中国粮食生产的时空格局及分布动态演进

doi:10.13304/j.nykjdb.2023.0142

摘要/Abstract

摘要：

从县域尺度全面考察粮食生产的差异及时空演化特征对提高我国粮食产量和保障粮食安全具有重要参考价值，基于2000—2020年2 545个县域尺度数据，运用标准差椭圆、三阶段嵌套泰尔指数和随机核密度估计等方法对我国粮食生产的时空格局及分布动态演进进行研究。结果发现，我国粮食生产整体水平以缓慢的速度上升，粮食生产增长率在2000—2020年呈现明显的波动趋势，在未来很长一段时期内我国粮食产量将保持稳定但粮食生产增长率会略有下滑。空间分布格局上，我国粮食生产的重心向西北方向迁移，呈现出东北-西南的方向性、东南-西北的发展分散性特征。空间差异上，我国粮食生产的总体差异越来越高，虽然我国粮食生产的县域差异贡献率从49.94%下降到29.51%，但县域尺度依然是总体差异最重要的来源。所以，空间差异主要以县域差异为主，其次分别是省间差异、市间差异和区域间差异。空间效应上，我国粮食生产水平呈现增长趋势并存在局部收敛的可能，其空间效应具有连续性，并且在不同时间、县域空间下具有明显的正相关空间效应。协同我国区域经济结构转型发展趋势与现实需求，“双循环”发展格局下细密且深入捕捉区域粮食生产韧性差异，利于各区域因地制宜制定合理有效的粮食生产政策，对于突破粮食生产困境及协调粮食生产安全具有重要意义。

关键词: 粮食生产, 时空差异, 动态演进, 随机Kernel密度估计

Abstract:

Comprehensive examination of the differences in food production and the spatio-temporal evolution characteristics from the county scale is an important reference value for improving China?’?s grain production and guaranteeing food security issues. The spatio-temporal pattern and distributional dynamic evolution of China?’?s grain production were investigated based on data from 2 545 county scales from 2000 to 2020， using standard deviation ellipse， three-stage nested Theil index and stochastic Kernel density estimation etc methods. The results showed that， the overall level of China’?s grain production rose at a slow pace， the growth rate of food production showed an obvious fluctuating trend from 2000 to 2020， and China’?s grain production would remain stable but the growth rate of fgrain production would decline slightly over a long period of time in the future. In terms of spatial distribution pattern， the center of grain production in China was moving to the northwest， showing the directionality of northeast-southwest， and the development dispersion of southeast-northwest. In terms of spatial difference， the overall difference in China’?s grain production was showing an increasingly high level， and although the contribution rate of county difference in China’?s grain production had decreased from 49.94% to 29.51%， the county scale was still the most important source of the overall difference. Therefore， spatial differences were mainly dominated by county differences， followed by inter-provincial differences， inter-municipal differences and inter-regional differences， respectively. In terms of spatial effect， China?’?s grain production level showed a growing trend and the possibility of local convergence， and its spatial effect had continuity and had obvious positive correlation spatial effect under different time and county space. In coordination with the development trend and practical needs of China’?s regional economic structure transformation， the “double cycle” development pattern captured the differences in regional grain production resilience in detail， which was conducive to the formulation of reasonable and effective grain production policies tailored to the local conditions of each region， and was of great significance for steadily breaking through the grain production dilemma and coordinating grain production security.

Key words: grain production, spatial and temporal differences, dynamic evolution, random Kernel density estimation

中图分类号:

F326.11

邓昊宇, 李成民, 曹健, 宋玉兰, 刘健. 中国粮食生产的时空格局及分布动态演进[J]. 中国农业科技导报, 2024, 26(9): 1-11.

Haoyu DENG, Chengmin LI, Jian CAO, Yulan SONG, Jian LIU. Spatial-temporal Pattern and Distribution Evolution of Grain Production in China[J]. Journal of Agricultural Science and Technology, 2024, 26(9): 1-11.

图/表 3

图1 2000—2020年我国粮食生产演变趋势注：数据来源于《中国县域统计年鉴》《中国区域经济统计年鉴》以及各县域统计年鉴，不含港澳台数据。

Fig. 1 Evolutionary trend of China’s grain production from 2000 to 2020Note： Data sources are from China County Statistical Yearbook， China Regional Economic Statistical Yearbook and each County Statistical Yearbook， excluding data from Hong Kong， Macao and Taiwan.

图2 三阶段嵌套Theil指数分解结果注：数据来源于《中国县域统计年鉴》《中国区域经济统计年鉴》以及各县域统计年鉴，不包含港澳台地区。

Fig. 2 Three stage nested decomposition results of Theil indexNote： Data sources are from China County Statistical YearbookChina Regional Economic Statistical Yearbook and each County Statistical Yearbook， excluding data from Hong Kong， Macao and Taiwan regions.

图3 我国粮食生产的Kernel密度及密度等高线

Fig. 3 Kernel density and density contour of China’s grain production

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