黏粒阳离子交换量估测模型的优化研究

为了优化土壤黏粒阳离子交换量估测模型,提升估测结果准确性,为土壤系统分类检索提供可靠数据支持,以江西省土壤为主要研究对象,根据有机质、粉粒阳离子交换量对现有估测模型的影响,将有机质含量低于6 g·kg–1的土壤样本定为低有机质样本,高于6 g·kg–1的土壤定为高有机质样本开展分类建模.高有机质样本中主要误差因子为有机...     

Effect of Clay Minerals and Organic Matter on the Cation Exchange Capacity of Silt Fractions

The cation exchange capacity (CEC) and specific surface properties were investigated in four particle‐size fractions < 50 μm from three loess (one Kastanozem and two Phaeozems), a holocene (Fluvisol) and a basalt soil (Nitisol) before and after destruction of organic matter. Particle‐size fractions were separated by sedimentation after chemical and physical dispersion of the soil samples. Illite, amorphous minerals, mixed layers, smectite and kaolinite were the predominant clay minerals. They were detected in all size fractions. The CEC increased with increasing organic matter contents and this effect was more pronounced in coarser fractions. The organic matter content per unit surface area was two or three times larger in coarse silt than in clay, irrespective of the soil type.     

区域土壤CEC与相关控制因子的空间非平稳关系评估

土壤阳离子交换量(CEC)对土壤的保肥能力具有重要影响。了解土壤CEC的空间分布及相关控制因子的影响有助于区域土壤肥力的精准调控。以往多采用传统的最小二乘(OLS)回归模型探索相关因子对土壤CEC的影响。然而,该类模型是一种总体回归方法,不能反映局部空间区域内相关因子对土壤CEC的影响。采用一种局部空间回归技术——地理加权回归(GWR)探索表层和亚表层土壤中CEC与相关控制因子(土壤黏粒、土壤有机质和土壤pH)之间的空间非平稳关系。结果表明,各控制因子在不同的子区域和深度对土壤CEC的影响均有明显差异;同时,GWR模型有效地揭示了土壤CEC与相关土壤控制因子的空间非平稳关系。所得的空间非平稳关系图可以为更精确地调控区域土壤肥力提供依据。     

西藏土壤阳离子交换量的空间变化和影响因素研究

根据西藏17个土类150个骨干剖面的分析资料,应用统计方法,探讨土壤CEC的空间变化和影响因素。结果表明,在西藏广阔的高原面上,土壤CEC的空间变化具有明显的水平地带特征:从东南向西北,土壤CEC随高山草甸型→高山草原型→高山荒漠型而急剧降低。土壤CEC垂直变化亦有表现,特别是在藏东南地区,山地湿润森林土壤的CEC从基带黄壤向上增高,至暗棕壤和灰化土达到最高,而在森林线以上土壤CEC又随黑毡土→草毡土→寒冻土而降低。西藏土壤CEC的空间变化,主要决定于各类土壤有机质积累的差异;有机质含量较低的土壤,粘粒含量也有重要影响;高山草甸型土壤还受粉砂粒含量的影响。土壤速效钾含量与CEC呈显著正相关,显示了土壤CEC的重要保钾功能。     

Cation Exchange Capacity of Crop Roots in Relation with Ion Uptake

The initial step of nutrients uptake by plant roots is the physico-chemical process of the exchange and adsorption of nutrients to root surface. The properties of root's surface determining the nutrients adsorption and exchange on them are mainly the cation exchange capacity and intensity. The possible role of these properties in the nutrient uptake via roots will not be understood unless the nature of the cation exchange ability of roots is clarified. The authors reported the preliminary experimental results on cation exchange capacity of crop roots¹⁾. The present paper deals with the re-examination of the various procedures, applicable to the determination of C. E. C. of the various crop roots. Discussions on the importance of the intensity of bonding of cations were also made.     

