Spatiotemporal evolution and driving factors of soybean production in Sichuan Province
-
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。Abstract: Sichuan Province is one of the 13 major grain-producing areas in China, as well as an important component of China’s emerging soybean planting area and southwest-producing area. Studies of soybean production patterns are crucial to Sichuan Province’s implementation of food security strategy and soybean industry development in southwest China. This study analyzes the temporal and spatial evolution characteristics and driving factors of soybean production in China from 2000 to 2020 in Sichuan Province using the Gini coefficient, industrial concentration, exploring spatial data analysis, and optimal parameters-based geographical detector. The findings were as follows: 1) from 2000 to 2020, soybean production in Sichuan Province fluctuated and increased, the spatial distribution was very uneven, and the agglomeration level increased and gradually concentrated in the hilly areas of central Sichuan. 2) There was a strong positive spatial correlation in soybean production, and the overall manifestations were high-high aggregation and low-low aggregation types. 3) The influences of geographical climate, comparative income, transportation conditions, and economic and social factors on soybean production pattern change were highly significant, and most of them showed the interaction effect of nonlinear enhancement and two-factor enhancement. Among them, the impact of resource input, comparative benefits, and altitude on soybean production pattern has been the most significant and fluctuating upward trend for a long time. The influence of temperature and rural household size increases rapidly, the influence of transportation conditions and regional GDP decreases significantly, and the driving force of farming system is the weakest. Based on these results, soybean production in Sichuan Province should focus on breaking the resource and environmental constraints such as cultivated land fragmentation and rural labor shortage, vigorously developing the productive service industry, and comprehensively improving the mechanization level of soybean production. At the same time, it is necessary to strengthen the ability of scientific and technological innovation to improve the yield level of soybeans in Sichuan Province. Additionally, it is critical to further optimize the policy system of soybean production, agricultural machinery, socialized services, insurance, and other links. Finally, we should focus on natural risks, such as drought caused by climate change, and establish and improve the early warning and prevention mechanism of natural disaster risks of soybeans, to further strengthen the comprehensive risk resilience of the soybean industry in Sichuan Province.
-
Key words:
- Soybean production /
- Food security /
- Spatiotemporal pattern /
- Driving factors /
