scholarly journals How does PTF Interpret Soil Heterogeneity? A Stochastic Approach Applied to a Case Study on Maize in Northern Italy

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 275 ◽  
Author(s):  
Angelo Basile ◽  
Antonello Bonfante ◽  
Antonio Coppola ◽  
Roberto De Mascellis ◽  
Salvatore Falanga Bolognesi ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Spatial Variability ◽  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Water Retention Curve ◽  
Real Field ◽  
Retention Curve

Soil water balance on a local scale is generally achieved by applying the classical nonlinear Richards equation that requires hydraulic properties, namely, water retention and hydraulic conductivity functions, to be known. Its application in agricultural systems on field or larger scales involves three major problems being solved, related to (i) the assessment of spatial variability of soil hydraulic properties, (ii) accounting for this spatial variability in modelling large-scale soil water flow, and (iii) measuring the effects of such variability on real field variables (e.g., soil water storage, biomass, etc.). To deal with the first issue, soil hydraulic characterization is frequently performed by using the so-called pedotransfer functions (PTFs), whose effectiveness in providing the actual information on spatial variability has been questioned. With regard to the second problem, the variability of hydraulic properties at the field scale has often been dealt with using a relatively simple approach of considering soils in the field as an ensemble of parallel and statistically independent tubes, assuming only vertical flow. This approach in dealing with spatial variability has been popular in the framework of a Monte Carlo technique. As for the last issue, remote sensing seems to be the only viable solution to verify the pattern of variability, going by several modelling outputs which have considered the soil spatial variability. Based on these premises, the goals of this work concerning the issues discussed above are the following: (1) analyzing the sensitivity of a Richards-based model to the measured variability of θ(h) and k(θ) parameters; (2) establishing the predictive capability of PTF in terms of a simple comparison with measured data; and (3) establishing the effectiveness of use of PTF by employing as data quality control an independent and spatially distributed estimation of the Above Ground Biomass (AGB). The study area of approximately 2000 hectares mainly devoted to maize forage cultivation is located in the Po plain (Lodi), in northern Italy. Sample sites throughout the study area were identified for hydropedological analysis (texture, bulk density, organic matter content, and other chemical properties on all the samples, and water retention curve and saturated hydraulic conductivity on a sub-set). Several pedotransfer functions were tested; the PTF‒Vereckeen proved to be the best one to derive hydraulic properties of the entire soil database. The Monte Carlo approach was used to analyze model sensitivity to two measured input parameters: the slope of water retention curve (n) and the saturated hydraulic conductivity (k0). The analysis showed sensitivity of the simulated process to the parameter n being significantly higher than to k0, although the former was much less variable. The PTFs showed a smoothing effect of the output variability, even though they were previously validated on a set of measured data. Interesting positive and significant correlations were found between the n parameter, from measured water retention curves, and the NDVI (Normalized Difference Vegetation Index), when using multi-temporal (2004–2018) high resolution remotely sensed data on maize cultivation. No correlation was detected when the n parameter derived from PTF was used. These results from our case study mainly suggest that: (i) despite the good performance of PTFs calculated via error indexes, their use in the simulation of hydrological processes should be carefully evaluated for real field-scale applications; and (ii) the NDVI index may be used successfully as a proxy to evaluate PTF reliability in the field.

scholarly journals Evaluation of Water Retention Curves by Regression and Machine Learning Methods

2021 ◽  
Vol 1203 (3) ◽  
pp. 032088
Author(s):  
Milan Cisty ◽  
Barbora Povazanova
Keyword(s):  
Bulk Density ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Water Retention Curve ◽  
Support Vector ◽  
Data Set ◽  
Retention Curve ◽  
Vector Machines ◽  
The One

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

Testing an infiltrometer methodology to investigate water impact effects on both soil sealing and hydraulic properties of a loam soil under conventional tillage and no-tillage

