Changes in soil hydrodynamic parameters during intermittent rainfall following tillage

Todisco, Francesca; Vergni, Lorenzo; Iovino, Massimo; Bagarello, Vincenzo

doi:10.1016/j.catena.2023.107066

Cited by 7 publications

(3 citation statements)

References 60 publications

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“…An influence of the season on the reliability of soil loss prediction models has already been observed by Todisco et al [43], which attributed this to the different conditions of the soil surface. The climatic conditions (particularly precipitation and air temperature) roughly described by the variable "period" can affect the dynamics of various soil properties such as roughness [44], infiltration and bulk density [45,46], and hydraulic conductivity [47]. Therefore, even if the RF model already explicitly considers essential features such as Q R and R, the "period" becomes important to allow the model to evaluate the seasonal variability of their effect on soil loss.…”

Section: Discussionmentioning

confidence: 99%

A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events

Vergni

Todisco

2023

Water

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Predicting the occurrence of erosive rain events and quantifying the corresponding soil loss is extremely useful in all applications where assessing phenomenon impacts is required. These problems, addressed in the literature at different spatial and temporal scales and according to the most diverse approaches, are here addressed by implementing random forest (RF) machine learning models. For this purpose, we used the datasets built through many years of soil loss observations at the plot-scale experimental site SERLAB (central Italy). Based on 32 features describing rainfall characteristics, the RF classifier has achieved a global accuracy of 84.8% in recognizing erosive and non-erosive events, thus demonstrating slightly higher performances than previously used (non-machine learning) methodologies. A critical performance is the percentage of erosive events correctly recognized to the observed total (72.3%). However, since the most relevant erosive events are correctly identified, we found only a slight underestimation of the total rainfall erosivity (91%). The RF regression model for estimating the event soil loss, based on three event features (runoff coefficient, erosivity, and period of occurrence), demonstrates better performances (RMSE = 2.30 Mg ha−1) than traditional regression models (RMSE = 3.34 Mg ha−1).

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Section: Discussionmentioning

confidence: 99%

A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events

Vergni

Todisco

2023

Water

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“…There have four frequently-used empirical models, including Brooks-Corey model, Gardner model, Van-Genuchten model (VG model for short) and Gardner-Russo model. Others researched on domestic and overseas soil hydrodynamic parameters and found that VG model have not only better fitting effect but also can link with the mechanical composition and bulk density of soil [18].…”

Section: One-dimensional Consolidation Theory Of Unsaturated Soilmentioning

confidence: 99%

Engineering Characteristics of Unsaturated Cohesive Foundation Soil at High-Plateau Airport with Considering the Embankment Filling Interval

Feng,

Liu,

et al. 2023

Pol. J. Environ. Stud.

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As the indication of practical engineering, related accidents could be seen everywhere in airport engineering due to the pavement excessive settlement. According to the relevant regulations of the Civil Aviation Administration of China, the requirement of foundation settlement of high-plateau airports is stricter than that of airports in plain area for ensuring flight safety [1-3]. So it is necessary to correctly analyze the settlement characteristics of foundation in high-plateau airport.So far, there could be roughly divided into three categories in research methods on settlement characteristics of foundation soil at the high-plateau airport [4][5]. The first one is one-dimensional analytic

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“…The BEST method is widely used [15] due to its simplicity, robustness, accuracy and versatility of application since it enables the estimate of soil hydraulic proprieties (water retention curve and hydraulic conductivity function) based on simple field infiltration measurements, information on the soil particlesize distribution and a few physical parameters. Specifically, the comparative analysis between different types of soil and crop management systems was based on the following parameters, all derived from the hydraulic characterisation obtained by the BEST method: saturated hydraulic conductivity (K S ), sorptivity (S), scale parameter of the Van Genuchten retention curve (h g ), two soil porosity indicators [i.e., the flow-weighted mean pore size (λ m ) and the numbers of pores per unit area (N 0 )] and four capacity-based indicators [i.e., macroporosity (P MAC ), air capacity (A C ), relative filed capacity (R FC ) and plant available water content (AWC)] [16][17][18]. The main goal was to identify whether the different LTAEs considered have relevant roles in conditioning the soil's physical-hydraulic properties in the specific climatic and pedological conditions of the study area.…”

Section: Introductionmentioning

confidence: 99%

Using Beerkan Procedure to Estimate Hydraulic Soil Properties under Long Term Agroecosystems Experiments

Vergni,

Tosi,

Bertuzzi

et al. 2024

Applied Sciences

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The BEST (Beerkan Estimation of Soil Transfer parameters) method was used to compare the hydraulic properties of the soils in two Long-term Agroecosystem Experiments (LTAEs) located at the FIELDLAB experimental site of the University of Perugia (central Italy). The LTAE “NewSmoca” consists of a biennial maize-durum wheat crop rotation under integrated low-input cropping systems with (i) inversion soil tillage (INT) or (ii) no-tillage (INT+) and (iii) under an organic cropping system with inversion soil tillage (ORG). ORG and INT+ involve the use of autumn-sown cover crops (before the maize cycle). Pure stand durum wheat was grown in INT and INT+, while a faba bean–wheat temporary intercropping was implemented in ORG. The LTAE “Crop Rotation” consists of different crop rotations and residue management, a continuous soft winter wheat and biennial rotations of soft winter wheat with maize or faba bean. Each rotation is combined with two modes of crop residue management: removal or burial. For INT+, despite the high-bulk density (>1.50 g/cm3), we found that conductivity, sorptivity and available water are comparable to those of INT, probably due to a more structured and efficient micropore system. ORG soils show the highest conductivity, sorptivity and available water content values, probably due to the recent spring tillage occurring in the wheat inter-row with the faba bean incorporation into the soil. For LTAE Rotation, the residue burial seems to influence the capacity-based indicators positively. However, the differences in the removal treatment are minor, and this could be due to the inversion soil tillage, which limits the progressive accumulation of organic matter.

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Changes in soil hydrodynamic parameters during intermittent rainfall following tillage

Cited by 7 publications

References 60 publications

A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events

A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events

Engineering Characteristics of Unsaturated Cohesive Foundation Soil at High-Plateau Airport with Considering the Embankment Filling Interval

Using Beerkan Procedure to Estimate Hydraulic Soil Properties under Long Term Agroecosystems Experiments

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