Search

Autoclaved-citrate extractable soil protein (ACE protein, hereafter referred as “soil protein”) is a novel biological soil health indicator that can indirectly capture a soil’s capacity to supply nitrogen (N) but is relatively expensive to assess. To explore cost saving options, a dataset of 4,171 soil samples with texture, total carbon (C) and N, carbon-to-nitrogen ratio (C/N), soil protein, permanganate-oxidizable carbon (POXC), pH, and extractable magnesium (Mg) and iron (Fe), was used to develop three pedotransfer functions for soil protein. These included a full random forest (RF) model utilizing all variables, and a reduced RF model and a multiple linear regression model employing a subset of the variables. Models were validated using a US subset of the North American Project to Evaluate Soil Health Measurements dataset that contained 1,406 samples. The full RF model for soil protein reduced the root mean square error (RMSE) by 41.7 and 53.4% compared to reduced RF and multiple linear regression models, respectively. Total C was a more important variable in the model than total N. Additionally, POXC, sand, clay, and extractable Mg and Fe were found to be important in the model. Soil protein was sensitive to management at 36 of 57 long-term experiments. The full RF model was able to replicate 92% of those significant effects of management on soil protein. The new RF pedotransfer function for soil protein can improve prediction compared to traditional regression techniques and reduce the cost of comprehensive soil health assessment. [ABSTRACT FROM AUTHOR]

In soil science, machine learning algorithms are preferred for pedotransfer functions due to their rapid data acquisition and high prediction accuracy. The current study aims to evaluate the prediction of soil quality in agricultural lands dominated by the humid Black Sea climate using various algorithms. Both classification and regression-based algorithms (Random Forest-RF, Light Gradient Boosting-LGB, Extreme Gradient Boosting-XGBoost, k-nearest neighbors-kNN, Logistic Regression, multilayer perceptron-MLP, Linear Regression-LR and Bayesian Ridge- BR) were used in the method. The comparison of soil maps is also included. Furthermore, the present study evaluates the Grid Search optimization method with K-Fold Cross Validation (K = 5) for both classification and regression-based algorithms. The prediction of soil quality was performed using class-based and regression-based algorithms. As a result of the study, the RF and XGBoost algorithms achieved an approximate accuracy rate of 92 % in the class-based prediction. In regression-based predictions, the most successful algorithms were BR and LR, with an R2 Score of 0.84. The Grid Search optimization method was used to improve the R2 Score, resulting in an increase to 0.90 and 0.88 for BR and LR, respectively. The optimized hyperparameters showed improved performance in predicting the soil quality index. The present study found that Gaussian and Spherical models had the lowest prediction errors in spatial distribution maps. Tree-based algorithms were found to be suitable for class-based prediction of soil quality, while the linear regression method was appropriate for regression predictions. This study is characterized by a rainy climate resulting in acidic soils with high organic matter content. Planning of new studies in different climates and soil properties is recommended. [ABSTRACT FROM AUTHOR]

Understanding the effects of salinity on soil structure and hydraulic properties is critical for addressing environmental challenges in coastal saline and sodic areas. In this study, soil samples were collected from a coastal region in eastern China to investigate how salinity affected the soil structure and hydraulic properties based on lab experiments. A comprehensive soil dataset was also compiled from the experimental results to develop a salinity-based pedotransfer function (PTF-S) tailored to the coastal environment. The results showed that salinity significantly altered the soil aggregate size distribution and hydraulic properties. Higher salinity promoted the formation of larger aggregates (0.25–2 mm), particularly in silty clay soil. Salinity positively correlated with the saturated hydraulic conductivity (Ks) in sandy loam soil, regardless of the cation type (Na⁺ or Ca2⁺). By comparison, Na+ increased the Ks of silty clay soil up to a certain threshold, while Ca2+ enhanced the Ks regardless of the soil texture. Increased salinity also reduced the soil water retention of sandy loam soil; however, Na+ increased the soil water retention of silty clay soil and Ca2+ had different effects depending on the suction levels. The newly developed PTF-S model, which included the electrical conductivity (EC) and cation exchange capacity (CEC), showed better predictions for the volumetric water content (R = 0.886 and RMSE = 0.057 cm3/cm3) and log Ks (R = 0.991 and RMSE = 0.073 mm/h) than the traditional model that excludes the salinity variables EC and CEC (PTF-N) (R = 0.839 and RMSE = 0.066 cm3/cm3 for the volumetric water content, and R = 0.966 and RMSE = 0.140 mm/h for the log Ks). This study highlights the importance of developing salinity-based PTFs for addressing soil salinization challenges. [ABSTRACT FROM AUTHOR]

Accurate soil organic carbon storage (SOCS) estimation is crucial for sustaining ecosystem health and mitigating climate change impacts. This study investigated the accuracy and variability of SOCS predictions, focusing on the role of pedotransfer functions (PTFs) in estimating soil bulk density (BD). Utilizing a comprehensive dataset from the Korean Rural Development Administration (RDA database), which includes 516 soil horizons, we evaluated 36 widely-used BD PTFs, well-established formulas that estimate BD by considering soil properties, including soil organic carbon (SOC), soil organic matter (OM), sand, gravel, silt, and clay. These PTFs demonstrated varying levels of precision, with root mean squared errors (RMSE) ranging from 0.177 to 0.377 Mg m−3 and coefficients of determination (R2) from 0.176 to 0.658; hence, the PTFs have been classified into excellent, moderate, and poor-performing groups for predicting BD. Further, a novel PTF based on an exponential function of SOC was developed, showing superior predictive power (R2 = 0.73) compared to existing PTFs, using an independent validation dataset. Our findings reveal significant differences in SOCS predictions and observations among the PTFs, with a p-value

Abstract Key message The hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction. Context To overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic. Aims This study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails. Methods Four hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails. Results The compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(K sat)) and the soil water content at saturation (θ sat). For the skid trails, θ sat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(K sat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils. Conclusion Estimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm).

Novel soil datasets and the application of pedotransfer functions provide soil hydraulic input data for modelling hydrological processes at different scales. We aimed to evaluate the reliability of soil hydraulic parameters derived by indirect methods in simulation of soil moisture time series and water budgets at profile level of three sites (Forest, Orchard and Grassland) from a Central European catchment (Lake Balaton, Hungary). Five soil-vegetation-atmosphere model variants were set up with the Hydrus-1D model for each site, differing only in the parametrization of input soil data: i) a calibrated reference, ii) measured values, iii) values predicted from measured basic soil properties, iv) values predicted from national soil map information, v) values derived from the 3D soil hydraulic dataset of Europe. Calibrated soil parameters led to Nash-Sutcliffe efficiency 0.50, 0.54 and 0.71 for the Forest, Orchard and Grassland Site respectively. The outcomes for model efficiency of soil moisture underline the superiority of local databases over regional ones and the need for more detailed vertical discretization during modelling. The model performance according to soil moisture and water budget accuracy led to different rank order of model variants. Water budget comparisons indicated moderate differences between the hydrologic fluxes simulated by the different model variants, emphasizing the uncertainties associated with soil hydraulic parametrization either at local or at watershed scale.

