Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem

Wiesmeier, Martin; Barthold, Frauke; Blank, Benjamin; Kögel‐Knabner, Ingrid

doi:10.1007/s11104-010-0425-z

Cited by 359 publications

(167 citation statements)

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“…The results indicated that these characteristics increased in the process of restoration, which resulted from an increase in seed germination and seed bank for annual and perennial species when livestock was excluded [27]. We found the mean SOC storage in FG to be 14.12˘0.17 g¨kg´1, which is similar to the average SOC for the steppe in the Xilin River Basin, as reported by Wiesmeier et al [28]. The SOC increased by 23% in our plots subjected to six years of exclusion, a higher percentage than the 14%-16% increase in SOC in plots subjected to three years of exclusion, as reported by Wiesmeier et al [29], at the identical sites.…”

Section: Effect Of Grazing Exclusion On Vegetation Characteristics Ansupporting

confidence: 88%

Effect of Grazing Exclusion on Vegetation Characteristics and Soil Organic Carbon of Leymus chinensis Grassland in Northern China

Jiao

Tang

2016

Sustainability

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Abstract:Overgrazing has caused vegetation destruction and soil degradation in Leymus chinensis grassland, the widely distributed type of grassland in northern China. To restore the degraded ecosystem, grazing exclusion was implemented in 1979, 1999, and 2004. However, changes in the vegetation and soil organic carbon (SOC) in different years of grazing exclusion have not been thoroughly elucidated. This paper examines the changes in vegetation characteristics (i.e., biomass, cover, richness, degree of succession, and shannon diversity index) and SOC under free of grazing (FG), 6 years (6 GE), 11 years (11 GE), and 31 years (31 GE) of grazing exclusion plots in the Xilin River Basin, China. The results indicate that the vegetation characteristics and SOC increased during the restoration process. Both the vegetation characteristics and SOC in 6 GE did not differ significantly from FG (p > 0.05), while these indexes in 11 GE were significantly higher than in FG. The differences between the vegetation characteristics and SOC in 11 GE and those in 31 GE were not significant. To meet the tradeoff between ecosystem conservation and utilization, further studies with multi-year observation should be conducted to identify the optimal duration of grazing exclusion and the grazing exclusion time threshold in L. chinensis grassland. This study provides valuable insights into sustainable grassland management in northern China.

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Section: Effect Of Grazing Exclusion On Vegetation Characteristics Ansupporting

confidence: 88%

Effect of Grazing Exclusion on Vegetation Characteristics and Soil Organic Carbon of Leymus chinensis Grassland in Northern China

Jiao

Tang

2016

Sustainability

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“…Values are in the form of the mean ± standard error, sample size n = 3 sociated soil properties (Deng et al 2017). Steffens et al (2008) and Wiesmeier et al (2011) indicated that soil organic C contents and stocks decreased with increasing grazing intensities. Also, in our study, it was found that soil C stock in different soil layers and soil depths both decreased along with the grazing intensities increased (P < 0.05, Figure 2a,b).…”

Section: Resultsmentioning

confidence: 99%

Mehlich 3 extractant used for the evaluation of wheat-available phosphorus and zinc in calcareous soils

Sedlář¹,

Balík²,

Kulhánek³

et al. 2018

PLANT SOIL ENVIRON

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Relation between wheat (Triticum aestivum) nutritional status determined at the beginning of stem elongation and during anthesis, respectively, and available content of phosphorus (P-M3) and zinc (Zn-M3) determined by the Mehlich 3 extractant was studied. Both one-year pot experiment with spring wheat and two-year on-farm trials with winter wheat were run on various calcareous soils (pH values of 7.18–7.94, median 7.80, P-M3 1–289 ppm, median 54, and Zn-M3 2–14 ppm, median 4), in the Czech Republic (Central Europe). Phosphorus nutrition index (ratio of phosphorus concentration in shoot biomass to critical phosphorus concentration – P<sub>c</sub>) was calculated using the Belanger et al.’s model: P<sub>c</sub> = –0.677 + 0.221N – 0.00292N(2), where both phosphorus and nitrogen concentrations were expressed in g/kg shoot dry matter. Unlike phosphorus concentration in shoot biomass, phosphorus nutrition index significantly correlated with P-M3 content in soil. Optimal values of the phosphorus nutrition index were recorded if P-M3 was 51–68 ppm. Zinc concentration in shoot biomass more strongly correlated with P:Zn ratio (M3) in soil compared to Zn-M3 content in soil. P:Zn ratio in shoot biomass of 130:1 did not lead to phosphorus deficiency and corresponded to P:Zn (M3) ratio in soil of 9.3:1–14.3:1.

