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Plants allocate internal resources to fulfil essential, yet possibly conflicting, demands such as defence or growth, as hypothesized by the 'growth-differentiation balance theory' (GDB). This trade-off was examined in young tomato plants grown for 25 d using the nutrient film technique with seven nitrate concentrations ([NO(3)]). The modification of primary (growth-related: organic acids, carbohydrates) and secondary (defence-related: phenolics) metabolite concentrations in leaves was assessed. Then a simple model was devised to simulate the trade-off between growth and secondary metabolism in response to N nutrition. N affected growth and metabolite concentrations in the leaves. Dry biomass, leaf area, and concentrations of nitrate and organic acid (malic, citric) increased with rising [NO(3)], up to a threshold, above which they remained constant. Starch, sucrose, and organic N concentrations were invariant with [NO(3)]. Glucose, fructose, and phenolic (chlorogenic acid, rutin, and kaempferol-rutinoside) concentrations were highest at lowest [NO(3)]. They declined progressively with rising [NO(3)] until a threshold, above which they remained constant. Model predictions are in phase with experimental phenolic concentration data although the simulated metabolic rates differ from the GDBH proposals depicted in the literature. From the model output it is shown that a careful definition of the C reserve compounds is required.

Image analysis is used in numerous studies of root system architecture (RSA). To date, fully automatic procedures have not been good enough to completely replace alternative manual methods. DART (Data Analysis of Root Tracings) is freeware based on human vision to identify roots, particularly across time-series. Each root is described by a series of ordered links encapsulating specific information and is connected to other roots. The population of links constitutes the RSA. DART creates a comprehensive dataset ready for individual or global analyses and this can display root growth sequences along time. We exemplify here individual tomato root growth response to shortfall in solar radiation and we analyse the global distribution of the inter-root branching distances. DART helps in studying RSA and in producing structured and flexible datasets of individual root growth parameters. It is written in JAVA and relies on manual procedures to minimize the risks of errors and biases in datasets.