Integrated Modeling Approaches for Agricultural Digital twins: the role of Process Based Models, Agent Based Models, Machine Learning, and Model Coupling

Abstract Process-based models (PBMs) provide a mechanistic foundation for simulating complex genotype, environment, and management interactions across multiple scales. Integrating PBMs with agent-based models (ABMs), machine learning (ML), and advanced model coupling strategies (sequential, loose, tight, and framework-based) enables more comprehensive representations of agricultural systems. ABMs capture the adaptive behaviours of individual farmers, while ML techniques efficiently approximate nonlinear physiological processes or act as surrogates for computationally expensive submodules and reveal data-driven patterns, collectively enhancing the simulation of G × E × M interactions under variable climate scenarios. Digital twins (DTs), defined as systems that dynamically synchronize virtual models with real-time sensor data, extend coupled PBM–ABM–ML systems by enabling bidirectional feedback between computational models and farm operations. However, the implementation of DTs remains constrained by challenges such as data integration across heterogeneous sources, computational scalability, and semantic/technical interoperability making them appropriate only where continuous decision support and real-time feedback outweigh complexity and cost. This review evaluates the function of PBMs as the mechanistic core of DT architectures, surveys model integration techniques, and outlines the IT infrastructure required for operationalization. We conclude that digital twins are best deployed when real-time feedback is essential, whereas PBM–ABM–ML couplings suffice for research and policy applications requiring long-term scenario analysis.