ESTIMATING CO2 EMISSIONS FROM TILLED SOILS THROUGH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION

• Autor(a) principal: Vitória, Edney Leandro da
• Data de Publicação: 2022
• Outros Autores: Simon, Carla da Penha, Lacerda, Élcio das Graça, Freitas, Ismael Lourenço de Jesus, Gontijo, Ivoney
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Caatinga
• Texto Completo: https://periodicos.ufersa.edu.br/caatinga/article/view/11063
• topic: Gases de efeito estufa. Manejo do solo. Modelagem. Inteligência artificial.; Greenhouse gases. Soil management. Modeling. Artificial intelligence.

Quantifying soil gas emissions is costly, since it requires specific methodologies and equipment. The objective of this study was to evaluate modeling by nonlinear regression and artificial neural networks (ANN) to estimate CO2 emissions caused by soil managements. CO2 emissions were evaluated in two different soil management systems: no-tillage and minimum tillage. Readings of CO2 flow were carried out by an automated closed system chamber; soil temperature, water content, density, and total organic carbon were also determined. The regression model and the ANN models were adjusted based on the correlation of the variables measured in the areas where the soil was managed with no-tillage and minimum tillage with data of CO2 emission. Artificial neural networks are more accurate to determine correlations between CO2 emissions and soil temperature, water content, density, and organic carbon content than linear regression.