• 资源利用与管理 •

### 县域粮食单产及其生产投入因素的空间异质性——以河南省为例

1. 1. 聊城大学 环境与规划学院,山东 聊城 252059;
2. 河南大学 环境与规划学院,河南 开封 475004
• 收稿日期:2010-06-07 修回日期:2011-01-23 出版日期:2011-03-20 发布日期:2011-03-20
• 通讯作者: 秦耀辰(1959- ),男,教授,博士生导师,主要从事区域可持续发展理论、模型与信息系统研究。E-mail: qinyc@henu.edu.cn E-mail:qinyc@henu.edu.cn
• 作者简介:张金萍(1977- ),女,山东招远人,博士,讲师,主要研究空间分析与区域系统建模。E-mail: maryzhjp@126.com
• 基金资助:

国家教育部人文社会科学重点研究基地重大项目(10JJDZONGHE015);省部共建河南大学研究项目(SBGJ090111);聊城大学校级项目(X09016)。

### Spatial Heterogeneity of Grain Yield per Hectare and Factors of Production Inputs in Counties: A Case Study of Henan Province

ZHANG Jin-ping1,2, QIN Yao-chen2

1. 1. College of Environment and Planning, Liaocheng University, Liaocheng 252059, China;
2. College of Environment and Planning, Henan University, Kaifeng 475004, China
• Received:2010-06-07 Revised:2011-01-23 Online:2011-03-20 Published:2011-03-20

Abstract: In the current situation that is difficult to increase the total arable land area, the relationship between grain yield and the factors of production inputs is important for national food security. Global Moran’s I index of grain yield per hectare in Henan was 0.6921, which indicates a strong spatial autocorrelation. Seen from Moran scatter plot, grain yield per hectare in Henan was the high-high cluster pattern. Moreover, there was significant spatial autocorrelation on the residual of ordinary least squares estimation of grain yield per hectare and the four factors of production inputs did not meet the modeling conditions of the classic linear regression analysis. Geographically weighted regression model can overcome the defects of hypothesis that the coefficients of the independent variables affecting grain yield per hectare are homogeneous in the global model. In addition, it can carry out local parameter estimation. The practice of 108 counties in Henan Province shows that, GWR model reduces spatial autocorrelation of the residual effectively for considering geo-spatial effects, and all test indicators are better than the OLS estimates. Especially,the AICc value of the GWR model decreased by 18.7 compared with the OLS model, and the global spatial autocorrelation of the residual greatly reduced, which are the strong evidence of a more superior performance of GWR over OLS. More importantly, the global models can only obtain the contribution rate of factors in the whole region, however, GWR model gives a more profound and delicate information. Four factors of production inputs have different rules of spatial variation. The impact of irrigation in the OLS and GWR models are both positive. The coefficient estimations of the other three independent variables, mechanical power per hectare, the fold pure amount of chemical fertilizer per hectare and electricity consumption per hectare, are negative in the OLS model. While positive and negative coefficient estimations co-exist in the GWR model, indicating there is spatial heterogeneity. In high-yield area located in north of the Yellow River, grain yield per hectare is mainly affected positively by mechanical power, irrigation and electricity consumption with greater fertilizer inputs leading to lower yield. High-yield areas along the Huaihe River and in Xinyang are mainly affected positively by the amount of chemical fertilizer and electricity consumption. To increase grain yield per hectare, mechanical power inputs should be strengthened and the efficiency of electricity be improved in the low grain-producing areas in western mountainous and hilly counties. In the most western mountainous counties, the amount of chemical fertilizer should be appropriately increasedly in addition.

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