BACKGROUND AND AIMS:The morphogenesis and architecture of a rice plant, Oryza sativa, are critical factors in the yield equation, but they are not well studied because of the lack of appropriate tools for 3D measurement. The architecture of rice plants is characterized by a large number of tillers and leaves. The aims of this study were to specify rice plant architecture and to find appropriate functions to represent the 3D growth across all growth stages.METHODS:A japonica type rice, 'Namaga', was grown in pots under outdoor conditions. A 3D digitizer was used to measure the rice plant structure at intervals from the young seedling stage to maturity. The L-system formalism was applied to create '3D virtual rice' plants, incorporating models of phenological development and leaf emergence period as a function of temperature and photoperiod, which were used to determine the timing of tiller emergence.KEY RESULTS:The relationships between the nodal positions and leaf lengths, leaf angles and tiller angles were analysed and used to determine growth functions for the models. The '3D virtual rice' reproduces the structural development of isolated plants and provides a good estimation of the tillering process, and of the accumulation of leaves.CONCLUSIONS:The results indicated that the '3D virtual rice' has a possibility to demonstrate the differences in the structure and development between cultivars and under different environmental conditions. Future work, necessary to reflect both cultivar and environmental effects on the model performance, and to link with physiological models, is proposed in the discussion.

Most crop models simulate the crop canopy as an homogeneous medium. This approach enables modelling of mass and energy transfer through relatively simple equations, and is useful for understanding crop production. However, schematisation of an homogeneous medium cannot address the heterogeneous nature of canopies and interactions between plants or plant organs, and errors in calculation of light interception may occur. Moreover, conventional crop models do not describe plant organs before they are visible externally e.g. young leaves of grasses. The conditions during early growth of individual organs are important determinants of final organ size, causing difficulties in incorporating effects of environmental stresses in such models. Limited accuracy in describing temporal source-sink relationships also contributes to difficulty in modelling dry matter distribution and paramaterisation of harvest indices. Functional-architectural modelling aims to overcome these limitations by (i) representing crops as populations of individual plants specified in three dimensions and (ii) by modelling whole plant growth and development from the behaviour of individual organs, based on models of organs such as leaves and internodes. Since individual plants consist of numerous organs, generic models of organ growth applicable across species are desirable. Consequently, we are studying the development of individual organs, and parameterising it in terms of environmental variables and plant characteristics. Models incorporating plant architecture are currently applied in education, using dynamic visual representation for teaching growth and development. In research, the 3D representation of plants addresses issues presented above and new applications including modelling of pesticide distribution, fungal spore dispersal through splashing and plant to plant heterogeneity.

L-systems are widely used in the modelling of branching structures and the growth process of biological objects such as plants, nerves and airways in lungs. The derivation of such L-system models involves a lot of hard mental work and time-consuming manual procedures. A method based on genetic algorithms for automating the derivation of L-systems is presented here. The method involves representation of branching structure, translation of L-systems to axial tree architectures, comparison of branching structure and the application of genetic algorithms. Branching structures are represented as axial trees and positional information is considered as an important attribute along with length and angle in the database configuration of branches. An algorithm is proposed for automatic L-system translation that compares randomly generated branching structures with the target structure. Edit distance, which is proposed as a measure of dissimilarity between rooted trees, is extended for the comparison of structures represented in axial trees and positional information is involved in the local cost function. Conventional genetic algorithms and repair mechanics are employed in the search for L-system models having the best fit to observational data.

Information on the architecture and morphogenesis of plants is important in a range of research fields, such as in the development of ideotypes and direct-seeding techniques and for understanding competition against weeds and compensation for pest damage. Although several simulation models for predicting yield exist, there is no detailed model for simulating morphogenesis and plant architecture. In this study, the three-dimensional (3D) structure of plants was measured and the results were used to construct a virtual rice model that simulates morphogenesis and the development of plant architecture.

This paper presents a spatial simulation model of above-ground morphogenesis in a gramineous crop, grain sorghum (Sorghum bicolor (L.) Moench). Functions and parameters describing structural development were obtained from three-dimensional (3D) measurements of plants made at intervals during their development. The functions were expressed as specifications of morphogenesis in the L-system formalism and the specifications were interpreted by specialised software to create 3D virtual sorghum plants. Using the length and height of the collar of an early leaf as initial states, realistic images were generated of the lengths and shapes of subsequent leaves. The approach captures the dynamic interaction between partitioning and morphogenesis and presents the complex results as images that aid rapid interpretation.

Glasshouse experiments determined effects of a moth, Helicoverpa armigera (Lepidoptera: Noctuidae), and the anthracnose pathogen, Colletotrichum gloeosporioides (Penz.) Penz. and Sacc., on each other when attacking the same host plant, Stylosanthes scabra (Vog.) (Leguminosae) cv. Fitzroy. The host was treated with both organisms in 2 ways of succession and at 2 different life stages each. Larvae of the moth preferred to feed on healthy plants rather than plants recently infected with C. gloeosporioides, and preferred such newly infected plants to severely diseased ones. Adult female moths laid more eggs on healthy and recently infected plants than on diseased plants, when given a choice of all 3 plant types. Severity of anthracnose disease was neither promoted nor retarded by damage to leaves caused by larvae of the moth.

This paper summarizes the processes involved in designing a mathematical model of a growing pasture plant, Stylosanthes scabra Vog. cv. Fitzroy. The model is based on the mathematical formalism of Lindenmayer systems and yields realistic computer-generated images of progressive plant geometry through time. The processes involved in attaining growth data, retrieving useful growth rules, and constructing a "virtual plant" model are outlined. Progressive output morphological data proved useful for predicting total leaf area and allowed for easier quantification of plant canopy size in terms of biomass and total leaf area.

There are four processes that can change the size of a population: birth, death, immigration, and emigration. As originally developed for human demography, life tables were concerned only with rates of death and showed how life expectancy changed with age. In ecological studies, life tables can contain age-specific statistics for births, immigration, and emigration, but this is not appropriate for such animals as Heliothis spp. whose reproduction and long-distance dispersal occurs during one relatively short life stage. In these cases, single estimates of birth, immigration, and emigration rates for each generation provide quantitative links between successive generations.