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Registros recuperados: 6
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Classification of soil respiration in areas of sugarcane renewal using decision tree. Repositório Alice
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..
ABSTRACT: The use of data mining is a promising alternative to predict soil respiration from correlated variables. Our objective was to build a model using variable selection and decision tree induction to predict different levels of soil respiration, taking into account physical, chemical and microbiological variables of soil as well as precipitation in renewal of sugarcane areas. The original dataset was composed of 19 variables (18 independent variables and one dependent (or response) variable). The variable-target refers to soil respiration as the target classification. Due to a large number of variables, a procedure for variable selection was conducted to remove those with low correlation with the variable-target. For that purpose, four approaches of...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Mineração de dados; Emissão de gás carbônico no solo; Seleção de variável; Temperatura no solo; Matéria orgânica no solo; Árvore de decisão; Data mining; Variable selection; Decision tree; Respiração do Solo; Carbon dioxide; Soil temperature; Soil organic matter.
Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1105884
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Classification of the initial development of eucaliptus using data mining techniques. Repositório Alice
LIMA, E. de S.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; LOVERA, L. H.; FARHATE, C. V. V..
Abstract - Eucalyptus plantation has expanded considerably in Brazil, especially in regions where soils have low fertility, such as in Brazilian Cerrados. To achieve greater productivity, it is essential to know the needs of the soil and the right moment to correct it. Mathematical and computational models have been used as a promising alternative to help in this decisionmaking process. The aim of this study was to model the influence of climate and physicochemical attributes in the development of Eucalyptus urograndis in Entisol quartzipsamment soil using the decision tree induction technique. To do so, we used 30 attributes, 29 of them are predictive and one is the target-attribute or response variable regarding the height of the eucalyptus. We defined...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Mineração de dados; Eucalipto; Eucalyptus; Technology.
Ano: 2017 URL: http://www.alice.cnptia.embrapa.br/handle/doc/1072262
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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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Predictive models to estimate carbon stocks in agroforestry systems. Repositório Alice
MARÇAL, M. F. M.; SOUZA, Z. M. de; TAVARES, R. L. M.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M.; GALINDO, F. S..
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,...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Sequestro de carbono; Sistemas de uso da terra; Mineração de dados; Floresta aleatória; Sistemas agroflorestais; Modelo preditivo; Land use systems; Data mining technique; Random forest; Agroforestry systems; Predictive models; Matéria Orgânica; Uso da Terra; Carbon sequestration; Land use; Organic matter; Agroforestry.
Ano: 2021 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134318
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Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon. Repositório Alice
LÓPEZ-NORONHA, R.; SOUZA, Z. M. de; SOARES, M. D. R.; CAMPOS, M. C. C.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M..
In the Amazon, there are soils associated with continued human occupation known as "archeological black earth" (ABE). Due to its physical and chemical properties, ABE is more productive than other typical soils in the same region. Therefore, its carbon (C) sequestration mechanism has been a major topic of discussion by the scientific community, aiming to replicate similar characteristics in other soils. Thus, the objective of this study was to develop a predictive model using feature selection and decision tree induction methods for predicting soil C stock in ABE under different land use scenarios. The experiment was carried out in agricultural (coffee, cacao, and beans), pasture, and forest areas. Four feature selection approaches were used to identify...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Estoque de carbono; Modelo preditivo; Classificação de solo; Carbon stock; Soil classification.
Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125072
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Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field. Repositório Alice
FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; TAVARES, R. L. M.; CARVALHO, J. L. N..
Soil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Mineração de dados; Emissão de dióxido de carbono; Manejo de cultivos; Carbon dioxide emission; Data mining; Cana de açúcar; Dióxido de carbono; Carbon dioxide; Sugarcane; Crop management.
Ano: 2018 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1089160
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