Soil Based Vegetation Productivity Model for the North Dakota Coal Mining Region

Burley, Jon Bryan; Fowler, Gary W.; Polakowski, Ken; Brown, Terence James

doi:10.1076/ijsm.15.4.213.7415

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…The eigenvector of the first dimension contained larger coefficients for the variables ranging from 0.553 to 0.776. Usually, such values are strongly associated with the first dimension [40][41][42][43][44][45][46][47][48][49][50][51][52]. In other words, it would be possible to explain 47.969% of the variance in one dimension with all of the variables strongly associated with the first dimension.…”

Section: Resultsmentioning

confidence: 99%

“…For comparison purposes, the first four principal components and the factor analysis dimensions can be employed in linear equations to assess and compare additional villages and environments as illustrated by the multivariate efforts of other investigators [40][41][42][43][44][45][46][47][48][49][50][51][52].…”

Section: Discussionmentioning

confidence: 99%

“…Our study examined the evaluation of the rural landscape of the Xiangxi District in Hunan Province, China, by surveying the responses of planning and design experts to gain their perception of the environmental qualities that comprise the characteristics of the setting. Such studies often generate many variables to consider and may rely upon multivariate statistical analysis to clarify the results [40][41][42][43][44][45][46][47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

“…To provide a more quantitative approach, factor analysis (FA) and principal component analysis (PCA) are two methods of multivariate statistical analysis which have been widely used in soil science, water quality science, climatology, medicine, urban geography, and other fields and have achieved insights into the relationships amongst a larger set of variables [40][41][42][43][44][45][46][47][48][49][50][51][52]. These approaches attempt to reduce and group the number of dimensions/variables to glean a clearer understanding of underlying relationships amongst the variables.…”

Section: Introductionmentioning

confidence: 99%

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Expert Opinion Dimensions of Rural Landscape Quality in Xiangxi, Hunan, China: Principal Component Analysis and Factor Analysis

Wen

Burley

2020

Sustainability

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Scholars and planning/design professionals are interested in the quantitative, metric properties influencing the quality and assessment of rural landscape space. These metrics are important for guiding rural planning, design, and construction of cultural rural environments. Respondents and metrics from four sampled villages (Qixin, Hangsha, Yanpai Xi, and Lvdong) in the Xiangxi District of Hunan Province in China were examined, employing statistical principal component analysis and factor analysis methods to understand the identifying properties concerning planning and design features of these rural mountain village landscape spaces. The two approaches reveal different aspects from the same variables. Through factor analysis and rotation, four general dimensions were revealed explaining approximately 62% of the variance: a settlement and environmental axis, an intangible culture axis, a productive landscape axis, and a transportation and public space axis, supporting the standing notion that the variables were ordinated across four dimensions in these mountain villages and occupied an elliptical plane that was different than the predicted space occupied by nearby cites. In contrast, principal component analysis revealed that the variables could be grouped into one latent dimension explaining 48% of the variance and revealing an alternative interpretation and spatial plot of the sites.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Expert Opinion Dimensions of Rural Landscape Quality in Xiangxi, Hunan, China: Principal Component Analysis and Factor Analysis

Wen

Burley

2020

Sustainability

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“…Soil structure and nutrient status are among the key indicators that are used to assess the restoration and maintenance of ecological functions in degraded ecosystems [13]. And Burley et al [14,15,16] did several seminal works and important advances crossing top soil adaptation for vegetation growth. Therefore, the selection of topsoil substitutes has become a key part of the process of soil reconstruction in zones of topsoil scarcity.…”

Section: Introductionmentioning

confidence: 99%

Responses of Melilotus officinalis Growth to the Composition of Different Topsoil Substitute Materials in the Reclamation of Open-Pit Mining Grassland Area in Inner Mongolia

et al. 2019

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The purpose of this study was to reveal that reconstructed soil composed of different types and proportions of materials has different effects on the growth of Melilotus officinalis, and to determine the most suitable formula of reconstructed soil materials to use for soil replacement. Using topsoil, coal gangue, fly ash, and rock and soil stripping materials from Shengli Mining Area of Inner Mongolia as raw materials, stratified and mixed pot experiments were carried out in a greenhouse using different proportions of each material. The differences in the aboveground biomass, leaf width, plant height, and root length of Melilotus officinalis plants in pot experiments were then compared using analysis of variance. The results showed that using different combinations of materials in different proportions affected the growth status of Melilotus officinalis, and their effects on biomass were greater than their effects on plant height, root length, and leaf width. When topsoil, coal gangue, and rock and soil stripping materials were mixed at a ratio of 3:3:4, respectively, the biomass of Melilotus officinalis increased by nearly 30% compared with that of plants potted in pure topsoil. When the content of coal gangue was controlled to be 30%, the content of fly ash was below 10%, and the content of rock and soil stripping materials was below 40%, the reconstructed soil conditions clearly promoted the growth of Melilotus officinalis. Coal gangue, rock and soil stripping materials, and fly ash can thus be used as substitutes for topsoil. Mixing soil reconstruction materials in the optimal proportion can solve the scarcity of topsoil in the grassland mining areas in the study region and, at the same time, can effectively improve the utilization of solid waste in this mining area.

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

Burley¹,

Wu²,

He³

et al. 2020

JOAAT

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Soil Based Vegetation Productivity Model for the North Dakota Coal Mining Region

Cited by 6 publications

References 13 publications

Expert Opinion Dimensions of Rural Landscape Quality in Xiangxi, Hunan, China: Principal Component Analysis and Factor Analysis

Expert Opinion Dimensions of Rural Landscape Quality in Xiangxi, Hunan, China: Principal Component Analysis and Factor Analysis

Responses of Melilotus officinalis Growth to the Composition of Different Topsoil Substitute Materials in the Reclamation of Open-Pit Mining Grassland Area in Inner Mongolia

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

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