Web Application for Spatial Modelling of Field Trials
In plant improvement experimental designs are used to decompose the total phenotypic variance observed in field experiments into at least two components: A genetic and a non-genetic component that is attributable to the spatial variation or environment. Recently, new methodologies for the modelling...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Language: | Inglés |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/103767 |
| _version_ | 1855523891158450176 |
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| author | Aparicio, Johan Steven Ariza-Suárez, Daniel Raatz, Bodo |
| author_browse | Aparicio, Johan Steven Ariza-Suárez, Daniel Raatz, Bodo |
| author_facet | Aparicio, Johan Steven Ariza-Suárez, Daniel Raatz, Bodo |
| author_sort | Aparicio, Johan Steven |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In plant improvement experimental designs are used to decompose the total phenotypic variance observed in field experiments into at least two components: A genetic and a non-genetic component that is attributable to the spatial variation or environment. Recently, new methodologies for the modelling of spatial trends have been published using the arrangement of the experimental units in the field. These methodologies have shown an improvement in the prediction of the genetic potential of evaluated genotypes. However, the use of these tools may be limited because of the cost to access a licensed product and/or the requirement to be familiar with the language and environment that was used for their implementation. This, in turn limits the data analysis efficiency for decision making. These limitations led to the development of Mr.Bean, an easy-to-access, practical and user-friendly tool that integrates the spatial modelling capabilities of SpATS, the graphical versatility of plotly and the interactive and simple construction approach offered by Shiny for the development of Web applications. This tool incorporates descriptive analyses, measures of dispersion and centralization, graphical visualization for comparing multiple variables, the adjustment of
mixed models with or without spatial components and the identification of outlier data. All these capabilities are aimed at plant breeders and in general people working with agricultural field data to make precise decisions more quickly |
| format | Conference Paper |
| id | CGSpace103767 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| record_format | dspace |
| spelling | CGSpace1037672025-11-05T17:42:17Z Web Application for Spatial Modelling of Field Trials Aparicio, Johan Steven Ariza-Suárez, Daniel Raatz, Bodo phenotypes genetics agriculture field trials spatial data In plant improvement experimental designs are used to decompose the total phenotypic variance observed in field experiments into at least two components: A genetic and a non-genetic component that is attributable to the spatial variation or environment. Recently, new methodologies for the modelling of spatial trends have been published using the arrangement of the experimental units in the field. These methodologies have shown an improvement in the prediction of the genetic potential of evaluated genotypes. However, the use of these tools may be limited because of the cost to access a licensed product and/or the requirement to be familiar with the language and environment that was used for their implementation. This, in turn limits the data analysis efficiency for decision making. These limitations led to the development of Mr.Bean, an easy-to-access, practical and user-friendly tool that integrates the spatial modelling capabilities of SpATS, the graphical versatility of plotly and the interactive and simple construction approach offered by Shiny for the development of Web applications. This tool incorporates descriptive analyses, measures of dispersion and centralization, graphical visualization for comparing multiple variables, the adjustment of mixed models with or without spatial components and the identification of outlier data. All these capabilities are aimed at plant breeders and in general people working with agricultural field data to make precise decisions more quickly 2019-07 2019-09-26T14:00:00Z 2019-09-26T14:00:00Z Conference Paper https://hdl.handle.net/10568/103767 en Open Access application/pdf Aparicio, Johan; Ariza-Suarez, Daniel; Raatz, Bodo (2019). Web Application for Spatial Modelling of Field Trials. In: 15-19 of July 2019, Barranquilla, Colombia XXIX Simposio Internacional de Estadística. |
| spellingShingle | phenotypes genetics agriculture field trials spatial data Aparicio, Johan Steven Ariza-Suárez, Daniel Raatz, Bodo Web Application for Spatial Modelling of Field Trials |
| title | Web Application for Spatial Modelling of Field Trials |
| title_full | Web Application for Spatial Modelling of Field Trials |
| title_fullStr | Web Application for Spatial Modelling of Field Trials |
| title_full_unstemmed | Web Application for Spatial Modelling of Field Trials |
| title_short | Web Application for Spatial Modelling of Field Trials |
| title_sort | web application for spatial modelling of field trials |
| topic | phenotypes genetics agriculture field trials spatial data |
| url | https://hdl.handle.net/10568/103767 |
| work_keys_str_mv | AT apariciojohansteven webapplicationforspatialmodellingoffieldtrials AT arizasuarezdaniel webapplicationforspatialmodellingoffieldtrials AT raatzbodo webapplicationforspatialmodellingoffieldtrials |