A comprehensive understanding of the spatial distribution of soil organic matter (SOM) is essential for conserving soil fertility, ensuring food security and supporting sustainable agricultural management. Among digital soil mapping (DSM) techniques, geostatistical methods and machine learning (ML) models are the two most widely used approaches, each offering distinct advantages and limitations. Random Forest (RF), a representative ML model, effectively captures complex non-linear relationships between soil properties and environmental covariates but typically overlooks spatial dependencies. In contrast, the Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation (INLA-SPDE) is a geostatistical method that explicitly accounts for spatial structure. To harness the complementary strengths of both approaches, this study integrates RF and INLA-SPDE for SOM mapping in the cropland of Guangzhou City, a region characterized by lateritic red soils. Four variable selection strategies, including variance inflation factor with stepwise regression based on the Akaike information criterion (VIF+StepAIC), recursive feature elimination (RFE), forward recursive feature selection (FRFS) and Boruta, were evaluated to identify the optimal modelling strategy. Results showed that RF (R 2 = 0.40-0.73) outperformed INLA-SPDE (R 2 = 0.19-0.42) across all variable selection methods, highlighting the importance of modelling non-linear relationships for spatial prediction. Notably, coupling RF with INLA-SPDE predictions led to a maximum accuracy improvement of 86.5% (based on VIF+StepAIC), demonstrating that incorporating spatial information into the RF model significantly enhances predictive performance. These findings underscore the potential of integrating geostatistics and machine learning for improved SOM mapping in DSM applications.

Soil organic carbon (SOC) is not a single and uniform entity, therefore understanding SOC fractions, particularly particulate organic carbon (POC) and mineral-associated organic carbon (MAOC), offers valuable insights into SOC dynamics. However, traditional laboratory measurements of SOC fractions are labor-intensive and costly. Therefore, leveraging rapid and cost-effective soil spectroscopy holds significant promise for addressing this challenge. While previous studies have concentrated on predicting SOC fractions using mid-infrared (MIR) spectroscopy, the potential of visible and near-infrared (VNIR) spectroscopy remains relatively unexplored, especially for tropical soils. To fill this gap, we evaluated six machine learning approaches, including three global models (Cubist, random forest (RF), partial least squares regression (PLSR)) and three local models (memory-based learning fitted by applying partial least squares regression (MBL-PLSR) and Gaussian process local regressions (MBL-GPR), non-linear memory-based learning (N-MBL)), for predicting POC and MAOC (g C kg(-1) soil) based on a regional soil VNIR spectral library (224 samples) from lateritic red soil in the tropical region of Guangdong Province, China. We also assessed the impact of variable selection on improving model performance by iteratively evaluating and removing insignificant predictor variables to determine the optimal number of predictors. The results showed that: (1) MBL-PLSR and N-MBL demonstrated commendable predictive performance, attaining coefficients of determination (R-2) of 0.73 and 0.72 for POC, and 0.53 and 0.55 for MAOC on the validation set, respectively, outperforming Cubist and PLSR; (2) variable selection simplified predictive models by identifying the best spectral bands, leading to improved predictive accuracy for both POC (R-2 increased from 0.68 to 0.73) and MAOC (R-2 increased from 0.49 to 0.55); (3) the overall predictive performance of VNIR spectroscopy was higher for POC (R-2 of 0.73) compared to MAOC (R-2 of 0.55), while MAOC could be predicted more accurately by subtracting POC predictions from SOC observations (R-2 of 0.73). The favorable predictive accuracy underscores VNIR spectroscopy's viability for POC predictions. Additionally, MAOC can be well predicted by subtracting the predicted POC from the measured SOC. The outcomes of this study offers valuable insights for predicting SOC fractions using VNIR spectroscopy.

