Advancing RACiR: A new gas-exchange method for rapid measurements

Background/Question/Methods

Gas exchange is a widely used technique to obtain real-time measurement of leaf physiological properties, such as CO₂ assimilation (A), stomatal conductance to water vapor (g_sw), and intercellular CO₂ (C_i). Modern gas exchange systems offer greater portability than the laboratory-built systems of the past and take advantage of high precision infrared gas analyzers (IRGAs) as well as optimized system design that helps to obtain robust parameter estimates. However, the basic measurement paradigm has long remained unchanged: for open systems, steady state (or quasi-steady state) conditions are required for the measurement to be accurate. In practical terms, this means that conditions in the instrument leaf chamber must be stable, and the leaf biology must be stable (or nearly stable) as well. For CO₂ response curves, this requirement has meant that each point on the curve may take between 1-3 minutes and a full-range (0-2000 ppm) curve requires 25-40 minutes to obtain a sufficient number of points to estimate parameters such as the maximum velocity of carboxylation (V_c,max) or the maximum rate of electron transport (J_max). However, new developments have demonstrated that steady-state conditions are not necessarily required to obtain valid CO₂ response curve data.

Results/Conclusions

Recently, Stinziano et al. (2017) described a new technique, termed RACiR (rapid A-C_i response), that ramps chamber CO₂ concentration over time and CO₂ response curve data for a full-range curve can be obtained over a shorter time interval of 15-20 minutes. Here, we present a new approach that further reduces the time required to obtain full range CO₂ response curve data. Unlike RACiR, this new method does not require data post-processing using an empty-chamber correction. Results from CO₂ response curves from soybean (Glycine max) show that this method results in data substantially similar to the traditional steady-state method, while reducing uncertainty around the parameter estimates and offering the potential for reduced time requirements (4-10 minutes vs. 25-40 minutes) per response curve.