RESUMO
Computer models have been used to assess soil organic carbon (SOC) stock change. Commonly, models require to determine soil bulk density (Db), a variable that is often lacking in soil data bases. To partly overcome this problem, pedotransfer functions (PTFs) are developed to estimate Db from other easily available soil properties. However, only a few studies have determined the accuracy of these functions and quantified their effects on the final quality of the spatial variability maps. In this context, the objectives of this study were: i) to develop one PTF to estimate Db in soils of the Brazilian Central Amazon region; ii) to compare the performance of PTFs generated with three other models generally used to estimate Db in soils of the Amazon region; and iii) to quantify the effect of applying these PTFs on the spatial variability maps of SOC stock. Using data from 96 soil profiles in the Urucu river basin in Brazil, a multiple linear regression model was generated to estimate Db using SOC, pH, sum of basic cations, aluminum (Al+3), and clay content. This model outperformed the three other PTFs published in the literature. The average estimation error of SOC stock using our model was 0.03 Mg C ha1, which is markedly lower than the other PTFs (1.06 and 1.23 Mg C ha1, or 15 % and 17 %, respectively). Thus, the application of a non-validated PTF to estimate Db can introduce an error that is large enough to skew the significant difference in soil carbon stock change.(AU)
Assuntos
Carbono/administração & dosagem , Carbono/análise , Modelos LinearesRESUMO
Computer models have been used to assess soil organic carbon (SOC) stock change. Commonly, models require to determine soil bulk density (Db), a variable that is often lacking in soil data bases. To partly overcome this problem, pedotransfer functions (PTFs) are developed to estimate Db from other easily available soil properties. However, only a few studies have determined the accuracy of these functions and quantified their effects on the final quality of the spatial variability maps. In this context, the objectives of this study were: i) to develop one PTF to estimate Db in soils of the Brazilian Central Amazon region; ii) to compare the performance of PTFs generated with three other models generally used to estimate Db in soils of the Amazon region; and iii) to quantify the effect of applying these PTFs on the spatial variability maps of SOC stock. Using data from 96 soil profiles in the Urucu river basin in Brazil, a multiple linear regression model was generated to estimate Db using SOC, pH, sum of basic cations, aluminum (Al+3), and clay content. This model outperformed the three other PTFs published in the literature. The average estimation error of SOC stock using our model was 0.03 Mg C ha1, which is markedly lower than the other PTFs (1.06 and 1.23 Mg C ha1, or 15 % and 17 %, respectively). Thus, the application of a non-validated PTF to estimate Db can introduce an error that is large enough to skew the significant difference in soil carbon stock change.
Assuntos
Carbono/administração & dosagem , Carbono/análise , Modelos LinearesRESUMO
The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0-30 and the 0-100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km(2) and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m(-2), respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock.