Registro:
Título en inglés:
Enhancing maize grain dry - down predictive models
Autor/es:
Chazarreta, Yésica D.; Carcedo, Ana J.P.; Alvarez Prado, Santiago; Massigoge, José I.; Amas, Juan I.; Fernández, Javier A.; Ciampitti, Ignacio Antonio; Otegui, María Elena
Filiación:
Chazarreta, Yésica D. Kansas State University. Department of Agronomy. Manhattan, Kansas, United States.
Chazarreta, Yésica D. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Pergamino, Buenos Aires, Argentina.
Chazarreta, Yésica D. CONICET. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Pergamino, Buenos Aires, Argentina.
Chazarreta, Yésica D. Universidad Nacional del Noroeste de la Provincia de Buenos Aires (UNNOBA). Buenos Aires, Argentina.
Carcedo, Ana J. P. Kansas State University. Department of Agronomy. Manhattan, Kansas, United States.
Alvarez Prado, Santiago. Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR). Zavalla, Santa Fe, Argentina.
Alvarez Prado, Santiago. CONICET - Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR). Zavalla, Santa Fe, Argentina.
Alvarez Prado, Santiago. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Cátedra de Sistemas de Cultivos Extensivos-GIMUCE. Campo Experimental Villarino. Santa Fe, Argentina.
Massigoge, José I. Kansas State University. Department of Agronomy. Manhattan, Kansas, United States.
Amas, Juan I. Corteva Agriscience. Pergamino, Buenos Aires, Argentina.
Fernández, Javier A. University of Queensland. St. Lucia, Brisbane, Australia.
Ciampitti, Ignacio Antonio. Kansas State University. Department of Agronomy. Manhattan, Kansas, United States.
Otegui, María Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Pergamino, Buenos Aires, Argentina.
Otegui, María Elena. CONICET. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino (EEA Pergamino). Pergamino, Buenos Aires, Argentina.
Otegui, María Elena. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Buenos Aires, Argentina.
Título revista:
Agricultural and Forest Meteorology
Temas:
ZEA MAYS L.; POST-MATURITY DRYING; SOWING DATE; KERNEL MOISTURE
Url al documento en Intranet FAUBA:
Resumen:
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.
Citación:
---------- APA ----------
Chazarreta, Y. D.; Carcedo, A. J. P.; Alvarez Prado, S.; Massigoge, J. I.; Amas, J. I.; Fernández, J. A.; Ciampitti, I. A.; Otegui, M. E. (2023).Enhancing maize grain dry - down predictive models.Agricultural and Forest Meteorology,334,art.109427
10.1016/j.agrformet.2023.109427
---------- CHICAGO ----------
Chazarreta, Yésica D.,Carcedo, Ana J.P.,Alvarez Prado, Santiago,Massigoge, José I.,Amas, Juan I.,Fernández, Javier A., et al..2023. "Enhancing maize grain dry - down predictive models".Agricultural and Forest Meteorology 334:art.109427.
Recuperado de http://ri.agro.uba.ar/greenstone3/library/collection/arti/document/2023chazarreta