Using multivariate analysis of soil fertility as a tool for forest fertilization planning

In this paper, testing soil for forest formulated fertilization needs is analyzed, describes the system architecture and hierarchy, and designs the geospatial database. On the basis of the GIS software make thematic analysis for soil fertility, according to site nutrient effect model to make decisions of forest fertilization, build the soil testing for forest formulated fertilization systems, services the forestry sector and the foresters, provide technical support for science to achieve forest soil testing and fertilizer.

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Grouping soils of the Montreal Lowlands (Quebec) according to fertility and P sorption and desorption characteristics

Canadian Journal of Soil Science ◽

10.4141/s00-021 ◽

2001 ◽

Vol 81 (1) ◽

pp. 71-83 ◽

Cited By ~ 18

Author(s):

M L Leclerc ◽

M C Nolin ◽

D. Cluis ◽

R R Simard

Keyword(s):

Multivariate Analysis ◽

Soil Fertility ◽

Ammonium Oxalate ◽

Chemical Properties ◽

Soil Survey ◽

Classification Model ◽

Soil P ◽

P Sorption ◽

Physico Chemical ◽

Extractable P

Soil tests P (STP) developed to estimate P fertilizer needs and designed to produce optimal economic crop yields, are often not well suited to assess potential environmental impact of fertilization practices. The objective of this study was to develop interpretative soil groupings of the Montreal Lowlands area (MLA) based on soil physico-chemical properties and on soil P sorption and desorption characteristics. Soil P sorption and desorption characteristics together with STP may help in evaluating potential risks of soil P addition. Sixty-six soil types (phases of soil series based on surface texture) were selected as representative soils of the study area. Twenty-seven soil properties were used, including Mehlich-3 extractable P and Al, ammonium-oxalate extractable P, Fe and Al, P sorption index (Psi) and Bray-2 extractable P(BR2P). Multivariate analysis was applied to generate clusters and interpret soil groupings. Principal components analysis yielded two components related: (1) to soil inherent fertility (texture) and (2) to P sorption capacity and desorption intensity. The Ward's clustering method was then applied to the first two component scores. Five soil fertility groups were obtained. Multiple discriminant analysis proposed a classification model using a small subset of variables. Five variables were selected among the soil survey characteristics for discriminating soil groups: clay content, pH measurement in water, cation exchange capacity, BR2P and ammonium-acetate extractable Mg. Adding Psi in the model improved the classification correctness. The results of this study indicate that physico-chemical properties of the surface layer used together with soil P sorption-desorption characteristics contributed to the development of an interpretative grouping that may also be useful to assess vulnerability to water contamination by P. Key words: Soil behavior, soil fertility groups, multivariate analysis techniques

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Multivariate Analysis for Soil Fertility Characterization of Harve Sub-watershed, Chamarajanagar District, Karnataka

Journal of the Indian Society of Soil Science ◽

10.5958/0974-0228.2019.00046.x ◽

2019 ◽

Vol 67 (4) ◽

pp. 431

Author(s):

A. Sathish ◽

Vinay Kumar ◽

Ramakrishna Parama

Keyword(s):

Multivariate Analysis ◽

Soil Fertility

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Evaluation of Soil Fertility Using Multivariate Analysis and GIS in Moghra Oasis, Egypt

Egyptian Journal of Soil Science ◽

10.21608/ejss.2016.3331 ◽

2016 ◽

Vol 56 (4) ◽

pp. 589-603

Keyword(s):

Multivariate Analysis ◽

Soil Fertility

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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