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Given the positive effects of mediating the growth of greenhouse gases in the atmosphere, interest in soil carbon stock dynamics has greatly increased. Several questions still exist as to whether irrigation with sediment laden water benefits carbon sequestration in soil profiles. This case study documented how long-term irrigation with sediment laden water from the Yellow River affected soil carbon sequestration in the Ningxia Irrigation Zone, China. The study included eight durations of irrigation management (10, 20, 30, 50, 280, 1300, 2100, and 2200 years) and five soil types. Soil samples from 44 profiles were collected to a depth of 100 cm, divided into four layers (0–20, 20–30, 30–60, 60–100 cm), and analyzed for soil organic carbon (SOC). SOC stocks both of soil profiles (0–100 cm) and irrigation-silted soil (ISS) layer, were 28.2 Tg C and 24.1 Tg C, respectively. The ISS layer was formed by the overlapping actions of irrigation and tillage, manure addition, and sediment silting as a result of long-term irrigation from the sediment laden water of the Yellow River. Compared to non-irrigated and non-cultivated control soils of similar depths and thicknesses, SOC stocks of the ISS layer increased 16.9 Tg C, and accounted for 89.9% of a total increment of 18.8 Tg C in the 0–100 cm layer of irrigated cropland soils. A significant correlation was found between the SOC density increment of the ISS layer and the number of irrigation years. Long-term irrigation with sediment laden Yellow River water greatly influenced SOC stocks, especially in the ISS layer, which plays an important role in soil carbon sequestration.

Forest ecosystems contribute substantially to global terrestrial primary productivity and climate regulation, but, in contrast to grasslands, experimental evidence for a positive biodiversity-productivity relationship in highly diverse forests is still lacking1. Here, we provide such evidence from a large forest biodiversity experiment with a novel design2 in subtropical China. Productivity (stand-level tree basal area, aboveground volume and carbon and their annual increment) increased linearly with the logarithm of tree species richness. Additive partitioning3 showed that increasing positive complementarity effects combined with weakening negative selection effects caused a strengthening of the relationship over time. In 2-species mixed stands, complementary effects increased with functional distance and selection effects with vertical crown dissimilarity between species. Understorey shrubs reduced stand-level tree productivity, but this effect of competition was attenuated by shrub species richness, indicating that a diverse understorey may facilitate overall ecosystem functioning. Identical biodiversity-productivity relationships were found in plots of different size, suggesting that extrapolation to larger scales is possible. Our results highlight the potential of multi-species afforestation strategies to simultaneously contribute to mitigation of climate change and biodiversity restoration.

Background Most cardiovascular diseases occur in low‐ and middle‐income regions of the world, but the socioeconomic distribution within China remains unclear. Our study aims to investigate whether the prevalence of cardiovascular diseases differs among high‐, middle‐, and low‐income regions of China and to explore the reasons for the disparities. Methods and Results We enrolled 46 285 individuals from 115 urban and rural communities in 12 provinces across China between 2005 and 2009. We recorded their medical histories of cardiovascular diseases and calculated the INTERHEART Risk Score for the assessment of cardiovascular risk‐factor burden, with higher scores indicating greater burden. The mean INTERHEART Risk Score was higher in high‐ and middle‐income regions than in low‐income regions (9.47, 9.48, and 8.58, respectively, P P trend =0.0064). In high‐ and middle‐income regions, urban communities have higher INTERHEART Risk Score and higher prevalent rate than rural communities. In low‐income regions, however, the prevalence of total cardiovascular disease was similar between urban and rural areas despite the significantly higher INTERHEART Risk Score for urban settings. Conclusions We detected an inverse trend between risk‐factor burden and cardiovascular disease prevalence in urban and rural communities in high‐, middle‐, and low‐income regions of China. Such asymmetry may be attributed to the interregional differences in residents’ awareness, quality of healthcare, and availability and affordability of medical services.

Soil erosion is the most severe environmental problem in the red soil region of southern China. Erosion has been significantly reduced over the last 30 years through the deployment of a massive government re-forestation program. Nevertheless, soil erosion is still severe in some areas, and an efficient method for assessing soil erosion under secondary forest canopy is needed. Traditionally the vegetation indices derived from remotely sensed imagery have been used for identifying eroded areas. However, under high density forest canopy (HDFC) their applicability suffers due to biomass light absorption, which varies by canopy structure. A mapping method was developed by integrating remote sensing parameters to identify eroded areas. The remotely sensed vegetation indices, vegetation fractional canopy (VFC) and leaf area index (LAI) were calibrated based on soil erosion measurements from previous runoff plot experiments and extrapolated from USLE modeling to a regional scale. Using vegetation restoration degree (VRD), based on VFC (VRDVFC = VFC/VITVFC) and LAI (VRDLAI = LAI/VITLAI), soil erosion under HDFC was identified. Results indicate that the threshold value of VFC (VITVFC) and LAI (VITLAI) ranged from 0.45 to 0.60, and from 1.3 to 2.7, respectively. Secondary forest associated with VRDVFC > 100% or VRDLAI > 100% occupied 75.8% and 37.8% of the total study area, respectively. About 50% of the area distinguished as being eroded by LAI, was mapped as having no obvious erosion by VFC. LAI based mapping had a precision of 96.7% according to field validation. The eroded areas were primarily distributed in locations with elevation between 300 to 500 m and slope angles below 25°. The present method for distinguishing soil erosion under HDFC by combining VRDVFC and VRDLAI is effective and can be used for operational erosion monitoring in the red soil region of southern China.

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.

The conversion of cereal to vegetable represents a significant shift in land use in China, and it causes significant changes in soil properties. Most studies have only focused on chemical or biological properties; few have investigated soil structure. Soil structure, especially macropore space, is very important for plant growth because of its relation to important soil functions and processes, such as gas diffusion and water permeability. The objective of this study was to assess the effect of land use conversion from rice to vegetable on soil macropores (>50 μm) measured by computed tomography (CT) and to examine the relationships between CT-measured pore characteristics and soil chemical properties. By using space instead of time, we sampled three land uses – rice/wheat rotation (RWR), open-field vegetable (OFV) and plastic-greenhouse vegetable (PGV) – in a tilled and plow pan layer in a suburban area of Nanjing, China, and analyzed the basic physicochemical properties and CT-measured macropore characteristics. The results showed that the tilled layer soil had a significant response to the land use change. The macroporosity decreased from 11.5% under RWR to 8.0% under OFV and 5.8% under PGV, and the decreased portion consisted mainly of elongated large macropores (>1000 μm). In addition, the macropore morphology of vegetable fields also showed degradation, with a higher degree of anisotropy (DA) and lower fractal dimension (FD) and connectivity compared to those under RWR, but PGV experienced a higher degree of degradation than did OFV. This study also showed that soil structure degradation was significantly correlated with decreasing soil organic matter (SOM). Increasing the amount of organic fertilizer applied might improve the SOM content and therefore improve the soil structure. Based on the linear regression equation, adding 1 g of SOM per kilogram of soil can improve the macroporosity by 0.54 m3 m−3.

