Plant phenotypic plasticity plays an important role in nitrogen (N) acquisition and use under nitrogen-limited conditions. However, this role has never been quantified as a function of N availability, leaving it unclear whether plastic responses should be considered as potential targets for selection. A combined modelling and experimentation approach was adopted to quantify the role of plasticity on N uptake and plant yield. Based on a greenhouse experiment we considered plasticity in two maize traits: root-to-leaf biomass allocation ratio and emergence rate of axial roots. In a simulation experiment we individually enabled or disabled both plastic responses for maize stands grown across six N levels. Both plastic responses contributed to maintaining a higher N uptake and plant productivity as N-availability declined, compared to stands in which plastic responses were disabled. We conclude that plastic responses quantified in this study may be a potential target trait in breeding programs for greater N uptake across N levels while it may only be important for the internal use of N under N-limited conditions in maize. Given the complexity of breeding for plastic responses, an a priori model analysis is useful to identify which plastic traits to target for enhanced plant performance.

Architecture with and from living trees (Baubotanik) is a promising approach to sustainable, climate-adapted construction. Shaping and grafting allows one to create resilient structures that combine the ecological performance and aesthetics of trees with the functions of buildings. In order to design and engineer such living structures, it is necessary to predict the growth of different tree segments, especially when trunks, branches or roots are bent and jointed into a complex inosculated network. To address this, we have developed a tool to forecast the relative girth growth of different segments in such structures based on topological skeletons, the pipe model theory and circuit analogy. We have validated our results with a set of (scaled) photographs of inosculated tree structures of the so-called \'Tree Circus\', covering over 80 years of their growth. Our model has proven to predict the relative girth growth with sufficient accuracy for conceptual design purposes. So far, it does not allow the simulation of absolute growth in circumference over the course of time that is necessary to predict quantitative technical aspects, such as mechanical performance at a given time. We conclude by briefly outlining how this could be addressed in future research.

Branching is a main morphogenetic process involved in the adaptation of plants to the environment. In grasses, tillering is divided into three phases: tiller emergence, cessation of tillering and tiller regression. Understanding and prediction of the tillering process is a major challenge to better control cereal yields. In this paper, we present and evaluate WALTer, an individual-based model of wheat built on simple self-adaptive rules for predicting the tillering dynamics at contrasting sowing densities.
WALTer simulates the three-dimensional (3-D) development of the aerial architecture of winter wheat. Tillering was modelled using two main hypotheses: (H1) a plant ceases to initiate new tillers when a critical Green Area Index (GAIc) is reached, and (H2) the regression of a tiller occurs if its interception of light is below a threshold (PARt). The development of vegetative organs follows descriptive rules adapted from the literature. A sensitivity analysis was performed to evaluate the impact of each parameter on tillering and GAI dynamics. WALTer was parameterized and evaluated using an initial dataset providing an extensive description of GAI dynamics, and another dataset describing tillering dynamics under a wide range of sowing densities.
Sensitivity analysis indicated the predominant importance of GAIc and PARt. Tillering and GAI dynamics of expt 1 were well fit by WALTer. Once calibrated based on the agronomic density of expt 2, tillering parameters allowed an adequate prediction of tillering dynamics at contrasting sowing densities.
Using simple rules and a small number of parameters, WALTer efficiently simulated the wheat tillering dynamics observed at contrasting densities in experimental data. These results show that the definition of a critical GAI and a threshold of PAR is a relevant way to represent, respectively, cessation of tillering and tiller regression under competition for light.

Developing a sustainable agricultural model is one of the great challenges of the coming years. The agricultural practices inherited from the Green Revolution of the 1960s show their limits today, and new paradigms need to be explored to counter rising issues such as the multiplication of climate-change related drought episodes. Two such new paradigms are the use of functional-structural plant models to complement and rationalize breeding approaches and a renewed focus on root systems as untapped sources of plant amelioration. Since the late 1980s, numerous functional and structural models of root systems were developed and used to investigate the properties of root systems in soil or lab-conditions. In this review, we focus on the conception and use of such root models in the broader context of research on root-driven drought tolerance, on the basis of root system architecture (RSA) phenotyping. Such models result from the integration of architectural, physiological and environmental data. Here, we consider the different phenotyping techniques allowing for root architectural and physiological study and their limits. We discuss how QTL and breeding studies support the manipulation of RSA as a way to improve drought resistance. We then go over the integration of the generated data within architectural models, how those architectural models can be coupled with functional hydraulic models, and how functional parameters can be measured to feed those models. We then consider the assessment and validation of those hydraulic models through confrontation of simulations to experimentations. Finally, we discuss the up and coming challenges facing root systems functional-structural modeling approaches in the context of breeding.

The ability to assimilate C and allocate non-structural carbohydrates (NSCs) to the most appropriate organs is crucial to maximize plant ecological or agronomic performance. Such C source and sink activities are differentially affected by environmental constraints. Under drought, plant growth is generally more sink than source limited as organ expansion or appearance rate is earlier and stronger affected than C assimilation. This favors plant survival and recovery but not always agronomic performance as NSC are stored rather than used for growth due to a modified metabolism in source and sink leaves. Such interactions between plant C and water balance are complex and plant modeling can help analyzing their impact on plant phenotype. This paper addresses the impact of trade-offs between C sink and source activities and plant production under drought, combining experimental and modeling approaches. Two contrasted monocotyledonous species (rice, oil palm) were studied. Experimentally, the sink limitation of plant growth under moderate drought was confirmed as well as the modifications in NSC metabolism in source and sink organs. Under severe stress, when C source became limiting, plant NSC concentration decreased. Two plant models dedicated to oil palm and rice morphogenesis were used to perform a sensitivity analysis and further explore how to optimize C sink and source drought sensitivity to maximize plant growth. Modeling results highlighted that optimal drought sensitivity depends both on drought type and species and that modeling is a great opportunity to analyze such complex processes. Further modeling needs and more generally the challenge of using models to support complex trait breeding are discussed.