CarbOnto: Data Integration Toward Net Zero

Abstract

Global warming and climate change have been subjects of great interest in recent years. They are understood to be related to greenhouse gas (GHG) emissions. Although agriculture suffers the consequences of these changes, it is one of the top global emitters of GHG. While it is complex in environmental, social, and economic aspects, there is a need to advance solutions for more sustainable agriculture. In the farm environment, an important step is the generation of GHG inventories. A GHG inventory is a systematic process for measuring and recording gas emissions and sequestration. However, generating inventories on rural properties presents many challenges due to the many variables involved, such as land use, animal husbandry, use of electricity, and fuels. One of the main challenges is to deal with this data heterogeneity. The data landscape presents obstacles such as managing source diversity, handling massive data volumes, adapting to various data formats, and ensuring real-time integration for decision-making. In this complex data landscape, ontologies emerge as a potential solution. An ontology is defined as a formal representation of a set of concepts within a domain and the relationships between those concepts. This study proposes an ontological model called CarbOnto for the syntactic and semantic integration of heterogeneous databases. Using an ontology, we intend to contribute to the standardization and interpretation of domain concepts and the addition of semantic information to generate complete GHG inventories. CarboOnto provides the means to generate farm inventories, identify imbalances, and search for solutions to neutralize gas emissions. We report a Case Study and argue that using this ontology can support balancing these gases.

Keywords

• Carbon balance
• farm
• GHG inventories
• global warming
• greenhouse gases
• net zero