Using digital elevation models and image processing to follow clod evolution under rainfall
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Abstract:
Soil surface roughness plays an important role in determining how the soil will interact with its environment. Analysis of soil roughness at small scale matters both for preparation of soil in order to allow for plant emergence, and for decisions to favor soil conservation. Indeed, soil roughness may be shaped by tillage operations and then changes with time, under rainfall impact.
Soil surface roughness is usually estimated by various indices, computed on measured profiles or images of elevations. Another approach is focusing on soil cloddiness, either by sieving or by image segmentation. The objective of this study is to monitor the evolution of clods under rainfall with Digital Elevation Model (DEM) recording and image processing.
We prepared two trays of artificial soil surfaces in the laboratory with silt loam soil topped by pre-sieved clods. They were designed to look like a seedbed. Soil surface evolution was achieved by subjecting the trays to controlled artificial rainfalls, and DEM were recorded at each stage. We performed automatic clod segmentation and measurement of the volume of individual clods. Under rainfall impact, we could see smoothing and leveling of clods until disappearance of the smaller ones. We focused on the larger clods greater than 12 mm in diameter that remained till the last rainfall. They showed swelling (volume increase) followed by erosion (volume decrease), these two phenomena being size dependent. Clod volume decrease was modeled by an exponential function.
Now, the slope and the amplitude parameters decreased according to a power law, as a function of mean volume of clods. Monitoring of clod volume with cumulated precipitation with the help of DEM measurements is able to differentiate the dynamics of clod depending on their size. This technique improves the usual roughness description and allows for a better understanding of the processes.Keywords:
Silt
Elevation (ballistics)
Smoothing
Soil texture
Soil texture
Silt
Texture (cosmology)
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Soil texture is known to have an influence on the physical and biological processes that produce N 2 O emissions in agricultural fields, yet comparisons across soil textural types are limited by considerations of time and practicality. We used the DayCent biogeochemical model to assess the effects of soil texture on N 2 O emissions from agriculturally productive soils from four counties in Wisconsin. We validated the DayCent model using field data from 2 yr of a long‐term (approximately 20‐yr) cropping systems trial and then simulated yield and N 2 O emissions from continuous corn ( Zea mays L.) and corn–soybean ( Glycine max L.) cropping systems across 35 Wisconsin soil series classified as either silt loam, sandy loam, or loamy sand. Silt loam soils had the highest N 2 O emissions of all soil types, exhibiting 80 to 158% greater mean emissions and 100 to 282% greater emission factors compared with loamy sand and sandy loam soils, respectively. The model predicts that for these soils under these cropping systems, denitrification constituted the majority of the N 2 O flux only in the silt loam soils. However, across all soil textures, locations, and years, denitrification explained the most variation (74–98%) in total N 2 O emissions. Our results suggest that soil texture is an important factor in determining a range of N 2 O emission characteristics and is critical for estimating future N 2 O emissions from agricultural fields. Core Ideas Soil texture influences agricultural N 2 O emissions and emission factors. Soil texture influences absolute and relative rates of nitrification and denitrification. Understanding soil texture effects is vital to estimating N losses via N 2 O.
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Summary Various soil texture schemes are in current use. These differ in the size ranges of their particle fractions. There is a need to establish simple methods to correlate these conventional schemes. Therefore I have defined closed‐form exponential and power law functions to fit models to cumulative particle‐size distribution data. I have tested the functions for their suitability (i) to represent cumulative particle‐size distribution curves and (ii) to transfer data between distributions that differ in the size ranges of the particle fractions. I found that closed‐form exponential functions adequately represent the cumulative particle‐size distributions of fine‐textured soils (clay, silty clay, silty clay loam, clay loam, silt loam and loam texture), whilst closed‐form power functions better describe the cumulative particle‐size distributions of coarse‐textured soils (sand, loamy sand, sandy loam, sandy clay and sandy clay loam texture). The functions defined are found to be suitable to transfer data between different texture schemes. The use of this approach is illustrated by examples of data transformations between three widely used soil texture schemes: ISSS, Katschinski's and USDA.
