Modelagem da emissão de CO2 do solo em áreas de cana-de-açúcar por meio da lógica Fuzzy.
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Data
2021-04-07
Autores
Silva, Angélica da
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Universidade Estadual Paulista (Unesp)
Resumo
Ações ambientais ligadas às mudanças climáticas têm causado preocupações para lideranças governamentais e muito tem-se discutido sobre o tema. Um dos assuntos mais relevantes são os Gases de Efeito Estufa (GEE), e por ser um tema amplo e dinâmico, é importante que haja uma compreensão correta das causas, consequências e as dimensões desse efeito. Objetivou-se a caracterização dos padrões do fluxo de CO2 (FCO2) do solo, por meio da lógica Fuzzy, e os atributos do solo (físicos, químicos e biológicos) em área de cultivo de cana-de-açúcar. Os experimentos foram conduzidos nos municípios de Guariba - SP no ano de 2008, Jaboticabal – SP no ano de 2010, e Pradópolis no ano de 2012. As avaliações foram realizadas após a colheita de cana-de-açúcar, que estavam no sistema de manejo de colheita mecanizada em que os resíduos vegetais permanecem na superfície do solo. Nas áreas estudadas foram avaliados o FCO2 do solo, umidade do solo e temperatura do solo com relação aos atributos do solo. Foi utilizada a lógica Fuzzy para expressar ou traduzir o conhecimento, sobre o FCO2 do solo em áreas agrícolas em forma de regras. Todos os procedimentos estatísticos e de programação Fuzzy foram mensuradas no software livre R (R Development Core Team, 2020). Os índices de correlação para os atributos do solo e FCO2 se apresentaram de forma levemente positiva para todos as variáveis de estudo, porém, para macroporosidade e temperatura do solo a correlação está mais próxima de ser nula. O uso de 3 variáveis de estudo foi suficiente para a obtenção de uma boa classificação e culminando num total de 27 regras para análise por meio da lógica Fuzzy, nas quais as variáveis são consideradas altamente correlacionadas com a variável de resposta (FCO2). A técnica de modelagem baseada em modelagem de incertezas e constituído por um conjunto de regras, demonstrou que a lógica Fuzzy é eficaz para estimar o FCO2. A complexidade do sistema pode ser abstraída pelos conjuntos de regra (modelos determinístico). Entretanto, faltam variáveis para melhorar a explicação, como por exemplo, o gás carbônico atmosférico, e a captura de oxigênio. O que pode ser incorporado em novos trabalhos com a mesma análise.
Environmental actions linked to climate change have caused concerns for government leaders and much has been discussed on the topic. One of the most relevant issues is the Greenhouse Gases (GHG), and because it is a broad and dynamic theme, it is important that there is a correct understanding of the causes, consequences and dimensions of this effect. However, the objective was to characterize the FCO2 patterns of the soil, using the Fuzzy logic, in addition to studying the relationships of such patterns with the other attributes of the soil in a sugarcane cultivation area. The studies were conducted in the cities of Guariba - SP in 2008, Jaboticabal - SP in 2010, and Pradópolis in 2012. The evaluations were carried out after the sugarcane harvest, which were in the mechanized harvest management system in which the vegetable residues remain on the soil surface. In the studied areas, soil FCO2, soil moisture and soil temperature were evaluated in relation to soil attributes. Fuzzy logic was used to express or translate knowledge about soil FCO2 in agricultural areas in the form of rules. All statistical and Fuzzy programming procedures were measured using the free software R (R Development Core Team, 2020). The correlation indices for soil and FCO2 attributes were slightly positive for all study variables, however, for macroporosity and soil temperature, the correlation is closer to zero. The use of 3 study variables was sufficient to obtain a good classification and culminating in a total of 27 rules for analysis using the Fuzzy logic, where such variables are considered highly correlated with the response variable. (FCO2). The modeling technique based on uncertainty modeling and consisting of a set of rules, demonstrated that the Fuzzy logic is effective for estimating the FCO2. The complexity of the system can be abstracted by the rule sets (deterministic models). However, there is a lack of variables to improve the explanation, such as atmospheric [CO2], and O2 capture. What can be incorporated into new works with the same analysis.
Environmental actions linked to climate change have caused concerns for government leaders and much has been discussed on the topic. One of the most relevant issues is the Greenhouse Gases (GHG), and because it is a broad and dynamic theme, it is important that there is a correct understanding of the causes, consequences and dimensions of this effect. However, the objective was to characterize the FCO2 patterns of the soil, using the Fuzzy logic, in addition to studying the relationships of such patterns with the other attributes of the soil in a sugarcane cultivation area. The studies were conducted in the cities of Guariba - SP in 2008, Jaboticabal - SP in 2010, and Pradópolis in 2012. The evaluations were carried out after the sugarcane harvest, which were in the mechanized harvest management system in which the vegetable residues remain on the soil surface. In the studied areas, soil FCO2, soil moisture and soil temperature were evaluated in relation to soil attributes. Fuzzy logic was used to express or translate knowledge about soil FCO2 in agricultural areas in the form of rules. All statistical and Fuzzy programming procedures were measured using the free software R (R Development Core Team, 2020). The correlation indices for soil and FCO2 attributes were slightly positive for all study variables, however, for macroporosity and soil temperature, the correlation is closer to zero. The use of 3 study variables was sufficient to obtain a good classification and culminating in a total of 27 rules for analysis using the Fuzzy logic, where such variables are considered highly correlated with the response variable. (FCO2). The modeling technique based on uncertainty modeling and consisting of a set of rules, demonstrated that the Fuzzy logic is effective for estimating the FCO2. The complexity of the system can be abstracted by the rule sets (deterministic models). However, there is a lack of variables to improve the explanation, such as atmospheric [CO2], and O2 capture. What can be incorporated into new works with the same analysis.
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Palavras-chave
Solo, Gases estufa, Fuzzy sets, Cana-de-açúcar, Ciência do solo