全球粮食贸易网络演变及其驱动因素解析

doi:10.18306/dlkxjz.2022.05.002

Abstract

Abstract:

Based on the panel data of 238 countries and regions, this study examined the change of the spatial pattern of the global grain trade network at different scales from 1988 to 2018. The zero-inflated negative binomial was introduced to construct the gravity model for analyzing the driving factors of the global grain trade network. The results show that: 1) The scale of the global grain trade showed a rapid fluctuating rising trend, while the growth rate of trade fluctuated greatly between years. The structure of trade commodities was constantly diversified but is still dominated by wheat, corn, and barley. 2) The density of the global grain trade network is increasing, with polarization of some core nodes and increasing diversification of the overall trade network. For example, the position of North America, Western Europe, East Asia, and so on, in the grain trade network is relatively declining, while the position of Eastern Europe, South America, and so on, has increased. On the national scale, the interaction of grain trade among countries and regions has enhanced, as the structure of the grain trade network has changed from an "east-west axis" to "multi-point radiation" with a gradual shift from polarization to pluralism. Although the number of core nodes in the grain export network is increasing, regional grain export networks are still dominated by only several key countries and show a strong local polarization phenomenon, while the centralized characteristics of import networks are gradually weakened and more balanced. 3) Natural endowment of land resources, the structure of agriculture, and the degree of opening up to the outside world are the core driving factors for countries and regions to participate in the global grain trade network. The levels of price differences, language proximity, spatial distance, and societal governance also have a certain impact on the participation of countries and regions in the global grain trade network. The influence of other factors is relatively small.

Key words: global grain trade, spatial change of trade network, gravity model, zero-inflated negative binomial

GUAN Jing, SONG Zhouying, LIU Weidong. Change of the global grain trade network and its driving factors[J].PROGRESS IN GEOGRAPHY, 2022, 41(5): 755-769.

Figures/Tables 9

Fig.1

Tab.1

Indicators of SNA and their definitions, meanings and formulas

指标	定义	含义	公式
网络密度	网络实际存在的与理论上限存在的联系总数之比	各节点间联系的紧密程度	$D = l n (n - 1)$
度数中心度	与某一节点直接相连的其他节点个数	节点占据网络核心地位的程度	$C i, i n = ∑ a i j, i n; C i, o u t = ∑ a i j, o u t$
度数中心势	度数中心度最大值与其他节点的度数中心度的差值的总和,与理论上限的差值总和之比	网络整体的中心性程度,即网络向某点集中趋势的程度	$C = ∑ i = 1 n (C i, m a x - C i) n 2 - 3 n + 2$
中间中心势	中间中心度最大值与其他节点的中间中心度的差值的总和,与理论上限的差值总和之比	特定节点出现于其他点对间最短路径的次数,反映能动者控制网络资源的能力	$C B = ∑ i = 1 n (C A B m a x - C A B i) n 3 - 4 n 2 + 5 n - 2$
特征向量中心势	与节点及其邻近节点的数量和特征向量中心度的总和成正比	网络向重要节点核心集中的程度	$E C (i) = m - 1 ∑ j = 1 n a i j x j$

Tab.1

Tab.2

Definitions and meanings of Poisson distribution and negative binomial distribution

分布	定义	累积分布函数	意义
泊松分布 $X ~ P (p)$	$P (X = w) = p w w! e - p$	$P (X ≤ w) = ∑ i = 0 w p i i! e - p (w ≥ 0) 0 (w < 0)$	在一段固定长度的时间中,O地和D地间发生的贸易规模为 $w$
负二项分布 $X ~ N B (r, p)$	$P (X = W) = W - 1 r - 1 p r (1 - p) W - r$	$P (X ≤ W) = ∑ i = 0 W i - 1 r - 1 p r (1 - p) i - r (W ≥ r) 0 (W < r)$	O和D地间存在一定贸易规模 $r$ 时,网络全部贸易规模为 $W$

