Simulation in ecosys of root growth response to contrasting soil water and nitrogen

Abstract

If mathematical models of plant growth are to perform reliably under diverse conditions of soil and climate, then the effects of these conditions on root growth must be represented. A mathematical model of root and mycorrhizal growth is proposed to represent the effects of soil and climate on growth using the hypothesis that a functional equilibrium exists among root axes and shoot branches. In this model access to growth resources (C, N, P, water) by different axes or branches depends upon (1) proximity of the axis or branch to the point of resource acquisition, and (2) the rate at which resources are consumed by the axis or branch in relation to that by other axes or branches. This model was coupled to a plant growth model as part of the ecosystem simulation model ecosys, and its sensitivity to changes in model parameters and soil boundary conditions was tested. Simulated root growth was less sensitive to changes in soil water and nitrogen than was simulated shoot growth. This lower sensitivity allowed the model to simulate changes in root:shoot ratios with changes in soil water and nitrogen that were consistent with those commonly reported in the literature. Changes in soil water also caused changes in vertical distributions of root length density to be simulated that were also consistent with those reported. Changes in root:shoot partitioning and in root density distributions allowed improved access by plants in the model to limiting growth resources. The root model was parameterized from basic root growth studies conducted independently of the model, and without reference to site-specific patterns of seasonal root growth. Consequently the model is likely to be of general value in the simulation of root growth under diverse soil conditions, although such generality needs to be established through further testing under different soils, climates and crops. The precision of some of the model parameters is uncertain and the sensitivity of the model to this uncertainty is discussed.