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Detailed information on the spatio-temporal changes of cropland soil organic carbon (SOC) can significantly contribute to the improvement of soil fertility and mitigate climate change. Nonetheless, information and knowledge on the national scale spatio-temporal changes and the corresponding uncertainties of SOC in Chinese upland soils remain limited. The CENTURY model was used to estimate the SOC storages and their changes in Chinese uplands from 1980 to 2010. With the Monte Carlo method, the uncertainties of CENTURY-modelled SOC dynamics associated with the spatial heterogeneous model inputs were quantified. Results revealed that the SOC storage in Chinese uplands increased from 3.03 (1.59 to 4.78) Pg C in 1980 to 3.40 (2.39 to 4.62) Pg C in 2010. Increment of SOC storage during this period was 370 Tg C, with an uncertainty interval of −140 to 1110 Tg C. The regional disparities of SOC changes reached a significant level, with considerable SOC accumulation in the Huang-Huai-Hai Plain of China and SOC loss in the northeastern China. The SOC lost from Meadow soils, Black soils and Chernozems was most severe, whilst SOC accumulation in Fluvo-aquic soils, Cinnamon soils and Purplish soils was most significant. In modelling large-scale SOC dynamics, the initial soil properties were major sources of uncertainty. Hence, more detailed information concerning the soil properties must be collected. The SOC stock of Chinese uplands in 2010 was still relatively low, manifesting that recommended agricultural management practices in conjunction with effectively economic and policy incentives to farmers for soil fertility improvement were indispensable for future carbon sequestration in these regions.

Organic manure is beneficial for macro-aggregate formation and soil organic carbon (SOC), but how SOC change in aggregate fractions in time-series is still uncertain. Moreover, greenhouse systems converted from cereal fields quickly faced soil degradation. Thus, the role of organic manure here should be discussed. The main objectives of this study were to determine the change of SOC fractions in bulk soil and aggregation level affected by long-term organic manure application. Using 13C solid-state nuclear magnetic resonance (NMR) spectroscopy, we investigated the SOC and its fraction changes within bulk soil and aggregate fractions under 1-year, 9-year, and 14-year organic greenhouse vegetable cultivation, and we also analysed the soil properties of rice-wheat rotation (RWR) fields as the control. Soil aggregate samples were wet sieved into large macro-aggregates (> 2 mm), small macro-aggregates (2–0.25 mm), micro-aggregates (0.053–0.25 mm), and silt and clay particles (

Process-based models have been successfully applied to predict long-term changes in soil organic carbon (SOC) at plot scales, but considerable uncertainties are still introduced into regional or national extrapolations due to the lack of spatially explicit information on the model input parameters. Using the CENTURY model we predicted SOC changes in the uplands of Northeast China during the period from 1980 to 2050 and provided 95% confidence intervals regarding the uncertainties associated with variability in the key input parameters. Regional SOC estimation predicted by CENTURY was reliable for the uplands of Northeast China when considering the uncertainty associated with heterogeneous key input parameters. SOC stocks were estimated to be 0.99, 0.88 and 0.87 Pg C in 1980, 2010 and 2050, with 95% confidence intervals ranging from 0.69 to 1.31, 0.66 to 1.11, and 0.69 to 1.07 Pg C, respectively. Overall, the upland soils of Northeast China functioned as a carbon source from 1980 to 2010, with a net decrease of 106 (9–207) Tg C. The SOC losses mainly occurred where SOC contents were high (Heilongjiang Province and eastern Jilin Province). However, assuming unchanged management, whether the uplands of Northeast China will serve as a carbon sink/source over the next 40 years remains uncertain. Information collection on the most influential input parameters (the initial SOC content and clay content) is critical to reduce uncertainty and to provide meaningful information for decision makers.

The aim of this study was to reveal the regional patterns and the primary controls of three pools of SOC in Chinese croplands in 2010. The samples were analysed based on a combination method of acid hydrolysis and CO2-C release during a 100-day incubation. The data from acid hydrolysis-incubation experiments were fitted to a three-pool first-order kinetics model that divided SOC into active (Ca), slow (Cs) and passive (Cp) pools. The results showed that soils in the eastern and southern China contained larger proportions of Ca and Cs but a smaller proportion of Cp in total SOC (P

Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon (SOC) pool simulation due to their strong influences on the modeling. A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales, namely, 1:50 000 (C5), 1:200 000 (D2), 1:500 000 (P5), 1:1 000 000 (N1), 1: 4 000 000 (N4) and 1:14 000 000 (N14), in the Taihu Region of China. Both soil unit formats were used for regional SOC pool simulation with a DeNitrification-DeComposition (DNDC) process-based model, which spans the time period from 1982 to 2000 at the six map scales. Four indices, namely, soil type number (STN), area (AREA), average SOC density (ASOCD) and total SOC stocks (SOCS) of surface paddy soils that were simulated by the DNDC, were distinguished from all these soil polygon and grid units. Subjecting to the four index values (IV) from the parent polygon units, the variations in an index value (VIV, %) from the grid units were used to assess its dataset accuracy and redundancy, which reflects the uncertainty in the simulation of SOC pools. Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools, matching their respective soil polygon unit map scales. With these optimal raster resolutions, the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy, when VIV < 1% was assumed to be a criterion for all four indices. A quadratic curve regression model, namely, y =–0.80 × 10–6 x 2 + 0.0228x + 0.0211 (R 2 = 0.9994, P < 0.05), and a power function model R = 10.394Ŝ 0.2153 (R 2 = 0.9759, P < 0.05) were revealed, which describe the relationship between the optimal soil grid unit resolution (y, km) and soil polygon unit map scale (1:10 000x), the ratio (R, %) of the optimal soil grid size to average polygon patch size (Ŝ, km2) and the Ŝ, with the highest R 2 among different mathematical regressions, respectively. This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale, and be referenced to other landscape polygon patches’ mesh partition.

Process-based models such as CENTURY have been extensively validated for simulating soil organic carbon (SOC) dynamics at the homogeneous plot scale. However, considerable uncertainty may exist when upscaling a simulation from the plot scale to a larger scale because of variation in the model inputs. The objectives of this study were to assess the uncertainty of CENTURY-modeled SOC and to identify the most influential model inputs in various upland regions of China. Global sensitivity analysis was used to explore the sensitivity of CENTURY-modeled SOC to seven key model inputs. The uncertainties of the SOC simulated using various model inputs and climate-soil-management conditions were evaluated at 21 long-term monitoring sites located across upland areas in China. The identified sensitive model inputs differed among regions and periods due to diverse climate-soil-management conditions; nevertheless, initial SOC content (SOCi), soil clay content, and crop residue removal rate (Residuerr) were the most influential inputs. The site-to-region upscaling uncertainties remained moderately large (±42.7, ±49.4, and ±69.3 % at the 90, 95, and 100 % confidence levels, respectively) when currently available observation data were used. Therefore, the collection of detailed information on soil properties and crop residue removal, particularly legacy soil data such as the SOCi and clay content, is important for reducing the uncertainties in SOC modeling. Data on SOCi, Residuerr, and clay content need to be collected prior to other input data to reduce input-related uncertainty and thus to provide more reliable SOC assessment at the regional or national scale in China.

Large dam projects attract worldwide scientific attention due to their environmental impacts and socioeconomic consequences. One prominent example is the Three Gorges Dam (TGD) at the Yangtze River in China. Due to considerable resettlements, large-scale expansion of infrastructure and shifts in land use and management, the TGD project has irreversible impacts on the Upper Yangtze River Basin and strongly challenges the environmental conditions of this fast-developing region. Soil erosion and landslides are major geo-hazards. Knowing the extent and consequences of those geo-hazards for the landscape is essential to predict and evaluate their risk potential and allows for the development of strategies for a sustainable future land use in the Three Gorges Region (TGR). In this context, our research objectives are (1) to better understand the mechanisms of soil erosion, landslides, and diffuse matter fluxes in the TGR and their anthropogenic and environmental control factors, (2) to predict their hazard potential by combining spatial and temporal, scenario-driven high-resolution modeling in combination with multi-scale earth observation data, and (3) to develop a multi-component approach for the assessment and monitoring of geogene structures and processes. The paper describes the workflow of the project and introduces case studies, representing the current state of our research. It is shown that land-use changes as well as the water-level fluctuations of the reservoir are the crucial drivers for the soil erosion and landslide hazard. Furthermore, we present a framework aiming at the establishment of a monitoring and measuring network as well as an early warning system.

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

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.

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.

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.

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.

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.

Vegetation stress diagnoses based on plant sampling and physiochemical analysis using traditional methods are commonly time-consuming, destructive and expensive. The measurement of field spectral reflectance is one basis of airborne or spaceborne remote sensing monitoring. In this study, paddy plants were grown in the barrels evenly filled with 10.0 kg soil that was mixed respectively with 0, 2.5 × 207.2 and 5.0 × 207.2 mg Pb per 1,000 g soil. Rice canopy spectra were gathered by mobile hyper-spectral radiometer (ASD FieldSpec Pro FR, USA). Meanwhile, canopy leaves in the field-of-view (FOV) of spectroradiometer were collected and then prepared in the laboratory, (1) for chlorophyll measurement by Model 721 spectrophotometer, and (2) for Pb determination by atomic absorption spectrophotometer (SpectraAA-220FS). Canopy spectral reflectance in the region of visible-to-near-infrared light (VNIR) increased, because ascended Pb concentration caused the decrease of canopy chlorophyll content. In the agro-ecosystem, however, heavy metal contamination is presented typically as mixture and their interactions strongly affect actually occurring effects. Normalized spectral absorption depth (D n), and shifting distance (DS) of red edge position (REPs) revealed the differences in Pb concentration for canopy leaves, especially at the early tillering stage. Due to insufficient biomass of rice plants, the 30th day was not reliable enough for the selection of crucial growth stages. Some special sensitive bands might be omitted at the same time because of limited sample sets. Our initial experiments are still too few in the amounts of both metals and plants neither to build accurate prediction models nor to discuss the transformation from ground to air/spaceborne remote sensing. However, we are pleased to communicate that ground remote sensing measurements would provide reliable information for the estimation of Pb concentration in rice plants at the early tillering stage when proper features (such as DS and D n) of reflectance spectra are applied. Hyper-spectral remote sensing is a potential and promising technology for monitoring environmental stresses on agricultural vegetation. Further ground remote sensing experiments are necessary to evaluate the possibility of hyper-spectral reflectance spectroscopy in monitoring different kinds of metals’ stress on various plants.

