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Methods of mesophyll conductance estimation: its impact on key biochemical parameters and photosynthetic limitations in phosphorus‐stressed soybean across CO 2
Author(s) -
Singh Shardendu K.,
Reddy Vangimalla R.
Publication year - 2016
Publication title -
physiologia plantarum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.351
H-Index - 146
eISSN - 1399-3054
pISSN - 0031-9317
DOI - 10.1111/ppl.12415
Subject(s) - photosynthesis , stomatal conductance , conductance , carboxylation , chemistry , horticulture , botany , biology , mathematics , biochemistry , combinatorics , catalysis
Despite the development of various methods, the rapid estimation of mesophyll conductance (g m ) for a large number of samples is still a daunting challenge. Although the accurate estimation of g m is critical to partition photosynthetic limitations by stomatal (L s ) and mesophyll (L m ) conductance and by photo‐biochemical (L b ) processes, the impact of various g m estimation methods on this is ambiguous. As phosphorus (P) starvation and elevated CO 2 ( eCO 2 ) strongly affect photosynthetic processes, their combined effect on the proportional changes in these limitations are not well understood. To investigate this, while also evaluating distinct recent methods of g m estimation sharing few common theories and assumptions, soybean was grown under a range of P nutrition at ambient and eCO 2 . Methods significantly affected g m and carboxylation efficiency (V Cmax ) but not other photosynthetic parameters. In all the methods, all photosynthetic parameters responded similarly to treatments. However, the percentage difference between V Cmax assuming finite and infinite g m was highly inconsistent among methods. The primary mechanism responsible for P limitation to soybean photosynthesis was not CO 2 diffusion limitations but L b comprised of reduced chlorophyll, photochemistry and biochemical processes. The eCO 2 decreased L b but increased L m without affecting L s across leaf P concentration. Although each method explored advances of our understanding about g m variability, they all require assumptions of varying degrees, which lead to the discrepancy in the g m values. Among the methods, the oxygen sensitivity‐based g m estimation appeared to be suitable for the quick assessment of a large number of samples or genotypes. Digital tools are provided for the easy estimation of g m for some methods.