Dong-Sheng Yu

National Scale Analysis of Soil Organic Carbon Storage in China Based on Chinese Soil Taxonomy

Patterns of soil organic carbon (SOC) storage and density in various soil types or locations are the foundation for examining the role of soil in the global carbon cycle. An assessment of SOC storage and density patterns in China based on soil types as defined by Chinese Soil Taxonomy (CST) and the recently compiled digital 1:1000000 Soil Database of China was conducted to generate a rigorous database for the future study of SOC storage. First, SOC densities of 7292 soil profiles were calculated and linked by soil type to polygons of a digital soil map using geographic information system resulting in a 1:1000000 SOC density distribution map of China. Further results showed that soils in China covered 9281×10km^2 with a total SOC storage of 89.14 Gt and a mean SOC density 96.0t ha^(-1). Among the 14 CST orders, Cambosols and Argosols constituted high percentage of Chinas total SOC storage, while Andosols, Vertosols, and Spodsols had a low percentage. As for SOC density, Histosols were the highest, while Primosols were the lowest. Specific patterns of SOC storage of various soil types at the CST suborder, group, and subgroup levels were also described. Results obtained from the study of SOC storage and density of all CST soil types would be not only useful for international comparative research, but also for more accurately estimating and monitoring of changes of SOC storage in China.

Cross-Reference Benchmarks for Translating the Genetic Soil Classification of China into the Chinese Soil Taxonomy

ABSTRACT Soil classification is the foundation for exchange and extension of research findings in soil science and for modern management of soil resources. This study explained database and research methodology to create a cross-reference system for translating the Genetic Soil Classification of China (GSCC) into the Chinese Soil Taxonomy (CST). With the help of the CST keys, each of the 2540 soil species in GSCC has been interpreted to its corresponding soil order, suborder, great group, and sub-group in CST. According to the methodology adopted, the assigned soil species have been linked one another to their corresponding polygons in the 1:1000000 digital soil map of China. Referencibility of each soil species between the GSCC and CST systems was determined statistically on the basis of distribution area of each soil species at a high taxon level of the two systems. The soils were then sorted according to their maximum referencibility and classified into three categories for discussion. There were 19 soil great groups in GSCC with maximum referencibility > 90% and 22 great groups between 60%–90%. These soil great groups could serve as cross-reference benchmarks. There were 19 great groups in GSCC with maximum referencibility

Storage and Spatial Variation of Phosphorus in Paddy Soils of China

Abstract Due to the growing concern about the agricultural phosphorus (P) losses pollution, an in-depth understanding of P in paddy soils of China would be helpful in providing a national perspective of the environmental impact of P cycling and fertility on Chinas farms. In this study, we evaluated the P storage and the P density of paddy soils in China, characterized the spatial variations of P among the subgroups of paddy soils and soil regions in China, and evaluated the P data using GIS-based analysis, which included a newly compiled 1:1 000 000 digital soil map of China, and using 1 490 soil profiles. The available and total P densities of paddy soils were 6.7 and 698.5 g m −3 , respectively. Overall in China, the total P storage within 1 m of paddy soils was estimated to be 330.2 Tg. The P density of paddy soils varied substantially with subgroups due to the different soil water regimes such as groundwater table and soil drainage. The P availability in paddy soils, especially in surface layer, was higher in high temperature and precipitation areas. Further research is needed to examine more anthropogenic impact factors, such as increasing use of chemical fertilizer.

Scale Effect of Climate and Soil Texture on Soil Organic Carbon in the Uplands of Northeast China

Abstract Understanding how spatial scale influences commonly-observed effects of climate and soil texture on soil organic carbon (SOC) storage is important for accurately estimating the SOC pool at different scales. The relationships among climate factors, soil texture and SOC density at the regional, provincial, city, and county scales were evaluated at both the soil surface (0–20 cm) and throughout the soil profile (0–100 cm) in the Northeast China uplands. We examined 1 022 profiles obtained from the Second National Soil Survey of China. The results indicated that the relationships between climate factors and SOC density generally weakened with decreasing spatial scale. The provincial scale was optimal to assess the relationship between climate factors and SOC density because regional differences among provinces were covered up at the regional scale. However, the relationship between soil texture and SOC density had no obvious trend with increasing scale and changed with temperature. There were great differences in the impacts of climate factors and soil texture on SOC density at different scales. Climate factors had a larger effect on SOC density than soil texture at the regional scale. Similar trends were seen in Heilongjiang and eastern Inner Mongolia at the provincial scale. But, soil texture had a greater effect on SOC density compared with climate factors in Jilin and Liaoning. At the city and county scales, the influence of soil texture on SOC density was more important than climate factors.