Tab.2

Fig.2

Tab.3

Fig.3

Fig.4

Tab.4

Tab.5

References 57

[1]	王祥, 强文丽, 牛叔文, 等. 全球农产品贸易网络及其演化分析[J]. 自然资源学报, 2018, 33(6): 940-953.
	[ Wang Xiang, Qiang Wenli, Niu Shuwen, et al. Analysis on global agricultural trade network and its evolution. Journal of Natural Resources, 2018, 33(6): 940-953. ] doi: 10.31497/zrzyxb.20180403
[2]	王祥, 牛叔文, 强文丽, 等. 实物量与价值量加权的全球农产品贸易网络分析[J]. 经济地理, 2019, 39(4): 164-173.
	[ Wang Xiang, Niu Shuwen, Qiang Wenli, et al. Trade network of global agricultural products weighted by physical and value quantity. Economic Geography, 2019, 39(4): 164-173. ]
[3]	周墨竹, 王介勇. 基于复杂网络的全球稻米贸易格局演化及其启示[J]. 自然资源学报, 2020, 35(5): 1055-1067.
	[ Zhou Mozhu, Wang Jieyong. Implications from pattern and evolution of global rice trade: A complex network analysis. Journal of Natural Resources, 2020, 35(5): 1055-1067. ] doi: 10.31497/zrzyxb.20200504
[4]	董超. 气候变化背景下国际小麦贸易网络动态演化研究[D]. 北京: 北京理工大学, 2018.
	[ Dong Chao. The research on dynamic evolution of global wheat trade network under climate change. Beijing, China: Beijing Institute of Technology, 2018. ]
[5]	李天祥, 刘星宇, 王容博, 等. 2000-2019年全球猪肉贸易格局演变及其对中国的启示: 基于复杂贸易网络分析视角[J]. 自然资源学报, 2021, 36(6): 1557-1572.
	[ Li Tianxiang, Liu Xingyu, Wang Rongbo, et al. Dynamics of global pork trade networks during 2000-2019 and its implications for China: A complex network analysis. Journal of Natural Resources, 2021, 36(6): 1557-1572. ] doi: 10.31497/zrzyxb.20210616
[6]	冯梅. 世界棉花贸易网络演变特征及影响因素研究: 基于复杂网络方法与模型[D]. 石河子: 石河子大学, 2017.
	[ Feng Mei. Evolution characteristics and influence factor of the world cotton trade network: Based on the complex network method and model. Shihezi, China: Shihezi University, 2017. ]
[7]	Singh S. South Asian agrifood trade networks in India: A case study of onion exports[J]. Millennial Asia, 2013, 4(2): 159-184. doi: 10.1177/0976399613506316
[8]	Dalin C, Konar M, Hanasaki N, et al. Evolution of the global virtual water trade network[J]. PNAS, 2012, 109(16): 5989-5994. doi: 10.1073/pnas.1203176109
[9]	汪艺晗, 杨谨, 刘其芸, 等. “一带一路”国家粮食贸易下虚拟水和隐含能源流动[J]. 资源科学, 2021, 43(5): 974-986. doi: 10.18402/resci.2021.05.11
	[ Wang Yihan, Yang Jin, Liu Qiyun, et al. Virtual water and embodied energy transfer in grain trade across the countries along the "Belt and Road". Resources Science, 2021, 43(5): 974-986. ] doi: 10.18402/resci.2021.05.11
[10]	韩雪, 梁璇, 王倩. 中国主要农产品虚拟要素贸易网络结构特征分析[J]. 生态学报, 2020, 40(11): 3851-3865.
	[ Han Xue, Liang Xuan, Wang Qian. Analysis on the structure characteristics of virtual elements trading network in major crops trade of China. Acta Ecologica Sinica, 2020, 40(11): 3851-3865. ]
[11]	朱智洺, 李秀琴, 黄永春. “一带一路”沿线国家粮食虚拟水贸易网络研究[J]. 资源与产业, 2020, 22(3): 31-42.
	[ Zhu Zhiming, Li Xiuqin, Huang Yongchun. Provision-virtualized water trading network along "the Belt and Road". Resources & Industries, 2020, 22(3): 31-42. ]
[12]	李旻晶, 陈洋洋. 中美粮食贸易结构分析与建议[J]. 农业经济, 2020(6): 124-126.
	[ Li Minjing, Chen Yangyang. Analysis and recommendations on the structure of grain trade between China and the United States. Agricultural Economy, 2020(6): 124-126. ]
[13]	于晓华, 刘畅, 张国政. 国际粮食贸易背景下的市场整合与虚拟资源流动: 以中国巴基斯坦大米市场为例[J]. 自然资源学报, 2021, 36(6): 1505-1520.
	[ Yu Xiaohua, Liu Chang, Zhang Guozheng. Market integration and virtual flow of resources under the background of international food trade: Evidence from China-Pakistan rice market. Journal of Natural Resources, 2021, 36(6): 1505-1520. ] doi: 10.31497/zrzyxb.20210612
[14]	韩冬, 李光泗. 中国与“一带一路”沿线国家粮食贸易格局演变与影响机制: 基于社会网络学视角[J]. 农业经济问题, 2020, 41(8): 24-40.