Key message: The hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction. Context: To overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic. Aims: This study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails. Methods: Four hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails. Results: The compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(Ksat)) and the soil water content at saturation (θsat). For the skid trails, θsat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(Ksat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils. Conclusion: Estimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm). [ABSTRACT FROM AUTHOR]

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions. [ABSTRACT FROM AUTHOR]

This study focused on the hydrophysical characteristics of an abandoned limestone quarry in Czechia. Six sites were examined; two sites were undergoing natural succession (the Quarry Wall and Reed Canary Grass plots, which had undeveloped arboreal layers) and four sites were undergoing managed forest reclamation. Of the four forest reclamation sites, three were classified as prospering (the Prospering Lime, Prospering Maple and Prospering Lime + Oatgrass plots) and one was in decline (the Declining Larch + Lime plot). The arboreal layer included small-leaved lime (Tilia cordata Mill.), sycamore maple (Acer pseudoplatanus L.), and European larch (Larix decidua Mill.). Our results showed that Lime + Oatgrass plot retained more water than other plots. Field soil moisture measurements indicated that throughout the 1096-day monitoring period, only the soils at the successional sites reached the wilting point (Quarry Wall plot: 159 days; Reed Canary Grass plot: 43 days). Soil heterogeneity in the reclaimed areas was due to variation in the soil profile depth, disturbance from mining activities, reclamation efforts, and the availability of quality soil material. Soil conditions and the dynamics at the quarry created less than ideal conditions for tree regeneration. This primarily relates to limiting and significantly heterogeneous successional plots.

The capacity of soil to retain water and make it available to plants is an essential soil functions for the sustainability of terrestrial ecosystems. A lot of progress has been made in estimating water retention and availability as a function of soil texture. On the other hand, a lower effort has been dedicated to seeking correlations between plant-available water (AW) and soil compaction. In this study, we compiled a dataset with 2479 records from experiments conducted in Brazilian soils to evaluate the relationship between AW and bulk density (BD). The dataset was split into sub-datasets defined by soil textural classes to reduce the effect of texture on AW–BD relationships. In each sub-dataset, AW–BD relationships were described by linear regression. In general, there was a weak association between AW and BD. The strongest correlations were found in the Silty Loam (R2 = 0.26) and Loam (R2 = 0.13) classes. However, the partitioning of the overall dataset by textural classes was not effective to eliminate the effect of texture on AW–BD relationships. Still, the data showed that soil compaction may increase or reduce AW. Nevertheless, more experimental research is needed to bring a better understanding of how AW is affected by changes in BD. [ABSTRACT FROM AUTHOR]

This study focused on the hydrophysical characteristics of an abandoned limestone quarry in Czechia. Six sites were examined; two sites were undergoing natural succession (the Quarry Wall and Reed Canary Grass plots, which had undeveloped arboreal layers) and four sites were undergoing managed forest reclamation. Of the four forest reclamation sites, three were classified as prospering (the Prospering Lime, Prospering Maple and Prospering Lime + Oatgrass plots) and one was in decline (the Declining Larch + Lime plot). The arboreal layer included small-leaved lime (Tilia cordata Mill.), sycamore maple (Acer pseudoplatanus L.), and European larch (Larix decidua Mill.). Our results showed that Lime + Oatgrass plot retained more water than other plots. Field soil moisture measurements indicated that throughout the 1096-day monitoring period, only the soils at the successional sites reached the wilting point (Quarry Wall plot: 159 days; Reed Canary Grass plot: 43 days). Soil heterogeneity in the reclaimed areas was due to variation in the soil profile depth, disturbance from mining activities, reclamation efforts, and the availability of quality soil material. Soil conditions and the dynamics at the quarry created less than ideal conditions for tree regeneration. This primarily relates to limiting and significantly heterogeneous successional plots. [ABSTRACT FROM AUTHOR]

Available water capacity (AWC), field capacity (θFC), and permanent wilting point (θPWP) are regarded as key physical soil health indicators that directly capture the soil's capacity to store plant available water but are expensive components of a comprehensive soil health analysis. To reduce costs, pedotransfer functions for θFC, θPWP, and AWC were developed from a dataset of 7,232 soil samples with texture, soil organic matter (SOM), permanganate-oxidizable carbon, soil respiration, AWC, θFC, θPWP, wet aggregate stability, and extractable potassium, magnesium, iron, and manganese. Three functions were developed for each property: a full random forest (RF) model containing all variables, a reduced RF model and a multiple linear regression model containing texture and SOM. Pedotransfer functions were validated with an independent dataset that contained 1,406 samples. The full RF models for θFC, θPWP, and AWC reduced the root mean square error (RMSE) by 16.3, 13.3, and 12.8%, compared to multiple linear regression models, respectively. Furthermore, the full RF models for θFC, θPWP, and AWC reduced RMSE by 11.6, 6.7, and 12.8%, compared to the reduced RF model, respectively. Permanganate-oxidizable carbon, wet aggregate stability, and extractable magnesium, potassium, and iron were useful novel predictor variables for improving prediction of θFC and AWC. AWC was sensitive in 20/57 long-term experiments, and full RF models were able to replicate 5/20 of those significant results. New RF pedotransfer functions for θFC, θPWP, and AWC can enhance prediction compared to traditional modeling techniques, fits into existing interpretative frameworks, and improves cost-effectiveness of comprehensive assessments of soil health. [ABSTRACT FROM AUTHOR]

Understanding the effects of salinity on soil structure and hydraulic properties is critical for addressing environmental challenges in coastal saline and sodic areas. In this study, soil samples were collected from a coastal region in eastern China to investigate how salinity affected the soil structure and hydraulic properties based on lab experiments. A comprehensive soil dataset was also compiled from the experimental results to develop a salinity-based pedotransfer function (PTF-S) tailored to the coastal environment. The results showed that salinity significantly altered the soil aggregate size distribution and hydraulic properties. Higher salinity promoted the formation of larger aggregates (0.25–2 mm), particularly in silty clay soil. Salinity positively correlated with the saturated hydraulic conductivity (Ks) in sandy loam soil, regardless of the cation type (Na⁺ or Ca2⁺). By comparison, Na+ increased the Ks of silty clay soil up to a certain threshold, while Ca2+ enhanced the Ks regardless of the soil texture. Increased salinity also reduced the soil water retention of sandy loam soil; however, Na+ increased the soil water retention of silty clay soil and Ca2+ had different effects depending on the suction levels. The newly developed PTF-S model, which included the electrical conductivity (EC) and cation exchange capacity (CEC), showed better predictions for the volumetric water content (R = 0.886 and RMSE = 0.057 cm3/cm3) and log Ks (R = 0.991 and RMSE = 0.073 mm/h) than the traditional model that excludes the salinity variables EC and CEC (PTF-N) (R = 0.839 and RMSE = 0.066 cm3/cm3 for the volumetric water content, and R = 0.966 and RMSE = 0.140 mm/h for the log Ks). This study highlights the importance of developing salinity-based PTFs for addressing soil salinization challenges.

