Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables

Autores
Rodriguez, Ignacio Martín; Mercau, Jorge Luis; Cipriotti, Pablo Ariel; Hall, Antonio Juan; Monzón, Juan Pablo
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Problem: The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments. Objective: The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables. Methods: Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Niño-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista. Results: Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m-2 with revised CROPGRO-Sunflower and of 119 g m-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Niña" phase was associated with the lowest grain yields at both sites. Conclusions: Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions. Implications: This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.
EEA Balcarce
Fil: Rodríguez, Ignacio M. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada Balcarce; Argentina.
Fil: Mercau, Jorge Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; Argentina.
Fil: Cipriotti, Pablo. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Cipriotti, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Hall, Antonio Juan. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Hall, Antonio Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Monzón, Juan Pablo. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada Balcarce; Argentina.
Fil: Monzón, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad Integrada Balcarce; Argentina
Fuente
Field Crops Research 300 : 108986. (September 2023)
Materia
Girasol
Factores Ambientales
Manejo del Cultivo
Toma de Decisiones
Sistema de Apoyo a las Decisiones
Sunflowers
Helianthus annuus
Environmental Factors
Crop Management
Decision-making
Decision-support Systems
Nivel de accesibilidad
acceso restringido
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:20.500.12123/25482
Acceder
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oai_identifier_str oai:localhost:20.500.12123/25482
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variablesRodriguez, Ignacio MartínMercau, Jorge LuisCipriotti, Pablo ArielHall, Antonio JuanMonzón, Juan PabloGirasolFactores AmbientalesManejo del CultivoToma de DecisionesSistema de Apoyo a las DecisionesSunflowersHelianthus annuusEnvironmental FactorsCrop ManagementDecision-makingDecision-support SystemsProblem: The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments. Objective: The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables. Methods: Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Niño-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista. Results: Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m-2 with revised CROPGRO-Sunflower and of 119 g m-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Niña" phase was associated with the lowest grain yields at both sites. Conclusions: Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions. Implications: This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.EEA BalcarceFil: Rodríguez, Ignacio M. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada Balcarce; Argentina.Fil: Mercau, Jorge Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; Argentina.Fil: Cipriotti, Pablo. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaFil: Cipriotti, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaFil: Hall, Antonio Juan. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaFil: Hall, Antonio Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaFil: Monzón, Juan Pablo. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada Balcarce; Argentina.Fil: Monzón, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad Integrada Balcarce; ArgentinaElsevier2026-03-16T13:59:11Z2026-03-16T13:59:11Z2023-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/25482https://www.sciencedirect.com/science/article/pii/S037842902300179X0378-42901872-6852https://doi.org/10.1016/j.fcr.2023.108986Field Crops Research 300 : 108986. (September 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-04-01T11:49:58Zoai:localhost:20.500.12123/25482instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-04-01 11:49:58.694INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
title Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
spellingShingle Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
Rodriguez, Ignacio Martín
Girasol
Factores Ambientales
Manejo del Cultivo
Toma de Decisiones
Sistema de Apoyo a las Decisiones
Sunflowers
Helianthus annuus
Environmental Factors
Crop Management
Decision-making
Decision-support Systems
title_short Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
title_full Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
title_fullStr Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
title_full_unstemmed Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
title_sort Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables
dc.creator.none.fl_str_mv Rodriguez, Ignacio Martín
Mercau, Jorge Luis
Cipriotti, Pablo Ariel
Hall, Antonio Juan
Monzón, Juan Pablo
author Rodriguez, Ignacio Martín
author_facet Rodriguez, Ignacio Martín
Mercau, Jorge Luis
Cipriotti, Pablo Ariel
Hall, Antonio Juan
Monzón, Juan Pablo
author_role author
author2 Mercau, Jorge Luis
Cipriotti, Pablo Ariel
Hall, Antonio Juan
Monzón, Juan Pablo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Girasol
Factores Ambientales
Manejo del Cultivo
Toma de Decisiones
Sistema de Apoyo a las Decisiones
Sunflowers
Helianthus annuus
Environmental Factors
Crop Management
Decision-making
Decision-support Systems
topic Girasol
Factores Ambientales
Manejo del Cultivo
Toma de Decisiones
Sistema de Apoyo a las Decisiones
Sunflowers
Helianthus annuus
Environmental Factors
Crop Management
Decision-making
Decision-support Systems
dc.description.none.fl_txt_mv Problem: The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments. Objective: The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables. Methods: Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Niño-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista. Results: Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m-2 with revised CROPGRO-Sunflower and of 119 g m-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Niña" phase was associated with the lowest grain yields at both sites. Conclusions: Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions. Implications: This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.
EEA Balcarce
Fil: Rodríguez, Ignacio M. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada Balcarce; Argentina.
Fil: Mercau, Jorge Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; Argentina.
Fil: Cipriotti, Pablo. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Cipriotti, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Hall, Antonio Juan. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Hall, Antonio Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina
Fil: Monzón, Juan Pablo. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada Balcarce; Argentina.
Fil: Monzón, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad Integrada Balcarce; Argentina
description Problem: The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments. Objective: The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables. Methods: Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Niño-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista. Results: Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m-2 with revised CROPGRO-Sunflower and of 119 g m-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Niña" phase was associated with the lowest grain yields at both sites. Conclusions: Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions. Implications: This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.
publishDate 2023
dc.date.none.fl_str_mv 2023-09
2026-03-16T13:59:11Z
2026-03-16T13:59:11Z
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 http://hdl.handle.net/20.500.12123/25482
https://www.sciencedirect.com/science/article/pii/S037842902300179X
0378-4290
1872-6852
https://doi.org/10.1016/j.fcr.2023.108986
url http://hdl.handle.net/20.500.12123/25482
https://www.sciencedirect.com/science/article/pii/S037842902300179X
https://doi.org/10.1016/j.fcr.2023.108986
identifier_str_mv 0378-4290
1872-6852
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
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 restrictedAccess
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 Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Field Crops Research 300 : 108986. (September 2023)
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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score 13.09665

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