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Highlights • Assessed uncertainty implications on native afforestation feasibility in erodible land. • Complemented a deterministic economic optimisation approach with Monte-Carlo methods. • The socio-cultural circumstances of the native community make low discount rates possible. • Long-rotation species could be feasible at low discount rates and payments for services. • High public environmental investments are required to hedge against climatic uncertainty. Abstract Forestry's long-term nature and associated uncertainties play against its consideration in land-use decisions. This study's objective was assessing the implications of uncertainty on the feasibility of afforesting erodible land with natives. The New Zealand example has global relevance enabling: innovative statistical representation of climatic uncertainty, economic uncertainty analysis due to existing payments for services, and the profitability of long-rotation species due to Māori's socio-cultural aspirations. We complemented a deterministic optimisation approach with Monte-Carlo methods to identify optimal afforestation areas representing uncertainty probabilistically. With stochastic dominance/efficiency tests, relevant to landowners, we identified that the uncertainty and profitability of long-rotation species increase at low discount rates, plausible due to low opportunity costs and inter-generational aspirations (not possible in competitive circumstances). At low discount rates, the high profitability odds of the long-rotation alternative compensate its high uncertainty and make it the preferred one even for highly risk-averse owners. With a probabilistic cost-benefit analysis, relevant to policymakers, we identified that the benefits necessary to cover erosion-reduction investments would need to be higher than expected to hedge against climatic uncertainty. These results were obtained through the uncertainty analysis. The methods/results covered in this study are relevant to land-use studies/models seeking to measure uncertainty impacts simply. [ABSTRACT FROM AUTHOR]