Featured Application: Landowners, regional and national governments, and researchers can use predictions of the soil hydrological properties created in this work, such as wilting point, field capacity, macroporosity, and total available water content, to characterize soils for soil management decisions in New Zealand, e.g., in terms of irrigation requirements, or for policy, e.g., nutrient budgets and regulations. This paper describes a new pedotransfer function (PTF) for the soil water content of New Zealand soils at seven specific tensions (0, −5, −10, −20, −40, −100, −1500 kPa) using explanatory variables derived from the S-map soil mapping system. The model produces unbiased and physically plausible estimates of the response at each tension, as well as unbiased and physically plausible estimates of the response differences that define derived properties (e.g., macroporosity and total available water content). The PTF is a development of an earlier model using approximately double the number of sites compared with the earlier study, a change in fitting methodology to a semi-parametric GAM Beta response, and the inclusion of sample depth. The results show that the new model has resulted in significant improvements for the soil water content estimates and derived quantities using standard goodness-of-fit measures, based on validation data. A comparison with an international PTF using explanatory variables compatible with variables available from S-map (EUPTF2) suggests that the model is better for prediction of soil water content using the limited information available from the S-map system. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil temperature regime (STR) is important for soil classification and land use. Generally, STR is delineated by estimating the mean annual soil temperature at a depth of 50 cm (MAST50) according to the Chinese Soil Taxonomy (CST). However, delineating the STR of China remains a challenge due to the difficulties in accurately estimating MAST50. The objectives of this study were to explore environmental factors that influence the spatial variation of MAST50 and generate an STR map for China. Soil temperature measurements at 40 and 80 cm depth were collected from 386 National Meteorological Stations in China during 1971–2000. The MAST50 was calculated as the average mean annual soil temperature (MAST) from 1971–2000 between 40 and 80 cm depths. In addition, 2 048 mean annual air temperature (MAAT) measurements from 1971 to 2000 were collected from the National Meteorological Stations across China. A zonal pedotransfer function (PTF) was developed based on the ensemble linear regression kriging model to predict the MAST50 in three topographic steps of China. The results showed that MAAT was the most important variable related to the variation of MAST50. The zonal PTF was evaluated with a 10% validation dataset with a mean absolute error (MAE) of 0.66°C and root mean square error (RMSE) of 0.78°C, which were smaller than the unified model with MAE of 0.83°C and RMSE of 0.96°C, respectively. This study demonstrated that the zonal PTF helped improve the accuracy of the predicted MAST50 map. Based on the prediction results, an STR map across China was generated to provide a consistent scientific base for the improvement and application of CST and land use support.

Liming is an effective measure to increase the soil pH and to counterbalance soil acidification. Therefore, the liming recommendations (LRs) for agricultural practice consider two aspects: changing the initial pH to the desired pH and compensating for all pH decreases taking place within the liming interval. The separation of these aspects is essential to optimize LRs and to minimize lime losses to the environment. Therefore, we developed a pedotransfer function (PTF) to calculate the lime demand to change the initial pH to the desired pH and compared the results with the LRs for agricultural practice. Applying this PTF to a set of 126 soil samples that were analyzed for base neutralization capacity could explain approximately 78% of the variability in the pH changes after the addition of different amounts of Ca(OH)2. Consequently, the lime demand to change the initial pH to the desired pH increased by approximately one-sixth compared to the lime demand proposed by the liming recommendation scheme, which is commonly used in Germany. From the numerical difference between the lime demand according to the LRs and the PTF, we calculated the annual acidification rates based on the soil texture, organic matter content and initial pH. Decoupling the abovementioned two aspects of LRs might be helpful to optimize the LRs by adapting to different regions, diverse management strategies and a changing climate. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop the best transfer functions for estimating the soil water retention curve (SWRC) for Iraqi soils using multiple regression methods. Soil samples were collected from 30 different sites in Iraq at two depths (0-0.3 m and 0.3-0.6 m) to create a database for the development of predictive transfer functions. The database included information on soil particle size distribution, carbonate minerals, mass density, particle density, organic matter, saturated hydraulic conductivity, capillary height, and available water limits. Explanatory variables (EV) were the measured characteristics, while response variables (RV) were the volumetric water content measured at different potentials (0, 5, 10, 33, 500, 1000, 1500 kPa). Two methods were used to develop predictive transfer functions: the logit model and beta model. Prediction accuracy was assessed using mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that the variables included in the derivation of the two models for predicting θ( Ψ) were similar, except at θ(0). The variables w1 (w1 = 2Psand° - Psilt° - Pcaly° - Pcarbonate), capillary height, available water, and porosity were found to be included in most of the logit and beta models. Additionally, there were no statistically significant differences between the MAE, RMSE, and R2 values of the two models. However, the beta model performed better in terms of MBE compared to the logit model. The models also demonstrated highly significant R2 values (0.9819-1.00) for a linear relationship between the measured and predicted water content values. [ABSTRACT FROM AUTHOR]

Permafrost peatlands are vulnerable to rapid structural changes under climatic warming, including vertical collapse. Peatland water budgets, and therefore peat hydraulic properties, are important determinants of vegetation and carbon fluxes. Measurements of hydraulic properties exist for only a limited number of permafrost peatland locations, primarily concentrated in North America. The impacts of thaw‐induced collapse upon properties such as horizontal saturated hydraulic conductivity (Kh), and thus lateral drainage, remain poorly understood. We made laboratory determinations of Kh from 82 peat samples from a degrading Swedish palsa mire. We fitted a linear mixed‐effects model (LMM) to establish the controls on Kh, which declined strongly with increasing depth, humification and dry bulk density. Depth exerted the strongest control on Kh in our LMM, which demonstrated strong predictive performance (r2 = 0.605). Humification and dry bulk density were influential predictors, but the high collinearity of these two variables meant only one could be included reliably in our LMM. Surprisingly, peat Kh did not differ significantly between desiccating and collapsed palsas. We compared our site‐specific LMM to an existing, multi‐site model, fitted primarily to boreal and temperate peatlands. The multi‐site model made less skillful predictions (r2 = 0.528) than our site‐specific model, possibly due to latitudinal differences in peat compaction, floristic composition and climate. Nonetheless, low bias means the multi‐site model may still be useful for estimating peat Kh at high latitudes. Permafrost peatlands remain underrepresented in multi‐site models of peat hydraulic properties, and measurements such as ours could be used to improve future iterations. Key Points: Depth and humification are important controls for horizontal saturated hydraulic conductivity in a degrading Swedish palsa complexPeat hydraulic properties did not significantly differ between desiccating and collapsed areas of the palsa complexAn existing model, trained on lower‐latitude peatlands, predicted horizontal saturated hydraulic conductivity adequately, with low bias [ABSTRACT FROM AUTHOR]