	[ Han Dong, Li Guangsi. Research on the evolution and the influence mechanism of grain trade pattern between China and countries along "the Belt and Road": From the perspective of social network. Issues in Agricultural Economy, 2020, 41(8): 24-40. ]
[15]	王璐, 刘曙光, 段佩利, 等. 丝绸之路经济带沿线国家农产品贸易网络结构特征[J]. 经济地理, 2019, 39(9): 198-206.
	[ Wang Lu, Liu Shuguang, Duan Peili, et al. Network structure of agricultural product trade in countries of the Silk Road economic belt. Economic Geography, 2019, 39(9): 198-206. ]
[16]	马述忠, 任婉婉, 吴国杰. 一国农产品贸易网络特征及其对全球价值链分工的影响: 基于社会网络分析视角[J]. 管理世界, 2016(3): 60-72.
	[ Ma Shuzhong, Ren Wanwan, Wu Guojie. The characteristics of agricultural trade network and its effects on the global value chain division: A study based on social network analysis. Management World, 2016(3): 60-72. ]
[17]	聂常乐, 姜海宁, 段健. 21世纪以来全球粮食贸易网络空间格局演化[J]. 经济地理, 2021, 41(7): 119-127.
	[ Nie Changle, Jiang Haining, Duan Jian. Spatial pattern evolution of global grain trade network since the 21st Century. Economic Geography, 2021, 41(7): 119-127. ]
[18]	和聪贤, 李秀香. 世界粮食贸易网络结构特征与中国地位变迁研究[J]. 世界农业, 2021(5): 64-78.
	[ He Cong-xian, Li Xiuxiang. Study on the structural characteristics of world grain trade network and the transition of China's status. World Agriculture, 2021(5): 64-78. ]
[19]	别诗杰. 中国与“一带一路”国家农产品贸易网络结构及其影响因素分析[D]. 武汉: 华中农业大学, 2019.
	[ Bie Shijie. Analysis on trade network structure of agricultural products between China and countries along "the Belt and Road" and its influencing factors. Wuhan, China: Huazhong Agricultural University, 2019. ]
[20]	谢涛. 中国与“一带一路”沿线国家农产品出口贸易影响因素研究[J]. 世界农业, 2017(3): 132-138.
	[ Xie Tao. Study on the factors influencing the export trade of agricultural products of China and the countries along "the Belt and Road". World Agriculture, 2017(3): 132-138. ]
[21]	Vollrath T L, Hallahan C B, Gehlhar M J. Consumer demand and cost factors shape the global trade network in commodity and manufactured foods[J]. Canadian Journal of Agricultural Economics, 2006, 54(4): 497-511. doi: 10.1111/j.1744-7976.2006.00063.x
[22]	王介勇, 戴纯, 周墨竹, 等. 全球粮食贸易网络格局及其影响因素[J]. 自然资源学报, 2021, 36(6): 1545-1556.
	[ Wang Jieyong, Dai Chun, Zhou Mozhu, et al. Research on global grain trade network pattern and its influencing factors. Journal of Natural Resources, 2021, 36(6): 1545-1556. ] doi: 10.31497/zrzyxb.20210615
[23]	韩冬, 李光泗, 钟钰. “一带一路”沿线国家粮食贸易网络核心结构演变及中国的粮食政策响应[J]. 农村经济, 2021(5): 11-21.
	[ Han Dong, Li Guangsi, Zhong Yu. The evolution of the core network structure of the grain trade in the countries along the "One Belt and One Road" and China's response on grain policy. Rural Economy, 2021(5): 11-21. ]
[24]	魏素豪. 中国与“一带一路”国家农产品贸易: 网络结构、关联特征与策略选择[J]. 农业经济问题, 2018, 39(11): 101-113.
	[ Wei Suhao. Agricultural products trade between China and "the Belt and Road" countries: Network structure, correlation characteristics and strategy selection. Issues in Agricultural Economy, 2018, 39(11): 101-113. ]
[25]	Haq Z U, Meilke K, Cranfield J. Selection bias in a gravity model of agrifood trade[J]. European Review of Agricultural Economics, 2013, 40(2): 331-360. doi: 10.1093/erae/jbs028
[26]	Anderson M J, Legendre P. An empirical comparison of permutation methods for tests of partial regression coefficients in a linear model[J]. Journal of Statistical Computation and Simulation, 1999, 62(3): 271-303. doi: 10.1080/00949659908811936
[27]	Dekker D, Krackhardt D, Snijders T A B. Sensitivity of MRQAP tests to collinearity and autocorrelation conditions[J]. Psychometrika, 2007, 72(4): 563-581. doi: 10.1007/s11336-007-9016-1 pmid: 20084106
