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1.
ABSTRACT

Pedotransfer functions (PTFs), as an indirect forecasting method, offer an alternative for labor-intensive bulk density (BD) measurements. In order to improve the forecasting accuracies, support vector machine (SVM) method was first used to develop PTFs for predicting BD. Cross-validation and grid-search methods were used to automatically determine the SVM parameters in the forecasting process. Soil texture and organic matter content were selected as input variables based on results of predecessors, coupled with gray correlation theory. And additional properties were added as inputs for improving PTF's accuracy and reliability. The performance of the PTF established by SVM method was compared with artificial neural network (ANN) method and published PTFs using two indexes: root-mean-square error (RMSE) and coefficient of determination(R2). Results showed that the average RMSE of published PTFs was 0.1053, and the R2 was 0.4558. The RMSE of ANN–PTF was 0.0638, and the R2 was 0.7235. The RMSE of SVM–PTF was 0.0558, and the R2 was 0.7658. Apparently, the SVM–PTF had better performance, followed by ANN–PTF. Additionally, performances could be improved when accumulated receiving water was added as predictor variable. Therefore, the first application of SVM data mining techniques in the prediction of soil BD was successful, improved the accuracy of predictions, and enhanced the function of soil PTFs. The idea of developing PTFs using SVM method for predicting soil BD in the study area could provide a reference for other areas.  相似文献   

2.
The aim of this research is to study the efficiency of pedotransfer functions (PTFs) and artificial neural networks (ANNs) for cationic exchange capacity (CEC) prediction using readily available soil properties. Here, 417 soil samples were collected from the calcareous soils located in East-Azerbaijan province, northwest Iran and readily available soil properties, such as particle size distribution (PSD), organic matter (OM) and calcium carbonate equivalent (CCE), were measured. The entire 417 soil samples were divided into two groups, a training data set (83 soil samples) and test data set (334 soil samples). The performances of several published and derived PTFs and developed neural network algorithms using multilayer perceptron were compared, using a test data set. Results showed that, based on statistics of RMSE and R2, PTFs and ANNs had a similar performance, and there was no significant difference in the accuracy of the model results. The result of the sensitivity analysis showed that the ANN models were very sensitive to the clay variable (due to the high variability of the clay). Finally, the models tested in this study could account for 85% of the variations in cationic exchange capacity (CEC) of soils in the studied area.

Abbreviations: ANN: arti?cial neural networks; MLP: multilayer perceptron; MLR: multiple linear regression; PTFs: Pedotransfer Functions; RBF: Radial Basis Function; MAE: mean absolute error; MSE: mean square error; CEC: cationic exchange capacity  相似文献   


3.
ABSTRACT

Measuring of soil cation exchange capacity (CEC) is difficult and time-consuming. Therefore, it is essential to develop an indirect approach such as pedotransfer functions (PTFs) to predict this property from more readily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, adaptive neurofuzzy inference system, and an artificial neural network (ANN) model to develop PTFs for predicting soil CEC. One hundred and seventy-one soil samples were used into two subsets for training and testing of the models. The model's prediction capability was evaluated by statistical indicators that include RMSE, R2, MBE, and RI. Results showed that the ANN model had the most reliable prediction when compared with other models. This study provides a strong basis for predicting soil CEC and identifying the most determinant properties influencing soil CEC in the north regions of Iran. Analytical framework results could be applied to other parts of the world with similar challenges.

Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Network; CEC: Cation Exchange Capacity; CV: Coefficient of Variation; FFBP: Feed-Forward Back-Propagation; FIS: Fuzzy Inference System; MBE: Mean Bias Error; MF: Membership Function; MLR: Multiple Linear Regressions; MNLR: Multiple Non-Linear Regressions; MLP: Multi-layer Perceptron; OC: Organic Carbon; PTFs: Pedotransfer Functions; R2: Determination Coefficient; RI: Relative Improvement; RMSE: Root Mean Square Error; SD: Standard Deviation  相似文献   

