Pedotransfer Research Articles

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.

Point Pedotransfer Functions (PPTF) for Estimating Soil Water Retention Curve by Multiple Linear Regression Methods

The aim of this study was to derive and find the best Point Pedotransfer Functions (PPTF) for estimating SWRC for Iraqi soils using the multiple regression methods. Soil samples were collected from 30 different sites of Iraqi soil at two depths: 0-0.3 m and 0.3-0.6 m, in order to obtain the required database for the derivation of PTF, which includes the soil particle size distribution, carbonate minerals, bulk density, particles density, organic matter, saturated hydraulic conductivity, capillary height and the boundaries of available water. Measured characteristics were considered as explanatory variables (EV). Volumetric water content was measured at suctions 0, -5, -10, -33, -500, -100, -500, -1000, and -1500 kPa and considered as response variables (RV). PPTF was derived using SAS statistical software based on multiple linear regression. Three methods for fitting the PPTF to EV were Maximum R2 Improvement (MR), Backward Elimination (BW) and Forward selection (FW). The prediction accuracy was evaluated through Mean Bias Error (MBE), Mean Absolute Error (MAE), root mean squared error (RMSE) and Coefficient Of Determination (R2). The results showed that all EVs were included in the PPFT derivation by MR method, while in the BW and FW methods the significant level of entery (SLENTRY) of EV was 0.1. It was also found that variables w1, capillary height, available water, bulk density, and porosity were included in the derivation of most of the models using the BW and FW method. Besides no statistically significant differences were found among the values of MBE, MAE, RMSE, and R2 of the PPTF although the number of selected EV are different.

Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning

Globally, the strategic use of nitrogen (N) is important in optimizing economic returns and reducing soil nitrogen losses to the environment. Incorporating reliable estimates of nitrogen (N) mineralized over a growing season (GSN) into N-fertilizer rate prescriptions is critical, but may often lack a direct measurement. For this purpose, Pedotransfer functions (PTFs) of total nitrogen (TN) – representing the stable pool from which N is mineralized and biological nitrogen availability (BNA) – representing the labile pool of N mineralization were used to estimate GSN. GSN was calculated based on TN and BNA results from a soil health database (SHD), which also includes a suite of related soil health parameters (n = 2222). Using a process of recursive feature elimination (RFE) and cost-benefit feature elimination (CBFE), the best predictors of TN, BNA, and GSN were identified using a suite of machine learners (MLs) and regression analysis. For TN, RFE revealed that BNA, active carbon (AC), sand (Sa), and soil organic matter (OM) were the best predictors yielding a Lin’s concordance correlation coefficient (CCC) of 0.80 and a reduction in theoretical cost of 41 % compared to the control. CBFE resulted in AC, soil respiration (SR), clay, Sa, and OM as the most cost-effective predictors of TN with a CCC of 0.79 and a theoretical cost savings 49 % below the cost of using all appropriate soil health parameters in the SHD. With respect to BNA, the best predictors from RFE were aggregate stability (AS), AC, SR, and TN with a CCC of 0.78 and a theoretical cost reduction of 23 %. CBFE retained AC, SR, S, TN, OM and pH as predictors of BNA with a CCC of 0.78 and reduction of 29 % in theoretical cost. Finally, GSN results from RFE identified AS, AC, SR, OM and pH as the best predictors with a 0.82 CCC and 17 % reduction in theoretical cost. CBFE, on the other hand, identified AC, SR, sand, OM, and pH as the most cost-efficient predictors while maintaining a CCC of 0.82 and theoretical cost reduction of 29 %. Of the MLs used for pattern recognition (i.e., cubist, random forest, support vector machine, and stochastic gradient boosting), cubist model outperformed the others for the majority of iterations of the RFE and CBFE processes. The cost-effective framework, and the N-related PTFs developed in this study will greatly enhance our ability to predict of soil N-pool dynamics and the ability to incorporate GSN estimates into N-fertilizer recommendations for producers worldwide. Improvements in predictive strength could be achieved by incorporating climate and soil management practices into PTF development. Another area for improvement and future study would include addition of spatial and landscape variability related to N-measures via digital soil mapping applications.

