The soil‐landscape relationships frequently used for soil mapping activities are seldom documented. We developed a statistical model that relates soil drainage classes to eight landscape parameters describing slope morphology, proximity to surface drainage features, and soil parent material. Soil profiles and landscape parameters were described at 305 randomly selected sampling points within the Mifflintown 7.5‐min topographic quadrangle in the unglaciated ridge and valley physiographic province of central Pennsylvania. Variables defining the spatial structure of the landscape were derived from digitized 1:24 000 scale maps of streams and drainageways, surficial geology, bedrock geology, and a digital elevation model. These data were stored in a geographic information system and overlaid with the sampling point locations to define a database of 305 known soil‐landscape combinations. These soil‐landscape combinations were used to derive a statistical soil‐landscape model using multivariate discriminant analysis and class frequency information. For soil drainage class, a 74% overall agreement with field observations was found for the model using a cross‐validation approach, compared with 69% for the published soil survey. The model correctly predicted a majority of the observations within each drainage class and provided a consistent method of extrapolating point information about soils to the three‐dimensional landscape.
The direct application of quantitative soil‐landscape models for soil mapping has been limited by technological constraints. This study combines a statistically based soil‐landscape model and geographic information system (GIS) technology to create soil drainage class maps. An existing soil‐landscape model that predicts soil drainage class from parent material, terrain, and surface drainage feature proximity variables was used. A digital geographic database of parent material, terrain, and surface drainage feature proximity variables stored in a geographic information system were used as model inputs. Combinations of these landscape variables were defined by overlaying the digital maps and by applying the soil‐landscape model to create predictive maps of soil drainage class probability and most‐likely soil drainage class. The modeled soil drainage class map agreed with an Order II (1:20000 scale) soil survey for 67% of the study area. A majority of the disagreement was attributed to areas predicted as somewhat poorly to moderately well drained by the model and well drained by the soil survey. This technique consistently assigns soil drainage class based on landscape attributes, documents the data and decision criteria used for drainage class assignment, estimates the uncertainty associated with drainage class assignment, and generates a digital map for GIS applications.
In this study we compared soil characterization data from the NRCS Soil Survey Laboratory (SSL) at the National Soil Survey Center (NSSC) and Cornell Nutrient Analysis Laboratory (CNAL) for paired analyses of selected soil chemical and physical properties. This assessment of soil characterization data generated by CNAL and NSSC, as determined using simple linear regression, demonstrated substantial agreement between the two laboratories. The results can help local practitioners obtain data more rapidly through local university‐based laboratories, while still allowing comparison and statistical analyses with data provided by the national soil survey laboratory.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.