Pedotransfer functions to estimate bulk density from soil properties and environmental covariates: Rio Doce basin

Abstract

Soil bulk density (ρ b ) data are needed for a wide range of environmental studies.However, ρ b is rarely reported in soil surveys. An alternative to obtain ρ b for data-scarce regions, such as the Rio Doce basin in southeastern Brazil, is indirect estimation from less costly covariates using pedotransfer functions (PTF). This study primarily aims to develop region-specific PTFs for ρ b using multiple linear regressions (MLR) and random forests (RF). Secondly, it assessed the accuracy of PTFs for data grouped into soil horizons and soil classes. For that purpose, we compared the performance of PTFs compiled from the literature with those developed here. Two groups of data were evaluated as covariates: 1) readily available soil properties and 2) maps derived from a digital elevation model and MODIS satellite imagery, jointly with lithological and pedological maps. The MLR model was applied step-wise to select significant predictors and its accuracy assessed by means of cross-validation. The PTFs developed using all data estimated ρ b from soil properties by MLR and RF, with R 2 of 0.41 and 0.51, respectively. Alternatively, using environmental covariates, RF predicted ρ b with R 2 of 0.41. Grouping criteria did not lead to a significant increase in the estimates of ρ b . The accuracy of the 'regional' PTFs developed for this study was greater than that found with the 'compiled' PTFs. The best PTF will be firstly used to assess soil carbon stocks and changes in the Rio Doce basin.

Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

“…Souza et al (2016) compared model adjustments for predicting bulk density using SMLR and random forest, also obtaining better results with random forest, in consonance with this work.…”

“…In contrast, these results demonstrate that adjusting equations according to the depth of sampling tends to provide better models using SMLR. Souza et al (2016) used SMLR for bulk density prediction, comparing models created only for A horizon, only for B horizon and a general one, encompassing the two horizons, and also obtained better adjustments for the equations for horizons separately in relation to the general model. Table 4 shows the equations with R² values greater than or equal to 0.80 generated for 0 to 10 cm, 10 to 20 cm, 20 to 40 cm.…”

“…However, despite of classifying the variables according to their importance to the model (Archer;Kimes, 2008), this method does not generate a final equation of the model, as opposed to SMLR. Therefore, it is sometimes referred to as a black-box method (Grimm et al, 2008), although some works have pointed out that this method is robust and provides better results than other methods for both spatial and non-spatial predictions (Hengl et al, 2015;Lies;Glaser;Huwe, 2012;Souza et al, 2016).…”

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

“…PTF estimates of bulk density are always based on SOC content according to the model of Adams () where bulk density of a soil can be modeled as mixture of organic matter and mineral component. The mineral components expressed as clay and/or silt content are identified as statistically significant explanatory variables (Bernoux et al, ; Benites et al, ; Botula et al, ; De Souza et al, ; Manrique & Jones, ). Also, the coarse fraction (>2 mm) and soil depth has predictive potential (Nussbaum et al, ) as well as the sum of basic cations (Benites et al, ) and other environmental covariates (De Souza et al, ).…”

“…This technique has been widely used in recent years (Akpa et al, 2016;Koestel & Jorda, 2014;Sequeira et al, 2014) given its reported prediction performance. De Souza et al (2016) analyzed the performance of the random forest technique in predicting bulk density based on soil properties and environmental covariates. Koestel and Jorda (2014) used this method to predict the strength of preferential transport.…”