Biological profile from soil using components analysis
It is known that principal components analysis is used as a mathematical procedure that transforms a set of correlated response variables into a new set of non-correlated variables known as main components. The sensitivity of the variables in response to the changes in the soil environment under dif...
| Autores principales: | , |
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| Formato: | info:ar-repo/semantics/documento de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
Sociedad Argentina de Estadística
2024
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/17991 |
| _version_ | 1855037773544685568 |
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| author | Rossi, Maria Sol Mon, Rodolfo |
| author_browse | Mon, Rodolfo Rossi, Maria Sol |
| author_facet | Rossi, Maria Sol Mon, Rodolfo |
| author_sort | Rossi, Maria Sol |
| collection | INTA Digital |
| description | It is known that principal components analysis is used as a mathematical procedure that transforms a set of correlated response variables into a new set of non-correlated variables known as main components. The sensitivity of the variables in response to the changes in the soil environment under different health conditions of trees. In order to clarify the structure of interdependence between all properties, principal components analysis was applied to the data corresponding to all the soil properties studied. We analyzed this dataset and we found that the metabolic quotient (qCO2) and micC are the two parameters that differed most in the studied soil.
The metabolic quotient (RES:micC) was higher in the presence of pathogens, while the biological quotient (micC:orgC) was lower. The score plots indicated that the samples can be divided into two groups (LRs-BAs and LRc, LRh, BAc, BAh), which suggests that the two
different states of plant health conditions had a strong effect on soil properties. In PCA biochemical parameters explained 84 % of the system variability. Strong association between LRs with DH and b-Glu was observed. The score plot indicated that the samples can be defined into two groups: LRc, LRh, BAc, BAh and LRs, BAs which suggests that the presence of the patoghen had an effect on soil properties. The parameters studied showed the same variability within LR as BA soils. Our results empathize the importance of determining soil quality indicators and rizopherical bacterial communities in the presence of root pathogens. Bacterial communities from soil played a differential role in the presence of root pathogens. Microbiological activity correlated with biochemical properties from soil in locations with trees with fungal symptoms. We hypothesized that changes in the health state of trees could be related to changes in rhizospheric soil quality indicators. |
| format | info:ar-repo/semantics/documento de conferencia |
| id | INTA17991 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Sociedad Argentina de Estadística |
| publisherStr | Sociedad Argentina de Estadística |
| record_format | dspace |
| spelling | INTA179912024-05-31T13:17:33Z Biological profile from soil using components analysis Rossi, Maria Sol Mon, Rodolfo Organismos Indicadores Microorganismos del Suelo Propiedades Físico - químicas Suelo Indicator Organisms Soil Microorganisms Soil Chemicophysical Properties Main Components Analysis Soil Biological Indicators Análisis de Componentes Principales Indicadores Biológicos del Suelo It is known that principal components analysis is used as a mathematical procedure that transforms a set of correlated response variables into a new set of non-correlated variables known as main components. The sensitivity of the variables in response to the changes in the soil environment under different health conditions of trees. In order to clarify the structure of interdependence between all properties, principal components analysis was applied to the data corresponding to all the soil properties studied. We analyzed this dataset and we found that the metabolic quotient (qCO2) and micC are the two parameters that differed most in the studied soil. The metabolic quotient (RES:micC) was higher in the presence of pathogens, while the biological quotient (micC:orgC) was lower. The score plots indicated that the samples can be divided into two groups (LRs-BAs and LRc, LRh, BAc, BAh), which suggests that the two different states of plant health conditions had a strong effect on soil properties. In PCA biochemical parameters explained 84 % of the system variability. Strong association between LRs with DH and b-Glu was observed. The score plot indicated that the samples can be defined into two groups: LRc, LRh, BAc, BAh and LRs, BAs which suggests that the presence of the patoghen had an effect on soil properties. The parameters studied showed the same variability within LR as BA soils. Our results empathize the importance of determining soil quality indicators and rizopherical bacterial communities in the presence of root pathogens. Bacterial communities from soil played a differential role in the presence of root pathogens. Microbiological activity correlated with biochemical properties from soil in locations with trees with fungal symptoms. We hypothesized that changes in the health state of trees could be related to changes in rhizospheric soil quality indicators. Instituto de Suelos Fil: Rossi, María Sol. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina Fil: Mon, Rodolfo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina 2024-05-31T13:09:06Z 2024-05-31T13:09:06Z 2015-10-06 info:ar-repo/semantics/documento de conferencia info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/17991 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Sociedad Argentina de Estadística 1er. Congreso Argentino de Estadística, Buenos Aires, Argentina del 06 al 10 de octubre de 2015 |
| spellingShingle | Organismos Indicadores Microorganismos del Suelo Propiedades Físico - químicas Suelo Indicator Organisms Soil Microorganisms Soil Chemicophysical Properties Main Components Analysis Soil Biological Indicators Análisis de Componentes Principales Indicadores Biológicos del Suelo Rossi, Maria Sol Mon, Rodolfo Biological profile from soil using components analysis |
| title | Biological profile from soil using components analysis |
| title_full | Biological profile from soil using components analysis |
| title_fullStr | Biological profile from soil using components analysis |
| title_full_unstemmed | Biological profile from soil using components analysis |
| title_short | Biological profile from soil using components analysis |
| title_sort | biological profile from soil using components analysis |
| topic | Organismos Indicadores Microorganismos del Suelo Propiedades Físico - químicas Suelo Indicator Organisms Soil Microorganisms Soil Chemicophysical Properties Main Components Analysis Soil Biological Indicators Análisis de Componentes Principales Indicadores Biológicos del Suelo |
| url | http://hdl.handle.net/20.500.12123/17991 |
| work_keys_str_mv | AT rossimariasol biologicalprofilefromsoilusingcomponentsanalysis AT monrodolfo biologicalprofilefromsoilusingcomponentsanalysis |