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Objective of investigation: Land use conversion strongly alters soil structure and substantially affects soil organic carbon (SOC) sequestration. Changing from an anaerobic paddy field (PF) to a dry land easily causes SOC loss due to stimulation of C decomposition. However, no evidence of SOC loss from PF to intensive vegetable cultivation has been certainly presented. Experimental material: This study was conducted on the long-term cultivated open-field vegetable (OFV) and greenhouse vegetable (GHV) planting area converted from old PF in China. Undisturbed soil cores, natural structured soil, and disturbed soil from top soil layers were using for further analyses. Methods of investigation: To comprehensively investigate SOC and soil structure change in the land use conversion of PF to OFV and PF to GHV, we used 13C-CPMAS NMR spectroscopy to classify the SOC fractions. The soil macropores (> 50 μm) was valued by X-ray computed tomography, and soil aggregates distribution was determined by wet sieving method. Data collection: Data were obtained from the above-mentioned measurements and statistically analyzed in R. Results: The result showed that the SOC stock increased 1-fold from PF to GHV. SOC stability increased with recalcitrant C (aromatic-C and carbonyl-C) raised by 21 %–27 % in GHV bulk soil. Both macropores and macroaggregates (> 250 μm) increased in GHV, accompanied by an accumulation of recalcitrant C in large macroaggregates. Conclusions: we confirmed the expanded GHV cultivation sequestered more belowground SOC than PF, associated with the amplified physical protection by enhancing soil aggregation and by redistributing of soil macropores.

Background The predictive value of adiposity indices and the newly developed index for cardiometabolic risk factors and cardiovascular diseases ( CVD s) remains unclear in the Chinese population. This study aimed to compare the predictive value of A Body Shape Index with other 5 conventional obesity‐related anthropometric indices (body mass index, waist circumference, hip circumference, waist‐to‐hip ratio, waist‐to‐height ratio) in Chinese population. Methods and Results A total of 44 048 participants in the study were derived from the baseline data of the PURE ‐China (Prospective Urban and Rural Epidemiology) study in China. All participants’ anthropometric parameters, CVD s, and risk factors (dyslipidemia, abnormal blood pressure, and hyperglycemia) were collected by standard procedures. Multivariable logistic regression models and receiver operator characteristic curve analysis were used to evaluate the predictive values of obesity‐related anthropometric indices to the cardiometabolic risk factors and CVD s. A positive association was observed between each anthropometric index and cardiometabolic risk factors and CVD s in all models ( P AUC s) for predicting dyslipidemia ( AUC s: 0.646, sensitivity: 65%, specificity: 44%), hyperglycemia ( AUC s: 0.595, sensitivity: 60%, specificity: 45%), and CVD s ( AUC s: 0.619, sensitivity: 59%, specificity: 41%). Waist circumference showed the best prediction for abnormal blood pressure ( AUC s: 0.671, sensitivity: 66%, specificity: 40%) compared with other anthropometric indices. However, the new body shape index did not show a better prediction to either cardiometabolic risk factors or CVD s than that of any other traditional obesity‐related indices. Conclusions Waist‐to‐height ratio appeared to be the best indicator for dyslipidemia, hyperglycemia, and CVD s, while waist circumference had a better prediction for abnormal blood pressure.

Soil nitrogen (N) plays a central role in soil quality and biogeochemical cycles. However, little is known about the distribution and spatial variability of the different fractions of soil N within entire soil profiles. This study aimed to investigate the potential of laboratory-based hyperspectral imaging (HSI) spectroscopy to retrieve and map total N (TN), available N (AvailN), ammonium N (NH4-N), nitrate N (NO3-N), and microbial biomass N (MBN) in soil profiles at a high resolution. HSI images of eleven intact soil profiles of 100 ± 5 cm depth from three typical soil types were recorded. A variety of nonlinear machine learning techniques, such as artificial neural networks (ANN), cubist regression tree (Cubist), k-nearest neighbour (KNN), support vector machine regression (SVMR) and extreme gradient boosting (XGBoost), were compared with a partial least squares regression (PLSR) to determine the most suitable model for the prediction of the various soil N fractions. Overall, the results showed that nonlinear techniques performed better than PLSR in most cases, with a high coefficient of determination (R2) and low root mean square error (RMSE). Among the models, SVMR was found to be superior to the other tested models for TN (R2P = 0.94, RMSEP = 0.17 g kg−1), AvailN (R2P = 0.94, RMSEP = 13.35 mg kg−1), NO3-N (R2P = 0.82, RMSEP = 7.31 mg kg−1), and NH4-N (R2P = 0.70, RMSEP = 1.51 mg kg−1) based on independent validation, whereas MBN (R2P = 0.63, RMSEP = 6.62 mg kg−1) was predicted best by KNN. In addition, SVMR required less computational time and was less sensitive to spectral noise. It can therefore be concluded that HSI spectroscopy combined with SVMR is suitable for the high-resolution mapping of various soil N fractions in soil profiles.

