Search

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2006.09.020 Byline: D.S. Yu (a), X.Z. Shi (a), H.J. Wang (a), W.X. Sun (a), J.M. Chen (b), Q.H. Liu (a), Y.C. Zhao (a) Keywords: Soil organic carbon stock; 1:1,000,000 Soil Database of China Abstract: Soil organic carbon (SOC) is of great importance in the global carbon cycle. Distribution patterns of SOC in various regions of China constitute a nation-wide baseline for studies on soil carbon changes. This paper presents an integrated and multi-level study on SOC stock patterns of China, and presents baseline SOC stock estimates by great administrative regions, river watersheds, soil type regions and ecosystem. The assignment is done by means of a recently completed 1: 1,000,000 scale soil database of China, which is the most detailed and reliable one in China at the present time. SOC densities of 7292 soil profiles collected across China in the middle of the 1980s were calculated and then linked to corresponding polygons in a digital soil map, resulting in a SOC Density Map of China on a 1: 1,000,000 scale, and a 1kmx1km grid map. Corresponding maps of administrative regions, river watersheds, soil types (ST), and ecosystems in China were also prepared with an identical resolution and coordinate control points, allowing GIS analyses. Results show that soils in China cover an area of 9.281x10.sup.6 km.sup.2 in total, with a total SOC stock of 89.14Pg (1Pg=10.sup.15 g) and a mean SOC density of 96.0tC/ha. Confidence limits of the SOC stock and density in China are estimated as [89.23Pg, 89.08Pg] and [96.143tC/ha, 95.981tC/ha] at 95% probability, respectively. The largest total SOC stock (23.60Pg) is found in South-west China while the highest mean SOC density (181.9tC/ha) is found in north-east China. The total SOC stock and the mean SOC density in the Yangtze river watershed are 21.05Pg and 120.0tC/ha, respectively, while the corresponding figures in theYellow river watershed are 8.46Pg and 104.3tC/ha, respectively. The highest total SOC stocks are found in Inceptisols (34.39Pg) with SOC density of 102.8tC/ha. The lowest and highest mean SOC densities are found on Entisols (28.1tC/ha), and on Histosols (994.728.1tC/ha), repectively. Finally, the total SOC stock in shrub and forest ecosystem classes are 25.55 and 21.50Pg, respectively; the highest mean SOC density (209.9tC/ha) was recorded in the wetland ecosystem class and the lowest (29.0tC/ha) in the desert ecosystem class. Among five forest ecosystem types, Evergreen conifer forest stores the highest SOC stock (6.81Pg), and Deciduous conifer forest shows the highest SOC density (225.9tC/ha). Figures of SOC stocks stratified by Administrative regions, river watersheds, soil types and ecosystem types presented in the study may constitute national-wide baseline for studies of SOC stock changes in various regions in the future. Author Affiliation: (a) State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China (b) Department of Geography, University of Toronto, 100 St. George St., Room 5047,Toronto, Ont., Canada M5S 3G3 Article History: Received 1 November 2005; Revised 8 September 2006; Accepted 19 September 2006

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.