Nonlinear Growth Models for Modeling Oil Palm Yield Growth
By A. Khamis, Zuhaimy Ismail, K. Haron, A. T. Mohammed. 2005
102 citations recorded by the source corpus.
This study provided the basic needs of parameters estimation for nonlinear growth model such as partial derivatives of each model, determination of initial values for each parameter and statistical tests of industrial usage. Twelve nonlinear growth models and its partial derivatives for oil palm yield growth are presented in this study. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating oil palm yield growth data. The best model was selected based on the model performance and it can be used to estimate the oil palm yield at any age of oil palm. This study found that the Gompertz, logistic, log-logistic, Morgan-Mercer-Flodin and Chapman-Richard growth models have the ability for quantifying a growth phenomenon that exhibit a sigmoid pattern over time. Based on the statistical testing and goodness of fit, the best model is the Logistic model and followed by the Gompertz model, Morgan-Mercer-Flodin, Chapman-Richard (with initial stage) and Log-logistic growth models.
Subfields: Ecology, Management Science and Operations Research, Plant Science
View Paper: https://www.semanticscholar.org/paper/90d3395848ba846474bc9b6406dce4eeee2f110f
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In Ecology
In Management Science and Operations Research
In Plant Science
Citations in the corpus
- A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging (2014, methodology): “The Lorentzian function is known to have been commonly used for describing the laser profiles and light distribution patterns in optics research (Davis 1996), and the Gompertz function has been widely applied to describe the growth of bacteria, cells, and organisms (Chowdhury et al. 2007; Khamis et ”
- A nonlinear regression model with skew-normal errors (2010, methodology): “These data were also used by Azme et al. (2005) , where is obtained estimatives for the parameters of twelve nonlinear growth models by using the iterative Marquardt method.”