The Agri Collaboratory

Transforming Rural Finance

Objective

This is an executive summary of a brainstorming session between TAC and iSPIRT to achieve the following:

Collate issues related to Agri credit (and more broadly in rural finance) across perspectives; establish key linkages between various parts of the crop cycle, impacting rural finance.
Draft a “problem statement” and break it into nano components.
Brainstorm possible out-of-the-box solutions for the issues discussed.

Shortcomings in the Agricultural and Agri Credit Sector

Despite having the highest percentage of arable land, India is grossly underutilizing its land, water and human resources and has low productivity compared to several countries.

I. Incomplete definition of farmer

The definition and view of a farmer often excludes those involved in poultry farming, pisciculture, animal husbandry, dairy and horticulture. Within crop cultivators itself, there is a necessity to further understand that the farmer’s challenges vary based on agro climatic zones and types of crops, rainfed or irrigated farm, amounts of rainfall, soil type, weather, to name a few.

II. Limited cash flow for farmer

The farmer requires cash across all stages of the crop cycle but has access to cash only during certain months. Often farmers have maximum access to cash at the sowing stage of the crop cycle, (which is also the least cash intensive period) but are cash-strapped to finance post-harvest processes (access to mechanisation, cash for labour, warehousing).

III. Agri-chain heavily contingent on price fluctuation and market forces

The entire Agri value chain, and the success or failure of the farmer is greatly dependent on price and market fluctuations. The farmer is never a lone actor but is always affected by the greater issues in the Agri supply chain. The following factors determine price:

Quantity and Quality: dependent on weather, input quality, technical advice, water availability.
Access to open markets: exploitation of the farmer by local traders and local loan providers.
Time of sale: price fluctuations based on time of arrival of crop in the market.
Contract cultivation: closed loop; from start to end of the Agri chain, price often pre-determined.
Access to market prices: options within a 50Km distance.

IV. Existing credit in the system flows toward large, profitable farmers

Lenders are caught in the loop of repeating lending to the same set of borrowers owing to reliable credit history, and ability to repay. 70% Agri loans go to the large, profitable farmers at the top of the pyramid however 84% of Indian farmers are medium and small and do not have access to sufficient or timely credit. Often Agri input is provided by the middlemen/distributor on credit which affects the smaller farmer.

Why do small and marginal farmers not have access to credit instruments?

Accumulation of previous unpaid loans
Poor / incomplete paperwork (40%)
Lender does not trust repayment capacity (29%)

V. Glacial credit request processing

The farmer often requires NOC / no encumbrance certificate forms from all neighbouring banks for the loan to be processed. This paperwork is time consuming and sometimes incomplete, leading to delay in accessing finance.

VI. No products available for personal credit requirement

A farmer needs money throughout the year for household expenses and hence a part of Agri loans disbursed – especially if processed late and granted post sowing cycle – are often utilised for personal use owing to the seasonal nature of agriculture. There is often minimal understanding of this fact by lenders.

Key Takeaways from Discussion

Aspirational Cash-Flow Lending Story: The Vision for 2025

Selvam, a rice farmer with a two-acre farm near Madurai, requires a loan amount ahead of the agricultural season. He approaches a digitally trained entrepreneur in his village with his details (crop, land, income, identity, etc). Much like a LIC agent / independent financial advisor, this digitally trained village level entrepreneur (VLE) enters the Selvam’s details including crop, livestock, background etc. with his consent into the system and accesses pre-approved templates from various lending institutions: Axis, IndusInd, HDFC, Samunnati, etc. He helps Selvam understand their products, and choose a few options, based on best fit with requirements from all options. Upon selecting the correct product, the VLE submits the application on a digital platform. Within half an hour, Selvam closes the loan, and links insurance. Everything is coordinated by the VLE who not only aggregates information but also ensures technical advice is provided and is paid by Selvam for his efforts. Multiple sellers compete for his business in a digitally enabled process.

While the actors and their roles may be the same in the above approach, what is unique is the nature of engagement. Farmer is now participating in the formal economy. Through this model we are trying to make the VLE work for farmer and play a central role as an entrepreneur. Unlike an LIC agent selling a policy, the VLE is paid by the farmer to find the right solutions as his agent. He will start representing a larger ecosystem.

VLE touches different nodes: a) Multiple service providers, b) access to cheaper capital & c) advisory providers.

Outcome: A Funnel is created to capture transactions through a formal channel and in building a digital footprint & farmer profile. Farmer is now part of the larger economy and has access multiple options of market (due to dis-aggregation)

I. Cash Flow Lending as an Alternate Approach

Incorporating a cash-flow based lending system (which currently does not exist in agriculture) will allow for a gradual credit rating to build over time. The key elements of the cash flow lending model are:

Tenant Farmers

While collateral-based lending favours farmland owners, the cash flow lending model focuses on the “farmer” rather than the “owner” and works for both – landholder farmer and tenanted farmers.