Understanding the impacts of climate change and agricultural management practices on soil organic carbon (SOC) dynamics is critical for implementing optimal farming practices and maintaining agricultural productivity. This study examines the influence of climatic variables and agricultural management on carbon sequestration potentials in Tai-Lake Paddy soils of China using the DeNitrification-DeComposition (DNDC, version 9.1) model, with a high-resolution soil database (1:50,000). Model simulations considered the effects of no-tillage, the application rates of manure, N fertilization, and crop residue, water management, and changes in temperature and precipitation. We found that the carbon sequestration potential in the top soils (0–30 cm) for the 2.32 Mha paddy soils of the Tai-Lake region varied from 4.71 to 44.31 Tg C under the feasible management practices during the period of 2001–2019. The sequestration potential significantly increased with increasing application of N-fertilizer, manure, conservation tillage, and crop residues, with an annual average SOC changes ranged from 107 to 121 kg C ha− 1 yr− 1, 159 to 326 kg C ha− 1 yr− 1, 78 to 128 kg C ha− 1 yr− 1, and 489 to 1005 kg C ha− 1 yr− 1, respectively. Toward mitigating greenhouse emissions and N losses, no-tillage and increase of crop residue return to soils as well as manure application are recommended for agricultural practice in this region. Our analysis of climate impacts on SOC sequestration suggests that the rice paddies in this region will continue to be a carbon sink under future warming conditions. Specifically, with rising air temperature of 2.0 °C and 4 °C, the average annual SOC changes were 52 and 21 kg C ha− 1 yr− 1, respectively.

Soil organic carbon (SOC) and its labile C fractions play a central role in soil quality and C cycles. This study aimed to investigate the potential of laboratory-based hyperspectral imaging (HSI) spectroscopy to predict and map SOC and its labile C fractions (e.g., dissolved organic C, DOC; readily oxidizable organic C, ROC; and microbial biomass C, MBC) in soil profiles with a high resolution. The HSI images were captured from 16 intact paddy soil profiles to a depth of 100 ± 5 cm from four typical parent materials. The linear (i.e., partial least squares regression, PLSR) and nonlinear (i.e., artificial neural networks, ANN; cubist regression tree, Cubist; Gaussian process regression, GPR; and support vector machine regression, SVMR) multivariate techniques were compared to assess their ability to map the soil C fractions in the profiles. A spectral variable selection technique (i.e., competitive adaptive reweighted sampling, CARS) was applied to these multivariate models (i.e., CARS-PLSR, CARS-ANN, CARS-Cubist, CARS-GPR, and CARS-SVMR). Overall, the results showed that the nonlinear models performed better than the PLSR models in most cases. All optimized multivariate models with CARS achieved prediction performances similar to the full spectrum models, with high Lin's concordance correlation coefficient (LCC) and low root mean square error (RMSE). CARS-SVMR used only 37–70 spectral variables and took less time-consuming on computations (

The impacts of rapid industrialization on agricultural soil cadmium (Cd) accumulation and their potential risks have drawn major attention from the scientific community and decision-makers, due to the high toxicity of Cd to animals and humans. A total of 812 topsoil samples (0&ndash, 20 cm) was collected from the southern parts of Jiangsu Province, China, in 2000 and 2015, respectively. Geostatistical ordinary kriging and conditional sequential Gaussian simulation were used to quantify the changes in spatial distributions and the potential risk of Cd pollution between the two sampling dates. Results showed that across the study area, the mean Cd concentrations increased from 0.110 mg/kg in 2000 to 0.196 mg/kg in 2015, representing an annual average increase of 5.73 &mu, g/kg. Given a confidence level of 95%, areas with significantly-increased Cd covered approximately 12% of the study area. Areas with a potential risk of Cd pollution in 2000 only covered 0.009% of the study area, while this figure increased to 0.75% in 2015. In addition, the locally concentrating trend of soil Cd pollution risk was evident after 15 years. Although multiple factors contributed to this elevated Cd pollution risk, the primary reason can be attributed to the enhanced atmospheric deposition and industrial waste discharge resulting from rapid industrialization, and the quick increase of traffic and transportation associated with rapid urbanization.

