Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy

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.     

Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark

Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0−5, 5−15, 15−30, 30−60 and 60−100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg⁻¹ was reported for 0−5 cm soil, whereas there was on average 2.2 g SOC kg⁻¹ at 60−100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg⁻¹ was found at 60−100 cm soil depth. Average SOC stock for 0−30 cm was 72 t ha⁻¹ and in the top 1 m there was 120 t SOC ha⁻¹. In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.     

Evaluation of the CENTURY Model Using Long-Term Fertilization Trials under Corn-Wheat Cropping Systems in the Typical Croplands of China

Soil organic matter models are widely used to study soil organic carbon (SOC) dynamics. Here, we used the CENTURY model to simulate SOC in wheat-corn cropping systems at three long-term fertilization trials. Our study indicates that CENTURY can simulate fertilization effects on SOC dynamics under different climate and soil conditions. The normalized root mean square error is less than 15% for all the treatments. Soil carbon presents various changes under different fertilization management. Treatment with straw return would enhance SOC to a relatively stable level whereas chemical fertilization affects SOC differently across the three sites. After running CENTURY over the period of 1990–2050, the SOC levels are predicted to increase from 31.8 to 52.1 Mg ha⁻¹ across the three sites. We estimate that the carbon sequestration potential between 1990 and 2050 would be 9.4–35.7 Mg ha⁻¹ under the current high manure application at the three sites. Analysis of SOC in each carbon pool indicates that long-term fertilization enhances the slow pool proportion but decreases the passive pool proportion. Model results suggest that change in the slow carbon pool is the major driver of the overall trends in SOC stocks under long-term fertilization.     

Soil Organic Carbon Pools and Stocks in Permafrost-Affected Soils on the Tibetan Plateau

The Tibetan Plateau reacts particularly sensitively to possible effects of climate change. Approximately two thirds of the total area is affected by permafrost. To get a better understanding of the role of permafrost on soil organic carbon pools and stocks, investigations were carried out including both discontinuous (site Huashixia, HUA) and continuous permafrost (site Wudaoliang, WUD). Three organic carbon fractions were isolated using density separation combined with ultrasonic dispersion: the light fractions (<1.6 g cm⁻³) of free particulate organic matter (FPOM) and occluded particulate organic matter (OPOM), plus a heavy fraction (>1.6 g cm⁻³) of mineral associated organic matter (MOM). The fractions were analyzed for C, N, and their portion of organic C. FPOM contained an average SOC content of 252 g kg⁻¹. Higher SOC contents (320 g kg⁻¹) were found in OPOM while MOM had the lowest SOC contents (29 g kg⁻¹). Due to their lower density the easily decomposable fractions FPOM and OPOM contribute 27% (HUA) and 22% (WUD) to the total SOC stocks. In HUA mean SOC stocks (0–30 cm depth) account for 10.4 kg m⁻², compared to 3.4 kg m⁻² in WUD. 53% of the SOC is stored in the upper 10 cm in WUD, in HUA only 39%. Highest POM values of 36% occurred in profiles with high soil moisture content. SOC stocks, soil moisture and active layer thickness correlated strongly in discontinuous permafrost while no correlation between SOC stocks and active layer thickness and only a weak relation between soil moisture and SOC stocks could be found in continuous permafrost. Consequently, permafrost-affected soils in discontinuous permafrost environments are susceptible to soil moisture changes due to alterations in quantity and seasonal distribution of precipitation, increasing temperature and therefore evaporation.     

Simulation of Biomass Yield and Soil Organic Carbon under Bioenergy Sorghum Production

Developing sustainable management practices including appropriate residue removal and nitrogen (N) fertilization for bioenergy sorghum is critical. However, the effects of residue removal and N fertilization associated with bioenergy sorghum production on soil organic carbon (SOC) are less studied compared to other crops. The objective of our research was to assess the impacts of residue removal and N fertilization on biomass yield and SOC under biomass sorghum production. Field measurements were used to calibrate the DNDC model, then verified the model by comparing simulated results with measured results using the field management practices as agronomic inputs. Both residue removal and N fertilization affected bioenergy sorghum yields in some years. The average measured SOC at 0–50 cm across the treatments and the time-frame ranged from 47.5 to 78.7 Mg C ha⁻¹, while the simulated SOC was from 56.3 to 67.3 Mg C ha⁻¹. The high correlation coefficients (0.65 to 0.99) and low root mean square error (3 to 18) between measured and simulated values indicate the DNDC model accurately simulated the effects of residue removal with N fertilization on bioenergy sorghum production and SOC. The model predictions revealed that there is, in the long term, a trend for higher SOC under bioenergy sorghum production regardless of residue management.     