Development of pedotransfer functions for predicting hydraulic parameters of van Genuchten model by incorporating environmental variables on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) region lacks large database of soil hydraulic parameters (SHPs) due to difficulty in soil sampling, and time- and labor-consuming measurement, which prevents accurate modeling of hydrological and biogeochemical processes. Pedotransfer functions (PTFs) using easily measurable soil properties may provide an alternative way to indirectly estimate SHPs. In this study, soil properties (bulk density (BD), clay, silt, sand, soil organic carbon, porosity, fitting parameter related to inverse of air entry pressure (α) and soil pore distribution (n), residual and saturated soil water content (θr and θs), and environmental parameters (altitude, slope gradient, annual average precipitation, air temperature, and evaporation) were determined from a north-south transect (ca. 500 km) in Qinghai Province and an east-west transect (ca. 1800 km) in Tibet Autonomous Region for establishing PTFs for SHPs on the QTP. The coefficient of variation of α was the largest, followed by θr, θs, and n, suggesting that α had the greatest spatial variability at the regional scale. There were significant differences in BD among 0–10, 10–20, and 20–30 cm soil layers (p < 0.05), whereas there were no significant differences in α, n, θr, and θs among these soil layers (p > 0.05). The five most important variables from the random forest analysis were selected to establish the PTFs of the SHPs and BD for the 0–30 cm soil layer on the QTP. The established PTFs for predicting SHPs and BD using multiple linear regression analysis were jointly affected by relief, climate, and soil properties. The accuracy of our newly established PTFs with the consideration of gravel content (< 10% or 10–30%) and mattic epipedon (existence or non-existence) was higher than that of the existing PTFs. We developed the first PTFs of SHPs on the QTP, providing a foundation to construct SHPs prediction models for other alpine regions in the world.

Estimating pre-compression stress in agricultural Soils: Integrating spectral indices and soil properties through machine learning

Soil compaction resulting from heavy machinery use in agricultural and forestry operations poses a significant threat to sustainable agriculture. Advancements in remote sensing technology have enabled the acquisition of vegetation, water, and soil spectral indices, which offer valuable insights into soil properties. This study focuses on estimating the pre-compression stress (Pc) by developing pedotransfer functions (PTFs) using Sentinel-2 satellite-derived spectral indices and soil properties (clay, CaCo3, and bulk density) as inputs. Two machine learning methods, Random Forest (RF) and Boosted Regression Tree (BRT), are employed for the estimation. A total of 140 surface soil samples were collected randomly from agricultural areas in Qazvin province, Iran. The results indicate that the BRT method outperforms the RF method in terms of accuracy. The estimation of Pc achieved better results when the Redness Index (RI) was used as the soil spectral index and the Surface Water Capacity Index (SWCI) was employed as the water spectral index, along with the soil properties as inputs for PTF3 and PTF11. In the training and testing steps, the root mean square error (RMSE) decreased from 0.100 and 0.114 (kPa) in PTF1 to 0.071 and 0.098 in PTF3, and 0.072 and 0.097 in PTF11, respectively. These outcomes demonstrate the practical applicability of estimating Pc through the integration of soil properties and spectral indices. The findings highlight the potential of remote sensing technologies, such as spectral indices, as effective and cost-efficient tools for studying soil compaction. The proposed methodology contributes to our understanding of soil compaction processes and provides valuable insights for developing sustainable land management strategies. This study has implications for the agricultural sector and offers practical solutions to mitigate soil compaction and its detrimental effects on agricultural productivity.