4.
Different types of cation exchange capacity (CEC) and related chemical properties were determined in the main genetic horizons of meadow-chestnut soils in the mesodepressions at the Dzhanybek Research Station of the Institute of Forestry of the Russian Academy of Sciences. In the A horizon, the CEC is mainly due to the organic matter from the clay and coarse fractions, which provides 36% of the soil CEC, and to labile silicates and other clay minerals of the clay fraction. In the Bt horizon, the CEC is mainly provided by the labile minerals of the clay fraction and organic matter of the clay and coarse fractions. The standard soil CEC was found to be significantly higher than the sum of the exchangeable cations in the A horizon and slightly lower than the sum of the exchangeable cations in the Bt and Bca2 horizons. This difference can be related to the fact that the NH4+ ion, which is selectively adsorbed by clay minerals, is used as a displacing cation during the determination of the exchangeable bases, while the Ba2+ ion, which is more selectively adsorbed by organic matter, is used during the determination of the standard CEC. In all the genetic horizons, the experimentally determined value of the standard CEC almost coincides with the CEC value obtained by summing the standard CECs of the different particle-size fractions with account for their contents; hence, this parameter is additive in nature.  相似文献   

5.
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6.
ABSTRACT

The traditional methods for the measurement of soil cation exchange capacity (CEC) are time-consuming and laborious. It is also difficult to maintain stability for long-term experiments and projects. Therefore, it is necessary to develop an indirect approach such as pedotransfer functions (PTFs) to estimate this property from more easily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, classification and regression trees (C&RT), artificial neural network (ANN) model included multiple layer perceptron (MLP) and k-nearest neighbors (k-NN) to develop PTFs for predicting soil CEC. Soil samples, 929, were used into two subsets for training and testing of the models. Sensitivity and statistical analyzes were conducted to determine the most and the least influential variables affecting soil CEC. The prediction capability of models was assessed by statistical indicators included the normalized root-mean-square error (NRMSE) and the coefficient of determination (R2). Results of the present investigation showed that the k-NN and ANN models had the ability to estimate soil CEC by computing easily measurable variables with a guarantee of authenticity, reliability, and reproducibility. Therefore, the results of this study provide a superior basis for predicting soil CEC and could be applied to other parts of the world with similar challenges.  相似文献   

7.
Pedotransfer functions (PTFs) are widely used for hydrological calculations based on the known basic properties of soils and sediments. The choice of predictors and the mathematical calculus are of particular importance for the accuracy of calculations. The aim of this study is to compare PTFs with the use of the nonlinear regression (NLR) and support vector machine (SVM) methods, as well as to choose predictor properties for estimating saturated hydraulic conductivity (Ks). Ks was determined in direct laboratory experiments on monoliths of agrosoddy-podzolic soil (Umbric Albeluvisol Abruptic, WRB, 2006) and calculated using PTFs based on the NLR and SVM methods. Six classes of predictor properties were tested for the calculated prognosis: Ks-1 (predictors: the sand, silt, and clay contents); Ks-2 (sand, silt, clay, and soil density); Ks-3 (sand, silt, clay, soil organic matter); Ks-4 (sand, silt, clay, soil density, organic matter); Ks-5 (clay, soil density, organic matter); and Ks-6 (sand, clay, soil density, organic matter). The efficiency of PTFs was determined by comparison with experimental values using the root mean square error (RMSE) and determination coefficient (R2). The results showed that the RMSE for SVM is smaller than the RMSE for NLR in predicting Ks for all classes of PTFs. The SVM method has advantages over the NLR method in terms of simplicity and range of application for predicting Ks using PTFs.  相似文献   

8.
Abstract

The cation exchange capacity (CEC) at pH 7 was measured for samples of 347 A horizons and 696 B horizons of New Zealand soils. The mean CEC was 22.1 cmolc/kg for the A horizons and 15.2 cmolc/kg for the B horizons. Multiple regressions were carried out for CEC against organic carbon (C), clay content, and the content of seven groups of clay minerals. The results, significant at p <0.001, were consistent with most of the CEC arising from soil organic matter. For the samples of A horizon, the calculated CEC was 221 cmolc/kg per unit C and for the B horizons was 330 cmolc/kg C. There was also a contribution from sites on clay minerals. Multiple regression indicated that smectite had a higher CEC (70 cmolc/kg) than other minerals but it was not as high as that of type smectites; kaolin minerals had the lowest CEC. There was a significant effect of interaction between organic matter and some clay minerals on the CEC. Samples from B horizons containing allophane had lower CEC than those not containing allophane which is consistent with allophane reacting with carboxyl groups on organic matter. For the samples from the A horizons, however the CEC was higher when allophane was present.  相似文献   

9.
Acid soils in some mediterranean forests were investigated for the composition of the adsorption complex and the gradients in soil pH. The effective CEC (235–838 mmolc kg?1) and base saturation (93–98 %) are highest in ectorganic horizons. In the mineral horizons the effective CEC (23–52 mmolc kg?1) and base saturation (11–40 %) are much lower. The exchange complex of mineral horizons consists for 90 (AEh) to 40 percent (Bw2) of organic matter. The effective CEC of the mineral clay fraction is low (60 mmolc kg?1 clay). The clear trends in soil pH within the ectorganic layer of deciduous and sclerophyllous oak forests are attributed to vertical spatial separation of nitrogen mineralization (ammonification and strongly impeded nitrification) and nutrient uptake by roots (mainly NH4). This leads to a high effective CEC in the fermentation layer and acidification of the uppermost part of the mineral soil. In contrast to the situation in temperate forests this process is impeded in mediterranean coniferous forests, where litter decomposition is extremely slow and both proton production and consumption take place in the organic rich mineral horizon.  相似文献   

10.
Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R2; 0.29–0.69) outperformed both PTF‐1 (R2; 0.11–0.18) and PTF‐2 (R2; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R2; 0.61–0.86) performed comparably as PTF‐1 (R2; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R2; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).  相似文献   

11.
Bulk density (BD) is an important soil physical property and has significant effect on soil water conservation function. Indirect methods, which are called pedotransfer functions (PTFs), have replaced direct measurement and can acquire the missing data of BD during routine soil surveys. In this study, multiple linear regression (MLR) and artificial neuron network (ANN) methods were used to develop PTFs for predicting BD from soil organic carbon (OC), texture and depth in the Three-River Headwater region of Qinghai Province, China. The performances of the developed PTFs were compared with 14 published PTFs using four indexes, the mean error (ME), standard deviation error (SDE), root mean squared error (RMSE) and coefficient of determination (R2). Results showed that the performances of published PTFs developed using exponential regression were better than those developed using linear regression from OC. Alexander (1980)-B, Alexander (1980)-A and Manrique and Jones (1991)-B PTFs, which had good predictions, could be applied for the soils in the study area. The PTFs developed using MLR (MLR-PTFs) and ANN (ANN-PTFs) had better soil BD predictions than most of published PTFs. The ANN-PTFs had better performances than the MLR-PTFs and their performances could be improved when soil texture and depth were added as predictor variables. The idea of developing PTFs or predicting soil BD in the study area could provide reference for other areas and the results could lay foundation for the estimation of soil water retention and carbon pool.  相似文献   

12.
Abstract

Recently agricultural activity in the mountainous area of northern Thailand has increased and problems relating to soil fertility have arisen. In order to gain basic information about the soil properties associated with shifting cultivation, physicochemical properties of the surface soils (0–10 cm) and subsoils (30–40 cm) were investigated in selected villages in the area. The physicochemical properties of the soils studied are summarized as follows: 1) The soils were rich in organic matter, content of which ranged from 11.4 to 63.3 g C kg?1 in the surface soil. 2) The pH(H2O) of the soils mostly ranged from 5 to 7 and soil acidity was more pronounced in the deeper horizons. In the surface soils, exchangeable Ca and Mg were generally dominant, whereas exchangeable Al was often predominant in the subsoils. 3) Most of the soils showed a medium to fine texture with more than 30% clay. The clay mineral composition was characterized by various degrees of mixture of kaolin minerals and clay mica with, in some cases, a certain amount of 2:1-2:1:1 intergrades. 4) According to the ion adsorption curves, most of the B horizon soils were characterized by the predominance of permanent negative charges. On the other hand, organic matter contributed to the increase of variable negative charges in the surface soils. The content of organic matter and the percentage of the clay fraction were essential for determining the CEC of the soils of the surface 10 and 30–40 cm depths, respectively. Under the field conditions, the composition of exchangeable cations largely reflected the soil acidity. In addition, the content of organic matter also showed a significant correlation with that of available N in the surface soils. Thus, soil acidity both in the surface soils and subsoils, organic matter content in the surface soils, and clay content in the subsoils were considered to be the main factors that affected soil chemical fertility in the area.  相似文献   

