Preliminary use of YieldSAFE model to assess Eucalyptus globulus productivity in Portugal under future climate

Poste

Emotions and dog bites: Could predatory attacks be triggered by emotional states?

Dog biting events pose severe public health and animal welfare concerns. They result in several consequences for both humans (including physical and psychological trauma) and the dog involved in the biting episode (abandonment, relocation to shelter and euthanasia). Although numerous epidemiological studies have analyzed the different factors influencing the occurrence of such events, to date the role of emotions in the expression of predatory attacks toward humans has been scarcely investigated. This paper focuses on the influence of emotional states on triggering predatory attacks in dogs, particularly in some breeds whose aggression causes severe consequences to human victims. We suggest that a comprehensive analysis of the dog bite phenomenon should consider the emotional state of biting dogs in order to collect reliable and realistic data about bite episodes

Rainfall interception modelling: is the wet bulb approach adequate to estimate mean evaporation rate from wet/saturated canopies in all forest types?

The Penman–Monteith equation has been widely used to estimate the maximum evaporation rate (E) from wet/saturated forest canopies, regardless of canopy cover fraction. Forests are then represented as a big leaf and interception loss considered essentially as a one-dimensional process. With increasing forest sparseness the assumptions behind this big leaf approach become questionable. In sparse forests it might be better to model E and interception loss at the tree level assuming that the individual tree crowns behave as wet bulbs (‘‘wet bulb approach”). In this study, and for five different forest types and climate conditions, interception loss measurements were compared to modelled values (Gash’s interception model) based on estimates of E by the Penman–Monteith and the wet bulb approaches. Results show that the wet bulb approach is a good, and less data demanding, alternative to estimate E when the forest canopy is fully ventilated (very sparse forests with a narrow canopy depth). When the canopy is not fully ventilated, the wet bulb approach requires a reduction of leaf area index to the upper, more ventilated parts of the canopy, needing data on the vertical leaf area distribution, which is seldom-available. In such cases, the Penman–Monteith approach seems preferable. Our data also show that canopy cover does not per se allow us to identify if a forest canopy is fully ventilated or not. New methodologies of sensitivity analyses applied to Gash’s model showed that a correct estimate of E is critical for the proper modelling of interception loss

Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe

Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues. We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a prior probability distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter distributions. We assessed model performance by sampling from prior and posterior distributions and comparing the growth predictions of these samples to the observations at the PSPs. We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty

E1 Theme: Trust-building for collaborative win-win customer solutions. Opportunity Assessment Roadmap Report

This report proposes a five year roadmap that address the opportunities for understanding, building and measuring trust in the Australian energy sector. Providing customer education alone is not consistent with best practice. Deficits in public knowledge are not the problem; therefore, educating energy users about the energy system is not the solution. This roadmap leverages customers strengths, knowledge, and practices to cultivate trust using a shared value approach. The conceptual centrepiece of this report is the ‘ecosystem of shared value’ – an industry-wide approach to valuing consumers’ contributions to the creation of value

Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modellingPeer reviewe

Selecting parameters for Bayesian calibration of a process-based model: a methodology based on canonical correlation analysis

Bayesian statistics is becoming increasingly common in the environmental sciences because of developments in computers and sampling-based techniques for parameter estimation. However, the use of the Bayesian approach is still limited in forest research, especially for models with many parameters. Some studies have used parameter screening to make the calibration of a computationally expensive model possible. In this paper we introduce a new methodology for parameter screening, based on canonical correlation analysis. Furthermore we show how parameter screening impacts the performance of a process-based model. The methodology presented here can be generally applied and is particularly suitable for complex process-based models because it is not computationally demanding and is easy to implement. It provides an overall ranking in relation to all outputs of the model, as opposed to common sensitivity methods that analyze one model output variable at a time. We found that parameter screening can be used to reduce the computational load of Bayesian calibration, but only the least important parameters should be excluded from the calibration if we do not want to affect model performance. In this exercise, 25% of the parameters of a process-based forest model could be excluded from the calibration without affecting model performance. When calibration was limited to a more restricted number of parameters, model performance significantly deteriorated