Sequestering carbon in the subsoil benefits crop transpiration at the onset of drought

Abstract. Increasing soil organic carbon is promoted as a negative emission technology for the agricultural sector with a potential co-benefit for climate adaptation due to increased soil water retention. Field-scale hydrological models are powerful tools for evaluating how the agricultural systems would respond to the changing climate in upcoming years and decades, for predicting impacts, and for looking for measures that would help decrease drought-driven crop stress under current and future climatic conditions. We quantified how different levels of soil organic carbon (SOC) additions at varied soil depths are expected to influence drought-induced transpiration reduction (Treddry) in maize cultivated in Switzerland. Parameterization of the model based on a pedotransfer function (PTF) was validated against soil moisture data from a long-term lysimeter experiment with a typical Swiss soil, and the model was subsequently applied under climate forcing between 1981 until 2099, representative of three distinct climatic sites of Switzerland. We used the same PTF to indirectly assess the effects of SOC additions at different depths on soil hydraulic properties. We found a threshold in both the added amount of SOC (2 % added) and the depth of sequestering that SOC (top 65 cm), beyond which any additional benefit appears to be substantially reduced. However, adding at least 2 % SOC down to at least 65 cm depth can reduce Treddry in maize, i.e. increase transpiration annually but mostly at the onset of summer drought, by almost 40 mm. We argue that SOC increases in subsoils can play a supporting role in mitigating drought impacts in rain-fed cropping in Switzerland.

Predicting the Base Neutralization Capacity of Soils Based on Texture, Organic Carbon and Initial pH: An Opportunity to Adjust Common Liming Recommendation Approaches to Specific Management and Climate Conditions

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.

On evaluating the hypothesis of shape similarity between soil particle-size distribution and water retention function

Two pedotransfer functions (PTFs) are available in the literature enabling the soil water retention function (WRF) to be estimated from knowledge of the soil particle-size distribution (PSD), oven-dry soil bulk density (b), and saturated soil water content (s): i) the Arya and Heitman model (PTF-AH) and ii) the Mohammadi and Vanclooster model (PTF-MV). These physico-empirical PTFs rely on the hypothesis of shape similarity between PSD and WRF, and do not require the calibration of the input parameters. In the first stage, twenty-seven PSD models were evaluated using 4,128 soil samples collected in Campania (southern Italy). These models were ranked according to the root mean square residuals (RMSR), corrected Akaike Information Criterion (AICc), and adjusted coefficient of determination (R2adj). In the second stage, three subsets of PSD and WRF data (DS-1, DS-2, and DS-3), comprising 282 soil samples, were used to evaluate the two PTFs using the best three PSD models selected in the first stage. The hypothesis of shape similarity was assumed as acceptable only when the RMSR value was lower than the field standard deviation of the WRFs (*), which is viewed as a tolerance threshold and computed from the physically-based scaling approach proposed by Kosugi and Hopmans (1998). In the first study area (DS-1), characterized by a fairly uniform, loamy textured volcanic soil, the PTF-AH outperformed the PTF-MV and both PTFs provided reasonable performance within the acceptance threshold (i.e., RMSR &lt; *). In the other two heterogeneous field sites (DS-2 and DS-3, characterized by soil textural classes that span from clay and clay-loam to loam and even sandy-loam soils), the PTF-MV (with 3% to 6% RMSR surpassing *) outperformed the PTF-AH (with 8% to 30% RMSR surpassing *) and the majority of RMSR values were larger than those obtained in the original studies. The mean relative error (MRE) revealed that the PTF-MV systematically underestimates the measured WRFs, whereas the PTF-AH provided negative MRE values indicating an overall overestimation. The outcomes of our study provide a critical evaluation when using calibration-free PTFs to predict WRFs over large areas.