13.
ABSTRACT

Using easily measurable soil properties and pedotransfer functions (PTFs) is a time-saving, non-destructive and cost-saving way in the prediction of the cation exchange capacity (CEC). The purpose of this study was to compare and evaluate the regression tree (RT), multiple linear regression (MLR) and Mamdani fuzzy inference system (MFIS) in estimating CEC. For this work, 100 soil samples from unsaturated soil hydraulic database (UNSODA) data-set were used. %Organic matter (OM), bulk density (BD), the geometric mean particle diameter (dg) and fractal dimension of particle size (D) were applied as the input predictive variables. First, the type of relationship between easily measurable soil properties and CEC was investigated and, then used for the development of PTFs and fuzzy membership functions. The results showed that MLR method was developed only based on %OM (r = 0.68, p < .01) and D (r = 0.68, p < .01). While in the RT method, all of the predictive variables were appeared in the tree-like based on their correlation coefficient with CEC. The D and %OM also were considered as input variables in developing fuzzy membership functions. Results also revealed that RT method had a higher performance than MLR and MFIS in the estimation of CEC with the highest coefficient of determination (R2 = 0.77), smallest root-mean-square error (RMSE = 5.14 meq/100gsoil), normalized root-mean-square error (NRMSE = 0.25 meq/100gsoil) and mean error (ME = ?1.80 meq/100gsoil). In addition, the MFIS had a higher efficiency than the MLR in the CEC estimation.  相似文献   

14.
Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the predictive models by horizons to evaluate prediction performance and systematically compare prediction performance for A, B, and combined A+B horizons. Therefore, we investigated the performance of pXRF and vis-NIR spectra, as individual or combined, for predicting the clay, silt, sand, total carbon (TC), and pH of soils developed in loess, and compared their prediction performance for A, B, and A+B horizons. Soil samples (176 in A horizon and 172 in B horizon) were taken from Mollisols and Alfisols in 136 pedons in Wisconsin, USA and analyzed for clay, silt, sand, pH, and TC. The pXRF and vis-NIR spectrometers were used to measure the pXRF and vis-NIR soil spectra. Data were separated into calibration (n=244, 70%) and validation (n=104, 30%) datasets. The Savitzky-Golay filter was applied to preprocess the pXRF and vis-NIR spectra, and the first 10 principal components (PCs) were selected through principal component analysis (PCA). Five types of predictor, i.e., PCs from vis-NIR spectra, pXRF of beams at 0-40 and 0-10 keV (XRF40 and XRF10, respectively) spectra, combined XRF40 and XRF10 (XRF40+XRF10) spectra, and combined XRF40, XRF10, and vis-NIR (XRF40+XRF10+vis-NIR) spectra, were compared for predicting soil properties using a machine learning algorithm (Cubist model). A multiple linear regression (MLR) model was applied to predict clay, silt, sand, pH, and TC using pXRF elements. The results suggested that pXRF spectra had better prediction performance for clay, silt, and sand, whereas vis-NIR spectra produced better TC and pH predictions. The best prediction performance for sand (R2=0.97), silt (R2=0.95), and clay (R2=0.84) was achieved using vis-NIR+XRF40+XRF10 spectra in B horizon, whereas the best prediction performance for TC (R2=0.93) and pH (R2=0.79) was achieved using vis-NIR+XRF40+XRF10 spectra in A+B horizon. For all soil properties, the best MLR model had a lower prediction accuracy than the Cubist model. It was concluded that pXRF and vis-NIR spectra can be successfully applied for predicting clay, silt, sand, pH, and TC with high accuracy for soils developed in loess, and that spectral models should be developed for different horizons to achieve high prediction accuracy.  相似文献   

