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四川省大豆生产格局变化及驱动因素研究

常洁 林正雨 高文波 杜兴端

常洁, 林正雨, 高文波, 杜兴端. 四川省大豆生产格局变化及驱动因素研究[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−14 doi: 10.12357/cjea.20230386
引用本文: 常洁, 林正雨, 高文波, 杜兴端. 四川省大豆生产格局变化及驱动因素研究[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−14 doi: 10.12357/cjea.20230386
CHANG J, LIN Z Y, GAO W B, DU X D. Spatiotemporal evolution and driving factors of soybean production in Sichuan Province[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−14 doi: 10.12357/cjea.20230386
Citation: CHANG J, LIN Z Y, GAO W B, DU X D. Spatiotemporal evolution and driving factors of soybean production in Sichuan Province[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−14 doi: 10.12357/cjea.20230386

四川省大豆生产格局变化及驱动因素研究

doi: 10.12357/cjea.20230386
基金项目: 中国工程科技发展战略四川研究院2023年度战略研究与咨询项目(2022JDR0352)、四川省重点研发计划项目(2021YFYZ0028)、四川省财政自主创新专项(2022ZZCX036)、四川省农业科学院“1+9”揭榜挂帅科技攻关项目(1+9KJGG009)、国家成都农业科技中心“揭榜挂帅”项目资助
详细信息
    作者简介:

    常洁, 研究方向为乡村地理与可持续发展。E-mail: 150467084@qq.com

    通讯作者:

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

  • 中图分类号: F3231; F304.5

Spatiotemporal evolution and driving factors of soybean production in Sichuan Province

Funds: This study was supported by the 2023 Strategic Research and Consulting Project in Sichuan Academy of Chinese Engineering S&T Strategy for Development (2022JDR0352), the Provincial Key Research and Development Project of Sichuan Province (2021YFYZ0028), Sichuan Provincial Financial Independent Innovation Project (2022ZZCX036), “Unveiling” Scientific and Technological Research Project in SAAS (1+9KJGG009), and “Unveiling” Project in National Chengdu Agricultural Science and Technology Center.
More Information
  • 摘要: 四川省是我国13个粮食主产区之一, 也是我国大豆种植的新兴地区和西南产区的重要组成, 研究大豆生产格局对四川省落实粮食安全战略、推动西南地区大豆产业发展具有重大意义。文章基于2000—2020年四川省183个区市县的面板数据, 运用空间基尼指数、地理集中度指数、空间转移系数、ESDA方法、最优地理探测器分析了大豆生产的时空格局变化及驱动因素。研究结果发现: 1)2000—2020年, 四川省大豆产能波动上升, 空间分布极不均衡, 集聚水平逐步上升并逐步向川中丘陵区集中; 2)大豆生产存在较强的正向空间相关性, 总体表现为高-高聚集和低-低聚集; 3)地理气候、比较收益、交通条件、经济社会等因素对大豆生产格局变化的影响均高度显著, 且呈现非线性增强、双因子增强的交互效应。其中, 资源要素投入、比较收益、海拔高程长期以来对大豆生产格局的影响最为显著且呈波动上升趋势, 气温、乡村家庭规模的影响力提升较快, 交通条件、地区GDP的影响力则显著下降, 耕作制度长期以来驱动力最弱。基于此, 四川省大豆生产应着力破解耕地资源细碎化与劳动力短缺等资源环境约束, 大力发展生产性服务业, 全面提升大豆生产机械化水平。需通过强化科技创新提升川豆单产, 并进一步优化大豆生产、农机、服务、保险等环节的政策保障。同时, 应重点关注气候变化引发的干旱等自然风险, 健全农业领域的自然灾害风险预警与防范机制, 以进一步强化大豆产业的综合风险抵御能力。
  • 图  1  四川省地貌分区

    Figure  1.  Geographical regions in Sichuan Province

    图  2  2000—2020年四川省大豆产量时序变化

    Figure  2.  Temporal changes of soybean production in Sichuan Province from 2000 to 2020

    图  3  2000—2020年四川省大豆产量空间集聚特征

    Figure  3.  Spatial agglomeration characteristics of soybean production in Sichuan Province from 2000 to 2020

    图  4  2000—2020年四川省不同阶段大豆产量空间转移系数变化

    Figure  4.  Changes of spatial transfer coefficient of soybean yield at different stages in Sichuan Province from 2000 to 2020

    图  5  节点年份四川省大豆LISA聚类图

    H-L、H-H、L-H和L-L分别表示高-低、高-高、低-高和低-低聚集类型区域。H-L, H-H, L-H and L-L represent high-low, high-high, low-high and low-low aggregation type regions, respectively.