Every application of soil erosion models brings the need of proper parameterisation, that is, finding physically or conceptually plausible parameter values that allow a model to reproduce measured values. No universal approach for model parameterisation, calibration and validation exists, as it depends on the model, spatial and temporal resolution and the nature of the datasets used. We explored some existing options for parameterisation, calibration and validation for erosion modelling exemplary with a specific dataset and modelling approach. A new Morgan‐Morgan‐Finney (MMF)‐type model was developed, representing a balanced position between physically‐based and empirical modelling approaches. The resulting model termed 'calculator for soil erosion' (CASE), works in a spatially distributed way on the timescale of individual rainfall events. A dataset of 142 high‐intensity rainfall experiments in Central Europe (AT, HU, IT, CZ), covering various slopes, soil types and experimental designs was used for calibration and validation with a modified Monte‐Carlo approach. Subsequently, model parameter values were compared to parameter values obtained by alternative methods (measurements, pedotransfer functions, literature data). The model reproduced runoff and soil loss of the dataset in the validation setting with R2adj of 0.89 and 0.76, respectively. Satisfactory agreement for the water phase was found, with calibrated saturated hydraulic conductivity (ksat) values falling within the interquartile range of ksat predicted with 14 different pedotransfer functions, or being within one order of magnitude. The chosen approach also well reflected specific experimental setups contained in the dataset dealing with the effects of consecutive rainfall and different soil water conditions. For the sediment phase of the tested model agreement between calibrated cohesion, literature values and field measurements were only partially in line. The methods we explored may specifically be interesting for use with other MMF‐type models, or with similar datasets. [ABSTRACT FROM AUTHOR]

Accurate saturated hydraulic conductivity (Ksat) predictions are critical for precise water flow estimations. Pedotransfer functions (PTFs) have been used to estimate Ksat based on soil structural and textural properties. However, PTF accuracy must be validated with observed Ksat values to improve confidence in model predictions. A study was conducted in the seasonal wetlands of a representative mixed land-use watershed in West Virginia (WV), USA. The observed data included soil characteristics and observed piezometric Ksat using slug tests. Soil texture was predominantly sandy, and the observed average Ksat ranged from 35.90 to 169.64 m/d. The average bulk dry density (bdry) increased, while porosity and volumetric water content decreased significantly with a depth to 45 cm (p < 0.05). The degree of saturation varied significantly between monitoring sites (p < 0.05). A Pearson correlation matrix and Principal Component Analysis (PCA) revealed that Ksat was more connected to soil textural properties, specifically clay. Single parameter PTFs that estimated Ksat as a function of clay content performed better (ME = −90.19 m/d, RMSE = 102.87 m/d) than the PTFs that used silt or sand percentages (ME= −96.86 m/d, RMSE = 108.77). However, all five PTFs predicted Ksat with low accuracy (RMSE > 100 m/d), emphasizing the need to calibrate existing PTFs with observed data or develop site-specific PTFs. These results provide valuable insights into Ksat estimation in riparian wetlands of mixed land-use watersheds and are a helpful reference for land managers and future work. [ABSTRACT FROM AUTHOR]

Modelling the lime requirement (LR) is a fast and efficient way to determine the amount of lime required to obtain a pH that can overcome the adverse effects caused by soil acidification. This study aimed to model the LR based on the properties of soil and lime. A total of 17 acidic soils and 39 lime samples underwent soil–lime incubation in the laboratory. The predictive equations for the LR (t ha−1) were modelled using ∆pH (the difference between the target pH and initial pH), the neutralizing value (NV, mmol kg−1) of lime, soil pH, soil clay content (%), soil bulk density (BD, g cm−3), and the depth of soil (h, cm) as the factors in an exponential equation. The generic predictive equation, L R = ∆ p H × e − 3.88 − 0.069 × N V + 0.51 × p H + 0.025 × C l a y × B D × h , was validated as the most reliable model under field conditions. Simplified predictive equations for different soil textures when limed with quicklime and limestone are also provided. Furthermore, the LR proportions provided by hydrated lime, quicklime, limestone, and dolomite in commercially available lime can be expressed as 0.58:0.64:0.97:1.00. This study provides a novel and robust model for predicting the amount of lime product containing components with different neutralizing abilities that are required to neutralize soils with a wide range of properties. It is of great significance to agronomic activities and soil remediation projects. [ABSTRACT FROM AUTHOR]

This paper describes a new pedotransfer function (PTF) for the soil water content of New Zealand soils at seven specific tensions (0, −5, −10, −20, −40, −100, −1500 kPa) using explanatory variables derived from the S-map soil mapping system. The model produces unbiased and physically plausible estimates of the response at each tension, as well as unbiased and physically plausible estimates of the response differences that define derived properties (e.g., macroporosity and total available water content). The PTF is a development of an earlier model using approximately double the number of sites compared with the earlier study, a change in fitting methodology to a semi-parametric GAM Beta response, and the inclusion of sample depth. The results show that the new model has resulted in significant improvements for the soil water content estimates and derived quantities using standard goodness-of-fit measures, based on validation data. A comparison with an international PTF using explanatory variables compatible with variables available from S-map (EUPTF2) suggests that the model is better for prediction of soil water content using the limited information available from the S-map system.

The study was carried out to derive pedotransfer (PTFs) and spectrotransfer (STF) functions to estimate soil organic matter (SOM) content measured by time-consuming and expensive Walkley–Black wet combustion (W-B), using SOM content obtained from loss on ignition (LOI) and spectra reflectance bands at visible (Vis) and near infrared (NIR) regions. In total, 171 soil samples were collected from calcareous soils of southern Iran. The SOM content of the samples was determined using W–B wet combustion (SOMW-B) and LOI at temperatures of 360 °C during 2 h (LOI360-2); 550 °C during 3 h (LOI550-3); and 375°C during 16 h (LOI375-16). For the spectroscopy method, partial least squares regression (SpectPLSR) and forward stepwise multiple linear regression (SpectSMLR) approaches were used for function development. The LOI360-2 procedure with R2val of 0.9 for the validation dataset provided the best match with SOMW-B content. Furthermore, the SpectSMLR provided a STF using spectral reflectance bands at 495, 730, 970, 1906, 2262 and 2342 nm which predicted SOMW-B with R2val of 0.87. Results indicated both the SpectSMLR and LOI360-2, as easy and inexpensive approaches, could be recommended to accurately assess SOM content of calcareous soils. [ABSTRACT FROM AUTHOR]

