HOLOS-IE: A System Model for Assessing Carbon Emissions and Balance in Agricultural Systems
Bibliographic record
Abstract
Agriculture significantly contributes to greenhouse gas (GHG) emissions, mainly via enteric and manure methane (CH4) from livestock and fertilizer-induced nitrous oxide (N2O) from soils. Mitigation strategies include dietary changes, feed additives, and fertilisation with circularity approaches. Agroforestry further offsets GHGs through carbon sequestration (soil and biomass) while enhancing soil health and ecosystem services. Achieving carbon-neutral farms by 2050 requires sustainable agricultural transformation. System-based modelling is crucial for understanding agriculture, supporting informed decision-making, and balancing data needs. HOLOS-IE, evolving into HOLOS-EU, simplifies complex modelling for farmers and stakeholders, empowering them to reduce their environmental footprint and achieve sustainable production.The HOLOS-IE v3.0 (www.ucd.ie/holos-ie) utilises large datasets, evidence-based algorithms, GIS, Machine Learning, and C#.NET coding. The ongoing development focuses on refining model components (crops, grasses, livestock, agroforestry and farm infrastructure), and their sub-components. These components are driven by key soil, climate and relevant variables, which are automated or user-defined inputs. As a case study, HOLOS-IE was applied to a 30-hectare Irish dairy farm to explore agroforestry scenarios (silvopastoral systems with Oak and Sycamore hedgerows) by sparing 5% of land without reducing livestock density. The model predicted sectoral GHG emissions, carbon removal, and total/net carbon balance, quantifying soil and biomass carbon sequestration. This analysis highlighted the offsetting potential and provided insights into total and net carbon balances, guiding future land-use planning for climate change mitigation.The model successfully simulated GHGs, soil organic carbon (SOC), biomass carbon, and farm energy. On the dairy farm, the main GHG contributors were enteric CH4 (76%, 5148 tCO2eq ha-1), direct N2O (13%), and manure CH4 (9%), with indirect N2O contributing 2%, respectively. SOC density in grassland increased by 0.16 t C ha-1 y-1 over 23 years. After introducing silvopasture, grassland GHG contributions remained similar, but SOC density in the tree zone increased, especially in hedgerows. Silvopasture and hedgerows, covering 5% of the land, offset 19% of the farm’s carbon footprint without reducing livestock density, supporting future steps toward carbon neutrality.This paper introduces HOLOS-IE as a foundational step towards the development of HOLOSEU. As the model is still under development, a relatively comprehensive scenario demonstrating how to achieve carbon neutrality including soil health indices, production metrics, cost-benefit analyses and maintaining profitability on a dairy farm will be presented at the conference. Feedback from stakeholders will be gathered to guide further improvements, followed by validation and calibration.The HOLOS-IE project is funded by the Science Foundation Ireland (Currently Research Ireland) through the Gov.ie and the ECRRF (Grant No. 22/NCF/FD/10947) in collaboration with ReLive and HOLOSEU funded by Transnational ERA-NET and ICT-AGRI-FOOD, respectively through the Department of Agriculture, Food and the Marine, Ireland.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".