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R² (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R²: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).     

Nutrient composition of spent wash and its impact on sugarcane growth and biochemical attributes

Nutrient composition of crude and digested spent wash and effect of their application on sugarcane growth and biochemical attributes were studied. Higher concentrations of essential nutrients (P, S, Fe, Mn, Zn, Cu) and heavy metals (Cd, Cr, Ni and Pb) were present in crude spent wash (CSW) as compared to the digested spent wash (DSW); sulphur content was the highest (765 μg ml⁻¹ in DSW and 1,609 μg ml⁻¹ in CSW) among all nutrients analyzed. Sugarcane (Saccharum spp. hybrid cultivar CoLk 8102) setts grown in soil pot culture conditions with different rates of crude spent wash (5, 10, 20 and 100 ml kg⁻¹ soil) along with digested spent wash (100 ml kg⁻¹ soil) showed improvement in bud sprouting (10.5 %), settling height (40 %), root number (9.4 %), root length (13.2 %), chlorophyll a (52.9 %) and b (55.3 %) contents and activity of catalase (98 %) enzyme over control at low rate of crude spent wash (5 ml kg⁻¹ soil). Whereas, higher doses of spent wash (20 and 100 ml kg⁻¹ soil) decreased these parameters markedly except peroxidase which was found higher at all the levels of both CSW and DSW. Findings indicated stimulatory effect of low rate of crude spent wash (5 ml kg⁻¹ soil) on root and shoot growth and inhibitory effect of higher dose (100 ml kg⁻¹ soil) of both crude and digested spent wash, therefore, judicious application of spent wash will improve crop productivity and alleviate environmental pollution problems.     

近红外光谱分析法测定东北黑土有机碳和全氮含量

以我国东北黑土为研究对象,分析了2004-2005年采集的136个土壤样品在3699～12000 cm^-1范围的近红外光谱,利用偏最小二乘法建立了原始光谱吸光度与土壤有机碳、全氮和碳氮比之间的定量分析模型.结果表明：土壤有机碳和全氮的模型拟合效果良好,决定系数R²分别为0.92和0.91（P<0.001）,相对分析误差RPD分别为3.45和3.36,利用该模型对验证样本土壤有机碳和全氮的预测值与实测值之间的相关系数分别为0.94和0.93（P<0.001）,表明可以用近红外光谱分析法对黑土有机碳和全氮含量进行测定.但是利用近红外光谱分析法对土壤碳氮比的预测并不理想,虽然验证样本集黑土碳氮比模型预测值与实测值呈显著相关（r＝0.74,P<0.001）,但是校正模型的R²为0.61,RPD仅为1.61,建立的模型不能对黑土碳氮比做出合理的估测.     

Transfer of Cadmium from Soil to Vegetable in the Pearl River Delta area,South China

The purpose of this study was to investigate the regional Cadmium (Cd) concentration levels in soils and in leaf vegetables across the Pearl River Delta (PRD) area; and reveal the transfer characteristics of Cadmium (Cd) from soils to leaf vegetable species on a regional scale. 170 paired vegetables and corresponding surface soil samples in the study area were collected for calculating the transfer factors of Cadmium (Cd) from soils to vegetables. This investigation revealed that in the study area Cd concentration in soils was lower (mean value 0.158 mg kg⁻¹) compared with other countries or regions. The Cd-contaminated areas are mainly located in west areas of the Pearl River Delta. Cd concentrations in all vegetables were lower than the national standard of Safe vegetables (0.2 mg kg⁻¹). 88% of vegetable samples met the standard of No-Polluted vegetables (0.05 mg kg⁻¹). The Cd concentration in vegetables was mainly influenced by the interactions of total Cd concentration in soils, soil pH and vegetable species. The fit lines of soil-to-plant transfer factors and total Cd concentration in soils for various vegetable species were best described by the exponential equation (), and these fit lines can be divided into two parts, including the sharply decrease part with a large error range, and the slowly decrease part with a low error range, according to the gradual increasing of total Cd concentrations in soils.     