Society calls for protection of agricultural soils in order to sustain the production of foods for a growing population. Compaction of subsoil layers is an increasing problem in modern agriculture and a cause of serious concern because of the poor resilience in natural amelioration. The concept of soil precompression stress has been adapted from civil engineering, although in soil science it is applied to unsaturated soils that have developed a secondary structure from the action of weather, biota and tillage. It assumes strain is elastic at loads up to the precompression stress, while plastic deformation is expected at higher stresses. To determine this threshold we performed uniaxial, confined compression tests for a total of 584 minimally disturbed soil cores sampled at three subsoil layers on nine Danish soils ranging in clay content from 0.02 to 0.38 kg kg-1. The cores were drained to either of three matric potentials (-50, -100 or - 300 hPa) prior to loading. Stress was applied by a constant-strain rate method. We estimated the point of maximum curvature of the strain-log10(normal stress) relation by a numerical procedure. This point is considered here as a compactive stress threshold, typically labeled the soil precompression stress, σpc. The preload suction stress (PSS) was calculated as the product of initial (i.e., before loading) water suction and initial degree of pore water saturation. Multiple regressions were performed to evaluate the effect of soil properties (textural classes, volumetric water content, bulk density (BD), soil organic matter (SOM), and PSS) on σpc. The best model explained 39% of the variation in σpc, and indicated that σpc increases with increasing PSS, BD and SOM. For a given combination of clay, BD and SOM, PSS affected σpc negatively. We recommend our regression model for use in risk assessment tools for estimating sustainable traffic on agricultural soils. The model was validated by five independent data sets from the literature. Our study shows that caution should be applied when regarding σpc as a fixed threshold for compressive strength. We hypothesize that plastic deformation is initiated over a range of stress rather than at a distinctive single value. Further studies are needed to better understand--and potentially quantify--to what extent the predicted σpc can be regarded a central estimate of allowable stress for a given soil. [ABSTRACT FROM AUTHOR]

Precompression stress, compression index, and swelling index are used for characterizing the compressive behavior of soils, and are essential soil properties for establishing decision support tools to reduce the risk of soil compaction. Because measurements are time-consuming, soil compressive properties are often derived through pedotransfer functions. This study aimed to develop a comprehensive database of soil compressive properties with additional information on basic soil properties, site characteristics, and methodological aspects sourced from peer-reviewed literature, and to develop random forest models for predicting precompression stress using various subsets of the database. Our analysis illustrates that soil compressive properties data primarily originate from a limited number of countries. There is a predominance of precompression stress data, while little data on compression index or recompression index are available. Most precompression stress data were derived from the topsoils of conventionally tilled arable fields, which is not compatible with knowledge that subsoil compaction is a serious problem. The data compilation unveiled considerable variations in soil compression test procedures and methods for calculating precompression stress across different studies, and a concentration of data at soil moisture conditions at or above field capacity. The random forest models exhibited unsatisfactory predictive performance although they performed better than previously developed models. Models showed slight improvement in predictive power when the underlying data were restricted to a specific precompression stress calculation method. Although our database offers broader coverage of precompression stress data than previous studies, the lack of standardization in methodological procedures complicates the development of predictive models based on combined datasets. Methodological standardization and/or functions to translate results between methodologies are needed to ensure consistency and enable data comparison, to develop robust models for precompression stress predictions. Moreover, data across a wider range of soil moisture conditions are needed to characterize soil mechanical properties as a function of soil moisture, similar to soil hydraulic functions, and to develop models to predict the parameters of such soil mechanical functions. • Soil compressive properties data originate from a few countries. • Precompression stress data mainly from topsoils, neglecting subsoil compaction. • Lack of standardized methods hinders precompression stress modelling. • Data needed for soil compressive properties across wider moisture range. [ABSTRACT FROM AUTHOR]

Determination of thermal soil properties for optimization of cable dimensioning The dimensioning of extra‐high voltage direct current transmission lines (HVDC) is based on detailed knowledge of the ground with regard to its thermal conductivity. Therefore, a methodology to determine reproducible values of the thermal conductivity of soils was developed for the construction of the 700 km long wind power line SuedLink. With the aid of a highly efficient test rig, the corresponding thermal conductivity values can be determined from the saturated to the ovendried state of a soil sample. The evaluation of the thermal conductivity‐water content relationship has been significantly further developed, taking into account the stone content of the soil and the groundwater conditions. These results are used in the planning of the cable dimensioning for the optimization of cable axis distances in order to minimize the project costs and the environmental impact. In addition, a comprehensive database for thermal conductivity values will be created in order to limit the effort for soil exploration for future earthworks projects by means of so‐called pedotransfer functions. [ABSTRACT FROM AUTHOR]

Soil erodibility (K) reflects the sensitivity of soil to detachment and transport and is a key factor for estimating the loss of soil. Most of the models for estimating K are complex and experiential, simple and local estimation model in the hilly and mountainous southeastern China is rare. This study aims to build local pedotransfer functions (PTFs) for soil erodibility estimation and evaluate the performance of the built PTFs, i.e. multiple linear regression (MLR), MLR with deformed forms of variables (MLR-DFV) and artificial neutral network with deformed forms of variables (ANN-DFV) to estimating K in southeastern China. The local true K values were obtained by a comprehensive method that considering the optimization prediction model and runoff-plot monitoring data. The best predictive variables were determined using correlation analysis, principal component analysis, importance evaluation and minimum variable-set determination. Mean K in the study area was 0.043 t ha h ha−1 MJ−1 mm−1, ranging from 0.019 to 0.060 t ha h ha−1 MJ−1 mm−1, showed a moderate spatial variability. Soil organic-matter content (SOM) was the most important factor influencing K and accounted for 17.5 % of the total importance. Soil sand content, geometric mean diameter of aggregates, SOM and synthetic curvature were identified as the best predictive variables representing soil physical properties, aggregate characteristics, nutrient and topographical conditions, respectively. The accuracies of MLR-DFV and ANN-DFV were high and similar but higher than the accuracy of MLR. K estimated using ANN-DFV was more similar in magnitude, distribution, and spatial variability to the true K data than K estimated using MLR-DFV. We developed the first local PTFs for estimating K in the hilly and mountainous southeastern China, which could provide empirical basis and method support for studying K in similar regions.

In northern peatlands, near‐saturated surface conditions promote valuable ecosystem services such as carbon storage and drinking water provision. Peat saturated hydraulic conductivity (Ksat) plays an important role in maintaining wet surface conditions by moderating drainage and evapotranspiration. Peat Ksat can exhibit intense spatial variability in three dimensions and can change rapidly in response to disturbance. The development of skillful predictive equations for peat Ksat and other hydraulic properties, akin to mineral soil pedotransfer functions, remains a subject of ongoing research. We report a meta‐analysis of 2,507 northern peat samples, from which we developed linear models that predict peat Ksat from other variables, including depth, dry bulk density, von Post score (degree of humification), and categorical information such as surface microform type and peatland trophic type (e.g., bog and fen). Peat Ksat decreases strongly with increasing depth, dry bulk density, and humification; and increases along the trophic gradient from bog to fen peat. Dry bulk density and humification are particularly important predictors and increase model skill greatly; our best model, which includes these variables, has a cross‐validated r2 of 0.75 and little bias. A second model that includes humification but omits dry bulk density, intended for rapid field estimations of Ksat, also performs well (cross‐validated r2 = 0.64). Two additional models that omit several predictors perform less well (cross‐validated r2 ∼ 0.5), and exhibit greater bias, but allow Ksat to be estimated from less comprehensive data. Our models allow improved estimation of peat Ksat from simpler, cheaper measurements. Key Points: We report skillful statistical models to estimate saturated hydraulic conductivity in northern peats from simpler measurementsPeat dry bulk density and humification (von Post score) are particularly powerful predictorsOur models represent an improvement over existing pedotransfer functions for peat saturated hydraulic conductivity [ABSTRACT FROM AUTHOR]

