Soil science
Sh. Asghari; M. Hasanpour Kashani; H. Shahab Arkhazloo
Abstract
IntroductionThe penetration resistance (PR) of the soil shows the mechanical resistance of the soil against the penetration of a conical or flat probe; it is important in terms of seed germination, root growth and tillage operations. In general, if the PR value of a soil exceeds 2.5 MPa, the growth and ...
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IntroductionThe penetration resistance (PR) of the soil shows the mechanical resistance of the soil against the penetration of a conical or flat probe; it is important in terms of seed germination, root growth and tillage operations. In general, if the PR value of a soil exceeds 2.5 MPa, the growth and expansion of roots in the soil will be significantly limited. The direct measurement of PR is also a laborious and costly task due to instrumental errors. Therefore, it is useful the use of different models such as multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) to estimate PR through easily accessible and low-cost soil characteristics. The objectives of this research were: (1) to obtain MLR, ANN and GEP models for estimating PR from the easily accessible soil variables in forest, range and cultivated lands of Fandoghloo region of Ardabil province, (2) to compare the accuracy of the aforementioned models in estimating soil PR using the coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) criteria. Materials and MethodsDisturbed and undisturbed samples (n = 80) were nearly systematically taken from 0-10 cm soil depth with nearly 50 m distance in forest (n = 20), range (n = 23) and cultivated (n = 37) lands of Fandoghloo region of Ardabil province, Iran (lat. 38° 24' 10" to 38° 24' 25" N, long. 48° 32' 45" to 48° 33' 5" E) in summer 2023. The contents of sand, silt, clay, CaCO3, pH, EC, bulk (BD) and particle density (PD), organic carbon (OC), gravimetric field water content (FWC), mean weight diameter (MWD) and geometric mean diameter (GMD) were measured in the laboratory. Relative bulk density (BDrel) was calculated using BD and clay data. Mean geometric diameter (dg) and geometric standard deviation (σg) of soil particles were computed by sand, silt and clay percentages. The penetration resistance (PR) of the soil was measured in situ using cone penetrometer (analog model) at 5 replicates. Data randomly were divided in two series as 60 data for training and 20 data for testing of models. The SPSS 22 software with stepwise method, MATLAB and Gene Xpro Tools 4.0 software were used to derive multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) models, respectively. A feed forward three-layer (2, 5 and 6 neurons in hidden layer) perceptron network and the tangent sigmoid transfer function were used for the ANN modeling. A set of optimal parameters were chosen before developing a best GEP model. The number of chromosomes and genes, head size and linking function were selected by the trial and error method, as they are 30, 3, 8, and +, respectively. The rates of genetic operators were chosen according to literature studies. The accuracy of MLR, ANN and GEP models in estimating PR were evaluated by coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) statistics. Results and Discussion The studied soils had clay loam (n = 11), sandy clay loam (n = 6), sandy loam (n = 12), loam (n = 13), silty clay loam (n = 14), silty clay (n = 1) and silt loam (n = 23) textural classes. The values of sand (13.14 to 64.79 %), silt (21.11 to 74.96 %), clay (2.95 to 42.18 %), OC (1.01 to 7.17 %), FWC (11.58 to 50.47 mass percent), BD (0.84 to 1.43 g cm-3) and PR (1.03 to 5.83 MPa) showed good variations in the soils of the studied region. There were found significant correlations between PR with FWC (r = - 0.45**), silt (r = - 0.36**) and σg (r = 0.36**). Due to the multicollinearity of silt with σg (r = -0.84**), the σg was not used as an input variable to estimate PR. Generally, 3 MLR, ANN and GEP models were constructed to estimate PR from measured readily available soil variables. The results of MLR, ANN and GEP models showed that the most suitable variables to estimate PR were FWC, silt and BDrel. The values of R2, RMSE, ME and NS criteria were obtained equal 0.44, 1.19 MPa, 0.19 MPa and 0.36, and 0.92, 0.41 MPa, -0.05 MPa and 0.92, 0.79, 0.91 MPa, 0.13 MPa, 0.63 for the best MLR, ANN and GEP models, respectively. The former researchers also reported that there is a negative and significant correlation between PR with FWC. Conclusion The results indicated that field water content (FWC), silt and relative bulk density (BDrel) were the most important and readily available soil variables to estimate penetration resistance (PR) in the studied area. According to the lowest values of RMSE and the highest values of NS, the accuracy of ANN models to predict soil PR was higher than MLR and GEP models in this research.
ali baliani; Ali Reza Vaezi
Abstract
IntroductionRainfall erosion results from the expenditure of the energy of falling raindrops and flowing water when these two agents act either singly or together. Soil erosion by rainfall is a serious ongoing worldwide environmental issue that contributes to soil and water quality degradation. Understanding ...
