Estimating cation exchange capacity and clay content from agricultural soil testing data

Clay content and the ability to reversibly retain cations affect many essential chemical and physical properties of soil, such as pH buffering and carbon sequestration. Cation exchange capacity (CEC) and base saturation are also commonly used as criteria in soil classification. However, determination of CEC and particle-size distribution is laborious and not included in routine soil testing. In this study, pedotransfer functions including soil test cations (STCat; Ca2+ + Mg2+ + K+), pH and soil organic carbon (SOC, %) as explanatory variables were developed for estimating CEC, titratable acidity (TA; H+ + Al3+) and clay content (clay, %). In addition, reference values for potential CEC and its components were determined for Finnish mineral and organic soils. The mean of potential CEC extracted by 1 M ammonium acetate at pH 7.0 ranged from 14 (range 6.4−25) in coarse soils to 33 (21−45) cmol(+) kg-1 in heavy clay soils, and from 42 (24−82) in mull soils to 77 (25−138) cmol(+) kg-1 in peat soils. The average CEC of clay and SOC were 27 and 160 cmol(+) kg-1, respectively. Titratable acidity occupied 53% and around 40% of the CEC sites in organic and mineral soils, respectively, evidencing that it is a prominent component of the potential CEC in these predominantly acidic soils. STCat, pH and SOC explained 96% of the variation in potential CEC. STCat and pH can be used in estimating the clay content especially for soils containing over 30% clay. In coarse textured soils, in contrast, SOC hampers the STCat based estimation of clay content.

PERFORMANCE OF LINEAR MODELS IN PREDICTING CATION EXCHANGE CAPACITY OF CALCAREOUS SOILS

Determination of soil cation exchange capacity (CEC) in lab is cumbersome, time consuming, and costly. Accordingly, this article attempted to formulate pedotransfer functions for predicting it using some soil physical and chemical properties e.g., sand (SA), silt (SI), clay (CL), organic matter (OM) and calcium carbonate (CC). This research included four steps: preparing soil database; selecting independent variables which are related to CEC value; formulating models using NCSS 12.0.2 software, and the last step is to achieve specific objective of the research which is the comparsion among models by a series of efficiency criteria: root mean square error (RMSE), Nash-Sutcliffe model efficiency coefficient (EF), average absolute percent error (AAPE), and percentage of improving model efficiency (PIME). The statistical results of the research indicated that CEC of calcareous soils could be predicted from models that have one variable (CL), two variables (CL and OM), and three variables (CL, OM, and CC) with slight decrease in the RMSE (2.95402, 2.81180, and 2.79268) respectively, and slight increase in the EF (0.887360, 0.898448, and 0.90023) respectively. While the reliable models to predict soil CEC are formulated from the fewer number of independent variables with having the lowest points of the standardized residual of CEC that greater than +2 cmolc kg-1).

Impacts of Soil Properties, Topography, and Environmental Features on Soil Water Holding Capacities (SWHCs) and Their Interrelationships

Soil water holding capacities (SWHCs) are among the most important factors for understanding the water cycle in forested catchments because they control available plant water that supports evapotranspiration. The direct determination of SWHCs, however, is time consuming and expensive, so many pedotransfer functions (PTFs) and digital soil mapping (DSM) models have been developed for predicting SWHCs. Thus, it is important to select the correct soil properties, topographies, and environmental features when developing a prediction model, as well as to understand the interrelationships among variables. In this study, we collected soil samples at 971 forest sites and developed PTF and DSM models for predicting three kinds of SWHCs: saturated water content (θS) and water content at pF1.8 and pF2.7 (θ1.8 and θ2.7). Important explanatory variables for SWHC prediction were selected from two variable importance analyses. Correlation matrix and sensitivity analysis based on the developed models showed that, as the matric suction changed, the soil physical and chemical properties that influence the SWHCs changed, i.e., soil structure rather than soil particle distribution at θS, coarse soil particles at θ1.8, and finer soil particle at θ2.7. In addition, organic matter had a considerable influence on all SWHCs. Among the topographic features, elevation was the most influential, and it was closely related to the geological variability of bedrock and soil properties. Aspect was highly related to vegetation, confirming that it was an important variable for DSM modeling. Information about important variables and their interrelationship can be used to strengthen PTFs and DSM models for future research.

