By Shreya Deb
Budget FY22 announced an allocation of Rs 65,000 crore to the PM-Kisan scheme, accounting for pretty much half of the total agriculture price range. Since 2019, the PM-Kisan has been the biggest element of the agriculture price range every year, and is targeted at farmers who personal cultivable land as per land records of the state. Unfortunately, this leaves out vulnerable sections such as tenant farmers, ladies farmers, tribal households and landless labourers, who possibly have to have the revenue help the most. The exclusion is the outcome of the gigantic challenge of 1st identifying these people today, due to the fact our current systems do not formally recognise them as farmers.
What’s in a name? The have to have to determine farmers
Despite 73.2% of rural ladies engaging in agriculture, only 12.8% are reported to personal land. The rest are non-existent on land records, resulting in millions of ladies not becoming recognised as farmers. This problem is additional exacerbated owing to income employees such as patwaris, conditioned by tradition, assuming the head of the household a.k.a. male household members must be named in the land records. Among tribal communities, of the 20 million tribal households, much less than 2 million have received person forest rights pattas the rest are ‘invisible’ and left out of government security nets. Even then, forest rights pattas for tribal households usually do not get integrated with the income land records. Landless agricultural labourers and tenant farmers account for close to 150 million people today in rural India, and they also are not component of state land records. Overall, our state land records are not made to be inclusive, and usually not dependable.
Although there are several welfare schemes for farmers, there is no normal government definition of a farmer. The 2007 MS Swaminathan Committee referred to as out that the term ‘farmer’ would incorporate any particular person actively engaged in increasing crops and other agricultural commodities, and would incorporate not only landholders, but also cultivators, labourers, sharecroppers, tenants and tribal households, amongst other individuals. Unfortunately, the ground realities make it a challenge to implement such a definition. Our state land records, bound by legacy systems and laws, do not 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 more than 5 million little and marginal farmers, tenants, sharecroppers and landless agricultural labourers. The KALIA supplies an unconditional revenue help of Rs 12,500 to landless agricultural households and an annual Rs 10,000 to little and marginal land-owning farmers as effectively 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 current 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 employees at state, district and block levels to conduct in depth on-ground verification to determine eligible beneficiaries. This painstaking course of action was vital in the absence of a extensive and credible farmer database.
Telangana took a distinct strategy prior to rolling out the Rythu Bandhu Scheme, a direct advantage 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 just before implementing the scheme. The income and agriculture departments partnered to undertake a state-wide Land Records Updation Programme (LRUP). It involved 3,500 income officers going from village to village to update land records, covering 32 of the 33 districts in the state inside a period of 3 months. The LRUP drive covered 86% of the total location, of which 95% was declared cleared as it had no big disputes concerning land ownership. The digital Pattadar Passbooks had been issued in 93% of such cleared land parcels. This shows that what is usually deemed to be an not possible task—that of updating and digitising land records database—is attainable with focused efforts.
The way forward for PM-Kisan
Instead of every single scheme possessing its personal farmer beneficiary database, the excellent remedy would be to leverage the current land records databases in every single state. This would need some modifications in the structure of these databases, making certain they accurately capture all interests in the land, like ownership as effectively as tenancy. The design and style must make sure women’s names are not excluded, overcoming deep-rooted societal beliefs that guys are the house owners in a household. Implementation of the Forest Rights Act 2006 requires to be accelerated so that tribal households obtain forest rights pattas and develop into component of the land records database. The next challenge is to create in incentives in the course of action to encourage the upkeep of the land record database, such that all future transactions such as sale, present, inheritance, and so forth, are consistently updated to boost the reliability of the records.
A dependable land records database that contains details about landowners and cultivators and is inclusive by design and style is essential to minimise exclusion errors and implementation bottlenecks.
With the PM-Kisan comprising the biggest element in the agriculture price range, there is a have to have to address its deficiencies drawing from the experiences of Odisha’s KALIA scheme and Telangana’s Rythu Bandhu Scheme. The pandemic, more so than something else, has highlighted the have to have for the government to have robust social safety mechanisms to attain the most vulnerable sections of the population, and creating PM-Kisan more inclusive is an vital step in that path.
The author leads Omidyar Network’s investments in house rights in India