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Soil structure degradation in greenhouse vegetable fields reduces vegetable production. Increasing aeration porosity is the key to ameliorating soil structure degradation. Thus, we tested the effect of a porous material, porous clay ceramic (PLC), on the amelioration of soil structure degradation under greenhouse vegetable production. A 6-month pot experiment was conducted with four PLC application levels based on volume, i.e., 0% (control), 5% (1P), 10% (2P), and 15% (3P) using Brassica chinensis as the test plant. At the end of the experiment, soil columns were sampled, and the aeration pore network was reconstructed using X-ray computed tomography (CT). The degree of anisotropy (DA), fractal dimension (FD), connectivity, aeration porosity, pores distribution, and shape of soil aeration pores and plant biomass were determined. The DA, FD, and connectivity did not significantly differ as the PLC application rate increased. Nonetheless, aeration porosity significantly linearly increased. The efficiency of PLC at enhancing soil aeration porosity was 0.18% per Mg ha–1. The increase in aeration porosity was mainly due to the increase in pores > 2 000 μm, which was characterized by irregular pores. Changes in aeration porosity enhanced the production of B. chinensis. The efficiency of PLC at increasing the plant fresh weight was 0.60%, 3.06%, and 2.12% per 1% application rate of PLC for the 1P, 2P, and 3P treatments, respectively. These results indicated that PLC is a highly efficient soil amendment that improves soil structure degradation by improving soil aeration under greenhouse conditions. Based on vegetable biomass, a 10% application rate of PLC was recommended.

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 (

Organic farming can potentially mitigate soil compaction, which commonly occurs in vegetable plantations, particularly in greenhouses when compared with open‐air systems. Although several studies have addressed the effect of planting patterns on soil pore characteristics, few studies have focused on changes in pore attributes under organic fertilization. We used adjacent fields during green manure planting as a control to compare the differences in soil physicochemical and macropore parameters between 14‐year‐old greenhouse plots and open‐air plots under organic fertilization. Pearson's correlation analysis was performed to analyse the drivers of soil macropore characteristics in vegetable plantations in Jiangsu Province, China. A significant increase in connectivity, fractal dimensions and total macroporosity and a decrease in bulk density after 14 years of organic farming, particularly in the surface layer in the greenhouse, were observed. However, the difference in pores in the plough pan layer was not significant between the treatments. The volume of small pores (50–500 μm) and medium pores (500–1,000 μm) increased significantly (p 1,000 μm) showed a clear decrease, especially in open‐air fields, possibly due to the disappearance of the original straw residues in the surface layer. Small and medium pores in the plough layer increased in greenhouse fields, whereas the opposite occurred in open‐air fields. Overall, compared with open‐air systems, long‐term organic greenhouse patterns had a significantly positive effect on soil pore attributes.

This study evaluates the use of visible and near-infrared spectroscopy for rapid prediction of total carbon, total nitrogen, and total phosphorus concentrations in field crop samples. Two multivari...

Temperature and precipitation simultaneously influence the soil organic carbon (SOC) in agroecosystems. While these parameters' individual impacts have been extensively studied, their combined effects remain poorly understood at a regional scale. Thus we conducted this study to quantify the combined influence of temperature and precipitation on the SOC in upland-crop fields of the northern Jiangsu Province in eastern China. This quantification was achieved by using the DeNitrification-DeComposition (DNDC, version 9.5) model with the most detailed upland soil database in China. The model simulations indicated that under a scenario where the air temperature (T) increased by 2 °C (T2) and the precipitation (Prec) simultaneously decreased by 20%, the upland soils in the study region were estimated to a sequestered 39.10 Tg C from 2010 to 2039. Meanwhile, a scenario with T2 and a simultaneous increase in Prec by 20% led to a lower C sequestration of 37.39 Tg C. We found that the respective C sequestration from the combined effects of T and Prec in the above two scenarios was 1.24 (or 2.12) Tg C less compared to the sum of their individual effects. Moreover, if the increase of T changed from 2 °C to 4 °C, and the Prec continued to decrease (or increase) by 20%, the C sequestration was 34.69 (or 33.64) Tg C, which was 3.60 (or 3.82) Tg C lower than the sum of the individual effects. Our analysis of the combined effects of T and Prec on the SOC changes suggested that future warming and Prec changes in this region may cause a decrease in the SOC sequestration, but the upland agroecosystems will still serve as a long-term sink for atmospheric CO2 due to the high application rates of chemical fertilizer and farmyard manure. Furthermore, the separate and combined effects of temperature and precipitation on the SOC changes in the different upland soil groups varied widely due to the heterogeneity of soil properties, physicochemical conditions and fertilizer practices. This result emphasized the importance of using fine scale soil datasets and the need to understand the intricacies in the soil carbon sequestration potential at a regional scale in a changing climate.

