Photosynthetic responses of switchgrass to light and CO2 under different precipitation treatments

Abstract

AbstractSwitchgrass (Panicum virgatum L.) is a prominent bioenergy crop with robust resilience to environmental stresses. However, our knowledge regarding how precipitation changes affect switchgrass photosynthesis and its responses to light and CO2 remains limited. To address this knowledge gap, we conducted a field precipitation experiment with five different treatments, including −50%, −33%, 0%, +33%, and +50% of ambient precipitation. To determine the responses of leaf photosynthesis to CO2 concentration and light, we measured leaf net photosynthesis of switchgrass under different CO2 concentrations and light levels in 2020 and 2021 for each of the five precipitation treatments. We first evaluated four light and CO2 response models (i.e., rectangular hyperbola model, nonrectangular hyperbola model, exponential model, and the modified rectangular hyperbola model) using the measurements in the ambient precipitation treatment. Based on the fitting criteria, we selected the nonrectangular hyperbola model as the optimal model and applied it to all precipitation treatments, and estimated model parameters. Overall, the model fit field measurements well for the light and CO2 response curves. Precipitation change did not influence the maximum net photosynthetic rate (Pmax) but influenced other model parameters including quantum yield (α), convexity (θ), dark respiration (Rd), light compensation point (LCP), and saturated light point (LSP). Specifically, the mean Pmax of five precipitation treatments was 17.6 μmol CO2 m−2 s−1, and the ambient treatment tended to have a higher Pmax. The +33% treatment had the highest α, and the ambient treatment had lower θ and LCP, higher Rd, and relatively lower LSP. Furthermore, precipitation significantly influenced all model parameters of CO2 response. The ambient treatment had the highest Pmax, largest α, and lowest θ, Rd, and CO2 compensation point LCP. Overall, this study improved our understanding of how switchgrass leaf photosynthesis responds to diverse environmental factors, providing valuable insights for accurately modeling switchgrass ecophysiology and productivity.