Land-use/land-cover changes (LUCCs) have links to both human and nature inter- actions. China's Land-Use/cover Datasets (CLUDs) were updated regularly at 5-year inter- vals from the late 1980s to 2010, with standard procedures based on Landsat TM\ETM+ im- ages. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics, differences, and causes of land-use changes at a national scale were then examined. The main findings are summarized as fol- lows. Land-use changes (LUCs) across China indicated a significant variation in spatial and temporal characteristics in the last 20 years (1990-2010). The area of cropland change de- creased in the south and increased in the north, but the total area remained almost un- changed. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly, were mainly distributed in the east, and gradually spread out to central and western China. Woodland decreased first, and then increased, but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUC in China were found between the late 20th century and the early 21st century. The original 13 LUC zones were replaced by 15 units with changes of boundaries in some zones. The main spatial characteristics of these changes included (1) an accelerated expansion of built-up land in the

The 3-D spatial distributions of vegetation are of great significance for water and soil conservation but are rarely concerned in literatures. The live vegetation volume (LVV) was used to relate to water/soil loss under 144 natural erosive rainfall events from 2007 to 2010 in a typical water-eroded area of southern China. Quadratic polynomial regression models were established for five pure tree ( Pinus massoniana Lamb) plots between LVV and water (runoff)/soil conservation effects (RE/SE). RE/SE corresponds to the ratios of runoff depth/soil loss of the pure tree plots to that of the control plot under each rainfall event. Increasing LVV exhibits descending (DS), descending-ascending (DA), ascending-descending (AD), and ascending (AS) trends in the LVV–RE and LVV–SE curves. The effects of soil conservation on the plots were generally more noticeable than the effects of water conservation, and most of the RE and SE values reflected the positive effects of water and soil conservation. The effects were mainly positive under heavy rains (e.g., rainfall erosivity, R = 140 MJ mm ha −1 h, maximum 30 min intensity, I 30 = 16 mm h −1 ), whereas the effects were mainly negative under light rains (e.g., R = 45 MJ mm ha −1 h, I 30 = 8 mm h −1 ). The trees' water/soil conservation effects notably transformed when rainfall erosivity and intensity were lower than the positive or negative effects to a certain threshold. About 50% rainfall events led to obvious transform effects when LVVs were near 0.5 or 0.6. These results are able to aid in the decision making on the forest reconstruction in water-eroded areas.

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.

Accumulation and potential health risk of cadmium (Cd), lead (Pb), copper (Cu), and zinc (Zn) in a plot-scale vegetable production peri-urban area near Nanjing city, China was investigated through element balance method, model simulation and dietary risk assessment. The heavy metals accumulated in the surface soils were due to long-term and heavy application of organic fertilizers, among which the accumulation of Cu and Zn were greater than those of Cd and Pb. The result of a mass balance model simulation indicated that intensive vegetable production would result in accumulation of Cd, Pb, Cu and Zn in soils exceeding the target values in 55, 36, 34 and 71 years, respectively. The estimated dietary intakes of Cd, Pb, Cu, and Zn were far below the tolerable limits and the hazard quotient values were below one for both children and adults. Although there is no imminent health risk from heavy metals through vegetable consumption, more attention should be paid to the long-term accumulation and risk, especially for children.

Agricultural intensity, a function of agricultural input and output, impacts agricultural soil organic carbon (SOC) pools. Investigating the agricultural intensity and SOC density relationship supports understanding of anthropogenic activities on SOC pools in agricultural regions. Cangshan County, one of the most important vegetable cultivation counties in China, was selected as the study area for this investigation. By using soil survey data compiled in 1980 and 2008, 21 towns in Cangshan County were used as study units to investigate the relationship between agricultural intensity indicators and index, with SOC density. Results demonstrate that single agricultural intensity indicators could not reflect SOC densities changes well. Conversely the agricultural intensity index, a composite measure of agricultural input, output and input–output indicators (AI1, AI2 and AI3, respectively), was a more reliable measure reflecting SOC density variability. An S-curve model, SOCd (t ha−1) = exp (α-β/AIi), was the best fit for the plot of SOC densities with the agricultural intensity index. Relationships between AI3 and SOC density were statistically significant for 1980, 2008 and their variation during 2008–1980 (p < 0.05), indicating that AI3 had a more robust relationship with SOC density compared with AI1 and AI2. Though the relationships have low precision (R2 = 0.24–0.40) for not including the natural factors and more indictors relative to variation of SOC density should be selected to improve the interpretability, it is still valuable to introduce the agricultural intensity index when detecting dynamics of SOC pools at a large regional scale.

Due to resettlements, construction of new infrastructure, and new land reclamation the rapid agricultural changes in the Three Georges Area (TGA) in Central China are expected to force the degradation of the cultivated terraced landscape. Consequently, increased soil erosion can hamper a sustainable land management in the mountainous TGA. This paper presents the model framework TerraCE (Terrace Condition Erosion) for determining the causes for different terrace conditions and terrace degradation based on field surveys and spatial data mining. For a total of 987 bench terrace plots in the Xiangxi catchment we collected data on their state of maintenance and terrace design to account for terrace stability and thus capability of soil conservation. Assessing the driving factors of terrace degradation was done by embedding terrain-based predictors and distance-transforms of remote-sensing data as indicators of environmental and anthropogenic influences. Random forests classification and regression models were applied for data mining. Terrace degradation in the Xiangxi catchment is obvious. The sequence of degradation ranges from ‘well maintained’ (21%), ‘fairly maintained’ (44%), and ‘partially collapsed’ (23%) to ‘completely collapsed’ (11%) terraces. The cross-validation error of the supervised TerraCE model is below 8%, allowing for reasonable and valid interpretations of the causes of terrace degradation. Data mining reveals indicators for anthropogenic effects such as the distance to settlements or to roads as major drivers for the spatial distribution of terrace conditions. The effect of relief, which can be regarded as the major natural driver for terrace degradation by erosive action is tributary but altered and overlaid by land use dynamics associated with the Three Gorges Dam. An important indicator representing a combined effect of terrain and human activity is the distance to stream channels of different orders. Applying TerraCE we reveal mechanisms of terrace degradation in disturbed environments and present a framework for standardized mapping and analysis of terrace degradation under cultivation. The approach might also be used to develop guidelines for terrace planning in mountainous terraced landscapes of limited carrying capacity, with respect to socio-economic as well as environmental conditions.