The impacts of rapid industrialization on spatio-temporal changes in soil pH have drawn major attention from the scientific community and decision-makers. A total of 1185 topsoil samples (0–20 cm) were collected from southern Jiangsu Province of China in 1980, 2000, and 2015, and the geostatistical sequential Gaussian simulation (SGS) was used to quantify the spatio-temporal patterns of changes in soil pH and identify the areas with significantly-declined pH. A significant topsoil acidification occurred in major croplands of the study area, with mean decrements of 0.56 and 0.52 units over the periods of 1980–2000 and 2000–2015, respectively. A significant decrease in pH occurred in the areas surrounding Taihu Lake during 1980–2000, and a further decrease was evident predominantly in the northeast during 2000–2015. Over the entire 35-year period, the soil pH for approximately 79% of the study area declined significantly. The decline over the first two decades (1980–2000) was largely attributed to increased chemical fertilizer inputs and the development of industries, while the further enhanced industrial emissions resulting from the significant expansion of factories were the primary reason for the accelerated soil acidification in the subsequent 15 years (2000–2015). Results from this study may serve as a basis for developing management strategies to achieve better soil quality in similar areas moving toward industrialization.

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 use of three discrete soil organic carbon (SOC) pools proposed by the CENTURY model to study the SOC dynamics helps to understand the changes in cropland SOC pool and its stability. This study focuses on a typical black soil (mollisol) region in Northeast China. Based on historical soil data from samples that were collected in the 1980s, we selected 44 sampling points and collected 88 soil samples from the surface (0–20 cm) and subsurface (20–40 cm) layers in 2010. A 100-day laboratory incubation for each sample was conducted to measure the decomposition rates of SOC at different times, and data from the incubation experiment were fitted to a three-pool first-order model that divided the total SOC into active (Ca), slow (Cs) and resistant (Cr) SOC fractions. A method for predicting the concentrations of the three SOC fractions was developed and used to obtain the concentrations of SOC fractions in the 1980s at each sampling point. The results showed that a power function model (Dsoct = a × tb) could be used as the universal SOC decomposition curve model. Using the universal model, the predictive method accurately estimated the concentrations of Ca, Cs and Cr of upland soils in this black soil region, which effectively solved the problem of a lack of soil samples and SOC fractions data in previous studies of the spatial-temporal variations of SOC fractions in regional soils. From 1980 to 2010, the estimated variations of the Ca, Cs and Cr in the surface upland soil were +0.37, −5.53 and −6.32 g kg−1, and the corresponding contributions to the loss of the total SOC were -3.2%, +48.2% and +55.1%, respectively, while the variations of the Ca, Cs and Cr in the subsurface soil were +0.19, −0.43 and −2.45 g kg−1, and the corresponding contributions to the loss of the total SOC were −7.1%, +16.0% and +91.1%, respectively. The decrease in the total SOC is primarily attributed to the decrease of the Cs and Cr fractions in the region, although the Ca fraction has significantly increased. The overall variations in the SOC fractions suggest a declining stability of the SOC pool in the black soil region of Northeast China.

