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

Autores
Severini, Alan David; Alvarez Prado, Santiago; Otegui, Maria Elena; Kavanová, Monika; Vega, Claudia Rosa Cecilia; Zuil, Sebastian; Ceretta, Sergio; Acreche, Martin Moises; Amarilla, Fidencia; Cicchino, Mariano Andrés; Fernández Long, María Elena; Crespo, Aníbal; Serrago, Roman Augusto; 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 feld 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, frst for each genotype, and then across MGs. We identifed base temperatures specifc for diferent developmental phases and an extra parameter for calculating the photoperiod efect afer the R1 stage (fowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly afecting the duration of vegetative and early reproductive phases. Even so, early phases of development were beter 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 diferent developmental stages.
Fil: Severini, Alan David. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Otegui, Maria Elena. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Kavanová, Monika. Instituto Nacional de Investigacion Agropecuaria;
Fil: Vega, Claudia Rosa Cecilia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Manfredi; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Zuil, Sebastian. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela; Argentina
Fil: Ceretta, Sergio. Estacion Experimental la Estanzuela ; Instituto Nacional de Investigacion Agropecuaria;
Fil: Acreche, Martin Moises. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Salta-Jujuy. Estación Experimental Agropecuaria Salta; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Amarilla, Fidencia. Instituto Paraguayo de Tecnología Agraria; Paraguay
Fil: Cicchino, Mariano Andrés. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Cuenca del Salado.; Argentina
Fil: Fernández Long, María Elena. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Crespo, Aníbal. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Serrago, Roman Augusto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miralles, Daniel Julio. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Bayesian
Decision-making
Dynamic model
Model development
Phenology
Soybean
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/239397
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/239397
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern ConeSeverini, Alan DavidAlvarez Prado, SantiagoOtegui, Maria ElenaKavanová, MonikaVega, Claudia Rosa CeciliaZuil, SebastianCeretta, SergioAcreche, Martin MoisesAmarilla, FidenciaCicchino, Mariano AndrésFernández Long, María ElenaCrespo, AníbalSerrago, Roman AugustoMiralles, Daniel JulioBayesianDecision-makingDynamic modelModel developmentPhenologySoybeanhttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Accurate 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 feld 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, frst for each genotype, and then across MGs. We identifed base temperatures specifc for diferent developmental phases and an extra parameter for calculating the photoperiod efect afer the R1 stage (fowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly afecting the duration of vegetative and early reproductive phases. Even so, early phases of development were beter 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 diferent developmental stages.Fil: Severini, Alan David. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Otegui, Maria Elena. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Kavanová, Monika. Instituto Nacional de Investigacion Agropecuaria;Fil: Vega, Claudia Rosa Cecilia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Manfredi; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Zuil, Sebastian. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Ceretta, Sergio. Estacion Experimental la Estanzuela ; Instituto Nacional de Investigacion Agropecuaria;Fil: Acreche, Martin Moises. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Salta-Jujuy. Estación Experimental Agropecuaria Salta; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Amarilla, Fidencia. Instituto Paraguayo de Tecnología Agraria; ParaguayFil: Cicchino, Mariano Andrés. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Cuenca del Salado.; ArgentinaFil: Fernández Long, María Elena. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Crespo, Aníbal. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Serrago, Roman Augusto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miralles, Daniel Julio. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaOxford University Press2024-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/239397Severini, Alan David; Alvarez Prado, Santiago; Otegui, Maria Elena; Kavanová, Monika; Vega, Claudia Rosa Cecilia; et al.; CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone; Oxford University Press; in silico Plants; 6; 1; 1-2024; 1-192517-5025CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diae005/7667638info:eu-repo/semantics/altIdentifier/doi/10.1093/insilicoplants/diae005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:40Zoai:ri.conicet.gov.ar:11336/239397instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:46:40.654CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