Determining the effects of the critical structure of forests on soil and water conservation aids in understanding forest functions and scientific decision-making in management practices. Quadratic polynomial regression models were established for five pure tree ( Pinus massoniana Lamb) plots between vegetation fractional coverage (VFC) and soil/water conservation effects (SCE/WCE) for 144 erosive rainfall events from 2007 to 2010 in Hetian Town, Changting County, Fujian Province, a typical water-eroded area in southern China. The SCE/WCE corresponds to the ratios of soil loss/runoff depth of each tree plot to those of the control plot under each rainfall event. The results show that increasing VFC exhibits descending (DS), descending–ascending (DA), ascending–descending (AD), and ascending (AS) trends in the VFC–SCE and VFC–WCE curves, which indicate positive or negative effects of plot trees on soil and water conservation. In general, the effects on soil conservation on the plots were more notable than those on water conservation, and most of the SCE and WCE values reflect the positive effect of trees on soil and water conservation. Most determination coefficients of both the VFC–SCE and VFC–WCE models exceeded 0.6, and varied with the year and the effect. The positive effects of the pure trees occurred mainly during rainfall events with relatively higher rainfall depth, stronger intensity, and shorter duration, whereas the negative effects occurred mainly during rainfall events with lower rainfall depth, weaker intensity, and longer duration. The transition effects DA and AD appeared largely during rainfall events with moderate rainfall depth, intensity, and duration. The critical VFCs for soil and water conservation during rainfall events were 0.4 and 0.5, respectively, which provided a reference for the evaluation and planning of forest reconstruction in this and similar water-eroded areas.

Paddy soils support important croplands in many parts of the world, especially in Asia, so an accurate estimation of the magnitude and dynamics of paddy soil organic carbon (SOC) stocks is critical to developing strategies for greenhouse gas mitigation. Soil databases of different map scales have been widely used in process-based models, but exactly how map scale affects the simulation of SOC dynamics on a national basis remains unclear. To improve the understanding of map scale associated uncertainty, the DeNitrification–DeComposition (DNDC) model were parameterized with three soil polygon-based databases at scales of 1:1,000,000, 1:4,000,000, and 1:14,000,000 to simulate SOC stocks (0–30 cm) for paddy soils of China and their changes from 1980 to 2008. Based on the relatively detailed 1:1,000,000 scale soil database, SOC stock for paddy soils of China ranged from 2.51 Pg C in 1980 to 2.65 Pg C in 2008. The estimates of SOC stock decreased as map scale decreased from 1:4,000,000 to 1:14,000,000, and differed by 27% and 40% from the 1:1,000,000 scale database. Map scale influences on simulation outcome are related to the total paddy soil area and initial SOC contents, of which the latter may account for 73.2%–79.5% of the variability of SOC density for paddy soils of China. The multi-scale simulation results indicate that the application of 1:1,000,000 scale soil polygon-based database could reduce the inaccuracy of national SOC stock estimates and contribute to improved assessment of continental and global carbon balance.

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.

AimsThe aim of our research was to understand small-scale effects of topography and soil fertility on tree growth in a forest biodiversity and ecosystem functioning (BEF) experiment in subtropical SE China.MethodsGeomorphometric terrain analyses were carried out at a spatial resolution of 5×5 m. Soil samples of different depth increments and data on tree height were collected from a total of 566 plots (667 m2 each). The soils were analyzed for carbon (soil organic carbon [SOC]), nitrogen, acidity, cation exchange capacity (CEC), exchangeable cations and base saturation as soil fertility attributes. All plots were classified into geomorphological units. Analyses of variance and linear regressions were applied to all terrain, soil fertility and tree growth attributes.Important FindingsIn general, young and shallow soils and relatively small differences in stable soil properties suggest that soil erosion has truncated the soils to a large extent over the whole area of the experiment. This explains the concurrently increasing CEC and SOC stocks downslope, in hollows and in valleys. However, colluvial, carbon-rich sediments are missing widely due to the convexity of the footslopes caused by uplift and removal of eroded sediments by adjacent waterways. The results showed that soil fertility is mainly influenced by topography. Monte–Carlo flow accumulation (MCCA), curvature, slope and aspect significantly affected soil fertility. Furthermore, soil fertility was affected by the different geomorphological positions on the experimental sites with ridge and spur positions showing lower exchangeable base cation contents, especially potassium (K), due to leaching. This geomorphological effect of soil fertility is most pronounced in the topsoil and decreases when considering the subsoil down to 50cm depth. Few soil fertility attributes affect tree height after 1–2 years of growth, among which C stocks proved to be most important while pHKCl and CEC only played minor roles. Nevertheless, soil acidity and a high proportion of Al on the exchange complex affected tree height even after only 1–2 years growth. Hence, our study showed that forest nutrition is coupled to a recycling of litter nutrients, and does not only depend on subsequent supply of nutrients from the mineral soil. Besides soil fertility, topography affected tree height. We found that especially MCCA as indicator of water availability affected tree growth at small-scale, as well as aspect. Overall, our synthesis on the interrelation between fertility, topography and tree growth in a subtropical forest ecosystem in SE China showed that topographic heterogeneity lead to ecological gradients across geomorphological positions. In this respect, small-scale soil–plant interactions in a young forest can serve as a driver for the future development of vegetation and biodiversity control on soil fertility. In addition, it shows that terrain attributes should be accounted for in ecological research.

In densely populated countries like China, clean water is one of the most challenging issues of prospective politics and environmental planning. Water pollution and eutrophication by excessive input of nitrogen and phosphorous from nonpoint sources is mostly linked to soil erosion from agricultural land. In order to prevent such water pollution by diffuse matter fluxes, knowledge about the extent of soil loss and the spatial distribution of hot spots of soil erosion is essential. In remote areas such as the mountainous regions of the upper and middle reaches of the Yangtze River, rainfall data are scarce. Since rainfall erosivity is one of the key factors in soil erosion modeling, e.g., expressed as R factor in the Revised Universal Soil Loss Equation model, a methodology is needed to spatially determine rainfall erosivity. Our study aims at the approximation and spatial regionalization of rainfall erosivity from sparse data in the large (3,200 km2) and strongly mountainous catchment of the Xiangxi River, a first order tributary to the Yangtze River close to the Three Gorges Dam. As data on rainfall were only obtainable in daily records for one climate station in the central part of the catchment and five stations in its surrounding area, we approximated rainfall erosivity as R factors using regression analysis combined with elevation bands derived from a digital elevation model. The mean annual R factor (R a) amounts for approximately 5,222 MJ mm ha−1 h−1 a−1. With increasing altitudes, R a rises up to maximum 7,547 MJ mm ha−1 h−1 a−1 at an altitude of 3,078 m a.s.l. At the outlet of the Xiangxi catchment erosivity is at minimum with approximate R a = 1,986 MJ mm ha−1 h−1 a−1. The comparison of our results with R factors from high-resolution measurements at comparable study sites close to the Xiangxi catchment shows good consistance and allows us to calculate grid-based R a as input for a spatially high-resolution and area-specific assessment of soil erosion risk.

