Optimization of Skid Trails and Log Yards on the Amazon Forest

Research highlights: We used Dijkstra Algorithm (DA) to define optimal allocation of yards in order to minimize total skid-trail’s distance in the Amazon Forest. DA minimized trails’ distances and associated transportation costs, leading to an even smaller value when the current planning was disregarded and suggesting the reduction of deleterious environmental externalities. Background and objectives: We sought to answer if it is possible to optimize distances and intrinsic costs in the management of Amazonian forests using DA. The objective was to minimize skid trails distances by best allocating yards using DA and to compare four scenarios of forest harvest planning in the Brazilian Amazon. Materials and methods: Tree census data from Genesis-Salem Farm, state of Para, Brazil, were used. The yards and roads located by Grupo Arboris (scenario 1) were compared to three alternative scenarios in terms of total skid distance, trails and road densities, and skidding costs for three successive harvests, seeking to minimize total skid-trails’ distance. Alternative scenarios were to keep the number of yards within work units (WU) and place them in the edge of existing roads (scenario 2); keep the number of yards within each WU (scenario 3); and place 23 yards, disregarding the current planning (scenario 4). Results: Total skid-trail’s distance, number of trees above optimal extraction distance and densities of skid trails and roads were smaller in scenarios 2, 3, and 4, compared to the current yard allocation (scenario 1). Scenario 4, with fewer restrictions, reduced skid-trails’ distances by 23%. Harvest costs decreased from scenario 1 to 4 in all three harvest cycles. Conclusions: DA allowed optimized distribution of yards and skid trails and generated efficient results for harvest planning. This reinforces the importance of optimized planning, which establishes satisfactory results in the effort to reduce costs and environmental impact keeping high efficiency.