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Effects of surface application of calcium-magnesium silicate and gypsum on soil fertility and sugarcane yield Rev. Bras. Ciênc. Solo
Crusciol,Carlos Alexandre Costa; Foltran,Rodrigo; Rossato,Otavio Bagiotto; McCray,James Mabry; Rossetto,Raffaella.
Lime application recommendations for amendment of soil acidity in sugarcane were developed with a burnt cane harvesting system in mind. Sugarcane is now harvested in most areas without burning, and lime application for amendment of soil acidity in this system in which the sugarcane crop residue remains on the ground has been carried out without a scientific basis. The aim of this study was to evaluate the changes in soil acidity and stalk and sugar yield with different rates of surface application of calcium, magnesium silicate, and gypsum in ratoon cane. The experiment was performed after the 3rd harvest of the variety SP 81-3250 in a commercial green sugarcane plantation of the São Luiz Sugar Mill (47º 25' 33" W; 21º 59' 46" S), located in Pirassununga,...
Tipo: Info:eu-repo/semantics/other Palavras-chave: Soil acidity correction; Silicon; Stalk and sugar yield; Green sugarcane.
Ano: 2014 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832014000600019
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Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach. Repositório Alice
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..
ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical,...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Green sugarcane; Mineração de dados; Data mining; Random Forest algorithm; Saccharum Officinarum; Argila; Cana de Açúcar; Soil respiration; Clay; Soil organic carbon; Sugarcane.
Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1092118
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Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach Scientia Agricola
Tavares,Rose Luiza Moraes; Oliveira,Stanley Robson de Medeiros; Barros,Flávio Margarito Martins de; Farhate,Camila Viana Vieira; Souza,Zigomar Menezes de; Scala Junior,Newton La.
ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical,...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Saccharum officinarum; Soil respiration; Green sugarcane; Clay.
Ano: 2018 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162018000400281
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