Pedotransfer Research Articles

High-resolution digital mapping of soil erodibility in China

Soil erodibility (K) is the intrinsic susceptibility of a soil to water erosion. Currently, its detailed and accurate spatial distribution information especially over large areas is urgently required for national and regional soil erosion assessment and soil conservation decision making. This study combined pedotransfer function with digital soil mapping techniques to develop a high-resolution map of soil erodibility across China. The First, based on a recent national soil survey, we adopted the erosion-productivity impact calculator (EPIC) to calculate soil erodibility values at 4710 soil sampling points. Then, with the caclulated values of points, we used five techniques including polygon linking (PL), ordinary kriging (OK), Cubist, extreme gradient boosting (XGBoost), and random forests (RF) to generate spatial distribution of soil erodibility. The three latter machine learning techniques modeled the quantitative relationships between soil erodibility and a set of environmental covariates. The results showed that machine learning methods exhibited much more spatial details than the PL and OK did. Among the five techniques the RF achieved the highest accuracy with R2 of 0.49 and RMSE of 0.0077 t ha h ha−1 MJ−1 mm−1 based on 10-fold cross-validation. Spatial uncertainty analysis of the RF predictions showed that high uncerntainty occurred in northwestern China and low uncertainty in the center and southeast. We found that topographical and climatic variables are major environmental factors indirectly controlling spatial variation of soil erodibility while the soil particle composition and SOC contents directly influence the variation.

Exchangeable acidity and pedotransfer functions for the soils of Ghana

AbstractSoil exchangeable acidity (EA) is an indicator of aluminium toxicity potential in acidic soils. Predicting the distribution and dynamics of EA is needed for the identification and management of acidic soils. In this study, we used datasets of 355 pedons from across Ghana and the Cubist rule‐based algorithm to generate pedotransfer functions (PTFs) of EA. Eight soil properties (pH, organic carbon, calcium, sodium, magnesium, total exchangeable bases, cation exchange capacity [CEC] to clay ratio and soil depth) were used to predict EA. We first used the whole dataset to construct generic PTFs and then stratified the dataset based on World Reference Base‐Reference Soil Groups (WRB‐RSGs) to generate soil‐specific PTFs. Goodness‐of‐fit statistics comprising the root mean squared error (RMSE), Lin's concordance correlation coefficient () and coefficient of determination (R2) were used to evaluate the prediction accuracy and reliability of the developed PTF models on both calibration and validation datasets. The Fluvisols EA‐PTF exhibited lower performance metrics in the validation (RMSE = 0.17 cmolc kg−1, = 0.19, R2 = 0.24), whereas the EA‐PTFs for all other WRB‐RSGs and whole dataset had above‐average performance metrics in the validation (0.05 ≤ RMSE ≤ 0.97 cmolc kg−1; 0.34 ≤ ≤ 0.94; 0.52 ≤ R2 ≤ 0.96). Soil pH sufficed for predicting EA in soils with pH above 5.0, but in soils with a pH < 5.0, the levels of exchangeable bases (e.g., Na+, K+, Mg2+, Ca2+), CEC to clay ratio (CCR) and soil depth improved the prediction of EA. The developed EA‐PTFs are useful for estimating the missing values of EA in soil databases.

High‐resolution mapping of available water content in Senegal using iSDA Africa dataset and USDA Rosetta3 model

AbstractThis data article presents a high‐resolution map of available water content (AWC) for Senegal, derived from the iSDA Africa dataset and the USDA Rosetta3 model, as well as the method used for its production. The map covers the entire country at a resolution of 30 m and provides a valuable resource for hydrological studies and spatialized crop model simulations in the region where water is a limiting factor for crop production. The dataset is based on existing soil properties data and leverages pedotransfer functions (PTFs) to estimate water retention capabilities from soil properties. This AWC map derived from datasets with enhanced accuracy offers a more precise estimate of soil water retention capacity, which can be instrumental in informing water and agricultural management, policy decisions and investments. The dataset, including intermediate variables, is available in geotiff format at Cirad Dataverse under the DOI 10.18167/DVN1/SGNSII and complies with FAIR data principles, allowing its broad reuse. The code used to produce this dataset is also made available under the DOI 10.5281/zenodo.10078399, so that AWC maps can be produced for any territory covered by the iSDA Africa product.