Spatial Distribution of Soil Organic Carbon and Its Influencing Factors in Desert Grasslands of the Hexi Corridor,Northwest China

Knowledge of the distribution patterns of soil organic carbon (SOC) and factors that influence these patterns is crucial for understanding the carbon cycle. The objectives of this study were to determine the spatial distribution pattern of soil organic carbon density (SOCD) and the controlling factors in arid desert grasslands of northwest China. The above- and belowground biomass and SOCD in 260 soil profiles from 52 sites over 2.7×10⁴ km² were investigated. Combined with a satellite-based dataset of an enhanced vegetation index during 2011–2012 and climatic factors at different sites, the relationships between SOCD and biotic and abiotic factors were identified. The results indicated that the mean SOCD was 1.20 (SD:+/− 0.85), 1.73 (SD:+/− 1.20), and 2.69 (SD:+/− 1.91) kg m⁻² at soil depths of 0–30 cm, 0–50 cm, and 0–100 cm, respectively, which was smaller than other estimates in temperate grassland, steppe, and desert-grassland ecosystems. The spatial distribution of SOCD gradually decreased from the southeast to the northwest, corresponding to the precipitation gradient. SOCD increased significantly with vegetation biomass, annual precipitation, soil moisture, clay and silt content, and decreased with mean annual temperature and sand content. The correlation between BGB and SOCD was closer than the correlation between AGB and SOCD. Variables could together explain about 69.8%, 74.4%, and 78.9% of total variation in SOCD at 0–30 cm, 0–50 cm, and 0–100 cm, respectively. In addition, we found that mean annual temperature is more important than other abiotic factors in determining SOCD in arid desert grasslands in our study area. The information obtained in this study provides a basis for accurately estimating SOC stocks and assessing carbon (C) sequestration potential in the desert grasslands of northwest China.     

Seasonal Dynamics of Soil Labile Organic Carbon and Enzyme Activities in Relation to Vegetation Types in Hangzhou Bay Tidal Flat Wetland

Soil labile organic carbon and soil enzymes play important roles in the carbon cycle of coastal wetlands that have high organic carbon accumulation rates. Soils under three vegetations (Phragmites australis, Spartina alterniflora, and Scirpusm mariqueter) as well as bare mudflat in Hangzhou Bay wetland of China were collected seasonally. Seasonal dynamics and correlations of soil labile organic carbon fractions and soil enzyme activities were analyzed. The results showed that there were significant differences among vegetation types in the contents of soil organic carbon (SOC) and dissolved organic carbon (DOC), excepting for that of microbial biomass carbon (MBC). The P. australis soil was with the highest content of both SOC (7.86 g kg^-1) and DOC (306 mg kg^-1), while the S. mariqueter soil was with the lowest content of SOC (6.83 g kg^-1), and the bare mudflat was with the lowest content of DOC (270 mg kg^-1). Soil enzyme activities were significantly different among vegetation types except for urease. The P. australis had the highest annual average activity of alkaline phosphomonoesterase (21.4 mg kg^-1 h^-1), and the S. alterniflora had the highest annual average activities of β-glycosidase (4.10 mg kg^-1 h^-1) and invertase (9.81mg g^-1 24h^-1); however, the bare mudflat had the lowest activities of alkaline phosphomonoesterase (16.2 mg kg^-1 h^-1), β-glycosidase (2.87 mg kg^-1 h^-1), and invertase (8.02 mg g^-1 24h^-1). Analysis also showed that the soil labile organic carbon fractions and soil enzyme activities had distinct seasonal dynamics. In addition, the soil MBC content was significantly correlated with the activities of urease and β-glucosidase. The DOC content was significantly correlated with the activities of urease, alkaline phosphomonoesterase, and invertase. The results indicated that vegetation type is an important factor influencing the spatial-temporal variation of soil enzyme activities and labile organic carbon in coastal wetlands.     