Effect of Soil Sampling Density on Detected Spatial Variability of Soil Organic Carbon in a Red Soil Region of China

Abstract Spatial variability of soil organic carbon (SOC) of different land use patterns and soil types was examined in a county-wide red soil region of South China, using six sampling densities, 14, 34, 68, 130, 255, and 525 samples designed by the method of grid sampling in 6 different grid sizes, labeled as D14, D34, D68, D130, D255, and D525, respectively. The results showed that the coefficients of variation (CVs) of SOC decreased gradually from 62.8% to 47.4% with the increase in soil sampling densities. The SOC CVs in the paddy field change slightly from 30.8% to 28.7%, while those of the dry farmland and forest land decreased remarkably from 58.1% to 48.7% and from 99.3% to 64.4%, respectively. The SOC CVs of the paddy soil change slightly, while those of red soil decreased remarkably from 82.8% to 63.9%. About 604, 500, and 353 ( P 0.05) samples would be needed a number of years later if the SOC change was supposedly 1.52 g kg −1 , based on the CVs of SOC acquired from the present sampling densities of D14, D68, and D525, respectively. Moreover, based on the same SOC change and the present time CVs at D255, the ratio of samples needed for paddy field, dry farmland, and forest land should be 1:0.81:3.33, while the actual corresponding ratio in an equal interval grid sampling was 1:0.74:0.46. These indicated that the sampling density had important effect on the detection of SOC variability in the county-wide region, the equal interval grid sampling was not efficient enough, and the respective CV of each land use or soil type should be fully considered when determining the sampling number in the future.

Regional Simulation of Soil Organic Carbon Dynamics for Dry Farmland in East China by Coupling a 1:500 000 Soil Database with the Century Model

Abstract Changes in soil organic carbon (SOC) in agricultural soils influence soil quality and greenhouse gas concentrations in the atmosphere. Dry farmland covers more than 70% of the whole cropland area in China and plays an important role in mitigating carbon dioxide (CO 2 ) emissions. In this study, 4 109 dry farmland soil polygons were extracted using spatial overlay analysis of the soil layer (1:500 000) and the land use layer (1:500 000) to support Century model simulations of SOC dynamics for dry farmland in Anhui Province, East China from 1980 to 2008. Considering two field-validation sites, the Century model performed relatively well in modeling SOC dynamics for dry farmland in the province. The simulated results showed that the area-weighted mean soil organic carbon density (SOCD) of dry farmland increased from 18.77 Mg C ha −1 in 1980 to 23.99 Mg C ha −1 in 2008 with an average sequestration rate of 0.18 Mg C ha −1 year −1 . Approximately 94.9% of the total dry farmland area sequestered carbon while 5.1% had carbon lost. Over the past 29 years, the net SOC gain in dry farmland soils of the province was 19.37 Tg, with an average sequestration rate of 0.67 Tg C year −1 . Augmentation of SOC was primarily due to increased consumption of nitrogen fertilizer and farmyard manure. Moreover, SOC dynamics were highly differentiated among dry farmland soil groups. The integration of the Century model with a fine-scale soil database approach could be conveniently utilized as a tool for the accurate simulation of SOC dynamics at the regional scale.

A Model for Estimating Total Forest Coverage with Ground-Based Digital Photography

Vegetation fractional coverage (VFC) is one of the key indicators of vegetation distribution. In the work a measurement-based model was developed to derive total forest VFC (TG) as well as the VFC of trees (T) and shrub-grasses (G) separately in a subtropical forest area in Nanjing, China. Both upward and downward photographs were taken with a digital camera in 72 quadrats (10 m × 10 m each). Fifteen models were established and validated. Models jointly using both T and G performed better than those using the T and G separately. The best model, TG = T + G − 1.134 × T × G − 0.025 (R2 =0.9115, P < 0.01, root mean squared error = 0.0789), is recommended for application. This model provides a good way to obtain total forest VFC values through taking tree and shrub-grass photos on ground below tree canopy rather than above tree canopy.

