Predictive models to estimate carbon stocks in agroforestry systems

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversity of Rio Verde (UniRV)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.creatorMarçal, Maria Fernanda Magioni-
Autor(es): dc.creatorde Souza, Zigomar Menezes-
Autor(es): dc.creatorTavares, Rose Luiza Moraes-
Autor(es): dc.creatorFarhate, Camila Viana Vieira-
Autor(es): dc.creatorOliveira, Stanley Robson Medeiros-
Autor(es): dc.creatorGalindo, Fernando Shintate-
Data de aceite: dc.date.accessioned2025-08-21T20:03:39Z-
Data de disponibilização: dc.date.available2025-08-21T20:03:39Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/f12091240-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/222449-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/222449-
Descrição: dc.descriptionThis 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSchool of Agricultural Engineering (Feagri) University of Campinas (Unicamp)-
Descrição: dc.descriptionSchool of Agronomy University of Rio Verde (UniRV)-
Descrição: dc.descriptionSchool of Agricultural and Veterinarian Sciences University State of São Paulo (Unesp)-
Descrição: dc.descriptionBrazilian Agricultural Research Corporation (Embrapa)-
Descrição: dc.descriptionSchool of Agronomy University State of São Paulo (Unesp)-
Descrição: dc.descriptionSchool of Agricultural and Veterinarian Sciences University State of São Paulo (Unesp)-
Descrição: dc.descriptionSchool of Agronomy University State of São Paulo (Unesp)-
Idioma: dc.languageen-
Relação: dc.relationForests-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCarbon sequestration-
Palavras-chave: dc.subjectData mining technique-
Palavras-chave: dc.subjectLand use systems-
Palavras-chave: dc.subjectOrganic matter-
Palavras-chave: dc.subjectRandom forest-
Título: dc.titlePredictive models to estimate carbon stocks in agroforestry systems-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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