Despite the soil science discipline in the USA hitting hard times in the 1980s and 1990s, there were still many positive advances within soil science in the USA during these two decades. There was an increased use of geophysical instrumentation, remote sensing, geographic information systems (GIS), and global positioning systems (GPS), and research began in digital soil mapping, all of which lead to better understanding of the spatial distribution and variability of soils. Digital soil mapping is being incorporated into the National Cooperative Soil Survey, and the impact of humans on the soil system is being fully recognized. The expansion of soils into new areas and widening recognition of the importance of soils gives the field hope for a bright future in the USA.
This raster contains the natural, inherent, soil wetness of the lower 48 states, as determined by the ordinally based Natural Soil Drainage Index (DI). The DI is intended to reflect the amount of water that a soil can supply to growing plants under natural conditions. It ranges from 0 for the very driest soils and exposed bedrock, to … Continue reading Drainage Index Grid (conterminous U.S.)
This raster describes the inherent, soil productivity of the lower 48 states, as determined by the ordinally based Natural Soil Productivity Index (PI). The PI uses family-level Soil Taxonomy information, i.e., interpretations of taxonomic features or properties that tend to be associated with naturally low or high soil productivity, to rank soils from 0 (least productive) to 19 (most … Continue reading Productivity Index Grid (conterminous U.S.)
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.