Modeling Seedling Emergence and Growth in Green Bean, Sunflower and Maize by Some Nonlinear Models

• Published in: Majallah-i dānish-i kishāvarzī va tawlīd-i pāydār Vol. 20; no. 2; pp. 129 - 140
• Main Authors: B Behtari, Z Nemati, H Hassanpour, J Rezapour Fard
• Format: Journal Article
• Language: Persian
• Published: University of Tabriz 01.05.2010
• Subjects: Model fitting; Modeling seedling emergence; Nonlinear regression
• ISSN: 2476-4310; 2476-4329

Seedling emergence is one of important phenological event that influences the success of an annual crop probably. There has been accomplished numerous researches in recent years to understand and predict the emergence patterns of crop and weed species for different objectives. Nonlinear regression models have been developed to explain crop and weed emergence patterns as a function of time. In this study, some seedling emergence molels by field data of three crop species including green bean (Phaseolus vulgaris var. sunray), sunflower (Helianthus annuus L. var. alistar) and maize (Zea mays L. var. merit) were evaluated. Prediction of crop seedling emergence with the France and Thornley model and growth by the Logistic, Gompertz and Monomolecular models were also attempted. Emergence indices (SOE, MED, ERI, T0.5) showed that seedling emergence of maize was greater than green bean and sunflower. The values of the median emergence date (T0.5) predicted by Logistic model were in close agreement with the time required for 50% emergence calculated directly from interpolation of the raw emergence data. While shoot length (Lf) of crop seedling emergence fitted by Logistic and Monomolecular models were significant, it was not significant in Gompertz model statistically. Among the three models, the Gompertz and the Logestic models gave quite satisfactory results as the predicted values from the model and the observed values from the experiment were close (EF 0.9 in most of the cases and RMSE12 in all cases). Results showed that the empirical models with an inflection point are recommendable because thay predicted growth of crops seedling superiorly.