Spatial dependency and correlation of properties of soil cultivated with oil palm, Elaeis guineensis, in agroforestry systems in the eastern Brazilian Amazon

ABSTRACT Geostatistics is a tool that can be used to produce maps with the distribution of nutrients essential for the development of plants. Therefore, the present study aimed to analyze the spatial variation in chemical attributes of soils under oil palm cultivation in agroforestry systems in the eastern Brazilian Amazon, and their spatial dependence pattern. Sixty spatially standardized and georeferenced soil samples were collected at each of three sampling sites (DU1, DU2, and DU3) at 0-20 cm depth. Evaluated soil chemical attributes were pH, Al3+, H+Al, K+, Ca2+, Mg2+, cation exchange capacity (CEC), P, and organic matter (OM). The spatial dependence of these variables was evaluated with a semivariogram analysis, adjusting three theoretical models (spherical, exponential, and Gaussian). Following analysis for spatial dependence structure, ordinary kriging was used to estimate the value of each attribute at non-sampled sites. Spatial correlation among the attributes was tested using cokriging of data spatial distribution. All variables showed spatial dependence, with the exception of pH, in one sampling site (DU3). Highest K+, Ca2+, Mg2+, and OM levels were found in the lower region of two sampling sites (DU1 and DU2). Highest levels of Al3+ and H+Al levels were observed in the lower region of sampling site DU3. Some variables were correlated, therefore cokriging proved to be efficient in estimating primary variables as a function of secondary variables. The evaluated attributes showed spatial dependence and correlation, indicating that geostatistics may contribute to the effective management of agroforestry systems with oil palm in the Amazon region.

摘要 地质统计学（Geostatistics）是一种可用于绘制植物生长必需营养元素空间分布图谱的工具。因此，本研究旨在分析巴西亚马逊东部地区农林复合系统中油棕种植下土壤化学属性的空间变异特征及其空间依赖性模式。本研究在3个采样点（DU1、DU2、DU3）的0~20 cm土层分别采集了60份经过空间标准化并带有地理坐标的土壤样品。本次评估的土壤化学属性包括pH、三价铝离子（Al³+）、氢离子-铝离子复合体（H+Al）、钾离子（K+）、钙离子（Ca²+）、镁离子（Mg²+）、阳离子交换量（cation exchange capacity, CEC）、磷（P）以及有机质（organic matter, OM）。通过半方差函数分析对上述变量的空间依赖性进行评估，并拟合了球状、指数型和高斯型三种理论模型。在明确空间依赖性结构后，采用普通克里金法对未采样点的各属性值进行估算。借助数据空间分布的协同克里金法，对各属性间的空间相关性进行了检验。除DU3采样点的pH外，所有变量均表现出空间依赖性。DU1与DU2两个采样点的地势较低区域的钾离子（K+）、钙离子（Ca²+）、镁离子（Mg²+）与有机质（OM）含量最高。DU3采样点的地势较低区域则检测到最高的三价铝离子（Al³+）与氢离子-铝离子复合体（H+Al）含量。部分变量间存在相关性，因此协同克里金法可有效基于次级变量对主变量进行估算。本次评估的土壤属性均表现出空间依赖性与相关性，表明地质统计学可为亚马逊地区油棕农林复合系统的高效管理提供技术支撑。