Estimation of Infiltration Parameters: The Role of Pedotransfer Functions and Initial Moisture Conditions

Infiltration is one of the key components of rainfall-runoff processes at small catchment scale and is therefore a vital component of hydrological models. However, small catchments typically lack direct infiltration measurements and associated soil hydraulic characteristics, which are being substituted with soil databases and pedotransfer functions built upon these databases. We utilized two sets of pedotransfer functions Rosetta 3 and EUPTFv2 derived from two well known soil databases: UNSODA, based on US and global soil data, and EU-HYDI, based on EU soil data. Our study evaluates the impact of choosing one of these pedotransfer functions on modeled infiltration in relation to variable initial moisture content- It investigates the behavior of the simplified infiltration models of Green-Ampt and Philip under different initial conditions, in comparison with Richards’ complex solution, which is used as a benchmark. Our evaluation was conducted using soil property data available in the Czech Republic, covering ten USDA soil textures that are prevalent within the European Union. The applied initial moisture conditions were derived for specific suction pressure values that correspond to hydrological initial conditions ranging from near saturation to near the permanent wilting point. Variation in the derived parameters for the Green-Ampt and Philip models across the pedotransfer function sources results in considerable variation in the modeled infiltration. Our results imply different sensitivities of the simplified infiltration models to the initial moisture for different pedotransfer functions. The Green-Ampt and Philip infiltration models demonstrate better alignment with Richards’ complex solution with a low and consistent RMSE when using the Rosetta 3 pedotransfer function. By contrast, the EUPTFv2 model scenarios, show a clear trend of increasing RMSE towards dry initial conditions. As the European soil database is not an open data source, we only hypothesize that the distributions of soil characteristics in the European soil database differ systematically from those in UNSODA, and that this would cause different results of pedotransfer functions for different initial moisture conditions. The findings of this study are important for selecting appropriate infiltration models and, appropriate pedotransfer functions for estimating model parameters, and for assessing uncertainties connected with varying initial conditions.

ESTABLISHING THE FIRST DATABASE OF SOIL HYDRAULIC PROPERTIES IN TUNISIA BASED ON PEDOTRANSFER FUNCTIONS

Soil hydraulic properties play a crucial role in the unsaturated soil water supply process. Currently, Tunisia lacks a database containing essential values for soil retention properties and soil saturated hydraulic conductivity. The main objective of this study is to create the inaugural soil hydraulic properties database through the utilization of open-access data and pedotransfer functions (PTFs). To achieve this goal, the harmonized world soil database (FAO) was employed to identify 752 measurement points across Tunisia. Subsequently, the soil texture, organic carbon content, and bulk density were determined at each point. These acquired values were then entered into the CalcPTF software to estimate the van Genuchten soil retention parameters. The calculation of soil saturated hydraulic conductivity was accomplished using two widely recognized pedotransfer functions (Saxton and Rosetta). The outcomes facilitated the creation of a catalog containing soil hydraulic parameter value for each soil texture. Significance of discrepancies between values obtained from the PTFs was assessed using a Tukey test. The spatial variability of each soil hydraulic property was studied using the simple kriging. In conclusion, the establishment of this significant soil hydraulic properties database holds diverse applications in agricultural, hydrological, and environmental studies in Tunisia.