Bayesian
Decision-making
Dynamic model
Model development
Phenology
Soybean
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
Alvarez Prado, Santiago
Otegui, Maria Elena
Kavanová, Monika
Vega, Claudia Rosa Cecilia
Zuil, Sebastian
Ceretta, Sergio
Acreche, Martin Moises
Amarilla, Fidencia
Cicchino, Mariano Andrés
Fernández Long, María Elena
Crespo, Aníbal
Serrago, Roman Augusto
Miralles, Daniel Julio
author Severini, Alan David
author_facet Severini, Alan David
Alvarez Prado, Santiago
Otegui, Maria Elena
Kavanová, Monika
Vega, Claudia Rosa Cecilia
Zuil, Sebastian
Ceretta, Sergio
Acreche, Martin Moises
Amarilla, Fidencia
Cicchino, Mariano Andrés
Fernández Long, María Elena
Crespo, Aníbal
Serrago, Roman Augusto
Miralles, Daniel Julio
author_role author
author2 Alvarez Prado, Santiago
Otegui, Maria Elena
Kavanová, Monika
Vega, Claudia Rosa Cecilia
Zuil, Sebastian
Ceretta, Sergio
Acreche, Martin Moises
Amarilla, Fidencia
Cicchino, Mariano Andrés
Fernández Long, María Elena
Crespo, Aníbal
Serrago, Roman Augusto
Miralles, Daniel Julio
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Bayesian
Decision-making
Dynamic model
Model development
Phenology
Soybean
topic Bayesian
Decision-making
Dynamic model
Model development
Phenology
Soybean
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
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 feld 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, frst for each genotype, and then across MGs. We identifed base temperatures specifc for diferent developmental phases and an extra parameter for calculating the photoperiod efect afer the R1 stage (fowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly afecting the duration of vegetative and early reproductive phases. Even so, early phases of development were beter 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 diferent developmental stages.
Fil: Severini, Alan David. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Otegui, Maria Elena. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Kavanová, Monika. Instituto Nacional de Investigacion Agropecuaria;
Fil: Vega, Claudia Rosa Cecilia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Manfredi; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Zuil, Sebastian. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela; Argentina
Fil: Ceretta, Sergio. Estacion Experimental la Estanzuela ; Instituto Nacional de Investigacion Agropecuaria;
Fil: Acreche, Martin Moises. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Salta-Jujuy. Estación Experimental Agropecuaria Salta; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Amarilla, Fidencia. Instituto Paraguayo de Tecnología Agraria; Paraguay
Fil: Cicchino, Mariano Andrés. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Cuenca del Salado.; Argentina
Fil: Fernández Long, María Elena. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Crespo, Aníbal. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Serrago, Roman Augusto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Miralles, Daniel Julio. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; 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 feld 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, frst for each genotype, and then across MGs. We identifed base temperatures specifc for diferent developmental phases and an extra parameter for calculating the photoperiod efect afer the R1 stage (fowering). Also, an optimum photoperiod length for each MG was found. Model selection showed that the determinants of phenology across MGs were mainly afecting the duration of vegetative and early reproductive phases. Even so, early phases of development were beter 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 diferent developmental stages.
publishDate 2024
dc.date.none.fl_str_mv 2024-01
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/11336/239397
Severini, Alan David; Alvarez Prado, Santiago; Otegui, Maria Elena; Kavanová, Monika; Vega, Claudia Rosa Cecilia; et al.; CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone; Oxford University Press; in silico Plants; 6; 1; 1-2024; 1-19
2517-5025
CONICET Digital
CONICET
url http://hdl.handle.net/11336/239397
identifier_str_mv Severini, Alan David; Alvarez Prado, Santiago; Otegui, Maria Elena; Kavanová, Monika; Vega, Claudia Rosa Cecilia; et al.; CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone; Oxford University Press; in silico Plants; 6; 1; 1-2024; 1-19
2517-5025
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/insilicoplants/article/doi/10.1093/insilicoplants/diae005/7667638
info:eu-repo/semantics/altIdentifier/doi/10.1093/insilicoplants/diae005
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
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 reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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