Spectral reflectance technology has emerged as a promising tool for estimating soil properties while offering a rapid, non-destructive, and cost-effective alternative to traditional methods. Free iron is an important soil property, and it reflects the occurrence and evolution of soil. An accurate and efficient determination of soil free iron content is important. To evaluate the feasibility of using spectral reflectance and machine learning methods to estimate soil free iron content, we collected the spectral reflectance of 540 soil samples from 135 locations. We looked at the original spectrum and transforms such as the first derivative (FD), standard normal variate (SNV), and continuum removed (CR). The full spectrum, correlated spectrum, and principal components from principal component analysis (PCA) were considered as model variable selection. We used machine learning algorithms, such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and deep neural network (DNN) algorithms for model construction. We found that FD was a more efficient transform than the original, SNV and CR spectra. The average R2, RMSE, and RRMSE when using the FD transform for training were 0.797, 5.550 g/kg, and 25.1%, respectively. In testing models, CR had a higher accuracy than the other transforms and its R2, RMSE, and RRMSE were 0.644, 7.140 g/kg, and 32.7%. Variable selection based on PCA projection improved model accuracy compared to using full and correlated spectra. The average model R2, RMSE, and RRMSE following PCA were 0.821, 5.260 g/kg, and 23.9% in training and 0.692, 6.744 g/kg, and 30.9% in testing, which had a higher R2 and lower RMSE and RRMSE than when using the full and correlated spectra. Over-fitting may have occurred in our study when employing the CR transform and RF algorithm. Their models had high accuracy in training and low accuracy in testing. The model R2 using the DNN showed better performance than those using the PLS and SVM algorithm, but the DNN showed poorer performance in RMSE and RRMSE than that of the model utilizing the SVM and PLS algorithm. The best combination of spectral transform, variable selection, and modeling method was FD + PCA + SVM. The R2, RMSE and RRMSE of this combination were 0.876, 4.085 g/kg and 18.8%, respectively, in training; these reached 0.803, 5.203 g/kg and 23.9%, respectively, in testing. Hence, our study showed spectral reflectance and machine learning could be used to estimate soil free iron content rapidly, non-destructively, and economically. Given these valuable findings, the present study benefits soil properties mapping, crop nutrient management and improving environmental issues.

Accurate monitoring of soil organic carbon (SOC) is critical for sustainable management of soil for improving its quality, function, and carbon sequestration. As a nondestructive, efficient, and low-cost technique, mid-infrared (MIR) spectroscopy has shown a great potential in rapid estimation of SOC, despite limited studies of the global scale. The objective of this work was to use a globally distributed topsoil MIR spectral library with 33,039 samples to predict SOC using different modeling methods. Effects of nine fractional-order derivatives (FODs) on the predicted accuracy of SOC were evaluated using four regression algorithms (i.e., ratio index-based linear regression, RI-LR; partial least squares regression, PLSR; Cubist; convolutional neural network, CNN). Square-root transformation to SOC data was performed to minimize the skewness and non-linearity. Results indicated FOD to capture the subtle spectral details related to SOC, leading to improved predictions that may not be possible by the raw absorbance and common integer-order derivatives. Concerning the RI-LR models, the optimal validation result for SOC was obtained by 0.75-order derivative, with the ratio of performance to inter-quartile distance (RPIQ) of 1.85. Regarding the full-spectrum modeling for SOC, the CNN outperformed PLSR and Cubist models, irrespective of raw absorbance or eight FODs; the best-performing CNN model was achieved by 1.25-order derivative (validation RPIQ = 6.33). It can be concluded that accurate estimation of SOC using large and diverse MIR spectral library at the global scale combined with deep-learning CNN model is feasible. This global-scale database is extremely valuable for us to deal with the shortage of soil data and to monitor the soils at different geographical scales.

