| 摘要: |
| ［目的］通过探究耕地产能空间分异规律与影响因子，识别地区粮食生产关键制约因素，提升高标准基本农田建设的针对性和建设效果。［方法］采用空间自相关模型刻画了村级尺度耕地产能空间分异规律，对比分析了线性回归模型和地理加权回归模型在耕地产能影响因子识别的效果，并依此划分产能提升类型区。［结果］（1）海伦市耕地产能呈现中西部高北部偏低的态势，且具有较强的空间自相关特征。（2）海伦市耕地产能与黑土层厚度、出现障碍层深度、耕作距离、田块状况和质地等变量显著相关，且均呈正相关，但在空间上表现出不同程度的异质性。（3）基于地理加权回归模型的耕地产能影响因子，提出了产能保持区、耕作条件改良区、质地改良区、障碍层改良区和保护性耕作区5类耕地产能提升类型区。［结论］文章所采用的基于村级尺度耕地产能及其空间分异因素模型相关研究，能够较好揭示村级耕地产能空间分布特点、空间自相关性特征及影响因子分布特征，基于此所划分的耕地产能提升类型区较已有研究对耕地分类管理具有更精准的支撑作用。 |
| 关键词: 耕地产能空间分异空间自相关线性回归地理加权回归产能提升类型区 |
| DOI： |
| 分类号:F3034 |
| 基金项目:国家自然科学基金青年基金（40901287） |
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| SPATIAL VARIATION AND IMPACT FACTORS IN CULTIVATED LAND PRODUCTIVITY AT VILLAGE LEVEL |
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Zhou Jingru1, Zhao Huafu1,2※, Song Wen3, Hou Xinyue1
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1.College of Land Science and Technology, China University of Geosciences（Beijing）,Beijing 100083, China; 2.Key Laboratory of Land Regulation Ministry of Land and Resources. Beijing, 100035，China; 3.College of Resources and Environment, Shandong Agricultural University, Tai′an，Shandong 271018,China
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| Abstract: |
| By exploring the spatial differentiation and impact factors of cultivated land productivity, the key constraints of regional grain production are identified, and the pertinence and construction effects of high standard basic farmland construction are improved. The spatial autocorrelation model was used to describe the spatial differentiation of cultivated land productivity at village level. At the same time, this paper analyzed the effects of linear regression model and geographically weighted regression model on the identification of impact factors of cultivated land productivity, and then the capacity improvement type areas were divided accordingly. As a result, there were three conclusions as follows. Firstly, the cultivated land productivity in Hailun was high in the central and western regions, and low in the north, showing strong spatial autocorrelation characteristics. Secondly, the cultivated land productivity in Hailun was significantly correlated with the thickness of the black soil layer, the depth of the barrier layer, the distance of cultivation, the condition of the field and the texture, and they were all positively correlated, but showed different degrees of heterogeneity in space. Thirdly, based on the geographically weighted regression results, five types of cultivated land capacity improvement areas were proposed, namely, capacity retention area, cultivation condition improvement area, texture improvement area, barrier improvement area and conservation tillage area. In summary, the research based on the village scale arable land productivity and spatial differentiation factors model can better reveal the spatial distribution characteristics, spatial autocorrelation characteristics and distribution factors of village level cultivated land productivity. The cultivated land capacity improvement type area has more precise support for the classification management of cultivated land than the existing research. |
| Key words: cultivated land productivity spatial variation spatial autocorrelation linear regression GWR model capacity improvement type |
