Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains

Autores
Song, Feng; Salvagiotti, Fernando; Schmer, M.R.; Wingeyer, Ana Beatriz; Weiss, Albert
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Simulation models of soil-related biological processes usually require soil temperature data. Frequently these soil temperatures are simulated, and the soil temperature algorithms cannot be more complicated than the original process model. This situation has led to the use of semi-empirical-type relationships in these process models. The objective of this study was to evaluate a hybrid soil temperature model, which combines empirical and mechanistic approaches, in an agroecosystem and a tallgrass prairie in the Great Plains. The original hybrid soil temperature model was developed and verified for a temperate forest system. This model simulated soil temperatures on a daily basis from meteorological inputs (maximum and minimum air temperatures) and soil and plant properties. This model was modified using different extinction coefficients for the plant canopy and ground litter. The agroecosystem consisted of a no-till rotation system of corn (Zea mays L.) and soybeans (Glycine max [L.] Merr.). Soil temperatures were measured at different depths in multiple years (three years and two-and-a-half years in the agroecosystem and tallgrass prairie, respectively). In the agroecosystem, the root mean square error of the modified model simulation varied from 1.41º to 2.05ºC for the four depths (0.1, 0.2, 0.3, and 0.5 m). The mean absolute error varied from 1.06º to 1.53ºC. The root mean square error and mean absolute error of the modified model were about 0.1º–0.3ºC less than the original model at the 0.2–0.5 m depths. For the tallgrass prairie, the mean absolute errors of the simulated soil temperatures were slightly greater than the agroecosystem, varying from 1.48º to 1.7ºC for all years and from 1.09º to 1.37ºC during the active growing seasons for all years.
EEA Oliveros
Fil: Song, Feng. University of Nebraska–Lincoln. School of Natural Resources; Estados Unidos
Fil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros. Departamento de Agronomía; Argentina
Fil: Schmer, M.R. University of Nebraska–Lincoln. Department of Agronomy and Horticulture; Estados Unidos
Fil: Wingeyer, Ana Beatriz. University of Nebraska–Lincoln. Department of Agronomy and Horticulture; Estados Unidos
Fil: Weiss, Albert. University of Nebraska–Lincoln. School of Natural Resources; Estados Unidos
Fuente
Great Plains Research 20 (2) : 249–260 (2010)
Materia
Maíz
Soja
Agroecosistemas
Modelos de Simulación
Temperatura
Suelo
Maize
Soybeans
Agroecosystems
Simulation Models
Temperature
Soil
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/6458

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oai_identifier_str oai:localhost:20.500.12123/6458
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network_name_str INTA Digital (INTA)
spelling Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plainsSong, FengSalvagiotti, FernandoSchmer, M.R.Wingeyer, Ana BeatrizWeiss, AlbertMaízSojaAgroecosistemasModelos de SimulaciónTemperaturaSueloMaizeSoybeansAgroecosystemsSimulation ModelsTemperatureSoilSimulation models of soil-related biological processes usually require soil temperature data. Frequently these soil temperatures are simulated, and the soil temperature algorithms cannot be more complicated than the original process model. This situation has led to the use of semi-empirical-type relationships in these process models. The objective of this study was to evaluate a hybrid soil temperature model, which combines empirical and mechanistic approaches, in an agroecosystem and a tallgrass prairie in the Great Plains. The original hybrid soil temperature model was developed and verified for a temperate forest system. This model simulated soil temperatures on a daily basis from meteorological inputs (maximum and minimum air temperatures) and soil and plant properties. This model was modified using different extinction coefficients for the plant canopy and ground litter. The agroecosystem consisted of a no-till rotation system of corn (Zea mays L.) and soybeans (Glycine max [L.] Merr.). Soil temperatures were measured at different depths in multiple years (three years and two-and-a-half years in the agroecosystem and tallgrass prairie, respectively). In the agroecosystem, the root mean square error of the modified model simulation varied from 1.41º to 2.05ºC for the four depths (0.1, 0.2, 0.3, and 0.5 m). The mean absolute error varied from 1.06º to 1.53ºC. The root mean square error and mean absolute error of the modified model were about 0.1º–0.3ºC less than the original model at the 0.2–0.5 m depths. For the tallgrass prairie, the mean absolute errors of the simulated soil temperatures were slightly greater than the agroecosystem, varying from 1.48º to 1.7ºC for all years and from 1.09º to 1.37ºC during the active growing seasons for all years.EEA OliverosFil: Song, Feng. University of Nebraska–Lincoln. School of Natural Resources; Estados UnidosFil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros. Departamento de Agronomía; ArgentinaFil: Schmer, M.R. University of Nebraska–Lincoln. Department of Agronomy and Horticulture; Estados UnidosFil: Wingeyer, Ana Beatriz. University of Nebraska–Lincoln. Department of Agronomy and Horticulture; Estados UnidosFil: Weiss, Albert. University of Nebraska–Lincoln. School of Natural Resources; Estados UnidosUniversity of Nebraska Press2019-12-05T13:53:09Z2019-12-05T13:53:09Z2010info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://digitalcommons.unl.edu/greatplainsresearch/1129/http://hdl.handle.net/20.500.12123/64581052-5165Great Plains Research 20 (2) : 249–260 (2010)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-16T09:29:42Zoai:localhost:20.500.12123/6458instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:29:42.805INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
title Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
spellingShingle Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
Song, Feng
Maíz
Soja
Agroecosistemas
Modelos de Simulación
Temperatura
Suelo
Maize
Soybeans
Agroecosystems
Simulation Models
Temperature
Soil
title_short Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
title_full Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
title_fullStr Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
title_full_unstemmed Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
title_sort Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great plains
dc.creator.none.fl_str_mv Song, Feng
Salvagiotti, Fernando
Schmer, M.R.