This work is devoted to bridging the gap between large-area, economically driven macromodels such as the Canadian Regional Agriculture Model (CRAM) and small-area biophysically based process models used in environmental assessments through the development of a Land Use Allocation Model (LUAM). LUAM is designed to enable environmental assessments of economic scenarios to be conducted by allocating crop area changes predicted for large areas by CRAM to much smaller Soil Landscapes of Canada (SLC) polygons through an optimization method based on land capability, relative crop productivity and current land use. To develop the procedures, we used linear programming to optimize crop production for large areas under current commodity prices and land productivity ratings and then allocated the results to much smaller soil-landscape polygons based on land capability. To assess the validity of our prototype LUAM, we compared the predicted crop areas with actual crop data from the Census of Agriculture using the method of cumulative residuals (MCR). We concluded that this version of the LUAM model can predict the location of land use to some extent, but requires further refinement. The potential for further development of LUAM using the Land Suitability Rating System (LSRS) is discussed.

An extensive knowledge of how sampling density affects soil organic C (SOC) estimation at regional scale is imperative to reduce uncertainty to a meaningful confidence level and aid in the development of sampling schemes that are both rational and economical. Using kriging prediction, this paper examined the effect of sampling density on regional SOC-concentration estimations in cultivated topsoils at six scales in a 990 km2 area of Yucheng County, a typical region in the N China Plain. Except the original data set (n = 394), five other sampling densities were recalculated using grids of 8 km × 8 km (n = 28), 8 km × 4 km (n = 44), 4 km × 4 km (n = 82), 4 km × 2 km (n = 142), and 2 km × 2 km (n = 257), respectively. Experimental SOC semivariances and kriging interpolations at six sampling density scales were calculated and modeled to estimate regional SOC variability. Accuracy of the effects of the five sampling densities on regional SOC estimations was assessed using the indices of mean error (ME) and root mean square error (RMSE) with 100 independent validation samples. By comparison with the kriged grid map derived from the 394 samples data set, the relative error (RE,%) was spatially calculated to highlight the spatial variability of prediction errors at five sampling-density scales due to the intrinsic limitations of ME and RMSE in accuracy assessment. The results indicated that sampling density significantly affected the estimation of regional SOC concentration. Particularly when the sampling density was < 4 km × 4 km, the large spatial variation of SOC was concealed. Semivariance analysis indicated that different sampling density had significant effect on reasonable detection of the dominant factors which influenced SOC spatial variation. Greater sampling density could more exactly reveal regional SOC variation caused by human management. The prediction accuracy for regional SOC estimation increased with the increasing of sampling density. The critical areas with larger RE values should be intensified in the future sampling scheme, and the areas of lower RE values should be decreased relatively. A specific sampling scheme should be considered in accordance with the demand to the estimation accuracy of regional SOC stock at a certain confidence level. Our results will facilitate a better understanding of the effect of sampling density on regional SOC estimation for future sampling schemes by providing meaningful confidence levels.

Regional soil organic carbon (SOC) modeling is the dominant approach for regional and global carbon cycling assessment, but the models are often applied to regions with high heterogeneity that are not adequately represented by the spatially limited soil data. This study used version 9.1 of the denitrification–decomposition (DNDC) model with the most detailed soil database for the paddy region of China. The database is a 1:50,000 record derived from 1107 paddy soil profiles with 52,034 polygons. The simulations suggested that the 2.3 Mha of paddy soils in the Tai-Lake region had a net sequestration of about 1.48 Tg C from 1982 to 2000, with the annual SOC change ranging from −45 to 92 kg C ha −1 y −1 . In general, paddy soils in the Tai-Lake region were a weak sink of atmospheric CO 2 . Highest SOC loss (−201 kg C ha −1 y −1 ) was associated with the gleyed paddy soil subgroup. Highest SOC sequestration (205 kg C ha −1 y −1 ) was associated with the submergenic paddy soil subgroup. On a regional basis, model simulations indicated a C loss in the polder region (−39 kg C ha −1 y −1 ), but this was offset by increases in the alluvial plain (104 kg C ha −1 y −1 ), low mountainous and hilly region (87 kg C ha −1 y −1 ), and Tai-Lake Plain (7 kg C ha −1 y −1 ). At the administrative scale, SOC of most counties in Zhejiang Province decreased, while it increased in Jiangsu Province and Shanghai City. Overall, the SOC change in the Tai-Lake region was strongly influenced by paddy subgroup, sub-region, and administrative area, because of the high variability in soil properties. This emphasizes that the use of detailed soil data sets with high-resolution digital soil maps and robust soil profile data essential for creating accurate models of the soil carbon cycle.

article i nfo Paddy soils support important croplands in many parts of the world, especially in Asia, so an accurate estima- tion of the magnitude and dynamics of paddy soil organic carbon (SOC) stocks is critical to support adoption of appropriate soil conservation measures and development of strategies for greenhouse gas mitigation. To reduce uncertainties from upscaling spatial processes of the DeNitrification-DeComposition (DNDC) model for improving the definition of China's paddy soils SOC stocks (0-30 cm) and their changes from 1980 to 2008, a soil polygon-based modeling method was developed by integrating DNDC with the latest 1:1,000,000 soil database, derived from 1490 paddy soil profiles collected during the Second National Soil Survey of China. The simulations demonstrated that Chinese paddy soils (0-30 cm), which cover 45.0 M ha, storaged 2.51 Pg in 1980 and 2.65 Pg in 2008 with an average sequestration rate of 5.0 Tg C year �1 . Approximately 83.3% of the paddy soils sequestrated C, while 16.1% lost C and only 0.6% were balanced over the past 28 years. There were, however, large regional differences in sequestration out- comes. Increases in paddy soil SOC stocks were found predominantly in South, Eastern and Southwest China, where topsoil SOC increased by 59.6, 56.4 and 28.3 Tg, respectively. SOC stock in Northwest and North China increased only 0.6 and 0.3 Tg, respectively, while that in Northeast China lost 0.5 Tg. Enhancement in the soil databases with higher resolution inputs should improve the C sequestration estimates of any area of interest. © 2011 Elsevier B.V. All rights reserved.

