## 4 Discussion
This study was deliberately limited to a single case application at a composting facility in Cajamarca, Colombia, due to its alignment with the research scope of the doctoral project and the availability of a controlled environment appropriate for initial validation. This methodological choice reflects a strategic decision to conduct a deep, context-sensitive validation under real operating conditions. Implementing DTs in low-tech environments such as rural composting facilities requires a high degree of customization, close control over contextual variables, and extended observation periods. These constraints justify the adoption of an intensive single-case study rather than a multi-site or extensive approach, which could have compromised the level of detail, control, and iterative refinement necessary at this stage of development. Far from being a limitation, this focused approach enabled rigorous control of operational variables and robust validation under real-world conditions. The modular architecture of the proposed model, along with explicitly defined minimum replication conditions, ensures adaptability to other sectors and use cases. Rather than relying on abstract claims, the study presents verifiable evidence through high-power statistical metrics and a quantifiable return on investment (ROI), reinforcing both its scientific rigor and its practical relevance. Although the experimental validation focused on composting, the selected performance indicators, such as process duration, material homogeneity, and operational efficiency, represent fundamental metrics that are applicable across various production and service systems. These indicators were chosen for their practical relevance, ease of measurement, and direct responsiveness to DT interventions, especially in low-digitalization contexts. Their statistical significance demonstrates the feasibility and added value of the proposed methodology under real-world conditions. While the current study prioritizes clarity and robustness over scope, future research will expand the range of indicators, such as emissions, process variability, or economic return, to further validate the methodology's applicability in sectors like precision agriculture, water treatment, and decentralized manufacturing. Additionally, the methodology was carefully designed to balance academic validity with operational feasibility in rural, low-digitalization contexts, prioritizing traceability, interpretability, and the use of accessible technologies. The absence of complex explainable AI (XAI) components is not a shortcoming, but a strategic choice aimed at maximizing sustainability and technology transfer in constrained environments. In this regard, the study not only delivers an effective technical solution but also introduces a replicable conceptual framework aligned with international DT standards, digital maturity models, and explicit scalability criteria. This positions the work as a robust and transferable contribution to the field of DTs in low-tech environments and opens the door to future multi-case validations, progressive XAI integration, and cross-sector applications. A direct performance comparison between the proposed DT methodology and other existing approaches was not possible due to the lack of standardized quantitative metrics or publicly available field data in most reviewed studies. As identified in the literature review, the majority of related works remain at a conceptual or simulated stage, often lacking empirical validation in real-world conditions. This study contributes to closing that gap by offering one of the first field-tested implementations specifically applied to rural composting systems, with measurable improvements in efficiency, consistency, and economic return. The results obtained serve as a practical benchmark for future DT deployments in low-tech environments.