- 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 undulationTER 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 patternCOK 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) $ X1和X2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 W1 W2 W3 2000 0.304*** 0.078*** 0.307*** 2001 0.312*** 0.078*** 0.281*** 2002 0.401*** 0.106*** 0.344*** 2003 0.407*** 0.109*** 0.415*** 2004 0.445*** 0.130*** 0.447*** 2005 0.487*** 0.137*** 0.440*** 2006 0.412*** 0.112*** 0.360*** 2007 0.456*** 0.109*** 0.468*** 2008 0.471*** 0.112*** 0.477*** 2009 0.493*** 0.115*** 0.501*** 2010 0.518*** 0.118*** 0.527*** 2011 0.536*** 0.119*** 0.546*** 2012 0.554*** 0.119*** 0.565*** 2013 0.569*** 0.118*** 0.573*** 2014 0.571*** 0.117*** 0.567*** 2015 0.572*** 0.114*** 0.552*** 2016 0.554*** 0.109*** 0.533*** 2017 0.555*** 0.109*** 0.534*** 2018 0.557*** 0.110*** 0.544*** 2019 0.558*** 0.111*** 0.551*** 2020 0.567*** 0.118*** 0.570*** ***表示在P<0.01水平显著; W1、W2和W3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
Variable2000 2001 2008 2010 2016 2020 q值
q-value位序
Orderq值
q-value位序
Orderq值
q-value位序
Orderq值
q-value位序
Orderq值
q-value位序
Orderq值
q-value位序
OrderPEO 0.6232*** 1 0.5851*** 1 0.8052*** 1 0.8488*** 1 0.8332*** 1 0.7942*** 1 LAD 0.4527*** 2 0.4594*** 2 0.5448*** 2 0.6081*** 2 0.5770*** 2 0.5808*** 2 MCE 0.2114*** 6 0.2692*** 3 0.3613*** 5 0.3871*** 4 0.4077*** 3 0.4304*** 3 BEF 0.2514*** 3 0.2588*** 5 0.3813*** 3 0.4614*** 3 0.2875*** 6 0.3950*** 4 ELE 0.2564*** 4 0.2659*** 4 0.2813*** 6 0.2971*** 6 0.2889*** 5 0.2940*** 5 TEP 0.1798*** 11 0.2087*** 6 0.3675*** 4 0.3512*** 5 0.2908*** 4 0.2726*** 6 SLP 0.1861*** 9 0.2002*** 8 0.2584*** 7 0.2470*** 8 0.2415*** 7 0.2602*** 7 TER 0.1936*** 7 0.2017*** 7 0.2377*** 8 0.2526*** 7 0.2309*** 8 0.2536*** 8 ROD 0.2373*** 5 0.1861*** 10 0.2246*** 9 0.2220*** 9 0.1417*** 11 0.2249*** 9 HOU 0.1222*** 13 0.1951*** 9 0.2101*** 10 0.1674*** 11 0.1467*** 9 0.2112*** 10 GDP 0.1914*** 8 0.1064*** 14 0.0989*** 14 0.1356*** 12 0.1343*** 12 0.1316*** 11 PRE 0.1741*** 12 0.1238*** 13 0.1388*** 11 0.2004*** 10 0.0518 14 0.1292*** 12 URA 0.1092** 14 0.1262*** 12 0.1115*** 13 0.0863** 13 0.1451*** 10 0.1174*** 13 COK 0.1802*** 10 0.1558*** 11 0.1336*** 12 0.0563 14 0.0754** 13 0.0551** 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
Variable2000 2001 2008 2010 2016 2020 q值总和
Sum of q位序
Orderq值总和
Sum of q位序
Orderq值总和
Sum of q位序
Orderq值总和
Sum of q位序
Orderq值总和
Sum of q位序
Orderq值总和
Sum of q位序
OrderPEO 0.6936 1 0.6988 1 0.8541 1 0.8772 1 0.8826 1 0.8431 1 LAD 0.5853 2 0.5725 2 0.6490 2 0.6937 2 0.6486 2 0.6560 2 MCE 0.4507 6 0.4347 6 0.5524 4 0.5906 4 0.5755 3 0.6213 3 BEF 0.4962 4 0.5109 3 0.6107 3 0.6534 3 0.5477 4 0.6618 4 HOU 0.4488 7 0.4061 13 0.5124 11 0.4836 11 0.4735 10 0.5446 5 ELE 0.4174 12 0.4269 8 0.4674 10 0.4908 10 0.4971 5 0.4880 6 TER 0.4485 8 0.4296 7 0.5111 7 0.5179 7 0.4846 7 0.5171 7 TEP 0.3764 14 0.4034 14 0.5591 5 0.5461 5 0.4784 8 0.4845 8 SLP 0.4144 13 0.4105 11 0.4879 9 0.5019 9 0.4749 9 0.5035 9 ROD 0.4609 5 0.4180 9 0.4864 12 0.4599 12 0.3786 14 0.4690 10 GDP 0.4970 3 0.4853 4 0.4727 6 0.5282 6 0.4929 6 0.4724 11 URA 0.4433 10 0.4448 5 0.4849 8 0.5137 8 0.4371 11 0.4733 12 PRE 0.4473 9 0.4132 10 0.4022 13 0.4584 13 0.3888 13 0.4160 13 COK 0.4381 11 0.4078 12 0.4527 14 0.4415 14 0.4075 12 0.3959 14 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
-
