Progress in Generating Unique Digital Farmer IDs

Why in news

As of July 20, 2026, over 10.18 crore Farmer IDs have been created, facilitating direct transfer of subsidies and technical advisories to farmers and streamlining various agricultural schemes.

Exam relevance

GS2, GS3 · Governance, Economy, Science & Technology · Prelims · Mains

Key facts

1. Over 10.18 crore Farmer IDs have been created as of July 20, 2026.
2. Farmer IDs facilitate direct transfer of subsidies and technical advisories.
3. Farmer IDs enable integration of Direct Benefit Transfer (DBT) schemes like PM Kisan, PMFBY, MSP procurement, credit access, input distribution, and disaster relief.
4. Integration of satellite imagery has optimized PMFBY operations.
5. YES-TECH (Yield Estimation System based on Technology) uses satellite remote sensing and crop yield estimation models.
6. Smart Sampling for CCEs uses satellite-derived vegetation indices to optimize planning and reduce bias.

Prelims pointers

• More than 10.18 Crore Farmer IDs created as of July 20, 2026.
• Farmer IDs facilitate Direct Benefit Transfer (DBT) for schemes like PM Kisan and PMFBY.
• YES-TECH is a system for crop yield estimation using satellite data.
• Smart Sampling for CCEs utilizes satellite-derived vegetation indices.

Mains practice question

Discuss the significance of unique digital farmer IDs in enhancing the efficiency and transparency of agricultural subsidies and schemes in India. What role does technology play in optimizing these initiatives?

Static linkage

The generation of unique digital farmer IDs is linked to the broader government initiative of leveraging technology for efficient governance and direct benefit transfer. It connects to the implementation of agricultural schemes like PM Kisan and PMFBY, and the use of remote sensing technology for agricultural monitoring and assessment, which falls under the domain of agricultural economics and technology.

Significance

The creation of unique digital farmer IDs is a significant step towards digitizing the agricultural sector, enabling more efficient and targeted delivery of government benefits and support. It enhances transparency and accountability in subsidy distribution and scheme implementation. The integration of technologies like satellite imagery and data analytics for yield estimation and smart sampling in Crop Cutting Experiments (CCEs) promises to improve the accuracy and efficiency of crop insurance schemes, leading to better risk management for farmers and more effective policy-making.