SRINAGAR: The Jammu and Kashmir Government on Wednesday nominated 20 senior JKAS officers as District Mentors to oversee the implementation, monitoring and timely completion of the digitisation of land and revenue records across all districts of the Union Territory.
The appointments were made through Government Order No. 1066-JK(GAD) of 2026, issued by the General Administration Department (GAD), with the objective of ensuring effective supervision of the digitisation of revenue records under the Digital India Land Records Modernisation Programme (DILRMP).
According to the order, the designated officers have been assigned specific districts and will be responsible for monitoring progress, resolving implementation bottlenecks and ensuring adherence to prescribed standards, formats and timelines.
Among the officers nominated are Rajinder Singh Tara for Jammu, Sukhdev Singh Samyal for Samba, Pawan Kumar Sharma for Kathua, Charandeep Singh for Udhampur, Rajesh Kumar Shavan for Rajouri, Inderjeet for Poonch and Puneet Sharma for Reasi.
Similarly, Naresh Kumar has been assigned Doda, Amarjeet Singh Kishtwar, Vikas Gupta Ramban, Munir-ul-Islam Srinagar, Naseer Ahmad Wani Ganderbal, Ghulam Rasool Budgam, Tassaduq Hussain Mir Anantnag, Altaf Ahmad Khan Shopian, Khalid Majeed Kulgam, Khurshid Ahmad Shah Pulwama, Shahbaz Ahmad Mirza Bandipora, Mohammad Rafi Kupwara and Shabir Hussain Keen Baramulla.
The government has tasked the District Mentors with monitoring and reporting the progress of digitisation within their respective jurisdictions, facilitating timely resolution of implementation-related issues and conducting random sample verification of digitised records. They will also be required to escalate unresolved matters to the Revenue Department for necessary intervention.
As part of the monitoring mechanism, the officers have been directed to submit monthly reports to the Revenue Department by the seventh day of every succeeding month. The reports will detail achievements, shortfalls and key issues requiring administrative attention under the DILRMP framework.
