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The use of three conceptual pools for soil organic carbon (SOC) proposed by the CENTURY model to study soil organic C (SOC) dynamics helps to more clearly understand the mechanism of C sequestration and the stability of SOC pool in farmlands. We studied a typical rice-growing area, Chengdu Plain in southwestern China. Based on historical soil data collected in the early 1980s, 48 sampling points that include four major types of paddy soils from the surface layer (0–20 cm) and the subsurface layer (20–40 cm) were selected to collect soil samples (n = 96) in 2010. A 100-d laboratory incubation was conducted to measure the SOC decomposition rates at different times, and data from the incubation experiment were fitted to a three-pool first-order model that divided SOC pool into active (Cₐ), slow (Cₛ), and resistant (Cᵣ) SOC fractions. Based on these data, a universal predictive method for the concentrations of SOC fractions was developed and used to obtain the 1980s’ concentrations of SOC fractions. The results showed that an exponential function model (Dₛₒcₜ = a - b × cᵗ) can be used as the optimal prediction model for the SOC decomposition process. The Cₐ, Cₛ, and Cᵣ concentrations increased in surface and subsurface soils in the study area from 1980 to 2010. The increases of Cₛ and Cᵣ contributed to more than 90% of the increase in the paddy SOC pool, whereas the contribution of the Cₐ increase was less than 10%. Thus, the increase in the paddy SOC pool mainly derived from the increases of Cₛ and Cᵣ.

A proper soil raster unit resolution for grid sampling design is important to estimate the soil organic carbon (SOC) pool at certain map scales, which is related to the soil sampling density and the accuracy of the estimation. A series of raster soil unit data sets at varying resolutions were derived from different vector soil unit data sets at six map scales of 1:50,000, 1:200,000, 1:500,000, 1:1,000,000, 1:4,000,000, and 1:14,000,000 in the Tai-Lake region of China. Four indices—soil type number (STN) and area (AREA), average SOC density (ASOCD), and total SOC stocks (SOCS) of surface paddy soils—were attributed from all these vector and raster units data sets. Subjected to the four index values (IV) from parent vector unit data set, the relative variability (VIV, %) from raster unit data set was used to assess its accuracy and redundancy, which reflects uncertainty and workload of SOC estimation, respectively. Optimal raster unit resolutions were generated and suggested for each map scale’s SOC estimation, in which the soil raster unit data set can hold the same accuracy as its parent vector unit data set without any redundancy when VIV < 1% of all the four indices was assumed as criteria to the assessment. A relationship between map scale (1:x) of soil vector unit and its optimal grid resolution (y, km) was found to be: y = −8.03 × 10⁻⁶x² + 0.0256x- 0.087 (R² = 0.998, p < 0.05). The results may serve for soil unit conversion from vector to raster and soil grid sampling design at a certain map scale in the investigation of regional SOC pool.

Soil polygons are the preferred format for the modeling of denitrification-decomposition (DNDC) at regional scale because a large area of relatively homogeneous properties can be encompassed within a single boundary. Despite this, it is not yet fully understood how map scales of the soil polygons affect modeling. Six soil polygonal data sets were generated from soil vector maps at scales of 1:50,000∼1:14,000,000 to estimate CH₄ emissions from paddy soils in the Tai-Lake region of China using the DNDC model. The 1:50,000 scale data set (P005) was the most detailed and accurate soil database of the region. DNDC-simulated CH₄ concentrations from input of the other five data sets were compared with that obtained by input of the P005 data set using metrics with the following outcomes: (i) Relative variations (VIV, %) of three indices, paddy soil area (APS, ha), annual mean CH₄ emission (AME, Gg yr⁻¹), and emission rate (RGE, kg ha⁻¹ yr⁻¹), calculated for 1: 200,000 (P02) data were all 20%, the greatest equaling 138%. Accuracy and computational efficiency assessments of regional-scale DNDC modeling indicate that P02 scale input are preferred, those at scales of P4 and P14 are the source of unacceptable error, and even greater uncertainty exists when assessment units at scales of P05 and P1 are used. The results provide guidelines for modeling soil carbon–nitrogen cycle and climate change impacts in China. Further, they help build a global understanding concerning appropriate scale input data for carbon–nitrogen cycle modeling.

Digital soil maps of different scales have been compiled in China, but exactly how map scale affects the estimation of regional SOC (soil organic carbon) stocks remains unclear. To test the effect, median, mean, and a pedological professional knowledge based method (PKB) were used to link soil profiles to soil maps at five scales ranging from 1:500000 to 1:10000000 for the Hebei Province. Excluding the 1:4000000 soil map, SOC stocks decreased as the map scale decreased. The estimated SOC stocks obtained using the mean were always higher than those using the median or PKB method. The changes in estimation due to different map scales and linking methods affected the process of assigning SOCD (soil organic carbon density) values to digital soil surveys. The differences in SOCD values resulted from the change in the total nonurban land area of each soil type as a result of the different methods and scales of maps used in the regional SOC stock estimation process.

Soil classification systems are not consistent among countries or organizations thereby hindering the communication and organizational functions they are intended to promote. The development of translations between systems will be critical for overcoming the gap in understanding that has resulted from the lack of a single internationally accepted classification system. This paper describes the application of a process that resulted in the translation of the Genetic Soil Classification of China (GSCC) to Soil Taxonomy (ST). A brief history of soil classification in China is also provided to familiarize readers with GSCC and its origins. Genetic Soil Classification of China is the attribute base for the recently assembled digital form of the 1:1 000 000 soil map of The People's Republic of China. The translation between GSCC and ST was based on profile, chemical, and physical descriptions of 2540 soil series. First, the 2540 soil series were classified to their equivalent soil order, suborder, great group, and subgroup according to ST and GSCC subgroup descriptors. Order names for both classification systems were then linked to corresponding map units in the 1:1 000 000 digital soil map of China using a geographic information system (GIS). Differences in classification criteria and in the number of orders of the two systems (there are more GSCC orders than ST orders) meant that each GSCC order could possibly be assigned to more than one ST order. To resolve the differences, the percent correspondence in area between orders was determined and used as the criterion for assigning GSCC orders to ST orders. Some percentages of correspondence were low so additional processing was used to improve the assignment process. The GSCC suborders were then matched with ST orders. When the area for each order was summarized, the percentage of correspondence increased except for two subgroups in the Ferrasols order.