Odisha’s KALIA and Telangana’s Rythu Bandhu show that a reliable land records database is crucial to minimise exclusion errors for PM-Kisan
By Shreya Deb
Budget FY22 announced an allocation of Rs 65,000 crore to the PM-Kisan scheme, accounting for almost half of the total agriculture budget. Since 2019, the PM-Kisan has been the largest component of the agriculture budget each year, and is targeted at farmers who own cultivable land as per land records of the state. Unfortunately, this leaves out vulnerable sections such as tenant farmers, women farmers, tribal families and landless labourers, who probably need the income support the most. The exclusion is the result of the gigantic challenge of first identifying these people, since our existing systems do not formally recognise them as farmers.
What’s in a name? The need to identify farmers
Despite 73.2% of rural women engaging in agriculture, only 12.8% are reported to own land. The rest are non-existent on land records, resulting in millions of women not being recognised as farmers. This issue is further exacerbated owing to revenue staff such as patwaris, conditioned by tradition, assuming the head of the household a.k.a. male family members should be named in the land records. Among tribal communities, of the 20 million tribal families, less than 2 million have received individual forest rights pattas; the rest are ‘invisible’ and left out of government safety nets. Even then, forest rights pattas for tribal families often don’t get integrated with the revenue land records. Landless agricultural labourers and tenant farmers account for close to 150 million people in rural India, and they too are not part of state land records. Overall, our state land records are not designed to be inclusive, and often not reliable.
Although there are multiple welfare schemes for farmers, there is no standard government definition of a farmer. The 2007 MS Swaminathan Committee called out that the term ‘farmer’ would include any person actively engaged in growing crops and other agricultural commodities, and would include not only landholders, but also cultivators, labourers, sharecroppers, tenants and tribal families, amongst others. Unfortunately, the ground realities make it a challenge to implement such a definition. Our state land records, bound by legacy systems and laws, don’t capture tenancy and other rights.
Alternate approaches: Experiences from Odisha and Telangana
Odisha has been a frontrunner in implementing an inclusive farmer welfare scheme, the KALIA, which benefited over 5 million small and marginal farmers, tenants, sharecroppers and landless agricultural labourers. The KALIA provides an unconditional income support of Rs 12,500 to landless agricultural households and an annual Rs 10,000 to small and marginal land-owning farmers as well as tenant farmers. The scheme also supports and trains landless labourers in allied agricultural activities such as goat rearing, duckery, dairy farming, beekeeping and fishery. Odisha leveraged existing databases such as the Paddy Procurement Automation System, the Pradhan Mantri Fasal Bima Yojana and the National Food Security Act, and deployed close to 50,000 government staff at state, district and block levels to conduct extensive on-ground verification to identify eligible beneficiaries. This painstaking process was necessary in the absence of a comprehensive and credible farmer database.
Telangana took a different approach prior to rolling out the Rythu Bandhu Scheme, a direct benefit transfer scheme for land-owning farmers. The Rythu Bandhu Scheme targeted only land-owning farmers, but the state took on the onus of updating land records before implementing the scheme. The revenue and agriculture departments partnered to undertake a state-wide Land Records Updation Programme (LRUP). It involved 3,500 revenue officers going from village to village to update land records, covering 32 of the 33 districts in the state within a period of three months. The LRUP drive covered 86% of the total area, of which 95% was declared cleared as it had no major disputes regarding land ownership. The digital Pattadar Passbooks were issued in 93% of such cleared land parcels. This shows that what is often deemed to be an impossible task—that of updating and digitising land records database—is possible with focused efforts.
The way forward for PM-Kisan
Instead of every scheme having its own farmer beneficiary database, the ideal solution would be to leverage the existing land records databases in every state. This would require some changes in the structure of these databases, ensuring they accurately capture all interests in the land, including ownership as well as tenancy. The design should ensure women’s names are not excluded, overcoming deep-rooted societal beliefs that men are the property owners in a family. Implementation of the Forest Rights Act 2006 needs to be accelerated so that tribal families receive forest rights pattas and become part of the land records database. The next challenge is to build in incentives in the process to encourage the maintenance of the land record database, such that all future transactions such as sale, gift, inheritance, etc, are regularly updated to increase the reliability of the records.
A reliable land records database that includes information about landowners and cultivators and is inclusive by design is crucial to minimise exclusion errors and implementation bottlenecks.
With the PM-Kisan comprising the largest component in the agriculture budget, there is a need to address its deficiencies drawing from the experiences of Odisha’s KALIA scheme and Telangana’s Rythu Bandhu Scheme. The pandemic, more so than anything else, has highlighted the need for the government to have robust social security mechanisms to reach the most vulnerable sections of the population, and making PM-Kisan more inclusive is an important step in that direction.
The author is Director, Omidyar Network India, an investment firm focused on social impact