-022-1291-3
Citation: TIAN Tian, MA Yanji, 2022. Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China. Chinese Geographical Science, 32(4): 643−664 doi:  10.1007/s11769-022-1291-3

doi: 10.1007/s11769-022-1291-3

Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China

Funds: Under the auspices of Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA19040500)
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  • Figure  1.  Driving mechanism between agricultural development level and transport superiority degree

    Figure  2.  The location of the central Jilin Province, China. Data from: Resource and Environment Science and Data Center (https://www.resdc.cn/)

    Figure  3.  Spatial distribution of agricultural development level in main grain-producing areas of the central Jilin Province, China

    Figure  4.  Spatial pattern of accessibility of central consumer cities in main grain-producing areas of the central Jilin Province, China

    Figure  5.  Spatial pattern of road connectivity in transportation networks in main grain-producing areas of the central Jilin Province, China

    Figure  6.  Spatial pattern of external accessibility in main grain-producing areas of the central Jilin Province, China

    Figure  7.  Spatial distribution of transport superiority degree in main grain-producing areas of the central Jilin Province, China

    Figure  8.  Moran’s I of agricultural development level in main grain-producing areas of the central Jilin Province, China. H means high, Lmeans low.

    Figure  9.  Moran’s I of transport superiority degree in main grain-producing areas of the central Jilin Province, China. H means high, Lmeans low.

    Figure  10.  Spatial distribution of combination type of agricultural development level and transport superiority degree in main grain-producing areas of the central Jilin Province, China

    Table  1.   Assessing the index system for agricultural development level

    Primary variableSecondary variableIndex descriptionDirectionWeight
    Agricultural production factor inputs Agricultural mechanization level / (kW/ha) Total power of agricultural machinery/cultivated land area + 0.137
    Effective irrigation rate / % (Effective irrigated area/cultivated land area) ×100% + 0.111
    Arable land per laborer / (ha/person) Cultivated land area/agriculturally employed persons + 0.262
    Cultivated land replanting index Total sown area /cultivated land area + 0.307
    Share of facility agriculture / % Facility agriculture area/cultivated land area + 0.334
    Integrated agricultural output capacity Land output rate / (million yuan(RMB)/ha) Gross output value of agriculture products/
    cultivated land area
    + 0.256
    Agricultural labor productivity/ (yuan/person) Total output of agricultural value/total agricultural workforce + 0.263
    Grain yields per ha / (t/ha) Total output of grain/sown area of grain crops + 0.061
    Per capita share of non-grain products / (t/person) (Meat+oil+vegetable+fruit yield)/total population + 0.297
    Rural socioeconomic development level Per capita net income of rural residents / yuan + 0.184
    Tap water popular rate / % Tap water benefiting village/total number of villages + 0.084
    Agricultural industry structure Value-added primary sector as a proportion of GDP / % (Value-added agriculture/GDP) × 100% + 0.216
    Share of labor force engaged in non-agricultural industries / % (Number of non-agricultural labor personnel/total number of labor personnel) × 100% + 0.173
    Proportion of livestock output value to total agricultural output value / % (Proportion of livestock output value/total agricultural output value) × 100% + 0.307
    Grain to economic crop ratio Grain crop area/economic crop area 0.144
    Sustainability of agricultural production Fertilizer use intensity / (t/ha) Consumption of chemical fertilizer/cultivated land 0.076
    Pesticide use intensity / (t/ha) Consumption of pesticides/cultivated land 0.061
    Ecological environmental protection funds as a proportion of fiscal expenditure / % (Ecological environmental protection funds/fiscal expenditure) ×100% + 0.143
    下载: 导出CSV

    Table  2.   Classification standards

    Division standardsDivision level
    0<ADL(TDL)<(M−SD)Low level
    (M−SD)<ADL(TDL)<MMiddle-low level
    M<ADL(TDL)<(M+SD)Middle-high level
    (M+SD)<ADL(TDL)<1High level
    Notes: M is the mean, SD is the standard deviation and ADL is agricultural development level
    下载: 导出CSV

    Table  3.   Spatial self-correlation test

    VariablesTypes20082019
    Moran’s IZ(I)Moran’s IZ(I)
    Global single variableADL0.4174.332***0.6643.313***
    TDL0.3616.529***0.4907.004***
    Global dual variablesLnADL (LnTDL)0.3695.341***0.3304.309***
    LnTDL (LnADL)0.3545.804***0.3755.597***
    Notes: *** represents significant at the 1% level, Ln ADL (LnTDL) indicates that the log of the agricultural development level is the independent variable, and the log of transport superiority degree is the dependent variable in the model, while LnTDL (LnADL) is the opposite
    下载: 导出CSV

    Table  4.   OLS, SLM and SEM estimation results

    Model 1: Region-wide spatially driven effects of agricultural development level on transport superiority degree across the regionModel 2: Region-wide spatially driven effects of transport superiority degree on agricultural development level
    VariablesOLSSLMSEMVariablesOLSSLMSEM
    C–0.962*
    (–1.691)
    0.457***
    (3.128)
    0.273**
    (1.899)
    C0.072
    (1.600)
    0.415*
    (3.848)
    0.792***
    (5.677)
    LnADL0.432**
    (0.133)
    0.603***
    (0.081)
    0.545***
    (0.211)
    LnTDL0.301*
    (0.568)
    0.487**
    (0.057)
    0.579**
    (0.021)
    λ0.484***
    (2.157)
    λ0.202***
    (0.725)
    Log likelihood944.6951059.3641269.770Log likelihood107.914114.337126.360
    R20.5070.6530.617R20.3380.4750.533
    AIC577.411500.274525.690AIC201.433186.745106.277
    SC563.244529.451539.562SC166.634122.664108.590
    F110.770***(0.000)F76.127***(0.000)
    LM-lag50.667***(0.003)LM-lag67.856*** (0.001)
    Robust LM-lag11.203***(0.000)Robust LM-lag1.661(0.570)
    LM-error19.637**(0.004)LM-error3.522***(0.006)
    Robust L-error3.682*(0.006)Robust LM-error10.425***(0.011)
    Notes: ***, ** and * denote significant at the 1%, 5%, and 10% level, respectively
    下载: 导出CSV

    Table  5.   The transfer matrix of types during 2008–2019 / %

    Types2019 (T2)
    ADL Strong DriveADL Weak DriveTDL Strong DriveTDL Weak DriveMutual Strong DriveMutual Weak DriveP1Reduction
    2008 (T1)
    ADL Strong Drive13.640.004.550.0013.640.0031.8218.1
    ADL Weak Drive4.550.000.000.000.009.013.6413.6
    TDL Strong Drive0.000.0013.640.004.550.0018.184.55
    TDL Weak Drive0.000.000.000.000.004.554.554.55
    Mutual Strong Drive0.000.004.550.009.090.0013.644.55
    Mutual Weak Drive4.554.554.550.000.004.5518.1813.64
    P222.734.5527.270.0027.2718.18100.00
    Increments9.094.5513.640.0018.1813.64
    Notes: ADL is agricultural development level and TDL is transport superiority degree; P is the percentage of the number of counties of each type in the total count of the counties in the time T
    下载: 导出CSV
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  • 收稿日期:  2021-07-27
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Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China

doi: 10.1007/s11769-022-1291-3
    基金项目:  Under the auspices of Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA19040500)
    通讯作者: MA Yanji. E-mail: mayanji@iga.ac.cn