[28]	刘林青, 闫小斐. 国际粮食贸易网络多核集聚格局的形成机制研究[J]. 华中农业大学学报(社会科学版), 2021(4): 47-59, 179-180.
	[ Liu linqing, Yan Xiaofei. Research on the formation mechanism of clustered structure with multi-hubs of the international cereal trade network. Journal of Huazhong Agricultural University (Social Sciences Edition), 2021(4): 47-59, 179-180. ]
[29]	苗文. 中国人口流动网络现状与形成机制研究: 基于指数随机图模型的分析[D]. 临汾: 山西师范大学, 2019.
	[ Miao Wen. A research on the current situation and formation mechanism of China's population mobility network: Based on ERGMs. Linfen, China: Shanxi Normal University, 2019. ]
[30]	陈艺文, 李二玲. “一带一路”国家粮食贸易网络空间格局及其演化机制[J]. 地理科学进展, 2019, 38(10): 1643-1654. doi: 10.18306/dlkxjz.2019.10.018
	[ Chen Yiwen, Li Erling. Spatial pattern and evolution of cereal trade networks among the Belt and Road countries. Progress in Geography, 2019, 38(10): 1643-1654. ] doi: 10.18306/dlkxjz.2019.10.018
[31]	曹粲然. “一带一路”农产品贸易网络的特征、演化与影响因素研究[D]. 北京: 北京工商大学, 2019.
	[ Cao Canran. Research on the characteristics,evolution and influencing factors of agricultural products trade network among the "One Belt, One Road" countries. Beijing, China: Beijing Technology and Business University, 2019. ]
[32]	陈艺文. “一带一路”国家粮食贸易网络演化特征及影响因素研究[D]. 开封: 河南大学, 2020.
	[ Chen Yiwen. Study on the evolution characteristics and influencing factors of one belt and one road national cereal trade network. Kaifeng, China: Henan University, 2020. ]
[33]	虞洋, 宋周莺, 史坤博. 基于百度指数的中国省域间信息联系网络格局及其动力机制[J]. 经济地理, 2019, 39(9): 147-155.
	[ Yu Yang, Song Zhouying, Shi Kunbo. Network pattern of inter-provincial information connection and its dynamic mechanism in China: Based on Baidu index. Economic Geography, 2019, 39(9): 147-155. ]
[34]	Leibenstein H. Shaping the world economy: Suggestions for an international economic policy[J]. The Economic Journal, 1966, 76: 92-95. doi: 10.2307/2229041
[35]	许和连, 孙天阳, 成丽红. “一带一路”高端制造业贸易格局及影响因素研究: 基于复杂网络的指数随机图分析[J]. 财贸经济, 2015(12): 74-88.
	[ Xu Helian, Sun Tianyang, Cheng Lihong. Trade patterns and influence factors of high-end manufacturing on "one belt and one road": A study based on the exponential random graph models. Finance & Trade Economics, 2015(12): 74-88. ]
[36]	王存同. 零膨胀模型在社会科学实证研究中的应用: 以中国人工流产影响因素的分析为例[J]. 社会学研究, 2010, 25(5): 130-148, 245.
	[ Wang Cuntong. Zero-inflated Poisson/Negative Binomial Modeling for Sociologists: Based on the analysis of induced abortion in China. Sociological Studies, 2010, 25(5): 130-148, 245. ]
[37]	Mullahy J. Specification and testing of some modified count data models[J]. Journal of Econometrics, 1986, 33(3): 341-365. doi: 10.1016/0304-4076(86)90002-3
[38]	Lambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing[J]. Technometrics, 1992, 34(1): 1-14. doi: 10.2307/1269547
[39]	Greene W H. Accounting for excess zeros and sample selection in poisson and negative binomial regression models[R]. New York University Working Paper, No. EC-94-10. New York, USA: New York University, 1994.
[40]	雷渊才, 张雄清. 长白落叶松林分进界模型的研究[J]. 林业科学研究, 2013, 26(5): 554-561.
	[ Lei Yuancai, Zhang Xiongqing. Tree recruitment model of larix olgensis. Forest Research, 2013, 26(5): 554-561. ]
[41]	徐昕, 袁卫, 孟生旺. 零膨胀负二项回归模型的推广与费率厘定[J]. 系统工程理论与实践, 2012, 32(1): 127-133.
	[ Xu Xin, Yuan Wei, Meng Shengwang. Generalization of zero-inflated negative binomial regression model and ratemaking. Systems Engineering: Theory & Practice, 2012, 32(1): 127-133. ]
[42]	曹瓅, 罗剑朝. 农户对农地经营权抵押贷款响应及其影响因素: 基于零膨胀负二项模型的微观实证分析[J]. 中国农村经济, 2015(12): 31-48.
	[ Cao Li, Luo Jianchao. Farmers' response to mortgage loan of agricultural land management right and its influencing factors: A micro empirical analysis based on zero inflation negative binomial model. Chinese Rural Economy, 2015(12): 31-48. ]
