Multi‑objective forest harvesting under sustainable and economic principles.

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dc.contributorTALLES HUDSON SOUZA LACERDA; LUCIANO CAVALCANTE DE JESUS FRANÇA, UNIVERSIDADE FEDERAL DE UBERLÂNDIA; ISÁIRA LEITE E LOPES, UNIVERSIDADE FEDERAL DE LAVRAS; SÂMMILLY LORRAYNE SOUZA LACERDA, UNIVERSIDADE FEDERAL DE LAVRAS; EVANDRO ORFANO FIGUEIREDO, CPAF-AC; BRUNO HENRIQUE GROENNER BARBOSA, UNIVERSIDADE FEDERAL DE LAVRAS; CAROLINA SOUZA JAROCHINSKI E SILVA, UNIVERSIDADE FEDERAL DE LAVRAS; LUCAS REZENDE GOMIDE, UNIVERSIDADE FEDERAL DE LAVRAS.
dc.creatorLACERDA, T. H. S.
dc.creatorFRANÇA, L. C. de J.
dc.creatorLOPES, I. L. e
dc.creatorLACERDA, S. L. S.
dc.creatorFIGUEIREDO, E. O.
dc.creatorBARBOSA, B. H. G.
dc.creatorSILVA, C. S. J. e
dc.creatorGOMIDE, L. R.
dc.date2024-02-08T15:32:21Z
dc.date2024-02-08T15:32:21Z
dc.date2024-02-08
dc.date2023
dc.date.accessioned2026-06-30T22:54:03Z
dc.descriptionSelective logging is well-recognized as an efective practice in sustainable forest management. However, the ecological efciency or resilience of the residual stand is often in doubt. Recovery time depends on operational variables, diversity, and forest structure. Selective logging is excellent but is open to changes. This may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary algorithms. The function maximizes remaining stand diversity, merchantable logs, and the inverse of distance between trees for harvesting and log landings points. The Brazilian rainforest database (566 trees) was used to simulate our 216-ha model. The log landing design has a maximum volume limit of 500 m3 . The nondominated sorting genetic algorithm was applied to solve the main optimization problem. In parallel, a sub-problem (p-facility allocation) was solved for landing allocation by a genetic algorithm. Pareto frontier analysis was applied to distinguish the gradients α-economic, β-ecological, and γ-equilibrium. As expected, the solutions have high diameter changes in the residual stand (average removal of approximately 16 m3 ha−1). All solutions showed a grouping of trees selected for harvesting, although there was no formation of large clearings (percentage of canopy removal<7%, with an average of 2.5 ind ha−1). There were no diferences in foristic composition by preferentially selecting species with greater frequency in the initial stand for harvesting. This implies a lower impact on the demographic rates of the remaining stand. The methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.
dc.identifierJournal of Forestry Research, v. 34, p. 1379-1394, Oct. 2023.
dc.identifier1007-662X
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1161859
dc.identifierhttps://doi.org/10.1007/s11676-023-01614-5
dc.identifier.urihttp://hdl.handle.net/123456789/370606
dc.languageeng
dc.rightsopenAccess
dc.subjectSelective logging
dc.subjectManejo florestal sustentável
dc.subjectBosques primarios
dc.subjectSilvicultura sustentable
dc.subjectRegeneración forestal
dc.subjectRegeneración natural
dc.subjectAnálise de Fronteira de Pareto
dc.subjectPareto frontier analysis
dc.subjectBujari (AC)
dc.subjectAcre
dc.subjectAmazônia Ocidental
dc.subjectWestern Amazon
dc.subjectAmazonia Occidental
dc.subjectFloresta Nativa
dc.subjectExploração Florestal
dc.subjectExtração da Madeira
dc.subjectRegeneração Natural
dc.subjectModelo Matemático
dc.subjectPrimary forests
dc.subjectSustainable forestry
dc.subjectNatural regeneration
dc.subjectForest regeneration
dc.subjectMathematical models
dc.titleMulti‑objective forest harvesting under sustainable and economic principles.
dc.typeArtigo de periódico

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