Cuatro funciones de pedotransferencia para estimar la retención de humedad en suelos: Evaluación de desempeño e implicaciones en el manejo del recurso hídrico

Simulation of water flow and solute transport in the soil environment requires accurate estimates of the soil water retention characteristics (SWRC). In this study, multiple linear regression (MLR) was used to develop two site-specific pedotransfer functions (PTFs), a point (MLRP) and a parametric (MLRF), using soil properties of 43 soil samples collected from the Jazan region, southwest of Saudi Arabia. The accuracy of the developed PTFs and four existing PTFs to determine the SWRC, predict soil water content (SWC) at − 10, − 33, and − 1500 kPa, and estimate available water content (AWC) was assessed. The MLRP and the Schaap PTFs produced the best estimate of SWC, with smaller root mean square error (RMSE) (0.023–0.053 cm3 cm−3), and larger D-index (0.8–0.9) and Akaike information criterion (AIC) (− 303.7 to − 240.7), respectively. The largest prediction errors in the estimation of SWC were observed at matric potential close to field capacity (FC). For the AWC, the Schaap PTF provided the best prediction (RMSE = 0.014 cm3 cm−3, D-index = 0.93, AIC = − 359.9), followed by the MLRP PTF (RMSE = 0.027 cm3 cm−3, D-index = 0.83, AIC = − 302.1), whereas the Vereecken, Gupta and Larson, and the MLRF PTFs produced less accurate predictions of the AWC. The MLRP PTF proved to be more accurate compared to other tested PTFs in the prediction of SWC at both FC and permanent wilting point (PWP). In contrast, the MLRF PTF produced a relatively large error in the estimation of SWC at FC.

Using basic soil properties could save time and costs in determining field capacity (FC) and permanent wilting point (PWP). The objectives of this study were to investigate the relationship between FC and PWP and basic soil properties, develop two new equations for estimating FC and PWP, and evaluating their performance as compared to some existing pedotransfer functions (PTFs) in predicting FC and PWP. For this purpose, 210 soil samples of UNSODA dataset and 45 soil samples of HYPRES dataset were used for development and validation of the PTF, respectively. Graphical exploration of relations between soil texture component, geometric mean particle-size diameter (dg), bulk density (BD), and organic matter (OM) with FC and/or PWP showed that relations of FC was nonlinearly related to percentage of clay (positive) and dg (negative) and relations of PWP was linearly and nonlinearly related to percentage of clay (positive) and dg (negative), respectively. Based on standardized independent variable weight (W), dg showed the highest influence on FC (W = 0.81), followed by percentage of clay (W = 0.70), OM and BD (W = 0.49). PWP was primarily affected by percentage of clay (W = 0.89) and dg (W = 0.64), whereas BD and OM with Wof 0.30 were less effective. The two new functions suggested and evaluated for predicting FC and PWP had root mean squares error (RMSE) of 0.06 and 0.02 m3 m−3, geometric mean error (GMER) 1.03 and 1.10 m3 m−3 and Akaike's information criterion (AIC) of −262 and −349, respectively. As such, their prediction performance was higher than that of other FC and PWP PTFs found in literature.