[43]	郭福涛, 胡海清, 金森, 等. 基于负二项和零膨胀负二项回归模型的大兴安岭地区雷击火与气象因素的关系[J]. 植物生态学报, 2010, 34(5): 571-577. doi: 10.3773/j.issn.1005-264x.2010.05.011
	[ Guo Futao, Hu Haiqing, Jin Sen, et al. Relationship between forest lighting fire occurrence and weather factors in Daxing'an Mountains based on negative binomial model and zero-inflated negative binomial models. Chinese Journal of Plant Ecology, 2010, 34(5): 571-577. ] doi: 10.3773/j.issn.1005-264x.2010.05.011
[44]	戴卓. 国际贸易网络结构的决定因素及特征研究: 以中国东盟自由贸易区为例[J]. 国际贸易问题, 2012(12): 72-83.
	[ Dai Zhuo. Determinants and characteristics of international trade network structure: An example about China-ASEAN free trade area. Journal of International Trade, 2012(12): 72-83. ]
[45]	曾寅初, 吴金炎, 夏薇. 农产品国际贸易争端及其影响因素的实证研究[J]. 世界经济研究, 2007(3): 50-54, 88.
	[ Ceng Yanchu, WuYan, XiaWei. What makes for international agricultural trade disputes? An empirical study. World Economy Studies, 2007(3): 50-54, 88. ]
[46]	洪俊杰, 商辉. 国际贸易网络枢纽地位的决定机制研究[J]. 国际贸易问题, 2019(10): 1-16.
	[ Hong Junjie, Shang Hui. The determination mechanism of centrality in international trade networks. Journal of International Trade, 2019(10): 1-16. ]
[47]	Henderson V, Squires T, Storeygard A, et al. The global distribution of economic activity: Nature, history, and the role of trade[J]. The Quarterly Journal of Economics, 2018, 133(1): 357-406. doi: 10.1093/qje/qjx030
[48]	Dal Bianco A, Boatto V L, Caracciolo F, et al. Tariffs and non-tariff frictions in the world wine trade[J]. European Review of Agricultural Economics, 2016, 43(1): 31-57. doi: 10.1093/erae/jbv008
[49]	宋海英. 中国-拉美农产品贸易的影响因素: 基于引力模型的实证分析[J]. 农业经济问题, 2013, 34(3): 74-78, 112.
	[ Song Haiying. An empirical analysis of influence factors of China's agricultural trade with Latin America: Based on gravity model. Issues in Agricultural Economy, 2013, 34(3): 74-78, 112. ]
[50]	Gygli S, Haelg F, Potrafke N, et al. The KOF globalisation index: Revisited[J]. The Review of International Organizations, 2019, 14(3): 543-574. doi: 10.1007/s11558-019-09344-2
[51]	苏剑, 葛加国. “一带一路”倡议背景下双边贸易的语言效应: 抑制还是促进[J]. 学术月刊, 2018, 50(9): 74-80, 55.
	[ Su Jian, Ge Jiaguo. The effect of the language for one Belt and Road initiative strategy: Barrier or promotion. Academic Monthly, 2018, 50(9): 74-80, 55. ]
[52]	Isphording I E, Otten S. The costs of Babylon-linguistic distance in applied economics[J]. Review of International Economics, 2013, 21(2): 354-369. doi: 10.1111/roie.12041
[53]	Lohmann J. Do language barriers affect trade?[J]. Economics Letters, 2011, 110(2): 159-162. doi: 10.1016/j.econlet.2010.10.023
[54]	齐欣, 孙凤洋. WTO深度发展与国际贸易: 基于政府采购协定视角的经验分析[J]. 经济社会体制比较, 2020(3): 141-152.
	[ Qi Xin, Sun Fengyang. The depth development of WTO and international trade: A study based on the perspective of government procurement agreement. Comparative Economic & Social Systems, 2020(3): 141-152. ]
[55]	Subramanian A, Wei S J. The WTO promotes trade, strongly but unevenly[J]. Journal of International Economics, 2007, 72(1): 151-175. doi: 10.1016/j.jinteco.2006.07.007
[56]	Dutt P, Mihov I, van Zandt T. The effect of WTO on the extensive and the intensive margins of trade[J]. Journal of International Economics, 2013, 91(2): 204-219. doi: 10.1016/j.jinteco.2013.08.001
[57]	Kraay A, Kaufmann D, Mastruzzi M. The worldwide governance indicators: Methodology and analytical issues[R]. World Bank policy research working paper No. 5430. World Bank, 2010. doi: 10.1596/1813-9450-5430. doi: 10.1596/1813-9450-5430