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“…Recently, these principals have been applied to the prediction of both D b (Jalabert et al, 2010;Martin et al, 2009) and organic carbon stock (Wiesmeier et al, 2011;Grimm et al, 2008) at the point scale with considerable success. Methods commonly used to explicitly include landscape attributes in the modelling process are artificial neural networks (ANNs) (Keshavarzi et al, 2010) and random forests (RFs) (Prasad et al, 2006).…”

Section: K P Taalab Et Al: Modelling Soil Bulk Density At the Landmentioning

confidence: 99%

“…A 10 m resolution digital terrain model (DTM) was used to derive the following landscape attributes: elevation, slope, aspect, curvature (plan, profile and mean), SAGA wetness index (SWI) and sediment transport index (STI), all of which are commonly used topographic features in digital soil mapping (Wiesmeier et al, 2011). The SWI is based on the ratio of contributing upslope area per unit contour width and local slope angle (Böhner et al, 2001).…”

Section: Topographic Datamentioning

confidence: 99%

“…As many countries already have soil, land use and geological maps at a variety of scales, it makes sense to see if further information can be extracted from them in the form of predictive models. Secondly, a key research theme in spatial mapping is the assessment soil carbon stocks because they relate to the global carbon budget (Bellamy et al, 2005;Tornquist et al, 2009;Wiesmeier et al, 2011). One issue of interest here is the lack of spatial representations of D b .…”

Section: Modelling Without Using Measured Soil Propertiesmentioning

confidence: 99%

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Modelling soil bulk density at the landscape scale and its contributions to C stock uncertainty

et al. 2013

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Soil bulk density (D_b) is a major contributor to uncertainties in landscape-scale carbon and nutrient stock estimation. However, it is time consuming to measure and is, therefore, frequently predicted using surrogate variables, such as soil texture. Using this approach is of limited value for estimating landscape-scale inventories, as its accuracy beyond the sampling point at which texture is measured becomes highly uncertain. In this paper, we explore the ability of soil landscape models to predict soil D_b using a suite of landscape attributes and derivatives for both topsoil and subsoil. The models were constructed using random forests and artificial neural networks.

Using these statistical methods, we have produced a spatially distributed prediction of D_b on a 100 m × 100 m grid, which was shown to significantly improve topsoil carbon stock estimation. In comparison to using mean values from point measurements, stratified by soil class, we found that the gridded method predicted D_b more accurately, especially for higher and lower values within the range. Within our study area of the Midlands, UK, we found that the gridded prediction of D_b produced a stock inventory of over 1 million tonnes of carbon greater than the stratified mean method. Furthermore, the 95% confidence interval associated with total C stock prediction was almost halved by using the gridded method. The gridded approach was particularly useful in improving organic carbon (OC) stock estimation for fine-scale landscape units at which many landscape–atmosphere interaction models operate

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Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem

Cited by 359 publications

References 56 publications

Effect of Grazing Exclusion on Vegetation Characteristics and Soil Organic Carbon of Leymus chinensis Grassland in Northern China

Effect of Grazing Exclusion on Vegetation Characteristics and Soil Organic Carbon of Leymus chinensis Grassland in Northern China

Mehlich 3 extractant used for the evaluation of wheat-available phosphorus and zinc in calcareous soils

Modelling soil bulk density at the landscape scale and its contributions to C stock uncertainty

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