Canopy-scale relationships between stomatal conductance and photosynthesis in irrigated rice.

Modeling stomatal behavior is critical in research on land-atmosphere interactions and climate change. The most common model uses an existing relationship between photosynthesis and stomatal conductance. However, its parameters have been determined using infrequent and leaf-scale gas-exchange measurements and may not be representative of the whole canopy in time and space. In this study, we used a top-down approach based on a double-source canopy model and eddy flux measurements throughout the growing season. Using this approach, we quantified the canopy-scale relationship between gross photosynthesis and stomatal conductance for 3 years and their relationships with leaf nitrogen content throughout each growing season above a paddy rice canopy in Japan. The canopy-averaged stomatal conductance (gsc ) increased with increasing gross photosynthesis per unit green leaf area (Ag ), as was the case with leaf-scale measurements, and 41-90% of its variation was explained by variations in Ag adjusted to account for the leaf-to-air vapor-pressure deficit and CO2 concentration using the Leuning model. The slope (m) in this model (gsc versus the adjusted Ag ) was almost constant within a 15-day period, but changed seasonally. The m values determined using an ensemble dataset for two mid-growing-season 15-day periods were 30.8 (SE = 0.5), 29.9 (SE = 0.7), and 29.9 (SE = 0.6) in 2004, 2005, and 2006, respectively; the overall mid-season value was 30.3 and did not greatly differ among the 3 years. However, m appeared to be higher during the early and late growing seasons. The ontogenic changes in leaf nitrogen content strongly affected Ag and thus gsc . In addition, we have discussed the agronomic impacts of the interactions between leaf nitrogen content and gsc . Despite limitations in the observations and modeling, our canopy-scale results emphasize the importance of continuous, season-long estimates of stomatal model parameters for crops using top-down approaches.