Does using soil chemical variables in cokriging improve the spatial modelling of the commercial wood volume of Brazilian mahogany in an Amazonian agroforestry system?

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 (v(c)) 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 m(2) to determine the soil chemical variables. In these plots, 108 standing trees were scaled to compute v(c) 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 y e of the remaining trees within the plots. Lastly, v(c) 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 v(c) (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 v(c), followed by scenario one.

Automated summary

This study aimed to test whether adding soil chemical variables as secondary variables to ordinary kriging could improve the precision of simulating the volume of Brazilian-mahogany commercial wood. The exponential geostatistical model was found to be the best choice in all scenarios, but the models in scenario two were not good alternatives due to lack of strong spatial dependence. The approach in scenario three proved to be the best alternative for interpolating v(c), followed by scenario one.