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Schönhart, M., Schauppenlehner, T., Kuttner, M., Kirchner, M., & Schmid, E. (2014). Integrated Assessment of Climate Change Mitigation and Adaptation Impacts at Field and Farm level in the Austrian Mostviertel Region..
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Mitter, H., Schönhart, M., & Schmid, E. (2014). Integrated climate change impact and adaptation assessment for the agricultural sector in Austria..
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Schönhart, M., Schauppenlehner, T., & Schmid, E. (2014). Integrated Land Use modelling of climate change impacts in two Austrian case study landscapes at field level..
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Schönhart, M., Schauppenlehner, T., & Schmid, E. (2014). Integrated land use modelling of climate change impacts – preliminary results from two Austrian case study landscapes. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: We present an integrated land use modelling framework (ILM) to analyze impacts of climate change and CAP reform as well as farm adaptation using economic, biotic and abiotic indicators at field, farm and landscape scales. The IML is applied on the two contrasting landscapes in the Austrian MACSUR regional pilot study. The scenarios cover climate and policy changes until 2040. The anticipated policy changes lead to declines in farm gross margins by -36% and -5% in the two landscapes, respectively. In contrast, climate change leads to higher gross margins, where farms can reach pre-reform levels on average. Environmental impacts such as removing of landscape elements and increasing fertilization can be moderated by an agri-environmental program, but the opportunity costs of program participation may increase.
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Sanna, M., Acutis, M., & Bellocchi, G. (2014). Interrelationship between evaluation metrics to assess agro-ecological models (Vol. 3).
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. No Label
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