Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon

ABSTRACT: Ultisols are the most common soil order in the Brazilian Amazon. The Legal Amazon (LA) has an area of 5 × 106 km2, with few accessible areas, which restricts studies of soils at a detailed level. The pedological properties can be estimated more efficiently using statistical procedures and machine learning techniques, tools which are capable of recognizing patterns in a large soil database. We analyzed the main chemical and physical properties of the B horizons of the Ultisols of the Brazilian Amazon, as well as the spatial variability of the most explanatory properties of these horizons. Physical and chemical data of 1,068 profiles of the RadamBrasil Project were used. A principal component analysis (PCA) was applied and the most explanatory variables were separated by morphostructural units and climate zones. The technique of machine learning was used for spatialization of the explanatory variables based on predictive covariates. In general, the horizons are thick, clay, with a predominance of negative charges, and low levels of exchangeable cations. The variables retained in the PCA were: sum of bases (SB), Al3+, degree of flocculation (Floc), ∆pH, and organic carbon content (C). Areas of greater precipitation have low SB, with higher values in the basement complex (BC) and in areas under the Andean influence. Higher levels of Al3+ and degrees of flocculation were also associated with greater precipitation. However, the soils are predominantly electronegative, showing a kaolinitic mineralogy. The C contents in general were low, with an increase in more humid zones due to the process of mineralization and illuviation (podzolization), and in the BC due to the protection of C by the aggregation of clay. The use of multivariate analysis allowed a better understanding of the Ultisols’ main properties in different morphostructural and climatic domains, and its spatialization facilitated the interpretation of properties and their relationships with environmental characteristics in the Legal Amazon.摘要：老成土（Ultisols）是巴西亚马逊地区最常见的土壤纲。法律亚马逊地区（Legal Amazon，LA）面积达5×10⁶平方千米，可及区域稀少，这限制了高精度土壤学研究的开展。利用统计方法与机器学习技术可更高效地估算土壤发生学属性，这类工具能够从大规模土壤数据库中识别潜在模式。本研究分析了巴西亚马逊地区老成土B层的主要化学与物理属性，同时探究了这些土层最具解释性的属性的空间变异特征。研究采用了RadamBrasil项目中1068个土壤剖面的物理与化学数据。通过主成分分析（principal component analysis, PCA），依据地貌构造单元与气候区对最具解释性的变量进行了归类；并借助机器学习技术，基于预测协变量对解释性变量进行空间化处理。总体而言，研究区土层厚度较大，质地黏重，以负电荷为主，交换性阳离子含量较低。主成分分析保留的变量包括：盐基总量（sum of bases, SB）、Al³⁺含量、絮凝度（degree of flocculation, Floc）、∆pH值以及有机碳含量（organic carbon content, C）。降水量更高的区域盐基总量较低，而在基底杂岩（basement complex, BC）与受安第斯山脉影响的区域盐基总量则更高。更高的Al³⁺含量与絮凝度同样与更高降水量区域相关。不过，研究区土壤以电负性为主，矿物学类型以高岭石为主。有机碳含量总体偏低，在更湿润的区域，由于矿化作用与淀积作用（灰化作用），有机碳含量有所升高；而在基底杂岩区域，由于黏土团聚体对有机碳的保护作用，有机碳含量同样有所提升。多变量分析的应用使得我们能够更好地理解不同地貌构造与气候分区内老成土的核心属性，而空间化处理则助力我们解读法律亚马逊地区土壤属性及其与环境特征间的关联。