Abstract: When evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub.

Abstract: Political, technical, environmental, and scientific developments in the last decades have affected the aims and scope of modern land use research. Attention has shifted from a relatively narrow analysis of food and non-food production to more comprehensive studies of land based ecosystem services. A growing number of integrated assessments attempt to guide the future development of agricultural lands, managed forests, and terrestrial ecosystems in the coming decades towards efficiency and sustainability. The increasing links between distinct disciplines create many scientific opportunities but also new challenges. This talk will provide a brief summary of past achievements in integrated land use modeling and outline strategies for future development.

Abstract: Impact Assessment for Sustainable Development: Knowledge Systems for the Future The ex ante Impact Asssessment of planned policies has developed as an important part of policy making within the European institutions as well as in Member States. The analysis of expected economic, social and environmental impacts informs the decision making. Collecting relevant and trustworthy evidence is a challenge for policy decisions. At the same time, Impact Assessment is an opportunity for researchers, research organisations and funding agencies to develop knowledge relevant for societal decision making.As a European research consortium LIAISE investigated over the past 4.5 years the Impact Assessment (IA) practices in relation to Sustainable Development (SD). Specific attention was given to the question how the process of IA in various venues (i.e. nation states, supra national organizations and local organizations) is related to the processes of research and knowledge production.