The shift from rice-wheat rotation (RWR) to greenhouse vegetable soils has been widely practiced in China. Several studies have discussed the changes in soil properties with land-use changes, but few studies have sought to address the differences in soil pore properties, especially for fields based on long-term organic fertilization under greenhouse vegetable system from RWR fields. This study uses the X-ray computed tomography (CT) scanning and statistical analysis to compare the long-term effects of the conversion of organic greenhouse vegetable fields (over one year, nine years, and fourteen years) from RWR fields on the soil macropore structure as well as the influencing factors from samples obtained in Nanjing, Jiangsu, China, using the surface soil layer and triplicate samples. The results demonstrated that the macropore structure became more complex and stable, with a higher connectivity, fractal dimension (FD) and a lower degree of anisotropy (DA), as the greenhouse vegetable planting time increased. The total topsoil macroporosity increased considerably, but the rate of increase gradually decelerated with time. The transmission pores (round pores ranging from 50 to 500μm) increased with time, but the biopores (>2000μm) clearly decreased after nine years of use as greenhouse vegetable fields. Soil organic matter (OM) has a significant relationship with the soil pore structure characteristics, especially for the transmission pores. In addition, organic fertilization on the topsoil had a short-term effect on the pores, but the effect stabilized and had a weak influence on the pores over longer periods. These results suggested that organic fertilization was conducive for controlling soil degradation regarding it physical quality for water and oxygen availability in the short term.

Digital soil maps with different scales have been widely used to quantify soil organic carbon (SOC) dynamics in the upland-crop fields. However, most of the regional SOC modeling often uses soil maps with a fixed scale, thus how varying map scales influence modeled SOC dynamics has rarely been quantified. Different map scales have different levels of details in describing the soil properties (e.g., soil texture, SOC content, bulk density, and pH) for a specific area, with the high resolution map having finer spatial representation. In this study, six digital upland soil databases of the northern Jiangsu Province in Eastern China at scales of 1:50,000 (P5), 1:250,000 (P25), 1:500,000 (P50), 1:1,000,000 (P100), 1:4,000,000 (P400), and 1:10,000,000 (P1000) have been used to drive the DeNitrification-DeComposition (DNDC, version 9.5) model to quantify SOC changes for the period of 1980–2009. This suite of scales selections covers all basic soil map scales in China. Our simulations indicate that the regional total SOC changes from 1980 to 2009 in the top layer (0–50 cm) for upland soils using P5, P25, P50, P100, P400, and P1000 soil maps were 37.89, 34.91, 36.04, 37.05, 39.28 and 38.46 Tg C, respectively. Taking the regional total SOC changes based on the most detailed soil map, P5, as a reference, the relative deviation of those derived from the P25, P50, P100, P400, and P1000 were 7.86%, 4.86%, 2.21%, 3.67%, and 1.51%, respectively. Although the relative deviation of regional total SOC changes for most soil maps are low, disparities exist among SOC changes for different soil groups (i.e. 4.8 to 981%) and for administrative areas level (i.e. 0.32 to 151%). The results indicate that SOC estimates are significantly influenced by soil map scale. Lack of detailed soil information, like representation of many soil types and spatial variations in soil types, in coarse-scale maps result in the large uncertainties in the estimates of SOC changes. This study stresses the needs of detailed soil digital maps for accurate quantification of regional SOC changes.

The successful determination of soil properties by visible and near-infrared (Vis–NIR) reflectance spectroscopy (350–2500 nm) depends on the selection of an appropriate multivariate calibration technique. In this study, four multivariate techniques (principal components regression, PCR; partial least squares regression, PLSR; back-propagation neural network, BPNN; and support vector machine regression, SVMR) were compared with the aim of rapidly and accurately predicting soil properties, including soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and total potassium (TK). A total of 148 intact soil cores (8.4 cm internal diameter and 40 cm long) collected from paddy fields in Yujiang, China were used as the dataset for the calibration-validation procedure. The Vis–NIR spectra were measured on flat, horizontal surfaces of soil core sections at four depths (i.e., 5, 10, 15, and 20 cm) in the laboratory. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) were used to evaluate the accuracy of the calibration models. Both the cross-validation and independent validation data sets showed that the SVMR models outperformed the BPNN, PCR, and PLSR models for SOM, TN, and TP predictions, whereas BPNN outperformed the other models for TK. Furthermore, BPNN and SVMR provided better performance than PCR and PLSR. The best predictions were obtained by the SVMR model for SOM (R2P = 0.88; RMSEP = 4.87; RPDP = 2.84) and TN (R2P = 0.86; RMSEP = 0.31; RPDP = 2.69), which were classified as good model predictions. The predictions of TP (R2P = 0.76; RMSEP = 0.080; RPDP = 2.03) by SVMR were approximately quantitative predictions, whereas the TK (R2P = 0.65; RMSEP = 3.54; RPDP = 1.65) prediction with BPNN was unsuccessful. Vis–NIR spectroscopy combined with SVMR has great potential to accurately determine the selected soil properties of intact soil cores of paddy fields.