At present, various interpolation methods have been used for obtaining the spatial variability of soil organic carbon (SOC) in some regions. However, systematic research on accuracy comparison and the influence of method selection on revealing SOC variability is still lacking for the hilly red soil region of China. In this study, the spatial distributions of SOC were interpolated via three kriging methods (ordinary kriging (OK), kriging combined with soil type information (KSt), and with land-use information (KLu)) and three polygon-based methods (linking sample points data to the sampling grid (PGr), to soil type polygon (PSt), and to land-use polygon (PLu)) in Yujiang County, Jiangxi Province, China. To illustrate the influence of method selection on revealing SOC variability, the prediction efficiencies of these methods were compared with soil validation samples. The results showed that the KLu and PLu had high prediction accuracy, followed immediately by the KSt and PSt, while the OK and PGr had very low accuracies. Meanwhile, the SOC distribution contours obtained by KLu, PLu, KSt, and PSt could better reflect the real conditions of the study area, as they were consistent with the variation of land-use or soil types. However, the contours from the OK and PGr had poor performance with sketchy borderlines and regular homogeneous grid cells, respectively. Therefore, great differences exist in the prediction efficiency of different methods, and the interpolation method selection has an important impact on revealing SOC variability in the hilly red soil region of China. The results can provide a useful reference for the efficient SOC prediction and simulation in similar soil regions.

China's terrestrial ecosystems have functioned as important carbon sinks. However, previous estimates of carbon budgets have included large uncertainties owing to the limitations of sample size, multiple data sources, and inconsistent methodologies. In this study, we conducted an intensive field campaign involving 14,371 field plots to investigate all sectors of carbon stocks in China's forests, shrublands, grasslands, and croplands to better estimate the regional and national carbon pools and to explore the biogeographical patterns and potential drivers of these pools. The total carbon pool in these four ecosystems was 79.24 ± 2.42 Pg C, of which 82.9% was stored in soil (to a depth of 1 m), 16.5% in biomass, and 0.60% in litter. Forests, shrublands, grasslands, and croplands contained 30.83 ± 1.57 Pg C, 6.69 ± 0.32 Pg C, 25.40 ± 1.49 Pg C, and 16.32 ± 0.41 Pg C, respectively. When all terrestrial ecosystems are taken into account, the country's total carbon pool is 89.27 ± 1.05 Pg C. The carbon density of the forests, shrublands, and grasslands exhibited a strong correlation with climate: it decreased with increasing temperature but increased with increasing precipitation. Our analysis also suggests a significant sequestration potential of 1.9-3.4 Pg C in forest biomass in the next 10-20 years assuming no removals, mainly because of forest growth. Our results update the estimates of carbon pools in China's terrestrial ecosystems based on direct field measurements, and these estimates are essential to the validation and parameterization of carbon models in China and globally.

Tree diversity improves forest productivity Experimental studies in grasslands have shown that the loss of species has negative consequences for ecosystem functioning. Is the same true for forests? Huang et al. report the first results from a large biodiversity experiment in a subtropical forest in China. The study combines many replicates, realistic tree densities, and large plot sizes with a wide range of species richness levels. After 8 years of the experiment, the findings suggest strong positive effects of tree diversity on forest productivity and carbon accumulation. Thus, changing from monocultures to more mixed forests could benefit both restoration of biodiversity and mitigation of climate change. Science , this issue p. 80

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.

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.

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.

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.

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.

