scholarly journals Geostatistical analysis of microrelief of an oxisol as a function of tillage and cumulative rainfall

2009 ◽  
Vol 66 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Eva Vidal Vázquez ◽  
Sidney Rosa Vieira ◽  
Isabella Clerici De Maria ◽  
Antonio Paz González
Keyword(s):  
Surface Roughness ◽  
Soil Surface ◽  
Geostatistical Analysis ◽  
Soil Tillage ◽  
Model Parameters ◽  
Natural Rainfall ◽  
Cumulative Rainfall ◽  
Data Points ◽  
Plot Area ◽  
Chisel Plow

Surface roughness can be influenced by type and intensity of soil tillage among other factors. In tilled soils microrelief may decay considerably as rain progresses. Geostatistics provides some tools that may be useful to study the dynamics of soil surface variability. The objective of this study was to show how it is possible to apply geostatistics to analyze soil microrelief variability. Data were taken at an Oxisol over six tillage treatments, namely, disk harrow, disk plow, chisel plow, disk harrow + disk level, disk plow + disk level and chisel plow + disk level. Measurements were made initially just after tillage and subsequently after cumulative natural rainfall events. Duplicated measurements were taken in each one of the treatments and dates of samplings, yielding a total of 48 experimental surfaces. A pin microrelief meter was used for the surface roughness measurements. The plot area was 1.35 × 1.35 m and the sample spacing was 25 mm, yielding a total of 3,025 data points per measurement. Before geostatistical analysis, trend was removed from the experimental data by two methods for comparison. Models were fitted to the semivariograms of each surface and the model parameters were analyzed. The trend removing method affected the geostatistical results. The geostatistical parameter dependence ratio showed that spatial dependence improved for most of the surfaces as the amount of cumulative rainfall increased.

scholarly journals Fractal dimension and geostatistical parameters for soil microrelief as a function of cumulative precipitation

2010 ◽  
Vol 67 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Eva Vidal Vázquez ◽  
Sidney Rosa Vieira ◽  
Isabella Clerici De Maria ◽  
Antonio Paz González
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Agricultural Practices ◽  
Soil Surface ◽  
Soil Tillage ◽  
Model Parameters ◽  
Semivariogram Model ◽  
Scale Free ◽  
Chisel Plow ◽  
Disc Level

Surface roughness isinfluenced by type and intensity of soil tillage among other factors, and it changes considerably with rain. In microrelief studies the advantages of using indices such as the fractal dimension, D, and the crossover length, l, is that they allow the partition of the roughness characteristics into properties that depend purely on the scale and on a scale free component, respectively. On the other hand, some geostatistical parameters may provide different ways to understand soil surface variability not addressed with fractal parameters. Changes in fractal dimension and semivariogram parameters for surface roughness evolution were evaluated as a function of cumulative rainfall on Oxisol samples over six tillage treatments, namely, disc harrow, disc plow, chisel plow, disc harrow+disc level, disc plow+disc level and chisel plow+disc level. Measurements were taken in each tillage treatment after rainfall events yielding a total of 48 experimental surfaces measured with a pin microrelief meter. The plot had 135 cm by 135 cm and the sample spacing was 25 mm. Trends due to plot slope component with its concavities and convexities and to agricultural practices were removed from field data sets. A semivariogram model was fitted to each of the surfaces and the model parameters were analyzed and related to the fractal dimension, D, and crossover length, l. A relationship was found between the fractal dimension, D, and semivariogram model parameters. The cross over length, l,did not show as strong relationships with the semivariogram model parameters, even though there was a power relation between D and l.