English Abstract

TIAN Tian, MA Yanji, 2022. Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China. Chinese Geographical Science, 32(4): 643−664 doi:  10.1007/s11769-022-1291-3
Citation: TIAN Tian, MA Yanji, 2022. Interactive Effect and Spatial Characteristics of Agricultural Development Level and Transport Superiority Degree in Main Grain-producing Areas of the Central Jilin Province, China. Chinese Geographical Science, 32(4): 643−664 doi:  10.1007/s11769-022-1291-3
    • The United Nations 2030 Agenda for Sustainable Development provides a blueprint for sustainable development that concerns the global economy, agriculture, and ecology (Gao and Bryan, 2017; Zhang et al., 2019), includes achieving sustainable development in agriculture as one of its goals (SDG 2). The main grain-producing area is the core area of food supply, and because sustainable food security is related to the sustainable development of agriculture (FAO, 2019), it is important to increase the agricultural development potential and agricultural competitiveness to make agriculture an engine of inclusive economic development and rural prosperity in low-income countries (Loizou et al., 2019; Miah et al., 2020). Rural infrastructure is a basic condition for agricultural development and the survival of farmers, and infrastructure and agricultural development are generally manifested in two aspects—infrastructure supporting agricultural development and agricultural development promoting infrastructure construction. Among these, transportation is an important factor supporting rural agricultural development (Shamdasani, 2021). Therefore, the government should adopt an active agricultural policy to develop sustainable agriculture, establish sustainable transportation systems, and build high-quality resilient infrastructure.

      Agricultural development in main grain-producing areas is not an isolated system, but is constantly interacting with the external environment and achieving sustainable development mainly through factor integration, structural optimization, and functional diversification (Zhai and Liu, 2009). Currently, the theoretical and methodological research on agricultural development continues to deepen, and the research perspectives involve geography, economics, sociology, management, and other fields of study (Long ang Liu, 2016; Duvernoy et al., 2018; Liu, 2018; Plummer et al., 2018; Chivu et al., 2020). The research areas include various aspects such as agricultural modernization (Kansanga et al., 2018), agricultural multifunctionality (Pribadi et al., 2017), agricultural sustainability (Ayambire et al., 2019), and agroecological security (Wang et al., 2021). In terms of evaluation methods, there are model evaluation methods (Zhou et al., 2013), multi-indicator integrated evaluation method (Sarkar et al., 2021), water footprint method (Moghaddam et al., 2018), and environmental calculation framework method (Skaf et al., 2019). The evaluation indicators include various elements such as production, economy, ecology, society, agricultural function, and food security (FAO, 2014; Peng et al., 2015; Salimova et al., 2020).

      Transportation is a basic requirement for regional economic and social development (Dimitriou, 2019), village roads at all levels facilitate spatial organization among village collectivities (Wudad et al., 2021), promote the free flow of factors and resource redistribution in rural territories, and play a pivotal role in the development of agricultural development (Gutiérrez et al., 2010; Liu et al., 2020). Theoretical and empirical studies have been conducted on the relationship between transportation and economy, and transportation and agricultural development. On the theoretical side, the theory of agricultural location reveals the principle of the spatial differentiation of agriculture arising from the same climate, soil, and other conditions, related mainly to the distance of production sites from markets (Li, 2006), and this principle also guided the determination of the transport superiority degree index in this thesis. In terms of empirical evidence, scholars have focused on transportation construction and agricultural production (Wang et al., 2012; Branco et al., 2021), transport superiority degree and agricultural economy (He et al., 2019; Pokharel et al., 2021), transportation network complexity and rural social development (Gharehbaghi et al., 2020), transportation accessibility and agricultural external linkages (Llanto, 2012; Bacior and Prus, 2018), and other aspects. Therefore, it is important to discover the interactive effect of transport superiority degree and agricultural development.

      Researchers around the world are currently enriching the study of agricultural development and transport superiority degree. First, the research scales and regions are mainly focused on countries, cities, and urban agglomerates, and there are few studies on food-producing regions. Second, there is no discussion on the driving principles of agricultural development and transport superiority degree. Therefore, this paper focuses on specifying and discussing the driving principles of agricultural development and transport superiority degree indicators in food-producing regions. In terms of the driving mechanism of agricultural development by the degree of transportation advantage, the location condition elements constitute the spatial carrier of regional agricultural development, and the agricultural economic factors of agricultural industrial structure and development foundation determine, to a certain extent, the level of current development and future growth potential of agricultural regions through path dependence (Fig. 1). The transportation network, under the influence of regional layout, road scale level, and road network density, actively integrates into the agricultural development system of the main food-producing areas through the transportation and road construction—driving the regional consumption, transformation, and upgrading of agriculture, improving agricultural economic benefits, and gradually realizing the goal of sustainable agricultural development. With regard to the driving mechanism of the impact of agricultural development on transportation advantage, agricultural development, under the joint action of an endogenous force and external support force, gradually generates high-level demands, such as developing agricultural agro-tourism and opening up agricultural ‘green transportation channels’ by adjusting agricultural structure and establishing advantageous characteristic agricultural products. The new development needs to improve transportation conditions, promote the ‘integrated’ development of urban and rural transportation, and drive the overall optimization of regional transportation.

      Figure 1.  Driving mechanism between agricultural development level and transport superiority degree

      As an important grain-producing area in China, the central region of Jilin Province is taken as the research object, which has reference significance for the development of other main grain-producing areas in the country. This study seeks to combine the agricultural development kernel system and rural development outer edge system in order to construct an agricultural development evaluation model, and uses the traffic dominance degree as the entry point of the agricultural development outer edge system to explore the mutual driving effect of the two. This paper employs traditional econometric models, GIS spatial analysis, and spatial econometric models to quantitatively analyze the basic patterns of traffic dominance and agricultural development in 2008 and 2019 reflecting the trend of change in the 10-yr period (2009–2018), as well as their mutual driving mechanisms. The objective is to explore the coordination and spatial distribution of agriculture and traffic, and to clarify the intrinsic correlation between agricultural development and traffic development, which is not only a practical test of the classical agricultural location theory in the new economic era, but also a way to reveal agricultural development level under the transport development orientation.

    • The central region of Jilin Province is an important grain producing area and is the commodity grain base in Jilin Province and the entire country (Liu et al., 2018). The black soil arable land area accounts for more than 40% of the province’s arable land and is concentrated in Yushu, Gongzhuling and Lishu, the country’s leading grain-producing counties. During 2005–2019, the annual output of grain was stable at 25 million t, accounting for 85% of the province. In addition, the region of central Jilin Province is also the central urban agglomeration of Jilin Province, and has a dense transportation network consisting of a transportation network connecting the various cities and counties in different directions. This study is based on the administrative division of Jilin Province in 2014, including 22 counties (county-level cities), excluding the municipal districts of Changchun, Jilin, Siping, Liaoyuan and Songyuan (Fig. 2). Using the county scale as the measurement unit of agricultural development level, a more detailed study of the development of the county’s agriculture and transportation.

