Gross Primary Productivity (GPP) is considered a key component in the study of carbon balance between the atmosphere and the biosphere. Large scale GPP estimations can be addressed by two ways:
1. Direct measurements with the use of eddy covariance technique (Baldocchi, 2003).
2. Development of mathematical models (Verbeeck et al., 2006).
While eddy covariance technique is considered as the only method of direct measurement of CO2 fluxes on ecosystem level, it is strongly limited by its spatial application and, additionally, it is very costly. On the other hand, models consist an affordable way to upscale processes from leaf to canopy and finally to the ecosystem. One of their main advantages is that models can be used not only for past ecosystem fluxes estimation, but also for the prediction of their possible future operation under changing climatic conditions.
A canopy level photosynthesis model has been developed in our laboratory, which consists of an empirical leaf level photosynthesis model, based on a modified non-rectangular hyperbola (Markos & Kyparissis, 2011) and a multi-layer canopy integration model (Leuning et al., 1995). The model uses as inputs simple ecophysiological parameters measured with typical equipment in the field (e.g. Amax, LAI, Photo 1), as well as meteorological parameters (PAR, temperature, Photo 2) and estimates canopy carbon fluxes with a default time step of 5 min and integrates to the desired time step (e.g. daily, Figure 1). It has been built on Simile visual modeling environment (Simulistics, Ltd.) and has been given the name MANTIS, from the greek word for prognosticator. Except GPP (Figure 1), the model also accounts for canopy Absorbed Photosynthetically Active Radiation (APAR), Light Use Efficiency (LUE) and total canopy transpiration.
Photo 1. Measuring leaf photosynthesis of Quercus frainetto in the field.
Photo 2. Setting up a meteorological station.
Figure 1. A 3-year seasonal fluctuation of GPP (output of the Mantis photosynthesis model) for a Fagus sylvatica forest (a) and the corresponding meteorological (b, c) and physiological (d, e) data that were used as inputs in the model. (b) daily Photosynthetically Active Radiation, PARd, (c) daily Temperature, Td, (d) leaf maximum photosynthetic rate, Amax, (e) Leaf Area Index, LAI.
Baldocchi, D. D. (2003). Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems : past , present and future. Global Change Biology, (October 2002), 479–492.
Leuning, R., Kelliher, F. M., De Pury, D. G. G., & Schulze, E.-D. (1995). Leaf nitrogen, photosynthesis, conductance and transpiration: scaling from leaves to canopies. Plant, Cell and Environment, 18(10), 1183–1200.
Markos, N., & Kyparissis, A. (2011). Ecophysiological modelling of leaf level photosynthetic performance for three Mediterranean species with different growth forms. Functional Plant Biology, 38(4), 314–326.
Verbeeck, H., Samson, R., Verdonck, F., & Lemeur, R. (2006). Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: a Monte Carlo analysis. Tree physiology, 26(6), 807–17.