scholarly journals EFFECT OF SOIL TILLAGE AND PLANT RESIDUE ON SURFACE ROUGHNESS OF AN OXISOL UNDER SIMULATED RAIN

2015 ◽  
Vol 39 (1) ◽  
pp. 268-278 ◽  
Author(s):  
Elói Panachuki ◽  
Ildegardis Bertol ◽  
Teodorico Alves Sobrinho ◽  
Paulo Tarso Sanches de Oliveira ◽  
Dulce Buchala Bicca Rodrigues
Keyword(s):  
Surface Roughness ◽  
Soil Surface ◽  
Conventional Tillage ◽  
Plant Residue ◽  
Soil Tillage ◽  
No Tillage ◽  
Maize Crop ◽  
Minimum Tillage ◽  
Roughness Index ◽  
Simulated Rain

Surface roughness of the soil is formed by mechanical tillage and is also influenced by the kind and amount of plant residue, among other factors. Its persistence over time mainly depends on the fundamental characteristics of rain and soil type. However, few studies have been developed to evaluate these factors in Latossolos (Oxisols). In this study, we evaluated the effect of soil tillage and of amounts of plant residue on surface roughness of an Oxisol under simulated rain. Treatments consisted of the combination of the tillage systems of no-tillage (NT), conventional tillage (CT), and minimum tillage (MT) with rates of plant residue of 0, 1, and 2 Mg ha-1 of oats (Avena strigosa Schreb) and 0, 3, and 6 Mg ha-1 of maize (Zea mays L.). Seven simulated rains were applied on each experimental plot, with intensity of 60±2 mm h-1 and duration of 1 h at weekly intervals. The values of the random roughness index ranged from 2.94 to 17.71 mm in oats, and from 5.91 to 20.37 mm in maize, showing that CT and MT are effective in increasing soil surface roughness. It was seen that soil tillage operations carried out with the chisel plow and the leveling disk harrow are more effective in increasing soil roughness than those carried out with the heavy disk harrow and leveling disk harrow. The roughness index of the soil surface decreases exponentially with the increase in the rainfall volume applied under conditions of no tillage without soil cover, conventional tillage, and minimum tillage. The oat and maize crop residue present on the soil surface is effective in maintaining the roughness of the soil surface under no-tillage.

scholarly journals Application of profilograph for evaluation of mechanical tillage of slope lands

2020 ◽  
Vol 193 ◽  
pp. 01066
Author(s):  
Mikhail Vasiliev ◽  
Sergey Vasiliev ◽  
Alexey Vasiliev
Keyword(s):  
Surface Roughness ◽  
Average Method ◽  
Soil Cover ◽  
Moving Average ◽  
Surface Profile ◽  
Soil Surface ◽  
Morphological Parameters ◽  
Wide Range ◽  
Data Points ◽  
Measured Profile

In this paper, we analyze a wide range of measurements of the day surface profile of tilled soil in order to substantiate the moving average method for evaluating morphological parameters and studying the influence of the base profile length on the accuracy of the obtained values. The surface roughness of the elementary plot was 4 mm, the surface ridgeness formed by technological furrows was 16 mm, and the slope of the plot was 0.198. The accuracy of the obtained values of roughness and ridgeness of the tilled soil surface depends significantly on the profile length determined by the number of measurements performed. We obtained 5273 data points per one turn of the device on a 6.3 m profile length (1 measurements are made per 1 mm) for the elementary plot. The surface roughness varied from 3, 1 mm to 4, 3 mm, and the ridgeness – 12 ... 21 mm when the number of measurements is from 2000 to 5273. When the measured profile length is about 1.3 m or less (not more 2000 points), the parameters of the tilled soil cover are not considered important. However, they are more critical for ploughed soil than for harrowed soil. On flatter surfaces, the base profile length of 2.5 m may be sufficient to adequately calculate the parameters of the day soil surface.

scholarly journals A multifractal approach to characterize cumulative rainfall and tillage effects on soil surface micro-topography and to predict depression storage

