FarmCarbon AI: An AI-Based Framework for Carbon Mitigation and Financing in Central European Dairy Farms

Abstract

Agriculture is a major source of greenhouse-gas (GHG) emissions, particularly through enteric fermentation and manure management in dairy farming. This paper presents FarmCarbon AI, an AI-assisted framework that combines deterministic IPCC Tier-2 calculations, mitigation-scenario screening, economic screening, and structured grant-draft generation for Central-European dairy farms. The evaluation uses 100 constrained synthetic farm profiles across eight country contexts; it is therefore a test of workflow behaviour under modeled inputs, not evidence of field performance or national representativeness. The calculation engine estimates enteric CH?, manure CH?, and manure N?O, while the retrieval-augmented language-model component is limited to source-grounded explanation and draft documentation rather than numerical calculation. Under the specified scenario assumptions, modeled emission reductions were 8–19%. A one-way sensitivity analysis of the illustrative high-intensity profile shows that economic screening is highly dependent on the assumed low-carbon milk premium, co-financing rate, and operating costs; it should not be interpreted as a market forecast. The study contributes a transparent prototype and an explicit validation agenda for future testing with measured farm records, country-specific costs, expert review, and benchmark comparisons with established calculators and human workflows.

Keywords: sustainable agriculture; artificial intelligence; greenhouse gas emissions; dairy farming; IPCC Tier-2 methodology; carbon footprinting; climate finance; decision support; Central Europe; Common Agricultural Policy