• Defining Colluvium and Alluvium

    To better understand how researchers differentiate these two materials and to look for common ground between different perspectives, this poster is promoting a survey of how people define them. One early respondent described the survey as “a lot of fun.” We hope you enjoy it too!

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  • History of soil geography in the context of scale

    Categories of cartographic scale correspond to the selection of environmental soil predictors used to initially create historical soil maps. Paradigm shifts in soil mapping and classification can be best explained by not only their correlation to historical improvements in scientific understanding, but also by differences in purpose for mapping, and due to advancements in geographic technology. Although the hierarchy of phenomena scales observed in this study is generally known in pedology today, it also represents a new view on the evolution of soil science.

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  • Oracles and Science: The Trouble with Predictions

    We all want to know the future, but what is the best way to make predictions? Assuming we don’t have a crystal ball, we have to find patterns in the available information and make our best, informed guess. This is what scientists do.

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  • What is Colluvium? An Interactive Poster Seeking a Common Definition to Improve International Communication

    This poster first attempts to summarize the variety of definitions for colluvium. Then it begins an experiment of asking viewers to identify the terminology they would use to define colluvium and alluvium as well as distinguish between the two.

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  • Towards mapping soil carbon landscapes: Issues of sampling scale and transferability

    This study examines the spatial patterns and accuracies of predictions made by different spatial modelling methods on sample sets taken at two different scales. These spatial models are then tested on independent validation sets taken at three different scales. Each spatial modelling method produced similar, but unique, maps of soil organic carbon content (SOC%). Kriging approaches excelled at internal spatial prediction with more densely spaced sample points.

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  • Soil mapping, classification, and pedologic modeling: History and future directions

    Soil mapping, classification, and pedologic modelling have been important drivers in the advancement of our understanding of soil. Advancement in one of these highly interrelated areas tend to lead to corresponding advances in the others. Traditionally, soil maps have been desirable for purposes of land valuation, agronomic planning, and even in military operations. The expansion of the use of soil knowledge to address issues beyond agronomic production, such as land use planning, environmental concerns, energy security, water security, and human health, to name a few, requires new ways to communicate what we know about the soils we map as well as bringing forth research questions that were not widely considered in earlier soils studies.

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  • The Real Benefits of Digital Soil Mapping

    Traditional soil mapping methods are not as bad as they are sometimes made out to be, which can cause us to misunderstand the advancements we’ve made with digital soil mapping. In many cases, the most significant difference between traditional and digital soil maps is the quality and amount of data available to make the map. This is where digital soil mapping truly shines, because it would be practically impossible to utilize all of the data we have now for mapping soil using traditional methods.

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  • Surficial Geology of Iowa

    Raster of the surficial geology of Iowa based on the parent material interpretations of the US Soil Survey. The map is built from delineations made at a 1:15,840 scale, then converted to a 10 m resolution grid. This data set is based on the spatial information in the USDA-NRCS gSSURGO database and soil series characteristics described in the official soil series descriptions (OSD).

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  • Spatial modeling of organic carbon in degraded peatland soils of northeast Germany

    The objective of this study was to evaluate the ability of high-resolution, minimally invasive sensor data to predict spatial variation of soil organic carbon stocks within highly degraded peatland soils in northeast Germany. Soil organic carbon density was related to elevation, electrical conductivity, and peat thickness. Modeling peat thickness based on sensor data needs additional research, but seems to be a valuable set of covariates in digital soil mapping.

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  • Types of Measurement Error

    We always want to avoid error, but it is a fact of life. At the foundation of analysis and modelling, we rely on measurements. Because errors in measurements are inescapable, the important question is how much does the error affect the result? I start the conversation by explaining what measurement error is, including its component parts, and what we can do to minimize its effect.

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