From EU-SoilHydroGrids to HU-SoilHydroGrids: A leap forward in soil hydraulic mapping

Spatially explicit, quantitative information on soil hydraulic properties is required in various modelling schemes. At European scale, EU-SoilHydroGrids proved its applicability in a number of studies, in ecological predictions, geological and hydrological hazard assessment, agri-environmental models, among others. Inspired by its continental antecedent, an analogous, but larger scale, national, 3D soil hydraulic database was elaborated for the territory of Hungary (HU-SoilHydroGrids) supported by various improvements (i–iv) in the computation process. Pedotransfer functions (PTFs) were built in the form of i) advanced machine learning methods and ensemble models, and trained on the ii) national soil hydrophysical dataset. The set of predictors used in PTFs was supplemented by iii) additional environmental auxiliary variables. Spatial layers of the soil hydraulic parameters were generated using iv) 100 m resolution information on primary soil properties, namely DOSoReMI.hu. HU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties (water content at saturation, field capacity and wilting point, saturated hydraulic conductivity and van Genuchten parameters for the description of the moisture retention curve) with national coverage at 100 m spatial resolution down to 2 m depth for six GSM standard depth layers. The HU-SoilHydroGrids has significantly lower squared error in the case of describing the moisture retention curve and hydraulic conductivity than the EU-SoilHydroGrids. The derived 3D soil hydraulic database (ver1.0) is presently available in National Laboratory for Water Science and Water Safety for project partners in order to test its functional performance in describing hydrological and ecological processes.

Macropore flow in relation to the geometry and topology of soil macropore networks: Re-visiting the kinematic wave equation

The rapid flow of water through soil macropores significantly affects the partitioning of precipitation between surface runoff and infiltration and also the rate of solute transport in soil, both of which have an impact on the risk of contamination of surface water and groundwater. The kinematic wave equation is often employed as a model of gravity-driven water flow through soil macropores. The exponent in this simple model influences the pore water velocity attained in the macropores at any given input rate and is usually estimated by inverse modelling against measured flow rates or water contents. In theory, the exponent in the kinematic wave equation should depend on the geometry and topology of the conducting macropore networks, although these relationships have not so far been investigated. In this study, we related metrics of soil structure derived from X-ray images to values of the kinematic exponent estimated from drainage experiments on twenty-two columns sampled at three different field sites under two contrasting land uses and at three different depths.We found that smaller values of the exponent in the kinematic wave equation, which would equate to more rapid flow of water through soil macropores, were found in plough pan and subsoil columns of smaller macroporosity, for which biopores comprised a significant fraction. The macroporosity in these columns was more vertically oriented and poorly inter-connected, though still continuous across the sample. In contrast, topsoil columns from both arable land and grassland had better connected, denser and more isotropically-distributed macropore networks and larger values of the kinematic exponent. Our results suggest that for predictive modelling at large scales, it may be feasible to estimate the kinematic exponent using class pedotransfer functions based on pedological information such as land use and horizon type.

Development of pedotransfer functions for predicting saturated hydraulic conductivity in a Himalayan catchment: Sikkim, India

AbstractSaturated hydraulic conductivity (Ks) plays a vital role in irrigation and drainage system design. Generally, Ks is estimated in the laboratory; however, it is expensive and tedious, especially in the Himalayan ranges where soil sampling is challenging due to topographical constraints. Therefore, in this study, pedotransfer functions were generated using multiple linear regression (MLR) models for the predictability of Ks in a Himalayan catchment in India. Fifty soil samples were collected and divided into two groups at a 70:30 ratio. Different soil attributes derived from 70% of samples were used for MLR generation, and attributes of the remaining 30% of samples were used for model validation. Six different MLR models constituting different independent soil attributes were generated and compared statistically. The results indicate that the MLR model comprising soil texture, bulk density, particle density, soil moisture content (MC), organic carbon content and porosity results in the highest adjusted coefficient of determination (R2; 0.93 and 0.89 during model generation and validation, respectively). Additionally, it was found that the weight basis MC ranged from 14% to 29% with a median value of 24%. These results demonstrate that simple MLR models can be used as an alternative to laborious experimental setups for Ks estimation. These findings can be used as guidelines for proper irrigation planning and design in mountainous catchments.

