How much agroforestry is needed to achieve multifunctional landscapes at the forest frontier?-Coupling expert opinion with robust goal programming

Esther Reith, Elizabeth Gosling, Thomas Knoke, Carola Paul

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Agroforestry has been promoted as a key forest landscape restoration (FLR) option to restore ecosystem services in degraded tropical landscapes. We investigated the share and type of agroforestry selected in an optimized landscape, accounting for a mosaic of alternative forest landscape restoration options (reforestation and natural succession) and forest and common agricultural land-uses. We extend previous studies on multi-objective robust optimization and the analytic hierarchy process by a systematic sensitivity analysis to assess the influence of incorporating agroforestry into a landscape. This approach accounts for multiple objectives concurrently, yet data and computational requirements are relatively low. Our results show that experts from different backgrounds perceive agroforestry (i.e., alley cropping and silvopasture) very positively. Inclusion of large shares of agroforestry (41% share of landscape) in the FLR mix enhanced simulated ecosystem service provision. Our results demonstrate that landscapes with high shares of agroforestry may also comprise of high shares of natural forest. However, landscapes dominated by single agroforestry systems showed lower landscape multifunctionality than heterogeneous landscapes. In the ongoing effort to create sustainable landscapes, our approach contributes to an understanding of interrelations between land-covers and uncertain provisions of ecosystem services in circumstances with scarce data.

Original languageEnglish
Article number6077
JournalSustainability (Switzerland)
Volume12
Issue number15
DOIs
StatePublished - Aug 2020

Keywords

  • Agroforestry
  • Analytic hierarchy process
  • Ecosystem services
  • Forest landscape restoration
  • Multifunctionality
  • Optimization
  • Uncertainty

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