Effects of slope aspect, grazing, and sampling position on the soil penetration resistance curve

Bayat, Hossein; Sheklabadi, Mohsen; Moradhaseli, Mohsen; Ebrahimi, Eisa

doi:10.1016/j.geoderma.2017.05.003

Cited by 40 publications

(16 citation statements)

References 70 publications

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“…PR significantly influenced soil infiltration properties and the development of root systems, and thus was closely related to soil erodibility (Parker et al, 1995;Silva et al, 2011;Bayat et al, 2017). In this study, PR was positively correlated with BD, and negatively related to BSC thickness and LD (Table V ).…”

Section: Factors Influencing Soil Erodibilitymentioning

confidence: 51%

“…Small BD was caused by slight soil consolidation, and thus soil with small BD was easier to penetrate (Parker et al, 1995;Silva, 2011;Trabaquini et al, 2015;Wang et al, 2018a). The decreases in PR with BSC thickness and LD can probably be attributed to the increase in soil porosity caused by vegetation growth (Bayat et al, 2017;Wang et al, 2018a). Wang et al (2018a) investigated the variation in soil erodibility (reflected by PR) with vegetation restoration age in the same watershed on the Loess Plateau, and found that PR was directly related to BD, BSC thickness, and LD.…”

Section: Factors Influencing Soil Erodibilitymentioning

confidence: 99%

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Soil erodibility as impacted by vegetation restoration strategies on the Loess Plateau of China

Wang

Zhang

et al. 2018

Earth Surf Processes Landf

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Near soil surface characteristics change significantly with vegetation restoration, and thus, restoration strategies likely affect soil erodibility. However, few studies have been conducted to quantify the effects of vegetation restoration strategies on soil erodibility in regions experiencing rapid vegetation restoration. This study was conducted to evaluate the effects of vegetation restoration strategies on soil erodibility, reflected by soil cohesion (Coh), penetration resistance (PR), saturated conductivity (Ks), number of drop impacts (NDI), mean weight diameter of soil aggregates (MWD), and soil erodibility K factor on the Loess Plateau. One slope farmland and five 25‐year‐restored lands covered by old world bluestem, korshinsk peashrub, shrub sophora, sea‐buckthorn, and black locust were selected as test sites. The old world bluestem was restored via natural succession, while the other four lands were restored by artificial planting. A comprehensive soil erodibility index (CSEI) was produced by a weighted summation method to quantify the effects of vegetation restoration strategies on soil erodibility completely. The results showed that Coh, Ks, NDI, and MWD of the five restored lands were greater than those of the slope farmland. However, the PR and K of the five restored lands were less than those of the slope farmland. CSEI varied greatly under different restoration strategies, from 1 to 0.214. Compared with the control, these indices decreased on average by 68.2%, 78.6%, 72.7%, 75.8%, and 62.8% for old world bluestem, korshinsk peashrub, shrub sophora, sea‐buckthorn, and black locust, respectively. The variation in soil erodibility was significantly influenced by biological crust thickness, bulk density, organic matter content, plant litter density, and root mass density. Shrub‐lands via artificial planting, especially korshinsk peashrub, were considered the most effective restoration strategies to reduce soil erodibility on the Loess Plateau. The results are helpful for selecting vegetation restoration strategies and asking their benefits in controlling soil erosion. © 2018 John Wiley & Sons, Ltd.

