Monetising certain agricultural data has been proposed under a new Agriculture Data Management Policy released by the Telangana government on July 6th. The policy, which aims to codify norms, processes, guidelines, etc. for handling agricultural data, is open to comments from the public until 6th August.
Apart from a clause to monetise data, the policy also proposes instituting grievance redressal and data management officers for various entities, including government departments, handling agricultural data, a grievance redressal mechanism, and key principles on which data governance frameworks, Standard Operating Procedures (SOPs), etc. can be built for agricultural data. We have summarised these below.
Why it matters? In the report, the state government says “The Government of Telangana has been giving a high priority to the development of the agriculture sector in the state, given that 5 million farmers depend on it and that the sector contributes to about 15% of the GSDP…The Government is keen to make a further impact on the levels of production, productivity, and profitability of the farmers by promoting digital agriculture and extensive use of technologies including the emerging technologies like AI, ML, IoT, Drones and satellite imagery.”
Agri-tech initiatives, even from the Central government are up-and-coming in India.Telangana’s policy could guide the Central government’s approach in this regard as well, as the latter had said last year that it was working on creating a data policy for agriculture.
Comments can be sent in through this form specified in the policy or sent via email to email@example.com with a copy to firstname.lastname@example.org. The paper also specifies a format in which such comments should be sent.
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Committee set up for governing agricultural data
The policy proposes setting up an interdepartmental committee (IDC) which would lay out purposes under which personal data can be processed, SOPs for managing them, as well as the sharing of such data. Officials from the following departments will be a part of the committee, the policy says however it doesn’t specify any ranks for them:
(a) Irrigation & CAD Department;
(b) Planning Department;
(c) Agriculture and Cooperation Department;
(d) Land Administration;
(e) Agriculture Department;
(f) Telangana State Remote Application Centre; and
(g) Information Technology and Communications Department
Even besides the above, the IDC could invite officials from other departments which deal with agricultural, horticultural, and allied activities the policy says.
The principles Telangana plans to govern agricultural data with
For creating Standard Operating Procedures: As aforementioned the IDC will create SOPs. With regards to the same, the policy outlines certain aspects for which the SOPS will have to create procedures, guidelines, formats, checklists, and templates:
i) Data Management Officer
ii) Capacity building
iii) Compliance requirements
iv) De-identification and anonymisation
v) Quality of datasets
vi) Access control
vii) Technical methods for data sharing
viii) Data Service Providers.
For creating data governance frameworks: The policy asks that entities which deal with agricultural data form data governance frameworks for themselves. For the same it lays down certain guidelines.
Further, the policy categorises such entities into two groups:
i) Agriculture Information Users (AIU) or ‘persons, business entities, public and private organisation that need and use data in accordance with the policy’
ii) Data Service Providers (DSP) which compile data sets that AIUs can use, such as related to agricultural credit, crop management, pest management, etc. create data governance frameworks for themselves.
The principles for the frameworks are as follows:
i. Data architecture – Standards, rules and policies shall be defined by the entity to describe how data is collected, stored, integrated, processed and used internally within the entity. A comprehensive data architecture should be laid out that specifies the various functionaries responsible for, and the processes and technology used in data management.
ii. Meta-data management – The concerned entity shall devise a method to capture, manage and publish meta-data information with defined access controls.
iii. Data quality – Standards and procedures should be adopted to maintain the integrity and quality of data, in accordance with standards and best practices.
iv. Auditability – Decisions and processes related to data governance must be auditable, as per prescribed standards.
v. Accountability – Accountability of different teams within an entity must be prescribed in a manner that introduces checks-and-balances between domain and technology teams, and between those who create/collect information, those who manage it, those who use it, and those who introduce standards and compliance requirements.
Purposes for processing personal data: While the policy says that the IDC will outline purposes for which personal data can be processed under it, it outlines some such purposes in its annexure. To elaborate further on this, some of these are as follows:
For agricultural credit:
- Assessment of credit worthiness and extension of short, medium, and long-term loans to farmers and the associated processes like recovery.
- Extending credit to farmers basing on Negotiable Warehouse Receipts (NWR)
- Providing crop insurance cover and disbursing of the amount assured on meeting the defined criteria.
- Providing insurance cover for farm machinery
For payment services:
- Enabling electronic payments and receipts arising out of the transactions relating the purposes specified in this standard.
Types of data covered by the policy
The policy defines agricultural data as ‘data that is created, collected, processed, shared or used in the agricultural sector.’ It further classifies these into the following:
Agricultural operations and management
1. Agricultural credit
2. Agricultural insurance
3. Supply chain-related data
4. Rolling and fixed asset data
5. Farm machinery
6. Compliance-related data
1. Seed / variety sown
2. Dates of sowing/ field operations
3. Water management
4. Pest and disease management
5. Yield data
6. Land records
7. Field boundaries, GIS, GPS data
8. Soil fertility/ health
General agricultural services
4. Commodity prices
5. Market intelligence
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