Field capacity (FC) and permanent wilting point (PWP) are two critical input parameters required in various biophysical models. There are limited published data on FC and PWP of dryland cropping soils across north-western Victoria. Direct measurements of FC and PWP are time-consuming and expensive. Reliable prediction of FC and PWP from their functional relationships with routinely measured soil properties can help to circumvent these constraints. This study provided measured data on FC using undisturbed samples and PWP as functions of geomorphological unit, soil type, and soil texture class for dryland cropping soils of north-western Victoria. We used a balanced, nested sampling strategy and developed functional relationships of FC and PWP with routinely measured soil properties using residual maximum likelihood based mixed-effects regression modelling. Using the data, we also tested the adequacy of nine published pedotransfer functions (PTFs) in predicting FC and PWP. Significant differences were observed among the three soil types and nine texture classes for most soil properties. FC and PWP were higher for Grey Vertosols (FC 43.7% vol, PWP 29.1% vol) than Hypercalcic Calcarosols (38.4%, 23.5%) and Red Sodosols (20.2%, 9.2%). Of the several functional relationships developed for prediction of FC and PWP, a quadratic single-predictor model based on dg (geometric mean particle size diameter) performed better than other models for both FC and PWP. It was nearly bias-free, with a root mean square error (RMSE) of 3.18% vol and an R2 of 93% for FC, and RMSE 3.47% vol and R2 89% for PWP. Another useful model for FC was a slightly biased, two-predictor quadratic model based on clay and silt, with RMSE 3.14% vol and R2 94%. For PWP, two other possibly useful, though slightly biased, models included a single-predictor quadratic model based on clay (RMSE 3.45% vol, R2 89%) and a three-predictor model based on clay, silt, and sg (geometric standard deviation of particle size diameter) (RMSE 3.27% vol, R2 90%). We observed a strong quadratic relationship of FC with PWP (RMSE 1.61% vol, R2 98%). This suggests the possibility to further improve the prediction of FC indirectly through PWP. These predictive models for FC and PWP, though developed for the dryland cropping soils of north-western Victoria, may be applicable to other regions with similar soil and climatic conditions. Some validation is desirable before these models are confidently applied in a new situation. Of the nine published PTFs, the multiple linear regression and artificial neural network based NTh5 for FC and NTh3 and CAM for PWP performed better on our data for the prediction of FC and PWP. The root mean square deviation of these PTFs, for both FC and PWP, was higher than the RMSE of our models. Our models are therefore likely to perform better under the dryland cropping soils of north-western Victoria than these PTFs. As a safeguard against arriving at optimistic inferences, we suggest that the modelling of functional relationships needs to account for the hierarchical structure of the sampling design using appropriate mixed effects regression models.

Field capacity (FC) and permanent wilting point (PWP) are important physical properties for evaluating the available soil water storage, as well as being used as input variables for related agro-hydrological models. Direct measurements of FC and PWP are time consuming and expensive, and thus, it is necessary to develop related pedotransfer functions (PTFs). In this study, stepwise multiple linear regression (SMLR) and artificial neural network (ANN) methods were used to develop FC and PWP PTFs for the deep layer of the Loess Plateau based on the bulk density (BD),sand, silt, clay, and soil organic carbon (SOC) contents. Soil core drilling was used to obtain undisturbed soil cores from three typical sites on the Loess Plateau, which ranged from the top of the soil profile to the bedrock (0–200 m). The FC and PWP were measured using the centrifugation method at suctions of − 33 and − 1500 kPa, respectively. The results showed that FC and PWP exhibited moderate variation where the coefficients of variation were 11 and 23%, respectively. FC had significant correlations with sand, silt, clay, and SOC (P < 0.01), while there were also significant correlations between all of the variables and PWP. In addition, sand was an important input variable for predicting FC, and clay and BD for predicting PWP. The performance of the SMLR and ANN approaches was similar. In this study, we developed new PTFs for FC and PWP as the first set of PTFs based on data obtained from deep profiles in the Loess Plateau. These PTFs are important for evaluating the soil water conditions in the deep profile in this region.

Characterization of soil water retention, e.g., water content at field capacity (FC) and permanent wilting point (PWP) over a landscape plays a key role in efficient utilization of available scarce water resources in dry land agriculture; however, direct measurement thereof for multiple locations in the field is not always feasible. Therefore, pedotransfer functions (PTFs) were developed to estimate soil water retention at FC and PWP for dryland soils of India. A soil database available for Arid Western India (N=370) was used to develop PTFs. The developed PTFs were tested in two independent datasets from arid regions of India (N=36) and an arid region of USA (N=1789). While testing these PTFs using independent data from India, root mean square error (RMSE) was found to be 2.65 and 1.08 for FC and PWP, respectively, whereas for most of the tested ‘established’ PTFs, the RMSE was >3.41 and >1.15, respectively. Performance of the developed PTFs from the independent dataset from USA was comparable with estimates derived from ‘established’ PTFs. For wide applicability of the developed PTFs, a user-friendly soil moisture calculator was developed. The PTFs developed in this study may be quite useful to farmers for scheduling irrigation water as per soil type.