2020 ◽  
Author(s):  
Mirko Castellini ◽  
Simone Di Prima ◽  
Anna Maria Stellacci ◽  
Massimo Iovino ◽  
Vincenzo Bagarello
Keyword(s):  
Hydraulic Conductivity ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Soil Management ◽  
Water Retention Curve ◽  
Soil Hydraulic Properties ◽  
Retention Curve ◽  
Soil Sealing ◽  
Loam Soil ◽  
Soil Hydraulic

<p>Testing new experimental procedures to assess the effects of the drops impact on the soil sealing formation is a main topic in soil hydrology.</p><p>In this field investigation, the methodological approach proposed first by Bagarello et al. (2014) was extended to account for a greater soil infiltration surface (i.e., about 3.5 times higher), a higher range and number of heights of water pouring and to evaluate the different impact on soil management. For this purpose, the effects of three water pouring heights (low, L=3 cm; medium, M=100 cm; high, H=200 cm) on both no-tilled (NT) and conventionally tilled (CT) loam soil were investigated by Beerkan infiltration runs and using the BEST-procedure of data analysis to estimate the soil hydraulic properties.</p><p>Final infiltration rate decreased when perturbing runs (i.e., M and H) were carried out as compared with the non-perturbing (L) ones (by a factor of 1.5-3.1 under NT and 3.4-4.4 under CT). Similarly, the water retention scale parameter, h<sub>g</sub>, increased (i.e., higher in absolute terms) by a factor 1.6-1.8 under NT and by a factor 1.7 under CT. Saturated hydraulic conductivity, K<sub>s</sub>, changed significantly as a function of the increase of water pouring height; regardless of the soil management, perturbing runs caused a reduction in soil permeability by a factor 5 or 6. Effects on hydraulic functions (i.e., soil water retention curve and hydraulic conductivity function), obtained with the BEST-Steady algorithm, were also highlighted. For instance, differences in water retention curve at fixed soil pressure head values (i.e., field capacity, FC, and permanent wilting point, PWP) due to perturbing and non-perturbing runs, were estimated as higher under NT (3.8%) than CT (3.4%) for FC, and equal to 2.1% or 1.6% for PWP.</p><p>Main results of this investigation confirm that a recently tilled loamy soil, without vegetation cover, can be less resilient as compared to a no-tilled one, and that tested water pouring heights methodology looks promising to mimic effects of high energy rainfall events and to quantify the soil sealing effects under alternative management of the soil.</p><p><strong>Acknowledgments</strong></p><p>The work was supported by the project “STRATEGA, Sperimentazione e TRAsferimento di TEcniche innovative di aGricoltura conservativA”, funded by Regione Puglia–Dipartimento Agricoltura, Sviluppo Rurale ed Ambientale, CUP: B36J14001230007.</p><p><strong> </strong><strong>References</strong></p><p>Bagarello, V., Castellini, M., Di Prima, S., Iovino, M. 2014. Soil hydraulic properties determined by infiltration experiments and different heights of water pouring. Geoderma, 213, 492–501. https://doi.org/10.1016/j.geoderma.2013.08.032</p>

scholarly journals Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1431 ◽  
Author(s):  
Alessandro D’Emilio ◽  
Rosa Aiello ◽  
Simona Consoli ◽  
Daniela Vanella ◽  
Massimo Iovino
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Water Retention Curve ◽  
Retention Data ◽  
Soil Hydraulic Properties ◽  
Retention Curve ◽  
Artificial Neural

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρb), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρb, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike’s information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes.

scholarly journals Effect of Chia Seed Mucilage on the Rhizosphere Hydraulic Characteristics

2021 ◽  
Vol 13 (6) ◽  
pp. 3303
Author(s):  
Faisal Hayat ◽  
Mohanned Abdalla ◽  
Muhammad Usman Munir
Keyword(s):  
Hydraulic Conductivity ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Numerical Models ◽  
Chemical Properties ◽  
Water Retention Curve ◽  
Soil Drying ◽  
Retention Curve ◽  
Unsaturated Hydraulic Conductivity ◽  
Chia Seed