2010 ◽  
Vol 7 (10) ◽  
pp. 2989-3004 ◽  
Author(s):  
E. Vidal Vázquez ◽  
J. G. V. Miranda ◽  
J. Paz-Ferreiro
Keyword(s):  
Experimental Data ◽  
Multifractal Analysis ◽  
Spatial Configuration ◽  
Vertical Component ◽  
Soil Surface ◽  
Data Sets ◽  
Natural Rainfall ◽  
Cumulative Rainfall ◽  
Disc Level ◽  
Micro Topography

Abstract. Most of the indices currently employed for assessing soil surface micro-topography, such as random roughness (RR), are merely descriptors of its vertical component. Recently, multifractal analysis provided a new insight for describing the spatial configuration of soil surface roughness. The main objective of this study was to test the ability of multifractal parameters to assess in field conditions the decay of initial surface roughness induced by natural rainfall under different soil tillage systems. In addition, we evaluated the potential of the joint use of multifractal indices plus RR to improve predictions of water storage in depressions of the soil surface (MDS). Field experiments were performed on an Oxisol at Campinas, São Paulo State (Brazil). Six tillage treatments, namely, disc harrow, disc plough, chisel plough, disc harrow + disc level, disc plough + disc level and chisel plough + disc level were tested. In each treatment soil surface micro-topography was measured four times, with increasing amounts of natural rainfall, using a pin meter. The sampling scheme was a square grid with 25 × 25 mm point spacing and the plot size was 1350 × 1350 mm (≈1.8 m2), so that each data set consisted of 3025 individual elevation points. Duplicated measurements were taken per treatment and date, yielding a total of 48 experimental data sets. MDS was estimated from grid elevation data with a depression-filling algorithm. Multifractal analysis was performed for experimental data sets as well as for oriented and random surface conditions obtained from the former by removing slope and slope plus tillage marks, respectively. All the investigated microplots exhibited multifractal behaviour, irrespective of surface condition, but the degree of multifractality showed wide differences between them. Multifractal parameters provided valuable information for characterizing the spatial features of soil micro-topography as they were able to discriminate data sets with similar values for the vertical component of roughness. Conversely, both, rough and smooth soil surfaces, with high and low roughness values, respectively, can display similar levels of spectral complexity. Although in most of the studied cases trend removal produces increasing homogeneity in the spatial configuration of height readings, spectral complexity of individual data sets may increase or decrease, when slope or slope plus tillage tool marks are filtered. Increased cumulative rainfall had significant effects on various parameters from the generalized dimension, Dq, and singularity spectrum, f(α). Overall, micro-topography decay by rainfall was reflected on a shift of the singularity spectra, f(α) from the left side (q>>0) to the right side (q>0) to the left side (q

scholarly journals Analysis of persistent macroaggregates on the surface of agricultural soils

2021 ◽  
Author(s):  
Tian Tian ◽  
Joann K. Whalen ◽  
Pierre Dutilleul
Keyword(s):  
Soil Moisture ◽  
Digital Images ◽  
Agricultural Soils ◽  
Soil Surface ◽  
Organic Soil ◽  
Organic Soils ◽  
Biological Processes ◽  
Time Intervals ◽  
Natural Rainfall ◽  
Cumulative Rainfall

In humid regions, the number of macroaggregates on the soil surface could decline because of rainfall disturbance, or increase due to rainfall-activated chemical and biological processes. We took digital images of macroaggregates at the surface of clay and organic soils six times during a 68-d period with 264 mm natural rainfall. Based on the constant or increasing number of surface macroaggregates during the five time intervals, rainfall did not disturb macroaggregates. Macroaggregate persistence was positively correlated with cumulative rainfall (both soils) and soil moisture (organic soil), so we infer that rainfall promoted macroaggregate assemblage through chemical and biological processes.

scholarly journals A multifractal approach to characterize cumulative rainfall and tillage effects on soil surface micro-topography and to predict depression storage

2010 ◽  
Vol 7 (2) ◽  
pp. 2099-2141 ◽  
Author(s):  
E. Vidal Vázquez ◽  
J. G. V. Miranda ◽  
J. Paz-Ferreiro
Keyword(s):  
Spatial Configuration ◽  
Vertical Component ◽  
Soil Surface ◽  
Data Sets ◽  
Natural Rainfall ◽  
Cumulative Rainfall ◽  
Generalized Dimension ◽  
The Right ◽  
Disc Level ◽  
Micro Topography

Abstract. Most of the indices currently employed for assessing soil surface micro-topography, such as random roughness (RR), are merely descriptors of its vertical component. Recently, multifractal analysis provided a new insight for describing the spatial configuration of soil surface roughness. The main objective of this study was to test the ability of multifractal parameters to assess decay of initial surface roughness induced by natural rainfall under different soil tillage systems in field conditions. In addition, we evaluated the potential of the joint use of multifractal indices plus RR to improve predictions of water storage in depressions of the soil surface (MDS). Field experiments were performed on an Oxisol at Campinas, São Paulo State (Brazil). Six tillage treatments, namely, disc harrow, disc plough, chisel plough, disc harrow + disc level, disc plough + disc level and chisel plough + disc level were tested. In each treatment soil surface micro-topography was measured four times, with increasing amounts of natural rainfall, using a pin meter. The sampling scheme was a square grid with 25×25 mm point spacing and the plot size was 1350×1350 mm (≈1.8 m2), so that each data set consisted of 3025 individual elevation points. Duplicated measurements were taken per treatment and date, yielding a total of 48 experimental data sets. MDS was estimated from grid elevation data with a depression-filling algorithm. Multifractal analysis was performed for experimental data sets as well as for oriented and random surface conditions obtained from the former by removing slope and slope plus tillage marks, respectively. All the investigated microplots exhibited multifractal behaviour, irrespective of surface condition, but the degree of multifractality showed wide differences between them. Multifractal parameters provided valuable information for characterizing the spatial features of soil micro-topography as they were able to discriminate data sets with similar values for the vertical component of roughness. Both, rough and smooth soil surfaces, with high and low roughness values, respectively, can display similar levels of spectral complexity. Although in most of the studied cases trend removal produces increasing homogeneity in the spatial configuration of height readings, spectral complexity of individual data sets may increase or decrease, when slope or slope plus tillage tool marks are filtered. Increased cumulative rainfall had significant effects on various parameters from the generalized dimension, Dq, and singularity spectrum, f(α). Overall, micro-topography decay by rainfall produced was reflected on a shift of the singularity spectra, f(α) from the left side (q>>0) to the right side (q<<0) and also on a shift of the generalized dimension spectra from the right side (q>>0) to the left side (q<<0). The use of an exponential model of vertical roughness indices, RR, and multifractal parameters accounting for the spatial configuration such as D1, D5, and D10 improved estimation of water stored in surface depressions.

scholarly journals Describing soil surface microrelief by crossover length and fractal dimension

2007 ◽  
Vol 14 (3) ◽  
pp. 223-235 ◽  
Author(s):  
E. Vidal Vázquez ◽  
J. G. V. Miranda ◽  
A. Paz González
Keyword(s):  
Surface Roughness ◽  
Soil Surface ◽  
Statistical Index ◽  
Surface Microrelief ◽  
Random Roughness ◽  
Natural Rainfall ◽  
Primary Tillage ◽  
Soil Surface Roughness ◽  
D Values ◽  
Disc Level

