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

2018 ◽  
Vol 47 (4) ◽  
pp. 718-725 ◽  
Author(s):  
Elias Mendes Costa ◽  
Wagner de Souza Tassinari ◽  
Helena Saraiva Koenow Pinheiro ◽  
Sidinei Julio Beutler ◽  
Lucia Helena Cunha dos Anjos
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Organic Matter Fractions

Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon

2017 ◽  
Vol 20 ◽  
pp. 76-91 ◽  
Author(s):  
Huichun Ye ◽  
Wenjiang Huang ◽  
Shanyu Huang ◽  
Yuanfang Huang ◽  
Shiwen Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Regression Kriging

scholarly journals Effect of Different Tillage Systems on Soil Organic Carbon and Enzymatic Activity

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 208
Author(s):  
Małgorzata Szostek ◽  
Ewa Szpunar-Krok ◽  
Renata Pawlak ◽  
Jadwiga Stanek-Tarkowska ◽  
Anna Ilek
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Organic Farming ◽  
Total Nitrogen ◽  
Farming Systems ◽  
Conventional Tillage ◽  
Tillage Systems ◽  
Organic Farming Systems ◽  
Organic Matter Fractions

The aim of the study was to compare the effect of conventional, simplified, and organic farming systems on changes in the content of soil organic carbon, organic matter fractions, total nitrogen, and the enzymatic activity. The research was conducted from 2016–2018 on arable land in the south-eastern part of Poland. The selected soils were cultivated in conventional tillage (C_Ts), simplified tillage (S_Ts), and organic farming (O_Fs) systems. The analyses were performed in soil from the soil surface layers (up to 25 cm depth) of the experimental plots. The highest mean contents of soil organic carbon, total nitrogen, and organic matter fractions were determined in soils subjected to the simplified tillage system throughout the experimental period. During the study period, organic carbon concentration on surface soil layers under simplified tillage systems was 31 and 127% higher than the soil under conventional tillage systems and organic farming systems, respectively. Also, the total nitrogen concentration in those soils was more than 40% and 120% higher than conventional tillage systems and organic farming systems, respectively. Moreover, these soils were characterised by a progressive decline in SOC and Nt resources over the study years. There was no significant effect of the analysed tillage systems on the C:N ratio. The tillage systems induced significant differences in the activity of the analysed soil enzymes, i.e., dehydrogenase (DH) and catalase (CAT). The highest DH activity throughout the experiment was recorded in the O_Fs soils, and the mean value of this parameter was in the range of 6.01–6.11 μmol TPF·kg−1·h−1. There were no significant differences in the CAT values between the variants of the experiment. The results confirm that, regardless of other treatments, such as the use of organic fertilisers, tillage has a negative impact on the content of SOC and organic matter fractions in the O_Fs system. All simplifications in tillage reducing the interference with the soil surface layer and the use of organic fertilisers contribute to improvement of soil properties and enhancement of biological activity, which helps to maintain its productivity and fertility.

Spatial modelling of soil organic carbon stocks with combined principal component analysis and geographically weighted regression

2018 ◽  
Vol 156 (6) ◽  
pp. 774-784 ◽  
Author(s):  
Long Guo ◽  
Mei Luo ◽  
Chengsi Zhangyang ◽  
Chen Zeng ◽  
Shanqin Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Prediction Models ◽  
Principal Component ◽  
Weighted Regression ◽  
Auxiliary Variables ◽  
Soc Stocks

AbstractWith the development of remote sensing and geostatistical technology, complex environmental variables are increasingly easily quantified and applied in modelling soil organic carbon (SOC). However, this emphasizes data redundancy and multicollinearity problems adding to the difficulty in selecting dominant influential auxiliary variables and uncertainty in estimating SOC stocks. The current paper considers the spatial characteristics of SOC density (SOCD) to construct prediction models of SOCD on the basis of reducing the data dimensionality and complexity using the principal component analysis (PCA) method. A total of 260 topsoil samples were collected from Chahe town, China. Eight environmental variables (elevation, aspect, slope, normalized difference vegetation index, normalized difference moisture index, nearest distance to construction area and road, and land use degree comprehensive index) were pre-analysed by PCA and then extracted as the main principal component variables to construct prediction models. Two geostatistical approaches (ordinary kriging and ordinary co-kriging) and two regression approaches (ordinary least squares and geographically weighted regression (GWR)) were used to estimate SOCD. Results showed that PCA played an important role in reducing the redundancy and multicollinearity of the auxiliary variables and GWR achieved the highest prediction accuracy in these four models. GWR considered not only the spatial characteristics of SOCD but also the related valuable information of the auxiliary attributes. In summary, PCA-GWR is a promising spatial method used here to predict SOC stocks.

scholarly journals Podzolisation affects the spatial allocation and chemical composition of soil organic matter fractions

Soil Research ◽  
2020 ◽  
Vol 58 (8) ◽  
pp. 713
Author(s):  
Agnes Krettek ◽  
Ludger Herrmann ◽  
Thilo Rennert
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Soil Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Allocation ◽  
Organic Matter Fractions ◽  
Som Decomposition

Podzols are soils that display a unique vertical distribution of soil organic matter (SOM). We hypothesise that podzolisation, as a pedogenetic process, influences or even controls content, allocation and quality of SOM. We determined soil organic carbon (SOC) and nitrogen (N) contents in six SOM fractions obtained from mineral horizons of five soils with increasing degree of podzolisation: sand and stable aggregates (S + A), particulate organic matter (POM) > 63 µm and <63 µm, silt and clay (s + c), resistant SOC and dissolved organic matter. We applied infrared spectroscopy to evaluate SOM decomposition state, relative abundance of functional groups and SOM-metal complexation. In topsoil horizons, relative SOC allocation shifted from the larger to the smaller size POM fraction with increasing podzolisation. Accompanied with size reduction, the POM < 63 µm fraction was progressively less decomposed, as derived from infrared spectroscopy and C:N ratios. In illuvial subsoils, the proportion of SOC in the S + A fraction increased with increasing podzolisation, implying SOM accumulation in aggregates and coatings on sand grains. Elevated abundance of carboxylate and aromatic C in the s + c fractions of subsoil horizons indicated their preferred sorption. Additionally, metal-carboxyl complexation increased during podzolisation.

scholarly journals Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach

2020 ◽  
Vol 12 (22) ◽  
pp. 9330
Author(s):  
Tao Liu ◽  
Huan Zhang ◽  
Tiezhu Shi
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Spatial Interpolation ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Carbon Density ◽  
Soil Organic Carbon Density

Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km2) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.

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