Bayesian estimation of linear and nonlinear mixed models of fertilizer dosing with independent normally distributed random components

3rd International Seminar On Sciences "Sciences On Precision And Sustainable Agriculture"

Masjkur, M.^1 ; Folmer, H.^2,3

Department of Statistics, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Indonesia^1

Faculty of Spatial Sciences, University of Groningen, Netherlands^2

Department of Agricultural Economics, College of Economics and Management, Northwest AandF University, Yangling, Shaanxi, China^3

关键词: Bayesian estimations;    Linear and nonlinear mixed models;    Linear plateaux;    Potassium fertilization;    Random components;    Random parameter model;    Random parameters;    Yield prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/58/1/012006/pdf
DOI  :  10.1088/1755-1315/58/1/012006

来源: IOP

【 摘 要 】

Many linear and nonlinear mixed response models are proposed to predict the optimum dose of fertilizer. However, a major restriction of this class of models is the normality assumption of the random parameter component. The purpose of this paper is to analyze the performance of linear and nonlinear mixed models of fertilizer dosing with independent normally distributed random parameter components. We compare the Linear Plateau, Spillman-Mitscherlich, and Quadratic random parameter models with different random effects distribution assumption, i.e. the normal, Student-t, slash, and contaminated normal distributions and the random errors following their symmetric normal independent distributions. The method is applied to datasets of multi-location trials of potassium fertilization of soybeans. The results show that the Student-t Spillman-Mitscherlich Response Model is the best model for soybean yield prediction.