There is an increasing call for tools that provide insight into the complex nature of agricultural systems and that deal with a broad range of sustainability issues related to policy intervention, technological innovations, and changes in environmental conditions (e.g., climate change). Sustainability indicators are useful, but only if their number is limited and the interactions between indicators are taken into consideration. In this context, we propose a methodology for an integrated analysis of tradeoffs between economic and environmental indicators. The analysis to quantify these relationships should be based on a multi-disciplinary approach and as such requires the usage of bio-physical as well as econometric-process simulation models. The communication between these very different models is based on explicit definitions of spatial and temporal scales and model integration software. The methodology is based on spatially explicit econometric simulation models linked to spatially referenced bio-physical simulation models to simulate land use and input use decisions. The methodology has been applied for the potato–pasture production system in the Ecuadorian Andes. Results of the analysis are presented in the form of tradeoff curves between different indicators, but also as maps, and risks diagrams. Besides an analysis of the current status, the approach allows for the analysis of alternative scenarios showing the effect of those scenarios on the position and slope of the tradeoff curve.
Agroecosystems are arguably the most important managed ecosystems in the world. An increasingly diverse array of leading public policy issues – including sustaining agricultural livelihoods, protecting water quality and other natural resources, and assessing global climate change mitigation strategies – create a demand for information about the economic and environmental properties of agricultural production systems. To make informed decisions, public stakeholders ranging from community leaders to national policy decision makers need to be able to assess how agricultural systems respond to changes in external stimuli such as changes in production technologies, policies, and shocks such as climate change.