In order to implement optimal farming practices for increasing soil organic carbon (SOC) in agro-ecosystems, there is a need for understanding how management practices and climate change alter SOC levels. This study quantified the influence of agricultural management practices and climatic factors on SOC changes in Eastern China’s upland-crop fields in northern Jiangsu Province for the period of 2010–2039, by using the DeNitrification-DeComposition (DNDC, version 9.5) model. We utilized the currently most detailed soil database, which is at a scale of 1:50,000, containing 17,024 soil polygons derived from 983 upland soil profiles. Across all the examined scenarios of agricultural management practices, our results show that the carbon sequestration potential in the upper layer soil (0–50 cm) of the study area varied from 6.93 to 155.11 Tg C during 2010–2039, with an average rate of 59 to 1317 kg C ha −1 year −1 . As a promising alternative, the combined scenario of crop residue return rate of 50% and farmyard manure incorporation rate of 50% is recommended for agricultural management practice in this region. Meanwhile, climate conditions play a significant role in the annual SOC changes as well. Air temperature increase of 2–4 °C leads to 3.41–7.51 Tg C decrease in SOC under conventional management for the entire study region. Decreasing or increasing precipitation by 20% would increase 0.57 Tg C or decrease 1.09 Tg C under the conventional management scenario, respectively. Additionally, among all the soil groups, the fluvo-aquic soils have the highest C sequestration rate in most scenarios. Our findings could be used to inform optimal agricultural management toward climate mitigation.

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

Reporting modeling results with uncertainty information can benefit decision making by decreasing the extent that variability exerts a disproportionate influence on the options selected. For making decisions with more confidence, the uncertainty interval should be as narrow as possible. Here, the soil organic carbon (SOC) dynamics of the major paddy soil subgroup from 4 different paddy field regions of China (located in 4 counties under different climate-soil-management combinations) were modeled using the DeNitrification-DeComposition (DNDC) model for the period from 1980 to 2008. Uncertainty intervals associated with the SOC dynamics for these 4 subgroups were estimated by a long-term global sensitivity and uncertainty analysis (i.e., the Sobol? method), and their sensitivities to 7 influential factors were quantified using the total effect sensitivity index. The results, modeled with high confidence, indicated that in the past 29 years, the studied paddy soils in Xinxing, Yixing, and Zhongjiang counties were carbon (C) sinks, while the paddy soil in Helong County was a C source. The 3 C sinks sequestered 12.2 (5.4, 19.6), 17.1 (8.9, 25.0), and 16.9 (−1.2, 33.6) t C ha −1 (values in the parentheses are the 5th and 95th percentiles, respectively). Conversely, the C source had a loss of −5.4 (−14.2, 0.06) t C ha −1 in the past 29 years. The 7 factors, which changed with the climate-soil-management context, exhibited variable influences on modeled SOC. Measures with potential to conserve or sequestrate more C into paddy soils, such as incorporating more crop residues into soils and reducing chemical fertilizer application rates, were recommended for specific soils based on the sensitivity analysis results.

Changes in soil organic matter (SOM) can affect food security, soil and water conservation, and climate change. However, the drivers of changes in SOM in paddy soils of China are not fully understood because the effects of agricultural management and environmental factors are studied separately. Soil, climate, terrain, and agricultural management data from 6 counties selected based on representative soil types and cropping systems in China were used in correlation analysis, analysis of variance, and cforest modeling to analyze the drivers of changes in SOM in paddy soils in the Middle and Lower Yangtze River Plain from 1980 to 2011. The aims of this study were to identify the main factors driving the changes in SOM and to quantitatively evaluate their individual impacts. Results showed that the paddy SOM stock in the study area increased by 12.5% at an average rate of 0.023 kg m−2 year−1 over the 31-year study period. As a result of long-term rice planting, agricultural management practices had a greater influence than soil properties, climate, and terrain. Among the major drivers, straw incorporation, the most influential driver, together with fertilization and tillage practices, significantly increased the accumulation of SOM, while an increase in temperature significantly influenced SOM decomposition. Therefore, to confront the challenge of rising temperatures, it is important to strengthen the positive effects of agricultural management. Rational fertilizer use for stabilizing grain production and crop straw incorporation are promising measures for potential carbon sequestration in this region.

