用时空图神经网络预测奶牛场可持续性，支持政策模拟。
Spatio-Temporal Graph Neural Networks for Dairy Farm Sustainability Forecasting and Counterfactual Policy Analysis
- 构建端到端框架，用VAE增强数据并保留联合分布。
- 首次在县级尺度上实现2026-2030年可持续性指数多期预测。
- 融合地理依赖与非线性时序动态，适合农业政策研究者使用。
本研究提出一种新型数据驱动框架，首次将时空图神经网络（STGNN）应用于县级尺度的奶牛场可持续性预测。方法基于爱尔兰牛种育种联合会（ICBF）的畜群级运营数据，采用变分自编码器（VAE）进行数据增强，在保持变量联合分布的同时缓解数据稀疏问题。通过主成分分析首次构建基于四个支柱的评分体系：繁殖效率、遗传管理、群体健康与畜群管理，生成加权综合指数。采用新型STGNN架构，显式建模地理依赖关系与非线性时间动态，实现对2026至2030年可持续性指标的多期预测。
原文摘要 · Abstract (English)
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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