Faced with increasing global soil degradation, spatially explicit data on cropland soil organic matter (SOM) provides crucial data for soil carbon pool accounting, cropland quality assessment and the formulation of effective management policies. As a spatial information prediction technique, digital soil mapping (DSM) has been widely used to spatially map soil information at different scales. However, the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance. To overcome this limitation, this study systematically assessed a framework of “information extraction-feature selection-model averaging” for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou, China in 2021. The results showed that using the framework of dynamic information extraction, feature selection and model averaging could efficiently improve the accuracy of the final predictions (R2: 0.48 to 0.53) without having obviously negative impacts on uncertainty. Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM, which improved the R2 of random forest from 0.44 to 0.48 and the R2 of extreme gradient boosting from 0.37 to 0.43. Forward recursive feature selection (FRFS) is recommended when there are relatively few environmental covariates (<200), whereas Boruta is recommended when there are many environmental covariates (>500). The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty. When the structures of initial prediction models are similar, increasing in the number of averaging models did not have significantly positive effects on the final predictions. Given the advantages of these selected strategies over information extraction, feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales, so this approach can provide more reliable references for soil conservation policy-making.

Visible-to-near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy have been widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis-NIR and MIR data can be hypothesized to provide accurate and reliable prediction for SOC because spectral data within a specific range of each individual sensor may lack important absorptive features associated with SOC. In this study, six data fusion strategies, principally direct concatenation-partial least squares regression (DC-PLSR), outer product analysis-PLSR (OPAPLSR), OPA-competitive adaptive reweighted sampling-PLSR (OPA-CARS-PLSR), sequentially orthogonalizedPLSR (SO-PLSR), DC-convolutional neural network (DC-CNN), and parallel input-CNN (PI-CNN), were compared for the spectral estimations of SOC. The data fusion and individual sensor models were developed using soil samples collected from Zhejiang Province, East China, and scanned under laboratory conditions with both vis-NIR and MIR spectrophotometers. The validation results of vis-NIR (validation coefficient of determination [R2] = 0.63-0.73) were generally better than those of MIR (validation R2 = 0.45-0.59). For data fusion, the best validation accuracy was achieved by the PI-CNN (validation R2 = 0.84), followed in descending order by DC-CNN (validation R2 = 0.78), SO-PLSR (validation R2 = 0.73), OPA-CARS-PLSR (validation R2 = 0.69), OPAPLSR (validation R2 = 0.66), and DC-PLSR (validation R2 = 0.64). The better performance of PI-CNN over DCCNN demonstrates the necessity of using different sizes of convolutional kernels before feeding into the fully connected layers in the CNN network for fusing vis-NIR and MIR spectral data. The deep-learning fusion method based on PI-CNN can be considered an efficient tool for integrating data from multiple sensors for estimating soil properties in the field of soil spectral modeling.

Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg−1 in the plough layer (PL) and 13.92 g kg−1 in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg−1. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making.

Islands have special geographical landscapes and complex soil forming conditions, where the soil formation is different from that in mainland areas. To understand the genesis characteristics and taxonomic classification of island hilly soils in Zhejiang Province, 80 soil profiles were surveyed combining with historical literature data in this study. The results showed that the geomorphic structures, parent materials, climatic conditions, and vegetation types of the soils were relatively single, but the soils were frequently affected by the island scales, distances from land, tide, and human activities. The terrain slope was large; the lithification was obvious; the clayization was weak; the desilicification-allitization was changeable; the weathering-leaching coefficient was medium; the restoring base cations were obvious; the pH value and base saturation percentage were higher than those in the mainland at the same latitude; and the soil forming environment was affected by both ancient and modern factors. Four soil orders, namely Ferrosols, Argosols, Cambosols, and Primosols, were identified from the islands of Zhejiang Province, including 7 suborders of Ustic Ferrosols, Udic Ferrosols, Ustic Argosols, Udic Argosols, Ustic Cambosols, Udic Cambosols, and Orthic Primosols, as well as 10 soil groups, 11subgroups and 25 soil families. In conclusion, the direction of soil formation from the islands of Zhejiang Province is basically the same as that of mainland hilly soil at the same latitude, belonging to the traditional ‘red soil zone’.

