Predictive models to estimate carbon stocks in agroforestry systems
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/f12091240 http://hdl.handle.net/11449/222449 |
Resumo: | This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems. |
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Predictive models to estimate carbon stocks in agroforestry systemsCarbon sequestrationData mining techniqueLand use systemsOrganic matterRandom forestThis study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)School of Agricultural Engineering (Feagri) University of Campinas (Unicamp)School of Agronomy University of Rio Verde (UniRV)School of Agricultural and Veterinarian Sciences University State of São Paulo (Unesp)Brazilian Agricultural Research Corporation (Embrapa)School of Agronomy University State of São Paulo (Unesp)School of Agricultural and Veterinarian Sciences University State of São Paulo (Unesp)School of Agronomy University State of São Paulo (Unesp)Universidade Estadual de Campinas (UNICAMP)University of Rio Verde (UniRV)Universidade Estadual Paulista (UNESP)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Marçal, Maria Fernanda Magionide Souza, Zigomar MenezesTavares, Rose Luiza MoraesFarhate, Camila Viana Vieira [UNESP]Oliveira, Stanley Robson MedeirosGalindo, Fernando Shintate [UNESP]2022-04-28T19:44:45Z2022-04-28T19:44:45Z2021-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/f12091240Forests, v. 12, n. 9, 2021.1999-4907http://hdl.handle.net/11449/22244910.3390/f120912402-s2.0-85115203989Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengForestsinfo:eu-repo/semantics/openAccess2022-04-28T19:44:46Zoai:repositorio.unesp.br:11449/222449Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:47:12.370800Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Predictive models to estimate carbon stocks in agroforestry systems |
title |
Predictive models to estimate carbon stocks in agroforestry systems |
spellingShingle |
Predictive models to estimate carbon stocks in agroforestry systems Marçal, Maria Fernanda Magioni Carbon sequestration Data mining technique Land use systems Organic matter Random forest |
title_short |
Predictive models to estimate carbon stocks in agroforestry systems |
title_full |
Predictive models to estimate carbon stocks in agroforestry systems |
title_fullStr |
Predictive models to estimate carbon stocks in agroforestry systems |
title_full_unstemmed |
Predictive models to estimate carbon stocks in agroforestry systems |
title_sort |
Predictive models to estimate carbon stocks in agroforestry systems |
author |
Marçal, Maria Fernanda Magioni |
author_facet |
Marçal, Maria Fernanda Magioni de Souza, Zigomar Menezes Tavares, Rose Luiza Moraes Farhate, Camila Viana Vieira [UNESP] Oliveira, Stanley Robson Medeiros Galindo, Fernando Shintate [UNESP] |
author_role |
author |
author2 |
de Souza, Zigomar Menezes Tavares, Rose Luiza Moraes Farhate, Camila Viana Vieira [UNESP] Oliveira, Stanley Robson Medeiros Galindo, Fernando Shintate [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) University of Rio Verde (UniRV) Universidade Estadual Paulista (UNESP) Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) |
dc.contributor.author.fl_str_mv |
Marçal, Maria Fernanda Magioni de Souza, Zigomar Menezes Tavares, Rose Luiza Moraes Farhate, Camila Viana Vieira [UNESP] Oliveira, Stanley Robson Medeiros Galindo, Fernando Shintate [UNESP] |
dc.subject.por.fl_str_mv |
Carbon sequestration Data mining technique Land use systems Organic matter Random forest |
topic |
Carbon sequestration Data mining technique Land use systems Organic matter Random forest |
description |
This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-01 2022-04-28T19:44:45Z 2022-04-28T19:44:45Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/f12091240 Forests, v. 12, n. 9, 2021. 1999-4907 http://hdl.handle.net/11449/222449 10.3390/f12091240 2-s2.0-85115203989 |
url |
http://dx.doi.org/10.3390/f12091240 http://hdl.handle.net/11449/222449 |
identifier_str_mv |
Forests, v. 12, n. 9, 2021. 1999-4907 10.3390/f12091240 2-s2.0-85115203989 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Forests |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129118172086272 |