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IntroductionRainfall erosion results from the expenditure of the energy of falling raindrops and flowing water when these two agents act either singly or together. Soil erosion by rainfall is a serious ongoing worldwide environmental issue that contributes to soil and water quality degradation. Understanding raindrop-impact-induced erosion processes are key to design and apply soil management techniques that minimize and control soil erosion risk. Water erosion and especially raindrop-impact-induced erosion is the primary agents that cause soil erosion-induced degradation and has been identified as one of the major processes contributing to the soil and water quality degradation. Soil degradation caused by rainfall raindrops impacts the soil surface disperses and splashes the soil, and displaces particles from their original position. Raindrops striking the soil surface develop a raindrop-soil particle momentum before releasing their energy in the form of the splash. Other causes of soil degradation are including compaction and penetration resistance.
Materials and Methods: This study was conducted to investigate the raindrop-impact-induced erosion in relation to slope gradient (0, 10, 20, and 30%) and antecedent moisture content or AMC (air dried, quarter saturation, semi saturation, and saturation). Toward this, six soil texture classes were exposed to simulated rainfalls with 40 mm h-1 in intensity for 15-min in four slope gradients and four antecedent moisture contents. Rainfall was simulated using rainfall simulator from soil erosion laboratory of the University of zanjan with 3-meter height and surface of 2 m2. A total of 288 experimental soil boxes with 25 cm × 35 cm dimensions and 5-cm depth were investigated using the completely randomized block design with three replications. Data of soil erosion processes include splash erosion particles amount caused raindrop impact, soil resistance ratio after rainfall using penetrometer, and compaction percent using bulk density after and before rainfall was measured and then compared using Duncan's test among the slope steepness and antecedent moisture content
Results and Discussion: Significant relationships were found between the splash erosion rate, soil resistance ratio and soil compaction means (P<0.01. (The results showed that silt soil carried the highest mean value in splash erosion rate with 1574.93 gm-2 h-1, soil resistance ratio with 10.53 and soil compaction with 17.43 percent, while sand soil carried the lowest mean value in splash rate with 437.37 gm-2 h-1, soil resistance ratio with 2.66 and soil compaction with 0.25 percent. Soil erosion processes were significantly affected by slope gradient and AMC. Soil erosion processes showed a decreasing rate in 0 slope degree and increasing rate in 30 slope degree and also decreasing rate in air dried and increasing rate in semi saturation AMC. Significant correlations (P< 0.01 and 00.05) were found between soil erosion processes and sand, silt, geometric mean particle diameter, bulk density, saturated hydraulic conductivity, and calcium bicarbonate equivalent. among the physical properties of the studied soils, the sand percentage, bulk density, and Geometric mean diameter showed a negative significant correlation with splash erosion, soil compaction, and soil resistance, and the percentage of silt and calcium carbonate content with splash erosion, soil compaction, and soil resistance were positive significant correlated. The cause of this negative and positive correlation might be dependent on particles size and more percent of coarse particles, the transfer of particles from the soil mass is reduced due to raindrops and degradation processes occur with less intensity. In addition, destruction processes with more intensity occurred with increasing silt and lime percent.
Conclusion: Increasing the slope gradient has an incremental effect on the amount of rainfall erosion processes i.e. sediment load, penetration ratio, and soil compaction value. However, antecedent moisture content in various soil textures has the different effect on the amount of rainfall erosion processes. Among the soil chemical properties, only calcium carbonate equivalent with splash erosion, density, and soil surface resistance was positively correlated and chemical properties such as a percentage of organic matter and exchangeable sodium percent no significant correlated with soil erosion processes. In other words, the physical nature of soil-forming particles such as particle size, as well as some of the chemical properties of soil particles such as organic matter, have a more effect on soil degradation, density, and soil resistance ratio. also the role of soil physical properties such as sand percent and calcium carbonate equivalent on the rainfall processes were more than soil chemical properties. In general, increasing the percent of silt and lime in the soil, unlike sand, was increased the sensitivity of the soil to the rainfall erosion and as a result increasing the splash erosion leads to increased soil compaction and soil resistance ratio.
