Landscape-scale simulations as a tool in multi-criteria decision making to support agri-environment schemes. (November 2019)

• Record Type: Journal Article
• Authors: Topping, Christopher J.; Dalby, Lars; Valdez, Jose W.
• Is Part Of: Agricultural systems. Volume 176(2019)
• Publication Date: 2019-11
• Subjects: Agricultural systems -- ALMaSS -- AES -- Agent-based modelling -- MCDA -- Wildlife management; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2019.102671
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: Increasing concerns over the environmental impacts of agriculture in Europe has led to the introduction of agri-environment schemes (AES) into the Common Agricultural Policy to help mitigate biodiversity loss by encouraging farmers with subsidies for implementing environmentally-friendly farming techniques. However, effectiveness of AES has been mixed and only partially successful in achieving desired outcomes. To improve effectiveness and reduce costs, multi-criteria decision analysis (MCDA) can help support decision-making and determine the most effective management action. Although MCDA has great potential for evaluating policy measures, it rarely considers the context-dependency of species responses to management practices across different landscapes. Landscape simulations can, therefore, be valuable for reducing the uncertainties when predicting the consequences of management actions. A potential suitable simulation system is the Animal, Landscape, and Man Simulation System (ALMaSS), a mechanistic simulation with can improve MCDA with the automatic integration of landscape context and a species ecology and behaviour. The aim of this study was to demonstrate the effectiveness of ALMaSS in evaluating AES management practices across different landscapes and estimate their ability to achieve the proposed conservation outcomes in three species of conservation interest. In this study, the effect of a particular management strategy on a species was dependent on the landscape context, in our case, a combination of landscape structure and the type and distribution of farms, and varied depending on the metrics being measured. We demonstrate how simulations can be used for MCDA to select between management strategies with different costs. Despite the complexity of ALMaSS models, the simulation results provided are easy to interpret. Landscape simulations, such as ALMaSS, can be an important tool in multi-criteria decision making by simulating a wide range of managements and contexts and provide supporting information for filtering management options based on specific conservation goals. Highlights: Species are impacted differently by various management practices across different landscapes. The effect of a particular management strategy on a species is dependent on landscape context. Landscape models help predict effect of management on various species across landscapes. Landscape simulations can provide an integrated approach to multi-criteria decision-making.