Stochastic simulation of restoration outcomes for a dry afromontane forest landscape in northern Ethiopia

• Main Authors: Aynekulu, E. (Author), Gebru, Y. (Author), Luedeling, E. (Author), Muchiri, C. (Author), Negussie, A. (Author), Shepherd, K. (Author), Tamba, Y. (Author), Wafula, J. (Author), Whitney, C. (Author), Yigzaw, N. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2021
• Subjects: Biomass accumulation; Decision analysis; Decision making; Decision Making; Dry afromontane forests; Ecological functions; Ecology; Forest and landscape restoration; Investments; Land reclamation; Land Reclamation; Landscape restoration; Monte Carlo methods; Monte Carlo risk analysis; Natural regeneration; Reforestation; Restoration; Returns on investment; Sensitivity analysis; Soil conservation; Soil moisture; Soil-water conservation; Stochastic models; Stochastic simulations; Stochastic systems; Uncertainty; Uncertainty analysis; Value of information; Water conservation; Water Management
• Online Access: View Fulltext in Publisher
• ISBN: 13899341 (ISSN)
• DOI: 10.1016/j.forpol.2021.102403

Forest and Landscape Restoration (FLR) is carried out with the objective of regaining ecological functions and enhancing human well-being through intervention in degrading ecosystems. However, uncertainties and risks related to FLR make it difficult to predict long-term outcomes and inform investment plans. We applied a Stochastic Impact Evaluation framework (SIE) to simulate returns on investment in the case of FLR interventions in a degraded dry Afromontane forest while accounting for uncertainties. We ran 10,000 iterations of a Monte Carlo simulation that projected FLR outcomes over a period of 25 years. Our simulations show that investments in assisted natural regeneration, enrichment planting, exclosure establishment and soil-water conservation structures all have a greater than 77% chance of positive returns. Sensitivity analysis of these outcomes indicated that the greatest threat to positive cashflows is the time required to achieve the targeted ecological outcomes. Value of Information (VOI) analysis indicated that the biggest priority for further measurement in this case is the maturity age of exclosures at which maximum biomass accumulation is achieved. The SIE framework was effective in providing forecasts of the distribution of outcomes and highlighting critical uncertainties where further measurements can help support decision-making. This approach can be useful for informing the management and planning of similar FLR interventions. © 2021 The Authors