Modeling branching in cereals

Abstract

Cereals and grasses adapt their structural development to environmental conditions and the resources available. The primary adaptive response is a variable degree of branching, called tillering in cereals. Especially for heterogeneous plant configurations the degree of tillering varies per plant. Functional–structural plant modeling (FSPM) is a modeling approach allowing simulation of the architectural development of individual plants, culminating in the emergent behavior at the canopy level. This paper introduces the principles of modeling tillering in FSPM, using (I) a probability approach, forcing the dynamics of tillering to correspond to measured probabilities. Such models are particularly suitable to evaluate the effect structural variables on system performance. (II) Dose–response curves, representing a measured or assumed response of tillering to an environmental cue. (III) Mechanistic approaches to tillering including control by carbohydrates, hormones, and nutrients. Tiller senescence is equally important for the structural development of cereals as tiller appearance. Little study has been made of tiller senescence, though similar concepts seem to apply as for tiller appearance.

Canopy architectural and physiological characterization of near-isogenic wheat lines differing in the tiller inhibition gene tin

“…However, it is still unclear to what extent such reduced levels of tillering plasticity in tin genotypes are mediated by light quality and especially R:FR signal transduction through the different phytochromes present in higher plants (Franklin and Whitelam, 2005 ). It will be interesting to further evaluate the tillering plasticity of tin genotypes in light and R:FR environments generated by the growing of plants on regular grids of different plant densities (Evers et al, 2006 ) or by manipulating light levels and/or R:FR artificially (Mandoli and Briggs, 1981 ; Casal et al, 1990 ) to explore the idea of a defined, genotype-specific R:FR threshold at which tillering ceases to be implemented in models of crop growth and development (Evers and Vos, 2013 ).…”

“…Thus, k is mainly useful to show that there are differences in optical properties but to dissect and further understand any sources of variation in k in NILs contrasting for tin requires a more integrated approach that explicitly takes into account the spatial distribution of plant organs. The most feasible approach for achieving this is arguably spatially-explicit modeling of plant architecture, growth, development and physiological functioning called functional-structural plant (FSP) modeling (Vos et al, 2010 ; Evers and Vos, 2013 ). Based on existing FSP models of wheat (Evers et al, 2005 ), contrasting seasonal time-courses of tiller production and mortality as well as leaf area expansion and arrangement could be simulated to explore (i) the relationship with cumulative radiation interception, which is important for potentially achieving high productivity (Monteith, 1977 ), and (ii) the possible consequences for the timing of transpirational water-use relative to anthesis and seed-filling, which is important for crop adaptation to water-limited environments (Passioura and Angus, 2010 ; Rebetzke et al, 2013 ).…”

“…The relationships were assessed in terms of leaf and tiller appearance, sizes of fully grown organs, and light quality and quantity. This study is a first step in the development of an architectural model (Vos et al, 2010 ; Evers and Vos, 2013 ) of tin phenotypes—a tool that could assist in the identification of desirable plant architectural traits for use in trait-based selection (Long et al, 2006 ; Richards et al, 2010 ; Rebetzke et al, 2013 ).…”

Multiple pathways regulate shoot branching

“…To assess how plant growth and environment affect the levels of the various players near each bud of a given plant, one approach can be to develop a functional–structural plant model. This modeling approach consists in representing plant physiological functioning in a realistic plant botanical structure ( Prusinkiewicz and Lindenmayer, 1990 ; Evers et al, 2011 ; Evers and Vos, 2013 ). Each organ is individually represented and positioned in the plant, so that the hormonal, nutrient, or physical environments (e.g., light) can be estimated locally for each organ.…”

How virtual shade sheds light on plant plasticity

“…FSP models can capture the complex dynamic feedback between the changing plant phenotype and the surrounding light environment by simulating plant phenotypic development and biomass growth over time in three dimensions at the organ level (Vos et al, 2010;Evers, 2016). In this model, the ability of plants to exhibit plastic responses is regarded by using response curves; dose-response relationships or step-wise relationships that relate organ trait change to a range of R:FR conditions (Gautier et al, 2000;Evers et al, 2007;Evers & Vos, 2013). By simulating the R:FR distribution as a function of the dynamic 3D plant phenotypes that are created by the interaction of resource acquisition and growth at the organ level, the plastic responses at the organ level are quantitatively linked to whole-plant performance during competition (Chapter 2; Bongers et al, 2014).…”