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- Title
ESTIMATING CO<sub>2</sub> EMISSIONS FROM TILLED SOILS THROUGH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION.
- Authors
LEANDRO DA VITÓRIA, EDNEY; DA PENHA SIMON, CARLA; GRAÇA LACERDA, ELCIO DAS; DE JESUS FREITAS, ISMAEL LOURENÇO; GONTIJO, IVONEY
- Abstract
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.
- Subjects
ARTIFICIAL neural networks; ARTIFICIAL plant growing media; SOIL management; NONLINEAR regression; SOIL temperature; SOIL air
- Publication
Revista Caatinga, 2022, Vol 35, Issue 4, p964
- ISSN
0100-316X
- Publication type
Article
- DOI
10.1590/1983-21252022v35n424rc