Assessment and Creating a Digital Identity for farmer

Replicating an account aggregator model for data portability, using an Agri Data Exchange for creating an Agri data marketplace with the farmer at the centre; digitising the farmer’s land records are needed.

Insurance

This model automatically connects to the farmer’s already existing insurance or enables insurance to the extent of the loan taken with recourse to the lender.

Linkage to markets / Mandi

Market linkages will be democratised making it easier for the stakeholders to seamlessly trade. Price discovery, supply & demand mapping will bring in transparency and build trust among the eco system players.

VLE

VLE could be an independent entity and can be aligned to an FPO. He will be the center of the ecosystem that we are trying to build.

The enabler, VLE, will ensure lender-borrower best interests are maintained and serves as an agent for the farmer. Technology will have to be used to ensure that the VLE does not develop vested interests. Now the incentives are being aligned and the costs associated are streamlined across VLE, farmer and the service provider.

We can leverage the VLE model to retired army jawans and post office workers who have a closer proximity to the villages.

FPO

The local collectives like SHGs, cooperatives, FPO’s to be empowered with technology and support to reach the individual farmer, through the VLE to enable the cash flow lending model.

DBT Linkage

Visibility of the DBT inflow from government inflow to the farmer is an important facet of the cash flow lending model.

Warehouse storage and receipts

This will enable the farmer to store his produce and sell it when the prices are higher and at the same time make available finances to repay previous loans and have funds for the next crop.

Retail Digital rupee

We can start thinking about leveraging RBI’s initiative of using digital e-rupee for direct procurement with input sellers, service and advisory providers.

II. Creating a range of loan products

There is a need for a range of loan options available in the market so a farmer can select the best suited loan option based on interest and repayment cycle. Ideally the loan products accrued by each farmer should be customizable at a software level; this will require a base infrastructure to guide the same.

III. Design a credit rating system of farmers & providing specific and relevant ri-financial advice

The advantages of the cash flow lending model are that it helps formalize largely invisible “tenanted” farmers, avoids the need for precise assessment of farmland and linkage to govt records, enables ability to provide funds to farmers in a timely manner, is process driven, and enabled by a digital platform. The target for this innovation should be small-scale, high-risk farmers, with inbuilt income growth potential.

Next Steps

Further consultation within the Industry and the Govt. are needed to flesh out and strengthening of this narrative is now needed for a successful model. It should be piloted in a sub-district with the potential to demonstrate revenue / productivity gain through cash injection and the suitable choice of crops.

Participants

Sharad Sharma, Co-Founder, iSPIRT Foundation
Anil Kumar SG, Founder and CEO Samunnati Financial Intermediation & Services Private Limited
Nipun Mehrotra, Founder & Ceo, The Agri Collaborative (TAC) – Convener
Ram Kaundinya, DG National Seed Federation of India, Ex-Axis Bank board, Ex- MD, Advanta
Ravishankar A, Ex Madura Microfinance, Ex Centre for Good Governance, AP
Vineet Saraogi, Volunteer, iSPIRT

Project AgCx – Automated Rural Finance Assessment: Concept Note

Motivation – The “Why”

As per Agriculture Census, 2015-16 India has 126 million small and marginal farmers and requires Rs 37-40 Lakh Crore (400-500 US$ Billion) of working capital annually. RBI estimates that approx. 59.1 % of small and marginal farmers and 30 % of agricultural households avail credit from non-institutional sources probably since they cannot offer collateral to avail institutional credit. As per NAFIS Survey 2016-17, only 10.5 % of agricultural households have a valid Kisan Credit Card since several households have multiple cards and, in several states, (e.g., Tamil Nadu, Andhra Pradesh, Kerala, Karnataka), 71% of the crop loans are disbursed outside of KCC.

As per RBI (2019) – aggressive efforts are needed to improve institutional credit through technology-driven solutions:

Credit is largely dependent on the operated area due to constraints on data available on other credit determinants, such as district-wise input cost for a crop, type of crops being grown, crop-wise sown area, etc.
Banks to explore collaborations with Agri Start-ups to provide access to credit efficiently to farmers.
IBA to come out with a tech-driven portal to ease farmer credit.

Scope – The “What”

The Agri Collaboratory is helping build common digital infrastructure (“Digital Rail”) as a digital public good, in open source for the ecosystem and the government. Project AgCx aims to simplify Farm Credit Assessment for Small & Marginal farmers by triangulating consented, multi-year, disparate, public, and private data sets. TAC is an “Agri ecosystem builder” and the primary point of contact for stakeholders and the general administrators of the project.