Effects of Cement Treatment on Water Retention Behavior and Collapse Potential of Gypseous Soils: Experimental Investigation and Prediction Models

Gypseous soil poses a significant challenge in geotechnical engineering due to its susceptibility to collapse under saturation. This type of soil covers approximately 33% of regions in Iraq, primarily in unsaturated conditions. This study focuses on two types of gypseous soils: one with moderate gypsum content from Karbala city (G1) and the other with high-gypsum content from Tikrit city (G2), to investigate the effects of cement treatment on their water retention behavior and potential for reducing collapse. Different percentages of cement were mixed with both soils to determine the optimum soil–cement mixture for reducing collapse potential (CP) through single oedometer tests. The water retention characteristics and water retention behavior of samples with varying gypsum content and different levels of cement treatment were examined and compared using a controlled-suction oedometer. The soil–water retention curve (SWRC) of these natural and treated gypseous soils was also investigated and compared in both wetting and drying paths. Additionally, multiple pedotransfer functions (PTFs) were assessed to identify or adapt prediction equation(s) for the SWRC of gypseous soil both with and without cement treatment with acceptable accuracy. The results show that adding cement can decrease the CP of gypseous soils; it also affects their SWRC significantly. By making some simple modifications, the PTFs demonstrate acceptable estimations for the water retention curve of both natural and cement-treated gypseous soils.

Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils.

Hydraulic conductivity (Kψ) is one of the most important soil properties that influences water and chemical movement within the soil and is a vital factor in various management practices, like drainage, irrigation, erosion control, and flood protection. Therefore, it is an essential component in soil monitoring and managerial practices. The importance of Kψ in soil-water relationship, difficulties for its measurement in the field, and its high variability led us to evaluate the potential of stepwise multiple linear regression (SMLR), and multilayer perceptron (MLPNNs) and radial-basis function (RBFNNs) neural networks approaches to predict Kψ at tensions of 15, 10, 5, and 0 cm (K15, K10, K5, and K0, respectively) using easily measurable attributes in calcareous soils. A total of 102 intact (by stainless steel rings) and composite (using spade from 0-20 cm depth) soil samples were collected from different land uses of Fars Province, Iran. The common physico-chemical attributes were determined by the common standard laboratory approaches. Additionally, the mentioned hydraulic attributes were measured using a tension-disc infiltrometer (with a 10 cm radius) in situ. Results revealed that the most of studied soil structure-related parameters (soil organic matter, soluble sodium, sodium adsorption ratio, mean weight diameter of aggregates, pH, and bulk density) are more correlated with K5 and K0 than particle-size distribution-related parameters (sand, silt, and standard deviation and geometric mean diameter of particles size). For K15 and K10, the opposite results were obtained. The applied approaches predicted K15, K10, K5, and K0 with determination coefficient of validation data (R2val) of 0.52 to 0.63 for SMLR; 0.71 to 0.82 for MLPNNs; and 0.58 to 0.78 for RBFNNs. In general, the capability of the applied methods for predicting Kψ at all the applied tensions was ranked as MLPNNs > RBFNNs > SMLR. Although the SMLR method provided easy to use pedotransfer functions for predicting Kψ in calcareous soils, the present study suggests using the MLPNNs approach due to its high capability for generating accurate predictions.

New empirical-point pedotransfer functions for water retention data for a wide range of soil texture and climates

Knowing soil hydraulic properties is essential to support soil use and management practices; however, their measuring is commonly expensive and time-consuming. Thus, pedotransfer functions (PTFs) have been used to quantify physical properties such as the soil water retention curve (SWRC). SWRC relates the volumetric soil water content (θ) as a function of the matric potential (h) and plays a vital role in soil hydraulic modeling. Point-PTFs estimate key-points of the SWRC, often from measured texture, bulk density, and organic matter. This study aimed to formulate new point-PTFs to estimate θ(h) data ranging from θ(-0.1 m) to θ(-150 m) to be applied in subtropical, tropical and temperate soils. The PTF equations were derived from linear and non-linear regressions of measured soil physical properties against to the water retention data. The prediction performance of the new-formulated PTFs overcame the performance of already existing and widely-known PTFs recognized in the literature and can be, therefore, applied in soil water retention topics under a wider textural range.

