Stochastic forestry harvest planning under soil compaction conditions

Autores
Rossit, Daniel Alejandro; Pais, Cristóbal; Weintraub, Andrés; Broz, Diego Ricardo; Frutos, Mariano; Tohmé, Fernando Abel
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Fil: Pais, Cristóbal. University of California at Berkeley; Estados Unidos
Fil: Weintraub, Andrés. Universidad de Chile; Chile
Fil: Broz, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Misiones; Argentina
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Materia
FOREST HARVEST PLANNING
PROGRESSIVE HEDGING
RAINFALL REGIME
SOIL COMPACTION
STOCHASTIC MODELLING
SUSTAINABLE MANAGEMENT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/140296
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/140296
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Stochastic forestry harvest planning under soil compaction conditionsRossit, Daniel AlejandroPais, CristóbalWeintraub, AndrésBroz, Diego RicardoFrutos, MarianoTohmé, Fernando AbelFOREST HARVEST PLANNINGPROGRESSIVE HEDGINGRAINFALL REGIMESOIL COMPACTIONSTOCHASTIC MODELLINGSUSTAINABLE MANAGEMENThttps://purl.org/becyt/ford/2.7https://purl.org/becyt/ford/2We 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.Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: Pais, Cristóbal. University of California at Berkeley; Estados UnidosFil: Weintraub, Andrés. Universidad de Chile; ChileFil: Broz, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Misiones; ArgentinaFil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaAcademic Press Ltd - Elsevier Science Ltd2021-10-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/140296Rossit, Daniel Alejandro; Pais, Cristóbal; Weintraub, Andrés; Broz, Diego Ricardo; Frutos, Mariano; et al.; Stochastic forestry harvest planning under soil compaction conditions; Academic Press Ltd - Elsevier Science Ltd; Journal of Environmental Management; 296; 15-10-2021; 1-420301-47971095-8630CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0301479721012196info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jenvman.2021.113157info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:45:03Zoai:ri.conicet.gov.ar:11336/140296instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:45:03.461CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Stochastic forestry harvest planning under soil compaction conditions
title Stochastic forestry harvest planning under soil compaction conditions
spellingShingle Stochastic forestry harvest planning under soil compaction conditions
Rossit, Daniel Alejandro
FOREST HARVEST PLANNING
PROGRESSIVE HEDGING
RAINFALL REGIME
SOIL COMPACTION
STOCHASTIC MODELLING
SUSTAINABLE MANAGEMENT
title_short Stochastic forestry harvest planning under soil compaction conditions
title_full Stochastic forestry harvest planning under soil compaction conditions
title_fullStr Stochastic forestry harvest planning under soil compaction conditions
title_full_unstemmed Stochastic forestry harvest planning under soil compaction conditions
title_sort Stochastic forestry harvest planning under soil compaction conditions
dc.creator.none.fl_str_mv Rossit, Daniel Alejandro
Pais, Cristóbal
Weintraub, Andrés
Broz, Diego Ricardo
Frutos, Mariano
Tohmé, Fernando Abel
author Rossit, Daniel Alejandro
author_facet Rossit, Daniel Alejandro
Pais, Cristóbal
Weintraub, Andrés
Broz, Diego Ricardo
Frutos, Mariano
Tohmé, Fernando Abel
author_role author
author2 Pais, Cristóbal
Weintraub, Andrés
Broz, Diego Ricardo
Frutos, Mariano
Tohmé, Fernando Abel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv FOREST HARVEST PLANNING
PROGRESSIVE HEDGING
RAINFALL REGIME
SOIL COMPACTION
STOCHASTIC MODELLING
SUSTAINABLE MANAGEMENT
topic FOREST HARVEST PLANNING
PROGRESSIVE HEDGING
RAINFALL REGIME
SOIL COMPACTION
STOCHASTIC MODELLING
SUSTAINABLE MANAGEMENT
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.7
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv 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.
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Fil: Pais, Cristóbal. University of California at Berkeley; Estados Unidos
Fil: Weintraub, Andrés. Universidad de Chile; Chile
Fil: Broz, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Misiones; Argentina
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021-10-15
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/140296
Rossit, Daniel Alejandro; Pais, Cristóbal; Weintraub, Andrés; Broz, Diego Ricardo; Frutos, Mariano; et al.; Stochastic forestry harvest planning under soil compaction conditions; Academic Press Ltd - Elsevier Science Ltd; Journal of Environmental Management; 296; 15-10-2021; 1-42
0301-4797
1095-8630
CONICET Digital
CONICET
url http://hdl.handle.net/11336/140296
identifier_str_mv Rossit, Daniel Alejandro; Pais, Cristóbal; Weintraub, Andrés; Broz, Diego Ricardo; Frutos, Mariano; et al.; Stochastic forestry harvest planning under soil compaction conditions; Academic Press Ltd - Elsevier Science Ltd; Journal of Environmental Management; 296; 15-10-2021; 1-42
0301-4797
1095-8630
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0301479721012196
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jenvman.2021.113157
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Ltd - Elsevier Science Ltd
publisher.none.fl_str_mv Academic Press Ltd - Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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