When using indirect approaches for accurate estimation of soil hydraulic properties, it is important to consider time and cost savings as part of water management. Models based on an adaptive neuro fuzzy inference system (ANFIS) outperform artificial neural networks (ANNs) in terms of forecasting error, computational speed, and estimation. The present study used ANFIS to develop pedotransfer functions (PTFs) to estimate soil hydraulic parameters (van Genuchten water retention curve parameters α, n, and residual water content [θr]), soil water retention at saturation, field capacity (FC), and permanent wilting point (PWP) using basic soil properties such as soil particle size distribution, bulk density (BD), medium porosity (0.2 to 30 μm [7.9 × 10−6 to 0.001 in]), and organic carbon (C). The ANN-based Rosetta model and ANFIS-based PTF were compared for accuracy of prediction of saturated soil water content (θs), FC, and PWP of soil in the flood spreading areas of Iran. The ANFIS-based models were able to estimate soil hydraulic properties with reasonable accuracy. It was concluded that adding medium porosity (0.2 to 30 μm) as an input variable to the ANFIS-based models improved model accuracy for FC, α, and n. Except for the SSCBDθ33 model, prediction of water content at FC and PWP by Rosetta improved significantly over those obtained using the ANFIS-based PTFs.

The dynamics of water availability for plant growth is particularly important for crop productivity simulation. Critical for the prediction of crop growth and development is the accurate simulation of soil moisture variation time. Soil capacity-based models assume that the vertical movement of water in the soil is mostly controlled by the intrinsic soil water retention capacities (WRCs), mainly field capacity (FC) and wilting point (WP). However, FC and WP are difficult to measure directly. Pedotransfer functions (PTFs) have been developed to determine these parameters from basic, more readily available soil attributes such as texture and soil organic carbon content. Functional evaluation, a procedure to assess the appropriateness of a PTF, entails testing the sensitivity of the different PTFs to model&amp;#8217;s target simulation outcomes. This study constitutes an attempt to quantify and understand the impact of different PTFs on crop yield in a soil capacity-based model.Six PTFs were used in the crop model HERMES to test their ability to simulate soil water dynamics and to determine their effect on yield simulation. This study, carried out in Germany, included three sandy soil sites in Brandenburg and a silty soil site in Bavaria. Five lysimeters at a site in Brandenburg provided a complete record for assessing the performance of PTFs. Measured soil texture and organic carbon were used as inputs in HERMES, which by applying the PTFs under study, produced the corresponding estimates of WRPs used for soil water dynamic simulations and yield predictions. Soil water records were statistically compared with model outputs to assess the accuracy of each PTF-based simulation. Differences in yield predictions were measured to estimate the sensitivity of the crop model to the PTFs tested.Not a single PTF performed best in all sites. PTFs by Batjes and Rosetta were the best performers at the three Brandenburg sites. At Duernast, Bavaria, all PTFs resulted in higher errors than at the other sites. At this site, the measured soil water content maxima during the rainy months appeared very variable from year to year, which was unexpected if assumed that the maxima should stay around FC and be fairly constant. In general, HERMES simulations followed the trends in measured soil water dynamics regardless of the PTF applied, whereas differences between PTFs appear on the magnitude of the water maxima during the winter months. This shows that the accuracy of PTFs largely depended on their ability to correctly estimate FC. The highest variability in yield prediction for the different PTFs was observed in the three Brandenburg sites, which also corresponded with higher differences in FC estimation. A closer look at the sandy sites, and simulations with a synthetic soil database showed that differences in yield simulation between PTFs increased proportionally with soil sand percent. This points out at the empirical nature of PTFs and the care that needs to be taken when applied in new situations.