黄土高原小流域土壤阳离子交换量分布特征及影响因子

通过对黄土高原陕北地区3个小流域(朱家沟、纸坊沟和泥河沟)27个采样点的54个土壤样品分析,应用统计方法讨论土壤阳离子交换量的分布特征和影响因子。结果表明:(1)在3个所选定的典型小流域中,土壤CEC呈现明显的地带性,从北到南,CEC值显著升高。(2)在同一流域,CEC垂直地带变化基本表现为随高度降低而增大;而在同一剖面中,表层土壤CEC值总是高于下层。通过相关性分析和逐步回归检验,得出在粘土矿物类型基本相同的前提条件下,影响CEC值变化的主要因素有pH值、土壤黏粒含量和有机质含量,粉粒含量的影响较小,而砂粒含量则与CEC表现出显著负相关。     

河南三种土壤阳离子交换量相关性及预测模型研究

土壤阳离子交换量是一项重要的土壤理化性质,它是衡量土壤肥力和作物养分有效性的一个重要指标。通过对河南3种土壤1177个数据的相关性及回归分析来研究阳离子交换量与土壤有机质、pH值、粘粒含量、粉粒含量和砂粒含量的关系。结果表明:(1)对于阳离子交换量来说,砂姜黑土>水稻土>褐土;3种土壤的上层土>下层土;3种土壤的阳离子交换量比第二次土壤普查分别降低15.7%,12.7%,6.5%;(2)对与砂姜黑土和褐土来说,其阳离子交换量与pH值和0.02~2 mm砂粒含量成显著负相关,褐土与粘粒含量成显著正相关;对于水稻土来说,其阳离子交换量与有机质含量和粘粒含量成显著正相关,与砂粒含量成显著负相关;(3)利用这些数据做出的这三个土壤类型的6个回归模型是有科学依据的。总的来说,土壤有机质、pH值、粘粒含量与砂粒含量与CEC有着紧密联系,但还有其他因素影响着预测模型的准确性。     

Prediction of Soil Cation Exchange Capacity Using Different Soil Parameters by Intelligent Models

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.     

Selection of a Suitable Soft Computing Model for Estimation of Soil Cation Exchange Capacity

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     

Improving on the Prediction of Cation Exchange Capacity for Highly Weathered and Structurally Contrasting Tropical Soils from Their Fine-Earth Fractions

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.     

Spatial Variability of Soil Organic Matter and Cation Exchange Capacity in an Oxisol under Different Land Uses

ABSTRACT

Soil properties may exhibit large spatial variability. Frequently this variability is auto-correlated at a certain scale. In addition to soil-forming factors, soil management, land cover, and agricultural system may affect the spatial variability of agricultural soils. Soil organic matter (OM) is an important soil property contributing toward soil fertility and a key attribute in assessing soil quality. Increasing soil OM increases cation exchange capacity (CEC) and enhances soil fertility. We analyzed the impact of land use on the spatial variability of OM and CEC in a tropical soil, an Oxisol, within São Paulo state, Brazil. Land uses were prairie, maize, and mango. Soil samples were taken at 0–10 and 10–20 cm depths at 84 points within 1-ha plots, i.e., 100 m × 100 m. Statistical variability was higher for soil OM than for CEC. The mango plot contained the highest soil OM, whereas prairie the lowest. Also, soil OM and CEC were significantly related at all land use treatments and depths studied. All soil OM data sets and most of the CEC data sets (with two exceptions) exhibited spatial dependence. When spatial variability was present, the semivariograms showed a nugget effect plus a spherical or an exponential structure. Patterns of soil OM and CEC spatial variability (i.e., model type, ranges of spatial dependence, and nugget effects) were different between land uses and soil depths. In general, CEC exhibited a lower spatial autocorrelation and a weaker spatial structure than soil OM. Moreover, soil OM displayed a higher autocorrelation and was more strongly structured at the 0–10 cm depth than at the 10–20 cm depth. Interpolation by kriging or inverse distance weighting (IDW) allowed to illustrate how the spatial variability of soil OM and CEC differed due to land cover and sampling depth. Modeling and mapping the spatial distribution of soil OM and CEC provided a framework for spatially implicit comparisons of these soil properties, which may be useful for practical applications.     

Modeling Main and Interactional Effects of Some Physiochemical Properties of Egyptian Soils on Cation Exchange Capacity Via Artificial Neural Networks

Eurasian Soil Science - The application of artificial neural networks (ANNs) in agricultural sciences has proved its importance as a mathematical modeling technique for prediction and providing...     

