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<p>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 (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(R^2 = 0.979\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\textrm{RMSE} = 0.0274\)</EquationSource></InlineEquation>) and was validated against observed adoption data using a Kolmogorov-Smirnov test (<InlineEquation ID="IEq3"><EquationSource Format="TEX">\(D = 0.2407\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(p &lt; 0.001\)</EquationSource></InlineEquation>). 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.</p>

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Agent-based modeling of low-emission fertilizer adoption for dairy farm decarbonisation using empirical farm data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月25日

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  • Surya Jayakumar,
  • Kieran Sullivan,
  • John McLaughlin,
  • Christine O’Meara,
  • Indrakshi Dey

摘要

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 (\(R^2 = 0.979\), \(\textrm{RMSE} = 0.0274\)) 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.