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首页> 外文期刊>Forest Ecology and Management >Optimal soil-sampling design for rubber tree management based on fuzzy clustering.
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Optimal soil-sampling design for rubber tree management based on fuzzy clustering.

机译：基于模糊聚类的橡胶树优化土壤采样设计。

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Farming management practices related to nutrient recommendation for rubber tree plantations have been a challenge for scientists, farm managers and local producers. Specific caves and building contour ledges to prevent nutrient losses through soil erosion often cause spatial variation of topsoil nutrients in such plantations of rubber trees (Hevea brasiliensis). The design of soil-sampling schemes to test chemical properties of the soil is critical for successful nutrient recommendation for rubber trees. Our objectives were to characterize the spatia variability of soil pH, macronutrient NPK and organic matter in rubber plantations and to evaluate the rationality of soil sampling schemes in rubber plantations for tree nutrient management. The study was conducted in an area of 84 m2 consistent of nine rubber trees and soil samples (0-0.2 m depth) were taken from 168 grid points with a dimension of 1 m x 0.5 m. Concentrations of total nitrogen, organic matter, available phosphorus, available potassium and pH levels were determined for each soil sample. Based on their spatial variability patterns, the analyzed variables were divided into several homogeneous zones through fuzzy cluster algorithm. The number of subzones was determined using fuzzy performance index and normalized classification entropy to optimize the classification algorithm. The classification results showed that there were three optimal sampling zones for the soil chemical properties. The analysis of variance indicated that chemical properties were significantly different between the delineated zones. The delineated management zones could be used as a reference for making soil-sampling scheme in the rubber plantation. The results of this study have the implication in optimization of soil sampling planning for soil testing for nutrient recommendation. Fuzzy cluster algorithms could classify soil chemical properties into three practical zones by reducing intrazone variability, which would provide with useful information for making effective soil-sampling schemes in rubber tree plantations.
机译：与橡胶树人工林推荐养分有关的耕作管理实践对科学家，农场主和当地生产者构成了挑战。在这种橡胶树（巴西橡胶树）人工林中，特定的洞穴和建筑物轮廓壁架可防止因土壤侵蚀而造成的养分流失，通常会导致表土养分的空间变化。测试土壤化学性质的土壤采样方案的设计对于成功推荐橡胶树养分至关重要。我们的目标是表征橡胶人工林中土壤pH，常量营养素NPK和有机质的空间变异性，并评估橡胶人工林中用于树木养分管理的土壤采样方案的合理性。该研究在9棵橡胶树的面积为84 m 2 的区域内进行，从168个网格点（尺寸为1 m x 0.5 m）上采集了土壤样品（深度为0-0.2 m）。测定每个土壤样品的总氮，有机质，有效磷，有效钾和pH值。根据变量的空间变异性，通过模糊聚类算法将分析变量分为几个同质区域。使用模糊性能指标和归一化分类熵确定分区数，以优化分类算法。分类结果表明，针对土壤化学性质，存在三个最佳采样区。方差分析表明，所划定区域之间的化学性质显着不同。划定的管理区可以作为橡胶园土壤取样方案的参考。这项研究的结果对于优化土壤养分推荐的土壤采样计划具有重要意义。模糊聚类算法可以通过减少区域内的变异性将土壤化学性质分为三个实用区域，这将为在橡胶树上制定有效的土壤采样方案提供有用的信息。

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