MANAGEMENT ZONES DESIGN FOR SOYBEAN CROP USING PRINCIPAL COMPONENTS AND GEOSTATISTICS

ABSTRACT In precision agriculture, determining management zones for soil and plant attributes is a complex process that requires knowledge of several variables, which complicates management and decisionmaking processes. This study evaluated the spatial variability of soybean yield and soil chemical properties using geostatistical and multivariate analyses to define management zones in an Oxisol. The soybean yield and soil chemical properties between 0 to 0.2 and 0.2 to 0.4 m soil depths were sampled at 70 points. Geostatistical and multivariate analyses were then performed on these data. The soil chemical properties showed higher variability at 0.2 to 0.4 m soil depth. The semivariogram parameters of the principal component analysis (PCA) data (PCA 1, PCA 2, and PCA 3) for both depths were more homogeneous than the original data. The maps of soil chemical properties showed high similarity to the soybean yield map. The PCA explained 65.34% (0 to 0.2 m) and 70.50% (0.2 to 0.4 m) of data variability, grouping the soybean yield, organic matter, pH, phosphorous, potassium, calcium, magnesium, and sodium. PCA spatialization allowed for the definition of management zones indicated by PCA 1, PCA 2, and PCA 3 for both depths. The result indicates that the area must be managed using different strategies of soil fertility management to increase soybean yield.摘要 在精准农业领域，划定土壤与植株性状的管理分区是一项复杂工作，需综合考量多项变量，这会加大田间管理与决策的难度。本研究采用地统计与多元分析方法，探究某氧化土（Oxisol）中大豆产量与土壤化学性质的空间变异特征，以划定合理的管理分区。研究人员在0~0.2 m与0.2~0.4 m两个土层深度布设70个采样点，采集大豆产量及土壤化学性质数据，随后对所获数据开展地统计与多元分析。结果显示，土壤化学性质在0.2~0.4 m土层中的变异性更高；两个土层的主成分分析（PCA）数据（主成分1、主成分2与主成分3）的半方差图参数，均较原始数据集更为均质。土壤化学性质的空间分布图与大豆产量分布图具有较高的相似性。主成分分析分别解释了0~0.2 m土层65.34%、0.2~0.4 m土层70.50%的数据变异性，并将大豆产量、有机质、pH值、磷、钾、钙、镁及钠等指标进行了聚类分组。借助主成分分析的空间可视化结果，可基于两个土层的主成分1、主成分2与主成分3划定管理分区。研究结果表明，该区域需采用差异化的土壤肥力管理策略，以提升大豆产量。