SciELO - Scientific Electronic Library Online

vol.51 issue9Digital mapping of sand, clay, and soil carbon by Random Forest models under different spatial resolutionsPrediction of soil fertility of the agricultural hub of the state of Rio de Janeiro using soil x landscape modeling author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




Related links


Pesquisa Agropecuária Brasileira

Print version ISSN 0100-204XOn-line version ISSN 1678-3921


VASQUES, Gustavo Mattos et al. Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil. Pesq. agropec. bras. [online]. 2016, vol.51, n.9, pp.1371-1385. ISSN 0100-204X.

The objective of this work was to compare ordinary kriging with regression kriging to map soil properties at different depths in a tropical dry forest area in Brazil. The 11 soil properties evaluated were: organic carbon content and stock; bulk density; clay, sand, and silt contents; cation exchange capacity; pH; water retention at field capacity and at permanent wilting point; and available water. Samples were taken from 327 sites at 0.0-0.10, 0.10-0.20, and 0.20-0.40-m depths, in a tropical dry forest area of 102 km2. Stepwise linear regression models for particle-size fractions and water retention properties had the best fit. Relief and parent material covariates were selected in 31 of the 33 models (11 properties at three depths) and vegetation covariates in 29 models. Based on external validation, ordinary kriging obtained higher accuracy for 21 out of 33 property x depth combinations, indicating that the inclusion of a linear trend model before kriging does not necessarily improve predictions. Therefore, for similar studies, the geostatistical methods employed should be compared on a case-by-case basis.

Keywords : caatinga; digital soil mapping; gamma radiometric survey; geostatistics; pedometrics.

        · abstract in Portuguese     · text in English     · English ( pdf )