Abstract: The latest Common Agricultural Policy (CAP) reforms could bring substantial changes to Scottish farming communities. Two major components of this reform package, an introduction of environmental measures into the Pillar 1 payments and a move away from historical farm payments towards regionalized area payments, would have a significant effect on altering existing support structures for Scottish farmers, as it would for similar farm types elsewhere in Europe where historic payments are used. An optimizing farm-level model was developed to explore how Scottish beef and sheep farms might be affected by the greening and flat rate payments under the current CAP reforms. Nine different types of beef and sheep farms were identified and detailed biophysical and financial farm-level data for these farm types were used to parameterize the model. Results showed that the greening measures of the CAP did not have much impact on net margins of most of the beef and sheep farm businesses, except for ‘Beef Finisher’ farm types where the net margins decreased by 3%. However, all farm types were better off adopting the greening measures than not qualifying for the greening payments through non-compliance with the measures. The move to regionalized farm payments increased the negative financial impact of greening on most of the farms but it was still substantially lower than the financial sacrifice of not adopting greening measures. Results of maximizing farm net margin, under a hypothetical assumption of excluding farm payments, showed that in most of the mixed (sheep and cattle) and beef suckler cattle farms the optimum stock numbers predicted by the model were lower than actual figures on farm. When the regionalized support payments were allocated to each farm, the proportion of the mixed farms that would increase their stock numbers increased whereas this proportion decreased for beef suckler farms and no impact was predicted in sheep farms. Also under the regionalized support payments, improvements in profitability were found in mixed farms and sheep farms. Some of the specialized beef suckler farms also returned a profit when CAP support was added.

Abstract: Europe is the largest producer of wheat, the second most widely grown cereal crop after rice. The increased occurrence and magnitude of adverse and extreme agroclimatic events are considered a major threat for wheat production. We present an analysis that accounts for a range of adverse weather events that might significantly affect wheat yield in Europe. For this purpose we analysed changes in the frequency of the occurrence of 11 adverse weather events. Using climate scenarios based on the most recent ensemble of climate models and greenhouse gases emission estimates, we assessed the probability of single and multiple adverse events occurring within one season. We showed that the occurrence of adverse conditions for 14 sites representing the main European wheat-growing areas might substantially increase by 2060 compared to the present (1981-2010). This is likely to result in more frequent crop failure across Europe. This study provides essential information for developing adaptation strategies.

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.