指标	1988年	1998年	2008年	2018年
联系数量	1032	1728	2476	3155
网络密度	0.0183	0.0306	0.0439	0.0559
出度中心势/%	1.991	1.684	1.946	1.997
入度中心势/%	0.630	0.557	0.475	0.446
中间中心势/%	4.51	8.54	9.47	8.57
特征向量中心势/%	82.08	102.28	103.69	101.32

因素层	要素层	指标名称	变量简称	预期作用
因素层	要素层	指标名称	变量简称	出口地	进口地
经济因素	经济水平	人均GDP(现价美元)^a	ln O_perGDP/ln D_perGDP	+	+
	产业结构	农业增加值占GDP比重(%)^b	ln O_Ind/ln D_Ind	+	-
	市场规模	人口总数(人)^a	ln O_POP/ln D_POP		+
	开放程度	全球化指数KOF ^c	ln O_KOF/ln D_KOF	+	+
	价格差异	价格水平指数^a	ln O_PLI/ln D_PLI	+	-
自然因素	土地资源禀赋	人均耕地面积(hm²/人)^b	ln O_Land/ln D_Land	+	-
	水资源禀赋	人均可再生内陆淡水资源(m³)^b	ln O_Water/ln D_Water	+	-
社会因素	社会治理水平	全球治理指标WGI ^d	ln O_WGI/ln D_WGI	+	-
文化因素	文化相近性	语言邻近度^e	ln Lan	+
制度因素	国际组织	10大贸易自由协定区域	ln FTA	+
模型基础因素	距离	重心间距离(100 km)	ln Dis	-
	存量	14种粮食作物生产总量(t)^b	ln O_Pro/ln D_Pro	+	+

