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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
02/03/2022 |
Data da última atualização: |
03/03/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
PARRAM J. S.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; FARHATE, C. V. V.; MARQUES JÚNIOR, J.; SIQUEIRA, D. |
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. |
Título: |
Phosphorus adsorption prediction through Decision Tree Algorithm under different topographic conditions in sugarcane fields. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Catena, v. 213, p. 1-11, June 2022. |
DOI: |
https://doi.org/10.1016/j.catena.2022.106114 |
Idioma: |
Inglês |
Notas: |
Article 106114. |
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. |
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. |
Thesagro: |
Cana de Açúcar. |
Thesaurus Nal: |
Phosphorus; Sugarcane. |
Categoria do assunto: |
-- |
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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Registros recuperados : 10 | |
4. |  | 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.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Agricultura Digital. |
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5. |  | 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.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
Biblioteca(s): Embrapa Agricultura Digital. |
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6. |  | 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.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Agricultura Digital. |
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7. |  | 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.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Agricultura Digital. |
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8. |  | 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.Tipo: Resumo em Anais de Congresso |
Biblioteca(s): Embrapa Agricultura Digital. |
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9. |  | 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.Tipo: Resumo em Anais de Congresso |
Biblioteca(s): Embrapa Agricultura Digital. |
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