On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes

[EN] The achievement of goals in forest landscape restoration strongly relies on successful plantation establishment, which is challenging in drylands, especially under climate change. Improvement of field performance through stock quality has been used for decades. Here, we use machine learning (ML...

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Autores: Campo García, Antonio Dámaso Del|||0000-0002-5279-4215, GONZÁLEZ-SANCHIS, MARÍA DEL CARMEN, Reyna Domenech, Santiago, Segura-Orenga, Guillem, Molina, Antonio J., Hermoso, Javier, Ceacero, Carlos J.
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/190533
Acceso en línea:https://riunet.upv.es/handle/10251/190533
Access Level:acceso abierto
Palabra clave:Forest restoration
Aleppo pine
Quercus ilex
Quercus faginea
Arbutus unedo
Pinus pinaster
Juniperus phoenicea
Machine learning
TECNOLOGIA DEL MEDIO AMBIENTE
INGENIERIA HIDRAULICA
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spelling On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic ExtremesCampo García, Antonio Dámaso Del|||0000-0002-5279-4215GONZÁLEZ-SANCHIS, MARÍA DEL CARMENReyna Domenech, SantiagoSegura-Orenga, GuillemMolina, Antonio J.Hermoso, JavierCeacero, Carlos J.Forest restorationAleppo pineQuercus ilexQuercus fagineaArbutus unedoPinus pinasterJuniperus phoeniceaMachine learningTECNOLOGIA DEL MEDIO AMBIENTEINGENIERIA HIDRAULICA[EN] The achievement of goals in forest landscape restoration strongly relies on successful plantation establishment, which is challenging in drylands, especially under climate change. Improvement of field performance through stock quality has been used for decades. Here, we use machine learning (ML) techniques to identify key stock traits involved in successful survival and to refine previous specifications that were developed under more conventional stock quality assessments carried out at the lifting-shipping phases in the nursery. Two differentiated stocklots in each species were used, both fitting in the regional quality standard. ML was used to infer a set of attributes for planted seedlings that were subsequently related to survival at the short-term (two years) and mid-term (ten years) in six different species planted in a harsh site with shallow soil that suffered the driest year on record during this study. Whilst stocklot quality, as measured in the lifting-shipping stage, had very poor importance to the survival response, individual plant traits presented a moderate to high diagnostic ability for seedling survival (area under the receiver operating characteristic (ROC) curve between 0.59 and 0.99). Early growth traits catch most of the importance in these models (approximate to 40%), followed by individual morphology traits (approximate to 28%) and site variation (approximate to 2%), with overall means varying across species. Aleppo pine and Phoenician juniper stocklots presented survival rates of 66-78% after ten years, and these rates were below 27% for the remaining species that suffered during the historical drought. In Aleppo pine, the plant attributes related to early field performance (growth in the first growing season) were more important in the drought-mediated mid-term performance than stock quality at the nursery stage. Within the technical framework of this study, our results allow for both testing and refining the regional quality standard specifications for harsh conditions such as those found in our study.This study is part of research projects: "Comprehensive quality control of the reforestation works in the public forest of Cortes de Pallas, Valencia" signed between UPV-ReForeST and the state-owned company TRAGSA, and "Monitoring and evaluation of the reforestation in the forest V-143 Muela de Cortes, in the municipality of Cortes de Pallas (Valencia), 10 years after its execution" (contract number CNMY18/0301/26), signed between UPV-ReForeST and Valencia Regional Government (CMAAUV, Generalitat Valenciana).MDPI AGDepartamento de Ingeniería Hidráulica y Medio AmbienteInstituto Universitario de Tecnologías de la Información y ComunicacionesEscuela Técnica Superior de Ingeniería Agronómica y del Medio NaturalUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-01-22journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/190533reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUniversitat Politècnica de València https://doi.org/10.13039/501100004233 CNMY18%2F0301%2F26open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1905332026-06-13T07:49:27Z
dc.title.none.fl_str_mv On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
title On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
spellingShingle On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
Campo García, Antonio Dámaso Del|||0000-0002-5279-4215
Forest restoration
Aleppo pine
Quercus ilex
Quercus faginea
Arbutus unedo
Pinus pinaster
Juniperus phoenicea
Machine learning
TECNOLOGIA DEL MEDIO AMBIENTE
INGENIERIA HIDRAULICA
title_short On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
title_full On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
title_fullStr On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
title_full_unstemmed On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
title_sort On the Need to Further Refine Stock Quality Specifications to Improve Reforestation under Climatic Extremes
dc.creator.none.fl_str_mv Campo García, Antonio Dámaso Del|||0000-0002-5279-4215
GONZÁLEZ-SANCHIS, MARÍA DEL CARMEN
Reyna Domenech, Santiago
Segura-Orenga, Guillem
Molina, Antonio J.
