Resumo
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 variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipitation > macroporosity > soilmoisture > potential acidity.
Assuntos
Análise do Solo , Dióxido de Carbono/análise , Matéria Orgânica , Mineração de Dados , SaccharumResumo
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 variable selection were evaluated: no variable selection, correlation-based feature selection (CFS), chisquare method (χ2) and Wrapper. To classify soil respiration, we used the decision tree induction technique available in the Weka software package. Our results showed that data mining techniques allow the development of a model for soil respiration classification with accuracy of 81 %, resulting in a knowledge base composed of 27 rules for prediction of soil respiration. In particular, the wrapper method for variable selection identified a subset of only five variables out of 18 available in the original dataset, and they had the following order of influence in determining soil respiration: soil temperature > precipitation > macroporosity > soilmoisture > potential acidity.(AU)
Assuntos
Mineração de Dados , Dióxido de Carbono/análise , Análise do Solo , Matéria Orgânica , SaccharumResumo
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, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.(AU)
Assuntos
Saccharum , Dióxido de Carbono , Análise do Solo , Argila/análise , Mineração de Dados , 24444 , Estação Seca , Estação ChuvosaResumo
Intercropping with cool season species has been used as an alternative to increase productivity off-season (fall/winter) of irrigated pastures. This study was conducted from May to October 2009 in Icaraíma, Paraná State, to evaluate the productivity and morphological composition of the Tifton 85 grass overseeded with winter forage in irrigated system. The experiment was a randomized block design with four replicates and repeated measures over time. The experimental plots consisted of five treatments, three as oats overseeding using the genotypes IAPAR 61, IPR 126, and FMS2 on Tifton 85 pasture and one as overseeding of oats combined with rye (IPR 126 + IPR 89) also on Tifton 85 grass and, finally, a control with only Tifton 85 without overseeding. The overseeding of IPR 126 oats achieved the highest cumulative productivity, 4102 kg DM ha-1, with leaf/stem ratio higher than that of exclusive Tifton 85 exclusive, 1.77 and 1.08, respectively. However, overseeding of winter forages did not increase the overall productivity of the pasture.(AU)
A consorciação com espécies hibernais vem sendo utilizada como alternativa para aumentar a produtividade de entressafra (outono/inverno) das pastagens irrigadas. Este trabalho foi conduzido no período de maio a outubro de 2009, no município de Icaraíma, Estado do Paraná, com o objetivo de avaliar a produtividade e a composição morfológica do capim Tifton 85 com sobressemeadura de forrageiras de inverno em sistema irrigado. O delineamento utilizado foi de blocos casualisados com quatro repetições e com medidas repetidas no tempo. As parcelas experimentais foram implantadas por meio de cinco tratamentos, sendo três na forma de sobressemeadura de aveia, utilizando os genótipos Iapar 61, IPR 126, e FMS2 em pastagem de capim Tifton 85 e, um na forma de sobressemeadura de aveia em consórcio com centeio (IPR 126 + IPR 89), também em capim Tifton 85 e, por fim, um tratamento testemunha com apenas capim Tifton 85 sem sobressemeadura. A sobressemeadura com aveia IPR 126 obteve a maior produtividade acumulada, igual 4.102 kg MS ha-1, com razão folha/colmo superior ao capim Tifton 85 exclusivo, iguais 1,77 e 1,08, respectivamente. No entanto, as sobressemeaduras de forrageiras de inverno não aumentaram a produtividade total da pastagem.(AU)
Assuntos
Pastagens/efeitos adversos , Pastagens/análise , Avena/anatomia & histologia , Secale/anatomia & histologia , Cynodon/análiseResumo
Intercropping with cool season species has been used as an alternative to increase productivity off-season (fall/winter) of irrigated pastures. This study was conducted from May to October 2009 in Icaraíma, Paraná State, to evaluate the productivity and morphological composition of the Tifton 85 grass overseeded with winter forage in irrigated system. The experiment was a randomized block design with four replicates and repeated measures over time. The experimental plots consisted of five treatments, three as oats overseeding using the genotypes IAPAR 61, IPR 126, and FMS2 on Tifton 85 pasture and one as overseeding of oats combined with rye (IPR 126 + IPR 89) also on Tifton 85 grass and, finally, a control with only Tifton 85 without overseeding. The overseeding of IPR 126 oats achieved the highest cumulative productivity, 4102 kg DM ha-1, with leaf/stem ratio higher than that of exclusive Tifton 85 exclusive, 1.77 and 1.08, respectively. However, overseeding of winter forages did not increase the overall productivity of the pasture.
A consorciação com espécies hibernais vem sendo utilizada como alternativa para aumentar a produtividade de entressafra (outono/inverno) das pastagens irrigadas. Este trabalho foi conduzido no período de maio a outubro de 2009, no município de Icaraíma, Estado do Paraná, com o objetivo de avaliar a produtividade e a composição morfológica do capim Tifton 85 com sobressemeadura de forrageiras de inverno em sistema irrigado. O delineamento utilizado foi de blocos casualisados com quatro repetições e com medidas repetidas no tempo. As parcelas experimentais foram implantadas por meio de cinco tratamentos, sendo três na forma de sobressemeadura de aveia, utilizando os genótipos Iapar 61, IPR 126, e FMS2 em pastagem de capim Tifton 85 e, um na forma de sobressemeadura de aveia em consórcio com centeio (IPR 126 + IPR 89), também em capim Tifton 85 e, por fim, um tratamento testemunha com apenas capim Tifton 85 sem sobressemeadura. A sobressemeadura com aveia IPR 126 obteve a maior produtividade acumulada, igual 4.102 kg MS ha-1, com razão folha/colmo superior ao capim Tifton 85 exclusivo, iguais 1,77 e 1,08, respectivamente. No entanto, as sobressemeaduras de forrageiras de inverno não aumentaram a produtividade total da pastagem.