Mapping soil carbon particle size fractions and water retention in tropical dry forest in Brazil

The study evaluated the accuracy of ordinary kriging versus regression-kriging for mapping soil attributes in a Brazilian tropical dry forest. While stepwise linear regression models showed the best fit for grain size fractions and water retention properties, ordinary kriging demonstrated higher accuracy for 21 out of 33 attribute-depth combinations, suggesting that adding a linear trend model does not always improve predictions.

• Ordinary kriging provided higher accuracy than regression-kriging for 21 of the 33 combinations of soil attributes and depths, indicating that incorporating a linear trend model before kriging does not consistently improve prediction results.
• Stepwise linear regression models were the most effective for fitting grain size fractions and water retention properties. Relief and parent material covariates were selected in 31 of 33 models, while vegetation covariates were selected in 29 models.

APA

Inter-American Institute for Global Change Research (2025). Mapping soil carbon particle size fractions and water retention in tropical dry forest in Brazil. https://iai.int/es/pub_acad/mapping-soil-carbon-particle-size-fractions-and-water-retention-in-tropical-dry-forest-in-brazil/

Chicago

Inter-American Institute for Global Change Research. Mapping soil carbon particle size fractions and water retention in tropical dry forest in Brazil. 2025. https://iai.int/es/pub_acad/mapping-soil-carbon-particle-size-fractions-and-water-retention-in-tropical-dry-forest-in-brazil/.

Wikipedia

{{cite report |author=Inter-American Institute for Global Change Research |title=Mapping soil carbon particle size fractions and water retention in tropical dry forest in Brazil |date=13 January 2025 |url=https://iai.int/es/pub_acad/mapping-soil-carbon-particle-size-fractions-and-water-retention-in-tropical-dry-forest-in-brazil/ |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{interamericaninstituteforglobalchangeresearch2025mapping, author = {{Inter-American Institute for Global Change Research}}, title = {{Mapping soil carbon particle size fractions and water retention in tropical dry forest in Brazil}}, institution = {Inter-American Institute for Global Change Research}, year = {2025}, month = jan, url = {https://iai.int/es/pub_acad/mapping-soil-carbon-particle-size-fractions-and-water-retention-in-tropical-dry-forest-in-brazil/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }