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| Registros recuperados : 10 | |
| 3. |  | FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; LOVERA, L. H.; OLIVEIRA, I. N. de; GUIMARÃES, E. M. Data mining techniques for classification of soil CO2 emission. In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: abstracts. Viçosa, MG: SBCS, 2018. Não paginado. WCSS 2018.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 4. | | FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; CARVALHO, J. L. N.; LA SCALA JÚNIOR, N.; SANTOS, A. P. G. Classification of soil respiration in areas of sugarcane renewal using decision tree. Scientia Agricola, v. 75, n. 3, p. 216-224, May/June 2018.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 6. | | 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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| 7. | | 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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| 8. | | LÓPEZ-NORONHA, R.; SOUZA, Z. M. de; SOARES, M. D. R.; CAMPOS, M. C. C.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M. Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon. Agronomy Journal, v. 112, n. 5, p. 4437-4450, Sept./Oct. 2020.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 9. | | NORONHA, R. L.; SOARES, M. D. R.; OLIVEIRA, I. N. de; FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M. Soil carbon stock predictive models on archaeological black lands - natural and transformed. In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: abstracts. Viçosa, MG: SBCS, 2018. Não paginado. WCSS 2018.| Biblioteca(s): Embrapa Agricultura Digital. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
02/03/2022 |
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Data da última atualização: |
03/03/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
PARRAM J. S.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; FARHATE, C. V. V.; MARQUES JÚNIOR, J.; SIQUEIRA, D. |
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Afiliação: |
JEISON SANCHEZ PARRA, FEAGRI/UNICAMP; ZIGOMAR MENEZES DE SOUZA, FEAGRI/UNICAMP; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA, FEAGRI/UNICAMP; CAMILA VIANA VIEIRA FARHATE, FEAGRI/UNICAMP; JOSE MARQUES JÚNIOR, FCA/UNESP; DIEGO SIQUEIRA, FCA/UNESP. |
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Título: |
Phosphorus adsorption prediction through Decision Tree Algorithm under different topographic conditions in sugarcane fields. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Catena, v. 213, p. 1-11, June 2022. |
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DOI: |
https://doi.org/10.1016/j.catena.2022.106114 |
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Idioma: |
Inglês |
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Notas: |
Article 106114. |
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Conteúdo: |
Abstract. Phosphorus availability in the soil is essential for plant growth. In Brazil, phosphorous is poorly available in the soil due to its high adsorption in the form of phosphates. This phenomenon requires much studying to assist in the nutritional management of crops. To that end, predicting the fraction of adsorbed phosphorus can be approximated by using attributes that influence soil formation and structure. This study aimed to predict soil phosphorus adsorption based on soil attributes in sugarcane crops with different relief types using data mining techniques. The experiment was carried out in sugarcane agricultural areas, experimental plots with differentiated relief (concave or convex), and identical agricultural practices. The soil was classified as an Alfisol with udic moisture (Udalf) regime and medium to clayey texture. The dataset constituted a matrix of 4580 observations. The analyzed variables corresponded to the chemical, physical, geophysical, and mineralogical attributes in the 0-0.2 m topsoil. Data analysis was carried out based on a decision tree induction model, with an 85% accuracy rate and a high level of agreement between variables. The decision tree recognized magnetic susceptibility as the attribute with the most significant influence on the prediction of soil phosphorus adsorption, validating the relation among adsorption processes and the magnetic properties of oxide minerals characteristic of Brazilian agricultural regions. |
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Palavras-Chave: |
Adsorção de fósforo no solo; Árvore de decisão; Atributos do solo; Classification techniques; Data mining; Macronutrientes; Macronutrients; Magnetic susceptibility; Mineração de dados; Soil attributes; Susceptibilidade magnética; Técnicas de classificação. |
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Thesagro: |
Cana de Açúcar. |
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Thesaurus NAL: |
Phosphorus; Sugarcane. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02686naa a2200385 a 4500
001 2140450
005 2022-03-03
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.catena.2022.106114$2DOI
100 1 $aPARRAM J. S.
245 $aPhosphorus adsorption prediction through Decision Tree Algorithm under different topographic conditions in sugarcane fields.$h[electronic resource]
260 $c2022
500 $aArticle 106114.
520 $aAbstract. Phosphorus availability in the soil is essential for plant growth. In Brazil, phosphorous is poorly available in the soil due to its high adsorption in the form of phosphates. This phenomenon requires much studying to assist in the nutritional management of crops. To that end, predicting the fraction of adsorbed phosphorus can be approximated by using attributes that influence soil formation and structure. This study aimed to predict soil phosphorus adsorption based on soil attributes in sugarcane crops with different relief types using data mining techniques. The experiment was carried out in sugarcane agricultural areas, experimental plots with differentiated relief (concave or convex), and identical agricultural practices. The soil was classified as an Alfisol with udic moisture (Udalf) regime and medium to clayey texture. The dataset constituted a matrix of 4580 observations. The analyzed variables corresponded to the chemical, physical, geophysical, and mineralogical attributes in the 0-0.2 m topsoil. Data analysis was carried out based on a decision tree induction model, with an 85% accuracy rate and a high level of agreement between variables. The decision tree recognized magnetic susceptibility as the attribute with the most significant influence on the prediction of soil phosphorus adsorption, validating the relation among adsorption processes and the magnetic properties of oxide minerals characteristic of Brazilian agricultural regions.
650 $aPhosphorus
650 $aSugarcane
650 $aCana de Açúcar
653 $aAdsorção de fósforo no solo
653 $aÁrvore de decisão
653 $aAtributos do solo
653 $aClassification techniques
653 $aData mining
653 $aMacronutrientes
653 $aMacronutrients
653 $aMagnetic susceptibility
653 $aMineração de dados
653 $aSoil attributes
653 $aSusceptibilidade magnética
653 $aTécnicas de classificação
700 1 $aSOUZA, Z. M. de
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aFARHATE, C. V. V.
700 1 $aMARQUES JÚNIOR, J.
700 1 $aSIQUEIRA, D.
773 $tCatena$gv. 213, p. 1-11, June 2022.
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