Construction and evaluation of pedotransfer functions for saturated hydraulic conductivity in the granite red soil regions of southern China

Study regionSix typical regions of granite red soil in southern China. Study focusRegional large-scale Ks analysis of granite red soil in South China is still limited to a large number of repetitive and expensive workloads. It is necessary to construct Ks PTFs suitable for the granite red soil region of South China. In this study, six different regions granite red soil in South China were used to validate the prediction of classical PTFs for Ks, the best combination of predictors was screened and PTFs developed based on stepwise regression methods and machine learning algorithms. New hydrologic insights for the region(1) The coefficient of variation for Ks is 69.87% in the granite red soil region of South China, with a large spatial variability. (2) The six typical PTFs of Ks were poor predictors of Ks for soils in southern China, R2 < 0.4. (3) "Clay+Silt+Sand+BD+SOM+NCP" is the best combination of predictors of soil Ks in the granite red soil region of South China, which can be used in different machine learning algorithms. (4) The constructed MLR model “Ks= 1.501 + 0.043 ×NCP-0.995 ×BD+ 0.034 ×SOM-0.013 ×Clay” with KNN, ANN and BRT machine algorithm models have a prediction accuracy R2 greater than 0.8. This study provides different prediction models for soil Ks in the granitic regions of southern China and lays a theoretical basis for hydrological research in the these regions.

Variations, controls and predictions of soil saturated hydraulic conductivity under different land use types in the alpine region of Tibet, China

Understanding the variations and controls of soil saturated hydraulic conductivity (Ks) under different land use types in the alpine region of Tibet is fundamental for predicting Ks in high-altitude regions where sample points are difficult to obtain and is important for establishing the regional soil hydrological model. In this study, soils samples under farmland, forestland, and grassland were collected from an east-west transect in southeastern Tibet, China. Basic soil properties and Ks were measured to analyze the influencing factors of Ks under different land use types and to establish suitable pedotransfer functions (PTFs) for predicting Ks in the alpine region. The average value of Ks followed the decreasing order of grassland (2.33 cm h−1) > farmland (2.12 cm h−1) > forestland (1.59 cm h−1). Ks of grassland (0.25–9.19 cm h−1) decreased gradually with increasing soil depth. Ks of farmland (0.11–8.82 cm h−1) increased initially and then decreased with increasing soil depth, whereas Ks of forestland (0.12–4.46 cm h−1) exhibited the opposite trend. There were significant differences in the main controlling factors of Ks under different land use types. In farmland, soil bulk density (BD), non-capillary porosity (Pn), clay and gravel content (GC) were the main drivers of Ks (P < 0.05). The dominant influencing factors of Ks in forestland were BD and total porosity (Pt) (P < 0.05), while Pn and GC were the main factors controlling Ks in grassland (P < 0.05). Compared with the multiple linear stepwise regression models and published PTFs, the BP neural network transfer model had better prediction accuracy for Ks in the alpine region. Our results not only provide hydraulic parameters for regional hydrological modeling and but also lay a foundation to construct Ks prediction models for other alpine regions in the world.

Soil water retention and hydraulic conductivity measured in a wide saturation range

Abstract. Soil hydraulic properties (SHPs), particularly soil water retention capacity and hydraulic conductivity of unsaturated soils, are among the key properties that determine the hydrological functioning of terrestrial systems. Some large collections of SHPs, such as the UNSODA and HYPRES databases, have already existed for more than 2 decades. They have provided an essential basis for many studies related to the critical zone. Today, sample-based SHPs can be determined in a wider saturation range and with higher resolution by combining some recently developed laboratory methods. We provide 572 high-quality SHP data sets from undisturbed, mostly central European samples covering a wide range of soil texture, bulk density and organic carbon content. A consistent and rigorous quality filtering ensures that only trustworthy data sets are included. The data collection contains (i) SHP data, which consist of soil water retention and hydraulic conductivity data, determined by the evaporation method and supplemented by retention data obtained by the dewpoint method and saturated conductivity measurements; (ii) basic soil data, which consist of particle size distribution determined by sedimentation analysis and wet sieving, bulk density and organic carbon content; and (iii) metadata, which include the coordinates of the sampling locations. In addition, for each data set, we provide soil hydraulic parameters for the widely used van Genuchten–Mualem model and for the more advanced Peters–Durner–Iden model. The data were originally collected to develop and test SHP models and associated pedotransfer functions. However, we expect that they will be very valuable for various other purposes such as simulation studies or correlation analyses of different soil properties to study their causal relationships. The data are available at https://doi.org/10.5880/fidgeo.2023.012 (Hohenbrink et al., 2023).