四川盆地西缘土壤阳离子交换量的特征及影响因素

土壤阳离子交换量(CEC)因其对土壤肥力保护及污染评估具有重要意义而受到日益重视。对四川盆地西缘黄壤、黄棕壤、水稻土、紫色土、潮土和石灰土CEC进行系统比较及影响因素研究,结果表明:黄壤、水稻土和紫色土CEC显著低于黄棕壤(P<0.05),显著高于潮土(P<0.05)。同时,土壤CEC与年均温、积温呈负相关关系(P<0.01),而与年均降水量和湿润指数呈二次函数关系(P<0.01);山地土壤的CEC显著高于平原和丘陵土壤(P<0.05),且土壤CEC随海拔增加而极显著上升(P<0.01);冰碛物发育土壤的CEC显著高于河流冲积物和紫色粉砂岩发育土壤(P<0.05)。主成分分析结果表明,温度和降水组成的气候因素为该区土壤CEC的决定性影响因素。     

西北石灰性土壤阳离子交换量测定方法比较

阳离子交换量是评价土壤保肥性能和缓冲性能的重要指标,也是改良土壤和合理施肥的重要依据之一。为了给西北石灰性土壤阳离子交换量的测定提供科学依据,通过平行性实验、标准物质检测、实际样品检测,分析比较了目前普遍采用的测定方法乙酸钙交换法和三氯化六氨合钴浸提-分光光度法的优劣。结果表明,2种分析方法均具有较好的精密度和准确度,均适合西北地区石灰性土壤阳离子交换量的测定。从测定结果来看,三氯化六氨合钴浸提-分光光度法的土壤阳离子交换量测定值略高于乙酸钙交换法,在实际样品检测中相对标准偏差介于3.47%～9.46%。但相比之下乙酸钙交换法具有试剂成本较低的优势,而三氯化六氨合钴浸提-分光光度法具有节省时间的优势,检测人员可根据实际情况灵活选择使用。     

A Rapid and Simple Manifold to Study Exchangeable Cations and Cation Exchange Capacity in 24 Soil Samples Simultaneously

ABSTRACT

The outcomes of this work highlight the development and validation of a rapid and simple manifold for determination of exchangeable cations [calcium (Ca), magnesium (Mg), sodium (Na), and potassium (K)] and cation exchange capacity (CEC) in soil. First, the performance of the manifold was evaluated to determine the best conditions to use: filter assembly, volume and number of aliquots of extracting solution, and the use of vacuum. Second, the analytical performance was study from trueness and precision analysis. For that, soil samples with assigned values, an in house reference material and unknown soil samples, were used, comparing their results with those obtained using the classical extraction method (agitation, centrifugation, and filtration). The performance study showed that a filter system assembled with S&S Nº859 type filter paper, cotton, and plastic piece is better to the other studied options. Four aliquots of 10 mL extracting solution and a few seconds of vacuum between additions is recommended to achieve the best recovery. The extraction procedure proposed using the manifold demonstrated to be accurate, and so can effectively replace the classical method for the extraction of exchangeable cations and CEC in soils. Regarding simplicity, rapidity, and simultaneity, the manifold method could be the method of choice for extraction up to 24 samples. Moreover, the manifold method significantly reduces the laboratory supplies and instrument used in the extraction steps in the classical method, attaining better efficiency and reducing costs associated to this stage of the analysis.     

酸性紫色土的阳离子交换特征及其对酸缓冲容量的影响

明确酸性紫色土的交换性阳离子组成特征及其对土壤后续酸化进程的影响有助于评估紫色土的潜在酸化风险。为此,在重庆市合川区采集了38个酸性紫色土,进行了理化性质分析与酸缓冲容量测定,探讨了阳离子交换特征对土壤酸缓冲容量的影响。结果表明:部分紫色土的酸化程度较为严重,但酸性紫色土仍具有较高的交换性盐基阳离子含量和阳离子交换量(CEC)。紫色土的交换性Ca²⁺和交换性Mg²⁺含量分别为红壤的5.9倍和3.9倍。紫色土CEC也显著高于红壤和砖红壤。丰富的黏土矿物组成,尤其是较高的蒙脱石含量,是紫色土具有较高盐基阳离子含量和高CEC的主要原因。酸性紫色土的酸缓冲容量大小为3.18～25.6mmol·kg^–1·pH^–1。酸缓冲容量与交换性盐基总量和CEC间均呈极显著的正相关性。较高的盐基阳离子含量和CEC有助于增加紫色土的酸缓冲容量,减缓土壤的酸化速度。因此,尽管部分紫色土酸化较为严重,但受成土母质和发育程度的影响,丰富的盐基阳离子含量能对进入土壤中的酸进行缓冲,减缓紫色土酸化速度。这是紫色土相对于其他地带性酸性土...     

Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression,ANN (RBF,MLP), and ANFIS Models

Investigation of soil properties such as cation exchange capacity (CEC) and soil infiltration is an important role in environmental research. The measurement of these parameters is time-consuming and costly. In this study, intelligence-based models [artificial neural networks (MLP and RBF), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression (MR) techniques] are employed as alternatives to estimate the CEC and soil infiltration parameters from more readily available soil data. Two hundred soil samples were collected from soil 0–30 cm deep from two sites of the Ghoshe Region in Semnan Province, Iran. The first site was a flood plain and second site was agriculture land. The input data for models were electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ratio (SAR), and bulk density. To evaluate the performance of these models, the statistical parameters such as root mean square error (RMSE), mean absolute error (MAE), mean error (ME), and coefficient of determination (R²) were used. Then the results of the intelligence-based models and MR were compared to each other’s. The results show that the MLP model was better than ANFIS, MR, and RBF models. Finally, sensitivity analysis was conducted to determine the most and the least influential variables affecting the soil infiltration and CEC parameters. It was found that EC and bulk density have respectively the most and the least effect on soil infiltration, and for CEC they were clay percentage and bulk density, respectively.     

Nutrient Element Contents and Cation Exchange Capacity in Fine Fractions of Southeastern Nigerian Soils in Relation to Their Stability

Abstract

Soils collected from 15 locations from SE Nigeria at the 0‐ to 20‐cm depth were studied for the nutrient elements of fine fractions and their role in the stability of the soils. The objective was to understand the role of these elements in the stability of the aggregates. The fine fractions were clay and silt, and elements measured in the fine fractions were exchangeable sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺), magnesium (Mg²⁺), exchangeable acidity (EA), cation exchange capacity (CEC), and available phosphorus (P). The aggregate stability was measured at the microlevel with clay dispersible indices and water‐stable aggregate (WSA) <0.25 mm, and at macrolevel with other WSA indices and mean‐weight diameter (MWD). Soils varied from loamy sand to sandy clay. There were more exchangeable cations, CEC, EA, and available P in clay than in the silt fraction. Whereas EA values ranged from 2.8 to 10.4 cmol kg^?1, they were between 1.6 and 9.2 cmol kg^?1 in silt. The CEC in the clay fraction was from 7.4 to 70 cmol kg^?1 and between 4.0 and 32.8 cmol kg^?1 in the silt fraction. The WDC were from 50 to 310 g kg^?1 while the average dispersion ratio (DR) was generally higher than the corresponding clay‐dispersion ratio (CDR), and the MWD ranged from 0.45 to 2.68 mm. Soils with WSA skewed mostly to higher WSA (>2–1.00 mm) had a higher MWD. Exchangeable Ca²⁺ in clay correlated significantly with CDR and WSA sizes 1.0–0.5 mm and 0.5–0.25 mm (r=0.45,* 0.51,* and 0.60*), respectively, but negatively correlated with clay flocculation index (CFI) (r=?0.45*). Also, available P in clay correlated respectively with CDR and CFI (r=0.45*, ?0.45*), whereas K⁺ in silt correlated significantly with WDSi (r=0.64*), CFI (r=0.62*), and CDR (r=?0.65*). Principal component analysis revealed that elemental contents in the silt fraction can play very significant roles in the microaggregate stability.     

Long-Term Effects of Wheat Production Management Practices on Exchangeable Base Cations and Cation Exchange Capacity of a Plinthosol in Semi-arid South Africa

Changes in soil fertility indicators are mainly the result of management practices and usually influence crop yields over the long term. This study shows the effects of long-term wheat production management practices on exchangeable base cations and cation exchange capacity (CEC). Applied field treatments included two methods of straw management (unburned, burned), three methods of tillage (no tillage, stubble mulch, plowing), and two methods of weeding (chemical, mechanical). Samples were collected at six soil depths and analyzed for potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), and CEC. Burned wheat straw resulted in greater K and lower Ca concentrations compared to unburned wheat straw. No-tillage treatment increased K, Ca, Mg, Na, and CEC compared to both stubble mulch and plowing. Chemical weeding improved Na and CEC compared to mechanical weeding. The treatment combinations had positive influences mainly on CEC. Unburned straw and moldboard plowing with respect to burned straw and no tillage enhanced grain yield with 8%.