Sand-filled splash cups were used to study the erosivity of rainfall and throughfall in the humid subtropics of southeast China. Our results showed that the splash cup measurements yielded precise and reproducible results both under open field conditions and under forest vegetation. The splash cups were exposed to forest stands of different age and to selected species ( Schima superba , Castanopsis eyrei ) in the Gutianshan National Nature Reserve (GNNR). The measurements in the open field revealed a close relationship between unit sand loss (g m − 2 ), rainfall amount (mm) (R 2 = 0.94) and maximum rainfall intensity (mm h − 1 ) (R 2 = 0.90). The highest correlation was obtained between unit sand loss (g m − 2 ) and the average of the five highest five minute interval rainfall intensities (mm h − 1 ) (R 2 = 0.96). This underlines the reliability of the splash cups used. The best results on the relationship between variables related to precipitation and sand loss were obtained with the Vaisala sensor compared to a standard tipping-bucket rain gauge. This is mainly due to the fact that the drop impact-based Vaisala sensor (0.8–5.0 mm drops are recorded assuming their terminal velocity) does not measure drops with low kinetic energy, whereas drop size distribution plays no role for the measurements with the tipping-bucket rain gauge. The results obtained under forest vegetation show that the erosive power of throughfall drops is 2.59 times higher compared to the open field, which accentuates the importance of shrub, herb and litter layers in forest ecosystems to protect the soil against erosion. Coalescing drops from leaves and branches (drips) are responsible for this enormous gain in erosive power. Moreover, the results show that the erosion potential under forest is related to the forest structure, especially height and canopy cover. The erosion potential of medium and old grown forests is 1.53 times higher than of young forests. Further, differences in sand loss between Schima superba and Castanopsis eyrei indicate that the erosion potential and the spatial heterogeneity of throughfall is species-specific, highlighting the importance of selecting species for afforestation projects considering soil erosion potential.

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.

China's rice paddies, accounting for 19% of the world's total, play an important role in soil carbon (C) sequestration. In order to reduce uncertainties from upscaling spatial processes of the DeNitrification–DeComposition (DNDC) model for improving the understanding of C sequestration under recommended management practices (RMPs), we parameterized the DNDC model with a 1:1,000,000 polygonal soil database to estimate how RMPs influence potential C sequestration of the top 30 cm of Chinese paddy soils and to identify which management practices have the greatest potential to increase soil organic carbon (SOC) in these soils. These practices include reduced/no tillage, increasing crop residue return, and increasing manure applications. A baseline and eleven RMP scenarios were projected from 2009 to 2080, including traditional and conservation tillage, increasing crop residue return, increasing manure incorporation, and the combination of these practices. The results indicated that C sequestration potential under modeled RMPs increased compared to the baseline scenario, and varied greatly from 29.2 to 847.7 Tg C towards the end of the study period with an average rate of 0.7 to 20.2 Tg C yr − 1 . In general, increasing crop residue return was associated with higher rates of C sequestration when compared to increasing manure application or practicing conservation tillage. The simulations demonstrated that the most effective soil C sequestration strategy probably involves the implementation of a combination of RMPs, and that they vary by location.

Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI mod...

Soil erosion by water under forest cover is a serious problem in southern China. A comparative study was carried out on the use of leaf area index (LAI) and vegetation fractional coverage (VFC) in quantifying soil loss under vegetation cover. Five types of vegetation with varied LAI and VFC under field conditions were exposed to two rainfall rates (40 mm h−1 and 54 mm h−1) using a portable rainfall simulator. Runoff rate, sediment concentration and soil loss rate were measured at relatively runoff stable state. Significant negative exponential relationship (p < 0.05, R2 = 0.83) and linear relationship (p < 0.05, R2 = 0.84) were obtained between LAI and sediment concentration, while no significant relationship existed between VFC and sediment concentration. The mechanism by which vegetation canopy prevents soil loss was by reducing rainfall kinetic energy and sediment concentration. LAI could better quantify such a role than VFC. However, neither LAI nor VFC could explain runoff rate or soil loss rate. Caution must be taken when using LAI to quantify the role of certain vegetation in soil and water conservation.

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.

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.

Mercury (Hg) accumulation and transfer in soil ecosystems has been altered on local, regional, and even global scales, and their environmental risk has increasingly been a concern to the public and the scientific community. A county level region in Zhangjiagang County, the Yangtze River Delta (YRD) region of China and a factory with Hg-contaminated wastewater discharging within the region were selected to study the accumulation, bioavailability, and transfer of Hg from different sources in soils and crops under rapid industrialization, urbanization, and intensive agricultural activities. Regional soil samples close to and away from factories and local soil and crop samples around a typical factory were collected in the YRD region of China. Soil and crop Hg and basic soil properties were examined. Significant soil Hg accumulation was found in soils away from factories regardless of Cambosols (Entisols) and Anthrosols (Inceptisols), while the mobile HCl-extractable Hg (HCl-Hg) were greater in soils closer to factories due to a decrease and increase in soil pH and organic matter. A high level of soil total Hg (T-Hg) was found around the factory, and soil and crop Hg accumulation in the vicinity of the factory was localized with an exponential decrease as distance away from the wastewater discharge outlet increased. Although Hg accumulated in these soils, the T-Hg levels at only a few sampling sites in acidic Anthrosols area were found to exceed the second most stringent critical value of Chinese Environmental Quality Standards for Soils. Considering the cessation of Hg-containing agrochemicals and limitation of effects of industrial activities on Hg accumulation, more attention should be paid to the changes in soil properties and crop rotations than controlling the pathways of Hg entering soils because the current environmental risk is mobilization of accumulated soil Hg.

Previous studies suggest that vegetable cultivation increases soil organic carbon (SOC) storage. However, how stable the SOC increment is, and how greenhouse cultivation contributes to the SOC increment in terms of quantity and stability, remains unclear. Soil samples were taken from three typical vegetable cultivation pattern fields: open field (OF), seasonal greenhouse (SG), and permanent greenhouse (PG), as well as adjacent non-vegetable fields. Three conceptual SOC pools, including active (Ca), slow (Cs), and resistant (Cr) pools were fractionated to evaluate SOC sequestration and its stability in vegetable cultivation. The results indicate that vegetable cultivation is associated with greater stored SOC compared with non-vegetable cultivation (SOC increased by 57.9% on average). Using non-vegetable fields as a reference, SOC increments by vegetable cultivation were associated with a higher proportion of Ca (3.7&ndash, 6.6%) than the reference fields (1.0&ndash, 2.0%), indicating that the SOC increments might be easily decomposed. Among the three vegetable cultivation patterns, SG, with a higher increase in Cr, is recommended due to its relatively more stable SOC sequestration. Overall, vegetable cultivation could enhance the quantity of SOC, but the stability of the SOC increment is affected by the vegetable cultivation pattern.

Sand-filled splash cups were used to study the erosive power of rainfall and throughfall in the humid subtropics of Southeast China. The splash cup measurements yielded precise and repro- ducible results under both open field conditions and forest vegetation. The splash cups were exposed to specific forest stands of different ages and to selected species (Schima superba, Castanopsis eyrei, Daph- niphyllum oldhamii, Lithocarpus glaber) in the Gutianshan (古田山) National Nature Reserve (GNNR). The results of the measurements under forest vegetation show that the erosive power of throughfall drops to be 2.59 times higher compared to the open field. This accentuates the importance of shrub, herb and litter layers in forest ecosystems to protect the soil against erosion. Coalescing drops from leaves and branches (drips) are responsible for this notable gain in erosive power. Moreover, differ- ences in sandloss between the investigated tree species (deciduous, evergreen) revealed that the erosion potential and the spatial heterogeneity of throughfall are species-specific. This highlights the impor- tance of selecting specific species for afforestation projects considering the prevention of soil erosion.

Though numerous studies have evaluated the effect of prediction method on soil organic carbon (SOC) spatial distribution, the influence of prediction method on detecting the temporal evolution of SOC has rarely been reported. This study was conducted in Yujiang County, Jiangxi Province, China using 174 and 257 samples collected in 1982 and 2007, respectively. The SOC spatial distributions for those 2 years and the associated temporal evolution were predicted via ordinary kriging (OK) and kriging combined with soil types and land-use (KSTLU) patterns. Results showed that KSTLU had better spatial prediction accuracy than OK for both years. The mean absolute errors (MAEs) of OK for 1982 and 2007 were 2.45 and 5.99 g kg−1, and the root mean square errors (RMSEs) were 3.37 and 7.23 g kg−1, respectively. Meanwhile, the MAEs of KSTLU for 1982 and 2007 were 1.99 and 3.23 g kg−1 which are 19 and 46% lower than those of OK, respectively. The RMSEs were 2.76 and 3.97 g kg−1 which were 18 and 45% lower than those of OK, respectively. Moreover, the result of temporal evolution from the KSTLU prediction showed that the area of increasing SOC (70%) is remarkably larger than the area of decreasing SOC (30%). The KSTLU prediction was more consistent with the statistical results than the OK prediction. Furthermore, the contours of SOC temporal evolution from KSTLU had a more detailed description for local change of SOC than OK. Thus, the prediction methods greatly affect the detection of SOC temporal evolution. Results of this study indicated that KSTLU is more efficient and rational than OK in studying the SOC temporal evolution in the Hilly Red Soil Region of South China.

Field sampling remains a vital step for assessing the spatial distribution of various soil properties. In regional soil surveys, unclassified grid-based mode (Grid), soil type-based mode (SoTy), land-use pattern-based mode (Lu), and land-use pattern–soil type-based mode (Lu–SoTy) are all used popularly in allocating sampling points. However, few studies have identified which mode is most effective for sampling point allocation in SOC studies. This study on the effectiveness of sampling modes was conducted in Yujiang County, Jiangxi Province, China, where red soil prevails on hills. A total of 561 soil samples were collected on the basis of 2 km × 2 km grids. Then the collected samples were classified according to the four aforementioned modes respectively where SOC spatial variability data was compared and assessed. The coefficient of variation (CV) of SOC from the classification of Grid was 47%. Mean CVs from the classification of SoTy were 46%, 38%, and 34% at the soil great group, subgroup, and family categories respectively. The mean CV of SOC from the classification of Lu was 42%, and the mean CVs from the classification of Lu–SoTy were 41%, 37%, and 31% at soil great group, subgroup, and family categories, respectively. The mean CVs of SOC from the three latter classification modes were all lower than that from the classification of Grid. The widest decrease in CV was found in the classification of the Lu–SoTy case. A decrease in the mean CV of SOC indicates a decrease in the uncertainty of sampling point allocation for getting SOC spatial distribution. For soil survey in China's hilly red soil areas with complicated soil types and diversified land-use patterns at a county scale, the most effective way to assess the spatial distribution of SOC is to allocate sampling points based on Lu–SoTy.

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.

Soil classification is the basis for the exchange of soil science research results and the foundation for the application of modern soil resource management methods. A WebGIS-based system designed to relate genetic soil classification of China (GSCC) to soil taxonomy (ST) was developed to enhance global cooperation and to support communication between China and the other countries on important agricultural and environmental issues. The system has a Browse Server (B/S) structure and exploits the 1:1,000,000 soil databases of China using WebGIS functionality. This paper describes the application of the WebGIS system for easily accessing cross-reference information between GSCC to ST. First, we describe the three-level B/S structure of the system. The cross-reference methodologies, referenceability and maximum referenceability, are then explained and applied at three geographic scales (i.e. nation, region and pedon). Finally, three sub-modules based on the supported scales are described and illustrated with application scenarios to familiarize users with the inquiry system and its usage. The main advantage of the system is that it considers statistical similarity in the spatial distributions between the two different classification systems. Users with limited knowledge are able to obtain soil cross-reference information using an intuitive interface, which supports query, visualization and analysis via a web browser at the most detailed level. The inquiry system benefits the development of soil classification science and international academic exchange.

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.

Information and knowledge about the spatial variability and multi-scale sources of soil heavy metal variability are important for risk assessment, soil remediation, as well as effective management recommendations. Topsoil samples (0–20 cm) (n = 330) were collected from Luhe County, China. Geostatistical multivariate factorial kriging with robust estimates of variograms, spatial overly analysis and indicator kriging were applied to explore the correlations among soil heavy metals (Cu, Zn, Pb, Cr, Ni, Cd, and Hg) across different spatial scales, identify the sources of spatial variability, and evaluate the potential risk of soil contamination. High variability of soil heavy metals was observed and the correlations among the selected soil heavy metals in the study area depend on spatial scale. Copper, Zn, Cr, and Ni at short-range scale (2 km) are mainly controlled by geology (bed rock), while land use greatly affected the long-range variations of Cd and Hg. The strong correlations between Cr and Ni at short- and long-range (11 km) scale indicate that the sources of Cr and Ni are predominately geochemical. However, anthropogenic activities had profound impacts on the concentrations of Cu, Zn, Pb, Cd, and Hg. The potential contamination risk, defined here as measured soil heavy metal concentrations which exceeded the background values of Chinese Environmental Quality Standard for Soils, was observed in Cu, Zn and Pb, and the areas covered 3%, 3.4%, and 1.4%, respectively. The possible sources of Cu and Zn contamination can be considered as the current and historic industrial emissions from Dachang industrial zone, and the potential areas of Pb contamination could be attributed to the industrial emissions from Dachang industrial zone, small factories and heavy traffic flows in/nearby downtown area of the county. While the Hg contamination risk was closely related to the widely use of Hg-related pesticide during 1940–1970s.

Predicting soil organic carbon (SOC) content distribution accurately from limited soil samples has received a great deal of attention recently in an effort to support soil fertility mapping and to improve our understanding of carbon sequestration variability. Kriging methods combined with auxiliary variables are frequently used at present. However, studies using categorical information, such as soil type and land use, which are closely related to local trends in SOC spatial variation, as auxiliary variables are seldom conducted. In the present investigation, a total of 254 surficial soil samples were collected in the study area, Yujiang county in the hilly red soil region of South China, and a comparison of performance of four kriging approaches was conducted, ordinary kriging (OK), kriging combined with soil-type information (KST), land use (KLU) and combined land use–soil type information (KLUST). Results of the assessment were based on 85 validation samples. The results indicate that the best ...