Spectral variable selection is widely accepted as an important step in the quantitative analysis of visible and near-infrared (Vis–NIR) reflectance spectroscopy (350–2500 nm), as it tends to have parsimonious data representation and can result in multivariate models with greater predictive ability. In this study, a total of 123 intact soil cores (8.4 cm internal diameter and 40 cm long) were collected from paddy fields in Yujiang, China. The Vis–NIR spectra, measured in the laboratory for flat, horizontal surfaces of soil core sections at two depths (5 cm and 10 cm), were used to estimate the rice root density by support vector machine regression (SVMR). In addition to full-spectrum SVMR (FS-SVMR), four spectral feature selection techniques (competitive adaptive reweighted sampling, CARS; genetic algorithm, GA; successive projections algorithm, SPA; and uninformative variable elimination, UVE) were applied with SVMR (i.e., CARS-SVMR, GA-SVMR, SPA-SVMR, and UVE-SVMR) to determine the technique with the most accurate predictions. The coefficient of determination ( R 2 ), root mean square error (RMSE), and residual prediction deviation (RPD) were used to evaluate the accuracy of the optimized calibration models. Based on the independent validation data set ( n = 73), the order of the prediction accuracy was CARS-SVMR ( R 2 P = 0.92; RMSE P = 2.56; RPD P = 3.42) > GA-SVMR ( R 2 P = 0.91; RMSE P = 2.92; RPD P = 2.99) > FS-SVMR ( R 2 P = 0.88; RMSE P = 4.28; RPD P = 2.83) > SPA-SVMR ( R 2 P = 0.87; RMSE P = 4.43; RPD P = 2.55) > UVE-SVMR ( R 2 P = 0.86; RMSE P = 4.49; RPD P = 2.51). The proposed method of CARS-SVMR had a relatively strong ability for spectral variable selection, with the fewest variables ( n = 60) and the shortest computation time (0.12 min), while retaining excellent prediction accuracy. In conclusion, SVMR combined with CARS has great potential to accurately determine the rice root density of intact soil cores of paddy fields using Vis–NIR spectroscopy.

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.

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.

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.

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.

The use of three conceptual pools for soil organic carbon (SOC) proposed by the CENTURY model to study soil organic C (SOC) dynamics helps to more clearly understand the mechanism of C sequestration and the stability of SOC pool in farmlands. We studied a typical rice-growing area, Chengdu Plain in southwestern China. Based on historical soil data collected in the early 1980s, 48 sampling points that include four major types of paddy soils from the surface layer (0–20 cm) and the subsurface layer (20–40 cm) were selected to collect soil samples (n = 96) in 2010. A 100-d laboratory incubation was conducted to measure the SOC decomposition rates at different times, and data from the incubation experiment were fitted to a three-pool first-order model that divided SOC pool into active (Cₐ), slow (Cₛ), and resistant (Cᵣ) SOC fractions. Based on these data, a universal predictive method for the concentrations of SOC fractions was developed and used to obtain the 1980s’ concentrations of SOC fractions. The results showed that an exponential function model (Dₛₒcₜ = a - b × cᵗ) can be used as the optimal prediction model for the SOC decomposition process. The Cₐ, Cₛ, and Cᵣ concentrations increased in surface and subsurface soils in the study area from 1980 to 2010. The increases of Cₛ and Cᵣ contributed to more than 90% of the increase in the paddy SOC pool, whereas the contribution of the Cₐ increase was less than 10%. Thus, the increase in the paddy SOC pool mainly derived from the increases of Cₛ and Cᵣ.

