Abstract Soil chemical variables are among the main factors that influence forest production; however, there is no consensus on which soil variables are most correlated with the individual volume spatial variability. Moreover, no studies that used the ordinary cokriging geostatistical technique to model the variability of the volume with soil chemical variables correlated as secondary variables in agroforestry systems were found. For these reasons, the objective of this study was to test whether the precision of the spatial modelling of the Brazilian-mahogany commercial wood volume (vc) by ordinary cokriging could be improved by adding soil chemical variables as secondary variables compared to ordinary kriging. Therefore, soil samples were collected at the centre of 36 georeferenced circular plots with approximate areas of 500 m2 to determine the soil chemical variables. In these plots, 108 standing trees were scaled to compute vc using Smalian’s formula. Subsequently, artificial neural networks were trained using the diameter at 1.3 m above the soil level and the commercial height of these sample trees to predict the vc of the remaining trees within the plots. Lastly, vc was spatially modelled by ordinary kriging (scenario one), ordinary cokriging using latent variables created from principal component analysis (scenario two), and soil chemical variables significantly correlated with vc (scenario three) as secondary variables. In all the scenarios, the exponential geostatistical model stood out as the best, as it presented better spatial dependence index and precision measures in the leave-one-out cross-validation process. However, none of the models applied in scenario two were good alternatives because of the lack of strong spatial dependence. The scenario-three approach proved to be the best alternative for interpolating vc, followed by scenario one.

中文翻译：

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在协同克里金法中使用土壤化学变量是否可以改善亚马逊农林业系统中巴西桃花心木商业木材体积的空间建模？

摘要 土壤化学变量是影响森林生产的主要因素之一；然而，对于哪些土壤变量与个体体积空间变异性最相关，还没有达成共识。此外，没有发现使用普通的协同克里金地质统计技术来模拟与作为农林业系统中的次要变量相关的土壤化学变量的体积变异性的研究。由于这些原因，本研究的目的是测试与普通克里金法相比，通过添加土壤化学变量作为次要变量，是否可以提高普通协同克里金法对巴西桃花心木商业木材体积 (vc) 空间建模的精度。所以，在大约 500 平方米面积的 36 个地理参考圆形地块的中心收集土壤样品，以确定土壤化学变量。在这些图中，使用 Smalian 公式对 108 棵立树进行了缩放以计算 vc。随后，人工神经网络使用土壤水平以上 1.3 m 处的直径和这些样本树木的商业高度进行训练，以预测地块内剩余树木的 vc。最后，vc 由普通克里金法（场景一）、使用主成分分析创建的潜在变量的普通协同克里金法（场景二）和与 vc 显着相关的土壤化学变量（场景三）作为次要变量进行空间建模。在所有情况下，指数地质统计模型都是最好的，因为它在留一法交叉验证过程中提供了更好的空间依赖性指数和精确度量。然而，由于缺乏强烈的空间依赖性，场景二中应用的模型都不是很好的替代方案。场景三方法被证明是内插 vc 的最佳选择，其次是场景一。