Introduction

Atmospheric CO₂ concentration is expected to rise from its current level of 354ppm to 530ppm by the year 2050 and to 700ppm by the year 2100 (Watson et al., 1990). Concurrently this and other changes in the concentration of the infra-red absorbing gases in the atmosphere are expected to produce a greenhouse warming of the global surface of 3-4^oC by 2100 (Bretherton et al., 1990). Predicting how vegetation will respond to these changes is critical to understanding the impacts of atmospheric change on both natural ecosystems and crop growth. Experimental manipulation of the climate and atmosphere of enclosures around plants provides some direct evidence of the effects of these changes. However, given the global scale of the changes in atmosphere and climate, and the diversity of natural and crop ecosystems, experiments can only provide a tiny fragment of the information needed. Further, they can only be conducted at the scale of square meters whereas understanding at the scale of square kilometres is needed. Predictive models provide the only alternative, and a number of empirical mathematical models have now been developed and deployed (review: Long & Drake, 1991;Long, 1985). The empirical approach however has obvious limitations. Whilst they may closely mimic the behaviour inside the limits of experimental information, there can be no certainty that their behaviour outside of these limits will mimic that of actual biological systems. Yet, understanding climate change effects requires prediction beyond our present information about biological systems. A solution would be the use of mechanistic models, i.e. a model based upon key biochemical and biophysical processes. To mathematically describe the biochemistry and biophysics of all major physiological processes in higher plants is obviously unrealistic at the present time. Variation in some of these physiological processes is uncertain whilst for others the biochemical mechanisms remain uncertain in any plant. However, in considering both the response of plants to rising atmospheric CO₂ concentration and the carbon balance of plants, the process of photosynthesis is central. Photosynthesis, is the process by which plants both sense and respond to change in atmospheric CO₂ concentration. It is the physiological process that governs the input of C to any model of plant, crop or ecosystem production and carbon balance, and thus forms the front-end to any model concerned with production or carbon flow. Finally, it is well suited to mechanistic modelling. With just two variations on the basic metabolic pathway, C₃ photosynthesis, the biochemical pathway and enzymes of photosynthetic carbon metabolism are identical across all plants. Further, Farquhar & von Caemmerer (1980) have shown from theory, that at steady-state the responses of leaf photosynthetic CO₂ uptake to light, temperature and ambient CO₂ concentration may be described by the biochemical properties of just two steps in the process, the carboxylation reaction and the regeneration of the acceptor for carboxylation. The kinetic properties of the primary carboxylase, Ribulose 1:5 bisphosphatase carboxylase/oxygenase (Rubisco) are also known to have been highly conserved in the evolution of terrestrial plants so that the broad properties of this model should be applicable to all C₃ vegetation. This mechanistic model, developed from theory, has been widely validated as an accurate predictor of photosynthetic carbon uptake by leaves with variation in environmental conditions (reviewed Long, 1985). Thus, whilst all processes underlying C-balance of vegetation cannot be mathematically described at the biochemical level, photosynthesis the process central to the response of C-balance to atmospheric change can be mechanistically modelled. This mechanistic model of leaf photosynthesis may then be combined with physical models of light and gas transport within canopies to scale from the leaf to vegetation (Long & Drake, 1991; Forseth & Norman, 1991). A few models designed to examine atmospheric change response have already incorporated these mechanistic principles, i.e. MAESTRO (Wang & Jarvis, 1990) and BIOMASS (McMurtrie et al, 1992). However these models were developed for forest stands and were developed for the specialist modellers rather than for non specialist user groups.

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