Spatial aggregation features of 2015 county-level grain yields of Huang-Huai-Hai Region have been analyzed systematically based on cumulative distribution function and spatial autocorrelation analysis method, and impact of 18 factors on grain yields of different categories of counties in Huang-Huai-Hai Region and their interaction have been analyzed by use of geographical detector. The results indicate that low county-level grain yields in Huang-Huai-Hai Region tend to aggregate and high county-level grain yields tend to scatter, showing significant homogeneous aggregation in space. The areas of significant high yields are mainly distributed in southeast Henan Province, north Anhui Province and north Jiangsu Province and areas of significantly low yields are mainly in Beijing-Tianjin-Hebei Region and coastal counties of Shandong Province. In consideration of spatial constraints and distribution difference of grain yields, Huang-Huai-Hai Region can be classified into 4 areas: high grain yield area, mid-high grain yield area, low-middle grain yield area and low grain yield area. The impacts of 18 factors on county-level grain yields of Huang-Huai-Hai Region vary and mainly manifest dual-factor enhancement type and nonlinear enhancement type. The leading factors of high yield area are added value of primary industry, consumption of fertilizers (total mass percent of nutrients) and total agricultural mechanical power, belonging to social economy and factor-input acting type. The leading factors of mid-high yield area are cultivated land area, regional registered population, added value of primary industry, gross agricultural mechanical power, showing as the combined acting type. The leading factors of low-middle yield area are cultivated land area and consumption of fertilizers (total mass percent of nutrients), showing as geographical environment and factor-input acting type. The leading factors of low yield area are vegetation index, cultivated land area, added value of primary industry, consumption of fertilizers (total mass percentage of nutrients) and total agricultural mechanical power, showing as a combined acting type. The targeted grain production and income increase strategy shall be formulated in the future based on actual conditions of different areas.

基于累积分布函数和空间自相关分析方法, 系统地分析2015年黄淮海地区县域粮食产量的空间集聚特征, 并借助地理探测器分析18个因子对黄淮海地区及不同类型县域粮食产量的影响及其交互作用, 提炼出主导因素, 得到如下结果。黄淮海地区县域粮食产量呈现“低值集聚、高值离散”的特征, 并在空间上呈现显著的同质集聚性。其中, 显著高值集聚区主要分布在豫东南、皖北和苏北地区, 显著低值集聚区主要分布在京津冀地区和山东临海县域。综合考虑空间约束和粮食产量分布差异, 将黄淮海地区分为粮食高产区、中高产区、中低产区和低产区4个类型区。18个因子对黄淮海地区县域粮食产量的影响不一, 主要表现为双因子增强型和非线性增强型。其中, 高产区的主导因素为第一产业增加值、化肥施用量(折纯)和农业机械总动力, 属于社会经济及要素投入作用型; 中高产区的主导因素为耕地面积、区域人口、第一产业增加值和农业机械总动力, 表现为综合作用型; 中低产区的主导因素为耕地面积和化肥施用量(折纯), 表现为地理环境及要素投入作用型; 低产区的主导因素为植被指数、耕地面积、第一产业增加值、化肥施用量(折纯)和农业机械总动力, 表现为综合作用型。针对不同区域的研究结果, 提出不同的粮食增产增收策略建议。