Controle espacial clonal em modelos de regulação florestal

Abstract

The forestry sector plays an important role to Brazilian economy. The use of charcoal by steel mill industries has increased as a more sustainable alternative of energy. The steel sector is closely related to the forest since the expansion potential of the first depends on the second. This way, a good forest planning is necessary for supplying information to decision-making. It is known that forest planning problems have a high number of variables that make the planning process increasingly complex. Mathematical programming helps in the resolution of this kind of problem by means of Integer Linear Programming (ILP). So, this study aimed to introduce a new application of space restrictions of URM type, also named restriction of clonal adjacency, which intends to help in preventing the proliferation of pests and diseases in eucalyptus plantations . This restriction was used in ILP models to the minimization of production costs. Space and non-space, volumentric, reform areas, abandonment and nonabandonment of stands restrictions were considered in this study. This research was shared in two chapter: the first one presents a reference about the current situation of steel sector and the forest planning problem, while the second one proposes the utilization of a new application of space restrictions in Silviculture, named restriction of clonal adjacency. Twelve separated scenarios were generated in three different groups, without restrictions, with URM restrictions and clonal adjacency restriction. All groups had yet volumetric, limit of area to be reformed and abandonment or non-abandonment of stands restrictions. Average volume harvested in the scenarios without restriction (group 1) was 0.64% higher in relation to that with space restrictions (group 2 and 3). In scenarios with restriction of reform area percentage had significant differences in the costs. The elaboration of result maps presented, in landscape matrix, the efficiency of space restrictions application. The application of clonal adjacency restriction showed be better than URM harvest. The scenarios that used the application of clonal adjacency restriction were, on average, 0.14% higher when compared to traditional planning scenarios (group 1), proving the viability of its application to the proposed objective.