Transposability of pedotransfer functions for estimating water retention of Algerian soils

Poor data availability on soil hydraulic properties in tropical regions hampers many studies, including crop and environmental modeling. The high cost and effort of measurement and the increasing demand for such data have driven researchers to search for alternative approaches. Pedotransfer functions (PTFs) are predictive functions used to estimate soil properties by easily measurable soil parameters. PTFs are popular in temperate regions, but few attempts have been made to develop PTFs in tropical regions. Regression approaches are widely used to develop PTFs worldwide, and recently a few attempts were made using machine learning methods. PTFs for tropical Sri Lankan soils have already been developed using classical multiple linear regression approaches. However, no attempts were made to use machine learning approaches. This study aimed to determine the applicability of machine learning algorithms in developing PTFs for tropical Sri Lankan soils. We tested three machine learning algorithms (artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF)) with three different input combination (sand, silt, and clay (SSC) percentages; SSC and bulk density (BD); SSC, BD, and organic carbon (OC)) to estimate volumetric water content (VWC) at −10 kPa, −33 kPa (representing field capacity (FC); however, most studies in Sri Lanka use −33 kPa as the FC) and −1500 kPa (representing the permanent wilting point (PWP)) of Sri Lankan soils. This analysis used the open-source data mining software in the Waikato Environment for Knowledge Analysis. Using a wrapper approach and best-first search method, we selected the most appropriate inputs to develop PTFs using different machine learning algorithms and input levels. We developed PTFs to estimate FC and PWP and compared them with the previously reported PTFs for tropical Sri Lankan soils. We found that RF was the best algorithm to develop PTFs for tropical Sri Lankan soils. We tried to further the development of PTFs by adding volumetric water content at −10 kPa as an input variable because it is quite an easily measurable parameter compared to the other targeted VWCs. With the addition of VWC at −10 kPa, all machine learning algorithms boosted the performance. However, RF was the best. We studied the functionality of finetuned PTFs and found that they can estimate the available water content of Sri Lankan soils as well as measurements-based calculations. We identified RF as a robust alternative to linear regression methods in developing PTFs to estimate field capacity and the permanent wilting point of tropical Sri Lankan soils. With those findings, we recommended that PTFs be developed using the RF algorithm in the related software to make up for the data gaps present in tropical regions.

Appropriate land use and management requires a good knowledge about the main hydraulic properties of the soil, in particular soil moisture at field capacity (FC), permanent wilting point (PWP), and water holding capacity (HC). In the absence of direct measurements, these characteristics could be estimated from data on texture and organic matter content, using pedotransfer functions (PTFs). In this study, FC, PWP, and HC of predominant soil types in Cap Bon (Vertisols, Cambisols, and Calcisols), are estimated using Saxton and Rawls (Soil Sci Soc Am J 70:1569–1578, 2006) PTFs and complete sets of soil analysis data. Results show that when all soil samples (61) are taken together, obtained estimations were well correlated with measured values for FC (R2 = 0.72) and PWP (R2 = 0.72). Similar correlations were observed for Cambisols and Calcisols taken separately (R2 = 0.65–0.70.); but there was an overestimation of FC and PWP for Vertisols (percentage of clay > 50%). However, relatively weak relationships were observed between estimated and measured values for HC in all cases (R2 = 0.15). PTFs seem appropriate to be used in combination with remote sensing methods for generation of soil FC and PWP maps needed in irrigation and agriculture efficient management.

As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM–Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.