Granite red soil (GRS) and Quaternary red clay (QRC) are two typical erodible soils in the red-soil region of southern China. Analytical and comparative studies of the characteristics of runoff and sediment yield for the two soils at various slopes are currently needed. The purpose of the current study was to clarify the characteristics of runoff and sediment yield for GRS and QRC at different slopes and to establish models for estimating sediment yield for the two soils. Forty-eight runoff microplots with four slopes (5°, 15°, 25°, and 35°) and two soils (GRS and QRC) were established and exposed to natural rainfall. Runoff and sediment yield were measured 10 times during the study period. Runoff and sediment yield for the two soils under the various slopes had similar temporal variations, and both increased with prior cumulative erosive rainfall. Runoff for GRS and QRC was moderately temporally variable, with coefficients of variation (CVs) from 46.2% to 60.6%, and sediment yield for QRC was strongly temporally variable, with CVs from 114.8% to 145.8%. Sediment yield for GRS increased with slope, but sediment yield for QRC first increased and then decreased, with a calculated inflection point of 18°, but runoff for both soils decreased with slope. The CVs of both runoff and sediment yield with slope for the two soils ranged from 3.6% to 88.0%, lower than the temporal variabilities, indicating that rainfall may have a larger impact than slope on runoff and sediment yield for QRC and GRS. Under the various slopes, runoff and sediment yield for both soils increased with rainfall and sediment yield increased with runoff, but the proportions of effective rainfall and runoff differed. Pedotransfer-function models based on rainfall, runoff, and slope accurately estimated sediment yield for the two soils, with the model fit coefficient of determination (R 2) > 0.81 and the R 2 for verification >0.79. These results improve the understanding of the laws governing erosion for different soil types in the red-soil region of southern China and are important for managing the erosion of collapsing gullies and sloping farmland in the region. [ABSTRACT FROM AUTHOR]

Hydrological, ecohydrological, and terrestrial biosphere models depend on pedotransfer functions for computing soil hydraulic parameters based on easily measurable variables, such as soil textural and physical properties. Several pedotransfer functions have been derived in the last few decades, providing divergent estimates of soil hydraulic parameters. In this study, we quantify how uncertainties embedded in using different pedotransfer functions propagate to ecosystem dynamics, including simulated hydrological fluxes and vegetation response to water availability. Using a state‐of‐the‐art ecohydrological model applied at 79 sites worldwide, we show that uncertainties related to pedotransfer functions can affect both hydrological and vegetation dynamics. Uncertainties in evapotranspiration, plant productivity, and vegetation structure, quantified as leaf area, are in the order of ∼10% at annual time scales. Runoff and groundwater recharge uncertainties are one order of magnitude larger. All uncertainties are largely amplified when small‐scale topography is taken into account in a distributed domain, especially for water‐limited ecosystems with low permeability soils. Overall, pedotransfer function related uncertainties for a given soil type are higher than uncertainties across soil types in both hydrological and ecosystem dynamics. The magnitude of uncertainties is climate‐dependent but not soil type‐dependent. Evapotranspiration, vegetation structure, and plant productivity uncertainties are higher in water‐limited semiarid climates, whereas groundwater recharge uncertainties are higher in climates where potential evapotranspiration is comparable to precipitation. Plain Language Summary: Models that simulate the water and carbon cycles at the land surface need to consider the properties of soil that affect water movement. As those properties are not easily observable, models typically rely on empirical functions for their estimation. These are called pedotransfer functions, which use easily measurable quantities, such as the description of soil texture, to predict less easily measurable soil hydraulic properties. In this study, we quantified how differences in many of those pedotransfer functions introduce uncertainties in the way ecosystem water and carbon cycles are simulated. We show that differences in soil hydraulic properties estimation can introduce significant uncertainty in both the water and carbon cycles. The uncertainties in the water cycle are largest for the prediction of water flowing toward rivers and groundwater replenishment rates. The uncertainties in the carbon cycle are largest in semiarid climates. All uncertainties are further amplified by the presence of prominent topographic features (e.g., valleys and mountains) that introduce further complexity in relation to soil water movement. Key Points: Uncertainties between pedotransfer functions are comparable to uncertainties in soil texturePedotransfer function choice has large effect on hydrological and smaller effects on ecosystem dynamicsComplex topography amplifies the importance of pedotransfer function uncertainties [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) plays a key role in ecosystems. Reduction of its content due to land-use changes has a negative impact on the soil, but also on the wider environment. Accordingly, SOM content is routinely analyzed in the laboratory. As these are expensive and/or time-consuming, indirect ones are also tested. The aim of this study was to examine the possibility of predicting SOM content by linear regression using soil color as the predictor, at three locations in Zagreb (Croatia), with different soil types (eutric cambisol anthropogenic, humofluvisol, pseudogley) and different land uses (plough land, meadow, forest, respectively). At each location, 5 samples of the surface soil layer were taken. Soil color was determined using the Munsell system, and the hue was 2.5Y and 10YR in dry and moist soil, respectively. Laboratory analyzes showed that the soils are very acid to neutral silt loams. In line with the land-use, they differed significantly in SOM content and were poorly humic (plough land), moderately to highly humic (meadow), and highly humic (forest). Correlation between soil color dimensions and SOM content was significant only for the dry samples, between chroma and SOM and between value/chroma ratio and SOM. Regression analysis showed high coefficients of determination for these two relationships (R2 = 0.88 for chroma-SOM, R2 = 0.76 for value/chroma-SOM). The results suggest that visual soil color determination can be used to estimate SOM content, but only in dry soil. The model calibrated in this paper needs to be validated using samples of other (different) soils.