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Land Surface Processes Experiment was conducted in the year 1997 in which land surface observations were collected over a tropical semi-arid region of Gujarat, India. Using these observations, Noah Land Surface Model version 2.7.1 (Noah-LSM) has been tested in the wet and dry surface conditions for four test sites, viz., Anand (22°35′ N, 72°55′ E), Derol (22°40′ N, 73°45′ E), Arnej (22°40′ N, 72°15′ E), and Khandha (22°02′ N, 73°11′ E) having different soil texture (sandy loam and clay). Model simulations for net radiation, skin temperature, and soil temperature at various depths were compared with observations. Initial results of soil and surface temperature showed good agreement for clay soil texture compared with sandy loam textures during dry periods. In contrast, for wet periods. Contrastingly, for wet periods, the net radiation and skin temperature showed better agreement for sandy loam textures than for clay textured soils. The model simulation was repeated for the sandy loam soil texture soil during dry period and for the clay texture soil during the wet period by replacing the model estimated soil thermal conductivity by the annual mean soil thermal conductivity of test stations. The results were improved for sandy loam texture but remain unchanged for clay texture. Comparison of simulated and observed parameters shows good correlation, high index of agreement, and low error. Overall, the results simulated by Noah-LSM for both soil textures are comparable with the observations.
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Abstract Soil texture affects many physical and chemical properties of soil. Knowledge of soil texture is essential for all water and soil studies. The aim of the research is to draw a map of the spatial distribution of soil texture in the region of eastern Wasit province and know the relationship of texture to the soil’s hydrological groups. Laboratory tests were conducted on 25 soil samples. With a depth of 50-75 cm, were selected from locations that represent the study area. According to the unified classification system, The results showed that the soil texture for the samples locations was 40% sand, 16% for both silt loam and sandy loam, 12% for loamy sand, 8% for both sandy clay loam and sandy loam. A soil texture classification map was produced for the study area. The first soil texture map for the area differs significantly from the World Food and Agriculture Organization soil texture classification map. It adopts signed tests of the site. The statistical analysis showed that the per cent sand’s standard deviation was 22.65%, silt 19.247%, and 6.416% clay. It turns out that 52% of the soil models from hydrologic group A, 24% from hydrologic group B and 24% from hydrologic group C, Arc GIS software was used to produce maps.
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Comparisons of the common used models for soil water characteristic curves were made,and the best model was selected.Soil water characteristic curve for five different texture soils of heavy loam,medium loam,light loam,tight sand and rough sand was successfully simulated with Van Genuchten equation.The variation tendency of soil water characteristic curve and its parameters were analyzed: water characteristic curve for rough sand changed sharply,but gently for tight sand,and light loam,and medium loam and heavy loam soils;curve shape factor n become bigger as the particle sizes increased and the content of physical clay particle reduced;alpha values for heavy clayed soil were found smaller larger for light soil.
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Soil texture
Silt
Akaike information criterion
Pedotransfer function
Texture (cosmology)
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Soil surface texture is an important environmental factor that influences crop productivity because of its direct effect on soil water and complex interactions with other environmental factors. Using 30-year data, an agricultural system model (DSSAT-CERES-Wheat) was calibrated and validated. After validation, the modelled yield and water use (WU) of spring wheat (Triticum aestivum L.) from two soil textures (silt loam and clay) under rain-fed condition were analyzed. Regression analysis showed that wheat grown in silt loam soil is more sensitive to WU than wheat grown in clay soil, indicating that the wheat grown in clay soil has higher drought tolerance than that grown in silt loam. Yield variation can be explained by WU other than by precipitation use (PU). These results demonstrated that the DSSAT-CERES-Wheat model can be used to evaluate the WU of different soil textures and assess the feasibility of wheat production under various conditions. These outcomes can improve our understanding of the long-term effect of soil texture on spring wheat productivity in rain-fed condition.
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Studies on the acidic buffering capacbility in different texture types in Xinxiang area,showed that acidic buffering capacities of clay soil is larger than sandy soil.The relation between physical clay and acidic buffering capacbility is a positive correlation with correlation coefficient of 0.98.The acidic buffering capacbility in different texture soils in the north China plain is clay soilmedium loam soillight loam soilsand loam soilsand soil.
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Clay soil
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