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| 2. |  | TAVARES, R. L. M.; OLIVEIRA, S. R. de M.; BARROS, F. M. M. de; FARHATE, C. V. V.; SOUZA, Z. M. de; LA SCALA JUNIOR, N. Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach. Scientia Agricola, Piracicaba, v. 74, n. 4, p. 281-287, July/Aug. 2018.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 3. | | MARÇAL, M. F. M.; SOUZA, Z. M. de; TAVARES, R. L. M.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M.; GALINDO, F. S. Predictive models to estimate carbon stocks in agroforestry systems. Forests, v. 12, n. 9, p. 1-15, Sept. 2021. Article 1240. Na publicação: Stanley Robson Medeiros Oliveira.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 4. | | BOLDRIN, P. F.; REIS, A. C. P. dos; BENITES, V. de M.; TAVARES, R. L. M.; MENEZES, J. F. S.; CANTÃO, V. C. G. Soil phosphorus and corn development under application of phosphate sources. Journal of Agricultural Science, v. 13, n. 9, p. 61-72, 2021.| Biblioteca(s): Embrapa Solos. |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
14/09/2021 |
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Data da última atualização: |
14/09/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
MARÇAL, M. F. M.; SOUZA, Z. M. de; TAVARES, R. L. M.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M.; GALINDO, F. S. |
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Afiliação: |
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. |
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Título: |
Predictive models to estimate carbon stocks in agroforestry systems. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Forests, v. 12, n. 9, p. 1-15, Sept. 2021. |
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DOI: |
https://doi.org/10.3390/f12091240 |
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Idioma: |
Inglês |
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Notas: |
Article 1240. Na publicação: Stanley Robson Medeiros Oliveira. |
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Conteúdo: |
Abstract: 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. MenosAbstract: 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 physic... Mostrar Tudo |
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Palavras-Chave: |
Agroforestry systems; Data mining technique; Floresta aleatória; Land use systems; Mineração de dados; Modelo preditivo; Predictive models; Random forest; Sequestro de carbono; Sistemas agroflorestais; Sistemas de uso da terra. |
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Thesagro: |
Matéria Orgânica; Uso da Terra. |
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Thesaurus NAL: |
Agroforestry; Carbon sequestration; Land use; Organic matter. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/225942/1/AP-Predictive-models-Forests-2021.pdf
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Marc: |
LEADER 03046naa a2200409 a 4500
001 2134318
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008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/f12091240$2DOI
100 1 $aMARÇAL, M. F. M.
245 $aPredictive models to estimate carbon stocks in agroforestry systems.$h[electronic resource]
260 $c2021
500 $aArticle 1240. Na publicação: Stanley Robson Medeiros Oliveira.
520 $aAbstract: 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.
650 $aAgroforestry
650 $aCarbon sequestration
650 $aLand use
650 $aOrganic matter
650 $aMatéria Orgânica
650 $aUso da Terra
653 $aAgroforestry systems
653 $aData mining technique
653 $aFloresta aleatória
653 $aLand use systems
653 $aMineração de dados
653 $aModelo preditivo
653 $aPredictive models
653 $aRandom forest
653 $aSequestro de carbono
653 $aSistemas agroflorestais
653 $aSistemas de uso da terra
700 1 $aSOUZA, Z. M. de
700 1 $aTAVARES, R. L. M.
700 1 $aFARHATE, C. V. V.
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aGALINDO, F. S.
773 $tForests$gv. 12, n. 9, p. 1-15, Sept. 2021.
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