Project AgCx Objective: Create an affordable assessment of 1) The Farmer, 2) Her Farm, 3) Target Crop + Credit Usage.

“In 50 | At 50 | For 50”: Realtime – in less than 50 min, at a base cost of Rs 50 (for the farmer), at national scale – for 50+ Million farmers, (directly or with FPOs), and monitor credit usage to improve recovery.

Roadmap – The “How”

The Agri Collaboratory (TAC) partners with Samunnati, Govt of Telangana, RICH, Cropin, Satsure, The Indian Institute of Science (IUDX), Digital Green, and others to build open-source digital assets and enable data flows. The insights from these diverse datasets, combined with local level information from the Govt, along with policy and regulatory changes, can provide accurate, real-time credit assessment for millions of small and marginal farmers.

The Project AgCx will focus on a 3-5 year, 4-phase implementation plan:

Phase 1: Concept Development & Buy-in:

Create a detailed conceptual design to get buy-in from stakeholders (domain, technology, government, funders).
Conduct experiments in a sandbox environment at Mulkanoor, Telangana for ~3000 farmers to test out various elements of Project AgCx, across multiple stakeholders.

Mulkanoor and Jammikunta, Telangana experiments:

Approach:

Collate issues related to agri credit (and more broadly, rural finance) across perspectives; a) Incomplete definition of a farmer, b) Insufficient cashflow for farmer, c) Limited flow of credit to small and marginal farmers e) Glacial credit request processing and f) Lack of personal financing offerings. Establish key linkages between various parts of the crop cycle, impacting rural finance. This project is organized into 3 workstreams.

As-is process mapping: Conduct a survey to capture farmer demographics, feedback on lending products and schemes (when and what stage they need loan, awareness of various government schemes, access to institutional credit) and feedback on credit process and hurdles (how much time it took them to avail loan, challenges / issues in getting the loan, reasons for loan delays / rejections and the advantages of being a member of a farmer collective)

Credit assessment report: Create a credit assessment report with inputs from various lending institutions (public and private sector banks, NBFCs & Co-operative societies. This report then can be used by the lending institutions using a configurable business rule engine depending on the type of loan, tenure, secured vs unsecured. Streamlining the credit assessment report will enable wider adoption and minimize loan rejections.

Data integration: Accurate and quality data is key for timely decision making, creating scale and interoperability. To validate this hypothesis, check the veracity of consented farmer data with Government Telangana data base, integrate this data with public and private data sets (e.g. Satellite data for crop history / yield, soil data, irrigation data, local demand, credit history and loan usage)

Phase 2: Proof of Concept (POC): Conduct several controlled (iterative) experiments (POC), in 3 districts, 6 talukas with about 30,000 farmers to develop and validate the following key steps for Project AgCx:

Agri Lender Ecosystem Buy-in: Concur with RBI, Banks, NBFCs, NABARD, etc. on an approach of using Data-based Farm Credit Assessment and fine-tune the alternate credit assessment report format.
Cash Flow Lending: Build an industry approach toward cash-flow lending for rural finance
Enable potential data sources: Test data availability with Govt., Private, and the public sector, validate data accuracy with ground sources, and match Farm ID to the Farmer through unique identifiers.
Aggregate Data: Collate data from multiple sources through a semi-automated process to validate the suitability to create the credit assessment report based on ground reality.
Automate: Test an automated Agri Data Exchange and Farm Credit Application with the selected Data streams.

Phase 3: Pilot: Conduct detailed pilots based on the learning from the POCs, in 3-4 states, 10 districts for 100,000 farmers:

Test suitability and interoperability of data at scale: Across several agronomic zones, and farmer types including tenanted. Test ability of AgCx with automated Agri Data Exchange to process transactions in minutes and at low cost (~ Rs 50 to 100 / farmer).
Build version 2 of the AgCx platform: Focusing on cashflow lending and integrations with MoF, MoA, NIC, and other agencies.
Test wider applicability: – include broader datasets by onboarding 15+ Data Providers and 10+ Agri lenders.

Phase 4: Field Trial Roll-Out: Rollout AgCx in 2-4 states as a production trial. Establish commercial business models for AgCx to be self-sustainable. Onboard 25+ Data Providers and 25+ Farm Credit lenders.