15.
Soil cation exchange capacity (CEC), which is considered to be an indicator of buffering capacity, is an important soil attribute that influences soil fertility but is costly, time‐consuming and labour‐intensive to measure. Pedotransfer functions (PTFs) have routinely been used to predict soil CEC from easily measured soil properties, such as soil pH, texture and organic matter content. However, uncertainty in which one to select can be substantial as different PTFs do not necessarily produce the same result. In this study, a total of 100 soil samples were collected from surface horizons (0–20 cm) in different regions of Qingdao City, China. Three ensemble PTFs (ePTFs), including simple ensemble mean (SEM), individually bias‐removed ensemble mean (IBREM) and collective bias‐removed ensemble mean (CBREM), were developed to reduce the uncertainty in CEC prediction based on 12 published regression‐based PTFs. In addition, a local PTF (LPTF) for CEC was also developed using multiple stepwise regression and basic soil properties. The performances of the three ePTFs were compared with those of the published PTFs and LPTF. Results show that the differences between the performances of the published PTFs were substantial. When the systematic bias of each published PTF was removed separately, the prediction capability of the PTFs was increased. The performance of LPTF was significantly better than that of SEM, but slightly worse than IBREM. It is noted that CBREM had higher accuracy than all of the other methods. Overall, CBREM is a promising approach for estimating soil CEC in the study area.  相似文献   

16.
The OAh and Ah horizons of acid brown and podzolic forest soils are reported to fix more radiocaesium than the mineral B horizons beneath them. We determined the respective influence of organic matter and clay minerals on the magnitude of Cs+ retention in a strongly acid brown forest soil in Belgium. The soil contained mica throughout the profile. Vermiculite was identified in the OAh and Ah horizons, and hydroxy interlayered vermiculite (HIV) in the Bw horizon. The OAh and Ah clay fraction retained much more Cs+ than the Bw horizon. The extraction of Al interlayers by Na-citrate resulted in a marked increase in Cs+ fixation in the Bw clays as well as the collapse of the vermiculitic layers after K+ saturation. Organic matter had a strong but indirect effect on Cs+ fixation. In the Bw horizon, acid weathering of layer silicates releases free Al and produces HIV minerals in which Al polymers block the access of radiocaesium onto Cs+-specific sites. In OAh and Ah horizons, free Al is complexed by organic acids. Consequently, the interlayer specific sites remain accessible for Cs+ fixation.  相似文献   

17.
Oxisols cover ≈ 23% of the land surface in the tropics and are utilized extensively for agricultural purposes in the tropical countries. Under the variable input types of agricultural systems practiced locally, some of these soils still appear to have problems in terms of proper soil classification and subsequently hinder attempts to implement sustainable agro‐management protocols. The definition for Oxisols in Soil Survey Staff (1999) indicates that additional input is still required to refine the definition in order to resolve some of the outstanding classification problems. Therefore, the objective of this study is to examine the properties of some Oxisols and closely related soils in order to evaluate the classification of these soils. Soils from Brazil, several countries in Africa, and Malaysia were used in this study. Field observations provided the first indication that some of the presently classified kandi‐Alfisols and kandi‐Ultisols were closer to Oxisols in terms of their properties. Water‐retention differences and apparent CEC of the subsurface horizons also supported this idea. The types of extractable Fe oxides and external specific surface areas of the clay fractions showed that many kandic horizons have surface properties that are similar to the oxic horizons. Micromorphology indicated that the genetic transition from the argillic to the oxic involves a diminishing expression of the argillic. Properties, such as CEC, become dominant. The kandic horizon is therefore inferred as a transition to the oxic horizon. It is proposed that the Oxisols be keyed out based only on the presence of an oxic horizon and an iso–soil temperature regime. The presence of a kandic horizon will be reflected at lower levels in Oxisols. The Oxisols will now be exclusive to the intertropical belt with an iso–soil temperature regime. The geographic extend of the Oxisols would increase and that of kandi‐Alfisols and Ultisols would decrease. A few kandi‐Alfisols and Ultisols in the intertropical area will have low CEC which would fail the weatherable mineral contents. The kandic subgroups of some Alfisols and Ultisols will be transitional between the low (< 16 cmolc [kg clay]–1)‐ and high (> 24 cmolc [kg clay]–1)‐activity clay soils. The proposed changes to classification will contribute to a better differentiation of the landscape units in the field. Testing of the proposed classification on some Malaysian soils showed that the new definition for Oxisols provides a better basis for the classification of the local soils and the development of meaningful soil‐management groups for plantations.  相似文献   

