CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone

Autores
Severini, Alan David; Álvarez-Prado, Santiago; Otegui, María Elena; Kavanová, Monika; Vega, Claudia Rosa Cecilia; Zuil, Sebastian; Ceretta, Sergio; Acreche, Martin Moises; Amarilla, Fidencia; Cicchino, Mariano Andres; Fernández-Long, María E.; Crespo, Aníbal; Serrago, Román; Miralles, Daniel Julio
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Accurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in field experiments located in Argentina, Uruguay and Paraguay. Experiments covered a broad range of maturity group (MG)s (2.2–6.8), sowing dates (SDs) (from spring to summer) and latitude range (24.9–35.6 °S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across MGs. We identified base temperatures specific for different developmental phases and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly affecting the duration of vegetative and early reproductive phases. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. In summary, we have constructed a soybean phenology model that simulates phenology accurately across various geographic locations and sowing dates. The model’s process-based approach has resulted in root mean square errors ranging from 5.8 to 9.5 days for different developmental stages.
EEA Pergamino
Fil: Severini, Alan D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Álvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos—GIMUCE. Campo Experimental Villarino; Argentina
Fil: Álvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Otegui, María Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Kavanová, Monika. Instituto Nacional de Investigación Agropecuaria (INIA). Programa de Investigación en Cultivos de Secano. La Estanzuela; Uruguay
Fil: Vega, C. R. C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina
Fil: Zuil, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina
Fil: Ceretta, Sergio. Instituto Nacional de Investigación Agropecuaria (INIA). Programa de Investigación en Cultivos de Secano. La Estanzuela; Uruguay
Fil: Acreche, Martin Moises. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina
Fil: Acreche, Martin Moises. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Amarilla, Fidencia. Instituto Paraguayo de Tecnología Agraria. Centro de Investigación Capitán Miranda; Paraguay
Fil: Cicchino, Mariano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cuenca del Salado; Argentina
Fil: Fernández-Long, María E. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Crespo, Aníbal. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Serrago, Román. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Serrago, Román. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Miralles, Daniel J. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miralles, Daniel J. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fuente
In silico Plants 6 (1) : diae005. (2024)
Materia
Soja
Fenología
Toma de Decisiones
Modelo Dinámico
Desarrollo de la Semilla
Soybeans
Phenology
Decision Making
Dynamic Models
Seed Development
Bayesian Model
Model Development
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:20.500.12123/21690
Acceder
id INTADig_b00785dae83f901d9aa5f4e49d0d6d16
oai_identifier_str oai:localhost:20.500.12123/21690
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern ConeSeverini, Alan DavidÁlvarez-Prado, SantiagoOtegui, María ElenaKavanová, MonikaVega, Claudia Rosa CeciliaZuil, SebastianCeretta, SergioAcreche, Martin MoisesAmarilla, FidenciaCicchino, Mariano AndresFernández-Long, María E.Crespo, AníbalSerrago, RománMiralles, Daniel JulioSojaFenologíaToma de DecisionesModelo DinámicoDesarrollo de la SemillaSoybeansPhenologyDecision MakingDynamic ModelsSeed DevelopmentBayesian ModelModel DevelopmentAccurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in field experiments located in Argentina, Uruguay and Paraguay. Experiments covered a broad range of maturity group (MG)s (2.2–6.8), sowing dates (SDs) (from spring to summer) and latitude range (24.9–35.6 °S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across MGs. We identified base temperatures specific for different developmental phases and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly affecting the duration of vegetative and early reproductive phases. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. In summary, we have constructed a soybean phenology model that simulates phenology accurately across various geographic locations and sowing dates. The model’s process-based approach has resulted in root mean square errors ranging from 5.8 to 9.5 days for different developmental stages.EEA PergaminoFil: Severini, Alan D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Álvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos—GIMUCE. Campo Experimental Villarino; ArgentinaFil: Álvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Otegui, María Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kavanová, Monika. Instituto Nacional de Investigación Agropecuaria (INIA). Programa de Investigación en Cultivos de Secano. La Estanzuela; UruguayFil: Vega, C. R. C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Zuil, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Ceretta, Sergio. Instituto Nacional de Investigación Agropecuaria (INIA). Programa de Investigación en Cultivos de Secano. La Estanzuela; UruguayFil: Acreche, Martin Moises. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Acreche, Martin Moises. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Amarilla, Fidencia. Instituto Paraguayo de Tecnología Agraria. Centro de Investigación Capitán Miranda; ParaguayFil: Cicchino, Mariano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cuenca del Salado; ArgentinaFil: Fernández-Long, María E. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Crespo, Aníbal. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Serrago, Román. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Serrago, Román. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Miralles, Daniel J. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miralles, Daniel J. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaOxford University Press2025-03-18T11:13:21Z2025-03-18T11:13:21Z2024-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21690https://academic.oup.com/insilicoplants/article/6/1/diae005/76676382517-5025 (online)https://doi.org/10.1093/insilicoplants/diae005In silico Plants 6 (1) : diae005. (2024)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)2025-10-23T11:19:26Zoai:localhost:20.500.12123/21690instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:19:27.199INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
title CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
spellingShingle CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
Severini, Alan David
Soja
Fenología
Toma de Decisiones
Modelo Dinámico
Desarrollo de la Semilla
Soybeans
Phenology
Decision Making
Dynamic Models
Seed Development
Bayesian Model
Model Development
title_short CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
title_full CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
title_fullStr CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
title_full_unstemmed CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
title_sort CRONOSOJA : a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone
dc.creator.none.fl_str_mv Severini, Alan David
Álvarez-Prado, Santiago
Otegui, María Elena
Kavanová, Monika
Vega, Claudia Rosa Cecilia
Zuil, Sebastian
Ceretta, Sergio
Acreche, Martin Moises
Amarilla, Fidencia
Cicchino, Mariano Andres
Fernández-Long, María E.
