The development of a crop ideotype with high agronomical performance depends on the identification of key architectural traits and their sensitivity to environmental factors.
In species with higher phenotypic plasticity like oilseed rape, stem branching pattern is an important trait for the development of such ideotype because the number, size and spatial distribution of branches determine source and sink dynamics, and in turn, grain yield.
Since stem branching pattern is greatly influenced by nitrogen and light dynamics, both factors could key determining crop architecture and grain yield.
Several studies have described a close association between nitrogen (N) fertilizer input and stem branching. In poaceae family, the tillering pattern has shown to be highly responsive to light distribution, suggesting that N availability together with light distribution play a key role in stem branching pattern. This could be particularly relevant in species with higher phenotypical plasticity as oilseed rape (Brassica napus).
However, the effects of agronomic practices such as N supply on plant architecture have been less explored than light environment, and have not been evaluated in oilseed rape. This is important because stem branching pattern and assimilate availability have a major influence on number and fertility of reproductive organs such as flowers and siliques, which are important components of grain yield in oilseed rape.
Since the factors determining stem branching and grain yield are part of a complex system with feedbacks at organ, plant and plant population levels, to elucidate the role of N and carbon economies in a mechanistic approach and considering different levels of biological organization, a modelling approach is needed.
Therefore, this study wants to evaluate i) the role of N availability in carbon assimilation and their effects on stem branching pattern, and ii) the impact of number of branches per plant on grain yield components in oilseed rape.
We will address these objectives using Functional-structural plant (FSP) modelling. This approach will permit to identify characteristics of a high-yielding plant type, which could be optimized by breeding or agronomic practices to increase crop yield based on quantitative terms.