      Figure 2.  The location of the central Jilin Province, China. Data from: Resource and Environment Science and Data Center (https://www.resdc.cn/)

    • Based on the principles of scientific and accessible indicators, and taking into account the main practical characteristics of the region of central Jilin Province, which are main grain-producing areas, and referring the research of Li et al. (2017), Lu et al., (2019), Liu et al. (2020) and Salimova et al. (2020), the evaluation index system of agricultural development level in main grain-producing areas was established on the basis of five criteria (Table 1), and a total of 18 indicators were selected for evaluation:

      Table 1.  Assessing the index system for agricultural development level

      Primary variableSecondary variableIndex descriptionDirectionWeight
      Agricultural production factor inputs Agricultural mechanization level / (kW/ha) Total power of agricultural machinery/cultivated land area + 0.137
      Effective irrigation rate / % (Effective irrigated area/cultivated land area) ×100% + 0.111
      Arable land per laborer / (ha/person) Cultivated land area/agriculturally employed persons + 0.262
      Cultivated land replanting index Total sown area /cultivated land area + 0.307
      Share of facility agriculture / % Facility agriculture area/cultivated land area + 0.334
      Integrated agricultural output capacity Land output rate / (million yuan(RMB)/ha) Gross output value of agriculture products/
      cultivated land area
      + 0.256
      Agricultural labor productivity/ (yuan/person) Total output of agricultural value/total agricultural workforce + 0.263
      Grain yields per ha / (t/ha) Total output of grain/sown area of grain crops + 0.061
      Per capita share of non-grain products / (t/person) (Meat+oil+vegetable+fruit yield)/total population + 0.297
      Rural socioeconomic development level Per capita net income of rural residents / yuan + 0.184
      Tap water popular rate / % Tap water benefiting village/total number of villages + 0.084
      Agricultural industry structure Value-added primary sector as a proportion of GDP / % (Value-added agriculture/GDP) × 100% + 0.216
      Share of labor force engaged in non-agricultural industries / % (Number of non-agricultural labor personnel/total number of labor personnel) × 100% + 0.173
      Proportion of livestock output value to total agricultural output value / % (Proportion of livestock output value/total agricultural output value) × 100% + 0.307
      Grain to economic crop ratio Grain crop area/economic crop area 0.144
      Sustainability of agricultural production Fertilizer use intensity / (t/ha) Consumption of chemical fertilizer/cultivated land 0.076
      Pesticide use intensity / (t/ha) Consumption of pesticides/cultivated land 0.061
      Ecological environmental protection funds as a proportion of fiscal expenditure / % (Ecological environmental protection funds/fiscal expenditure) ×100% + 0.143

      (1) Agricultural production factor inputs. Modern agriculture emphasizes the construction of agricultural infrastructure, and the improvement of agricultural machinery input and irrigation conditions is especially important for the sustainable and stable development of the main grain-producing areas. Thus, the level of agricultural mechanization, the effective irrigation rate, the average cultivated area of labor, the replanting index of cultivated land, and the proportion of facility agriculture are selected for measurement.

      (2) Integrated agricultural output capacity. Modern agriculture has a significantly higher level of output than traditional agriculture, and the core task of the main food-producing regions is to ensure national food security. Thus, the level of agricultural production, as well as the scale of the number of agricultural products supplied and the structure of varieties, must be considered. Grain yields, land output rates, agricultural labor productivity, and per capita non-grain product holdings are selected for measurement.

      (3) Rural socioeconomic development level. One of the goals of agricultural development is to realize the prosperity of rural industries in order to improve the level of rural economic development. Therefore, the net income per capita of rural residents and the penetration rate of piped water are measured in two aspects.

      (4) Agricultural industry structure. Modern agriculture has significant characteristics in terms of production mode and structure, and it is necessary to consider the optimization characteristics of the agricultural production structure in areas such as the terms of industry, labor force, and output value. Four indicators are selected to measure, the value-added ratio of factors such as primary industry, the proportion of labor force engaged in non-agricultural industry, the proportion of output value of animal husbandry, and the ratio of grain to economic crops.

      (5) Sustainability of agricultural production. In the process of agricultural development in the main food-producing areas, the excessive application of fertilizers, pesticides, and other modern agricultural production factors will inevitably lead to changes in the agricultural ecological environment and affect the sustainability of agricultural production. For this reason, three indicators are selected to reflect the sustainability of agricultural production: ecological and environmental protection expenditure investment, average fertilizer application on the land, and average pesticide application on the land.

      In this study, we used the entropy weighting method to reduce the subjective arbitrariness to a certain extent. By calculating the information entropy of the indicators, the weights of the indicators were determined according to the relative change in the indicators of the system as a whole (Shemshadi et al., 2011), which can profoundly reflect the differentiation ability of the indicators. This idea was consistent with the mechanism of indicator selection in our score evaluation model (Gao et al., 2020a). The specific method refers to Shen et al. (2015), and the composite index of agricultural development level (ADLij) can be defined by:

      $$ \mathop {ADL}\nolimits_{i j} = {c_{i j}} \times {X_{i j}} $$ (1)

      where cij is the weigh; Xij is the value after normalization of the data; i is county; j is index.

      Referring to the classification method of Ren and Ma (2018), the agricultural development level was classified into four types. The specific classification standards are shown in Table 2.

      Table 2.  Classification standards

      Division standardsDivision level
      0<ADL(TDL)<(M−SD)Low level
      (M−SD)<ADL(TDL)<MMiddle-low level
      M<ADL(TDL)<(M+SD)Middle-high level
      (M+SD)<ADL(TDL)<1High level
      Notes: M is the mean, SD is the standard deviation and ADL is agricultural development level
    • Central city accessibility refers to the spatial accessibility of agricultural production areas (counties) to major consumption areas (urban areas) under the conditions of regional transport networks (Khalili et al., 2020), which has a significant impact on the county accessibility. The accessibility of the central city from this perspective includes two meanings: 1) the ease of the transport networks’ connection from the node of production to the node of consumption; and 2) the market capacity or size of the node of consumption. By abstracting the main agricultural production and consumption areas in the study area as spatial nodes, main grain-producing areas in central Jilin Province include 22 agricultural production nodes (counties) and five main agricultural consumption market nodes (urban district of the prefecture level city).

      Drawing on the weighted average travel time calculation within the transport networks, the weighted average distance considered the relationship between the node and the central city (McKenzie, 2014); and it more realistically reflected the ease of travel from the destination to the central city as determined by the quality of the destination and transport services (Zhu et al., 2010; Jiao et al., 2012). The distance to the central city location relationship index (f1) can be measured by:

      $$ {f}_{1}={\sum }_{j=1}^{n}\left({R}_{i j}\times {Q}_{j}\right)/{\sum }_{j=1}^{n}{Q}_{j} $$ (2)

      where f1 is the central consumption city location index; Rij is the shortest travel time (h) from production node i to consumption node j, reflecting the travel cost between nodes; and Qj is the quality of consumption node j. One central node is selected as the center of economic activity in each area. To show the impact of the economic scale on the attractiveness of the central nodes, we define $ Q_{j}=\sqrt{G D P_{j} \times P O P_{j}} $ (GDP is gross domestic product, POP is total population).

    • The road connectivity of the intra-county transportation network (f2) can reflect the accessibility of the road network within the region, which can be calculated based on the time spent from any point within the county to its center (Xu et al., 2017). The shortest weighted distance from each grid to a particular destination grid was calculated from the raster data using the shortest path method (Israel, 2016). The time cost when passing through different spatial raster cells was summed to obtain the minimum distance from one point to another specific point (Kang and Liu, 2007).

      $$ {D_i} = \left\{ \begin{gathered} \frac{1}{2}\sum\limits_{i = 1}^n {\left( {{C_i} + {C_{i + 1}}} \right)} \hfill \\ \frac{{\sqrt 2 }}{2}\sum\limits_{i = 1}^n {\left( {{C_i} + {C_{i + 1}}} \right)} \hfill \\ \end{gathered} \right. $$ (3)

      where Ci is time cost of the ith image element; Ci + 1 is the time cost of the (i + 1)th image element along the direction of motion; n is the total number of image elements; and Di is the accessibility of the ith raster to the county center. The upper equation calculates the time cost in the vertical direction or parallel to the raster surface, and the lower equation calculates the time cost in the diagonal direction through the raster surface.

