Modelling temporal variation of parameters used in two photosynthesis models: influence of fruit load and girdling on leaf photosynthesis in fruit-bearing branches of apple.

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Background and Aims

Several studies have found seasonal and temporal variability in leaf photosynthesis parameters in different crops. This variability depends upon the environment, the developmental stage of the plant and the presence or absence of sinks. Girdling involves the removal of the bark and phloem down to the youngest xylem all around the stem and prevents export of photoassimilates out of the stem. The load of developing fruits has often been reported to influence the individual net leaf photosynthesis rate (Pn) in tree crops. In this study, we chose (1) to model the key parameters of photosynthesis models of leaves (Pgmax, Rd, α and θ) as a function of time and using these two means (girdling and low fruit load) to alter the source-sink balance and (2) to compare three models: the rectangular and non-rectangular hyperbola model by Thornley, as well as the non-rectangular hyperbola model by Marshall and Biscoe.


Six-year-old fruit-bearing branches of 10-year-old apple trees were used to study and model the seasonal variation of photosynthetic parameters in leaves of vegetative shoots, as a function of global fruit load (at the branch level), with or without girdling, during the growing season of 2015. Three treatments were applied: control, low load (LL) or low load + girdling (LLG). For each fruit-bearing branch, light-response curves of Pn for two leaves of vegetative shoots were measured at two different positions, proximal and distal.

Key Results

The model of Marshall and Biscoe was the most accurate for the simulation of Pn in fruit-bearing branches of apple trees with time (season) and the three treatments applied.


The present study proposed a way to model the photosynthesis rate by temporal and environmental variables only. A proper validation of this model will be necessary to extend its utilization and appreciate its predictive capacity fully.

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