In the context of increasing soil degradation worldwide, spatially explicit soil information is urgently needed to support decision-making for sustaining limited soil resources. Digital soil mapping (DSM) has been proven as an efficient way to deliver soil information from local to global scales. The number of environmental covariates used for DSM has rapidly increased due to the growing volume of remote sensing data, therefore variable selection is necessary to deal with multicollinearity and improve model parsimony. Compared with Boruta, recursive feature elimination (RFE), and variance inflation factor (VIF) analysis, we proposed the use of modified greedy feature selection (MGFS), for DSM regression. For this purpose, using quantile regression forest, 402 soil samples and 392 environmental covariates were used to map the spatial distribution of soil organic carbon density (SOCD) in Northeast and North China. The result showed that MGFS selected the most parsimonious model with only 9 covariates (e.g., brightness index, mean annual temperature), much lower than RFE (22 covariates), VIF (30 covariates), and Boruta (76 covariates). The repeated validation (50 times) showed that the MGFS derived model performed better (R2 of 0.60, LCCC of 0.74, RMSE of 13.80 t ha -1) than these using full covariates, Boruta, RFE and VIF (R2 of 0.48-0.57, LCCC of 0.64-0.72, RMSE of 14.24-15.79 t ha -1). Despite the similar performance of the uncertainty estimate (PICP), the model using MGFS and RFE had the lowest global uncertainty (0.86) as indicated by the uncertainty index. In addition, MGFS had the best computation efficiency when considering the steps of variable selection and map prediction. Given these advantages over Boruta, RFE and VIF, MGFS has a high potential in fine-resolution soil mapping practices, especially for these studies at a broad scale involving heavy computation on millions or billions of pixels.

Accurate quantification of urban soil organic carbon (SOC) is essential for understanding anthropogenic changes and further guiding effective city managements. Visible and near infrared (vis-NIR) spectroscopy can monitor the SOC content in a time-and cost-effective manner. However, processes and mechanisms dominating the re-lationships between SOC and spectral data in urban soils remain unknown. The main objective of this paper was to evaluate whether multiple stratification strategies (i.e., based on land-use/land-cover [LULC], pH, and spectral clustering) resulted in better predicted performance for SOC compared to the non-stratified (global) models. Results showed that regarding the non-stratified models, the convolutional neural network (CNN) model exhibited the best performance (validation R2 = 0.73), followed by Cubist (validation R2 = 0.66) and memory-based learning (validation R2 = 0.65). After LULC stratification, Cubist model achieved the best prediction (validation R2 = 0.76), improving the value of ratio of performance to interquartile distance by 0.11 compared to the global CNN model. Areas with high SOC values were mainly located in the city center. Stratification by LULC class is a promising strategy for addressing the impact of the soil-landscape diversity and complexity on vis-NIR spectral estimation of SOC in urban soil spectral library.

The knowledge of the spatial distribution of soil organic carbon (SOC) and of its influencing factors is crucial for understanding the global carbon cycle. Although the influence of climate, soil properties, and soil management on the SOC content has been extensively explored, their relative importance remains unclear, especially under dryland farming. Herein, we investigated the SOC density (SOCD) at different depths (0-10, 10-20, 20-30, and 30-40 cm) across an area of 37.79 x 10(4) km(2) (420 sites) located in the dryland farming regions of Northeast and North China. The total SOC storage (SOCS) was estimated to be 1922.38 Tg, with a mean density of 5.78 kg C m(-2) for the entire area. The three soil groups with the largest SOCS were Fluvo-aquic, Black, and Chernozem soils, accounting for 63.34% of the total SOCS in the study area. Overall, both the SOCD and SOCS increased from southwest to northeast at all investigated depths. A structural equation model was used to distinguish direct from indirect effects of different factors on SOCD. Soil properties (e.g., bulk density and pH) and natural conditions (e. g., mean annual temperature and mean annual precipitation) were found to be the main factors controlling the SOCD variation at depths of 10-20, 20-30, and 30-40 cm. However, the total (direct and indirect) effect of soil management on SOCD at a 0-10 cm depth was greater than that of natural conditions, and smaller than that of soil properties. With increasing soil depth, the total effect of natural conditions on SOCD changed from -0.33 to -0.58, whereas the indirect effect of soil management decreased from 0.47 to 0. These results indicate that compared with environmental factors, soil management practices such as tillage and fertilization had a greater influence on SOCD in the surface soil of dryland. Our study provides a valuable reference for future research on the long-term evolution of SOC in dryland farming regions.