A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression

Pedotransfer functions (PTFs) have been developed for many regions to estimate values missing from soil profile databases. However, globally there are many areas without existing PTFs, and it is not advisable to use PTFs outside their domain of development due to poor performance. Further, developed PTFs often lack accompanying uncertainty estimations. To address these issues, a framework is proposed where existing equation-based PTFs are recalibrated using a nonlinear least squares (NLS) approach and validated on two regions of Canada; this process is coupled with the use of quantile regression (QR) to generate uncertainty estimates. Many PTFs have been developed to predict soil bulk density, so this variable is used as a case study to evaluate the outcome of recalibration. New coefficients are generated for existing soil bulk density PTFs, and the performance of these PTFs is validated using three case study datasets, one from the Ottawa region of Ontario and two from the province of British Columbia, Canada. The improvement of the performance of the recalibrated PTFs is evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC). Uncertainty estimates produced using QR are communicated through the mean prediction interval (MPI) and prediction interval coverage probability (PICP) graphs. This framework produces dataset-specific PTFs with improved accuracy and minimized uncertainty, and the method can be applied to other regional datasets to improve the estimations of existing PTF model forms. The methods are most successful with large datasets and PTFs with fewer variables and minimal transformations; further, PTFs with organic carbon (OC) as one of or the sole input variable resulted in the highest accuracy.

The Applicability of Various Pedotransfer Functions to the Description of Soils

The Applicability of Various Pedotransfer Functions to the Description of Soils

Development and functional evaluation of pedotransfer functions for estimating soil hydraulic properties in Portuguese soils: Implications for soil water dynamics

Accurate knowledge of soil hydraulic properties is essential for effectively simulating soil water dynamics in field plots. However, determining these properties directly can be challenging, especially when a large amount of data is required. Therefore, the primary objective of this study was to develop four hierarchical pedotransfer functions (PTFs) to estimate the soil hydraulic parameters of the Mualem-van Genuchten model. The PTFs that were developed ranged from utilizing a limited set of variables to those comprising a more comprehensive set of variables. These included particle size distribution, soil bulk density, soil depth, and soil water contents at matric potentials of −33 and − 1580 kPa. The functional performance of these PTFs was also assessed in simulations of the soil water balance in ten agricultural fields over two growing seasons using the HYDRUS-1D model. The simpler PTFs exhibited moderate accuracy in estimating soil water contents at different matric potentials, while the more complex PTFs with a greater number of variables yielded increasing accuracy. However, estimation errors were more noticeable in the drier range of the soil water retention curve. The inclusion of bulk density as a predictor only affected the wetter range of the same curve. Estimation of log Ks improved from the simpler to the more complex PTFs, but the statistical indicators were consistently modest. The functional performance of the PTFs showed increased errors in simulating soil water contents in the ten study sites compared to reference simulations. The more complex PTFs with the greatest number of variables provided the best predictions in most cases. The PTFs primarily affected flow conditions in the soil profile, resulting in increased soil water storage and impacting predictions of deep percolation the most. Water availability in the soil profile then influenced actual transpiration, alleviating stress in some cases but causing waterlogging in one situation. The study emphasizes the cautious use of the PTFs and the need for additional information on clay-rich soils and hydraulic conductivity data to enhance their performance.