Long-term global sensitivity analyses have been made for DNDC modeling SOC.Six different uncertainty methods are compared for their suitability.Morris produced similar sensitivity results with fewer DNDC runs compared with Sobol'.Morris combined a user defined MSF or MC is recommended for uncertainty analysis. Process-based modeling is a powerful tool for identifying predominant factors that influence soil organic carbon (SOC) so that rational management decisions can be made for improving soil fertility and increasing carbon sequestration in agricultural soils. To achieve these goals, a sensitivity and uncertainty analysis of modeling output is required. Based on the data collected at a long-term observation site with a 21-year rice-bean planting history, two global sensitivity analysis (SA) methods and six uncertainty analysis (UA) methods were applied and compared for the DeNitrification-DeComposition (DNDC) model to simulate inter-annual SOC changes (dSOC). Morris and Sobol' SA methods were used to identify the most influential parameters, and then the dSOC uncertainty intervals estimated by Morris, Sobol', Morris and a joint use of Monte Carlo (MC) with the five most sensitive parameters considered (Morris-MC-5Fac), Morris and a joint use of MC with the four most sensitive parameters considered (Morris-MC-4Fac), DNDC embedded Most Sensitive Factor method (DNDC-MSF), and the user-modified Most Sensitive Factor method with the five most sensitive parameters considered (MSF-user-5Fac) were compared. Sensitivity analysis results indicated that the Morris and Sobol' methods produced similar sensitivity indices for 11 DNDC model input parameters. The initial SOC, bulk density, amount of manure applied, ratio of crop residue incorporated into soils, and amount of chemical fertilizer applied are the five most influential parameters on dSOC uncertainty at the site. In our case, Morris is recommended for a pure sensitivity analysis due to its low model running cost compared to that of Sobol'. Among the six uncertainty analysis methods, the Sobol' estimated uncertainty result is considered the most reliable, followed by that estimated with Morris-MC-5Fac, MSF-user-5Fac, Morris-MC-4Fac, Morris, and DNDC-MSF. DNDC-MSF has the lowest model running cost, followed by Morris, MSF-user-5Fac, Sobol', Morris-MC-4Fac, and Morris-MC-5Fac. Considering both the reliability of the uncertainty analysis results and the model running costs, MSF-user-5Fac is the most efficient. However, it should be noted that MSF-user-5Fac is not suitable for models in which the input parameters and modeling outputs are not monotonically related. For the latter models, Sobol' is a better choice. The information in this paper benefits DNDC users by providing guidance in proper sensitivity and uncertainty analysis method selection.

Using the DeNitrification-DeComposition (DNDC, version 9.5) model, we investigated the soil organic carbon (SOC) changes from 1980 to 2009 in Eastern China's upland-crop fields in northern Jiangsu Province. A currently most detailed high-resolution soil database, containing 17,024 polygons at a scale of 1:50,000, derived from 983 unique upland soil profiles, was used. A coarser county-level soil database was also used for a pair-wise simulation for comparison. We found that SOC changes modeled with the county-level soil database differ significantly from those with high-resolution soil data, with the deviation ranging from −64% to 8.0% in different counties. This implies that coarse soil data may lead to large biases in SOC simulation. With the high-resolution database, the model estimates a SOC increase of 37.89 Tg C in the top soils (0–50 cm) over the study area of 3.93 Mha for the past three decades, with an average rate of 322 kg C ha −1 year −1 . The SOC accumulation in the study region accounts for 10.2% of annual national carbon sequestration of upland soils, compared with the fraction of 3.7% in the total upland area of China. This underscores its significance to national climate mitigation. The annual SOC change varied between 61 to 519 kg C ha −1 year −1 , mainly driven by the variations in N-fertilizer and manure applications. This study highlights the significance of high-resolution soil databases in quantifying SOC changes. Our high-resolution estimates of SOC will support farming and carbon management in this region.

Agro-ecosystem models have been widely used to quantify soil organic carbon (SOC) dynamics based on digital soil maps. However, most of the studies use soil data of single or limited choices of map scales, thus the influence of map scales on SOC dynamics has rarely been quantified. In this study, six digital paddy soils databases of the Tai-Lake region in China at scales of 1:50,000 (P005), 1:200,000 (P02), 1:500,000 (P05), 1:1,000,000 (P1), 1:4,000,000 (P4), and 1:14,000,000 (P14) were used to drive the DNDC (DeNitrification & DeComposition) model to quantify SOC dynamics for the period of 2001⿿2019. Model simulations show that the total SOC changes from 2001 to 2019 in the top layer (0⿿30 cm) of paddy soils using P005, P02, P05, P1, P4, and P14 soil maps would be 3.44, 3.71, 1.41, 2.01, 3.57 and 0.10 Tg C, respectively. The simulated SOC dynamics are significantly influenced by map scales. Taking the total SOC changes based on the most detailed soil map, P005, as a reference, the relative deviation of P02, P05, P1, P4, and P14 were 7.9%, 58.9%, 41.6%, 3.9%, and 97.0%, respectively. Such differences are primarily attributed to missing soil types and spatial variations in soil types in coarse-scale maps. Although the relative deviation of P4 soil map for the entire Tai-Lake region is the lowest, substantial differences (i.e., 22⿿1010%) exist at soil subgroups level. Overall, soil map scale of P02 provides best accuracy for quantifying SOC dynamics of paddy soils in the study region. Considering the soil data availability of entire China, P1 soil map is also recommended. This study suggested how to select an appropriate scale of input soil data for modeling the carbon cycle of agro-ecosystems.

