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Home> Published Issues> 2020> Volume 7, No. 1, June 2020

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

Jon B. Burley 1, Zhen Wu 2, Shuyue He 3, and Xiaoying Li 3
1. School of Planning Design and Construction, Michigan State University, East Lansing, MI, USA
2. Nanjing Tech University, Nanjing, Jiangsu, P.R. China
3. College of Landscape Architecture, Nanjing Forestry University, Nanjing, Jiangsu, P.R. China

Abstract—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.
Index Terms—sustainable agriculture, environmental design, landscape architecture, disturbed land, landscape planning, soil conservation

Cite: Jon B. Burley, Zhen Wu, Shuyue He, and Xiaoying Li, "Soil-Based Vegetation Productivity Models for Disturbed Lands along the Northern and Central, Western Great Plains, USA," Journal of Advanced Agricultural Technologies, Vol. 7, No. 1, pp. 1-7, June 2020. Doi: 10.18178/joaat.7.1.1-7

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

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