Excess phosphorus (P) from agricultural soils contributes to eutrophication in water bodies. Samples ( n = 60) were taken from sites where rice paddies have been converted to vegetable fields for 0, 20 years and analyzed for five inorganic P (P i ) fractions, three organic P (P o ) fractions, and several soil parameters to investigate how land use conversion affects P i and P o fractions in a peri-urban area of China with soils characteristic of many agricultural areas of Asia. Significant increases of 33, 281, 293, and 438 mg kg −1 were found for soluble and loosely bound P i (SL-P i ), aluminum-bound P i (Al-P i ), calcium-bound P i (Ca-P i ), and iron-bound P i (Fe-P i ), respectively, after conversion from rice paddies to vegetable fields. Most of the increase in P i was in the form of Fe-P i , which increased from 8% of total P (TP) on paddy soil to 31% on the soil with > 20-year vegetable cultivation, followed by Al-P i , which increased from 2% to 19% of TP. For P o fractions, there was no significant change in P concentrations. The conversion of land use from paddy fields to high intensity vegetable fields was causing significant changes in soil P fractions. Management practices were causing a buildup of soil P, primarily in the Fe-P i fraction, followed by Ca-P i and Al-P i fractions. If current trends continue, a 30%–70% increase in TP could be expected in the next 20 years. Farmers in the area should reduce P application and use to maximize P uptake.

Soil classification systems are not consistent between countries or organizations thereby hindering the communication and organizational functions they are intended to promote. World Reference Base for soil resources (WRB) was endorsed and adopted by the International Union of Soil Sciences (IUSS) as the standard for soil correlation and international communication. As a widely used classification system in China, Genetic Soil Classification of China (GSCC) differs from WRB in its underlying understanding about the genetic process. The differences limit communication between Chinese and international soil scientists because there is no standard cross-reference between GSCC and WRB. This paper describes a cross-reference of GSCC to WRB at different scales. The basic soil data set used in the study includes 7292 soil profile data (representative of soil series) collected throughout China. First, a brief history of soil classification in China is provided to familiarize readers with GSCC and its origins. Second, cross-reference at the pedon scale is addressed based on data compiled from 51 monoliths acquired in China by the International Soil Reference and Information Centre (ISRIC) in the 1980s and 1990s. Each of GSCC's 7292 soil series is classified into their equivalent reference soil groups according to the WRB soil reference key. Pedon scale cross referencing is discussed using the database from the Second National Soil Survey of China. Third, the concept and calculation of referencibility is introduced and the process for cross-referencing soil classification systems at national scale is addressed. GIS based analysis generates 60 reference results between GSCC soil great groups and WRB reference group. Results demonstrate that there is great variability in the maximum referencibility between soil great groups of GSCC and WRB soil groups, which ranged from 29.4% to 100%. In terms of the maximum referencibility, it can be divided into three categories: high (80%–100%), intermediate (50%–80%), and low (

Soil organic carbon (SOC) plays an important role in maintaining and improving soil fertility and quality as well as mitigating climate change. Understanding SOC density spatial variability is fundamental for describing soil resources and predicting SOC. Three categories were used to create spatial scales: administrative category (county, city, province and region scale), soil taxonomic category (family, sub-group, great group and order scale) and soil type (zonal soil and azonal soil)-administrative category. Soil organic carbon density variability and its response to the expansion of scales in the topsoil (0–20 cm) and soil profile (a depth of 1 m) layers in the uplands of Northeast China were examined based on coefficient of variation (CV) values using data of 1041 profiles obtained from the Second National Soil Survey of China. The results depicted that SOC density variability increased not only in the topsoil layer but also in the soil profile layer with the expansion of scales in all categories. In the administrative category, there was a strong logarithmic relationship between upland areas or administrative areas and mean SOC density CV. Though mean SOC density CV within each soil order increased from family to order, the trend and range of increase varied greatly. Soil organic carbon density variability for zonal and azonal soils was similar in terms of trends but different in terms of rate with increasing scale from county to region. A strong logarithmic relationship between upland area and mean SOC density CV was also observed. These relationships indicated that reducing upland area by five orders of magnitude would halve the CV. Therefore, when estimating the SOC pool in uplands, both administrative and soil type scales should be considered in the sampling design, especially for azonal soil.

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 China's 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.

Determining soil nutrient distribution is critical to identify sites which are at risk of N and P loading. Equally important are determining factors that influence such distribution (e.g. land use, land management, topography, etc.). In this research, soil nutrient distribution and its influencing factors were studied in a small-scaled watershed in the purple soil region of Sichuan Province, China. The watershed is 1.3 km2 with a complex land management system including agriculture and forestry. Surface soil samples (0–20 cm) from 48 sites within the watershed were collected in the spring of 1999 and analysed for chemical properties. When spatial distribution patterns of soil organic matter and soil nutrients were considered, several patterns were evident. Soil organic matter and total nitrogen coincided with high soil nutrients in the center of the watershed. Total phosphorus was linked to low soil nutrient contents on hilltops, while total potassium was characterized by low variability and high soil nutrient content throughout the watershed. Water-soluble nutrients in soils were highly variable throughout the watershed. Results of this study indicate that land use, topography and other variables play important roles in controlling the spatial distribution of most soil nutrients. However, the relative roles of these indices were nutrient specific. The relationship was therefore complex. Land use, silt, and sand content contributed to a high level of heterogeneity for soil organic matter and total nitrogen with the former being the dominant factor. Land use, particle size distribution (silt and sand) and topographic factors (slope and elevation) contributed to the variability of total phosphorus. However, particle size distribution and topographic factors were the dominant factors affecting total phosphorus. Elevation influenced the concentration of total potassium and land use controlled the concentration of dissolved phosphorous. No significant relationship was found between the studied characteristics and nitrate nitrogen.

