Abstract: The aim of this paper is to provide a critical analysis of the strengths and weaknesses of a non-standardised Model Requirements Analysis (MRA) used for the purpose of developing the Sustainability Impact Assessment Tool (SIAT). By ‘non-standardised’ we mean not strictly following a published MRA method. The underlying question we are interested in addressing is how non-standardised methods, often employed in research driven projects, compare to defined methods with more standardised structure, with regards their ability to capture model requirements effectively, and with regards their overall usability. Through describing and critically assessing the specific features of the non-standardised MRA employed, the ambition of this paper is to provide insights useful for impact assessment tool (IAT) development. Specifically, the paper will (i) characterise kinds of user requirements relevant to the functionality and design of IATs; (ii) highlight the strengths and weaknesses of non-standardised MRA for user requirements capture, analysis and reflection in the context of IAT; (iii) critically reflect on the process and outcomes of having used a non-standardised MRA in comparison with other more standardised approaches. To accomplish these aims, we first review methods available for IAT development before describing the SIAT development process, including the MRA employed. Major strengths and weaknesses of the MRA method are then discussed in terms of user identification and characterisation, organisational characterisation and embedding, and ability to capture design options for ensuring usability and usefulness. A detailed assessment on the structural differences of MRA with two advanced approaches (Integrated DSS design and goal directed design) and their role in performance of the MRA tool is used to critique the approach employed. The results show that MRA is able to bring thematic integration, establish system performance and technical thresholds as well as detailing quality and transparency guidelines. Nevertheless the discussion points out to a number of deficiencies in application – (i) a need to more effectively characterise potential users, and; (ii) a need to better foster communication among the distinguished roles in the development process. If addressed these deficiencies, SIAT non-standardised MRA could have brought out better outcomes in terms of tool usability and usefulness, and improved embedding of the tool into conditions of targeted end-users. (C) 2013 Elsevier Ltd. All rights reserved.

Abstract: ► Delphi system, based on AFRCWHEAT2 model, for durum wheat forecast. ► AFRCWHEAT2 model was calibrated and validated for three years. ► A scenario approach was applied to simulation of durum wheat yield. ► Operational mode for eleven years in rainfed and water limiting conditions. ► Accurate forecast as an useful planning tool. Crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. The aim of this work was to test the predictive capacity of the Delphi system, calibrated and determined for each pedoclimatic factor affecting durum wheat during phenological development. at regional scale. We present an innovative system capable of predicting spatial yield variation and temporal yield fluctuation in long-term analysis, that are the main purposes of regional crop simulation study. The Delphi system was applied to simulate growth and yield of durum wheat in the major Italian supply basins (Basilicata, Capitanata, Marche, Tuscany). The model was validated and evaluated for three years (1995-1997) at 11 experimental fields and then used in operational mode for eleven years (1999-2009), showing an excellent/good accuracy in predicting grain yield even before maturity for a wide range of growing conditions in the Mediterranean climate, governed by different annual weather patterns. The results were evaluated on the basis of regression and normalized root mean squared error with known crop yield statistics at regional level. (c) 2012 Elsevier B.V. All rights reserved.

Abstract: The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.