• Productivity 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 natural low or high soil productivity, to rank soils from 0 (least productive) to 19 (most productive).

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  • The Soil Productivity Index (2012 AAG Conference)

    This poster introduces a new, ordinally based, Soil Productivity Index (PI). The demonstration map shows the PI for the lower 48 states. The PI uses family-level Soil Taxonomy information, i.e., interpretations of taxonomic features or properties that tend to be associated with natural low or high soil productivity, to rank soils from 0 (least productive) to 19 (most productive). The index has wide application, because, unlike competing indexes, it does not require copious amounts of soil data, e.g., pH, organic matter, or CEC, in its derivation.

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  • The Soil Productivity and Drainage Indexes (2011 NCSS Conference)

    This poster presents the Soil Drainage Index (DI) and introduces a new, Soil Productivity Index (PI). These indexes are taxonomically-based, ordinal estimates of relative soil properties and are shown mapped side-by-side for the lower 48 states. The DI is intended to reflect the amount of water that a soil can supply to growing plants under natural conditions. The PI uses 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 productive).

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  • A taxonomically based, ordinal estimate of soil productivity for landscape-scale analyses

    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.

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