[1] 杨辉, 林嘉柏, 林佳. 外生冲击下中国大豆产业安全: 现状、挑战与机遇[J]. 大豆科学, 2022, 41(3): 352−357YANG H, LIN J B, LIN J. Analysis of China’s soybean industry safety under exogenous impact: current situation, challenges and opportunities[J]. Soybean Science, 2022, 41(3): 352−357 [2] 朱文博, 韩昕儒, 问锦尚. 中国大豆生产自给的潜力、路径与挑战[J]. 华南师范大学学报(社会科学版), 2022(3): 122−135,207ZHU W B, HAN X R, WEN J S. The potential, path and challenge of soybean production in China[J]. Journal of South China Normal University (Social Science Edition), 2022(3): 122−135,207 [3] 石慧, 王思明. 大豆在中国的历史变迁及其动因探究[J]. 农业考古, 2019(3): 32−39SHI H, WANG S M. Research on historical development and motivations of soybeans in China[J]. Agricultural Archaeology, 2019(3): 32−39 [4] 李二玲, 位书华, 胥亚男. 中国大豆种植地理集聚格局演化及其机制[J]. 经济经纬, 2016, 33(3): 37−42LI E L, WEI S H, XU Y N. An analysis of the evolution and mechanism of China’s soybean planting agglomeration and geographic pattern[J]. Economic Survey, 2016, 33(3): 37−42 [5] 陈雨生, 江一帆, 张瑛. 中国大豆生产格局变化及其影响因素[J]. 经济地理, 2022, 42(3): 87−94CHEN Y S, JIANG Y F, ZHANG Y. Spatio-temporal evolution and influencing factors of soybean production in China[J]. Economic Geography, 2022, 42(3): 87−94 [6] 杨晓娟, 刘布春, 刘园, 等. 我国大豆种植时空分布与风险费率研究[J]. 中国农业资源与区划, 2021, 42(1): 106−115YANG X J, LIU B C, LIU Y, et al. Study of spatial-temporal distribution and premium rate of soybeans in China[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2021, 42(1): 106−115 [7] CHEN W G, ZHANG B B, KONG X B, et al. Soybean production and spatial agglomeration in China from 1949 to 2019[J]. Land, 2022, 11(5): 734 doi: 10.3390/land11050734 [8] 王静, 方锋, 王莺. 中国西南和华南粮食作物播种面积时序变化特征及其影响因素分析[J]. 生态环境学报, 2021, 30(10): 2010−2025WANG J, FANG F, WANG Y. Temporal variation characteristics and influencing factors of sown area variation of grain crops in southwest and South China[J]. Ecology and Environmental Sciences, 2021, 30(10): 2010−2025 [9] 秦婷婷, 曹鑫悦, 周泽群, 等. 1952年以来我国大豆单产变异特征及其影响因素研究[J]. 中国生态农业学报(中英文), 2022, 30(1): 47−56QIN T T, CAO X Y, ZHOU Z Q, et al. Variation characteristics of soybean yield since 1952 and its influencing factors in China[J]. Chinese Journal of Eco-Agriculture, 2022, 30(1): 47−56 [10] 盖钧镒. 盖钧镒: 中国大豆产业发展很大程度上取决于政策调控[J]. 黑龙江粮食, 2018(9): 40−45GAI J Y. Gai Junyi: the development of soybean industry in China largely depends on policy regulation[J]. Heilongjiang Grain, 2018(9): 40−45 [11] 董非非, 刘爱民, 封志明, 等. 大豆传统产区种植结构变化及影响因素的定量化评价−以黑龙江省嫩江县为例[J]. 自然资源学报, 2017, 32(1): 40−49 doi: 10.11849/zrzyxb.20160177DONG F F, LIU A M, FENG Z M, et al. Changes of planting structure and quantitative evaluation of influencing factors in traditional soybean producing areas: taking Nenjiang County in Heilongjiang Province as an example[J]. Journal of Natural Resources, 2017, 32(1): 40−49 doi: 10.11849/zrzyxb.20160177 [12] 王新刚, 司伟. 大豆补贴政策改革实现大豆扩种了吗?−基于大豆主产区124个地级市的实证[J]. 中国农村经济, 2021(12): 44−65WANG X G, SI W. Does the reform of soybean subsidy policy realize the expansion of soybean production? an empirical analysis based on 124 prefecture-level cities in soybean main producing areas[J]. Chinese Rural Economy, 2021(12): 44−65 [13] 韩振兴, 常向阳. 劳动力成本上升、主产区优势与中国大豆种植面积变化−基于1991—2019年的省级面板数据[J]. 农业现代化研究, 2022, 43(2): 296−305HAN Z X, CHANG X Y. Rising labor costs, advantages of main producing areas, and the changes of soybean planting area: based on a provincial panel data in China from 1991 to 2019[J]. Research of Agricultural Modernization, 2022, 43(2): 296−305 [14] 刘爱民, 于格, 于萧萌. 大豆主产区主要竞争农作物生产成本与收益分析[J]. 中国农业资源与区划, 2005, 26(2): 35−39LIU A M, YU G, YU X M. Analysis of production cost and gain of major competing crops at main soya bean production regions[J]. Journal of China Agricultural Resources and Regional Planning, 2005, 26(2): 35−39 [15] 刘爱民, 封志明, 阎丽珍, 等. 基于耕地资源约束的中国大豆生产能力研究[J]. 自然资源学报, 2003, 18(4): 430−436LIU A M, FENG Z M, YAN L Z, et al. Study on soybean throughput based on cultivated land resources restriction in China[J]. Journal of Natural Resources, 2003, 18(4): 430−436 [16] QIU B W, HU X, CHEN C C, et al. Maps of cropping patterns in China during 2015–2021[J]. Scientific Data, 2022, 9(1): 479 doi: 10.1038/s41597-022-01589-8 [17] ZHAO J C, WANG C, SHI X Y, et al. Modeling climatically suitable areas for soybean and their shifts across China[J]. Agricultural Systems, 2021, 192: 103205 doi: 10.1016/j.agsy.2021.103205 [18] 韩振兴, 常向阳. 劳动力价格上升、要素替代与要素投入结构变化−来自中国大豆生产的证据[J]. 农业现代化研究, 2021, 42(3): 507−516HAN Z X, CHANG X Y. Rising labor prices, input substitution, and input structure changes: evidence from soybean production in China[J]. Research of Agricultural Modernization, 2021, 42(3): 507−516 [19] 张雪, 周密, 张丽敏. 气候变化及农户适应性行为对大豆播种面积的影响[J]. 农业现代化研究, 2021, 42(1): 153−164ZHANG X, ZHOU M, ZHANG L M. Effects of climate change and farmers’ adaptive behaviors on soybean planting acreage[J]. Research of Agricultural Modernization, 2021, 42(1): 153−164 [20] 司伟, 韩天富. “十四五”时期中国大豆增产潜力与实现路径[J]. 农业经济问题, 2021, 42(7): 17−24SI W, HAN T F. China’s soybean yield increase potential and realization path during the “14th five-year plan” period[J]. Issues in Agricultural Economy, 2021, 42(7): 17−24 [21] 张晓峰, 王宏志, 刘洛, 等. 近50年来气候变化背景下中国大豆生产潜力时空演变特征[J]. 地理科学进展, 2014, 33(10): 1414−1423 doi: 10.11820/dlkxjz.2014.00.013ZHANG X F, WANG H Z, LIU L, et al. Spatial-temporal characteristics of soybean production potential change under the background of climate change over the past 50 years in China[J]. Progress in Geography, 2014, 33(10): 1414−1423 doi: 10.11820/dlkxjz.2014.00.013 [22] GUO S J, LV X Y, HU X D. Farmers' land allocation responses to the soybean rejuvenation plan: Evidence from “typical farm” in Jilin, China[J]. China Agricultural Economic Review, 2021, 13(3): 705−719 doi: 10.1108/CAER-06-2020-0118 [23] 周杨, 邵喜武, 吴佩蓉. 大豆生产者补贴政策改革促进农户种植结构调整了吗?−基于全国446个县的准自然实验[J]. 农林经济管理学报, 2021, 20(3): 305−315ZHOU Y, SHAO X W, WU P R. Does policy reform of subsidy for soybean producers promote farmers to adjust planting structure? based on quasi-natural experiments in 446 counties nationwide[J]. Journal of Agro-Forestry Economics and Management, 2021, 20(3): 305−315 [24] 封志明, 李文君, 李鹏, 等. 青藏高原地形起伏度及其地理意义[J]. 地理学报, 2020, 75(7): 1359−1372 doi: 10.11821/dlxb202007003FENG Z M, LI W J, LI P, et al. Relief degree of land surface and its geographical meanings in the Qinghai-Tibet Plateau, China[J]. Acta Geographica Sinica, 2020, 75(7): 1359−1372 doi: 10.11821/dlxb202007003 [25] 吕火明, 赵颖文, 刘宗敏, 等. 四川省粮食生产时空演变特征及其影响因素−基于90个粮食生产重点县视角[J]. 西南农业学报, 2022, 35(9): 2220−2228LYU H M, ZHAO Y W, LIU Z M, et al. Spatial and temporal evolution characteristics of food production in Sichuan Province and its influencing factors: from the perspective of 90 key food production counties[J]. Southwest China Journal of Agricultural Sciences, 2022, 35(9): 2220−2228 [26] 张玉芳, 吴冰洁, 郭斌. 气候变化背景下四川省玉米生育期农业气候资源变化趋势[J]. 