      The Di time cost is divided into five accessibility ranges: < 0.5 h, 0.5–1.0 h, 1.0–1.5 h, 1.5–2.0 h, and > 2.0 h. According to the principle that the closer to the county center, the smaller the time accessibility and the greater the road connectivity, f2 is assigned a functional value of 2.5, 2.0, 1.5, 1.0 or 0.5, respectively.

    • The county’s external transport access mainly includes road, rail, air, and water transport, and it is treated as a transport node, focusing on measuring the grade of the county’s proximity to such nodes and the distance to reach them. The external accessibility index was evaluated using the categorical assignment method by Jin et al. (2008). The external accessibility index (f3) can be expressed as:

      $$ {f}_{3}=\sum {c}_{im}\;\;\;\;i\in (\text{1, 2},\mathrm{ }...,n),m\in (1,\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }2,\mathrm{ }...,M) $$ (4)

      where cim is the assigned value corresponding to the node or line of category m in county i.

    • Based on the above calculations, the accessibility of integrated transport in the county can be defined as:

      $$ TDL = \sum\limits_{i = 1}^3 {{a_i}} \times {f_i} $$ (5)

      where TDL is the county’s transport superiority degree index; f1, f2, and f3 are the index of the location relationship with the central consumption city, the index of the intra-county connectivity and the index of the external accessibility of the county, respectively; a1, a2, and a3 are the coefficients (weights) of f1, f2, and f3, respectively; and their corresponding weights are 0.5, 0.25, and 0.25, referring to Liu et al. (2011). The classification method is shown in Table 2.

    • Because of the spatial diffusion effect and the spatial dependence of the dependent variable in a certain region (Conway et al., 2010), the SLM model can establish the influence of the county’s transport superiority degree on the neighboring county’s agricultural development level and the influence of the county’s agricultural development level on the neighbor’s transport superiority degree. The model expression of this is as follows:

      $$ Y = \rho WY + X\beta + \varepsilon $$ (6)

      where Y is the dependent variable vector; ρ is the spatial lag parameter, reflecting the degree of diffusion (spillover) between the neighboring spatial units; W is the spatial weight matrix; WY is the spatial lag dependent variable, reflecting the role of the spatial distance on the spatial behavior; X is the independent variable; β is the coefficient of the dependent variable with estimation, reflecting the degree of influence of X on Y; and ε is the random error vector.

    • In the driving effect of transport superiority degree on the level of rural development, only the effect of transport superiority degree is considered, whereas other drivers are not included in the model. Moreover, the SEM model assumes that these unincorporated variables are spatially correlated and have an effect on the level of agricultural development (Zhang et al., 2015; Jiang, 2020). The same applies to the driving effect of the level of agricultural development on the degree of transport superiority, the model expression of which is:

      $$ Y=X\beta +\varepsilon \text{, }\varepsilon =\lambda W\varepsilon +\mu $$ (7)

      where λ is the spatial error coefficient, which reflects the spatial dependence role in the sample observations; μ is the random error vector for the normal distribution; and Y, X, β, ε, and W are the same as in the SLM model.

    • The GWR model can show the local driving effect of county transport superiority degree and agricultural development level, reflecting the spatial variation characteristics through regression coefficients (Gao and Li, 2011; Mirbagheri and Alimohammadi, 2017; Gao et al., 2020b). The model thus achieves the goal of varying the relationship between the variables with spatial location. The structural form of the GWR model is shown as:

      $$ {y_i} = {\beta _0}\left( {{u_i},{v_i}} \right) + \sum\limits_{j = 1}^k {{\beta _k}\left( {{u_i},{v_i}} \right)} {x_{ik}} + {\varepsilon _i} $$ (8)

      where yi is the observed value of the explanatory variable in region i; xik is the observed value of the kth explanatory variable in region i; (ui,vi) is the geographical location of region i; β0(ui, vi) is the intercept term of the ith sample; βk (ui,vi) is the coefficient of the kth independent variable in region i; and εi is the random error term.

    • The indicator base data of this study were obtained from Jilin Statistical Yearbook, Changchun Statistical Yearbook, Jilin City Economic and Social Statistical Yearbook, Liaoyuan Statistical Yearbook, Siping Statistical Yearbook, Songyuan Statistical Yearbook, Tonghua Statistical Yearbook, and China Statistical Yearbook(County-Level). The base vector data were obtained from the Northeastern Asia Geographic Science Date Center (http://www.igadc.cn/nearest/index.html). The road transport data for 2008 and 2019 were obtained from the 1∶250 000 national basic geographic database (http://www.webmap.cn). The vector elements of the railways, highways, national roads, provincial roads, county roads and general roads were extracted from them, and the 2008 and 2019 editions of the Jilin Provincial Atlas and the China Highway and Urban-Rural Road Mileage Atlas were calibrated and updated. The geographic coordinate systems of all of the spatial data were unified with the 2000 national geodetic coordinate system, the 1985 national elevation datum, and the latitude and longitude coordinates.

      According to the Technical Standards for Highway Engineering of the People’s Republic of China (JTGB01-2003) and the Technical Standards for Highway Engineering of the People’s Republic of China (JTGB01-2014), the time cost was set as follows (https://www.mot.gov.cn/). In 2008, the highways were 100 km/h, the national roads were 70 km/h, the provincial roads were 60 km/h and the urban and rural roads were 30 km/h. In 2019, the highways were 120 km/h, the national roads were 80 km/h, the provincial roads were 60 km/h, the county roads were 40 km/h, and the urban and rural roads were 40 km/h.

    • The spatial distribution of the comprehensive index of agricultural development level is shown in Fig. 3. In general, the agricultural development level of most of the counties fluctuated. There were large differences in the agricultural development level of the counties from 2008 to 2019, and the variability expanded with time. However, in 2019, the overall agricultural development level of all of the counties has improved. In the central grain-producing areas of Jilin Province, the score of agricultural development level has improved from 0.4785 to 0.5964, the gap between regions has shrunk from 0.2338 to 0.2203, and the balance between the agricultural development level of the counties has increased.

      Figure 3.  Spatial distribution of agricultural development level in main grain-producing areas of the central Jilin Province, China

      The number and spatial distribution of the high-level areas were relatively stable from 2008 to 2019, and in 2019, driven by the strong radiation of Changchun, they gradually expanded from Lishu and Gongzhuling toward the northeast. As a large grain-producing county, Lishu has a large agricultural economy, a solid industrial foundation, and a high level of comprehensive agricultural output. The 2008 score was 0.5465, while the 2019 score was 0.5674. It ranked better than other regions in terms of agricultural labor productivity, the production of meat, eggs, and milk, total agricultural output per capita, and the intensity of pesticide use. Gongzhuling was also a large grain-producing county, had a strong agricultural industry base, and ranks second in terms of grain yield. The county’s proportion of the area of agriculture was greater than other counties, and its horticulture facilities were developing rapidly. It boasted a high level of production of cash crops, especially vegetables, melons, and other crops, at the forefront of the province, and was the supply base of green agricultural products. The county was also witnessing rapid development of the shed film economy, the per capita net income of farmers was high, and the level of agricultural development was mature and stable. Dehui is a traditional agricultural county in Jilin. In addition to the high level of agricultural mechanization and the high economic output of land, animal husbandry was the main driver of Dehui’s agricultural development. Animal husbandry in the region was ranked second in terms of the total agricultural output value, and the level of non-food production far exceeded that of other counties.

      The agglomeration characteristics of the middle-high level areas were also significant, and the number generally increased. This type of zone was mostly driven by the economically developed areas, the metropolitan areas and the main sales areas of the agricultural products, and it was strategically located close to the regional administrative center. In 2019, Yushu, Yongji, and Shuangliao rose to the top in terms agricultural output. Their levels of agricultural development rose to middle-high level, with the agricultural development score increasing by 0.07, 0.10, and 0.04, respectively. The advantages of agricultural resources were outstanding, and the levels of agricultural input were average, owing to the agricultural function phase expansion and integration and the rapid development of agriculture. Shulan, Huadian, and Jiaohe are located around the green leisure and modern agriculture belt in central Changchun-Jilin, at the space interface of rich agricultural and tourism resources, and the agricultural development has exhibited a good trend, with broad prospects for development.

      The middle-low development level areas were mainly scattered within and outside of the high and middle-high level areas, and the number of middle-low level areas decreased from 9 to 6. The spatial distribution of this type of area was highly variable and less stable. With a score of 0.13, Jiutai was strongly driven by the radiation of Changchun and Jilin. It was the most developed county in central Jilin Province. Additionally, the development of special agriculture and the establishment of special towns in Changchun and Jilin drove the continuous growth of agriculture in the region. However, performance indicators such as average arable land area, replanting index, and agricultural labor productivity were not satisfactory. In particular, the average arable land area was ranked low, which lowered the overall score. Other middle-level areas also lagged behind with respect to some indicators, such as the low level of cash-crop production in Fuyu, the low per capita net income of farmers in Changling, underdeveloped agriculture facilities in Qian’an, and insufficient production of economic crops in Huinan.

      The number of low-level development areas generally decreased, with a stable distribution in the southwest, indicating that agricultural development level in the main grain-producing areas in central Jilin Province has improved significantly. However, there were also individual counties whose development level was consistently slow, such as Meihekou and Dongfeng. They had insufficient investments in agriculture in the early stage. Meihekou’s primary industry value-added ratio, ecological and environmental protection funding input, fertilizer use intensity, and the proportion of agriculture facilities were all marked by obvious underdevelopment. As a result, its industrial base was not solid. Dongfeng also showed disadvantages in terms of the primary industry value-added ratio, the proportion of facility agriculture, and effective irrigation. In addition, the two areas were located in the southwest, and the influence of Changchun on these areas was relatively small. Even if there was some investment and improvement later, their progress was slow.

    • In 2019, compared to 2008, the accessibility of the central consumption cities in the main grain producing areas in central Jilin Province was optimized overall (Fig. 4), decreasing from 140.00 h to 101.60 h (by 27.240%). There were differences among the counties overall, but the distributions of the best and worse counties were relatively stable. The higher accessibility counties mainly centered around the Changchun metropolitan area, with the best counties being Gongzhuling, Lishu, Nong’an, Yongji, Jiutai, and Dehui in both 2008 and 2019 and the worst counties being Dongfeng, Liuhe, and Huinan. During this decade, the convenience of the counties to major agricultural consumption cities increased, and the agricultural economic linkages with the major consumption cities have improved. This trend became increasingly significant in the areas close to the major consumption cities. The increasingly improved road networks and the attractiveness of the major consuming cities have improved the accessibility time of the administrative units in the central grain producing areas of Jilin Province from 2–11 h in 2008 to 2–9 h in 2019. The number of administrative units within the 3-hour exchange circle did not change, but the range expanded. In contrast, Huinan and Liuhe in the south and Shuangliao and Changling in the west had relatively poor accessibility to the central consumer cities because of the constraints of the population movement, urban transportation corridors, and economic development. Overall, the central part of the study area was the best, the eastern part was significantly more accessible than the western part, and the northern part was better than the southern part.

      Figure 4.  Spatial pattern of accessibility of central consumer cities in main grain-producing areas of the central Jilin Province, China

    • From 2008 to 2019, the road accessibility of the main grain-producing areas in central Jilin Province improved substantially, reflecting the progress of the regional transportation networks’ construction toward multi-polarity, which assisted with the formation and development of agricultural industry clusters, as well as of leisure and tourism agriculture (Fig. 5). The road connectivity in the peripheral areas changed faster than that in the central areas, and the magnitude of the change was related to the initial value, highway construction, national policies, and regional physical and geographic conditions. The connectivity values of Changchun and Jilin in the geometric center were relatively high, and the connectivity values of the nearby counties such as Dehui, Jiaohe, Nong’an and Yushu were also advantageous, while the accessibility values of the peripheral areas were relatively low, but the differences between the regions narrowed over time. During the 10-year period, the road networks greatly improved the accessibility of the regions, and the completion of the highway caused the accessibility to follow the directionality of the traffic road. For example, the access to the areas near the Harbin-Dalian Line, National Highway 203 and National Highway 303 is better. In some areas such as Huadian and Huinan, the road connectivity change was not significant or even decreased, which was related to the road reconstruction, and the overall accessibility changed slowly.

      Figure 5.  Spatial pattern of road connectivity in transportation networks in main grain-producing areas of the central Jilin Province, China

    • The external accessibility also shows the best development pattern was located in the central part of the study area (Fig. 6). The three important transportation hubs in Changchun, Jilin, and Siping in central Jilin Province were both railroad transportation hubs and important road transportation nodes in the province. The surrounding places such as Jiutai, Gongzhuling, Dehui, Nong’an and Jiaohe all scored high. Among them, Jiutai is adjacent to the Changchun Longjia International Airport, which is an important air transport hub in Northeast Asia. Furthermore, Jiutai is close to Changchun Station and major highways, and it ranked first in external accessibility. The more significant change from 2008 to 2019 was in terms of railway stations. First, high-speed railway stations in the province have been built and put into use one after another, strengthening the external (i.e., other provinces and cities) connection capacity and shortening the external communication time. Second, some fourth-class stations were renovated, or passenger services were closed down. Instead, they now provide transportation of freight, grain, fertilizer, and other agricultural cargoes, enhancing the external transportation capacity.

      Figure 6.  Spatial pattern of external accessibility in main grain-producing areas of the central Jilin Province, China

    • The classification method was the same as the agricultural development level (Fig. 7). From 2008 to 2019, the overall distribution of transport superiority degree shows a clear characteristic of a core to periphery development trend. The scores of the counties gradually changed from low level and unbalanced to high level and balanced.

