Soil-Based Vegetation Productivity Model for Coryell County, Texas

Bin Wen and Jon Bryan Burley
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Bin Wen: College of Landscape Architecture and Art, Hunan Agriculture University, Changsha 410128, China
Jon Bryan Burley: Landscape Architecture, School of Planning, Design, and Construction, Michigan State University, East Lansing, MI 48824, USA

Sustainability, 2020, vol. 12, issue 13, 1-14

Abstract: Managers, scientists, planners and designers of landscapes are interested in systematic investigations, to predict the reconstruction of disturbed soil resources for optimum vegetation productivity. In this study, a predictive equation for estimating neo-soil plant growth in Coryell County, Texas was developed. The equation predicts the vegetation growth for wheat ( Triticum aestivum L.), oats [ Avena sativa L. (1753)], sorghum [ Sorghum bicolor (L.) Moench], cotton lint ( Gossypium hirsutum L.), Bermuda grass [ Cynodon dactylon (L.) Pers.], and rangeland production in general. The results suggest that an all-vegetation predictive model was highly significant ( p ≤ 0.0001), explaining over 80% of the variance. The equation employed hydraulic conductivity as a main-effect variable; bulk density and hydraulic conductivity as squared terms; and percent clay times bulk density, bulk density times soil reaction, hydraulic conductivity times available water holding capacity, and hydraulic conductivity times soil reactions as first order interaction terms, with each predicting variable containing a p -value equal to or less than 0.05. The results suggest that an annual crop equation and a plant-specific cotton lint equation also have merit.

Keywords: environmental design; landscape reclamation; landscape planning; soil science; physical geography; plant ecology; landscape architecture (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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