Digital twin (DT) technology refers to high-fidelity, continuously updated virtual replicas of physical assets, processes, or systems. Originally developed in manufacturing, DTs are now expanding into agriculture. Agriculture presents unique challenges such as biophysical uncertainty, spatiotemporal heterogeneity, and socio-economic constraints. These conditions demand the integration of IoT sensing, edge and cloud computing, AI/ML simulations, and automated decision-making across farms and landscapes. This chapter examines the role of DTs in farming systems. First, it provides a historical and conceptual overview of digital twins in agriculture. Second, it introduces a reference architecture comprising perception, integration, modelling, analytics, and actuation layers. Third, it presents practical applications and case studies in precision irrigation, soil health, greenhouse climate control, and livestock management. Fourth, it highlights design considerations such as data standards, model fidelity, uncertainty quantification, and human-centred decision support. Evidence from pilot studies indicates that DTs can reduce water use by 15–30%, chemical inputs by 10–20%, and livestock sickness by 24–48%. They also improve yield prediction by 8–15%. The chapter concludes with a road map for developing interoperable, smallholder-inclusive, and climate-smart DTs that integrate both biophysical and socio-economic outcomes.