Modeling Carbon Dynamics in Paddy Soils in Jiangsu Province of China with Soil Databases Differing in Spatial Resolution

A number of process-based models have been developed for quantifying carbon (C) sequestration in agro-ecosystems. The DeNitrification-DeComposition (DNDC) model was used to simulate and quantify long-term (1980–2008) soil organic carbon (SOC) dynamics in the important rice-producing province, Jiangsu, China. Changes in SOC storages were estimated from two soil databases differing in spatial resolution: a county database consisting of 68 polygons and a soil patch-based database of 701 polygons for all 3.7 Mha of rice fields in Jiangsu. The simulated SOC storage with the coarse resolution county database ranged between 131.0–320.6 Tg C in 1980 and 170.3–305.1 Tg C in 2008, respectively, while that estimated with the fine resolution database was 201.6 and 216.2 Tg C in 1980 and 2008, respectively. The results modeled with the soil databases differing in spatial resolution indicated that using the soil input data with higher resolution substantially increased the accuracy of the modeled results; and when lacking detailed soil datasets, the DNDC model, parameterized with the most sensitive factor (MSF) method to cope with attribute uncertainty, could still produce acceptable results although with deviations of up to 60% for the case study reported in this paper.

Variation of Sediment Concentration and Its Drivers Under Different Soil Management Systems

Abstract In order to prevent soil erosion in southern China, a study was performed to determine the drivers of sediment concentration variation using simulated rainfall and four soil management systems under field condition. Four soil management systems, i.e ., forest and grass coverage (FG), forest coverage with disturbed soil surface (FD), contour tillage (CT) and downslope tillage (DT), were exposed to two rainfall intensities (40 and 54 mm h −1 ) using a portable rainfall simulator. The drivers of sediment concentration variation were determined by the variations of runoff rate and sediment concentration as well as their relationships. The effects of the four soil management systems in preventing water and soil losses were compared using runoff rates and sediment concentrations at steady state. At runoff initial stage, sediment concentration variation was mainly driven by rainfall and management. The degree of sediment concentration variation driven by flow varied with different soil management systems. Three best relationships between runoff rate and sediment concentration were identified, i.e. , reciprocal (CT), quadratic (FG and FD) and exponential (DT). At steady state, runoff rates of the four soil management systems varied slightly, whereas their sediment concentrations varied greatly. FG and CT were recommended as the best soil management systems for preventing water and soil losses.

Regional Differences in the Effect of Climate and Soil Texture on Soil Organic Carbon

Abstract The agricultural soil carbon pool plays an important role in mitigating greenhouse gas emission and understanding the soil organic carbon-climate-soil texture relationship is of great significance for estimating cropland soil carbon pool responses to climate change. Using data from 900 soil profiles, obtained from the Second National Soil Survey of China, we investigated the soil organic carbon (SOC) depth distribution in relation to climate and soil texture under various climate regimes of the cold northeast region (NER) and the warmer Huang-Huai-Hai region (HHHR) of China. The results demonstrated that the SOC content was higher in NER than in HHHR. For both regions, the SOC content at all soil depths had significant negative relationships with mean annual temperature (MAT), but was related to mean annual precipitation (MAP) just at the surface 0–20 cm. The climate effect on SOC content was more pronounced in NER than in HHHR. Regional differences in the effect of soil texture on SOC content were not found. However, the dominant texture factors were different. The effect of sand content on SOC was more pronounced than that of clay content in NER. Conversely, the effect of clay on SOC was more pronounced than sand in HHHR. Climate and soil texture jointly explained the greatest SOC variability of 49.0% (0–20 cm) and 33.5% (20–30 cm) in NER and HHHR, respectively. Moreover, regional differences occurred in the importance of climate vs. soil texture in explaining SOC variability. In NER, the SOC content of the shallow layers (0–30 cm) was mainly determined by climate factor, specifically MAT, but the SOC content of the deeper soil layers (30–100 cm) was more affected by texture factor, specifically sand content. In HHHR, all the SOC variability in all soil layers was predominantly best explained by clay content. Therefore, when temperature was colder, the climate effect became stronger and this trend was restricted by soil depth. The regional differences and soil depth influence underscored the importance of explicitly considering them in modeling long-term soil responses to climate change and predicting potential soil carbon sequestration.