Enhancing maize grain dry-down predictive models

Autores
Chazarreta, Yésica Daniela; Carcedo, Ana Julia Paula; Alvarez Prado, Santiago; Massigoge, Ignacio; Amas, Juan Ignacio; Fernandez, Javier A.; Ciampitti, Ignacio Antonio; Otegui, María Elena
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Predicting the optimal harvest date after crop physiological maturity is highly relevant for maize (Zea mays L.). While harvesting before achieving the commercial kernel moisture implies additional costs of grain drying, a delayed harvest of maize crops is linked to grain yield and quality losses. The main objective of this work was to identify weather variables affecting the post-maturity grain dry-down coefficient (k) in order to develop models to predict kernel moisture loss and time to harvest (harvest readiness) under a wide range of sowing date environments. Kernel moisture datasets from field experiments in Pergamino (Argentina) and Kansas (US) were used for training and testing post-maturity grain dry-down models. Two k coefficients were defined based on the solar radiation and the VPD explored during the pre- and post-maturity period (kpre and kpost). Models including kpre and kpost were tested under a wide range of sowing date environments, presenting high accuracy in predicting kernel moisture (R2 ∼ 0.80; RRMSE ∼ 0.15) and harvest readiness (R2 = 0.99; RRMSE ∼ 0.05). This study provides the foundation for developing an interactive digital platform to estimate harvest time to assist farmers and agronomists with this critical decision.
EEA Pergamino
Fil: Chazarreta, Yésica Daniela. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Chazarreta, Yésica Daniela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Chazarreta, Yésica Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chazarreta, Yésica D. Universidad Nacional del Noroeste de la Provincia de Buenos Aires; Argentina
Fil: Carcedo, Ana Julia Paula. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina
Fil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina
Fil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos; Argentina
Fil: Massigoge, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Amás, Juan Ignacio. Corteva Agriscience; Argentina
Fil: Fernandez, Javier A. The University of Queensland; Australia
Fil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Otegui, María. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina.
Fil: Otegui, María. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Otegui, María. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina.
Fuente
Agricultural and Forest Meteorology 334 : 109427. (May 2023)
Materia
Maíz
Fecha de Siembra
Madurez
Secado
Maize
Sowing Date
Maturity
Drying
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:20.500.12123/25677
Acceder
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oai_identifier_str oai:localhost:20.500.12123/25677
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Enhancing maize grain dry-down predictive modelsChazarreta, Yésica DanielaCarcedo, Ana Julia PaulaAlvarez Prado, SantiagoMassigoge, IgnacioAmas, Juan IgnacioFernandez, Javier A.Ciampitti, Ignacio AntonioOtegui, María ElenaMaízFecha de SiembraMadurezSecadoMaizeSowing DateMaturityDryingPredicting the optimal harvest date after crop physiological maturity is highly relevant for maize (Zea mays L.). While harvesting before achieving the commercial kernel moisture implies additional costs of grain drying, a delayed harvest of maize crops is linked to grain yield and quality losses. The main objective of this work was to identify weather variables affecting the post-maturity grain dry-down coefficient (k) in order to develop models to predict kernel moisture loss and time to harvest (harvest readiness) under a wide range of sowing date environments. Kernel moisture datasets from field experiments in Pergamino (Argentina) and Kansas (US) were used for training and testing post-maturity grain dry-down models. Two k coefficients were defined based on the solar radiation and the VPD explored during the pre- and post-maturity period (kpre and kpost). Models including kpre and kpost were tested under a wide range of sowing date environments, presenting high accuracy in predicting kernel moisture (R2 ∼ 0.80; RRMSE ∼ 0.15) and harvest readiness (R2 = 0.99; RRMSE ∼ 0.05). This study provides the foundation for developing an interactive digital platform to estimate harvest time to assist farmers and agronomists with this critical decision.EEA PergaminoFil: Chazarreta, Yésica Daniela. Kansas State University. Department of Agronomy; Estados UnidosFil: Chazarreta, Yésica Daniela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Chazarreta, Yésica Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chazarreta, Yésica D. Universidad Nacional del Noroeste de la Provincia de Buenos Aires; ArgentinaFil: Carcedo, Ana Julia Paula. Kansas State University. Department of Agronomy; Estados UnidosFil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; ArgentinaFil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; ArgentinaFil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos; ArgentinaFil: Massigoge, Ignacio. Kansas State University. Department of Agronomy; Estados UnidosFil: Amás, Juan Ignacio. Corteva Agriscience; ArgentinaFil: Fernandez, Javier A. The University of Queensland; AustraliaFil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados UnidosFil: Otegui, María. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina.Fil: Otegui, María. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Otegui, María. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina.Elsevier2026-04-06T11:21:19Z2026-04-06T11:21:19Z2023-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/25677https://www.sciencedirect.com/science/article/pii/S01681923230011930168-19231873-2240https://doi.org/10.1016/j.agrformet.2023.109427Agricultural and Forest Meteorology 334 : 109427. (May 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/PNCYO/1127042/AR./Bases ecofisiológicas para el mejoramiento genético y la calidad diferenciada de cereales y oleaginosas.info: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)2026-04-09T08:29:30Zoai:localhost:20.500.12123/25677instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-04-09 08:29:30.961INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Enhancing maize grain dry-down predictive models
title Enhancing maize grain dry-down predictive models
spellingShingle Enhancing maize grain dry-down predictive models
Chazarreta, Yésica Daniela
Maíz
Fecha de Siembra
Madurez
Secado
Maize
Sowing Date
Maturity
Drying
title_short Enhancing maize grain dry-down predictive models
title_full Enhancing maize grain dry-down predictive models
title_fullStr Enhancing maize grain dry-down predictive models
title_full_unstemmed Enhancing maize grain dry-down predictive models
title_sort Enhancing maize grain dry-down predictive models
dc.creator.none.fl_str_mv Chazarreta, Yésica Daniela
Carcedo, Ana Julia Paula
Alvarez Prado, Santiago
Massigoge, Ignacio
Amas, Juan Ignacio
Fernandez, Javier A.