Topsoil samples (0–20 cm) (n = 237) were collected from Rugao County, China. Geostatistical variogram analysis, sequential Gaussian simulation (SGS), and principal component (PC) analysis were applied to assess spatial variability of soil nutrients, identify the possible areas of nutrient deficiency, and explore spatial scale of variability of soil nutrients in the county. High variability of soil nutrient such as soil organic matter (SOM), total nitrogen (TN), available P, K, Fe, Mn, Cu, Zn, and B concentrations were observed. Soil nutrient properties displayed significant differences in their spatial structures, with available Cu having strong spatial dependence, SOM and available P having weak spatial dependence, and other nutrient properties having moderate spatial dependence. The soil nutrient deficiency, defined here as measured nutrient concentrations which do not meet the advisory threshold values specific to the county for dominant crops, namely rice, wheat, and rape seeds, was observed in available K and Zn, and the deficient areas covered 38 and 11%, respectively. The first three PCs of the nine soil nutrient properties explained 62.40% of the total variance. TN and SOM with higher loadings on PC1 are closely related to soil texture derived from different parent materials. The PC2 combined intermediate response variables such as available Zn and P that are likely to be controlled by land use and soil pH. Available B has the highest loading on PC3 and its variability of concentrations may be primarily ascribed to localized anthropogenic influence. The amelioration of soil physical properties (i.e. soil texture) and soil pH may improve the availability of soil nutrients and the sustainability of the agricultural system of Rugao County.

Balances of major nutrients such as nitrogen (N), phosphorus (P), and potassium (K) in small-scale farming systems are of critical importance to nutrient management and sustainable agricultural development. Mass balances of N, P, and K and some of their influencing factors were studied for two years from July 2003 to July 2005 on small-scale vegetable-farming systems in two contrasting peri-urban areas (Nanjing and Wuxi) of the Yangtze river delta region of China. This balance approach considered organic fertilizer inputs (cow manure, pig manure, and human biosolids), inorganic fertilizer inputs (urea, composite fertilizer, and phosphates), irrigation water, and atmospheric deposition; and considered outputs by vegetables. Input via organic fertilizers was significant for all element balances in the Nanjing area. Inorganic and organic fertilizer, particularly inorganic fertilizer, contributed major nutrient inputs to the system in the Wuxi area. Compared with nutrient output by vegetables, there were significant surpluses of N and P on two vegetable farm systems. Furthermore, N surplus in the Nanjing area was higher than that in the Wuxi area with an inverse relationship to P surplus. In contrast, the general trend of K balances was negative on both sites; hence, the nutrient use efficiency was significantly lower for N and P than K. The nutrient imbalance may be attributed to the differences between fertilizer types and management modes driven by social economic status differences among farmer households. The large N and P net excess creates an environmental threat because of potential losses to ground or surface waters, whereas negative K balance creates soil fertility risks. The results highlight researchers’ and farmers’ need to develop rational fertilization technology to optimize nutrient management on vegetable farmlands to promote sustainable agricultural development in peri-urban areas.

A total of 540 topsoil samples (0–15 cm), 188 subsoil samples (20–40 cm), and four individual soil profiles were collected in this study for mapping the Cu- and Pb-contaminated areas in soils of Zhangjiagang city, an industrialized city in the Yangtze River Delta region of China. Robust geostatistical methods were applied for identifying possible spatial outliers of Cu and Pb data, and then a sequential Gaussian simulation was employed for delineating the potential areas where Cu or Pb concentration was affected by diffuse pollution. The results showed that the spatial outliers of Cu and Pb were strongly associated with various types of factories. The anthropogenic input of Cu to soils at local hotspots was closely related to emissions of printing and dyeing, metallurgical, and chemical factories, whereas a lead oxide factory and a chemical factory resulted in a considerable increase of Pb in the topsoil of the study area. Approximately 30% of the total land area of the study was at potential risk from the Cu or Pb diffuse pollution resulting from rapid industrialization of the area over the past 20 years.