Digital Twin (DT) technology, a part of digital transformation, offers significant improvements to agriculture, especially in boosting efficiency. However, several obstacles that hinder the widespread use of DT can be mentioned as technical, socioeconomic, and operational challenges. Data interoperability, infrastructure limitations, model accuracy, and real-time calibration are related to technical barriers. These concerns can be addressed by embedding advanced technology like edge computing and artificial intelligence (AI) in DT systems to improve the agricultural management process. The socioeconomic challenges include high implementation costs, low-quality digital skills, and the hesitation of farmers to adopt modern technology. To address these impediments, it requires effective collaboration through public-private partnerships that will fund infrastructure construction, share expenditures, and disseminate digital knowledge through community training. In addition, a lifelong operation of DT in agriculture requires tight support and collaborations across sectors. Joining hands among farmers, the technology experts, and the decision-makers brings benefits in creating a resilient and secure agricultural environment.