PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

Henrique Seixas Barros,Philip Martin Fearnside

doi:10.1590/01000683rbcs20140358

Henrique Seixas Barros, Philip Martin Fearnside

Open Access

https://doi.org/10.1590/01000683rbcs20140358

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Abstract

Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.

Highlights

Soil bulk density is used in the quantification of soil C stocks (Veldkamp, 1994), and is an important parameter for national inventories of greenhouse gas (GHG) emissions under the United Nations Framework Convention on Climate Change (UNFCCC)
The result is that soil carbon stocks are commonly estimated from mean bulk density (Bd) values from the literature and from values for C concentration measured in the field (Bernoux et al, 1998)
Assuming that there should be no co-linearity between independent variables that make up the equations in the regression models, the Models 1 and 2, despite having high R2 values, are considered biased and unreliable for predicting soil density

Summary

Introduction

Soil bulk density is used in the quantification of soil C stocks (Veldkamp, 1994), and is an important parameter for national inventories of greenhouse gas (GHG) emissions under the United Nations Framework Convention on Climate Change (UNFCCC). Bd is calculated as the ratio of soil mass to volume, both of which are measured variables, reliable information on soil Bd is difficult This has stimulated predictions of soil density that exploit the relationship between this parameter and other variables commonly found in soil-related inventories in order to ensure the reliability of carbon stock assessments and to reduce evaluation costs (Federer et al, 1993; Bernoux et al, 1998, 2002; Tomasella and Hodnett, 1998; Calhoun et al, 2001; Heuscher et al, 2005; Benites et al, 2007; Tranter et al, 2007; Steller et al, 2008; Gharahi-Ghehi et al, 2012; Chaudhari et al, 2013)

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