Wingeyer, Ana Beatriz
Weiss, Albert
author Song, Feng
author_facet Song, Feng
Salvagiotti, Fernando
Schmer, M.R.
Wingeyer, Ana Beatriz
Weiss, Albert
author_role author
author2 Salvagiotti, Fernando
Schmer, M.R.
Wingeyer, Ana Beatriz
Weiss, Albert
author2_role author
author
author
author
dc.subject.none.fl_str_mv Maíz
Soja
Agroecosistemas
Modelos de Simulación
Temperatura
Suelo
Maize
Soybeans
Agroecosystems
Simulation Models
Temperature
Soil
topic Maíz
Soja
Agroecosistemas
Modelos de Simulación
Temperatura
Suelo
Maize
Soybeans
Agroecosystems
Simulation Models
Temperature
Soil
dc.description.none.fl_txt_mv Simulation models of soil-related biological processes usually require soil temperature data. Frequently these soil temperatures are simulated, and the soil temperature algorithms cannot be more complicated than the original process model. This situation has led to the use of semi-empirical-type relationships in these process models. The objective of this study was to evaluate a hybrid soil temperature model, which combines empirical and mechanistic approaches, in an agroecosystem and a tallgrass prairie in the Great Plains. The original hybrid soil temperature model was developed and verified for a temperate forest system. This model simulated soil temperatures on a daily basis from meteorological inputs (maximum and minimum air temperatures) and soil and plant properties. This model was modified using different extinction coefficients for the plant canopy and ground litter. The agroecosystem consisted of a no-till rotation system of corn (Zea mays L.) and soybeans (Glycine max [L.] Merr.). Soil temperatures were measured at different depths in multiple years (three years and two-and-a-half years in the agroecosystem and tallgrass prairie, respectively). In the agroecosystem, the root mean square error of the modified model simulation varied from 1.41º to 2.05ºC for the four depths (0.1, 0.2, 0.3, and 0.5 m). The mean absolute error varied from 1.06º to 1.53ºC. The root mean square error and mean absolute error of the modified model were about 0.1º–0.3ºC less than the original model at the 0.2–0.5 m depths. For the tallgrass prairie, the mean absolute errors of the simulated soil temperatures were slightly greater than the agroecosystem, varying from 1.48º to 1.7ºC for all years and from 1.09º to 1.37ºC during the active growing seasons for all years.
EEA Oliveros
Fil: Song, Feng. University of Nebraska–Lincoln. School of Natural Resources; Estados Unidos
Fil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros. Departamento de Agronomía; Argentina
Fil: Schmer, M.R. University of Nebraska–Lincoln. Department of Agronomy and Horticulture; Estados Unidos
Fil: Wingeyer, Ana Beatriz. University of Nebraska–Lincoln. Department of Agronomy and Horticulture; Estados Unidos
Fil: Weiss, Albert. University of Nebraska–Lincoln. School of Natural Resources; Estados Unidos
description Simulation models of soil-related biological processes usually require soil temperature data. Frequently these soil temperatures are simulated, and the soil temperature algorithms cannot be more complicated than the original process model. This situation has led to the use of semi-empirical-type relationships in these process models. The objective of this study was to evaluate a hybrid soil temperature model, which combines empirical and mechanistic approaches, in an agroecosystem and a tallgrass prairie in the Great Plains. The original hybrid soil temperature model was developed and verified for a temperate forest system. This model simulated soil temperatures on a daily basis from meteorological inputs (maximum and minimum air temperatures) and soil and plant properties. This model was modified using different extinction coefficients for the plant canopy and ground litter. The agroecosystem consisted of a no-till rotation system of corn (Zea mays L.) and soybeans (Glycine max [L.] Merr.). Soil temperatures were measured at different depths in multiple years (three years and two-and-a-half years in the agroecosystem and tallgrass prairie, respectively). In the agroecosystem, the root mean square error of the modified model simulation varied from 1.41º to 2.05ºC for the four depths (0.1, 0.2, 0.3, and 0.5 m). The mean absolute error varied from 1.06º to 1.53ºC. The root mean square error and mean absolute error of the modified model were about 0.1º–0.3ºC less than the original model at the 0.2–0.5 m depths. For the tallgrass prairie, the mean absolute errors of the simulated soil temperatures were slightly greater than the agroecosystem, varying from 1.48º to 1.7ºC for all years and from 1.09º to 1.37ºC during the active growing seasons for all years.
publishDate 2010
dc.date.none.fl_str_mv 2010
2019-12-05T13:53:09Z
2019-12-05T13:53:09Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://digitalcommons.unl.edu/greatplainsresearch/1129/
http://hdl.handle.net/20.500.12123/6458
1052-5165
url https://digitalcommons.unl.edu/greatplainsresearch/1129/
http://hdl.handle.net/20.500.12123/6458
identifier_str_mv 1052-5165
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv 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)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv University of Nebraska Press
publisher.none.fl_str_mv University of Nebraska Press
dc.source.none.fl_str_mv Great Plains Research 20 (2) : 249–260 (2010)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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