Predictive models to estimate carbon stocks in agroforestry systems
| Autor: | Rose Luiza Moraes Tavares, Fernando Shintate Galindo, Camila Viana Vieira Farhate, Zigomar Menezes de Souza, Maria Fernanda Magioni Marçal, Stanley Robson de Medeiros Oliveira |
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| Přispěvatelé: | Universidade Estadual de Campinas (UNICAMP), University of Rio Verde (UniRV), Universidade Estadual Paulista (UNESP), Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), MARIA FERNANDA MAGIONI MARÇAL, FEAGRI/UNICAMP, ZIGOMAR MENEZES DE SOUZA, FEAGRI/UNICAMP, ROSE LUIZA MORAES TAVARES, UNIVERSITY OF RIO VERDE, CAMILA VIANA VIEIRA FARHATE, FEAGRI/UNICAMP, UNESP, STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA, FERNANDO SHINTATE GALINDO, FEAGRI/UNICAMP, UNESP. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Carbon sequestration
Soil test Sistemas de uso da terra Sistemas agroflorestais Pasture Predictive models land use systems Floresta aleatória Agroforestry QK900-989 Agricultural productivity Agroforestry systems Plant ecology Stock (geology) Sequestro de carbono organic matter Modelo preditivo geography geography.geographical_feature_category Land use Mineração de dados business.industry Uso da Terra Forestry Soil carbon carbon sequestration Land use systems Matéria Orgânica Agriculture Environmental science Data mining technique Organic matter business Entisol data mining technique random forest Random forest |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP Forests Volume 12 Issue 9 Forests, Vol 12, Iss 1240, p 1240 (2021) Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
| Popis: | Made available in DSpace on 2022-04-28T19:44:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-09-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 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. 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) |
| Databáze: | OpenAIRE |
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