Abstract: Agreeing that increased future global food demand will have to be met by production intensification rather than land use expansion (Hertel, 2011), scientists have moved to empirically analysing the causes for differences between potentially attainable yields and actually realized yields – the yield gap (Neumann et al., 2010). We aim at disentangling the effects of biophysical, economic and political impacts and farmers’ response to them on crop yields based on reviewing theoretical and empirical literature and some descriptive data analysis.

Abstract: The most usual records of observable agro-meteorological quantities are in the form of time series and the knowledge about their scaling properties is fundamental for  transferring locally measured fluctuations to larger scales and vice-versa. However, the scaling analysis of these quantities is complicated because of  the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the  chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). The MDFA analysis was performed  for time series of the air temperature, wind velocity and relative air humidity (at the height of 2 m above the  active surface) as well as the soil temperature (at 10 cm depth in the soil). The studied data were hourly interval, 12 years’ time series from the agro-meteorological station in Felin, near Lublin, Poland. The empirical singularity spectra  indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating their considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response  indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality, that underlies the q-dependence of the generalized Hurst exponent, by analyzing the corresponding shuffled and surrogate time series. For majority of studied quantities, the multifractality was due to different long-range correlation for small and large fluctuations.

Abstract: The extreme weather events, projected to increase both in frequency and magnitude with climate change have significant impact on agro-ecosystem services and pose severe limitations to sustainable agricultural land management. The proposed activities start from the hypothesis that these meteorological risks act as drivers of environmental innovation in agro-ecosystem management. These activities deal with risks associated with extreme weather phenomena and with risks of biological origin (e.g. pests and diseases). In order to reach this objective, the following elements of the chain of risk should be considered: • Hazard (assessment of the likely frequency and magnitude of extreme meteorological events)• Impact (analysis of the potential bio-physical and socio-economic impact of extreme weather events on agro-ecosystems)• Vulnerability (identification of the most vulnerable agro-ecosystems)• Risk Management (uncovering innovative risk management and adaptation options)These activities will concentrate on promoting a robust and flexible framework by demonstrating its performance across Belgian agro-ecosystems, and by ensuring its relevance to policy makers and practitioners. Impacts developed from physically based models will not only provide information on the state of the damage at any given time, but also assist in understanding the links between different factors causing damage and determining bio-physical vulnerability. Socio-economic impacts will enlarge the basis for vulnerability mapping, risk management and adaptation options. A strong expert and end-user network will be established to help exploiting project results to meet user needs