18.
This paper discusses the development of pedotransfer functions (PTFs) and uses a multiple nonlinear regression technique to validate point and parametric PTFs for the estimation of a water-retention curve from basic soil properties such as particle-size distribution, bulk density and organic matter content. One hundred soil samples were collected at different depths from different locations in the Pavanje river basin that lies within the southern coastal region of Karnataka, India. Prediction accuracies were evaluated using the coefficient of determination (R 2), root mean square error (RMSE) and mean error (ME) between measured and predicted values. Overall, both point and parametric methods predicted water contents at selected water potentials with considerable accuracy. However, prediction of the soil water-retention curve using PTFs by point estimation method was relatively more successful (best case R 2 = 0.983) for the sampled soils. F-tests were also conducted for all cases. For one regression equation, the p-value was zero and for other equation, values were close to zero. Critical comparative analysis was carried out on the performances of the point and parametric methods. Use of the developed PTFs is suggested for the prediction of a water-retention curve for loamy sand and sandy loam soils in this area of the coastal region of southern India.  相似文献   

19.
The roles of fine-earth materials in the cation exchange capacity (CEC) of especially homogenous units of the kaolinitic and oxyhydroxidic tropical soils are still unclear. The CEC (pH 7) of some coarse-textured soils from southeastern Nigeria were related to their total sand, coarse sand (CS), fine sand (FS), silt, clay, and organic-matter (OM) contents before and after partitioning the dataset into topsoils and subsoils and into very-low-, low-, and moderate-/high-stability soils. The soil-layer categories showed similar CEC values; the stability categories did not. The CEC increased with decreasing CS but with increasing FS. Silt correlated negatively with the CEC, except in the moderate- to high-stability soils. Conversely, clay and OM generally impacted positively on the CEC. The best-fitting linear CEC function (R2, 68%) was attained with FS, clay, and OM with relative contributions of 26, 38, and 36%, respectively. However, more reliable models were attained after partitioning by soil layer (R2, 71–76%) and by soil stability (R2, 81–86%). Notably FS's contribution to CEC increased while clay's decreased with increasing soil stability. Clay alone satisfactorily modeled the CEC for the very-low-stability soils, whereas silt contributed more than OM to the CEC of the moderate- to high-stability soils. These results provide new evidence about the cation exchange behavior of FS, silt, and clay in structurally contrasting tropical soils.  相似文献   

20.
Buried horizons and lenses in riparian soil profiles harbor large amounts of carbon relative to the surrounding soil horizons. Because these buried soil horizons, as well as deep surface horizons, frequently lie beneath the water table, their impact on nitrogen transport across the terrestrial–aquatic interface depends upon their frequency and spatial distribution, and upon the lability of associated organic matter. We collected samples of 51 soil horizons from 14 riparian zones Rhode Island, USA, where soil profiles are characterized by glacial outwash and alluvial deposits. These soil samples came from as deep as 2 m and ranged in carbon content from <1% to 44% in a buried O horizon 54–74 cm deep. We used these samples to: (1) determine the extent to which carbon in buried horizons, and deep surface horizons, is potentially microbially available; (2) identify spatial patterns of carbon mineralization associated with surface and buried horizons; and (3) evaluate likely relationships between soil horizon types, chemical characteristics and carbon mineralization. Carbon mineralization rates associated with buried horizons during anaerobic incubations ranged from 0.0001 to 0.0175 μmol C kg soil?1 s?1 and correlated positively with microbial biomass (R=0.89, P<0.0001, n=21). Excluding surface O horizons from the analysis, carbon mineralization varied systematically with horizon type (surface A, buried A, buried O, lenses, A/C, B, C) (P<0.05) but not with depth or depth x horizon interaction (overall R2=0.59, P<0.0005, n=47). In contrast to this result and to most published data sets, 13C-to-12C and 15N-to-14N ratios of organic matter declined with depth (13C?26.9 to ?29.3 per mil, 15N+5.6 to ?0.8 per mil). The absence of a relationship between horizon depth and C availability suggests that carbon availability in these buried horizons may be determined by the abundance and quality of organic matter at the time of horizon formation or burial, rather than by duration since burial, and implies that subsurface microbial activity is largely disconnected from surface ecosystems. Our results contribute to the emerging view that buried horizons harbor microbially available C in quantities relevant to ecosystem processes, and suggest that buried C-rich soil horizons need to be incorporated into assessments of the depth of the biologically active zone in near-stream subsurface soils.  相似文献   

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