Selenium (Se) deficiency and excess in agricultural ecosystems are related to many human health problems through the food chain and depend on the spatial variability and bioavailability of Se in soils. This paper examines the spatial variability of Se in soils as affected by geologic and pedogenic processes and its effects on rice grains, groundwater, and human health using Rugao County, Jiangsu Province, an agricultural area in Yangtze River Delta Region, which has a high level of nonagenarians, as a case study. The results showed that total soil Se (T-Se) concentration in the study area (average 0.13 ± 0.02 mg kg-1, n = 203) was close to the Se Clark value, the Se mean concentration in earth crust, and had narrow spatial variation as a result of consistent geological processes. As a fraction of the potential bio-available Se in ecosystems, soil water soluble Se (WS-Se) (average 2.27 ± 1.09 μg kg-1, n = 203) had wide spatial variation. The higher concentrations of WS-Se were spatially distributed on loamy and highly weathered soils in the east and north of the county. Spatial heterogeneity of WS-Se was primarily due to changes in soil physicochemical properties caused by pedogenic processes related to cultivation. Average Se concentration in the rice grains and groundwater were 97 ± 69 μg kg-1 (n = 93) and 0.19 ± 0.21 μg L-1 (n = 95), respectively. Similar spatial variability and correlation between WS-Se and rice Se, groundwater Se, and longevity ratio (an index of nonagenarians per 100,000 inhabitants), were found, indicating that the soluble fractions of Se in soils play an important role in biogeochemical cycling in the ecosystem and might be an important indicator of Se deficiency or excess in soils. Thorough small-scale surveys of element availability in the soil should be emphasized where geologic settings are simple but pedogenic processes are complex due to intensive human activities. So WS-Se in soils was advised as a better indicator for Se environmental effect than T-Se, especially when dealing with simple geologic settings and complex pedogenic processes on a small scale.

Locust plagues have been the source of some of the most severe natural disasters in human history. Soil moisture content is among the most important of the numerous factors influencing plague onset and severity. This paper describes a study initiated in three pilot locust plague monitoring regions, i.e., Huangzao, Yangguanzhuang, and Tengnan in Huanghua county, Hebei province, China, to examine the impact of soil moisture status on oriental migratory locust [ Locusta migratoria manilensis (L.) Meyen] plague breakout as related to the life cycle, oviposition in autumn, survival in winter, and incubation in summer. Thirty-nine temperature vegetation dryness index (TVDI) data sets, which represent soil moisture content, were extracted from MODIS remote sensing images for two representative time periods: a severe locust plague breakout year (2001–2002) and a slight plague year (2003–2004). TVDI values demonstrated distinctive soil moisture status differences between the 2 years concerned. Soil moisture conditions in the severe plague year were shown to be lower than those in slight plague year. In all three pilot regions, average TVDI value in the severe plague year was 0.07 higher than that in slight plague year, and monthly TVDI values in locust oviposition period (September and October) and incubation period (March, April and May) were higher than their corresponding monthly figures in slight plague year. No remarkable TVDI differences were found in other months during the locust life cycle between the 2 years. TVDI values for September and October (2001), March, April and May (2002) were 0.11, 0.08, 0.16, 0.11 and 0.16 higher than their corresponding monthly figures in 2003–2004 period, respectively.

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy (350-2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis-NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0-20 cm) samples were collected for SOM analysis and scanned with a Vis-NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23-4.69 g kg-1; R2 (coefficient of determination) = 0.80-0.84; RPD (ratio of standard deviation to RMSE) = 2.19-2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88-3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55-3.49 g kg-1; R2 = 0.87-0.93; RPD = 2.67-3.12; RPIQ = 3.15-4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis-NIR spectroscopy.

The spatial patterns of soil organic carbon (SOC) are closely related to the global climate change. In quantifying the spatial patterns of SOC density, the concept of uncertainty of the SOC density values at unsampled locations is particularly important because such uncertainty can be propagated into the subsequent global climate change modelling and has fundamental impacts on the ultimate results of the model. A total of 361 SOC density data of topsoil (0-20 cm) in Hebei province and sequential indicator simulation (SIS) were applied to perform a conditional stochastic simulation in this study to quantitatively assess the uncertainty of mapping SOC density. The results showed that a great variation exists in the SOC density data. The conditional variance of 500 realizations generated by SIS was larger in mountainous areas of the study area where the SOC density fluctuated the most, and the uncertainty was smaller on the plain area where SOC density was consistently small. Realizations generated by SIS can represent the possible spatial patterns of SOC density without smoothing effect. A set of realizations can be used to explore all possible spatial patterns of SOC density and provide a visual and quantitative measure of the spatial uncertainty of mapping SOC density. With a given threshold of SOC density, SIS can quantitatively assess both local uncertainty and spatial uncertainty of SOC density that is greater the threshold.

