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Planners, designers, soil scientists, foresters, agronomists, government agencies, and concerned citizens are interested in reliable and predictable methods to reconstruct and manage disturbed and native soil resources for optimum plant productivity. In our study, we developed predictive models to assess neo-soil reconstruction for study areas in Montana, Wyoming, and Colorado. We developed models to predict plant growth based upon soil characteristics for agronomic crops, rangeland, and woody plants. Our results indicated that potentially three to four dimensions of plant growth could produce predictive models, p<0.0001, explaining 71% to 88% of the variance. Regression models employed the main-effect variables, squared terms, and first order interaction terms for: soil reaction, percent organic matter, electrical conductivity, percent slope, bulk density, hydraulic conductivity, available water holding capacity, topographic position, percent rock fragments, and percent clay, with each regressor containing a p-value less than 0.05.

Soil-Based Vegetation Productivity Models for Disturbed Lands along the Northern and Central, Western Great Plains, USA