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

Burley, Jon Bryan; Wu, Zhen; He, Shuting; Li, Xiaoying

doi:10.18178/joaat.7.1.1-7

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…The equation is consistent with many other reported equations containing main effect terms, square terms and interaction terms [11][12][13][14][15][16][17][18][19][23][24][25]. The variable percent slope appears to be a curvilinear responding variable (Figure 4).…”

supporting

confidence: 88%

“…The best predictor equations are those that explain the most variance without being over-specific, containing only significant variables (p ≤ 0.05)." [25]. The weighting formula examines four layers of the soil profile in increments of 30.48 cm to a depth of 1.2192 m. The average value for each variable within a specified layer is employed in the weighting formula.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, another Chinese and American team reported upon equations in the coal mining regions of Montana, Wyoming and Colorado [25]. The study in Montana, Wyoming and Colorado revealed differences in the equations concerning high altitude and crop production in extremely cold and dry environments.…”

Section: Introductionmentioning

confidence: 99%

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Soil-Based Vegetation Productivity Model for Coryell County, Texas

Wen

Burley

2020

Sustainability

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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.

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supporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

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Soil-Based Vegetation Productivity Model for Coryell County, Texas

Wen

Burley

2020

Sustainability

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“…The American Landscape Architecture Research Universe and a Higher Education Ordination… DOI: http://dx.doi.org/10.5772/intechopen.99119 quality [12][13][14][15] and in natural resources to develop soil reclamation Eqs. [16][17][18][19][20][21]. 'Human intrusion theory' in explaining visual quality equations [22] and 'mesic preference theory' for reclaiming surface mines [23] are explanations that are scientific theories developed by landscape scholars.…”

Section: Introductionmentioning

confidence: 99%

The American Landscape Architecture Research Universe and a Higher Education Ordination: Descriptive Insights into the Discipline and Profession of Landscape Architecture

Liu¹,

X²,

Yue³

et al. 2022

Landscape Architecture Framed From an Environmental and Ecological Perspective

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Landscape scholars, educators, and academics are interested in the structure and nature of the knowledgebase that comprises both the discipline of landscape architecture and the profession of landscape architecture. In this study, the latent nature of the landscape architecture discipline was revealed by constructing a principal component citation analysis representation (the landscape architecture research universe) concerning several decades of literature (1982–2017) in Landscape Journal, a preeminent American journal addressing landscape architecture research. In addition, an ordination was developed describing the curriculum relationships between fifteen top American universities teaching landscape architecture as identified by ‘DesignIntelligence,’ preparing students for practicing in the profession of landscape architecture. The results revealed that in the discipline, the research activity is highly diverse along many dimensions, constantly evolving as new topics are explored. The pattern in landscape architecture research is broad, as the discipline integrates knowledge and ideas in many fields. In contrast, landscape architecture curriculums, teaching the fundamentals of the profession, are fairly closely clustered together and quite similar, with small differences reflecting emphasis in either landscape history or the visual arts, and mathematics or course electives. This dual identity is both a source of conflict and a unique opportunity.

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“…This resulted in the study and publication of a silica mining region in Chippewa County, Wisconsin and a kaolinite mine area in Georgia [27]. Coal mining in Montana, Wyoming, Colorado, and Texas was also explored [5,28]. Corr et al (Dustin Corr was an American graduate student of Dr. Burley) studied developing equations in the iron mining region of the Upper Peninsula of Michigan, deriving mesic and the first xeric set of equations, concluding the current set of equations that have been developed with this methodology [6].…”

Section: Introduction and Literaturementioning

confidence: 99%

Predictive Models for Reforestation and Agricultural Reclamation: A Clearfield County, Pennsylvania Case Study

Yue¹,

Burley²

2022

Vegetation Index and Dynamics

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Natural resource scientists, concerned citizens, and government officials are interested in reconstructing disturbed environments for reforestation and agricultural productivity. We examined Clearfield County in Pennsylvania, USA, to develop a predictive model to reconstruct the landscape for seven agronomic crops (corn, corn silage, oats, alfalfa hay, red clover, bluegrass, and soybeans) and thirteen woody plants (white cedar, lilac, highbush cranberry, Amur maple, gray dogwood, peashrub, white spruce, white pine, red maple, red pine, jack pine, nannyberry, and white ash). A significant predictive model (p ≤ 0.001) was generated explaining 96.94% of the variance, with percent clay, bulk density, hydraulic conductivity, available water capacity, pH, percent organic matter, percent rock fragments, slope, topographic position, and electrical conductivity explored as main effect terms, plus squared terms, and first order interaction terms. The model is not over-specified and each predictor is significant (p ≤ 0.05). The modeling effort suggests that there are at least several clusters of vegetation preference dimensions based upon the terrain of the landscape. The model provides insight into how to reconstruct the disturbed environment for vegetation in the study area.

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

Cited by 4 publications

References 16 publications

Soil-Based Vegetation Productivity Model for Coryell County, Texas

Soil-Based Vegetation Productivity Model for Coryell County, Texas

The American Landscape Architecture Research Universe and a Higher Education Ordination: Descriptive Insights into the Discipline and Profession of Landscape Architecture

Predictive Models for Reforestation and Agricultural Reclamation: A Clearfield County, Pennsylvania Case Study

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