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Keys to Soil Taxonomy

01 Jan 2010-
TL;DR: In this paper, the Soils That We Classify (Soil Orders, Suborders, Great Groups, and Subgroups) is presented. And the taxonomic class of a Soil is identified.
Abstract: Chapter 1 The Soils That We Classify Chapter 2 Differentiae for Mineral Soils and Organic Soils Chapter 3 Horizons and Characteristics Diagonostic for the Higher Categories Chapter 4 Identification of the Taxonomic Class of a Soil Chapters 5 16 The Key to Soil Orders, Suborders, Great Groups, and Subgroups is accessible on-line and is enriched with information from Chapters 5 thru 16. Browse thru the heirarchy for detailed information, or use this Search for Subgroup. Type in a partial or full name of a soil Subgroup to get detailed infomation about it. Chapter 17 Family and Series Differentiae and Names Chapter 18 Designations for Horizons and Layers Master Horizons List of Master Horizons, and their meanings. Suffixes List of Suffixes, and their meanings.
Citations
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Reference EntryDOI
31 Oct 2001
TL;DR: The American Society for Testing and Materials (ASTM) as mentioned in this paper is an independent organization devoted to the development of standards for testing and materials, and is a member of IEEE 802.11.
Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

3,792 citations

Journal ArticleDOI
TL;DR: Soils collected across a long-term liming experiment were used to investigate the direct influence of pH on the abundance and composition of the two major soil microbial taxa, fungi and bacteria, and both the relative abundance and diversity of bacteria were positively related to pH.
Abstract: Soils collected across a long-term liming experiment (pH 4.0-8.3), in which variation in factors other than pH have been minimized, were used to investigate the direct influence of pH on the abundance and composition of the two major soil microbial taxa, fungi and bacteria. We hypothesized that bacterial communities would be more strongly influenced by pH than fungal communities. To determine the relative abundance of bacteria and fungi, we used quantitative PCR (qPCR), and to analyze the composition and diversity of the bacterial and fungal communities, we used a bar-coded pyrosequencing technique. Both the relative abundance and diversity of bacteria were positively related to pH, the latter nearly doubling between pH 4 and 8. In contrast, the relative abundance of fungi was unaffected by pH and fungal diversity was only weakly related with pH. The composition of the bacterial communities was closely defined by soil pH; there was as much variability in bacterial community composition across the 180-m distance of this liming experiment as across soils collected from a wide range of biomes in North and South America, emphasizing the dominance of pH in structuring bacterial communities. The apparent direct influence of pH on bacterial community composition is probably due to the narrow pH ranges for optimal growth of bacteria. Fungal community composition was less strongly affected by pH, which is consistent with pure culture studies, demonstrating that fungi generally exhibit wider pH ranges for optimal growth.

2,966 citations

Book
24 Nov 2017
TL;DR: The authors presented the most current methodology available for wetland classification and culminated a long-term effort involving many wetland scientists, which represented the most accurate methodology available in the literature.
Abstract: From foreword: "This report represents the most current methodology available for wetland classification and culminates a long-term effort involving many wetland scientists."

2,427 citations

Journal ArticleDOI
16 Feb 2017-PLOS ONE
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.

2,228 citations

01 Jan 2009

2,036 citations

References
More filters
Reference EntryDOI
31 Oct 2001
TL;DR: The American Society for Testing and Materials (ASTM) as mentioned in this paper is an independent organization devoted to the development of standards for testing and materials, and is a member of IEEE 802.11.
Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

3,792 citations