Non-invasive measurement of leaf water content and pressure-volume curves using terahertz radiation.

In this paper we describe a non-invasive method of measuring leaf water content using THz radiation and combine this with psychrometry for determination of leaf pressure-volume relationships. In contrast to prior investigations using THz radiation to measure plant water status, the reported method exploits the differential absorption characteristic of THz radiation at multiple frequencies within plant leaves to determine absolute water content in real-time. By combining the THz system with a psychrometer, pressure-volume curves were generated in a completely automated fashion for the determination of leaf tissue water relations parameters including water potential at turgor loss, osmotic potential at full turgor and the relative water content at the turgor loss point.

Optimal stomatal drought response shaped by competition for water and hydraulic risk can explain plant trait covariation.

The classical theory of stomatal optimization stipulates that stomata should act to maximize photosynthesis while minimizing transpiration. This theory, despite its remarkable success in reproducing empirical patterns, does not account for the fact that the available water to plants is dynamically regulated by plants themselves, and that plants compete for water in most locations. Here, we develop an alternative theory in which plants maximize the expected carbon gain under stochastic rainfall in a competitive environment.

Optimal stomatal behaviour under stochastic rainfall.

Vegetation CO2 uptake is always accompanied by water loss. The balance in this gas exchange is controlled by the stomata, through which CO2 and water vapour diffuse between the leaf and the atmosphere. The optimal stomatal behaviour theory proposes that vegetation should optimise its stomatal behaviour such that, for given water availability, photosynthesis is maximised.

A Variational Bayesian Approach to Robust Identification of Switched ARX Models.

A variational Bayesian approach to robust identification of switched auto-regressive exogenous models is developed in this paper. By formulating the problem of interest under a full Bayesian identification framework, the number of local-models can be determined automatically, while accounting for the uncertainty of parameter estimates in the overall identification procedure. A set of significance coefficients is used to assign proper importance weights to local-models.