Evaluation of different strategic planning approaches in a forest plantation in the North of Misiones Province, Argentina.

In this paper we evaluate different models and constraints to define strategic planning approaches. In addition, we analyze the best models to meet the expectations generated by the organization. A forest company situated in the province of Misiones, Argentina, provided the data. Hence, forest growth was simulated and, ultimately, optimized planning was used to evaluate different scenarios with 50-year horizon. The best results to stabilize log production were obtained when the harvest is relaxed in ±2 years. Relaxing the clear-cut age leads to a better balance in planting, thinning (1, 2, 3 and 4) and clear felling operations. We found that when maximizing the economic benefit, the NPV is slightly higher, however, this is not significant. In this sense, the planner chooses an economic or volumetric objective function. Furthermore, we demonstrated that model 1 presented better results than model 2 because it manages to stabilize production in the planning horizon. The results allow forest companies to see the implication of choosing the model for strategic planning.

Stochastic forestry harvest planning under soil compaction conditions.

We present a study of annual forestry harvesting planning considering the risk of compaction generated by the transit of heavy forestry machinery. Soil compaction is a problem that occurs when the soil loses its natural resistance to resist the movement of machinery, causing the soil to be compacted in excess. This compaction generates unwanted effects on both the ecosystem and its economic sustainability. Therefore, when the risk of compaction is considerable, harvest operations must be stopped, complicating the annual plan and incurring in excessive costs to alleviate the situation. To incorporate the risk of compaction into the planning process, it is necessary to incorporate the analysis of the soil's hydrological balance, which combines the effect of rainfall and potential evapotranspiration. This requires analyzing the uncertainty of rainfall regimes, for which we propose a stochastic model under different scenarios. This stochastic model yields better results than the current deterministic methods used by lumber companies. Initially, the model is solved analyzing monthly scenarios. Then, we change to a biweekly model that provides a better representation of the dynamics of the system. While this improves the performance of the model, this new formulation increases the number of scenarios of the stochastic model. To address this complexity, we apply the Progressive Hedging method, which decomposes the problem in scenarios, yielding high-quality solutions in reasonable time.