Spatialization of Fractions of Organic Matter in Soil in an Agroforestry System in the Atlantic Forest, Brazil

Silva, Camila Santos da; Pereira, Marcos Gervásio; Delgado, Rafael Coll; Assunção, Shirlei Almeida

doi:10.1590/01047760201723022318

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…The largest extrapolations were observed with SOC and HUM contents, which may be related, among other factors, with the greater scale of variation of these attributes. This result corroborates those found in the literature in which the values of HUM are higher than the values of the other fractions (Fontana et al, 2014, Loss et al, 2014; Melo et al, 2016; Silva et al, 2017). …”

Section: Resultssupporting

confidence: 93%

“…Among the advantages, the GWR method stands out as having superior performance to the classic linear regression model (Mishra et al, 2010; Zhang et al, 2011; Wang et al, 2014; Song et al, 2016) and the possibility of using environmental variables, unlike the classic ordinary kriging models. The method is basically used for the position in the space (coordinates) for interpolation (Melo et al, 2016; Silva et al, 2017). …”

Section: Resultsmentioning

confidence: 99%

“…Several studies were undertaken that aimed to map the distribution of carbon contents and stocks by using covariates (Mishra et al, 2010; Zhang et al, 2011; Kumar et al, 2012; Wang, 2012; Song et al, 2016; Zeng et al, 2016). However, few studies have mapped soil organic matter (SOM) fractions (Melo et al, 2016; Silva et al, 2017), mainly using environmental variables. The spatial analysis of the organic matter fractions can help us to better understand the soil carbon dynamics and their relationship to environmental variables.…”

mentioning

confidence: 99%

“…Sometimes the SOC is not as sensitive as the SOM fractions with soil cover variation (Loss et al, 2014). It may still be that carbon does not have a degree of spatial dependence lower than the SOM fractions (Melo et al, 2016; Silva et al, 2017). …”

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confidence: 99%

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Mapping Soil Organic Carbon and Organic Matter Fractions by Geographically Weighted Regression

et al. 2018

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The soil organic matter (SOM) content and dynamic are related to vegetation cover, climate, relief, and geology; these factors have strong variation in space in the southeastern of Brazil. The objective of the study was to compare and evaluate performance of classical multiple linear regressions (MLR) and geographically weighted regression (GWR) models to predict soil organic carbon (SOC) and chemical fractions of organic matter in the Brazilian southeastern mountainous region. The regression models were fitted based on SOC and chemical fractions of SOM. The points ( = 89) were selected by pedologist's experience along transects and toposequences. The covariates were also selected using the empirical knowledge of pedologists when choosing variables that drive soil carbon content and its dynamics. Geology map, legacy soils map, terrain attributes derived from digital elevation model, and remote sensing indices derived from RapidEye sensor bands were used as covariates. In all MLR models (except for fulvic acid fraction [FAF]), the legacy soil map was selected as a covariate by the stepwise approach. The geology map was not selected as important covariate to predict FAF and humin (HUM). At least one variable derived from remote sensing was selected by the adjusted models. For the prediction of the SOC, HUM, and FAF, the GWR models had the highest performance. The MLR models extrapolated the results, especially for SOC. The relationships among SOC, SOM fractions, and environmental covariates were affected by local landscape variability, and the GWR model was better at modeling.

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Section: Resultssupporting

confidence: 93%