      Figure 7.  Spatial distribution of transport superiority degree in main grain-producing areas of the central Jilin Province, China

      In terms of the temporal dynamics, the areas with a high level of transport superiority degree in 2008 were Dehui and Gongzhuling, while in 2019 they were Dehui, Nong’an, Gongzhuling, Yitong, Jiutai, Yongji, and Jiaohe, forming an n-shaped pattern around Changchun. These counties were close to the central city, near the main consumer market of agricultural products, and were convenient for transportation of agricultural products. With the number of mature enterprises in the agricultural processing industry, Haoyue and Dacheng have jointly promoted the development of transportation. In addition, these areas were strongly influenced by the economic radiation of the Changchun-Jilin Metropolitan Area and were located along the main transport routes, such as the Harbin-Dalian Railway Line (railway), the Changchun-Siping Expressway (highway), the Changchun Bypass Expressway (highway), the National Highway 102 (national highway), the Changchun-Jilin Line (provincial highway), and the Changchun Economic Circle Ring Road (highway). The traffic infrastructure conditions are good. The counties with poor transport superiority degree in 2008 included Dongfeng, Huinan, Changling and Liuhe, while in 2019, there included Qian’an, Shuangliao, and Huinan, with little change overall. These counties were all located on the fringes of the study area, away from the economic center and political center, Changchun, and were relatively weakly influenced by the consumption of the central cities. Owing to the lack of backbone roads and mature agricultural industrial clusters, the transport conditions were relatively poor.

    • The global univariate and bivariate spatial autocorrelation Moran’s I (Table 3) was calculated for agricultural development level and transport superiority degree in 2008 and 2019. The global univariate Moran’s I was positive and passed the 1% significance test for both 2008 and 2019, indicating that agricultural development level and transport superiority degree in the central grain-producing areas of Jilin Province exhibited significant spatial clustering characteristics over the past 10 years, with gradually increasing clustering characteristics. The global bivariate spatial autocorrelation test represents the degree of spatial autocorrelation between agricultural development level in a county and its neighboring counties’ transport superiority degree. The model results show that both the Moran’s I values were positive and pass the 1% significance test, indicating that there was significant spatial autocorrelation between the two values. LnTDL(LnADL) > LnADL(LnTDL) indicates that agricultural development level in main grain-producing areas of Jilin Province is more dependent on transport superiority degree of neighboring counties.

      Table 3.  Spatial self-correlation test

      VariablesTypes20082019
      Moran’s IZ(I)Moran’s IZ(I)
      Global single variableADL0.4174.332***0.6643.313***
      TDL0.3616.529***0.4907.004***
      Global dual variablesLnADL (LnTDL)0.3695.341***0.3304.309***
      LnTDL (LnADL)0.3545.804***0.3755.597***
      Notes: *** represents significant at the 1% level, Ln ADL (LnTDL) indicates that the log of the agricultural development level is the independent variable, and the log of transport superiority degree is the dependent variable in the model, while LnTDL (LnADL) is the opposite

      Based on the global autocorrelation, the local spatial autocorrelation analysis (Fig. 8) shows that agricultural development level of H-H agglomeration area was main agricultural production area in Jilin Province. These areas have superior agricultural production conditions, better water and heat conditions, a higher agricultural scale, and more intensive production. For example, Lishu and Gongzhuling are important grain production areas in the province and even in the country. Agglomeration area L-L was stably located in the southwestern corner of the study area. This region is mainly a transition zone between the central hills and the western mountains. The agricultural production selection space was small, with low scale and concentration level, and it contains the poor counties in the region, so the agricultural security capacity was weak, and it was easy to form a low level of agricultural development agglomeration.

      Figure 8.  Moran’s I of agricultural development level in main grain-producing areas of the central Jilin Province, China. H means high, Lmeans low.

      Transport superiority degree H-H agglomeration was mainly located around the Changchun-Jilin metropolitan area (Fig. 9), especially in 2019, in a curved belt around the two major urban areas of Changchun and Jilin. This area is a dense urban center of administration, economy, and culture, and it has the most complex road traffic and the highest road network density in the region. The southern zone of the study area was agglomeration L-L. The urban road network construction and the road network density lag behind those of the Changchun-Jilin Belt.

      Figure 9.  Moran’s I of transport superiority degree in main grain-producing areas of the central Jilin Province, China. H means high, Lmeans low.

    • To choose the specific form of the spatial panel econometric model, it is necessary to perform the least squares (Classic OLS) regression estimation in MATLAB and then to perform the spatial lag model (SLM) and spatial error model (SEM) test to check the manifestation of its spatial dependence and to select the optimal econometric model.

      For Model 1 (Table 4), the R2 values of the OLS, SLM, and SEM are 0.507, 0.653, and 0.617, respectively, with an F-statistic of 110.770, which passes the 1% significance test. According to the spatial correlation test of the OLS estimation, the LM-lag and LM-error passed the 1% and 5% significance tests, respectively, indicating that to some extent, the SLM model is better than the SEM model. In addition, the SLM has smaller AIC and SC values, larger R2 and Log likelihood values, and the variables C and LnADL all pass the significance test, which fully indicates that the SLM fits better. Therefore, we choose the SLM model from the three econometric models, which illustrates that agricultural development level in main grain-producing areas in central Jilin Province has a significant driving effect on transport superiority degree over the entire spatial scale.

      Table 4.  OLS, SLM and SEM estimation results

      Model 1: Region-wide spatially driven effects of agricultural development level on transport superiority degree across the regionModel 2: Region-wide spatially driven effects of transport superiority degree on agricultural development level
      VariablesOLSSLMSEMVariablesOLSSLMSEM
      C–0.962*
      (–1.691)
      0.457***
      (3.128)
      0.273**
      (1.899)
      C0.072
      (1.600)
      0.415*
      (3.848)
      0.792***
      (5.677)
      LnADL0.432**
      (0.133)
      0.603***
      (0.081)
      0.545***
      (0.211)
      LnTDL0.301*
      (0.568)
      0.487**
      (0.057)
      0.579**
      (0.021)
      λ0.484***
      (2.157)
      λ0.202***
      (0.725)
      Log likelihood944.6951059.3641269.770Log likelihood107.914114.337126.360
      R20.5070.6530.617R20.3380.4750.533
      AIC577.411500.274525.690AIC201.433186.745106.277
      SC563.244529.451539.562SC166.634122.664108.590
      F110.770***(0.000)F76.127***(0.000)
      LM-lag50.667***(0.003)LM-lag67.856*** (0.001)
      Robust LM-lag11.203***(0.000)Robust LM-lag1.661(0.570)
      LM-error19.637**(0.004)LM-error3.522***(0.006)
      Robust L-error3.682*(0.006)Robust LM-error10.425***(0.011)
      Notes: ***, ** and * denote significant at the 1%, 5%, and 10% level, respectively

      For Model 2 (Table 4), the R2 values of the three econometric models (OLS, SLM, and SEM) are 0.338, 0.475, and 0.533, respectively, with an F-statistic of 76.127, passing the 1% significance test. According to the OLS spatial dependence test, both LM statistics of the SEM model passed the 1% significance test and rejected the original hypothesis of no spatial error; while for the LM test of the SLM model, robust LM-lag did not pass the 10% significance test, and the LM-lag also rejected the original hypothesis of no spatial lag at the 1% significance level. Therefore, the spatially driven effect of transport superiority degree on agricultural development level can not be ignored, and the SEM model is superior to the SLM model.

      Based on a comparison of Model 1 and Model 2 for the region-wide spatially driven effect of transport superiority degree on the agricultural development level in the central Jilin Province grain-producing region, the spatial relationship coefficient of LnTDL is larger than that of the LnADL, which indicates that the growth rate of agricultural development level is larger and the growth rate of transport superiority degree is relatively lagging in the region-wide spatial scope of the central Jilin Province grain-producing region.

