Application of a coupled model of photosynthesis, stomatal conductance and transpiration for rice leaves and canopy

Abstract

Physiological assumptions incorporated in crop models need improvement to more realistically represent responses to heat stress, rising CO2, and genetic diversity. Coupling of leaf-level photosynthesis and stomatal conductance sub-models within an energy balance has been proposed to improve gas exchange predictions underlying many of these responses. The purpose of this study was to calibrate model subcomponents, assess individual- and coupled- sub-model performance, integrate this methodology with the ORYZA rice model, and evaluate the ability of the original and modified model to accurately simulate responses at canopy level using soil–plant-atmosphere research (SPAR) chamber data, with respect to plant age, CO2 concentration, and short-term high heat exposure. Individual and coupled sub-models were more accurate for leaf rice photosynthesis (average R2 of 0.81) than stomatal conductance or transpiration (average R2 of 0.46 and 0.77 respectively) when averaged across all treatments and measurement dates. Photosynthetic parameter Vcmac25 linearly decreased over the growing season, while Jmax25 and Tp were relatively constant over time and between CO2 levels, until about 50% heading when declines between 25% and 45% were observed through the beginning of grain filling. Diurnal and daily canopy net photosynthesis estimates were closer to observed values (ambient CO2. R2 = 0.71, elevated CO2. R2 = 0.70, P < 0.001) using this coupled approach compared with the original ORYZA model (ambient CO2. R2 = 0.263, elevated CO2. R2 = 0.420, P < 0.01). Canopy transpiration results were similar between the two approaches at 28 °C, but the energy balance method showed more realistic responses to elevated CO2 and warming temperature. These results indicated the use of a coupled energy balance model can more accurately predict rice gas exchange processes compared to uncoupled photosynthesis/transpiration methods when simulating responses to different CO2 and temperature conditions, and thus may provide more realistic assessments of current and future climate impacts on rice production.