Soil plays an important role in the global carbon cycle, and carbon sequestration in soil is important for mitigating global climate change. Historically, soil erosion led to great reductions of soil organic carbon (SOC) storage in China. Fortunately, with the economic development and remarkably effective soil erosion control measures in subtropical China over the past 20 years, soil erosion has been greatly decreased. As a result, soil organic carbon sequestration has gradually increased due to the rapid recovery of vegetation in the area. However, little information exists concerning the potential of soil carbon sequestration in the area. This paper introduces a case study in Xingguo County, Jiangxi Province, China, which used to be a typical area with significant soil loss in subtropical China. This work represents a systematic investigation of the interrelations of carbon sequestration potential with soil erosion types, altitudes, soil types and soil parent materials. In this study, 284 soil samples were collected from 151 sampling sites (51 are soil profile sites) to determine soil physicochemical properties including organic carbon content. Soil organic carbon distribution maps of the surface layer (0–20 cm) and whole profile (0–100 cm) were compiled by linking soil types to the polygons of digital soil maps using GIS. Assuming that SOC was lost following the destruction of native vegetation, these lands hold great promise for potentially sequestering carbon again. The potential of soil carbon sequestration in the study area was estimated by subtracting the organic carbon status in eroded soils from that in non-eroded soils under undisturbed forest. Results show that the potential of SOC in the surface layer is 4.47 Tg C while that in the whole profile is 12.3 Tg C for the entire county. The greatest potential for carbon sequestration (3.72 Tg C) is found in severely eroded soil, while non-eroded soil has the smallest potential. Also, soil carbon sequestration potential decreases with increasing altitude. Soils at altitudes of 800 m have the smallest potential (0.25 Tg C). Among various soil types, red earths (Humic Acrisols) have the greatest potential of carbon sequestration (5.32 Tg C), and yellow earths (Ferralic Cambisols) have the smallest (0.15 Tg C). As for soils derived from various parent materials, soils derived from phyllite possess the greatest carbon sequestration potential, and soils from Quaternary red clays have the smallest.

Accurate simulation of soil organic carbon (SOC) dynamics is vitally important in researching the carbon cycle in terrestrial ecosystems. Especially, the application of SOC model at the regional scale has major implications for regional and global carbon cycling. This paper addresses the regional simulation for SOC in the surface layer (0~15 cm) of paddy soils in Wuxi and Changzhou, Jiangsu Province, China from 1982~2000 by linking a model of agro-ecosystem SOC dynamic simulation with GIS data on the basis of 100 × 100 m grids. The dataset includes soil profile descriptions and chemical data of 187 paddy soil samples in the Second National Soil Survey of China in 1982 and 352 paddy soil samples taken in 2000. Soil maps at a scale of 1:50,000 were used for the simulation. Results showed that the area of differences between simulated and observed SOC in 2000 (in the range of −3.6 to 3.6 Mg C ha − 1) accounted for 50.6% of the total area. Also, when a 10% and 20% simulated increase, and a 10%, 20% and 30% simulated decrease in harvested crop straw and manure amendments were simulated to test the sensitivity of the model, simulated SOC would range from 10.26 to 50.40 Mg C ha − 1 with a standard deviation of 1.6 and 2.5, respectively. It is indicated that a key factor controlling SOC is the amount of residue and manure inputs. As for the assessment model suitability based on soil textures, soil types, and parent materials, the model seemed to perform better for clayey soils, groundwater-related paddy soils and soils derived from alluvial parent material than for loamy soils, surface water-related paddy soils and soil derived from loess material.

Climate factors, considered significant factors in regulating soil organic carbon (SOC), are not equally important at all spatial scales. However, the scale which provides the optimal relationship between climate and SOC and how that relationship varies at multiple scales are still unclear. Thus, it is crucial to study the relationship between climate factors and SOC at multiple scales when attempting to accurately predict the SOC pool and evaluate the influence of climate change on global carbon cycling. The objective of this research is to examine the scale effect of climate factors on SOC content in the Uplands of Northeast China. The relationship between climate factors and SOC content at the regional, provincial, city, and county scales was evaluated at 0–20 and 0–100 cm using correlation and regression analysis. Data for the study in the Uplands of Northeast China were examined using 1,022 profiles obtained from the Second National Soil Survey of China. A nested sampling scheme was adopted at four scales for this research. Differences exist in the degree of correlation and relative importance of temperature and precipitation at different scales. Temperature is the main climate factor controlling SOC content at the regional scale. At the provincial scale, temperature is also the main climate factor controlling SOC content in the Uplands of Heilongjiang and Eastern Inner Mongolia. SOC content in Jilin and Liaoning is influenced jointly by temperature and precipitation. Additionally, obvious regional differences occurred in the relationship between climate factors and SOC content at the provincial scale. Although this scale differs from those by other researchers, similar results have been reported in China, Spain, and the USA. The reasons might be attributed to the differences in soil type, ecosystem, and topographic position. At both the city and county scales, weak or no relation was observed between climate factors and SOC content. Climate factors have limited ability to explain SOC content variability given that SOC is affected by multiple factors which were not taken into account by this research. Therefore, multiple scales should be considered when the feedback relationship between global climate change and SOC content is studied. The relationship between climate factors and SOC content weakens with decreasing scale, especially from the provincial to city scale. The main climate factors controlling SOC content vary with different scales. The provincial scale is optimal for studying the relationship between climate factors and SOC content in the Uplands of Northeast China. Seasonal change of temperature and precipitation may also influence SOC. Thus, it is necessary to consider climate factors at multiple timescales. Moreover, further studies focusing on the scale effect of other factors (including climate) on SOC content variability will be carried out at different scales to determine the main factors controlling SOC content.

The images of post atmospheric correction reflectance (PAC), top of atmosphere reflectance (TOA), and digital number (DN) of a SPOT5 HRG remote sensing image of Nanjing, China were used to derive four vegetation indices (VIs), that is, normalized difference vegetation index (NDVI), transformed vegetation index (TVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI). Based on these VIs and the vegetation fractional coverage (VFC) data obtained from field measurements, thirty-six VI-VFC relationship models were established. The results showed that cubic polynomial models based on NDVI and TVI from PAC were the best, followed by those based on SAVI and MSAVI from DN, with their accuracies being slightly higher than those of the former two models when VFC>0.8. The accuracies of these four models were higher in medium densely vegetated areas (VFC = 0.4–0.8) than in sparsely vegetated areas (VFC = 0–0.4). All the models could be used elsewhere via the introduction of a calibration model. In VI-VFC modeling, using VIs derived from different radiometric correction levels of remote sensing images could help explore and show valuable information from remote sensing data and thus improve the accuracy of VFC estimation.

Influencing factor analysis is required for assessing the performance of vegetation fractional coverage (VFC) models with remote sensing imagery. This paper analyses influences of the radiometric correction level (RL), vegetation index (VI) and polynomial exponential power (EP) choice on VI-VFC relationship models. A SPOT 5 HRG image of Nanjing, China was chosen, and three RLs (digital number, top of atmosphere reflectance and post atmospheric correction reflectance) were used to derive six VIs, such as normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). Fifty-four models describing the VI-VFC relationship were established, and the influences of the RL, VI and EP choice on the VI-VFC models were analysed based on a statistical analysis of determination coefficients (R 2) of these models. The results showed that model robustness was jointly influenced by the three factors, so these three factors should be synthetically taken into account in VI-VFC modelling. It is recommended to establish different models between the VFC and various VIs derived from different RLs, and then to select the better ones.