Analysis of greenhouse air temperature distribution using geostatistical methods

Geostatistical analyses have been used very little for the study of the variability of environmental conditions under greenhouse conditions. The spatio‐temporal temperature variation was assessed inside two types of greenhouses in the Bogotá Plateau, Colombia, applying geostatistical methods. A sing...

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Autores:
Tipo de recurso:
Article of journal
Fecha de publicación:
2009
Institución:
Universidad de Bogotá Jorge Tadeo Lozano
Repositorio:
Expeditio: repositorio UTadeo
Idioma:
eng
OAI Identifier:
oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12431
Acceso en línea:
https://elibrary.asabe.org/abstract.asp?aid=27393
http://hdl.handle.net/20.500.12010/12431
http://expeditiorepositorio.utadeo.edu.co
Palabra clave:
Air temperature
Geostatistics
Greenhouses
Kriging
Microclimate
Rights
License
Acceso restringido
id UTADEO2_988b851c91b05e0c8f61bbced8016381
oai_identifier_str oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/12431
network_acronym_str UTADEO2
network_name_str Expeditio: repositorio UTadeo
repository_id_str
dc.title.spa.fl_str_mv Analysis of greenhouse air temperature distribution using geostatistical methods
title Analysis of greenhouse air temperature distribution using geostatistical methods
spellingShingle Analysis of greenhouse air temperature distribution using geostatistical methods
Air temperature
Geostatistics
Greenhouses
Kriging
Microclimate
title_short Analysis of greenhouse air temperature distribution using geostatistical methods
title_full Analysis of greenhouse air temperature distribution using geostatistical methods
title_fullStr Analysis of greenhouse air temperature distribution using geostatistical methods
title_full_unstemmed Analysis of greenhouse air temperature distribution using geostatistical methods
title_sort Analysis of greenhouse air temperature distribution using geostatistical methods
dc.subject.spa.fl_str_mv Air temperature
Geostatistics
Greenhouses
Kriging
Microclimate
topic Air temperature
Geostatistics
Greenhouses
Kriging
Microclimate
description Geostatistical analyses have been used very little for the study of the variability of environmental conditions under greenhouse conditions. The spatio‐temporal temperature variation was assessed inside two types of greenhouses in the Bogotá Plateau, Colombia, applying geostatistical methods. A single‐layer polyethylene greenhouse (PGH, 5100 m2), naturally ventilated through a fixed open ridge over the complete length of the roof, and a single‐layer polyethylene greenhouse with automated roof ventilation (RPGH, 9792 m2) were selected for the research. In the PGH, a horizontal grid of sensors consisting of 30 measurement locations was installed, and in the RPGH, a 35‐sensor horizontal grid was installed across the greenhouse. Hourly temperature measurements over 28 days were used to perform the analyses. Isotropic classical semivariograms were constructed. Spherical, cubic, rational quadratic, Gaussian, circular, and exponentialsemivariogram models were evaluated to fit each dataset, and an ordinary kriging method was used to predict temperature values. A circular model was selected to represent the spatial variability of greenhouse temperatures for almost all hours of the day. The highest horizontal temperature gradients inside each greenhouse were found between 08:00 and 16:00 h. Horizontal differences of up to 3.2°C were predicted for the PGH, while the maximum difference predicted for the RPGH was 3.5°C. The results suggest important temperature distribution gradients that have to be accounted for when studying the response of biological processes inside greenhouse environments. Geostatistical methods proved to be a useful tool for the study of the distribution of climate variables such as the temperature inside greenhouses. The possibilities offered by geostatistics to assess the greenhouse environment include better management practices and improved productivity.
