Spatial distribution of organic carbon and humic substances in irrigated soils under different management systems in a semi-arid zone in Ceará, Brazil

Melo, Antônia Arleudina Barros de; Valladares, Gustavo Souza; Ceddia, Marcos Bacis; Pereira, Marcos Gervásio; Soares, Ismail

doi:10.5433/1679-0359.2016v37n4p1845

Cited by 4 publications

(15 citation statements)

References 9 publications

(12 reference statements)

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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%

“…The most labile fraction of SOM, the fulvic acid fraction (FAF), had the lowest number of covariates selected and the worst prediction results, using both the MLR and GWR models ( Table 2). This may be related to the fact that FAF varies widely due to land use and may have a low spatial dependence (Melo et al, 2016), which reduces the adjustment quality of geographically weighted models such as GWR. Moreover, this fraction is more dynamic and can easily change with the change of land use (Fontana et al, 2014;Loss et al, 2014).…”

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%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

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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Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Wang,

Qi,

et al. 2024

Front. Earth Sci.

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Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

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Availability and Spatial Variability of Copper, Iron, Manganese and Zinc in Soils of the State of Ceará, Brazil

Oliveira¹,

Valladares²,

Vieira³

et al. 2021

Ciências Agrárias: O Avanço Da Ciência No Brasil - Volume 2

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The aim of the present work was to: (i) determine and map the micronutrient content (copper -Cu, iron -Fe, manganese -Mn and zinc -Zn) in soils of the State of Ceará; (ii) evaluate soil attributes related to micronutrient availability; and (iii) evaluate, by ordinary kriging, the spatial dependence of the micronutrient content. The study area is located in the central region of Ceará, in the basin of the River Banabuiú. Soil samples were collected in the surface and subsurface layers to determine the chemical attributes, granulometry, and micronutrient content, extracted with Mehlich-1 solution. The sites of the principal types of soil in the region were spatialised and the results submitted to descriptive analysis and Pearson correlation analysis. The semivariograms with the best fit were tested using the ordinary kriging method, and the maps of spatial variability were prepared. The highest values for the mean, maximum and minimum levels of Cu and Fe are found in the subsurface layers, while for Mn and Zn, they are found in the surface layers. In the surface layers, the majority of soils present Fe, Mn and Zn in levels suitable for agricultural production, while the levels of Cu are classified as medium to low. The micronutrients showed a significant positive correlation, especially with the soil attributes and with each other. Cu, Fe, Mn and Zn are classified as having medium to strong spatial dependence in the surface and subsurface layers.

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Spatial distribution of organic carbon and humic substances in irrigated soils under different management systems in a semi-arid zone in Ceará, Brazil

Cited by 4 publications

References 9 publications

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

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

Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Availability and Spatial Variability of Copper, Iron, Manganese and Zinc in Soils of the State of Ceará, Brazil

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