Evaluation of Water Retention Curves by Regression and Machine Learning Methods

The paper presents two methods that simplify the estimation of the water retention curves. The case study is evaluated for the soils of Záhorská lowland in the paper. These methods are based on the supposed dependence of the soil water content on the percentage content of the 1st, 2nd, 3rd and 4th Kopecký grain categories, and the dry bulk density. The representative set of the drying branch of water retention curves was measured using soil samples from the Záhorská lowland region in a laboratory. Particle size distribution and dry bulk density were also determined. In this paper support vector machines and multiple linear regression is compared to estimate the pedotransfer functions that can be used for the prediction of the drying branch of the water retention curve. Both methods were verified on other data set of measured water retention curves than the one which was used for building the models with a close agreement to measured results.

Development and Evaluation of Pedotransfer Functions to Estimate Soil Moisture Content at Field Capacity and Permanent Wilting Point for South African Soils

This study was undertaken to develop new pedotransfer functions (PTFs) for the estimation of soil moisture content at field capacity (FC, at −33 kPa) and permanent wilting point (PWP, at −1500 kPa) for South African soils based on easily measurable soil physico-chemical properties. The new PTFs were developed using stepwise multiple linear regressions with the dependent variable (either FC or PWP) against clay, silt, sand and soil organic carbon (SOC) content from a total of 3171 soil horizons as the explanatory variables. These new PTFs were evaluated and compared with five well-established PTFs using a total of 3136 soil horizons as an independent dataset. The coefficient of determination (r2) values for the existing PTFs ranged from 0.65–0.72 for FC and 0.72–0.81 for PWP, whilst those developed in this study were 0.77 and 0.82 for FC and PWP, respectively. The root mean square error (RMSE) values for the well-established PTFs ranged from 0.052–0.058 kg kg−1 for FC and 0.030–0.036 kg kg−1 for PWP, whilst those developed in this study were 0.047 and 0.029 kg kg−1 for FC and PWP, respectively. These findings suggest that PTFs derived locally using a large number of soil horizons acquired from different agro-climatic locations improved the estimation of soil moisture at FC and PWP. Due to the range of conditions and large soil datasets used in this study, it is concluded that these new PTFs can be applied with caution in other regions facing data scarcity but with similar soil types and climatic conditions.

Mapping the Available Water Capacity in tropical climate soils for soybean (Glycine max) cultivation in the state of Tocantins-Brazil

Brazilian soybean has undergone considerable economic growth. Its production depends on the demand for some inputs. One of these inputs is the soil water supply, which can be made artificially or obtained by natural rainfall. Knowledge of available water capacity (AWC), which depends on total water availability (TWA), is poorly accessible and difficult to measure in the field. This study aimed to map the AWC of the state of Tocantins, based on pedotransfer functions (PTFs), to evaluate the water availability of the soils of the microregions of that state. We used the Arya and Paris model, aided by a computer program, Qualisolo, made by Embrapa Instrumentação. One hundred fifty-seven tropical soil samples were extracted from the Embrapa Solos portal. Preliminarily, the soil water retention curve (SWRC) was obtained and, subsequently, the TWA and AWC for this oilseed were estimated. Multiple linear regressions show the correlation between TWA and clay (CL), Silt (ST) and total sand (TS) contents. The correlation established was TWA = 3.2993 – 0.0028TS – 0.0034CL. This main conclusion reflects a fruitful AWC for decision-making by the soybean agribusiness and exposes the regional weaknesses for this crop under a rainfed regime in some regions of Tocantins. We could observe that, in terms of water availability, agribusiness is a potential threat to the environment protection area (APA) of the Ilha do Bananal/Cantão, Formoso River microregion.