publishDate 2009
dc.date.created.none.fl_str_mv 2009
dc.date.accessioned.none.fl_str_mv 2020-08-29T03:47:59Z
dc.date.available.none.fl_str_mv 2020-08-29T03:47:59Z
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_3248
dc.type.local.spa.fl_str_mv Artículo
dc.type.driver.spa.fl_str_mv http://purl.org/redcol/resource_type/CAP_LIB
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dc.identifier.issn.spa.fl_str_mv 0001-2351
dc.identifier.other.spa.fl_str_mv https://elibrary.asabe.org/abstract.asp?aid=27393
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12010/12431
dc.identifier.repourl.spa.fl_str_mv http://expeditiorepositorio.utadeo.edu.co
dc.identifier.doi.spa.fl_str_mv 10.13031/2013.27393
identifier_str_mv 0001-2351
10.13031/2013.27393
url https://elibrary.asabe.org/abstract.asp?aid=27393
http://hdl.handle.net/20.500.12010/12431
http://expeditiorepositorio.utadeo.edu.co
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv C. R. Bojacá, R. Gil, S. Gómez, A. Cooman, & E. Schrevens. (2009). Analysis of Greenhouse Air Temperature Distribution Using Geostatistical Methods. Transactions of the ASABE, 52(3), 957–968. doi:10.13031/2013.27393
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dc.rights.local.spa.fl_str_mv Acceso restringido
rights_invalid_str_mv Acceso restringido
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dc.format.extent.spa.fl_str_mv 12 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.coverage.spatial.spa.fl_str_mv http://expeditiorepositorio.utadeo.edu.co
dc.publisher.spa.fl_str_mv Transactions of the ASABE
institution Universidad de Bogotá Jorge Tadeo Lozano
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spelling http://expeditiorepositorio.utadeo.edu.co2020-08-29T03:47:59Z2020-08-29T03:47:59Z20090001-2351https://elibrary.asabe.org/abstract.asp?aid=27393http://hdl.handle.net/20.500.12010/12431http://expeditiorepositorio.utadeo.edu.co10.13031/2013.2739312 páginasapplication/pdfengTransactions of the ASABEAir temperatureGeostatisticsGreenhousesKrigingMicroclimateAnalysis of greenhouse air temperature distribution using geostatistical methodsArtículohttp://purl.org/redcol/resource_type/CAP_LIBhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_3248Acceso restringidohttp://purl.org/coar/access_right/c_14cbC. R. Bojacá, R. Gil, S. Gómez, A. Cooman, & E. Schrevens. (2009). Analysis of Greenhouse Air Temperature Distribution Using Geostatistical Methods. Transactions of the ASABE, 52(3), 957–968. doi:10.13031/2013.27393Geostatistical analyses have been used very little for the study of the variability of environmental conditions under greenhouse conditions. The spatio‐temporal temperature variation was assessed inside two types of greenhouses in the Bogotá Plateau, Colombia, applying geostatistical methods. A single‐layer polyethylene greenhouse (PGH, 5100 m2), naturally ventilated through a fixed open ridge over the complete length of the roof, and a single‐layer polyethylene greenhouse with automated roof ventilation (RPGH, 9792 m2) were selected for the research. In the PGH, a horizontal grid of sensors consisting of 30 measurement locations was installed, and in the RPGH, a 35‐sensor horizontal grid was installed across the greenhouse. Hourly temperature measurements over 28 days were used to perform the analyses. Isotropic classical semivariograms were constructed. Spherical, cubic, rational quadratic, Gaussian, circular, and exponentialsemivariogram models were evaluated to fit each dataset, and an ordinary kriging method was used to predict temperature values. A circular model was selected to represent the spatial variability of greenhouse temperatures for almost all hours of the day. The highest horizontal temperature gradients inside each greenhouse were found between 08:00 and 16:00 h. Horizontal differences of up to 3.2°C were predicted for the PGH, while the maximum difference predicted for the RPGH was 3.5°C. The results suggest important temperature distribution gradients that have to be accounted for when studying the response of biological processes inside greenhouse environments. Geostatistical methods proved to be a useful tool for the study of the distribution of climate variables such as the temperature inside greenhouses. The possibilities offered by geostatistics to assess the greenhouse environment include better management practices and improved productivity.Bojacá, C. R.Gil, R.Gómez, S.Cooman, A.Schrevens, E.LICENSElicense.txtlicense.txttext/plain; charset=utf-82938https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12431/2/license.txtabceeb1c943c50d3343516f9dbfc110fMD52open accessTHUMBNAILAnalysis of greenhouse air temperature distribution.pdf.jpgAnalysis of greenhouse air temperature distribution.pdf.jpgIM Thumbnailimage/jpeg14422https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12431/3/Analysis%20of%20greenhouse%20air%20temperature%20distribution.pdf.jpg09e8bb4dde02de3565c2360574b6b4d2MD53open access20.500.12010/12431oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/124312022-09-12 16:17:51.834metadata only accessRepositorio Institucional - Universidad Jorge Tadeo Lozanoexpeditiorepositorio@utadeo.edu.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