Abstract: This paper describes the environmental consequences of agricultural adaptation on eutrophication of the nearby ecological network for a study area in the Netherlands. More specifically, we explored (i) likely responses of farmers to changes in climate, technology, policy, and markets; (ii) subsequent changes in nitrogen (N) emissions in responses to farmer adaptations; and (iii) to what extent the emitted N was deposited in nearby nature reserves, in view of the potential impacts on plant species diversity and desired nature targets. For this purpose, a spatially-explicit study at landscape level was performed by integrating the environmental model INITIATOR, the farm model FSSIM, and the land-use model RULEX. We evaluated two alternative scenarios of change in climate, technology, policy, and markets for 2050: one in line with a ‘global economy’ (GE) storyline and the other in line with a ‘regional communities’ (RC) storyline. Results show that the GE storyline resulted in a relatively strong increase in agricultural production compared to the RC storyline. Despite the projected conversions of agricultural land to nature (as part of the implementation of the National Ecological Network), we project an increase in N losses and N deposition due to N emissions in the study area of about 20 %. Even in the RC storyline, with a relatively modest increase in agricultural production and a larger expansion of the nature reserve, the N losses and deposition remain at the current level, whereas a reduction is required. We conclude that more ambitious green policies are needed in view of nature protection.

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.