Funções de pedotransferência para estimativa da condutividade hidráulica e umidade do solo no bioma Cerrado

ABSTRACT The Cerrado biome is a strategic region for Brazilian agriculture, and obtaining physical hydraulic properties is fundamental to understanding the dynamics of soil water and its impact on productivity. However, the lack and difficulty of obtaining such properties opens an opportunity to use pedotransfer functions (PTFs). In the context, the objective of the present study was to develop PTFs using multiple linear regression to estimate hydraulic conductivity of the saturated soil (Ks) and soil moisture at tensions of 0, 6, 10, 33, 100, and 1,500 kPa for the Cerrado biome. For this, two different predictor datasets were used. Dataset 1 consists of sand, silt, clay, bulk density, particle density, total porosity, microporosity, and macroporosity, and dataset 2 consists of the variables in dataset 1 plus soil moisture at field capacity (FC) and soil moisture at the permanent wilting point (PMP). Dataset 2 presented the best performance compared to Dataset 1 for estimating Ks and soil moisture, highlighting the importance of moisture in FC and PWP as predictors in the development of PTFs. However, the PTFs developed for Ks presented a low predictive capacity in all predictor datasets, unlike the soil moistures that presented high precision, with R² greater than 0.8 and errors close to zero.

Mapping soil corrosivity potential to exclusion fencing using pedotransfer functions and open‐source soil data

AbstractSoil corrosivity is a term used to describe the corroding susceptibility (risk) of metal infrastructure in different soil environments. Soil corrosivity mapping is a crucial step in identifying potentially problematic, high‐maintenance fence lines and can help improve fence longevity by identifying soil environments where the use of more expensive, corrosion‐resistant materials would be more cost‐effective in the long term. Soil corrosion damage sustained on exclusion fences can be a serious management issue for conservation programs and initiatives, as it weakens the fence netting and provides opportunities for invasive animal migration and occupation (e.g. feral cats and foxes) into areas of high conservation value. The increasing accessibility of geospatial analysis software and the availability of open‐source soil data provide land managers with the opportunity to implement digital soil databases and pedotransfer functions to produce fence corrosion risk maps using commonly measured soil attributes. This paper uses open‐source government agency soil data (shapefiles) to map fence corrosion risk in the southern part of the Yorke Peninsula in South Australia, with the intention to assist with the installation of a new barrier (exclusion) fence as part of the Marna Banggara rewilding project. The risk classifications (low, moderate and high risk) made by this map were compared with rates of zinc corrosion (μm/year zinc loss) observed at field sites and correctly predicted the amount of fence damage sustained at five of the eight sites. The mapping approach outlined in this study can be implemented by environmental managers in other areas to inform strategies for enhancing fence longevity.

Hierarchical pedotransfer functions for predicting bulk density in Brazilian soils

Hierarchical pedotransfer functions for predicting bulk density in Brazilian soils

Determinants of Field‐Saturated Soil Hydraulic Conductivity Across Sub‐Saharan Africa: Texture and Beyond

AbstractSoil infiltration is critical for water security and related ecosystem services. This infiltration, the ability of soils to absorb water at their surface, is controlled by the soil hydraulic conductivity. Despite recent efforts in assembling measurements of soil hydraulic conductivity, global databases and derived pedotransfer functions lack coverage in the tropics. Here, we present soil infiltration measurements and other indicators of soil and land health collected systematically in 3,573 plots from 83 100 km2 sites across 19 countries in sub‐Saharan Africa. We use these data to (a) determine field‐saturated hydraulic conductivity (K fs) and (b) explore which variables best predict variation in K fs. Our results show that sand content, soil organic carbon (SOC), and woody cover had a positive relationship with K fs, whereas grazing intensity and soil pH had a negative relationship. Our findings highlight that, despite soil texture being important, structure also plays a critical role. These results indicate considerable potential to improve soil hydrological functioning through management and restoration practices that target soil structure. Enhancing SOC content, limiting animal stocking, promoting trees, shrubs, and other vegetation cover, and preventing soil erosion can increase K fs and improve water security. This data set can contribute to improving Earth system and land surface models for applications in Africa.

Determination of the best model for predicting soil available phosphorus based on soil organic carbon

There are several methods for determining the determination of soil available phosphorous. These methods require many chemicals and different devices, and determining soil available phosphorous in the laboratory is difficult, time-consuming, and costly. Consequently, pedotransfer functions more suitable and economical were used to predict the soil available phosphorous(AP) from soil organic carbon (OC). To predict soil available phosphorous (AP) from soil organic carbon (OC), five models were utilized, including exponential, linear, logarithmic, polynomial, and power models. Soil AP was predicted as a function of soil OC. The soil AP determined by laboratory tests was compared with the predicted soil AP based on the AP-soil OC model using the Bland-Altman method. The 95% limits of agreement for comparison of soil AP were determined with laboratory tests, and the soil AP pedotransfer function was computed at -1.707 and 1.542 mg kg -1. The mean soil AP difference between the two methods was - 0.083 mg kg -1. The polynomial model (AP = 38.173 - 4.462 x OC+ 0.146 x OC2) is given the best fit to predict soil available phosphorus, due to its high R2 (0.868) and low RMSE (0.846).

Long-term vinasse application enhanced the initial dissipation of atrazine and ametryn in a sugarcane field in Tucumán, Argentina.

The production of sugarcane bioethanol generates large volumes of vinasse, an effluent whose final disposal can produce an environmental impact that is of concern. The long-term disposal of vinasse in sugarcane fields could challenge crop management, such as the performance of traditional herbicides, by changing soil properties. This study aimed to evaluate the effect of long-term vinasse application on the field and the dissipation of atrazine and ametryn herbicides in a subtropical sugarcane agroecosystem, and to discuss the potential processes involved in it. Vinasse affected soil properties by increasing pH (12%), electrical conductivity (160%), and soil organic carbon (25%) at 0-10 cm depth of soil. Differences in the herbicide calculated sorption coefficient (Kd) varied according to the pedotransfer function applied and the herbicide type (atrazine or ametryn). During the first seven days after herbicide application, the soil underwent long-term vinasse application and increased atrazine and ametryn dissipation 45% and 33%, respectively, compared with the conventional fertilization scheme (control). The Pesticide Root Zone Model revealed that dissipation was mediated mainly by the degradation process rather than transport or other processes. The long-term application of vinasse in a typical sugarcane field of Tucumán, Argentina decreased the potential groundwater pollution of triazines and, adversely, reduced their bioavailability for weed control. For this, the present study presents original information about how long-term treatment with vinasse may require an adaptation of conventional management practices such as the application of herbicides in Argentina and other sugarcane-producing regions. Integr Environ Assess Manag 2024;20:1075-1086. © 2023 SETAC.

Reuse of Tunisian excavated material into composite soil for rainwater infiltration within urban green infrastructure