Soil TPH Concentration Estimation Using Vegetation Indices in an Oil Polluted Area of Eastern China

Assessing oil pollution using traditional field-based methods over large areas is difficult and expensive. Remote sensing technologies with good spatial and temporal coverage might provide an alternative for monitoring oil pollution by recording the spectral signals of plants growing in polluted soils. Total petroleum hydrocarbon concentrations of soils and the hyperspectral canopy reflectance were measured in wetlands dominated by reeds (Phragmites australis) around oil wells that have been producing oil for approximately 10 years in the Yellow River Delta, eastern China to evaluate the potential of vegetation indices and red edge parameters to estimate soil oil pollution. The detrimental effect of oil pollution on reed communities was confirmed by the evidence that the aboveground biomass decreased from 1076.5 g m⁻² to 5.3 g m⁻² with increasing total petroleum hydrocarbon concentrations ranging from 9.45 mg kg⁻¹ to 652 mg kg⁻¹. The modified chlorophyll absorption ratio index (MCARI) best estimated soil TPH concentration among 20 vegetation indices. The linear model involving MCARI had the highest coefficient of determination (R ² = 0.73) and accuracy of prediction (RMSE = 104.2 mg kg⁻¹). For other vegetation indices and red edge parameters, the R² and RMSE values ranged from 0.64 to 0.71 and from 120.2 mg kg⁻¹ to 106.8 mg kg⁻¹ respectively. The traditional broadband normalized difference vegetation index (NDVI), one of the broadband multispectral vegetation indices (BMVIs), produced a prediction (R ² = 0.70 and RMSE = 110.1 mg kg⁻¹) similar to that of MCARI. These results corroborated the potential of remote sensing for assessing soil oil pollution in large areas. Traditional BMVIs are still of great value in monitoring soil oil pollution when hyperspectral data are unavailable.     

Soil Respiration in Semiarid Temperate Grasslands under Various Land Management

Soil respiration, a major component of the global carbon cycle, is significantly influenced by land management practices. Grasslands are potentially a major sink for carbon, but can also be a source. Here, we investigated the potential effect of land management (grazing, clipping, and ungrazed enclosures) on soil respiration in the semiarid grassland of northern China. Our results showed the mean soil respiration was significantly higher under enclosures (2.17μmol.m⁻².s⁻¹) and clipping (2.06μmol.m⁻².s⁻¹) than under grazing (1.65μmol.m⁻².s⁻¹) over the three growing seasons. The high rates of soil respiration under enclosure and clipping were associated with the higher belowground net primary productivity (BNPP). Our analyses indicated that soil respiration was primarily related to BNPP under grazing, to soil water content under clipping. Using structural equation models, we found that soil water content, aboveground net primary productivity (ANPP) and BNPP regulated soil respiration, with soil water content as the predominant factor. Our findings highlight that management-induced changes in abiotic (soil temperature and soil water content) and biotic (ANPP and BNPP) factors regulate soil respiration in the semiarid temperate grassland of northern China.     

N rate and transport under variable cropping history and fertilizer rate on loamy sand and clay loam soils: II. Performance of LEACHMN using different calibration scenarios

Testing of existing agronomic models is needed to ensure their validity and applicability to different soils, cropping systems and environments. Data collected from a 3-year field experiment of maize (zea mays L.) on a loamy sand and a clay loam soil were used to validate the research version of the LEACHMN model for water flow and N fate and transport. Three calibration scenarios with increasing levels of generalization for transformation rate coefficients were used based on: (i) each year, treatment and soil type (ii) 3-year average values for each treatment and soil type, and (iii) average over years and soil types. Model accuracy was tested using both graphical and statistical methods including 1:1 scale plot, root mean square error and normalized root mean square error, and correlation coefficient values. The model accurately predicted drainage water flow rate and volume under both sites. Calibrated N transformation rate constants for each treatment, year and soil type provided satisfactory predictions of growing season cumulative NO₃–N leaching losses, and accurate predictions of growing season cumulative maize N uptake at both sites. The use of 3-year average rate constant values for each site resulted in fairly satisfactory predictions of NO₃–N leaching losses on the clay loam site, but inaccurate predictions on the loamy sand site. The model provided accurate predictions of cumulative maize N uptake for both sites. Using the rate constant values averaged over years and soil types resulted mostly in inaccurate predictions. Use of year and soil type-specific N rate coefficients results in accurate LEACHMN predictions of N leaching and maize N uptake. When rate coefficients are generalized over years for each soil type, satisfactory model predictions may be expected when N dynamics are not strongly affected by yearly variations in organic N inputs.     

