Abstract: Assessing the impact of macro-level policy driven land use changes on regional sustainability is an important task that can facilitate complex decision making processes of introducing reforms. The research work demonstrates the ability of Sustainability Impact Assessment Tool (SIAT), a meta-model, in conducting ex ante spatially explicit cross sectoral impact assessments of changes in common agricultural policy (CAP). The meta-model is able to appraise impacts of CAP amendments on land use and their repercussions on multiple indicators of sustainability. The presented study comprehensively analyses the possible impacts of discontinuing direct financial support to farmers under CAP. The simulations of the meta-model are able to reveal the land use changes both at EU and regional levels as well as to bring forth the subsequent changes in a number of indicators representing the regional sustainability (for five case study regions). In a nutshell, the simulations indicate that a reduction in direct support brings in general, a decrease in farmed area, an increase in forested land, less fluctuation in natural vegetation coverage, increase in abandoned arable land area and negligible changes in built-up area despite regionally diverging land use trends. The simulated changes in sustainability indicators for the study regions in consequence to these land use changes show that the discontinuation of subsidies evokes responses that are in general climate friendly (reduction in methane and N2O emissions, diminishing energy use and reduction in global warming potential), economically beneficial (increase in gross value of agriculture) and socially desired (decrease in unemployment rate) as well as environmentally harmful (increase in pesticide use). Even though the appraisals of diversity indicators such as forest deadwood and farmland birds are not conclusive for all regions, the changes are positive for the former indicator and slightly negative for the latter in general. The trade-offs among these regional sustainability indicators using their directional associations are also presented for a comprehensive assessment of the impacts. (C) 2013 Elsevier Ltd. All rights reserved.

Abstract: Climate change poses a major challenge for farmers, but agricultural sustainability, mitigation, and adaptation can effectively decrease climate impacts on agricultural systems. Changes in farming practices are necessary to reduce emissions and to adapt to climate change. However, such modifications to common practices depend, to a large extent, on farmers’ knowledge and attitudes towards climate risks. An empirical study of farmers’ attitudes and knowledge of climate change mitigation and adaptation practices is useful to understand how farmers’ knowledge influences their attitudes and practices towards climate change mitigation and adaptation. Based on a case study characterised by four agricultural farming systems (extensive dairy sheep, intensive dairy cattle, horticultural farming, and rice farming) in the Province of Oristano in Italy, this study contains an investigation of (i) farmers’ knowledge of climate change causes and effects, how they construct such knowledge, and how they adapt to the phenomenon; (ii) what and how are farmers’ attitudes towards climate change causes are shaped under their contextual social interests and values; and (iii) if their practices in responding to climate variability are influenced by their constructed knowledge. The research results showed that farmers’ declarative knowledge of climate change did not affect their adaptation practices but directed farmers’ attitudes towards climate change causes. The findings also underscore the necessity of facilitating social learning spaces for enhancing virtuous behaviours towards climate change mitigation and the sharing and co-production of procedural knowledge for developing shared sustainable climate adaptation practices at the farm level.

Abstract: Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.