    Figure  5.  LISA cluster plots of soybean production in Sichuan Province in node years from 2000 to 2020

    图  6  节点年份各影响因素交互作用探测结果

    实心圆的直径表示交互力的大小。LAD、PEO、MCE、ELE、SLP、TER、PRE、TEP、BEF、ROD、COK、GDP、URA和HOU分别表示耕地面积、大豆从业人员、农业机械总动力、平均高程、平均坡度、地形起伏度、年均降水量、年均温、大豆玉米产值差、路网密度、耕作制度、人均GDP、人口城镇化率和乡村家庭规模。Diameter of the solid circle indicate the strength of the interaction force. LAD, PEO, MCE, ELE, SLP, TER, PRE, TEP, BEF, ROD, COK, GDP, URA and HOU represent arable land area, soybean industry practitioners, total power of agricultural machinery, average elevation, average slope, terrain undulation, average annual precipitation, average annual temperature, soybean production value minus corn production value, road network density, cropping patterns, GDP per capita, population urbanization rate and size of rural households.

    Figure  6.  Interaction detection results of the factors in Sichuan Province in node years from 2000 to 2020

    图  7  节点年份各维度因子交互作用力结构

    Figure  7.  The interaction force structure of the factors in Sichuan Province in node years from 2000 to 2020

    表  1  四川省大豆产量的地理探测因子

    Table  1.   Indicators of geographical detector of soybean production in Sichuan Province

    划分依据
    Division basis
    驱动因子(单位)
    Driving factor(Unit)
    简写
    Abbreviation
    说明
    Instruction
    资源要素投入
    Resources inputs
    耕地面积
    Arable land area (×103 hm2)
    LAD指示土地资源投入
    Indicates land resource inputs
    大豆从业人员
    Soybean industry practitioners (persons)
    PEO指示人力资源要素投入
    Indicates the input of human resources
    农业机械总动力
    Total power of agricultural machinery (kW)
    MCE指示机械装备投入
    Indicates the input of mechanical equipment
    地理气候
    Geographical and climatic conditions
    平均高程
    Average elevation (m)
    ELE指示总体海拔特征
    Indicates the overall altitude characteristics
    平均坡度
    Average slope (°)
    SLP指示耕作地形条件
    Indicates tillage terrain conditions
    地形起伏度
    Terrain undulation
    TER指示海拔变化特征
    Indicates elevation change characteristics
    年均降水量
    Average annual precipitation (mm)
    PRE指示降水量气候因子
    Indicates precipitation factors
    年均温
    Average annual temperature (℃)
    TEP指示温度气候因子
    Indicates the temperature factor
    比较收益
    Comparative benefits
    大豆和玉米产值之差
    Soybean production value minus corn production
    value (×104 ¥)
    BEF指示市场驱动与竞争因子
    Indicates the market competitive factor
    交通条件
    Traffic conditions
    公路里程/行政区面积
    Road network density (km∙km−2)
    ROD指示生产区交通设施条件
    Indicates the conditions of transportation facilities
    种植习惯
    Planting habits
    耕作制度
    Cropping pattern
    COK指示作物种植制度
    Indicates cropping patterns
    社会经济环境
    Socio-economic environment
    人均GDP
    GDP per capita (¥ per cap.)
    GDP指示地区经济发展水平
    Indicates the level of regional economic development
    人口城镇化率
    Population urbanization rate (%)
    URA指示地区城镇化水平
    Indicates the level of urbanization in the region
    乡村家庭规模
    Size of rural households (persons per household)
    HOU指示乡村社会结构变化
    Indicates changes in the social structure of the village
    下载: 导出CSV

    表  2  交互作用类型

    Table  2.   The interaction type

    交互作用 Interaction描述 Description
    非线性减弱 Nonlinear weakening$ q\left({X}_{1}\cap {X}_{2}\right) < \mathrm{M}\mathrm{i}\mathrm{n}[q\left({X}_{1}\right),q\left({X}_{2}\right)] $
    单因子非线性减弱Single factor and nonlinear weakening$ \mathrm{M}\mathrm{i}\mathrm{n}[q\left({X}_{1}\right),q\left({X}_{2}\right)] < q\left({X}_{1}\cap {X}_{2}\right) < \mathrm{M}\mathrm{a}\mathrm{x}[q\left({X}_{1}\right),q\left({X}_{2}\right)] $
    双因子交互增强 Double factor interaction enhancement$ q\left({X}_{1}\cap {X}_{2}\right) > \mathrm{M}\mathrm{a}\mathrm{x}[q\left({X}_{1}\right),q\left({X}_{2}\right)] $
    非线性增强 Nonlinear enhancement$ q\left({X}_{1}\cap {X}_{2}\right) > q\left({X}_{1}\right)+q\left({X}_{2}\right) $
    相互独立 Independent$ q\left({X}_{1}\cap {X}_{2}\right)=q\left({X}_{1}\right)+q\left({X}_{2}\right) $
      X1X2表示影响因子。$ Min[q\left({X}_{1}\right),q\left({X}_{2}\right)] $表示在q(X1)和q(X2)两者取小值; $ \mathrm{M}\mathrm{a}\mathrm{x}[q\left({X}_{1}\right),q\left({X}_{2}\right)] $ 表示在q(X1)和q(X2)两者取大值; $ q\left({X}_{1}\right)+q\left({X}_{2}\right) $表示两者求和; $ q\left({X}_{1}\cap {X}_{2}\right) $表示两者交互。X1 and X2 indicate the impact factors. $ \mathrm{M}\mathrm{i}\mathrm{n}[q\left({X}_{1}\right),q\left({X}_{2}\right)] $ indicates the minimum value of q(X1) and q(X2); $ Max[q\left({X}_{1}\right),q\left({X}_{2}\right)] $ indicates the maximum value of q(X1) and q(X2); $ \text{q}\left({X}_{1}\right)+q\left({X}_{2}\right) $ indicates the sum of q(X1) and q(X2); $ q\left({X}_{1}\cap {X}_{2}\right) $ indicate the interaction of q(X1) and q(X2).
    下载: 导出CSV

    表  3  2000—2020年四川大豆产量Global Moran’s I指数变化

    Table  3.   Changes in the Global Moran’s I index of soybean production in Sichuan Province from 2000 to 2020

    年份
    Year
    空间权重矩阵 Spatial weight matrix
    W1W2W3
    20000.304***0.078***0.307***
    20010.312***0.078***0.281***
    20020.401***0.106***0.344***
    20030.407***0.109***0.415***
    20040.445***0.130***0.447***
    20050.487***0.137***0.440***
    20060.412***0.112***0.360***
    20070.456***0.109***0.468***
    20080.471***0.112***0.477***
    20090.493***0.115***0.501***
    20100.518***0.118***0.527***
    20110.536***0.119***0.546***
    20120.554***0.119***0.565***
    20130.569***0.118***0.573***
    20140.571***0.117***0.567***
    20150.572***0.114***0.552***
    20160.554***0.109***0.533***
    20170.555***0.109***0.534***
    20180.557***0.110***0.544***
    20190.558***0.111***0.551***
    20200.567***0.118***0.570***
      ***表示在P<0.01水平显著; W1、W2W3分别表示邻接矩阵、地理距离矩阵和经济距离矩阵。*** indicates the significant differences at P<0.01 level. W1, W2 and W3 indicate adjacency matrix, geographic distance matrix and economic distance matrix, respectively.
    下载: 导出CSV

    表  4  节点年份各影响因素对大豆产量的驱动力(q值)及位序变化

    Table  4.   Driving force(q value) and its order each influencing factor of soybean production in Sichuan Province in node years from 2000 to 2020

