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

E. de Souza, Elpídio Inácio Fernandes Filho, Carlos Ernesto Gonçalves Reynaud Schaefer, Niels H. Batjes, Gerson Rodrigues dos Santos, Lucas Machado Pontes

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Scopus)

Abstract

Soil bulk density (ρb) data are needed for a wide range of environmental studies. However, ρb is rarely reported in soil surveys. An alternative to obtain ρb 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 ρb 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 ρb from soil properties by MLR and RF, with R2 of 0.41 and 0.51, respectively. Alternatively, using environmental covariates, RF predicted ρb with R2 of 0.41. Grouping criteria did not lead to a significant increase in the estimates of ρb. 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.

Original languageEnglish
Pages (from-to)525-534
JournalScientia agricola
Volume73
Issue number6
DOIs
Publication statusPublished - 2016

Keywords

  • Multiple linear regressions
  • Random forests
  • Soil predictors
  • Spatial prediction

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