This work proposes an approach for establishing coffee management classes mainly supported by pedological information (soil survey) and land parcels, taking into account peculiarities of Brazilian coffee crops (land parcels already implemented with different crop ages, cultivars and density) and inspired by some management zone concepts. Two initial datasets were used based on soil survey and/or coffee crop management information. Eight sequences of tests were developed, involving: ranking of the most important variables for coffee yield modeling by random forest, reduction of data dimensionality through principal component analysis (PCA) or factorial analysis of mixed data (FAMD), generation of clusters with the hierarchical cluster on principal component (HCPC), applying hierarchical tree by using Ward's minimum variance method and improved by k-means classification. Cluster effectiveness was assessed by statistical difference in coffee yield. A total of 3 clusters were considered the most proper number of management classes, composed by the most accurate random forest model (crop age, crop density, silt fraction and soil organic matter content ranked as most important variables) and highest % of variables explanation by PCA. Although not well explored for such a purpose, HCPC applied in this study case was effective on generating homogeneous management classes, differing statistically from each other by means of coffee yield.

这项工作提出了一种建立主要由土壤学信息（土壤调查）和地块支持的咖啡管理类的方法，考虑到巴西咖啡作物的特性（已经实施了不同作物年龄、品种和密度的地块）并受到一些管理的启发区概念。基于土壤调查和/或咖啡作物管理信息使用了两个初始数据集。开发了八个测试序列，包括：通过随机森林对咖啡产量建模的最重要变量进行排序，通过主成分分析 (PCA) 或混合数据的因子分析 (FAMD) 降低数据维度，生成具有层次结构的聚类主成分 (HCPC) 上的集群，通过使用 Ward' 应用分层树k -均值分类。通过咖啡产量的统计差异评估集群有效性。共有 3 个集群被认为是最合适的管理类别数量，由最准确的随机森林模型（作物年龄、作物密度、淤泥比例和土壤有机质含量列为最重要的变量）和最高百分比的变量解释组成主成分分析。尽管没有为此目的进行深入探索，但在本研究案例中应用的 HCPC 可有效生成同质管理类别，通过咖啡产量在统计上彼此不同。