Enhanced Soil Moisture Retrieval through Integrating Satellite Data with Pedotransfer Functions in a Complex Landscape of Ethiopia

Remotely sensed soil moisture products potentially provide a valuable resource for monitoring agricultural drought and assessing food security. The agriculture dominated countries of Eastern Africa experience high inter-annual variability of rainfall, but the monitoring and assessment of the predominantly rainfed agriculture systems is hindered by an absence of ground-based observations. This study evaluates the accuracy of three soil moisture products: ASCAT SWI 12.5 km, SMAP soil moisture data 9 km (SPL3SMP_E), and enhanced surface soil moisture map derived through integrating ASCAT SWI and Pedotransfer Functions (PTFs) (ASCAT_PTF_SM), in Ethiopia, through comparison with in situ-observed soil moisture datasets. Additionally, a new water retention PTF, developed for Ethiopian soils, is integrated with a high-resolution soil property dataset to enhance the spatial resolution of the soil moisture product. The results show that the new integrated dataset performs better in terms of unbiased root mean square error (ubRMSE = 0.0398 m3/m3) and bias (0.0222 m3/m3) in comparison with ASCAT SWI 12.5 km (ubRMSE = 0.0.0771 m3/m3, bias = 0.1065 m3/m3). SMAP is found to have limitations during the wet season, overestimating soil moisture. The finer spatial resolution of the data allows for a better depiction of heterogeneity of soil moisture across the landscape and can be used to identify water-related issues and improve hydrological models for agricultural water management.

The appraisal of pedotransfer functions with legacy data; an example from southern Africa

Predictions of soil hydraulic properties by pedotransfer functions (PTFs) must be treated with caution when they are used in an application domain which differs from the domain of their original development and calibration. However, in some settings, scientists may have little alternative but to use PTFs calibrated elsewhere. In this paper we consider how legacy data can be used to evaluate PTFs in new regions, paying particular attention to the challenges that arise when, as is often the case, the legacy data are not obtained by independent random sampling, and may be clustered at multiple scales. We undertook this work in southern Africa (Zimbabwe, Zambia and Malawi) where PTFs have been little-used, despite the scarcity of direct measurements of the soil properties of interest. We evaluated the extent to which existing PTFs provide a useful tool for the prediction of soil moisture content at field-capacity (−33 kPa) and permanent wilting-point (−1500 kPa) at different spatial scales. Soil legacy data for Zambia, Zimbabwe and Malawi were collated from various sources and PTFs from temperate and tropical domains were evaluated. We examined error variance components of predictions at within-profile, within-site and between-site scales; and estimated their mean errors. In general the better-performing PTFs (with respect to bias and the size of the error variance components) were ones calibrated with data from a tropical domain. This was most apparent at −1500 kPa. However, not all PTFs calibrated with data on tropical soils performed well, and predictions from some PTFs calibrated over a temperate domain were better at −33 kPa. The observations were spatially clustered, with data from different depth intervals in the same profile, from profiles in the same experimental site or farm, and from clusters across the region. This enabled us to show, with an appropriate mixed model analysis, that PTFs which effectively capture regional-scale variation may be less useful for predicting variation within a profile. We propose that such studies, based on legacy data, and with a suitable linear mixed model, should be used to screen PTFs of any provenance before their wider application.

CLSoilMaps: A national soil gridded database of physical and hydraulic soil properties for Chile

Spatially explicit soil information is crucial for comprehending and managing many of Earth´s processes related to carbon, water, and other biogeochemical cycles. We introduced a gridded database of soil physical properties and hydraulic parameters at 100 meters spatial resolution. It covers the continental area of Chile and binational basins shared with Argentina for six standardized depths following the specifications of the GlobalSoilMap project. We generated soil maps based on digital soil mapping techniques based on more than 4000 observations, including unpublished data from remote areas. These maps were used as input for the pedotransfer function Rosetta V3 to obtain predictions of soil hydraulic properties, such as field capacity, permanent wilting point, total available water capacity, and other parameters of the water retention curve. The trained models outperformed several other DSM studies applied at the national and regional scale for soil physical properties (nRMSE ranging from 6.93% to 15.7%) and delivered acceptable predictions (nRMSE ranging from 10.4% to 15.6%) for soil hydraulic properties, making them suitable for countless environmental studies.