The information on the pedotransfer function of soil is vital for crop cultivation planning and management. The present study evaluated the pedotransfer function of ten locations (different villages) in the Kalyanpur block of Bihar state, India. The various soil parameters like soil texture, infiltration rate, soil penetration resistance, Bulk density, porosity, water holding capacity, available water content, field capacity, permanent wilting point, gravimetric moisture content and organic carbon were evaluated. The infiltration rate and soil penetration resistance were measured in the field and other parameters were evaluated in the laboratory with suitable techniques. The observed data of the above parameters were analyzed for given locations. A good coefficient of determination (R2 = 0.61) was discovered for the linear relationship between the infiltration rate as determined by the double ring infiltrometer and the soil penetration resistance (measured using a cone penetrometer down to a depth of 60 cm). Further, the nomographic representation of soil texture, final infiltration rate (mm h−1) and weighted mean of penetration resistance (kPa) was also developed. Furthermore, the regression analysis was done on five combinations (C-1, C-2, C-3, C-4 and C-5) of obtained parameters in which infiltration rate was taken as output and other parameters as input parameters to develop the pedotransfer model. Based on the obtained results, all combinations showed good correlation and C-3 with input parameters such as sand, silt, clay, porosity, GMC and AWC were found superior with good coefficient of determination values (R2 = 0.914). C-3 combination has the edge over C-4 combination (input: sand, silt, clay, porosity and organic carbon), having R2 values of 0.910. Therefore, the information obtained from the study will be helpful for policymakers in irrigation and other crop cultivation activities. [Display omitted] • The soil samples at 15 cm, 30 cm, 45 cm and 60 cm depths to evaluate different soil parameters. • IR and soil PR were evaluated by infiltrometer and cone penetrometer, resp. • Established a significant relationship between infiltration rate and PR. • Nomograph developed between texture, IR and PR for all villages. • Relation b/w soil texture, η, GMC, AWC and OC with IR using regression analysis. [ABSTRACT FROM AUTHOR]

• Hand-feel soil texture classes used as predictors in a pedotransfer function. • Clay and silt content simulated from distributions by hand-feel texture class. • Confusion in hand-feel texture class allocation is considered. • Confusion in hand-feel texture classes propagated to uncertainty in the prediction. Pedotransfer functions (PTFs) are increasingly being used to derive difficult-to-measure or cost prohibitive soil properties from more readily available soil data. Soil texture (ST) is one of the most commonly used predictors in PTFs. Soil texture can be determined in the laboratory or estimated manually by soil surveyors in the field. Soil texture classes are sometimes used either for producing class PTFs or to generate distributions of clay, silt and sand. Here, we develop a method to assess the uncertainty due to the confusion or error in hand-feel soil texture (HFST) classes allocation. We show that this error does not have a large impact on the coefficients of the linear regressions used to calibrate the PTFs. However, they may have a large impact on PTFs prediction performances. [ABSTRACT FROM AUTHOR]

Determination of the threshold velocity (TV) is a crucial step for wind erosion evaluation. Due to the difficulties of direct field measurements, pedotransfer functions (PTFs) and easily measurable soil properties could be used to save time and cost in predicting TV. Therefore, the present study was conducted to predict the TV using PTFs and to assess its influential parameters for calcareous soils of Fars Province, southern Iran. To this end, the TV was measured by a portable wind tunnel at 72 locations in different land uses and soil types across the study site. Various physicochemical and mechanical soil properties were used to develop six PTFs using multiple linear regression. Results showed that the TV varied from 3.0 m s−1 in poor rangelands to 12.83 m s−1 in saline lands. Soil surface shear strength (SS) with a correlation coefficient of 0.85 was the most influential parameter affecting the TV, followed by aggregate mean weight diameter (MWD). Results of the predictive models revealed that PTF 5, which was developed using SS and penetration resistance (PR;R2 = 0.86, RMSE = 0.85 m s−1), and PTF 6, which was developed using MWD and PR (R2 = 0.81, RMSE = 1.07 m s−1), had the highest performance for predicting the TV. PTF 5 was selected as the final model for predicting the TV since it only needed easily measurable soil properties without soil sample collection. We concluded that the use of PTFs could be an applicable alternative way to predict the TV, particularly at large scales. [ABSTRACT FROM AUTHOR]

Context. The depth-to a constraint determines how much of the soil profile, and the water it contains, can be accessed by plant roots. Information describing the impacts of soil constraints on available water capacity (AWC) and yield is important for farm management, but is rarely considered in a spatial context. Aims and methods. The depth-to three yield-limiting constraints (sodicity, salinity, and alkalinity) was mapped across ~80 000 ha in northern New South Wales, Australia using machine learning and digital soil mapping techniques. Soil AWC was calculated using soil data and pedotransfer functions, and water use efficiency equations were used to determine potential yield loss due to the presence of soil constraints. From this, the most-limiting constraint to yield was mapped. Key results. One or more constraints were found to be present across 54% of the study area in the upper 1.2 m of the soil profile, overall reducing the AWC by ~50 mm and potential yield by an average of 1.1 t/ha for wheat and 0.8 bales/ha for cotton. Sodicity (Exchangeable Sodium Percentage > 15%) was identified as the most-limiting constraint to yield across the study area. Implications. The simplification of multiple sources of information into a single decision-making tool could prove valuable to growers and farm managers in managing soil constraints and understanding important interactions with available water and yield. [ABSTRACT FROM AUTHOR]

The most applied pedotransfer functions (PTFs) often suffer from two main limitations: (a) the soil hydraulic models (SHMs) only account for capillary forces and/or the models show an unrealistic decrease near saturation for fine‐textured soils; (b) the observations of soil hydraulic properties (SHPs) used to generate the PTF generally do not cover very dry conditions. In this paper, we first present a simple method for predicting SHPs in the dry range from soil texture information. Together with measurements that cover only a relatively high matric potential range, the method yielded a good prediction of the complete SHPs from saturation to oven dryness. With this method, we extended a public dataset to cover dry conditions, and then applied it to develop a new PTF for a SHM that accounts for both capillary and adsorption forces and overcomes the unrealistic decrease near saturation for fine‐textured soils. A comparison with other PTF that was developed for the capillary‐based soil hydraulic model showed that the new PTF provided the most accurate predictions of SHPs. It reduced the root‐mean‐square‐error value from 0.055 to 0.045 cm3 cm−3 in predicting water content and from 0.84 to 0.66 log10 (cm d−1) in predicting hydraulic conductivity. We further applied this method to extend an existing capillary‐based PTF to dry conditions. The results showed an improved performance, with reported RMSE reduced from 0.058 (original) to 0.056 (extended) cm3 cm−3 and from 1.43 (original) to 1.20 (extended) log10 (cm d−1) for prediction of water content and hydraulic conductivity, respectively. Key Points: A simple method is presented for predicting a complete SHP with limited measurements covering only high matric potential rangeA new PTF was developed by considering limitations of model structure and observation, which significantly improved the prediction of SHPsA method is proposed to extend an existing capillary‐based PTF to dry conditions [ABSTRACT FROM AUTHOR]

【Background】 Accurate prediction of soil water dynamics requires soil hydraulic parameters and the PedoTransfer Function (PTF) is an indirect method estimating soil hydraulic parameters based on easy-to-measure soil properties. 【Objective】 The purpose of this paper is to compare different machine learning methods as a pedotransfer function to estimate soil hydraulic parameters. 【Method】 We used the UNSODA soil hydraulic property database and compared three machine learning methods: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The hydraulic parameters were described by the van Genuchten formula, and the relationship between its parameters with fractions of sand, silt and clay, as well as soil bulk density was analyzed using the three methods. The accuracy of each method was evaluated using measured water retention curves and saturated hydraulic conductivity from soils with different textures. 【Result】 The SVM model was most accurate and KNN the least to predict soil hydraulic parameters using these easy-to-measure soil properties. Evaluation of operating efficiency of all three methods revealed that the ANN model was least efficient and the KNN the most in model training. In contrast, the ANN model was most efficient while KNN the least in predicting soil hydraulic parameters. Comparison of the three models against the Rosetta model - a commonly used neural-network pedotransfer function with a single hidden layer - found that neural-network models with multiple hidden layers, as used in this paper, were more accurate. We also found that for all three models, increasing the number of input data improved their estimation accuracy. 【Conclusion】 Of the three models, SVM is most accurate for predicting soil hydraulic parameters using fractions of clay, sand and silt, and bulk density, followed by ANN, when the database was not large enough. With the size of the database increasing, the ANN model becomes increasingly more efficient. Since ANN can use the mini-batch method to train the model without increasing computational costs, our results suggest that selecting a suitable method to calculate soil hydraulic parameters should consider computational cost and estimation accuracy when the size of the database increases.

