Soil-geographic database of Russia

Шоба, С. А.; Stolbovoi, V.; Alyabina, I. O.; Молчанов, Э. Н.

doi:10.1134/s1064229308090019

Cited by 13 publications

(8 citation statements)

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“…WISE30sec used the soil map from HWSD with minor corrections and climate zone maps as categorical covariates. Many national and regional agencies around the world have organized their soil surveys by linking soil maps and soil profiles, including the USA State Soil Geographic Database (STATSGO2) (Soil Survey Staff, 2017), Soil Landscapes of Canada (Soil Landscapes of Canada Working Group, 2010), the ASRIS (Johnston et al, 2003), the Soil-Geographic Database of Russia (Shoba et al, 2008), the European Soil Database (ESB, 2004), and the China dataset of soil properties (Shangguan et al, 2013).…”

Section: Global and National Soil Datasetsmentioning

confidence: 99%

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Shangguan¹

2019

Preprint

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Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.

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Section: Global and National Soil Datasetsmentioning

confidence: 99%

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Shangguan¹

2019

Preprint

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“…GSDE as an improvement of HWSD incorporated more soil maps and more soil profiles related to the soil maps, with more soil properties. GSDE (Johnston et al, 2003), the Soil-Geographic Database of Russia (Shoba et al, 2008) the European Soil Database (ESB, 2004), the China dataset of soil properties (Shangguan et al, 2013) and so on.…”

Section: Global and National Soil Datasetsmentioning

confidence: 99%

A review on the global soil datasets for earth system modeling

Dai

Shangguan

Wang

et al. 2018

Preprint

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Abstract. Global soil dataset is a pillar to the challenge of earth system modeling. But it is one of the most important uncertainty sources for Earth System Models (ESMs). Soil datasets function as model parameters, initial variables and benchmark datasets for model calibration, validation and comparison. For modeling use, the dataset should be geographically continuous, scalable and with uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse resolution soil maps. Updated and comprehensive soil information needs to be incorporated in ESMs. New generation soil datasets derived by digital soil mapping with abundant soil observations and environmental covariates are preferred to those by the linkage method for ESMs. Because there is no universal pedotransfer function, an ensemble of them may be more suitable to provide secondary soil parameters to ESMs. Aggregation and upscaling of soil data are needed for model use but can be avoid by taking a subgrid method in ESMs at the cost of increases in model complexity. Uncertainty of soil data needs to be incorporated in ESMs.

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“…This paper aims to identify the redistribution of C-cycle-related enzymes and associated microbial cells within hierarchy levels of soil microstructure in two Silt Loam Phaeozems contrasting in land use type—native forest soil and arable land. Phaeozems are one of the main soil types of the Far East, Siberia, and European Russia, occupying 1.8% of the area, and formed within humid and sub-humid forest and steppe–forest zones [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Localization of C Cycle Enzymes in Arable and Forest Phaeozems within Levels of Soil Microstructure

et al. 2023

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Soil microbial and enzyme activities are closely related to the spatial variability of soil environmental conditions at the microscale (μm-mm). The origin and localization of the enzymes are somewhat neglected when the measured activity is used to evaluate specific soil functions. The activity of four hydrolytic enzymes (β-glucosidase, Cellobiohydrolase, Chitinase, Xylanase) and microbial diversity based on community-level physiological profiling were determined in samples of arable and native Phaeozems with increasing physical impact to soil solids. The level of impact on the soil solids had a significant effect on enzyme activity and depended on both the enzyme type and soil land use. The highest proportion of the activity of Xylanase and Cellobiohydrolase of arable Phaeozem was determined at the dispersion energy in the range of 450–650 J·mL−1 and was associated with the primary soil particles’ hierarchy level. The highest proportions of β-glucosidase and Chitinase activities were determined for forest Phaeozem after applying energies lower than 150 J·mL−1 and characterizing the level of soil microaggregates. The increased activity of Xylanase and Cellobiohydrolase in primary soil particles of arable soil compared to those in forest soil might be a reflection of the substrates being unavailable to decomposition, leading to enzyme accumulation on the solid surface. For the Phaeozems, the lower the level of soil microstructure organization, the greater the differences observed between soils of different land use type, i.e., microbial communities, associated with lower microstructure levels, were more specific to land use type.

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Soil-geographic database of Russia

Cited by 13 publications

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Reply to Anonymous Referee #1's first comment (same as the previous reply to this comment)

Reply to Anonymous Referee #1's first comment (same as the previous reply to this comment)

A review on the global soil datasets for earth system modeling

Localization of C Cycle Enzymes in Arable and Forest Phaeozems within Levels of Soil Microstructure

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