The rhizosphere is one of the major components in the soil–plant–atmosphere continuum which controls the flow of water from the soil into roots. Plant roots release mucilage in the rhizosphere which is capable of altering the physio-chemical properties of this region. Here, we showed how mucilage impacted on rhizosphere hydraulic properties, using simple experiments. An artificial rhizosphere, treated or not with mucilage, was placed in a soil sample and suction was applied to mimic the negative pressure in plant xylem. The measured water contents and matric potential were coupled with numerical models to estimate the water retention curve and hydraulic conductivity. A slower loss of water was observed in the treated scenario which resulted in an increase in water retention. Moreover, a slightly lower hydraulic conductivity was initially observed in the treated scenario (8.44 × 10−4 cm s−1) compared to the controlled one in saturated soil. Over soil drying, a relatively higher unsaturated hydraulic conductivity was observed. In summary, we demonstrated that mucilage altered the rhizosphere hydraulic properties and enhanced the unsaturated hydraulic conductivity. These findings improve our understanding of how plants capture more water, and postulate that mucilage secretion could be an optimal trait for plant survival during soil drying.

scholarly journals Soil physical and hydraulic properties in the Donato stream basin, RS, Brazil. Part 2: Geostatistical simulation

2019 ◽  
Vol 23 (9) ◽  
pp. 675-680
Author(s):  
Tirzah M. Siqueira ◽  
José A. S. Louzada ◽  
Olavo C. Pedrollo ◽  
Nilza M. dos R. Castro ◽  
Marquis H. C. de Oliveira
Keyword(s):  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Geostatistical Simulation ◽  
Sequential Gaussian Simulation ◽  
Water Retention Curve ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve ◽  
Gaussian Simulation

ABSTRACT Geostatistical simulation has been the most promising and used technique for the analysis of uncertainties of soil physical and hydraulic properties, with high spatial heterogeneity. This study carried out a stochastic analysis of saturated hydraulic conductivity (Ksat) and soil water retention curve parameters in the Donato stream basin, located in the municipality of Pejuçara, in the state of Rio Grande do Sul, Brazil, with geographic coordinates between 28º 25’ 34” S and 53º 40’ 30” W, and 28º 24’ 50” S and 53º 41’ 30” W, 590 m of altitude. Soil samples were collected during the period from August to November of 2012. Sequential Gaussian simulation technique was used to generate 100 random fields of each variable. The results showed great uncertainties for Ksat and the parameter α of the soil water retention curve. The uncertainties between the percentiles 5 and 95% for Ksat indicated values from 24 to 44 cm d-1, and for the parameter α, the uncertainties could be estimated from 0.622 to 1.122 cm-1.

Estimating soil hydraulic properties from saturation to complete dryness

2021 ◽  
Author(s):  
Budiman Minasny ◽  
Rudiyanto Rudiyanto ◽  
Federico Maggi
Keyword(s):  
Soil Moisture ◽  
Hydraulic Conductivity ◽  
Soil Water ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Soil Hydraulic Properties ◽  
Soil Moisture Dynamics ◽  
Soil Hydraulic ◽  
Moisture Dynamics

<p>To study the effect of drought on soil water dynamics, we need an accurate description of water retention and hydraulic conductivity from saturation to complete dryness. Recent studies have demonstrated the inaccuracy of conventional soil hydraulic models, especially in the dry end. Likewise, current pedotransfer functions (PTFs) for soil hydraulic properties are based on the classical Mualem-van Genuchten functions.</p><p>This study will evaluate models that estimate soil water retention and unsaturated hydraulic conductivity curves in full soil moisture ranges. An example is the Fredlund-Xing scaling model coupled with the hydraulic conductivity model of Wang et al. We will develop pedotransfer functions that can estimate parameters of the model. We will compare it with existing PTFs in predicting water retention and hydraulic conductivity.</p><p>The results show that a new suite of PTFs that used sand, silt, clay, and bulk density can be used successfully to predict water retention and hydraulic conductivity over a range of moisture content. The prediction of hydraulic properties is used in a soil water flow model to simulate soil moisture dynamics under drought. This study demonstrates the importance of accurate hydraulic model prediction for a better description of soil moisture dynamics.</p><p> </p>

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