Multi-Dimensional Analysis of Dairy Production Costs and Optimization of Consumption Reduction Strategies Based on Deep Learning and Causal reasoning

Dairy production systems face increasing challenges in cost optimization due to fluctuating resource availability, environmental constraints, and market volatility. Traditional modeling approaches struggle to capture the complex, dynamic, and interdependent nature of dairy operations. To address these limitations, we propose a novel computational framework that integrates deep learning and causal reasoning for multi-dimensional cost analysis and resource optimization in dairy production. The core of our method combines the AgroStochastic Graph Operator (ASGO), which models herd-level temporal dynamics via structured latent variables and dynamic interaction graphs, with the Policy-Aware Production Inference Scheme (PAPIS), which enables adaptive decision-making under uncertainty using model-predictive control and risk-sensitive planning. Extensive experiments were conducted using four large-scale datasets, including DHI, CDN, MmCows, and FAOSTAT, covering both animal-level and farm-level metrics. Results show that our method consistently outperforms ten recent baseline models across all key metrics, achieving up to 5-point gains in precision, NDCG, and MAP. Ablation studies further validate the critical roles of our structured latent modules and constraint-aware optimization. In conclusion, the proposed framework provides a scalable, interpretable, and data-driven solution for intelligent dairy production management, bridging the gap between predictive modeling and operational decision-making.