Excavated material or soil is one of the most abundant types of waste produced by civil works. The study draws a methodology for assessing through selected standard tests the environmental, geotechnical, and agronomical features of excavated material to highlight the most ecological way to reuse it. In view of making a constructed soil to be implemented within green infrastructure part of new urban landscape, imperative skills of soil or material are referred to as SWOFI: safety, workability, fertility, infiltrability, and guidelines are derived accordingly. As a practical case of the application of the methodology, a non-cohesive sedimentary parent rock with a sandy loam texture excavated at Bou Argoub in Tunisia has been characterised. This excavated material is a negligible source of pollution hazard by heavy metals. It classifies in a category very sensitive to compaction and strongly influenced by the clay content, so difficult to reuse in road applications. It is very poor in organic matter and too much alkaline, so grape marc waste is used to improve its fertility, thus making a composite soil that prefigures a constructed soil. This allows the valorisation of local agriculture waste throughout the designed strategy.Novelty resides in the selection of tests and the association of technics issued from usually separated disciplines and the integrated workflow of the soil at three stages of the value-chain: excavated-homogenised, amended, then repacked at a given bulk density as a composite soil. Immediate compactibility changes as a linear function of repacked bulk density BD; from the literature, this result is assumed true in the composite soil as well as the excavated material. Based on required intervals of permeability for the projected use in green infrastructure requiring rainwater infiltration, a decision-making table is applied with, as input, the most probable value of permeability determined from pedotransfer function as topsoil or as subsoil. Combining pollution hazard assessment, geotechnical improvement, agronomical amendment, an innovative approach leads to pedological and hydrological functionality for constructed soil, a nature-based solution.

Use of Pedotransfer Equations (PTF) to Estimate soil Density in the Hydrographic Basins of The Amambai, Iguatemi and Ivinhema Rivers in the State of Mato Grosso do Sul

Theoretical framework: Bulk density (Bd) is a physical property of soil used as an indicator of soil compaction, influencing root growth and density, water movement and retention in the soil, it is an essential characteristic for assessments of carbon stocks or other nutrients in the soil. Objective: This studyevaluates the accuracy and selects pedotransfer functions (PTFs) for Bd estimates for soils in the Amambai, Iguatemi, and Ivinhema River basins in Mato Grosso do Sul - MS. Method: Estimates of Bd values of the samples were made using eleven PTFs-Bd that were calibrated to estimate Bd for Brazilian soils. The accuracy of PTFs-Bd predictions is evaluated based on the lowers root mean square error (RMSE) and the highers coefficient of determination (R²). Results and conclusion: Among the eleven analyzed PTFs-Bd, the most accurate ones were PTFs- Ds 1, 2, 3, and 4 by Huf dos Reis et al. (2024) and the one by Tomasella and Hodnett (1998). PTF-Bd 3, which uses fine and coarse sand and clay as predictors, showed the highest accuracy for the total dataset (88 samples). PTF-Bd 5 obtained better accuracy in estimating Bd in Latossolos Vermelhos, Argissolos Vermelho Amerelos, and Neossolos Quartzarênicos. Research implications: Identifying the best PTFs for estimating Bd for the study area's soils will facilitate other research as carbon stocks estimation and use of hydrophysical PTFs that use Bd as predictor. Originality/Value: PTFs for estimating Bd for soils in the state of MS have not yet been developed, highlighting the need to evaluate and indicate between existing PTFs the more accurate.

Estimation of Saturated Hydraulic Conductivity using Pedotransfer Function (PTFS) in Gypsiferous Soil

This study was conducted to evaluate the performance of pedotransfer functions (PTFS) in estimating the saturated hydraulic conductivity in gypsum soil. The saturated hydraulic conductivity was measured for 26 unexcited samples using the permeability of the falling head, and then some chemical and physical properties of the study soil were measured. Rosetta and a number of models were used to predict the saturated hydraulic conductivity. Three statistical criteria, namely NSE, RMSE, and R2, were used to evaluate the performance of the models. The results showed that the second model of Rosetta, which depends on sand, silt, clay, and bulk density as input, is the best, as the values of NSE and RMSE were -13.28 and 0.31, respectively, while the other models, Puckett, Smettem, Bristow, and Saxton, were the best in predicting the saturated hydraulic conductivity of Among the models, the RMSE values were 0.52, 0.54, and 0.47, respectively, and the NSE values were 0.92, 0.91, and 0.93, respectively.