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Section: Factors Influencing Soil Erodibilitymentioning

confidence: 51%

Section: Factors Influencing Soil Erodibilitymentioning

confidence: 99%

Soil erodibility as impacted by vegetation restoration strategies on the Loess Plateau of China

Wang

Zhang

et al. 2018

Earth Surf Processes Landf

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“…1). However, the crotalaria crop when used in succession with maize provided higher MRP values in relation to the other treatments at depths 150-200 and 200-250 mm (Table 2), probably due to the lower contributions of the plant residues left by the plants used as cover [22], and higher tillage of the soil that led to the destruction of the larger aggregates in smaller aggregates and to the soil particle size distribution [23].…”

Section: Resultsmentioning

confidence: 97%

Soil Mechanical Resistance Penetration after Fifteen Years with Previous Crops and Tillage Systems and Productivity of Green Corn Cob in Northeast Brazil

Pedrotti

Filho

Assunção

et al. 2019

JEAI

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Conservation systems for less soil movement and when associated with previous crops may reduce the effects of soil compaction. The objective of this work was to evaluate soil mechanical resistance penetration (MRP) in different cropping systems associated with previous corn crop after fifteen years. The experimental design was composed of experimental strips with subdivided plots, combining three soils management systems: CC- conventional cultivation, MC- minimum cultivation and NT- no-tillage, and four species of crops antecedent to maize for the production of commercial ears of green corn. In the determination of soil MRP, the electronic penetrometer (FALKER model SoloTrack PLG 5200) was used, with readings up to a depth of 400 mm. The results showed that there was a significant effect on the MRP values of the soil when submitted to the different cropping systems and previous crops at the end of fifteen years. The lowest MRP values were located in the superficial layers in the CC and MC. The NT cultivar system showed higher MRP values, at depth 0-100 mm. At conditions of tableland, after fifteen years, it was observed that the NT system provides better corn productivity levels combined with lower MRP values along the profile.

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“…To determine the mathematical model of soil cone index as a dependent variable in relation to the percentage of soil moisture content, data from soil bulk density, electrical conductivity and sampling depth were used as independent variables (Bayat et al, 2017 index prediction using the Levenberg-Marquardt algorithm with a middle layer (middle layers) and a different number of neurons in the middle layer (layers). The sigmoid tangent function was used between the input and middle layers and a linear type was used between the middle layers and the output layer.…”

Section: Presenting a Regression Model For Soil Cone Indexmentioning

confidence: 99%

“…Research has demonstrated a correlation between evaluations of the soil cone index and soil bulk density as parameters related to soil compaction and compression (Bayat et al, 2017;Henderson et al, 1988) and between the soil cone index and moisture content (Faure and Da Mata, 1994). Abbaspour-Gilandeh et al (2006) reported that soil electrical conductivity (EC) data were highly correlated to soil texture (% clay content) with a correlation coefficient of 0.916 and that there was a strong linear correlation between soil EC and draft force across a field.…”

mentioning

confidence: 99%

Application of Computational Intelligence Methods for Predicting Soil Strength

Abbaspour‐Gilandeh

Abbaspour-Gilandeh

2019

Acta Technologica Agriculturae

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The aim of this study was to make predictions for soil cone index using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and a regression model. Field tests were conducted on three soil textures and obtained results were analyzed by application of a factorial experiment based on a Randomized Complete Block Design with five replications. The four independent variables of percentage of soil moisture content, soil bulk density, electrical conductivity and sampling depth were used to predict soil cone index by ANNs, ANFIS and a regression model. The ANNs design was that of back propagation multilayer networks. Predictions of soil cone index with ANFIS were made using the hybrid learning model. Comparison of results acquired from ANNs, ANFIS and regression models showed that the ANFIS model could predict soil cone index values more accurately than ANNs and regression models. Considering the ANFIS model, a novel result on soil compaction modeling, relative error (ε), and regression coefficient (R2) were calculated at 2.54% and 0.979, respectively.

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Effects of slope aspect, grazing, and sampling position on the soil penetration resistance curve

Cited by 40 publications

References 70 publications

Soil erodibility as impacted by vegetation restoration strategies on the Loess Plateau of China

Soil erodibility as impacted by vegetation restoration strategies on the Loess Plateau of China

Soil Mechanical Resistance Penetration after Fifteen Years with Previous Crops and Tillage Systems and Productivity of Green Corn Cob in Northeast Brazil

Application of Computational Intelligence Methods for Predicting Soil Strength

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