Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data

Abstract:To understand complex system dynamics in dairy farming requires tools that capture farm heterogeneity, social interactions, and cumulative environmental impacts. This study proposes an agent-based modelling(ABM) framework to simulate nitrogen management and low-emission fertiliser adoption across 295 Irish dairy farms over a 15-year period. Using empirical data, the model replicates farm communication through a social network, where adoption probabilities are driven by social contagion, farm-scale factors, and policy interventions such as subsidies and carbon taxes. The framework computes sectoral greenhouse gas emissions, cumulative abatement, and private-social costs, with Monte Carlo and sensitivity analyses quantifying uncertainty. The model achieved high predictive accuracy (R2 = 0.979, RMSE = 0.0274) and was validated against observed adoption data using a Kolmogorov-Smirnov test (D = 0.2407, p < 0.001). Adoption dynamics were fitted to Rogers logistic curves, reproducing a realistic saturation plateau (91%) while acknowledging structural laggard effects. By conceptualizing decarbonization as a socio-technical evolution rather than a purely monetary calculation, this study establishes an exploratory policy framework for evaluating the diffusion of climate strategies prior to implementation.

Artificial Intelligence (cs.AI)

arXiv:2605.03648 [cs.AI]

(or arXiv:2605.03648v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.03648 Focus to learn more arXiv-issued DOI via DataCite

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