Crespo, Aníbal
Serrago, Román
Miralles, Daniel Julio
author Severini, Alan David
author_facet Severini, Alan David
Álvarez-Prado, Santiago
Otegui, María Elena
Kavanová, Monika
Vega, Claudia Rosa Cecilia
Zuil, Sebastian
Ceretta, Sergio
Acreche, Martin Moises
Amarilla, Fidencia
Cicchino, Mariano Andres
Fernández-Long, María E.
Crespo, Aníbal
Serrago, Román
Miralles, Daniel Julio
author_role author
author2 Álvarez-Prado, Santiago
Otegui, María Elena
Kavanová, Monika
Vega, Claudia Rosa Cecilia
Zuil, Sebastian
Ceretta, Sergio
Acreche, Martin Moises
Amarilla, Fidencia
Cicchino, Mariano Andres
Fernández-Long, María E.
Crespo, Aníbal
Serrago, Román
Miralles, Daniel Julio
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Soja
Fenología
Toma de Decisiones
Modelo Dinámico
Desarrollo de la Semilla
Soybeans
Phenology
Decision Making
Dynamic Models
Seed Development
Bayesian Model
Model Development
topic Soja
Fenología
Toma de Decisiones
Modelo Dinámico
Desarrollo de la Semilla
Soybeans
Phenology
Decision Making
Dynamic Models
Seed Development
Bayesian Model
Model Development
dc.description.none.fl_txt_mv Accurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in field experiments located in Argentina, Uruguay and Paraguay. Experiments covered a broad range of maturity group (MG)s (2.2–6.8), sowing dates (SDs) (from spring to summer) and latitude range (24.9–35.6 °S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across MGs. We identified base temperatures specific for different developmental phases and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly affecting the duration of vegetative and early reproductive phases. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. In summary, we have constructed a soybean phenology model that simulates phenology accurately across various geographic locations and sowing dates. The model’s process-based approach has resulted in root mean square errors ranging from 5.8 to 9.5 days for different developmental stages.
EEA Pergamino
Fil: Severini, Alan D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Álvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos—GIMUCE. Campo Experimental Villarino; Argentina
Fil: Álvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Otegui, María Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina
Fil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Kavanová, Monika. Instituto Nacional de Investigación Agropecuaria (INIA). Programa de Investigación en Cultivos de Secano. La Estanzuela; Uruguay
Fil: Vega, C. R. C. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina
Fil: Zuil, Sebastián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Rafaela; Argentina
Fil: Ceretta, Sergio. Instituto Nacional de Investigación Agropecuaria (INIA). Programa de Investigación en Cultivos de Secano. La Estanzuela; Uruguay
Fil: Acreche, Martin Moises. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina
Fil: Acreche, Martin Moises. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Amarilla, Fidencia. Instituto Paraguayo de Tecnología Agraria. Centro de Investigación Capitán Miranda; Paraguay
Fil: Cicchino, Mariano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Cuenca del Salado; Argentina
Fil: Fernández-Long, María E. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Crespo, Aníbal. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Serrago, Román. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Serrago, Román. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Miralles, Daniel J. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miralles, Daniel J. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
description Accurate prediction of phenology is the most critical aspect for the development of models aimed at estimating seed yield, particularly in species that exhibit variable sensitivity to environmental factors throughout the cycle and among genotypes. With this purpose, we evaluated the phenology of 34 soybean varieties in field experiments located in Argentina, Uruguay and Paraguay. Experiments covered a broad range of maturity group (MG)s (2.2–6.8), sowing dates (SDs) (from spring to summer) and latitude range (24.9–35.6 °S), thus ensuring a wide range of thermo-photoperiodic conditions during the growing season. Based on the observed data, daily time-step models were developed and tested, first for each genotype, and then across MGs. We identified base temperatures specific for different developmental phases and an extra parameter for calculating the photoperiod effect after the R1 stage (flowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly affecting the duration of vegetative and early reproductive phases. Even so, early phases of development were better predicted than later ones, particularly in locations with cool growing seasons, where the model tended to overestimate their duration. In summary, we have constructed a soybean phenology model that simulates phenology accurately across various geographic locations and sowing dates. The model’s process-based approach has resulted in root mean square errors ranging from 5.8 to 9.5 days for different developmental stages.
publishDate 2024
dc.date.none.fl_str_mv 2024-05
2025-03-18T11:13:21Z
2025-03-18T11:13:21Z
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/20.500.12123/21690
https://academic.oup.com/insilicoplants/article/6/1/diae005/7667638
2517-5025 (online)
https://doi.org/10.1093/insilicoplants/diae005
url http://hdl.handle.net/20.500.12123/21690
https://academic.oup.com/insilicoplants/article/6/1/diae005/7667638
https://doi.org/10.1093/insilicoplants/diae005
identifier_str_mv 2517-5025 (online)
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 Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv In silico Plants 6 (1) : diae005. (2024)
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
_version_ 1846787601933008896
score 12.982451

Publicaciones similares