Forecasting the harvest date and yield of sweet corn by complex regression models

• Published in: Journal of the American Society for Horticultural Science Vol. 118; no. 4; pp. 450 - 455
• Main Authors: Lass, L.W, Callihan, R.H, Everson, D.O
• Format: Journal Article
• Language: English
• Published: Alexandria, VA American Society for Horticultural Science 01.07.1993
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agricultural and forest meteorology; Agronomy. Soil science and plant productions; AMANDE DE LA GRAINE; ANALISIS ESTADISTICO; ANALYSE STATISTIQUE; Biological and medical sciences; CALOR; CHALEUR; Climatic models of plant production; CONTENIDO DE HUMEDAD; DATE DE RECOLTE; DATE DE SEMIS; ENERGIA SOLAR; ENERGIE SOLAIRE; FECHA DE RECOLECCION; FECHA DE SIEMBRA; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production; GERMEN; HERBICIDAS; HERBICIDE; IDAHO; INSOLACION; INSOLATION; PROPIEDADES FISICO-QUIMICAS SUELO; PROPRIETE PHYSICOCHIMIQUE DU SOL; RENDEMENT DES CULTURES; RENDIMIENTO DE CULTIVOS; TECHNIQUE DE PREVISION; TECNICAS DE PREDICCION; TENEUR EN EAU; VARIEDADES; VARIETE; ZEA MAYS; Monocotyledones; Zea mays; Environmental factor; Cereal crop; Date; Harvest index; Ripening; Soils; Albedo; Genotype environment interaction; Gramineae; Angiospermae; Water content; Heat sum; Spermatophyta; Yield; Simulation model; Localization; Planting; Chemical weed control
• ISSN: 0003-1062; 2327-9788
• DOI: 10.21273/JASHS.118.4.450

Predicting sweet corn (Zea mays var. rugosa Bonaf.) harvest dates based on simple linear regression has failed to provide planting schedules that result in the uniform delivery of raw product to processing plants. Adjusting for the date that the field was at 80% silk in one model improved the forecast accuracy if year, field location, cultivar, soil albedo, herbicide family used, kernel moisture, and planting date were used as independent variables. Among predictive models, forecasting the Julian harvest date had the highest correlation with independent variables (R2 = 0.943) and the lowest coefficient of variation (cv = 1.31%). In a model predicting growing-degree days between planting date and harvest, R2 (coefficient of determination) = 0.85 and cv = 2.79%. In the model predicting sunlight hours between planting and harvest, R2 = 0.88 and cv = 6.41%. Predicting the Julian harvest date using several independent variables was more accurate than other models using a simple linear regression based on growing-degree days when compared to actual harvest time