Due to the importance of soil organic carbon (SOC) in supporting ecosystem services, accurate SOC assessment is vital for scientific research and decision making. However, most previous studies focused on single soil depth, leading to a poor understanding of SOC in multiple depths. To better understand the spatial distribution pattern of SOC in Northeast and North China Plain, we compared three machine learning algorithms (i.e., Cubist, Extreme Gradient Boosting (XGBoost) and Random Forest (RF)) within the digital soil mapping framework. A total of 386 sampling sites (1584 samples) following specific criteria covering all dryland districts and counties and soil types in four depths (i.e., 0–10, 10–20, 20–30 and 30–40 cm) were collected in 2017. After feature selection from 249 environmental covariates by the Genetic Algorithm, 29 variables were used to fit models. The results showed SOC increased from southern to northern regions in the spatial scale and decreased with soil depths. From the result of independent verification (validation dataset: 80 sampling sites), RF (R2: 0.58, 0.71, 0.73, 0.74 and RMSE: 3.49, 3.49, 2.95, 2.80 g kg−1 in four depths) performed better than Cubist (R2: 0.46, 0.63, 0.67, 0.71 and RMSE: 3.83, 3.60, 3.03, 2.72 g kg−1) and XGBoost (R2: 0.53, 0.67, 0.70, 0.71 and RMSE: 3.60, 3.60, 3.00, 2.83 g kg−1) in terms of prediction accuracy and robustness. Soil, parent material and organism were the most important covariates in SOC prediction. This study provides the up-to-date spatial distribution of dryland SOC in Northeast and North China Plain, which is of great value for evaluating dynamics of soil quality after long-term cultivation.

精准高效获取不同类型土壤的有机质含量,对促进东北土壤退化防治和耕地质量提升有重要意义.本研究以东北旱作农田典型土壤类型为研究对象,采集了黑土、黑钙土、潮土和棕壤共118个土壤样品,采用倒数对数、一阶微分、连续统去除和连续小波变换分别对其光谱曲线进行预处理.通过稳定性竞争自适应重加权采样(sCARS)算法筛选敏感波段,并建立偏最小二乘回归模型.研究结果表明:连续小波变换处理可以抑制背景和噪声的干扰,挖掘土壤光谱内隐含的有效信息,提高土壤光谱与有机质含量的相关性.sCARS算法能够提取与土壤有机质相关的重要特征信息变量,去除冗余、重叠的光谱信息,提高建模效率.黑土、黑钙土、潮土和棕壤的最佳模型均为连续小波变换模型,R2分别达到了 0.83、0.88、0.93和0.93;一 阶微分模型也有较好的表现,而倒数对数、连续统去除的模型效果不佳.连续小波变换处理后,模型的精度和稳定性得到了显著提升,建模集、验证集决定系数R2最高提升了 0.13、0.28,均方根误差(RMSE)最大降低了 2.48、2.40 g/kg.连续小波变换结合sCARS算法,为土壤有机质含量的高光谱快速精准估测提供了新途径.

