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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/21690
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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)2025-09-29T13:47:11Zoai: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:l2025-09-29 13:47:12.398INTA 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 |
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1844619201952612352 |
score |
12.559606 |