Spatial Variation in Carbon and Nitrogen in Cultivated Soils in Henan Province,China: Potential Effect on Crop Yield

Improved management of soil carbon (C) and nitrogen (N) storage in agro-ecosystems represents an important strategy for ensuring food security and sustainable agricultural development in China. Accurate estimates of the distribution of soil C and N stores and their relationship to crop yield are crucial to developing appropriate cropland management policies. The current study examined the spatial variation of soil organic C (SOC), total soil N (TSN), and associated variables in the surface layer (0–40 cm) of soils from intensive agricultural systems in 19 counties within Henan Province, China, and compared these patterns with crop yield. Mean soil C and N concentrations were 14.9 g kg⁻¹ and 1.37 g kg⁻¹, respectively, whereas soil C and N stores were 4.1 kg m⁻² and 0.4 kg m⁻², respectively. Total crop production of each county was significantly, positively related to SOC, TSN, soil C and N store, and soil C and N stock. Soil C and N were positively correlated with soil bulk density but negatively correlated with soil porosity. These results indicate that variations in soil C could regulate crop yield in intensive agricultural systems, and that spatial patterns of C and N levels in soils may be regulated by both climatic factors and agro-ecosystem management. When developing suitable management programs, the importance of soil C and N stores and their effects on crop yield should be considered.     

The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis–NIR spectroscopy and comparative methods

The development of a rapid, accurate and cost-effective method for the prediction of constituents related to soil nitrogen (N) supply is considered important. The potential of using visible (Vis) and near infrared reflectance (NIR) spectroscopy (400–2500 nm) as such a method was investigated. Vis–NIR calibrations were performed for organic carbon (C_org) and total N (N_tot) content and their potential mineralisation using 80 grassland soil samples of rather heterogeneous origin. Prediction accuracy was tested using a 'take-out-four' validation strategy (48 samples). Within investigated variables a ratio of standard deviation of reference data to standard error of bias corrected prediction (RPD) within 1.7 (r²=0.65) and 2.7 (r²=0.87) were achieved. Apparent differences in Vis–NIR prediction accuracy among the variables were partly due to errors in the reference values. Thawed moist samples tend to be more accurately predicted than dried samples, and no benefit was derived from the grinding of sieved (4 mm) and dried samples. Prediction accuracy did not differ using two different systems for sample presentation to the Vis–NIR analyses. Comparative predictions of C_org and N_tot and their potential mineralisations were performed using the take-out-four validation strategy and simple linear regression to loss on ignition (LOI) values and hot KCl extracted NH₄-N (N_hotKCl) values as predictors. Likewise, the reference values of C_org and N_tot were also used as predictors for each other and for the potential C and N mineralisation constituents. Accuracy obtained for the Vis–NIR predictions of investigated constituents was in general equal or better than prediction accuracy obtained by these comparative methods. The Vis–NIR method provided promising predictions of variables important for the soil N supply.     

基于数字土壤制图技术的土壤有机碳储量估算

精准的土壤属性空间分布信息有助于提升土壤有机碳储量估算的精度。本研究以河南省济源市南山林场为研究区,以地形因子为预测因子,利用模糊C均值(FCM)聚类方法对土壤有机碳含量、土壤容重、土壤厚度和土壤砾石含量进行数字土壤预测制图,基于数字制图结果实现土壤有机碳密度预测制图和土壤有机碳储量估算。结果表明: 基于数字土壤制图方法得到的研究区土壤有机碳密度平均值为4.24 kg·m^-2,其预测图的平均误差(ME)为0.08 kg·m^-2,平均绝对误差(MAE)为2.80 kg·m^-2,均方根误差(RMSE)为5.03 kg·m^-2,与传统类型方法相比,预测结果的精度和稳定性更高,具有较高的可信度,最终估算得到研究区土壤有机碳储量为3.08×10⁸ kg。基于数字土壤制图技术仅采用少量土壤样点即可实现较高精度的土壤有机碳密度制图和储量估算,且能表征土壤有机碳密度空间分布特征。本研究为土壤有机碳储量估算提供了新途径,有助于提升土壤有机碳储量估算的精度和效率。     

Organic carbon and nitrogen losses influenced by vegetation removal in a semiarid mediterranean soil

A reduction in plant cover can lead to an increase in the erosionprocesses that diminish soil quality. Any rise in temperature resulting frompredicted climate changes may aggravate this effect, particularly in semiaridMediterranean areas. Bearing this in mind, the capacity of a soil to preserveorganic matter becomes very important if the soil is to maintain its physicaland chemical properties. Soil organic carbon and nitrogen changes wereevaluatedin a non-disturbed (with natural vegetation) and a disturbed (all vegetationmanually clipped to ground level) pine system. Nine years after vegetationremoval significant differences (p < 0.01) were found in the soil organiccarbon content between plots (top 20 cm), but not in totalnitrogen. In the disturbed plot 0.0232 Mg ha^–1y^–1 of soil organic carbon were lost through erosionand4.30 Mg ha^–1 y^–1 throughmineralization. In the first 48 months after vegetation removal the soilorganiccarbon content fell from 40.3 to 28.0 g kg^–1. Inthe last 60 months of the experiment the amount of organic carbon in the soilfell from 28.0 to 27.7 g kg^–1. This result wasmainly attributable to the intense oxidization, which took place during thefirst 60 months, of organic matter linked to the coarse soil mineral fraction.Up to the 72^nd month the losses by erosion were a total of 532.7g, which rose to 1284.4 g by the end of theexperiment(108 months). The effect of vegetation removal in a Mediterranean semiarid arealeads to a rapid decline in the amount of organic carbon stored in the soil.Such perturbation is irreversible if left to nature.     

Spatial Analysis of Soil Organic Carbon in Zhifanggou Catchment of the Loess Plateau

Soil organic carbon (SOC) reflects soil quality and plays a critical role in soil protection, food safety, and global climate changes. This study involved grid sampling at different depths (6 layers) between 0 and 100 cm in a catchment. A total of 1282 soil samples were collected from 215 plots over 8.27 km². A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability and soil carbon content patterns. The mean SOC content in the 1282 samples from the study field was 3.08 g·kg⁻¹. The SOC content of each layer decreased with increasing soil depth by a power function relationship. The SOC content of each layer was moderately variable and followed a lognormal distribution. The semi-variograms of the SOC contents of the six different layers were fit with the following models: exponential, spherical, exponential, Gaussian, exponential, and exponential, respectively. A moderate spatial dependence was observed in the 0–10 and 10–20 cm layers, which resulted from stochastic and structural factors. The spatial distribution of SOC content in the four layers between 20 and 100 cm exhibit were mainly restricted by structural factors. Correlations within each layer were observed between 234 and 562 m. A classical Kriging interpolation was used to directly visualize the spatial distribution of SOC in the catchment. The variability in spatial distribution was related to topography, land use type, and human activity. Finally, the vertical distribution of SOC decreased. Our results suggest that the ordinary Kriging interpolation can directly reveal the spatial distribution of SOC and the sample distance about this study is sufficient for interpolation or plotting. More research is needed, however, to clarify the spatial variability on the bigger scale and better understand the factors controlling spatial variability of soil carbon in the Loess Plateau region.     

Mineralization of soil organic P induced by drying and rewetting as a source of plant-available P in limed and unlimed samples of an acid soil

This study assessed the effects of different farming systems, namely woodlot (WL), alley farming (AL), conventional tillage (CT) and natural fallow (NF) on the variability of organic carbon (OC) content and mean weight diameter (MWD) of a degraded Ferric Acrisol in the sub-humid zone of Ghana. The soils under woodlot accumulated the highest amount of organic carbon (18.6 g kg^–1) with the least spatial variability apparently due to the greater additions of litter and minimum tillage. The conventionally tilled soil had the least OC content (13.1 g kg^–1). Similar to the OC content, the woodlot soils also had the highest aggregate stability (MWD = 1.78 mm) and the least spatial variability. The stability of soil aggregates under the farming systems was greatly influenced by OC content; there was a good correlation between OC and MWD (r > 0.62^**). Correlograms showed that OC and MWD are space dependent. The correlation length for OC under the different farming systems followed the order WL > NF > AL > CT, indicating that WL ensured a greater uniform distribution soil organic matter. The spatial distribution in MWD followed the same trend observed for OC. The MWD in the other farming systems was poorly related from point to point with shorter k-values, suggesting lack of uniformity due to low accumulation of OC. Generally, the woodlot system appeared to be a better, low-input restorer of soil productivity.