    变量
    Variable
    200020012008201020162020
    q
    q-value
    位序
    Order
    q
    q-value
    位序
    Order
    q
    q-value
    位序
    Order
    q
    q-value
    位序
    Order
    q
    q-value
    位序
    Order
    q
    q-value
    位序
    Order
    PEO0.6232***10.5851***10.8052***10.8488***10.8332***10.7942***1
    LAD0.4527***20.4594***20.5448***20.6081***20.5770***20.5808***2
    MCE0.2114***60.2692***30.3613***50.3871***40.4077***30.4304***3
    BEF0.2514***30.2588***50.3813***30.4614***30.2875***60.3950***4
    ELE0.2564***40.2659***40.2813***60.2971***60.2889***50.2940***5
    TEP0.1798***110.2087***60.3675***40.3512***50.2908***40.2726***6
    SLP0.1861***90.2002***80.2584***70.2470***80.2415***70.2602***7
    TER0.1936***70.2017***70.2377***80.2526***70.2309***80.2536***8
    ROD0.2373***50.1861***100.2246***90.2220***90.1417***110.2249***9
    HOU0.1222***130.1951***90.2101***100.1674***110.1467***90.2112***10
    GDP0.1914***80.1064***140.0989***140.1356***120.1343***120.1316***11
    PRE0.1741***120.1238***130.1388***110.2004***100.0518140.1292***12
    URA0.1092**140.1262***120.1115***130.0863**130.1451***100.1174***13
    COK0.1802***100.1558***110.1336***120.0563140.0754**130.0551**14
      *表示在P<0.1水平显著, **表示在P<0.05水平显著, ***表示在P<0.01水平显著。PEO、LAD、MCE、BEF、ELE、TEP、SLP、TER、ROD、HOU、GDP、PRE、URA和COK分别表示大豆从业人员、耕地面积、农业机械总动力、大豆玉米产值差、平均高程、年均温、平均坡度、地形起伏度、路网密度、乡村家庭规模、人均GDP、年均降水量、人口城镇化率和耕作制度。* indicate the significant differences at P<0.1 level, ** indicate the significant differences at P<0.05 level, and *** indicate the significant differences at P<0.01 level. PEO, LAD, MCE, BEF, ELE, TEP, SLP, TER, ROD, HOU, GDP, PRE, URA and COK represent soybean industry practitioners, arable land area, total power of agricultural machinery, the difference of soybean production value and corn production value, average elevation, average annual temperature, average slope, terrain undulation, road network density, size of rural households, GDP per capita, average annual precipitation, population urbanization rate and cropping patterns.
    下载: 导出CSV

    表  5  节点年份各影响因子交互作用的q值总和及位序

    Table  5.   The sum of the q-value and the order of the impact factor interactions in Sichuan Province in node years from 2000 to 2020

    变量
    Variable
    200020012008201020162020
    q值总和
    Sum of q
    位序
    Order
    q值总和
    Sum of q
    位序
    Order
    q值总和
    Sum of q
    位序
    Order
    q值总和
    Sum of q
    位序
    Order
    q值总和
    Sum of q
    位序
    Order
    q值总和
    Sum of q
    位序
    Order
    PEO0.693610.698810.854110.877210.882610.84311
    LAD0.585320.572520.649020.693720.648620.65602
    MCE0.450760.434760.552440.590640.575530.62133
    BEF0.496240.510930.610730.653430.547740.66184
    HOU0.448870.4061130.5124110.4836110.4735100.54465
    ELE0.4174120.426980.4674100.4908100.497150.48806
    TER0.448580.429670.511170.517970.484670.51717
    TEP0.3764140.4034140.559150.546150.478480.48458
    SLP0.4144130.4105110.487990.501990.474990.50359
    ROD0.460950.418090.4864120.4599120.3786140.469010
    GDP0.497030.485340.472760.528260.492960.472411
    URA0.4433100.444850.484980.513780.4371110.473312
    PRE0.447390.4132100.4022130.4584130.3888130.416013
    COK0.4381110.4078120.4527140.4415140.4075120.395914
      PEO、LAD、MCE、BEF、HOU、ELE、TER、TEP、SLP、ROD、GDP、URA、PRE和COK分别表示大豆从业人员、耕地面积、农业机械总动力、大豆玉米产值差、乡村家庭规模、平均高程、地形起伏度、年均温、平均坡度、路网密度、人均GDP、人口城镇化率、年均降水量和耕作制度。PEO, LAD, MCE, BEF, HOU, ELE, TER, TEP, SLP, ROD, GDP, URA, PRE and COK represent soybean industry practitioners, arable land area, total power of agricultural machinery, soybean production value minus corn production value, size of rural households, average elevation, terrain undulation, average annual temperature, average slope, road network density, GDP per capita, population urbanization rate, average annual precipitation and cropping patterns.
    下载: 导出CSV
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  • 收稿日期:  2023-07-10
  • 录用日期:  2023-10-30
  • 修回日期:  2023-10-27
  • 网络出版日期:  2023-11-06

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