Hermoso, Javier
Ceacero, Carlos J.
author Campo García, Antonio Dámaso Del|||0000-0002-5279-4215
author_facet Campo García, Antonio Dámaso Del|||0000-0002-5279-4215
GONZÁLEZ-SANCHIS, MARÍA DEL CARMEN
Reyna Domenech, Santiago
Segura-Orenga, Guillem
Molina, Antonio J.
Hermoso, Javier
Ceacero, Carlos J.
author_role author
author2 GONZÁLEZ-SANCHIS, MARÍA DEL CARMEN
Reyna Domenech, Santiago
Segura-Orenga, Guillem
Molina, Antonio J.
Hermoso, Javier
Ceacero, Carlos J.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Hidráulica y Medio Ambiente
Instituto Universitario de Tecnologías de la Información y Comunicaciones
Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Forest restoration
Aleppo pine
Quercus ilex
Quercus faginea
Arbutus unedo
Pinus pinaster
Juniperus phoenicea
Machine learning
TECNOLOGIA DEL MEDIO AMBIENTE
INGENIERIA HIDRAULICA
topic Forest restoration
Aleppo pine
Quercus ilex
Quercus faginea
Arbutus unedo
Pinus pinaster
Juniperus phoenicea
Machine learning
TECNOLOGIA DEL MEDIO AMBIENTE
INGENIERIA HIDRAULICA
description [EN] The achievement of goals in forest landscape restoration strongly relies on successful plantation establishment, which is challenging in drylands, especially under climate change. Improvement of field performance through stock quality has been used for decades. Here, we use machine learning (ML) techniques to identify key stock traits involved in successful survival and to refine previous specifications that were developed under more conventional stock quality assessments carried out at the lifting-shipping phases in the nursery. Two differentiated stocklots in each species were used, both fitting in the regional quality standard. ML was used to infer a set of attributes for planted seedlings that were subsequently related to survival at the short-term (two years) and mid-term (ten years) in six different species planted in a harsh site with shallow soil that suffered the driest year on record during this study. Whilst stocklot quality, as measured in the lifting-shipping stage, had very poor importance to the survival response, individual plant traits presented a moderate to high diagnostic ability for seedling survival (area under the receiver operating characteristic (ROC) curve between 0.59 and 0.99). Early growth traits catch most of the importance in these models (approximate to 40%), followed by individual morphology traits (approximate to 28%) and site variation (approximate to 2%), with overall means varying across species. Aleppo pine and Phoenician juniper stocklots presented survival rates of 66-78% after ten years, and these rates were below 27% for the remaining species that suffered during the historical drought. In Aleppo pine, the plant attributes related to early field performance (growth in the first growing season) were more important in the drought-mediated mid-term performance than stock quality at the nursery stage. Within the technical framework of this study, our results allow for both testing and refining the regional quality standard specifications for harsh conditions such as those found in our study.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-22
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/190533
url https://riunet.upv.es/handle/10251/190533
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Universitat Politècnica de València https://doi.org/10.13039/501100004233 CNMY18%2F0301%2F26
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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