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...
- 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
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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 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
format |
http://purl.org/coar/resource_type/c_6501 |
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 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_14cb |
dc.rights.local.spa.fl_str_mv |
Acceso restringido |
rights_invalid_str_mv |
Acceso restringido http://purl.org/coar/access_right/c_14cb |
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 |
bitstream.url.fl_str_mv |
https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12431/2/license.txt https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/12431/3/Analysis%20of%20greenhouse%20air%20temperature%20distribution.pdf.jpg |
bitstream.checksum.fl_str_mv |
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bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Institucional - Universidad Jorge Tadeo Lozano |
repository.mail.fl_str_mv |
expeditiorepositorio@utadeo.edu.co |
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1818152953765167104 |
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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 |