Abstract Mango (Mangifera indica), a leading tropical fruit crop, is a prime candidate for intensification through modern orchard-management techniques, including canopy manipulation to improve light interception. This study investigated how leaf-level acclimation to light gradients within the canopy of a high-yield, dwarfing mango cultivar (Calypso™) could be used to examine integrated canopy-scale responses. We quantified foliar morphological, biochemical, and physiological traits across a range of canopy positions using this information to model canopy-scale productivity within digital-twin representations of mango under both conventional (i.e. open-vase) and espalier training canopy systems. Key findings demonstrated that leaves exposed to higher light exhibited increased leaf mass per unit area (LMA), nitrogen content, and photosynthetic capacity (Asat), but decreased chlorophyll-to-nitrogen ratios and photochemical reflectance indices, reflecting trade-offs between light capture and photoprotection. Phenolic content increased under high irradiance, indicating investment in photoprotective compounds at the expense of net carbon gain. Modelled leaf-level productivity increased with light availability, following a Michaelis-Menten saturating response, with diminishing returns under high light. Digital modelling of canopy light interception revealed that espalier-trellis training enhanced light distribution efficiency per unit leaf area but resulted in a 6.5% reduction in total canopy productivity due to a smaller total canopy leaf area. However, when normalized by total canopy leaf area, the espalier-trellis system showed a 3.6% productivity advantage over conventional canopies at the time of year modelled. These results highlight the role of canopy structure and light-use efficiency play in determining orchard productivity. Integrating spatially explicit mechanistic models with LiDAR-derived canopy data offers a promising pathway for designing high-density, resource-efficient mango orchards. Future work should expand modelling to account for dynamic canopy shape throughout the growing season and evaluate the interaction of modified canopy structures with environmental stressors, particularly under climate variability.
Cheesman et al. (Tue,) studied this question.