变量	模型1：OLS		模型2：双向固定效应		模型3：零膨胀负二项
变量	回归系数	z值	回归系数	z值	回归系数	z值
ln O_perGDP	0.257^***	9.04	0.352^***	7.95	-0.076^***	-7.67
ln D_perGDP	0.181^***	6.80	0.153^***	3.58	0.018^**	2.34
ln O_Ind	-0.438	-1.12	3.143^***	5.79	-1.064^***	-7.48
ln D_Ind	-0.412	-1.27	0.436	0.96	-0.260^***	-2.62
ln O_POP	0.588^***	41.22	-0.451^***	-4.69	0.101^***	31.15
ln D_POP	0.354^***	25.97	0.269^***	2.99	0.099^***	31.49
ln O_KOF	0.883^***	6.31	0.842^***	4.20	0.809^***	16.83
ln D_KOF	0.347^***	2.77	-0.114	-0.64	-0.010	-0.25
ln O_PLI	0.210^***	2.77	0.236^***	2.67	0.223^***	7.61
ln D_PLI	-0.064	-0.77	-0.220^**	-2.19	0.069^**	2.34
ln O_Land	4.712^***	42.24	3.178^***	9.57	1.070^***	55.26
ln D_Land	-0.692^***	-5.50	0.157	0.45	-0.341^***	-13.50
ln O_Water	0.101^***	8.05	0.139^*	1.81	0.027^***	9.47
ln D_Water	0.005	0.47	0.201^***	3.54	-0.009^***	-3.97
ln O_WGI	0.669^***	7.25	0.476^***	3.39	0.033	1.18
ln D_WGI	0.413^***	4.96	0.418^***	3.48	-0.146^***	-6.04
ln Lan	2.608^***	8.99	0	—	0.219^***	4.97
ln FTA	0.739^***	15.83	0.685^***	12.07	0.046^***	3.82
ln Dis	-0.986^***	-39.44	0	—	-0.191^***	-41.13
ln O_Pro	-0.004	-1.12	0.009^**	2.29	0.003^*	1.72
ln D_Pro	0.011^***	2.83	0.015^***	3.81	-0.001	-0.88
常数	-22.400^***	-37.44	-8.151^***	-2.82	-4.132^***	-20.36
N	53797		53797		53797
R²	0.0501		0.0600
AIC			213182.60		79755.85
BIC			213396.00		79978.17

Change of the global grain trade network and its driving factors

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