What this page shows

The dashboard above brings together every official AgriStack figure we could verify, each with its reporting date and a link to the Government document it comes from. We do not estimate, project or fill gaps. If a figure is not in an official source, it is not on this page.

All figures were transcribed from:

  • Lok Sabha replies by the Ministry of Agriculture and Farmers Welfare, which include state-wise tables (annexures), and
  • Press Information Bureau (PIB) releases summarising those replies.

We last checked all sources on 12 October 2026. The most recent official state-wise table we found refers to 6 August 2026.

How to read the numbers

Indian numbering is used throughout: 1 lakh = 1,00,000 and 1 crore = 1,00,00,000. So 10,33,93,603 is about 10.34 crore, or roughly 103 million.

Key findings in plain words

1. More than 10.33 crore Farmer IDs by August 2026

As on 6 August 2026, 10,33,93,603 Farmer IDs had been generated across 21 states and union territories. That is an increase of 1,15,23,918 since the 19 March 2026 table, which totalled 9,18,69,685.

The national count passed several milestones during 2026:

Date the figure refers toFarmer IDsWhere it was published
4 February 2026more than 8.48 crorePIB, 10 February 2026 (text only)
19 March 20269,18,69,685PIB, 24 March 2026 (state table)
30 July 202610,28,25,379Lok Sabha reply, 4 August 2026
3 August 2026more than 10.31 crorePIB, 7 August 2026 (text only)
6 August 202610,33,93,603Lok Sabha reply, 11 August 2026

2. Uttar Pradesh leads, and drove much of the growth

Uttar Pradesh had 2,47,59,182 Farmer IDs on 6 August 2026, close to a quarter (23.9%) of all IDs nationally. Between March and August its count rose by 47,83,531, which is about 41% of all the growth across India in that period.

The next largest were:

RankStateFarmer IDs (6 Aug 2026)Share of national total
1Uttar Pradesh2,47,59,18223.9%
2Maharashtra1,36,28,99213.2%
3Madhya Pradesh1,14,44,19311.1%
4Rajasthan87,46,2188.5%
5Karnataka66,05,5046.4%
6Gujarat62,46,9236.0%
7Bihar55,50,0755.4%

3. Fast percentage growth in states that started later

Some states with smaller counts grew quickly between March and August 2026:

  • Punjab went from 75,681 to 3,85,515, about five times its March figure.
  • Uttarakhand more than doubled, from 1,50,747 to 3,30,466.
  • Odisha grew by about 42%, from 14,70,707 to 20,87,004.
  • Bihar grew by about 18%, adding 8,55,677 IDs.

Sikkim (38,507) and Jammu and Kashmir (160) appear for the first time in the August table. PIB’s 7 August 2026 release also said Manipur “has initiated the process of DCS and participating in the process of creation of Farmer ID”.

Percentage growth from a small base can look dramatic. It mostly reflects when a state began, not how well it is doing compared with others.

4. Digital Crop Survey covered 648 districts in Rabi 2025-26

The Digital Crop Survey records which crop is grown on each plot, using geo-tagged photos. According to the Ministry:

  • Kharif 2025: 604 districts and more than 28.5 crore plots (PIB, 10 February 2026).
  • Rabi 2025-26: 648 districts and “more than 31.3 crore plots” (Lok Sabha, 11 August 2026).

In the Rabi 2025-26 state table, the largest numbers of plots surveyed were in Uttar Pradesh (5,56,88,914), Madhya Pradesh (4,81,34,982), Tamil Nadu (3,38,84,867), Odisha (3,01,09,994) and Rajasthan (2,72,66,008). Learn more in What is the Digital Crop Survey?

5. District-level data is available for Uttar Pradesh

The Lok Sabha reply of 4 August 2026 included Farmer IDs for all 75 districts of Uttar Pradesh as on 30 July 2026. The districts with the most IDs were Azamgarh (7,58,877), Sitapur (7,26,097), Jaunpur (7,05,897), Hardoi (6,86,929) and Kheri (6,26,190). The lowest were largely urban districts such as Gautam Buddha Nagar (45,708) and Ghaziabad (62,853).

You can search all 75 districts on our district and village guide. We have not found official district-wise tables for other states yet.

Inconsistencies we found in official documents

Official documents are not always internally consistent. Rather than silently “correcting” them, we show the figures as published and note the differences:

DocumentWhat we noticedWhat we show
PIB, 24 March 2026Headline says “more than 9.20 crore”; the table in the same release adds up to 9,18,69,685The table total, with a note
Lok Sabha 3879, Annexure II (DCS Rabi 2025-26)The printed total (31,40,04,270) is higher than the sum of the state rows (30,62,01,769)Each state row as printed, and both totals
Lok Sabha 2753Uttar Pradesh district rows add up to 2,46,48,335, while the state table in the same reply gives UP as 2,46,28,676 (a difference of 19,659)Both figures, with a note

These are small differences relative to the totals, but we think readers should know about them. If you are citing a figure in official work, use the original document.

How these numbers are produced

Farmer IDs are counted by the AgriStack systems when a state approves a farmer’s land claim and the ID is generated. The Ministry compiles state-wise counts and publishes them, usually when answering questions in Parliament. For a fuller explanation of what is counted and what is not, read How AgriStack statistics are collected.

What the numbers do not tell you

  • Coverage percentage. The Government has not published, in the documents we checked, a state-wise count of eligible farmers to compare against. We therefore do not show “percentage registered”.
  • Quality of records. A generated ID does not tell you whether all of a farmer’s land was linked correctly.
  • Benefits delivered. The count of IDs is not the count of farmers receiving any scheme.
  • Non-participating states. States absent from a table may be at an early stage, may use their own systems, or may not have reported.

Our method

  1. We download the official PDF or release and save a copy.
  2. We transcribe each state or district row exactly as printed.
  3. We add up the rows and compare them with the printed total. Any difference is recorded.
  4. Every record in our data file includes the state, metric, value, unit, reporting date, source URL, publishing organisation and the date we last verified it.
  5. When a new official table appears, we add it rather than overwrite older ones, so changes over time stay visible.

If you spot a transcription error, please tell us through the contact page, quoting the source document and row.