Enhancing agricultural productivity while maintaining ecological balance amidst climate change is a looming challenge. The future of resilient farming and food security will depend upon the effectiveness of collecting, interpreting, and acting on data. An agricultural digital twin (DT) can provide a feedback loop which improves both farm management and the computer system which informs it through integrating right-time sensor data, process-based models (PBMs), data-driven models (DDMs), and hybrid approaches. Three demonstrator DTs for farm ecosystems are currently under development, utilizing extensive datasets from three instrumented research farms at the North Wyke Farm Platform in Devon, UK to drive and evaluate the accuracy of models in simulating key agroecosystem processes, such as soil nutrient cycling, water balance, and crop performance. The implementation process will involve data collection and processing, model integration, and visualization. Key measurements are gathered up to every 15 minutes. PBMs along with DDMs and hybrid models will be utilized in an ensemble to enhance predictive accuracy and robustness. The DT architecture consists of three tiers. A client tier focuses on creating a user-friendly web frontend and API. An analysis and retrieval tier will facilitate the orchestration of services by a container registry and Kubernetes master node. A simulation tier will handle intensive data processing and model simulations with Apache Spark and high-performance computing nodes. We expect the DTs to improve decision-making, enhance system resilience against biotic and abiotic stresses, and pave the way for sustainable agricultural innovation.