[1] Fourteen grid data sets of different cell resolutions were generated, from 0.5 × 0.5 km to 64 × 64 km, to estimate CH4 and N2O emissions from paddy soils in the Tai Lake region of China using the Denitrification-Decomposition (DNDC) model. The grids were derived from a polygon-based data set (1:50,000 digital soil map/database), which was the most detailed soil database for the region. Comparison of simulated CH4 and N2O concentrations from input of the 14 grid data sets with the original polygon data demonstrated (1) no distinct variability (relative errors 10%) in the results when grid data sets with cell size of >8 km were applied as input. A grid data set with a cell size of 8 km was found to be optimal based on accuracy and computational efficiency of DNDC simulations. The results can be used as a guideline for optimizing field sampling strategies for locations where there is a lack of or insufficient soil data, whereby soil data can be collected through sampling in cell centers of designed grid frames.

Subtropical broad-leaved forests in southeastern China support a high diversity of woody plants. Using a comparative study design with 30×30 m plots (n1/427) from five successional stages (1 m in height in each plot and counted all woody recruits (bank of all seedlings ≤1 m in height) in each central 10×10 m quadrant of each plot. In addition, we measured a number of environmental variables (elevation, slope, aspect, soil moisture, pH, C, N, and C/N ratio) and biotic structural variables (height and cover of layers). Adult species richness varied from 25 to 69 species per plot, and in total 148 woody species from 46 families were recorded. There was a clear successional gradient in species composition as revealed by nonmetric multidimensional scaling (NMDS), but only a poor differentiation of different successional stages with respect to particular species. Adult richness per 100 individuals (rarefaction method) increased with successional stage. None of the measured abiotic variables were significantly correlated with adult species richness. We found no evidence that rare species were responsible for the increasing adult species richness, as richness of rare species among both adults and recruits was independent of the successional stage. Furthermore, the similarity between established adults and recruits did not increase with successional stage. There was a constant number of recruit species and also of exclusive recruit species, i.e., those that had not been present as adult individuals, across all successional stages, suggesting a continuous random immigration over time. © 2011 by the Ecological Society of America.

Accurate characterization of heavy-metal contaminated areas and quantification of the uncer- tainties inherent in spatial prediction are crucial for risk assessment, soil remediation, and effective management recommendations. Topsoil samples (0-15 cm) (n=547) were collected from the Zhangjiagang suburbs of China. The sequential indicator co-simulation (SIcS) method was applied for incorporating the soft data derived from soil organic matter (SOM) to simulate Hg concentrations, map Hg contaminated areas, and evaluate the associated uncer- tainties. High variability of Hg concentrations was observed in the study area. Total Hg concentrations varied from 0.004 to 1.510 mg kg −1 and the coefficient of variation (CV) accounts for 70%. Distribution patterns of Hg were identified as higher Hg concen- trations occurred mainly at the southern part of the study area and relatively lower concentrations were found in north. The Hg contaminated areas, identified using the Chinese Environmental Quality Standard for Soils critical values through SIcS, were limited and distributed in the south where the SOM concentration is high, soil pH is low, and paddy soils are the dominant soil types. The spatial correlations between Hg and SOM can be preserved by co-simulation and the realizations generated by SIcS represent the possible spatial patterns of Hg concentrations without a smooth- ing effect. Once the Hg concentration critical limit is given, SIcS can be used to map Hg contaminated areas and quantitatively assess the uncertainties inherent in the spatial prediction by setting a given critical probability and calculating the joint probability of the obtained areas.

8.29 � 10 15 g, representing 5.9–8.7% of the total global N storage. The total N storage in China is on average or slightly above the average of its share in the global N storage, even though low nitrogen content soils cover a large area in China. N density varies substantially with soil types and regions. Peat soils in the southeast of Tibet, southwest China, show the highest averaged N density with a value of 7314.9 g/m 3 among all soil types. This is more than 30 times of the lowest N density of brown desert soils in the western desert and arid region. The highest N storages among all the soil types are the felty soil in southeast of Tibet, dark-brown earths in northeast China, and red earths in southeast China with values of 921.1, 611.4, and 569.6 Tg, respectively. N density also varies with land cover types in China. Wetlands in southwest China exhibit the highest N density at 6775.9 g/m 3 and deserts in northwest China have the least at 447.5 g/m 3 . Our analysis also indicates that land cover types are poor predictors of N content. Further research is needed to examine how transformation from organic agriculture to increased use of fertilizers and pesticides has influenced N storage in China.