Abstract. Accurate description of soil surface topography is essential because different tillage tools produce different soil surface roughness conditions, which in turn affects many processes across the soil surface boundary. Advantages of fractal analysis in soil microrelief assessment have been recognised but the use of fractal indices in practice remains challenging. There is also little information on how soil surface roughness decays under natural rainfall conditions. The objectives of this work were to investigate the decay of initial surface roughness induced by natural rainfall under different soil tillage systems and to compare the performances of a classical statistical index and fractal microrelief indices. Field experiments were performed on an Oxisol at Campinas, São Paulo State (Brazil). Six tillage treatments, namely, disc harrow, disc plow, chisel plow, disc harrow + disc level, disc plow + disc level and chisel plow + disc level were tested. Measurements were made four times, firstly just after tillage and subsequently with increasing amounts of natural rainfall. Duplicated measurements were taken per treatment and date, yielding a total of 48 experimental surfaces. The sampling scheme was a square grid with 25×25 mm point spacing and the plot size was 1350×1350 mm, so that each data set consisted of 3025 individual elevation points. Statistical and fractal indices were calculated both for oriented and random roughness conditions, i.e. after height reading have been corrected for slope and for slope and tillage tool marks. The main drawback of the standard statistical index random roughness, RR, lies in its no spatial nature. The fractal approach requires two indices, fractal dimension, D, which describes how roughness changes with scale, and crossover length, l, specifying the variance of surface microrelief at a reference scale. Fractal parameters D and l, were estimated by two independent self-affine models, semivariogram (SMV) and local root mean square (RMS). Both algorithms, SMV and RMS, gave equivalent results for D and l indices, irrespective of trend removal procedure, even if some bias was present which is in accordance with previous work. Treatments with two tillage operations had the greatest D values, irrespective of evolution stage under rainfall and trend removal procedure. Primary tillage had the greatest initial values of RR and l. Differences in D values between treatments with primary tillage and those with two successive tillage operations were significant for oriented but not for random conditions. The statistical index RR and the fractal indices l and D decreased with increasing cumulative rainfall following different patterns. The l and D decay from initial value was very sharp after the first 24.4 mm cumulative rainfall. For five out of six tillage treatments a significant relationship between D and l was found for the random microrelief conditions allowing a covariance analysis. It was concluded that using RR or l together with D best allow joint description of vertical and horizontal soil roughness variations.

scholarly journals Application of laser profilograph in the assessment of mechanical tillage of slope lands

2021 ◽  
pp. 68-74
Author(s):  
Mikhail Andriyanovich Vasiliev ◽  
Sergey Anatolyevich Vasiliev ◽  
Alexey Anatolyevich Vasiliev
Keyword(s):  
Surface Roughness ◽  
Average Method ◽  
Soil Cover ◽  
Moving Average ◽  
Surface Profile ◽  
Soil Surface ◽  
Morphological Parameters ◽  
Treated Soil ◽  
Data Points ◽  
Measured Profile

The article analyzes an extensive set of measurements of the daily surface profile of cultivated soil in order to justify the moving average method for evaluating morphological parameters and studying the influence of the base length of the profile on the accuracy of the obtained values. The surface roughness for the elementary platform was 5.08 mm, the surface ridge formed by technological furrows was 21.9 mm, and the slope of the platform was 0.056 or 3.2 degrees. The accuracy of the obtained values of roughness and ridges of the surface of the treated soil depends significantly on the length of the profile under study, determined by the number of measurements performed. For one turn of the device for the elementary platform, 9616 data points were obtained on the profile length of 6.3 m (3 measurements are performed per 2 mm). The surface roughness varied from 2 mm to 6 mm, and the ridge – 16 ... 28 mm with the number of measurements from 2000 to 9600. When the measured profile length is about 1.3 m or less (no more than 2000 points), the parameters of the treated soil cover are greatly underestimated, and this error is greater for ploughed soil than for cored soil. On flatter surfaces, the base profile length of 2.5 m may be sufficient to adequately calculate the parameters of the daily soil surface.

scholarly journals Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion

2020 ◽  
Vol 12 (1) ◽  
pp. 232-241
Author(s):  
Na Ta ◽  
Chutian Zhang ◽  
Hongru Ding ◽  
Qingfeng Zhang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Soil Surface ◽  
Rainfall Events ◽  
Tillage Practices ◽  
Surface Microtopography ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
The Difference ◽  
Sheet Erosion

AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

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