An approach for the formulation of sustainable replanting policies in the Indonesian natural rubber industry

Rubber replanting is a key factor in sustaining natural rubber supplies because it replaces less productive rubber trees with fully productive ones. However, the time taken for rubber trees to become fully productive (approximately 6 years) can cause significant impacts on shorter-term rubber supplies. In addition, replanting can have environmental impacts, such as reduction of carbon stocks because immature trees convert less CO2 than mature ones, and social impacts, such as reduced need for tappers, the highly skilled employees who harvest latex from rubber trees, during the immature phase of trees’ lives. Early discussions with Indonesian natural rubber stakeholders highlighted a demand for methods and tools that allow the consideration of these impacts when formulating replanting policies. This paper proposes an approach to support the formulation of sustainable replanting policies in the Indonesian natural rubber supply network that allows users to consider trade-offs between three factors: economic, social and environmental. The approach uses the composite indicators method to represent the impacts of replanting on the sustainability of the supply network. These indices are used to drive computer simulations with a view to finding optimal replanting policies for given situations. The approach is illustrated through an application to the formulation of sustainable replanting scenarios in the Langkat, Deli Serdang, Asahan, Simalungun and South Tapanuli Districts at North Sumatera Province Indonesia. •Rubber trees have three lifecycle phases: immature, productive, less productive.•The balance of trees in a supply network is managed with replanting quotas.•Replanting quotas and policies impact all three dimensions of sustainability which are quantified using composite indices.•These indices are utilized in network simulations that use a novel hybrid approach to find optimal allocation quotas.•Simulation results identified different optimal quota allocations for each district.