Spatio-Temporal Graph Neural Networks for Dairy Farm Sustainability Forecasting and Counterfactual Policy Analysis

Surya Jayakumar, Kieran Sullivan, John McLaughlin, Christine O'Meara, Indrakshi Dey
12/23/2025
cs.LGeess.SY

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Abstract

This study introduces a novel data-driven framework and the first-ever county-scale application of Spatio-Temporal Graph Neural Networks (STGNN) to forecast composite sustainability indices from herd-level operational records. The methodology employs a novel, end-to-end pipeline utilizing a Variational Autoencoder (VAE) to augment Irish Cattle Breeding Federation (ICBF) datasets, preserving joint distributions while mitigating sparsity. A first-ever pillar-based scoring formulation is derived via Principal Component Analysis, identifying Reproductive Efficiency, Genetic Management, Herd Health, and Herd Management, to construct weighted composite indices. These indices are modelled using a novel STGNN architecture that explicitly encodes geographic dependencies and non-linear temporal dynamics to generate multi-year forecasts for 2026-2030.

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Cite this paper

@article{jayakumar2025spatiotemporal,
  title  = {Spatio-Temporal Graph Neural Networks for Dairy Farm Sustainability Forecasting and Counterfactual Policy Analysis},
  author = {Surya Jayakumar and Kieran Sullivan and John McLaughlin and Christine O'Meara and Indrakshi Dey},
  year   = {2025},
  eprint = {2512.19970},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.19970v1}
}

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