Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations.

Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.

Evaluation of a spatialized agronomic model in predicting yield and N leaching at the scale of the Seine-Normandie Basin.

The EU directive has addressed ambitious targets concerning the quality of water bodies. Predicting water quality as affected by land use and management requires using dynamic agro-hydrogeological models. In this study, an agronomic model (STICS) and a hydrogeological model (MODCOU) have been associated in order to simulate nitrogen fluxes in the Seine-Normandie Basin, which is affected by nitrate pollution of groundwater due to intensive farming systems. This modeling platform was used to predict and understand the spatial and temporal evolution of water quality over the 1971-2013 period. A quality assurance protocol (Refsgaard et al. Environ Model Softw 20: 1201-1215, 2005) was used to qualify the reliability of STICS outputs. Four iterative runs of the model were carried out with improved parameterization of soils and crop management without any change in the model. Improving model inputs changed much more the spatial distribution of simulated N losses than their mean values. STICS slightly underestimated the crop yields compared to the observed values at the administrative district scale. The platform also slightly underestimated the nitrate concentration at the outlet level with a mean difference ranging from -1.4 to -9.2 mg NO3 L-1 according to the aquifer during the last decade. This outcome should help the stakeholders in decision-making to prevent nitrate pollution and provide new specifications for STICS development.

[Going to the hospital or not? Limitations of the interpretation of treatment preferences in acute situations at home]. / Wel of niet naar het ziekenhuis?

When talking about 'advance care planning' (ACP), people regularly state that they do not want to go to the hospital any more. There are several reasons why this wish may be unclear in acute situations: preferred treatment is not always available or discussed, there may be different reasons for the 'no more hospital' statement and the emergency department is often insufficiently prepared for carrying out limited evaluations or treatments. To provide appropriate care, we recommend to (a) discuss the limitations of ACP, (b) evaluate preferences in the current context, (c) improve communication between primary and secondary care and to consider alternative options, (d) regularly evaluate current policies and (e) invest in local initiatives to prevent admission or long-term admission or to enable people to stay home. Treatment preferences, supported by earlier ACP discussions, always develop through the relationship between physicians and patients in specific situations.