Conclusions Leaf angles of heterogeneous canopies should be explicitly described as they have a big impact both on light distribution and photosynthesis. 8 % increase on simulated canopy photosynthesis. Taking into account the vertical variation of leaf photosynthetic parameters in the canopy, led to approx. The horizontal distribution of photosynthesis differed significantly between the top, middle and lower canopy layer. The fraction of light intercepted at a north–south orientation of rows differed from east–west orientation by 10 % on winter and 23 % on summer days. Explicitly described leaf angles resulted in higher light interception in the middle of the plant canopy compared with fixed and ellipsoidal leaf-angle distribution models, although the total light interception remained the same. Key Results Simulated light interception showed a good correspondence to the measured values. Different scenarios of horizontal and vertical distribution of light interception, incident light and photosynthesis were investigated under diffuse and direct light conditions. The model consisted of an architectural static virtual plant coupled with a nested radiosity model for light calculations and a leaf photosynthesis module. These data were used for the development and calibration of a functional–structural tomato model. Methods Detailed measurements of canopy architecture, light interception and leaf photosynthesis were carried out on a tomato crop. The aim of this paper is to analyse canopy heterogeneity of an explicitly described tomato canopy in relation to temporal dynamics of horizontal and vertical light distribution and photosynthesis under direct- and diffuse- light conditions. M.īackground and Aims At present most process-based models and the majority of three-dimensional models include simplifications of plant architecture that can compromise the accuracy of light interception simulations and, accordingly, canopy photosynthesis. Exploring the spatial distribution of light interception and photosynthesis of canopies by means of a functional–structural plant model
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