    • The GWR model in ArcGIS was used to derive the local spatially driven effect between agricultural development level and transport superiority degree in the counties of main grain-producing areas. For the estimation of the local spatially driven effect of the county’s agricultural development level on transport superiority degree, the AIC is −110.963 and the goodness-of-fit R2 is 0.648, so the overall fitting effect is better. For the estimation of the local spatially driven effect of the county’s transport superiority degree on agricultural development level, the AIC is −93.530 and the R2 of the goodness of fit is 0.525, which is higher than the goodness of fit of the OLS model, so the GWR model has a better fit overall.

      The regression coefficients of the local spatially driven effects were classified and grouped into six driving effect patterns (Fig. 10), and the types of county production mix shifted from each other (Ma et al., 2018). From the types’ transfer matrix (Table 5), there was a net increase of 18.18% in the counties with mutual strong drive effects from 2008 to 2019. The percentage of counties whose types changed from ‘ADL Strong Drive’ and ‘TDL Strong Drive’ to ‘Mutual Strong Drive’ were 13.64% and 4.55%, respectively. A total of 9.09% of counties changed from ‘ADL Weak Drive’ to ‘Mutual Weak Drive’; similarly, 4.55% of counties changed from ‘TDL Weak Drive’ to ‘Mutual Weak Drive’, and a small number of other county types also changed mutually.

      Table 5.  The transfer matrix of types during 2008–2019 / %

      Types2019 (T2)
      ADL Strong DriveADL Weak DriveTDL Strong DriveTDL Weak DriveMutual Strong DriveMutual Weak DriveP1Reduction
      2008 (T1)
      ADL Strong Drive13.640.004.550.0013.640.0031.8218.1
      ADL Weak Drive4.550.000.000.000.009.013.6413.6
      TDL Strong Drive0.000.0013.640.004.550.0018.184.55
      TDL Weak Drive0.000.000.000.000.004.554.554.55
      Mutual Strong Drive0.000.004.550.009.090.0013.644.55
      Mutual Weak Drive4.554.554.550.000.004.5518.1813.64
      P222.734.5527.270.0027.2718.18100.00
      Increments9.094.5513.640.0018.1813.64
      Notes: ADL is agricultural development level and TDL is transport superiority degree; P is the percentage of the number of counties of each type in the total count of the counties in the time T

      Figure 10.  Spatial distribution of combination type of agricultural development level and transport superiority degree in main grain-producing areas of the central Jilin Province, China

      In terms of the spatial distribution, between 2008 and 2019, the number of counties of the ‘Mutual Strong Drive’ type doubled in number, growing from 3 to 6, and were distributed immediately to the areas adjacent to the Changji metropolitan area. These types are mutually reinforcing and can jointly drive development, with transport superiority degree and agricultural development level forming a benign interaction. The types of ‘Mutual Weak Drive’, during 2008–2019, there was no change in quantity, and it was scattered and mainly distributed in the corner of the study area.

      The number of counties with an ‘ADL Strong Drive’ decreased over the decade. In 2008, the socioeconomic development of these areas drove the development of small-scale agriculture in the direction of increasing agricultural scale in a certain stage. Traditional agriculture gradually transitioned towards a capital and technology-based direction, and in 2019, due to the economic environment and the need for modern agricultural development, the material production conditions and scientific and technological equipment of modern agriculture increased the agricultural development level and decreased the constraints imposed by the spatial distribution factors of the market territory. Other agricultural development conditions (e.g., scientific and technological investments in agriculture, development of urban agriculture and green agriculture) have a great potential for future agricultural development if they can be used in the future. The counties of ‘ADL Weak Drive’ were consistently distributed in the southern and northern part of the study area, where there was an urgent need to adjust the agricultural structure and establish superior agricultural products with local resource advantages. The reason for this phenomenon may be that these areas were in the process of rapid development, and the pressure for the non-agricultural arable land was high, which decreased the stability of regional agricultural production.

      The number of counties with a ‘TDL Strong Drive’ increased from four in 2008 to six in 2019, indicating that the level of transportation construction in these areas is improving. The agricultural development of this type of county already has a good transportation base to support it, especially for the counties located around Changchun and Jilin, which are still located near the central cities and have the advantages of railroad and highway transportation hubs. Since the convenient transportation in these counties effects the development of the local agriculture, the green channel was opened for local agricultural products, and a comprehensive market for agricultural products is built, with the market as the hub for sending out the agricultural products. In 2008, Dongliao was the only county with a ‘TDL Weak Drive’. The scale of the regional transportation construction was insufficient relative to the agricultural development, so agricultural strategies and agricultural markets need to be developed in a focused manner. In addition, improving the transportation environment would further provide convenient conditions for regional and internal exchanges.

    • Agriculture is one of the strategically important sectors of economic activities in any country, and along with continuous globalization and urbanization, both developed and developing countries should actively promote agricultural development and rural reconfiguration (Liu and Li, 2017; Azunre et al., 2019). Since the implementation of the rural revitalization strategy in China, agriculture has provided strong support for the healthy and sustainable development of the national economy (Deng and Gibson, 2019; Liu et al., 2019). The transport base is the mediating and driving force of rural external linkages and material and information flows. Research on the relationship between agricultural development level and transport superiority degree in main grain-producing areas can strengthen inter-regional agricultural and economic interactions and linkages, thereby enhancing the regional agricultural exhibition potential and the location advantages of agricultural markets for agricultural activities. Although some studies have been conducted on the evaluation of agricultural development (Li et al., 2015; Hojman and Miranda, 2018; Wang et al., 2019), transport superiority degree (Elshahawany et al., 2017), and the correlation between the two (Yang et al., 2016), our study still includes several innovations. Firstly, the evaluation index system for agricultural development level in main grain-producing areas covers many aspects such as the production input, output, society, and nature, and the selected indicators reflect the characteristics of livestock development and non-agricultural development, which makes the evaluation more convincing. Secondly, in previous studies on the relationship between economic development level and transport superiority degree, the evaluation indexes were selected to focus on the transportation investment, transportation infrastructure construction, and other aspects (Salas-Olmedo et al., 2015). However, agricultural development is different from economic development and requires the consideration of the temporal and spatial factors of the locations of production and consumption. Owing to these innovations, we have gained a better understanding of the relationship between the two.

    • There are significant differences in the industrial development, functional positioning, and business models of the agriculture in urban areas with a high degree of urbanization, a good economic foundation, convenient transportation, and a developed agricultural processing industry in the city-ring areas and the agriculture in other areas. In terms of industrial development, in the regions with advantageous agricultural development mainly develop industries with higher income, such as the deep processing of agricultural products, cash crop, and the vegetable and fruit industries, an agricultural industrial structure that combines urban demand and resource advantages is built, whereas regions with less developed agricultural development do not have the advantage of large-scale operation of special industries. In terms of function positioning, regions with a higher agricultural development level are developing multifunctional agriculture that integrates production, life, and ecology, whereas regions with a lower level of agricultural development have not formed an interdependent relationship with multifunctional advantages, the main function is weakened, and the development is vulnerable to external factors. In terms of the business model, the advantageous regions mainly use new technology to transform traditional agriculture and develop green agricultural methods; while in the regions where agricultural development level is relatively backward, the business model is mainly to stabilize the agricultural industry base, to develop the plantation industry, to promote the transformation of food crops into cash crops, and to gradually expand the scale of agricultural industry.

