Global agriculture is facing a serious threat from climate change that compromises
global food security and impact the ecosystem services and biodiversity.
High temperatures affect plant development at the level of seed germination which represents the first step of plant establishment and also reduce the plant growth by affecting the shoot net assimilation rates and thus the total dry weight of the plant (1). It is the first climatic factor capping yields on a global scale for wheat (2) and rice (3). Projections predict a 9-15% decrease in the production of major cereals by 2020 (4). Climate change will increase the negative effect on crop productivity not only due to heat waves, i.e. an increase of several degrees over the seasonal temperature for a sustained period of days (5), but also by exacerbating broad-spectrum stresses such as drought, cold, salinity, flood, submergence and pests (1,6). Moreover, recent studies have revealed that the response of plants to combinations of two or more stress conditions is unique and cannot be directly extrapolated from the response of plants to each of the different stresses applied individually. Indeed the responses to the combined stresses are complex and largely controlled by different, and sometimes opposing, signaling pathways that may interact and inhibit each other (7).
Although genetic engineering (8) and agro-ecology (9) constitute the main topics of work to increase the resilience of crops to climate change, development of real time plant stress sensor is a strategic alternative for insuring a predictive and dynamic management of crop plants under field conditions. Since the first report (10), bioelectric activity during development and adaptation of plants to environmental changes became increasingly recognized and studied (11-15). Plant bioelectric activity, but also chemical, thermal & hydraulic responses, can be recorded locally or globally in short- and long-term
They represent a systemic and integrative response of the plant to environmental stimuli.
We have designed the project PACS that will delve into the use of plant activity and signatures as stress sensor with regard to climate changes.
Beyond the analysis of the signals emitted by the plant, an expected extension from the analysed stress response output may consist in building a model for the whole plant. Coupling the thermal, electric, hydrodynamic and chemical phenomena, the methodology would involve a nodal-type network and should allow for steady, pseudo-unsteady & unsteady simulations. Owing to an Onsager type force-flux approach, an elementary building block trying to mimic the behaviour of a (group of) similar plant cell(s) can first be designed then connected to others to form a network. The inputs/outputs and local features of this building block would be e.g.: chemical composition (like Ca++ concentration), temperature T, electro-chemical potential μ, local sap mass flow-rate and variation of the pressure P. According to the Lechatelier-Braun principle all the thermodanymic potentials are connected. For instance, the hydraulic and electric responses of plant are known to be highly correlated (Mancuso 1999) revealing the underlying Lechatelier-Braun coupling between P and μ. Similar observations of coupling between μ and T have been reported from vegetal thermoelectric responses (Goupil). All these thermodynamic-type analyses (cf. also e.g. Demirel & Sandler 2002, or Qian & Beard 2005) would benefit from a description of the energetic & material fluxes in order to build up solid knowledge about a plant metabolism. The dynamics energy budget (DEB) theory is the most popular non-species-specific theory of this kind. DEB aims to capture the quantitative aspects of the organization of metabolism at the organism level with implications for the sub- and supra-organismic levels (see e.g. Kooijman 2000, 2001; Nisbet et al. 2000). It is often claimed that when using DEB, the difference between species can be reduced to differences in the set of parameter values, since the theory may also consider complex phenomena such as simultaneous nutrient limitation, adaptation, co-metabolism, flocculated growth, product formation, aging, and syntrophy. However, this now quite standard modelling of biochemical networks neglects the spatial structure and the complex transport and allocation processes in the organism. Au contraire, the versatility of a spatio-temporal nodal approach would be able to handle both local to global scales (i.e. from coarse-grain to finer tuning) and also complex non-homogeneous structures and topologies. Moreover, possibly anisotropic, discontinuous transport and thermoelectric properties may also be included, hence mimicking the sharp interface between cells of different kinds. The properties can also be time or temperature/concentrations/electric potential dependent and allow for more complex behavior. The aim would be to be able to validate the network modeling against the observed data obtained from the PACS project, then to extrapolate the modeling to situations where no data can be measured (because e.g. of too high spatial and/or temporal resolution requirements, or difficult to measure properties.
References
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