To improve our understanding of biomass allocation and soil carbon cycling in paddy fields, information on the root biomass of rice is fundamental. The commonly used conventional methods to study root biomass are difficult and time-consuming. In this study, visible and near-infrared (Vis–NIR) reflectance spectroscopy (350–2500 nm) was evaluated for the rapid analysis of rice root density from the flat, horizontal surface of a soil core section. A total of 123 intact soil cores (0–40 cm depth, with a 8.4 cm diameter) were collected from paddy fields in Yujiang, China, and the Vis–NIR reflectance was measured in the laboratory at two depths (5 and 10 cm). Three multivariate regression methods (principal component regression, PCR; partial least square regression, PLSR; and support vector machine regression, SVMR) were used to develop models to predict root density, and these models were compared to determine the one with the most accurate predictions. Based on the comparisons with both cross-validation and independent validation data sets, the SVMR model outperformed the PCR and PLSR models. The predictions of root density with the SVMR model were more accurate (coefficient of determination [R2] = 0.88; root mean square error [RMSE] = 4.28; ratio of standard deviation to RMSE [RPD] = 2.83; ratio of performance to inter-quartile distance [RPIQ] = 3.23; Slope = 0.80) than those with the PCR model (R2 = 0.81; RMSE = 5.58; RPD = 2.17; RPIQ = 2.47; Slope = 0.74) or the PLSR model (R2 = 0.82; RMSE = 5.22; RPD = 2.32; RPIQ = 2.65; Slope = 0.76), based on 73 independent validation samples. In conclusion, the Vis–NIR spectra acquired from the intact soil cores were used to accurately predict rice root density. Therefore, Vis–NIR spectroscopy combined with SVMR calibrations has the potential to detect root densities in paddy soils under field conditions.

Rising temperatures and elevated atmospheric CO2 are two factors that simultaneously affect the dynamics of soil organic carbon (SOC). This study separately examines the effects arise from these two factors in Tai-Lake Paddy soils using DeNitrification–DeComposition (DNDC) model, with the currently most detailed soil database for the paddy region of China. The soil database is at a scale of 1:50,000, containing 52,034 paddy soil polygons derived from 1107 unique paddy soil profiles. Our simulations indicate that, the SOC in the top soils (0-30 cm) increases 0.83, 1.09, 1.32, and 1.51 Tg C under conventional management (3.44 Tg C) in the 2.32 Mha paddy soils of the Tai-Lake region from 2001 to 2019, respectively, with the atmospheric CO2 concentration increases at 1.5, 2.0, 2.5, and 3.0 times the normal rate (1.9 ppm year−1). By contrast, with rising air temperature of 0.5, 1.0, 1.5, 2.0, 3.0, and 4 °C, the SOC decreases 0.09, 0.54, 0.69, 1.13, 1.80, and 2.51 Tg C under conventional management, respectively. Thus, the effect of carbon sink induced from CO2 fertilization at the 2.0 times normal CO2 concentration increase rate could generally offset the effect of carbon source resulted from a 2.0 °C air temperature increase. In addition, the paddy soils in this region tend to persistently be a sink of atmospheric CO2 under warming and elevated CO2 scenarios, even if when the air temperature has increased by 4 °C. These results suggest that SOC storage in paddy soils of this region is prone to benefit from future global climate change and this carbon sequestration potential in the agro-ecosystems is likely to contribute to climate mitigation under current agricultural practices, despite any negative effects derived from warming. As a representative of paddy soils in eastern China, the insights gained from the Tai-Lake region may be potentially transferable to other paddy soils in eastern China where 95% of the total of China is located.

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.

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.

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.