Ciampitti, Ignacio Antonio
Otegui, María Elena
author Chazarreta, Yésica Daniela
author_facet Chazarreta, Yésica Daniela
Carcedo, Ana Julia Paula
Alvarez Prado, Santiago
Massigoge, Ignacio
Amas, Juan Ignacio
Fernandez, Javier A.
Ciampitti, Ignacio Antonio
Otegui, María Elena
author_role author
author2 Carcedo, Ana Julia Paula
Alvarez Prado, Santiago
Massigoge, Ignacio
Amas, Juan Ignacio
Fernandez, Javier A.
Ciampitti, Ignacio Antonio
Otegui, María Elena
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Maíz
Fecha de Siembra
Madurez
Secado
Maize
Sowing Date
Maturity
Drying
topic Maíz
Fecha de Siembra
Madurez
Secado
Maize
Sowing Date
Maturity
Drying
dc.description.none.fl_txt_mv Predicting the optimal harvest date after crop physiological maturity is highly relevant for maize (Zea mays L.). While harvesting before achieving the commercial kernel moisture implies additional costs of grain drying, a delayed harvest of maize crops is linked to grain yield and quality losses. The main objective of this work was to identify weather variables affecting the post-maturity grain dry-down coefficient (k) in order to develop models to predict kernel moisture loss and time to harvest (harvest readiness) under a wide range of sowing date environments. Kernel moisture datasets from field experiments in Pergamino (Argentina) and Kansas (US) were used for training and testing post-maturity grain dry-down models. Two k coefficients were defined based on the solar radiation and the VPD explored during the pre- and post-maturity period (kpre and kpost). Models including kpre and kpost were tested under a wide range of sowing date environments, presenting high accuracy in predicting kernel moisture (R2 ∼ 0.80; RRMSE ∼ 0.15) and harvest readiness (R2 = 0.99; RRMSE ∼ 0.05). This study provides the foundation for developing an interactive digital platform to estimate harvest time to assist farmers and agronomists with this critical decision.
EEA Pergamino
Fil: Chazarreta, Yésica Daniela. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Chazarreta, Yésica Daniela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Chazarreta, Yésica Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Chazarreta, Yésica D. Universidad Nacional del Noroeste de la Provincia de Buenos Aires; Argentina
Fil: Carcedo, Ana Julia Paula. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Alvarez Prado, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina
Fil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; Argentina
Fil: Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos; Argentina
Fil: Massigoge, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Amás, Juan Ignacio. Corteva Agriscience; Argentina
Fil: Fernandez, Javier A. The University of Queensland; Australia
Fil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unidos
Fil: Otegui, María. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina.
Fil: Otegui, María. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Otegui, María. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina.
description Predicting the optimal harvest date after crop physiological maturity is highly relevant for maize (Zea mays L.). While harvesting before achieving the commercial kernel moisture implies additional costs of grain drying, a delayed harvest of maize crops is linked to grain yield and quality losses. The main objective of this work was to identify weather variables affecting the post-maturity grain dry-down coefficient (k) in order to develop models to predict kernel moisture loss and time to harvest (harvest readiness) under a wide range of sowing date environments. Kernel moisture datasets from field experiments in Pergamino (Argentina) and Kansas (US) were used for training and testing post-maturity grain dry-down models. Two k coefficients were defined based on the solar radiation and the VPD explored during the pre- and post-maturity period (kpre and kpost). Models including kpre and kpost were tested under a wide range of sowing date environments, presenting high accuracy in predicting kernel moisture (R2 ∼ 0.80; RRMSE ∼ 0.15) and harvest readiness (R2 = 0.99; RRMSE ∼ 0.05). This study provides the foundation for developing an interactive digital platform to estimate harvest time to assist farmers and agronomists with this critical decision.
publishDate 2023
dc.date.none.fl_str_mv 2023-05
2026-04-06T11:21:19Z
2026-04-06T11:21:19Z
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/20.500.12123/25677
https://www.sciencedirect.com/science/article/pii/S0168192323001193
0168-1923
1873-2240
https://doi.org/10.1016/j.agrformet.2023.109427
url http://hdl.handle.net/20.500.12123/25677
https://www.sciencedirect.com/science/article/pii/S0168192323001193
https://doi.org/10.1016/j.agrformet.2023.109427
identifier_str_mv 0168-1923
1873-2240
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/PNCYO/1127042/AR./Bases ecofisiológicas para el mejoramiento genético y la calidad diferenciada de cereales y oleaginosas.
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 Agricultural and Forest Meteorology 334 : 109427. (May 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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