Response of bahiagrass carbon assimilation and photosystem activity to below optimum temperatures.

Photosynthesis and growth of tropical grasses are sensitive to cool season temperatures but information on the responsive mechanisms is limited in many species including bahiagrass (Paspalum notatum Flueggé). Therefore, an experiment was conducted in sunlit, controlled environment chambers to determine the effect of below optimum temperatures on leaf net photosynthesis (A) and chlorophyll fluorescence (F) and response to internal [CO] (C) and photosynthetic photon flux density (PPFD) of A and F of bahiagrass. Five day/night temperatures of 14/6, 18/10, 22/14, 26/18 and 30/22°C were imposed from 55 to 100 days after transplanting for plants grown initially for 55 days at 30/22°C. Leaf A and F were measured from 1000 to 1400 hours between -1 to 35 days after imposing temperature treatments. Leaf A-F/C and A-F/PPFD response curves were measured between 11 and 20 days after start of temperature treatments. After 35 days of treatment, the cold acclimation response of leaf A was assessed by lowering temperature in all treatments to 6°C and measuring A and F for a 3-day period. Repeated-measures analysis showed significant effects of time, temperature and time × temperature. The reduction of A on the first day of cold shock was 64, 37, 61, 64 and 81% in plants previously grown at 14, 18, 22, 26 and 30°C, respectively, which indicates acclimation at 18°C. Below optimum temperature significantly lowered CO-saturated net photosynthesis (A), carboxylation efficiency (CE) and electron transport rate (ETR) derived from A-F/C curves. Below optimum temperature also lowered light-saturated photosynthesis (A), Rd and ETR derived from A-F/PPFD curves. The relationship between φ and φ showed that bahiagrass A was more sensitive than electron transport at below optimum temperatures, which may be associated with increased CO leakage and over-cycling of C acid cycle. The leaf-level photosynthesis parameters and their response functions will also help to improve algorithms for simulating forage growth under variable temperature conditions.