Abstract: At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution.

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: Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved.