Soil depth prediction by digital soil mapping and its impact in pine forestry productivity in South Brazil

Based on the premise that the modeling and mining of soil and environmental data are capable of generating useful spatial information for use and planning in the forestry supply chain, we have established the following objectives: i) predict soil depth (SoD) in a topographically complex landscape via Digital Soil Mapping (DSM), ii) evaluate the potential of incorporating spatial data on SoD and topographic attributes in the prediction of the height of 30-year old Pinus taeda L., and iii) assess whether the global predictions of depth to bedrock (DTB) from Soil Grids is as effective as the local predictions for use in silviculture. The study was conducted in a 1.08-km(2) Pinus taeda L. forest, in first rotation, 30 years old, and in the mountain region of Santa Catarina, Brazil. The dendometric (tree height) and pedologic (SoD) data were measured at 102 points and used to train random forest (RF) models by leave-one-out cross-validation (LOOCV). Nine topographic covariates derived from a digital elevation model were used to spatially predict SoD. For spatial prediction of tree height, the models were trained using three set of covariates: 1) four topographic covariates (model 1), 2) SoD map predicted by RF plus four topographic covariates (model 2), and 3) DBT plus four topographic covariates (model 3). The RF model could adequately describe SoD and the general characteristics of the distribution of data measured in a landscape with complex topography using terrain attributes as covariates. The model obtained R-2 = 0.91 and RMSE = 0.17 m. The tree height was predicted with R-2 up to 0.93 and RMSE = 0.82 m. SoD and elevation were the most important covariates for it. The SoD covariate stood out compared to the others, improving the fit of model 2, while DBT was not considered important in model 3. Our results showed that SoD played a critical role to predict the tree height. However, local predictions of SoD are needed to obtain accurate predictions of tree height. These products, generated by DSM, showed to be useful for establishing methodologies to guide the long-term soil and forest management practices.

Automated summary

This study aimed to predict soil depth (SoD) and tree height in a complex landscape using digital soil mapping (DSM) and random forest (RF) models. Spatial data on SoD and topographic attributes were found to be crucial for accurately predicting tree height, highlighting the importance of localized predictions for effective silviculture practices.