      It was found that agricultural development level at the spatial interfaces located in areas rich in characteristic agriculture and tourism resources (e.g., Shulan and Jiaohe) was higher than in areas where tourism resources and characteristic agriculture were scarce. These areas paid attention to developing and protecting natural and cultural resources, creating rural tourism and green agricultural brands, diversifying agricultural structures, forming industrial chains and industrial clusters, and promoting the development of regional agriculture. This is consistent with the conclusions of previous studies, that is, the spatial interface where resources are abundant and the factors are concentrated and flow frequently will become a breakthrough for agricultural development (Liu and Qiao, 2017).

    • Compared with single traffic accessibility, the comprehensive transport superiority degree of a county can reflect the realistic pattern of the county’s transport development more comprehensively, and it has a more realistic influence on the development of the county’s agriculture. A good location facilitates the gathering of various factors, and the development level of a region with a complete agricultural industry chain will be qualitatively improved. Transport construction has changed the location environment at the edge of the study area, avoiding marginalization of the region and making the counties around the Changchun-Jilin integration with high level of agricultural development more closely connected, resulting in a scale effect and better agricultural development level and speed than in other areas, which is consistent with the results of Tong et al. (2013).

      The development of industrialization and urbanization in main grain-producing areas is not contradictory to agricultural production and food security, but rather it is a mutually reinforcing and symbiotic relationship. The decline in the proportion of agriculture in all of the industries does not mean the weakening of agriculture; and in fact, the development of secondary and tertiary industries provides more room for modern agriculture. In areas of urbanization, industrialization and rapid changes in the market demand, the transformation of agriculture is unstoppable. The agricultural transformation process is inevitably accompanied by the development of the agricultural processing industry. The requirements of agricultural enterprises in terms of location and transportation of agricultural products drive urbanization and infrastructure development, whereas the market demand in the central consumer cities pulls the agricultural economy and promotes agricultural prosperity.

      In general, the transportation construction in the grain-producing areas of central Jilin Province is consistent with agricultural development level. On the one hand, the transportation construction has improved the land transportation system in central Jilin Province, improved the location advantages of the counties, and met the agricultural development and transportation needs of different towns. However, it has increased the degree of connection between counties in main grain-producing areas, for example, enhancing the cohesion of the agricultural processing and manufacturing bases. The agricultural processing industry has basically formed a pattern based on the Harbin-Dalian line and the Songhua River, with Yushu-Dehui-Changchun-Nong’an-Guangzhuling-Siping as the first line, forming an agglomeration of a certain scale. However, there are also some counties in which agricultural development level and transport superiority degree are not coordinated. There are two reasons for this phenomenon. One may be that the agriculture is developing in the direction of capital and technology, which results in agricultural development level being less restricted by transport factors; and the other may be that the regional transport base is better, but the agricultural production is more unstable.

      The study of the relationship between agricultural development level and transport superiority degree is not the pursuit of large transport system, and the purpose of clarifying the synergistic relationship between the two is to establish a reasonable multi-level road transport system, to strengthen and optimize the exchange and integration of the multi-dimensional space for material production, cultural exchange, social life and ecological environment between urban and rural areas, and to achieve urban-rural integration in the true sense. In the background of the great development of e-commerce and logistics, a good transportation foundation is a necessary condition for the development of urban-rural integration, and the study of the relationship between the two is conducive to the development of agricultural modernization and provides a basic theory for rural road transport planning.

    • Through this study, it was found that in most of the counties in the grain-producing areas of central Jilin Province, there is a correlation between agricultural development level and transport superiority degree, but in some counties, this correlation is weak. Compared with the assumptions of a homogeneous plain, a single market center, and horse-drawn carriages as the main means of transportation, the background and conditions of von Thunen’s agricultural location theory have changed profoundly in the modern market economy.

      The reasons for this phenomenon are discussed below. The traditional agricultural location theory was created at a time when transport networks were not well developed, and agricultural production was directly satisfying the market demand. Transportation cost and transportation distance are an important part of agricultural development, and transportation is more decisive for agricultural production and consumption (Li, 2006). However, in the modern market economy, transportation networks and market systems have become increasingly developed, and the influence of transportation cost on agricultural development has decreased. This has significantly reduced the influences of certain traditional freight and distance factors on agricultural development within the county, and this decrease has been more obvious on small and medium spatial scales. Moreover, diversified agricultural development patterns such as the facility agriculture, characteristic farming, facility fruit and vegetable production, and leisure farming have broken far away from the pattern of the individual urban centers described by von Thunen’s agricultural location. The changes in these factors have gradually broken the interdependence between agricultural development and transport in some areas.

    • In areas where agricultural development level and transport superiority degree are both strong, it is suggested that we continue to stabilize the development of grain farming, promote the transformation of grain into value-added and efficient methods, build a clustered and efficient agricultural industry system, implement a spatially unbalanced development strategy, and focus on building the spatial development skeleton and backbone of the central region, that is, implement the point-axis spatial agricultural development model. To build an integrated urban-rural transportation model, the central city should play the main leading and radiation-driven role, the advantages of transportation should be used to continuously expand and integrate the agricultural functions with the surrounding areas, tourism +, ecology + and other modes should be used to create a green leisure and modern agriculture-based industrial development belt, and industrial clusters should be developed. A large-scale logistics center for agricultural products should be established to expand the target market and increase the agricultural development and farmers' income using the rich resources represented by agricultural products and the convenient and accessible transportation to the surrounding counties.

      In the middle level regions, efforts should be made to integrate the advantageous resources of planting, breeding, and processing resources and to accelerate the formation of an agricultural industry structure system with its own characteristics. We will accelerate the construction of transport networks in urban agglomeration and metropolitan areas, improve the agricultural support system and the production and circulation of agricultural products, and promote the development of industries toward the middle and high end of the value chain. In the weaker regions, agriculture should be planned scientifically and rationally to build transport links that mainly serve the rural economy and complement large transport links and to improve the overall depth of the transport access in the county. Neighboring counties should strengthen the cooperation with high agricultural level, adjust the agricultural structure, and develop special agriculture and diversified compound agriculture.

    • In this study, we applied geographic spatial analysis, agricultural development theory, and accessibility theory to quantitatively evaluate the agricultural development level, transport superiority degree and the mutually driven effects between agricultural development level and transport superiority degree in grain-producing areas of central Jilin Province and analyzed the spatial pattern characteristics and spatial evolution patterns. In addition, the application of agricultural location theory for the development of modern agriculture in main grain-producing areas was also discussed.

      (1) The overall agricultural development level of main grain-producing areas in central Jilin Province has changed from a low level to middle-high level, showing an elevated trend and a good development momentum. Transport superiority degree shows the characteristics of ‘high at the center and low at the edge’, and the overall level of the road networks connectivity has increased significantly during 10-year study period, the spatial pattern of accessibility has been optimized, and transport superiority degree of each region has been improved.

      (2) The correlation analysis revealed that the county’s transport superiority degree was significantly and positively correlated with its agricultural development level. Deeper analysis of spatial mutually driven effect of the two revealed that the agricultural development level increased at a greater rate and was more dependent on transportation.

      (3) Within the local spatial scope, there was spatial heterogeneity in the mutually driven effect between agricultural development level and transport superiority degree in the counties. The counties of the six mutually driven types are interconnected geographically, and only the individual counties are shaped independently, with significant spatial mosaic characteristics. This spatial pattern reflects the regional imbalance between agricultural development level and transport superiority degree.

      The regionalization factor of agricultural production and the pressure for the non-agricultural arable land will affect agricultural development. We can further study the multidimensional agricultural development, mechanism of urban-rural economic linkages and the performance of different agricultural types in the transport networks. These topics will continue to be explored in depth in future studies.

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