Author Information

Edward Gbur
Craig Beyrouty

Abstract

Several mechanistic models have been developed for the prediction of nutrient uptake at low concentrations from the soil by a plant root system. Claassen and Barber (1974 Plant Physiology 54, 564-568; 1976 Agronomy Journal 68, 961-964) presented an experimental procedure to obtain data from intact plants to fit an ion depletion curve and used the data in a model which they developed to predict nutrient uptake. Their model assumed that nutrient absorption from the soil solution followed Michaelis-Menten kinetics. In this paper, we develop a stochastic version of the Claassen-Barber model and illustrate its application to the estimation of the kinetic parameters associated with the uptake of potassium by corn plants. The analysis requires the fitting of a nonlinear regression equation which cannot be explicitly expressed in terms of the response variable. The analysis is potentially further complicated by heterogeneity of variance and an autocorrelated error structure.

Keywords

Nonlinear regression, Implicitly defined function, Michaelis-Menten kinetics

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Apr 28th, 10:30 AM

ESTIMATION OF KINETIC PARAMETERS ASSOCIATED WITH NUTRIENT UPTAKE BY AN INTACT PLANT ROOT SYSTEM

Several mechanistic models have been developed for the prediction of nutrient uptake at low concentrations from the soil by a plant root system. Claassen and Barber (1974 Plant Physiology 54, 564-568; 1976 Agronomy Journal 68, 961-964) presented an experimental procedure to obtain data from intact plants to fit an ion depletion curve and used the data in a model which they developed to predict nutrient uptake. Their model assumed that nutrient absorption from the soil solution followed Michaelis-Menten kinetics. In this paper, we develop a stochastic version of the Claassen-Barber model and illustrate its application to the estimation of the kinetic parameters associated with the uptake of potassium by corn plants. The analysis requires the fitting of a nonlinear regression equation which cannot be explicitly expressed in terms of the response variable. The analysis is potentially further complicated by heterogeneity of variance and an autocorrelated error structure.