#7501. Modelling carbon dioxide emissions under a maize-soy rotation using machine learning

October 2026publication date
Proposal available till 19-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Agronomy and Crop Science;
Food Science;
Soil Science;
Control and Systems Engineering;
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Abstract:
Climatic parameters influence CO2 emissions and the complexity of the relationship is not fully captured in biophysical models. Machine learning (ML) is now being applied to environmental problems, and it is, therefore, opportune to investigate ML models in CO2 predictions from agricultural soils. In this study, six ML models were compared for their predictive performance by comparing field measurements of CO2 emissions from two fertiliser treatments: inorganic fertiliser (IF) and solid cattle manure supplemented with inorganic fertiliser (SCM) applied to a maize-soy rotation.
Keywords:
Agricultural soils; Classic regression; CO2 emissions; Machine learning algorithms; Shallow neural networks

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