About: Soil texture is a research topic. Over the lifetime, 9346 publications have been published within this topic receiving 257808 citations. The topic is also known as: texture.
TL;DR: In this paper, the authors examined the association of soil organic carbon (SOC) content with climate and soil texture at different soil depths, and tested the hypothesis that vegetation type, through patterns of allocation, is a dominant control on the vertical distribution of SOC.
Abstract: As the largest pool of terrestrial organic carbon, soils interact strongly with atmospheric composition, climate, and land cover change. Our capacity to predict and ameliorate the consequences of global change depends in part on a better understanding of the distributions and controls of soil organic carbon (SOC) and how vegetation change may affect SOC distributions with depth. The goals of this paper are (1) to examine the association of SOC content with climate and soil texture at different soil depths; (2) to test the hypothesis that vegetation type, through patterns of allocation, is a dominant control on the vertical distribution of SOC; and (3) to estimate global SOC storage to 3 m, including an analysis of the potential effects of vegetation change on soil carbon storage. We based our analysis on .2700 soil profiles in three global databases supplemented with data for climate, vegetation, and land use. The analysis focused on mineral soil layers. Plant functional types significantly affected the vertical distribution of SOC. The per- centage of SOC in the top 20 cm (relative to the first meter) averaged 33%, 42%, and 50% for shrublands, grasslands, and forests, respectively. In shrublands, the amount of SOC in the second and third meters was 77% of that in the first meter; in forests and grasslands, the totals were 56% and 43%, respectively. Globally, the relative distribution of SOC with depth had a slightly stronger association with vegetation than with climate, but the opposite was true for the absolute amount of SOC. Total SOC content increased with precipitation and clay content and decreased with temperature. The importance of these controls switched with depth, climate dominating in shallow layers and clay content dominating in deeper layers, possibly due to increasing percentages of slowly cycling SOC fractions at depth. To control for the effects of climate on vegetation, we grouped soils within climatic ranges and compared distributions for vegetation types within each range. The percentage of SOC in the top 20 cm relative to the first meter varied from 29% in cold arid shrublands to 57% in cold humid forests and, for a given climate, was always deepest in shrublands, inter- mediate in grasslands, and shallowest in forests ( P , 0.05 in all cases). The effect of vegetation type was more important than the direct effect of precipitation in this analysis. These data suggest that shoot/root allocations combined with vertical root distributions, affect the distribution of SOC with depth. Global SOC storage in the to p3mo fsoil was 2344 Pg C, or 56% more than the 1502 Pg estimated for the first meter (which is similar to the total SOC estimates of 1500-1600 Pg made by other researchers). Global totals for the second and third meters were 491 and 351 Pg C, and the biomes with the most SOC at 1-3 m depth were tropical evergreen forests (158 Pg C) and tropical grasslands/savannas (146 Pg C). Our work suggests that plant functional types, through differences in allocation, help to control SOC distributions with depth in the soil. Our analysis also highlights the potential importance of vegetation change and SOC pools for carbon sequestration strategies.
TL;DR: In this article, a model of soil organic matter (SOM) quantity and composition was used to simulate steady-state organic matter levels for 24 grassland locations in the U.S. Great Plains.
Abstract: We analyzed climatic and textural controls of soil organic C and N for soils of the U.S. Great Plains. We used a model of soil organic matter (SOM) quantity and composition to simulate steady-state organic matter levels for 24 grassland locations in the Great Plains. The model was able to simulate the effects of climatic gradients on SOM and productivity. Soil texture was also a major control over organic matter dynamics. The model adequately predicted above-ground plant production and soil C and N levels across soil textures (sandy, medium, and fine); however, the model tended to overestimate soil C and N levels for fine textured soil by 10 to 15%. The impact of grazing on the system was simulated and showed that steady-state soil C and N levels were sensitive to the grazing intensity, with soil C and N levels decreasing with increased grazing rates. Regional trends in SOM can be predicted using four site-specific variables, temperature, moisture, soil texture, and plant lignin content. Nitrogen inputs must also be known. Grazing intensity during soil development is also a significant control over steady-state levels of SOM, and since few data are available on presettlement grazing, some uncertainty is inherent in the model predictions
TL;DR: Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%.
Abstract: This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.
TL;DR: In this paper, a power function relating soil moisture and hydraulic conductivity is used to derive a formula for the wetting front suction required by the Green-Ampt equation.
Abstract: The soil moisture characteristic may be modeled as a power curve combined with a short parabolic section near saturation to represent gradual air entry. This two-part function—together with a power function relating soil moisture and hydraulic conductivity—is used to derive a formula for the wetting front suction required by the Green-Ampt equation. Representative parameters for the moisture characteristic, the wetting front suction, and the sorptivity, a parameter in the infiltration equation derived by Philip (1957), are computed by using the desorption data of Holtan et al. (1968). Average values of the parameters, and associated standard deviations, are calculated for 11 soil textural classes. The results of this study indicate that the exponent of the moisture characteristic power curve can be predicted reasonably well from soil texture and that gradual air entry may have a considerable effect on a soil's wetting front suction.
TL;DR: In this paper, the authors developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and organic matter (OM), and combined these equations with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses.
Abstract: Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant-water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda. gov/soilwater/Index.htm.