Soil bulk density (ρb) is an important indicator of soil quality, productivity, compaction and porosity. Despite its importance, ρb is often omitted from global datasets due to the costs of making many direct ρb measurements and the difficulty of direct measurement on rocky, sandy, very dry, or very wet soils. Pedotransfer functions (PTFs) are deployed to address these limitations. Using readily available soil properties, PTFs employ estimator equations to fit existing datasets to estimate properties like ρb. However, PTF performance often declines when applied to soils outside those in the training dataset. Potentially, recalibrating existing PTFs using new observations would leverage the power of large datasets used in the original PTF derivation, while updating information based on new soil observations. Here, we evaluate such a recalibration approach for ρb estimation, benchmarking its performance against two alternatives: the original, uncalibrated PTFs, and novel, local PTFs derived solely from new soil observations. Using a ρb dataset of N = 360 total observations obtained in West Azerbaijan, Iran, we varied the local dataset size (with N = 15, 30, 60, and 360) and recalibrated four existing PTFs with these data. Local PTFs were generated based on stepwise multiple linear regression for the same datasets. The same PTFs (original, recalibrated, and local) were also applied to the study area, and the resulting ρb estimates were compared with the global SoilGrids dataset. Recalibration of PTFs reduced errors relative to the original uncalibrated PTFs; for instance, the NSE increased from −22.07 to 0.30 (uncalibrated) to 0.20–0.41 (recalibrated), and RMSE decreased from 0.12 to 0.60 Mg m−3 (uncalibrated) to 0.10–0.13 Mg m−3 (recalibrated). The recalibrated PTFs performance was comparable to or better than local PTFs applied to the same data. Recalibration of existing PTFs with local/regional uses provides a viable alternative to the use of global datasets or the development of local PTFs in data‐scarce regions. Highlights: Existing global PTFs were calibrated and tested using a small dataset for local utilisation.Several new local PTFs were also developed using the same datasets.Recalibration of existing global PTFs is comparable to or more accurate than developing new PTFs. [ABSTRACT FROM AUTHOR]

Studies on clayey soils developed in temperate areas have shown that their water retention properties are related to both the clay content and the specific pore volume of the clay, the latter being related to the hydric history of the soil, that is to the drying/wetting cycles. Our objective was to discuss the validity of these results for clayey soils developed in semi-arid areas. Samples were collected in soils located in Syria. Physico-chemical properties were determined. Water content was measured at field capacity and for water potentials ranging from −10 to −15,000 hPa. X-ray diffraction analyses were performed on the clay fraction to identify the clay. Results showed that the clays have both a high cation exchange capacity (0.707–0.891 mmol+ g−1 of clay) and a high external specific surface area (112 and 178 m2 g−1 of clay). These values are consistent with the X-ray diffraction results which showed the presence of a high proportion of smectite in most horizons and secondarily of varying proportions of illite and chlorite; kaolinite, while present, was not abundant. Results also showed that the amount of water retained by the clay according to the water potential was closely related to the specific pore volume of the clay at field capacity. Regression equations established by using both the data published earlier and those of this study enabled us to predict the water retention properties of clayey soils for a larger range of clay mineralogy and climatic environments including semi-arid environments than previously discussed in the literature. [ABSTRACT FROM AUTHOR]

In this study, different methods were compared in order to determine the soil hydraulic conductivity at the saturation (Ks) of two heavily anthropized soils in northern Italy: an irrigated field and a landfill cover. In situ, laboratory measurements (falling head and evaporation method) and pedotransfer functions (ROSETTA and HYPRES) were used for the Ks estimation. In accordance with scientific literature, the results have shown that Ks is largely dependent on the type of technique used in taking the measurements. The ROSETTA and HYPRES pedotransfer functions show quite similar performances, while their easiness and convenient use make them potential alternative techniques for the Ks estimation in comparison with the in situ and laboratory measurements. The Ks estimate is sensitive to the selected method and this sensitivity affects the hydrological model simulations. Therefore, none of the tested methods can be considered as a benchmark, but the results found in this study confirm that the applied method for the determination of Ks, may provide a first estimate of Ks to be subsequently optimised after the simulations.

ABSTRACT The saturated hydraulic conductivity (Ks) is an essential property for modeling water and contaminants movement into aquifers. However, Ks is extremely variable, even when considering nearby locations, which poses a challenge for modeling at catchment scales. Field measurements of Ks are most of the time expensive, time-consuming and labor-intensive. This study aimed to obtain, for modeling purposes, and using pedotransfer functions (PTFs), a composite value of Ks at a catchment scale, in a recharge area of the Guarani Aquifer System. Soil samples were taken across the study area, and the Ks for each sampling point were determined by several PTF methods. At the same locations, Ks field measurements were taken using a Guelph permeameter. Average values of Ks for all the sampling points calculated by PTFs were similar to the average value obtained by field measurements. The use of PTFs proved to be a faster and simpler method to efficiently determine the Ks value for the watershed and to capture the stochastic variation in terms of soil pore combination at the watershed scale.

Abstract Agriculture needs methodologies that assist in the determination of soil attributes and variability mapping attributes with greater levels of detail. Therefore, the objective of this research was to evaluate magnetic susceptibility as auxiliary variable for estimating soil attributes in areas of Indian Black Earths in the south of Amazonas State. Three Indian Black Earth areas are located in the municipalities of Apuí and Manicoré - Amazonas, under uses with coffee, cocoa and pasture. The soils were collected at the crossing points in the depth of 0.00 - 0.20 m, making a total of 88 sampling points/area, and totaling 264 samples. The points were georeferenced for geostatistical modeling. After that, physical and chemical analyzes were performed to obtain the values of soil and magnetic susceptibility attributes. Descriptive statistics, Pearson correlation, linear regression and geostatistical analyzes were applied for Pedotransfer Function modeling and the spatial variability of the analyzed attributes. Magnetic susceptibility showed a high degree of spatial dependence in the study areas, high range values, correlating with most of the assessed attributes, mainly physical, indicating potential in the prediction of the attributes in these environments. Pedotransfer functions vary among IBE’s sites in attribute prediction, ensuring moderate estimates for predicting soil attributes in IBE’s areas.