Valutazione del rischio legato ai cambiamenti climatici per il frumento duro

Recently, the availability of multi-model ensemble prediction methods has permitted to moving from the scenario-based approach to the risk-based approach in assessing the effects of climate change. This provides more useful information for decision-makers who need probability estimates to assess the seriousness of the projected impacts. In this study a probabilistic framework for evaluating the risk of durum wheat yield shortfall was exploited. An artificial neural network, trained to emulate outputs of a process-based crop growth model, was adopted to create yield response surfaces over which the probabilistic projections of future temperature and precipitation changes were overlaid to estimate probabilistic projections of future yields. The risk was calculated as the relative frequency of projected yield that not overcome the selected threshold. In contrast to previous studies suggesting that the beneficial effects of elevated atmospheric CO2 concentration over the next few decades would outweigh the detrimental effects of the early stages of climatic warming and drying, the results of our study are of more concern. Early in the next decades, the risk of reductions in yield below the long term yield average is likely (>66%). As the century progresses, the risk still increases, reaching its maximum by mid century (very likely). In the last decades, the risk slightly decreases as the effect of larger uncertainty in climate projections simulated for the end of the century

Climate Change and Tourism in Tuscany, Italy. What if heat becomes unbearable?

This paper investigates the empirical magnitude of climate conditions on tourist flows in Tuscany, exploring the use of a fine spatial scale analysis. In fact, we explore the use of an 8-year panel dataset of Tuscany’s 254 municipalities, examining how tourist inflows respond to variation in local weather conditions. In particular, as the area enjoys a fairly mild Mediterranean climate, our analysis focused on temperature extremes at key times of the tourist season, i.e., on maximum summer temperature and minimum winter temperature. Separate analyses are conducted for domestic and international tourists, so as to test the differences in the preferences among these distinct groups (or types of demand). Estimation results show the impact of climate change on tourist flows appears to vary significantly among destinations depending on the kind of attractions they offer, and those areas that host the main artistic and historical sights, affecting predominantly the domestic rather than the international tourists.Domestic Tourists, International Tourists, Municipalities, Maximum And Minimum Daily Temperature, Dynamic Model, Temperature Demand Elasticity, GMM

Concepts and methods developed for probabilistic evaluation of a number of alternative adaptation options

The purpose of this document is to define the protocol for a second study (IRS2) based on impact response surfaces (IRSs) in the frame of CropM/WP4. General considerations of IRS construction are described in the protocol developed for Phase I of the IRS analysis (IRS1)Access to the full document is restricted to MACSUR members until 2015-11-01

Relations between micrometeorological conditions and plant physiology

The changing climate and environmental conditions play a key role on plant physiology. In this context, crop simulation models represent a useful tool for investigating the main plant processes and provide a reliable estimation of crop productivity and quality. However, the most common crop models showed many limitations, with particular concern on the effect of some meteorological variables on plant processes during sensitive stages of development. Improving models by implementing the effect of such variables on crop processes may help to improve the accuracy of models, thus their usefulness. Here we focus on the analysis of the effect of high and low temperatures during flowering in grapevine. To this, the fruit-set index, developed for taking into account for the effect of temperature on setting the number of berries per cluster and the fruit-set percentage, was applied in a preliminary explorative study to assess the impact of different conditions during flowering at European scales. The sensitivity of the index allowed to identify the differential impact of temperature around flowering in different environment and for different varieties. Once meteorological variables are available at field or sub-field scale, the index can be used to provide information about the spatial variability of crop growth, thus allowing to identify the most appropriate interventions to improve productivity

Model comparison and improvement: Links established with other consortia

XC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls

Needs on model improvement

The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models

Adopting soil organic carbon management practices in soils of varying quality : Implications and perspectives in Europe

Acknowledgements We wish to thank all participants to the SmartSOIL project for their inspiring inputs and debates and for having shared their valuable expertise, contributing to the success of this project. Furthermore, we are grateful to the financial support from the 7th Framework Programme of the European Union (Call identifier: FP7-KBBE-2011-5; project number: 289694).Peer reviewedPostprin

Within-season predictions of durum wheat yield over the Mediterranean Basin

Crop yield is the result of the interactions between weather in the incoming season and how farmers decide to manage and protect their crops. According to Jones et al. (2000), uncertainties in the weather of the forthcoming season leads farmers to lose some productivity by taking management decisions based on their own experience of the climate or by adopting conservative strategies aimed at reducing the risks. Accordingly, predicting crop yield in advance, in response to different managements, environments and weathers would assist farm-management decisions(Lawless and Semenov, 2005). Following the approach described by Semenov and Doblas-Reyes (2007), this study aimed at assessing the utility of different seasonal forecasting methodologies in predicting durum wheat yield at 10 different sites across the Mediterranean Basin. The crop model, SiriusQuality (Martre et al., 2006), was used to compute wheat yield over a 10-years period. First, the model was run with a set of observed weather data to calculate the reference yield distributions. Then, starting from 1st January, yield predictions were produced at a monthly time-step using seasonal forecasts. The results were compared with the reference yields to assess the efficacy of the forecasting methodologies to estimate within-season yields. The results indicate that  durum wheat phenology and yield can be accurately predicted under Mediterranean conditions well before crop maturity, although some differences between the sites and the forecasting methodologies were revealed. Useful information can be thus provided for helping farmers to reduce negative impacts or take advantage from favorable conditions