以富春江上游典型农业小流域为研究对象,采集流域内 126 个农田表层土样测定 Cd含量,并借助地物光谱仪测量可见-近红外反射光谱,采用4 种常用方法对光谱数据进行预处理,应用主成分分析和模糊聚类分析量化土壤光谱数据的最佳分类数目,运用偏最小二乘回归(PLSR)构建土壤Cd含量的预测模型.结果表明:(1)流域内土壤光谱全波段反射率随土壤 Cd 含量的增加而减小,不同土壤 Cd含量下土壤光谱反射率曲线整体变化趋势相近;(2)流域内采样点依据光谱特征可划分为两种类型,类型 1所属样点的土壤光谱反射率高于类型2,但Cd含量平均值低于类型2,且存在明显空间集聚特征;(3)光谱数据经倒数对数处理后建立的PLSR预测模型决定系数达0.60 以上,均方根误差在 1.25 mg/kg以下,具有较好的预测能力和稳定性,是农业小流域土壤 Cd含量估算的有效方法.

The effects of environmental factors on topsoil nutrient distribution have been extensively discussed, but it remains unclear how they affect spatial characteristics of soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry at different depths. We collected 184 soil samples in the typical black soil region of northeast China. Ordinary kriging was performed to describe the spatial distribution of soil C, N, and P eco-stoichiometry. Redundancy analysis was used to explore relationships between C:N:P ratios and physicochemical characteristics. The soil classification was studied by hierarchical cluster analysis. The mean C, N, and P contents ranged from 15.67 to 20.08 g·kg−1, 1.15 to 1.51 g·kg−1, and 0.80 to 0.90 g·kg−1 within measured depths. C, N, and P concentrations and stoichiometry increased from southwest to northeast, and the Songhua River was identified as an important transition zone. At 0–20 cm, soil water content explained most of the C, N, and P content levels and ratios in cluster 1, while latitude had the highest explanatory ability in cluster 2. For 20–40 cm, soil bulk density was the main influencing factor in both clusters. Our findings contribute to an improved knowledge of the balance and ecological interactions of C, N, and P in northeast China for its sustainability.

Soil organic matter (SOM) and environmental factors have been shown to have a scale-location dependence relationship. However, few studies have considered the anisotropy, and the scale-location dependence relationship may not be fully characterized. In this study, transects with dominant directions of SOM variability in the dryland farming regions of Songliao Plain, China were extracted by anisotropy analysis. The scale-location specific multivariate relationships between SOM and environmental factors along the two transects were examined using multiple wavelet coherence. Results indicated that the scale and location-specific variations in SOM and environmental factors were direction-specific. The major direction with the most significant SOM variations was 56° east by north, while the minor direction was perpendicular to the major direction. The strongest single factor for explaining SOM variations differed between two dominant directions, sand along the major direction (average wavelet coherence (AWC) = 0.57, percentage area of significant coherence (PASC) = 40.32% at all scales) and bulk density (BD) along the minor direction (AWC = 0.66, PASC = 50.16% at all scales). The combination of mean annual temperature (MAT) and BD was the best to explain SOM variations along the major direction (AWC = 0.78, PASC = 46.23% at all scales). A two-factor combination is adequate to explain SOM variability along the major direction, whereas a single factor is sufficient for the explanation along the minor direction. More factors did not evidently increase or even decrease the percentage of scale-location domains where SOM variations were significantly explained. This work has important implications for developing future sampling strategies and preparing detailed digital soil maps.

Soil organic carbon density (SOCD) and soil organic carbon sequestration potential (SOCP) play an important role in carbon cycle and mitigation of greenhouse gas emissions. However, the majority of studies focused on a two-dimensional scale, especially lacking of field measured data. We employed the interpolation method with gradient plane nodal function (GPNF) and Shepard (SPD) across a range of parameters to simulate SOCD with a 40 cm soil layer depth in a dryland farming region (DFR) of China. The SOCP was estimated using a carbon saturation model. Results demonstrated the GPNF method was proved to be more effective in simulating the spatial distribution of SOCD at the vertical magnification multiple and search point values of 3.0×106 and 25, respectively. The soil organic carbon storage (SOCS) of 40 cm and 20 cm soil layers were estimated as 22.28×1011 kg and 13.12×1011 kg simulated by GPNF method in DFR. The SOCP was estimated as 0.95×1011 kg considered as a carbon sink at the 20–40 cm soil layer. Furthermore, the SOCP was estimated as −2.49×1011 kg considered as a carbon source at the 0–20 cm soil layer. This research has important values for the scientific use of soil resources and the mitigation of greenhouse gas emissions.

