Soils provide critical support essential for life on earth, regulate processes across diverse terrestrial and aquatic ecosystems, and interact with the atmosphere. In this paper, we present the outcomes of an initiative to identify pressing research questions as a tool for guiding future soil science research priorities.
County soil surveys document thin loess deposits across large tracts of Michigan’s western Upper Peninsula (UP), which we informally call the Peshekee loess. Our study is the first to examine the distribution, thickness and textural characteristics of these loess deposits, and speculate as to their origins. We introduce and describe a method by which the mixed sand data are removed, or “filtered out,” of the original particle size data, to better reflect the original textural characteristics of the loess.
Because laser diffractometry produces much more detailed data than does traditional pipette analysis, and because a much smaller sample is used in the analysis, precision or repeatability of laser-produced PSA data is a concern. The approach presented provides both a simple method for assessing the variation in PSA data sets and establishes a comparable standard for determining when additional measurements are needed to find a more precise result.
Soil Survey maps are the preeminent data set collected about our environment. Although there are other impressive data sets that are regularly used for studying and utilizing the environment, none match the wide utility and potential of soil maps. Recent innovations create opportunities to increase both the resolution and the efficiency at which Soil Survey maps are made.
We introduce, evaluate, and apply a new ordinally based soil Productivity Index (PI). The index has a wide application generally at landscape scales. Unlike competing indexes, it does not require copious amounts of soil data, for example, pH, organic matter, or cation exchange capacity, in its derivation. Geographic information system applications of the PI, in particular, have great potential. For regionally extensive applications, the PI may be as useful and robust as other indexes that have much more exacting data requirements.
The water regimes of contemporary wetlands when compared to their historic regimes suggest that many of today’s wetlands have different water regimes than they did prior to the onset of drainage. Because of the regional lowering of the groundwater table, many of today’s wetlands have drier water regimes, but some have wetter water regimes because they receive drainage tile inputs. Our results indicate that restoration has favored the wettest classes of wetlands and that temporarily to saturated wetland classes have not been restored in proportion to their relative abundance in the pre-drainage landscape.
This manuscript reports on the change in the undergraduate Agronomy student population at Iowa State University since instigating a marketing campaign. The most important elements in the development of the marketing campaign were a simple message, artistic style, branding, and advertising in smart locations. After four years, one hundred and six more students were studying Agronomy, which was an increase of 91%.
We estimated the pre-settlement density and area of different classes of palustrine wetlands on the Des Moines Lobe based on soil characteristics. Prior to drainage, wetlands covered nearly half of the Des Moines Lobe and there were differences in both the types and relative abundance of wetlands among the four geologic subdivisions of the Lobe (Bemis, Altamont, and Algona till plains and Altamont Lake).
The integration of soil survey maps with Geographic Information Systems (GIS) allows for an almost infinite level of collaboration across disciplines that use information related to soil databases. This study created a Quaternary geologic map by categorizing soil descriptions into a geologic context and joining the attributes with the Soil Survey Geographic (SSURGO) database in ArcGIS®. The resulting map communicates many of the spatial intricacies of the Des Moines Lobe landform with 15 map units based on geologic units.
Nitrate concentration and stream discharge data from USGS National Stream Quality Accounting Network monitoring stations in the upper Mississippi River (UMR) and Ohio River basins were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations. The model accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Modeling results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas.