基于模糊聚类分析的田间精确管理分区研究

【目的】以海涂围垦区盐碱土为研究对象，将从SPOT遥感影像提取出的NDVI数据和盐碱地土壤生产力的主要限制因子盐分数据及部分养分数据作为变量进行精确农业管理分区研究。【方法】模糊c均值聚类方法被用来进行分类分区，并引入了模糊聚类指数和归一化分类熵两种分区效果评价指标，对分区结果进行比较和评价。【结果】本研究区，最佳的分区数目为3个。对处于每一子区内土样的化学特性和实测棉花产量数据进行方差分析，发现其均值在所定义的每个管理分区内都存在着统计意义上的显著差异性，其中子区3具有最高的肥力水平和作物产量，而子区1最低。【结论】利用所选取的变量，模糊c均值聚类算法可以较好地进行管理分区划分。分区结果不但可以指导采样，而且可用于实施变量投入和精确施肥推荐，为样区土壤管理提供科学的决策依据。

Definition of Management Zones Based on Fuzzy Clustering Analysis in Coastal Saline Land

Objective] In this paper, the variables of NDVI data, soil salinity and some nutrient data were selected as data sources to study coastal saline field management zones. Method ] Fuzzy c-means a clustering algorithm was used to delineate management zones. A fuzzy performance index and normalized classification entropy were used to select the optimal cluster numbers. Result ] The optimal number of management zones for the study area was 3. To assess whether the defined management zones can be used to characterize spatial variability of soil chemical properties and crop productivity, 139 geo-referenced soil and yield sampling points across each management zone were examined by using one-way ANOVA. There were significantly statistical differences between the chemical properties of soil samples and yield data in each defined management. Management zone 3 presented the highest nutrient level and potential crop productivity, whereas management zone 1 was the lowest. Conclusion ] The results revealed that fuzzy c-means clustering algorithm could be used to delineate management zones by using the given variables. The defined management zones not only direct soil-sampling design, but can be used for fertilizer recommendation to manage soil nutrient and salinity more efficiently in coastal saline field.