Accurate estimation of soil hydraulic properties, specifically field capacity (FC) and wilting point (WP), collectively known as Water Holding Capacity (WHC), is crucial for effective water resource management in agriculture and the environment. Traditionally, WHC is obtained through soil sampling and laboratory analysis. Pedo Transfer Functions (PTFs) have been developed to estimate WHC from soil composition data, simplifying the process but still relying on accurate soil measurements.In response, we propose a novel algorithm for dynamic FC and WP estimation based on continuous soil moisture time series from remote sensing. This study includes a preliminary accuracy assessment of the downscaled 100-m soil-moisture time-series obtained from a combination of SMAP and Landsat data against in-situ stations from the International Soil Moisture Network (ISMN) which also include WP and FC measurements. Leveraging these long time series of soil moisture data enables a more nuanced and adaptive characterization of soil hydraulic properties over time. This approach recognizes the influence of factors such as precipitation, evapotranspiration, and land management practices on soil moisture variability.Furthermore, we perform a comparative analysis with SoilHydroGrids&amp;#8217; WP and FC as a benchmark, to underscore the advancements, enhancements and potential limitations of our approach. Our results demonstrate a noteworthy enhancement in the estimation of Field Capacity, reducing the Root Mean Square Error (RMSE) from 0.15m&amp;#179;/m&amp;#179; to 0.09m&amp;#179;/m&amp;#179;. Moreover, our algorithm exhibits slightly superior predictions for the wilting point when compared against laboratory measurements. Generally, our approach is capable of identifying a larger range of WP and FC values, which is also seen in the in-situ data.

The knowledge of soil water retention vs. soil water matric potential is applied to study irrigation and drainage scheduling, soil water storage capacity (plant available water), solute movement, plant growth and water stress. To measure field capacity and wilting point is expensive, laborious and is time consuming, so, frequently, matemathic models, called pedo-transfer functions (PTFs) are utilized to estimate field capacity and wilting point through physical-chemical soil characteristics. Six PTFs have been evaluated (Gupta and Larson, 1979; Rawls et al., 1982; De Jong et al., 1983; Rawls and Brakensiek, 1985; Saxton et al., 1986; Vereecken et al., 1989) by comparing measured soil moisture values with estimated ones at soil water matric potential of -33 and -1500 kPa. Soil samples were collected (361) from 185 pedons of Apulian Region (Southern Italy). Accuracy of the soil moisture predictions is quantified with Root Mean Square Deviation (RMSD) between estimated and measured water retention values. In Apulia Region the tested PTFs give different results on soils grouped on the basis of textural composition and organic matter (O.M.) content both at the Field Capacity (FC) and Wilting Point (WP). At the FC, Gupta and Larson model has given the best performance in Clayey (C), Sandy clay loam (SaCL), Sandy loam (SaL) and Silty (Si) soil, in loamy and tendency silty soils with O.M. content less than 1.9% and in tendency sandy soils with O.M. content less than 1.5% and greater than 2%; the Rawls model in Silty clay (SiC) and Silty loam (SiL) soils, in tendency clayey soils with O.M. less than 2.3% and in loamy and tendency silty soils with O.M. greater than 1.9%; the Rawls and Brakensiek model in tendency sandy soils with O.M. content between 1.5 and 2%; the Saxton model in Silty clay loam (SiCL), Loamy sand (LSa) soils and in tendency clayey soils with O.M. content greater than 2.3% and the Vereecken model in Sandy clay (SaC), Loamy (L), Clay loam (CL) and Sandy (Sa) soils. At the WP, the Gupta and Larson model has resulted the best in SiL, Si soils and, in general, in loamy and tendency silty and in tendency sandy soils with O.M. content greater than 1.9% and 2%, respectively; the Rawls model in Loamy soils and in loamy and tendency silty soils with O.M. between 1.0 and 1.9%; the De Jong model in C soils; the Rawls and Brakensiek model in SiC, SaC, CL, SiCL, SaCL soils and generally in tendency clayey soils with whatever O.M. content and in tendency sandy soils with O.M. content between 0.8 and 2%; the Saxton model in loamy and tendency silty soils with O.M. content less than 1% and in tendency sandy soils with O.M. less than 0.8%; the Vereecken model in SaL, Sa and LSa soils.

This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC), -33 kPa, and permanent wilting point (PWP), -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN), Artificial Neural Network (ANN) and pedotransfer functions (PTF) were used to predict the soil water at each sampling location. Mean square deviation (MSD) and its components, index of agreement (d), root mean square difference (RMSD) and normalized RMSD (RMSDr) were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

Pedotransfer functions development for modeling FC and PWP using Vis-NIR spectra combined with PLSR and regression models