西南农业学报, 2020, 33(9): 2102−2111ZHANG Y F, WU B J, GUO B. Characteristics of agro-meteorological climate resource during maize growing season under global climate change in Sichuan Province[J]. Southwest China Journal of Agricultural Sciences, 2020, 33(9): 2102−2111 [27] 傅玮韡, 张慧, 庄佩芬. 中国种植业生产集聚的影响因素研究[J]. 华中农业大学学报, 2021, 40(5): 89−97 doi: 10.13300/j.cnki.hnlkxb.2021.05.012FU W W, ZHANG H, ZHUANG P F. Factors affecting the production agglomeration of planting industry in China[J]. Journal of Huazhong Agricultural University, 2021, 40(5): 89−97 doi: 10.13300/j.cnki.hnlkxb.2021.05.012 [28] 李政通, 姚成胜, 梁龙武. 中国粮食生产的区域类型和生产模式演变分析[J]. 地理研究, 2018, 37(5): 937−953LI Z T, YAO C S, LIANG L W. Analysis on regional types of grain production and evolution of grain production models in China[J]. Geographical Research, 2018, 37(5): 937−953 [29] 张若婧, 陈跃红, 张晓祥, 等. 基于参数最优地理探测器的江西省山洪灾害时空格局与驱动力研究[J]. 地理与地理信科学, 2021, 37(4): 72−80ZHANG R J, CHEN Y H, ZHANG X X, et al. Spatial-temporal pattern and driving factors of flash flood disasters in Jiangxi Province analyzed by optimal parameters-based geographical detector[J]. Geography and Geo-Information Science, 2021, 37(4): 72−80 [30] 刘建志, 房艳刚, 王如如. 山东省农业多功能的时空演化特征与驱动机制分析[J]. 自然资源学报, 2020, 35(12): 2901−2915 doi: 10.31497/zrzyxb.20201207LIU J Z, FANG Y G, WANG R R. Spatio-temporal evolution characteristics and driving mechanisms of agricultural multifunctions in Shandong Province[J]. Journal of Natural Resources, 2020, 35(12): 2901−2915 doi: 10.31497/zrzyxb.20201207 [31] 梁建秋, 吴海英, 于晓波, 等. 四川种豆模式的演变、发展与育种对策[J]. 大豆科技, 2018(6): 27−30 doi: 10.3969/j.issn.1674-3547.2018.06.006LIANG J Q, WU H Y, YU X B, et al. Evolution, development and breeding countermeasures of soybean planting pattern in Sichuan[J]. Soybean Science & Technology, 2018(6): 27−30 doi: 10.3969/j.issn.1674-3547.2018.06.006 [32] 梁建秋, 吴海英, 冯军, 等. 四川大豆生产现状、主要问题及发展对策[J]. 大豆科技, 2020(3): 7−10LIANG J Q, WU H Y, FENG J, et al. Current situation, main problems and development countermeasures of soybean production in Sichuan Province[J]. Soybean Science & Technology, 2020(3): 7−10 [33] 郭声波. 历史时期四川粮食作物的地理分布[J]. 中国历史地理论丛, 1990, 5(3): 111−142GUO S B. Geographical distribution of grain crops in Sichuan in historical period[J]. Collections of Essays on Chinese Historical Geography, 1990, 5(3): 111−142 [34] 姚成胜, 杨一单, 殷伟. 中国非主粮生产的地理集聚特征及其空间演化机制[J]. 经济地理, 2020, 40(12): 155−165 doi: 10.15957/j.cnki.jjdl.2020.12.018YAO C S, YANG Y D, YIN W. Geographical agglomeration characteristics of China’s non-primary grain production and its spatial evolution mechanism[J]. Economic Geography, 2020, 40(12): 155−165 doi: 10.15957/j.cnki.jjdl.2020.12.018 [35] 李星月, 向运佳, 李其勇, 等. 四川丘区旱作生物灾害发生种类及其危害[J]. 农学学报, 2020, 10(2): 38−44 doi: 10.11923/j.issn.2095-4050.cjas19010030LI X Y, XIANG Y J, LI Q Y, et al. The types and damages of biological disasters in Sichuan dry-land[J]. Journal of Agriculture, 2020, 10(2): 38−44 doi: 10.11923/j.issn.2095-4050.cjas19010030 [36] 李清清. 近60年来(1960—2019)川中丘陵地区气候变化及极端气候事件研究[D]. 成都: 四川师范大学, 2021LI Q Q. Study on climate change and extreme climate events in the hilly area of central Sichuan Basin in recent 60 years (1960–2019)[D]. Chengdu: Sichuan Normal University, 2021 -
点击查看大图
图(7) / 表(5)
计量
- 文章访问数: 33
- HTML全文浏览量: 15
- PDF下载量: 3
- 被引次数: 0
