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Integrated Management of Forest Ecosystem Services: An Optimization Model Based on Multi-objective Analysis and Metaheuristic Approach

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Abstract

The goal of this research was to implement an optimization model that allows definition of best management strategies in the forest sector. The case study is depicted in a coniferous stand (silver fir—Abies alba Mill.) of Tuscany (central Italy). Four ecosystem services were quantified from biophysical (timber produced, carbon stored, ecosystem diversity, recreational function) and economic (total economic value of the above parameters) perspective. The indicators are aggregated through a multi-objective approach (compromise programming). In the framework of forest seen as complex adaptive systems, different management strategies can lead to emergent reaction of forest functions. Due to nonlinear and non-continuous interactions as well as to the presence of feedbacks and loops among environmental and socioeconomic forest components, results are optimized by means of differential evolution and particle swarm algorithm. The genetic algorithm was applied to minimize the distance from ideal point. The best value of the decision variable (rotation period) was defined for different scenarios based on compensatory level of criteria, constraints and presence/absence of forest thinning. Conflicting trends and trade-offs are highlighted when different criteria were optimized. Among outputs, thinning intervention seems to reduce the performance of several criteria in the study area. Strengths and weaknesses of the model as well as potential future improvements are finally discussed.

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  1. Department of Agricultural, Food and Forest Systems Management, University of Florence, 18, P.le delle Cascine, 50144, Florence, Italy

    Sandro Sacchelli & Iacopo Bernetti

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  1. Sandro Sacchelli
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  2. Iacopo Bernetti
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Correspondence to Sandro Sacchelli.

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Sacchelli, S., Bernetti, I. Integrated Management of Forest Ecosystem Services: An Optimization Model Based on Multi-objective Analysis and Metaheuristic Approach. Nat Resour Res 28 (Suppl 1), 5–14 (2019). https://doi.org/10.1007/s11053-018-9413-4

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