为准确评价东北旱作区耕层质量特征,针对全部初选指标采用主成分分析法(PCA)建立了东北旱作区耕层质量评价的最小数据集(Minimum data set,MDS),并运用最小数据集耕层质量指数(MDS-Plough horizon integrated quality index,MDS-PHIQI)和障碍因子诊断模型对研究区耕层质量及主导障碍因子进行分析.结果 表明:东北旱作区耕层质量评价的最小数据集由土壤有机质含量、全氮含量、有效磷含量、粘粒含量、耕作层穿透阻力和压实层厚度组成,最小数据集可替代全部初选指标对东北旱作区耕层质量进行评价;东北旱作区耕层质量指数分布在0.10 ～0.53之间,均值为0.30,整体处于低和中等水平.东北旱作区合理耕层指标参数的适宜范围为:有机质质量比大于等于37.16 g/kg,全氮质量比大于等于1.75 g/kg,有效磷质量比大于等于26.38 mg/kg,粘粒质量分数为4.60％ ～6.19％,耕作层穿透阻力小于等于364.56 kPa,压实层厚度小于等于8.18 cm.东北旱作区粮食产量低产区耕层多存在结构型障碍,中产区耕层结构型障碍和养分限制共存,而高产区耕层主要表现为养分限制型障碍.整体来看,研究区耕层质量的主要障碍因素为耕作层穿透阻力、土壤全氮含量、有机质含量,需针对上述指标采取针对性的耕作和培肥措施.

为探讨参数敏感性分析结果在区域尺度上表现出的不确定性问题,在温带季风气候类型黄淮海平原旱作区不同积温区内选取黄骅、商丘和驻马店3个站点,基于气象和作物生育期数据以及土壤实测数据,采用EFAST(Extended Fourier amplitude sensitivity test)方法,对WOFOST模型冬小麦和夏玉米参数进行全局敏感性分析,并对2种作物在不同生产水平和不同气候条件下的参数敏感性排序进行一致性检验.结果 表明:冬小麦产量在潜在生产水平下主要敏感参数有叶龄的低温阈值(TBASE)、储存器官同化物转化效率(CVO)、总同化速率在低温3℃时的校正因子(TMNFTB3)等,在水分限制生产水平下主要敏感参数有蒸散速率修正因子(CFET)和储存器官同化物转化效率(CVO)等;夏玉米在2种生产水平下产量敏感参数差异不大,主要为总同化速率低温10℃时的校正因子(TMNFTB10)、每日温度为40℃时单叶片同化CO2的初始光能利用效率(EFFTB40)、35℃时叶片生命周期(SPAN)等;冬小麦、夏玉米在不同生产水平下的TDCC系数(Top-down concordance coefficient)分别为0.82和0.98,P均小于0.01,参数敏感性排序的一致性均较高;冬小麦和夏玉米在不同气候条件下潜在生产水平TDCC系数分别为0.92和0.98,P均小于0.01,一致性较高,水分限制生产水平TDCC系数分别为0.61和0.86,P均小于0.01,一致性较差.WOFOST模型不同作物间参数敏感性差异明显,不同生产水平对参数敏感性的影响较小,但受水分胁迫程度的影响,不同气候条件对参数敏感性影响较大,且对不同生产水平下参数敏感性的影响不同,这主要与不同时空下的气候条件差异有关.