Predicting the Growth Interactions between Plants in Mixed Species Stands using a Simple Mechanistic Model

Abstract

The Conductance model is a simple mechanistic model used to predict the growth of species in monoculture or mixtures from parameter values derived from plants grown in isolation. In contrast to many mechanistic models that require extensive parameterization, the Conductance model is able to capture the growth of a broad range of species using a few simplified assumptions regarding plant growth and easily derived species-specific parameter values. We examine the assumptions within the Conductance model that total leaf area per plant is proportional to total plant weight, and that an isolated plant has a projected crown zone area that is proportional to the 2/3 power of its weight. Power rather than linear relations were found between weight and leaf area for Brassica oleracea , Daucus carota , Matricaria inodora , Solanum nigrum , Stellaria media , Trifolium repens and Veronica persica . For all seven species, the value of the power was less than unity. All species also exhibited a power relation between crown zone area and weight, with the slope of this relation being less than 2/3 for B. oleracea , D. carota and S. media . Although morphology type accounted for some of the variation in the parameter values relating to light interception, there were considerable differences between species within upright or prostrate foliage species groups. The Conductance model was used to predict yields of B. oleracea , S. nigrum and V. persica grown in both monoculture and binary weed-crop mixtures over a range of temporal and spatial scales. After calibrating the model to non-competing plants, the model was used to predict growth of the weed and crop species in contrasting densities and stand types. In some crop-weed combinations, predicted crop and weed weights were within 17% of observed values, with no systematic deviations. In others, systematic and large deviations occurred. Copyright 2001 Annals of Botany Compan

Modelling crop-weed competition: Why, what, how and what lies ahead?

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