Aspirational Cash-Flow Lending: The Vision for 2025

A cash-flow based lending system (which currently does not exist in agriculture) will allow for a gradual credit rating to be built over time and bring in the unserved and underserved rural population into the institutional credit framework. While collateral based lending favors farmland owners, the cash flow lending model focuses on the “farmer” rather than the “owner” and works for both – landholder farmer and tenanted farmers.
Desired state: Selvam, a rice farmer with a two-acre farm near Madurai, requires a loan amount ahead of the agricultural season. He approaches a digitally trained entrepreneur in his village with his details (crop, land, income, identity, etc). Much like a LIC agent / independent financial advisor, this digitally trained village level entrepreneur (VLE) enters the Selvam’s details including crop, livestock, background etc. with his consent into the system and accesses pre-approved templates from different banks: Axis, IndusInd, HDFC, Samunnati, etc. He helps Selvam understand their products, and choose a few options, based on best fit with requirements from all options. Upon selecting the correct product, the VLE submits the application on a digital platform. Within half an hour, Selvam closes the loan, and links insurance. Everything is coordinated by the VLE who not only aggregates information but also ensures technical advice is provided and is paid by Selvam for his efforts. Multiple sellers compete for his business in a digitally enabled process.

Digital Public Infrastructure (DPIs) for inclusive agriculture transformation

A collaborative national approach, open data interoperability, use-case led approach, governance, and farmer privacy is key.

In her budget speech, Finance Minister Nirmala Sitharaman stated, “A digital public infrastructure for agriculture will be built as an open source, open standard and interoperable public good that will help develop farmer-centric solution for crop planning, crop estimation, market intelligence, and support for growth of Agri-Tech industry and start-ups”.

Much has been written on the transformative scope of digital agriculture be it in improving sustainability, access to finance, inputs and markets, contextual advisory, etc. UPI and ONDC are examples of transformative cross-sectoral DPIs, and the building of an open, farmer-centric DPI is a key milestone in this journey.

Indian agriculture is highly heterogeneous and without a publicly funded DPI, digital agriculture is justifiably criticised for catering to large farmers with an ability to pay and large Agri-Tech players with deep pockets building monolithic, platforms. This excludes both – the small and marginal farmer and the long-tail of Agri start-ups from benefiting.

DPIs in agriculture, built in Public-Private Partnership mode, combining private innovation with this Government’s ability to drive transformation at mammoth scale can address issues of exclusion, making digital agriculture affordable and also encourage start-ups to proliferate and build businesses by addressing farmer expectations. More importantly, DPIs in Agriculture will enhance the efficiency of various budget initiatives by closing the promise-delivery gap in public service systems. Since Agriculture intersects with most other sectors – water, chemicals & fertilizers, finance, etc., Agriculture DPIs could be transformative for India.

We propose suggestions to consider while undertaking this mission, distilled from contributions by the Agri & Tech ecosystem (private sector, state governments, start-ups, global institutions, academia) during workshops organized by CGIAR and The Agri Collaboratory, last November and December at Delhi and Hyderabad.

1: Technology Recommendations:

a) Mobilising Open Data

Open data accelerates digital innovation across the food-land-water systems value chain and unlocks the economic potential of agriculture through the enhanced availability, aggregation, sharing and monetisation of Government and Private datasets, enabling significant economic and sustainability benefits to the farmer and the nation.

b) Data Interoperability, Governance, and Portability

Agriculture has dozens of siloed and disaggregated datasets. Due to lack of standardization, calibration, and certification, most are ineffective for use. Establishing sound data interoperability policies and data governance principles are of utmost importance to improve “data-trust” and farmer adoption. Enabling “data portability” protects farmer interests when switching service providers.

c) Consent Manager & Data Exchange

Consent managers are operational for Fintech and can be emulated keeping in mind specific requirements of Agriculture to reduce uncertainty and misuse while sharing farmer data. Similarly, a national Agri Data Exchange will create a homogenous, monetised and well governed data marketplace.

d) Digital Identities – Know Your Farmer (KYF)

On the lines of India Stack, attention should be paid to building an identity layer for the farmer. Like the “Know Your Customer”, “Know Your Farmer” (KYF) norms (including consent, registration, identity, and authentication) will be beneficial within the regulatory boundaries to make service delivery safer, efficient, and cost effective.

e) Digital Electronic Farm Record (EFR) – Know Your Farm

A digital public building block like EFR is an important step in building an Agri DPI, maintaining demographic information of the farmer and the topographical, annual information of the farmland including soil health, and crop yield. KYF and EFR should enable the inclusion of tenanted farmers and women farmers.

f) Disaggregate Building Blocks – leverage commonalities

A key promise of DPIs is cost reduction and quicker farmer benefits through their reusability across services. By building the functionality once, it is reused for several use cases. KYF, for example, is required for finance, advisory, market, and supply chain linkages in Agri and beyond in health and social services, thus providing exponential benefits.

While DPIs are transformative, building them takes years, hard work and a lot of patience. We suggest keeping some first principles in mind as we embark on this project.

2. Key principles

a) National collaborative approach across Centre and State

By design, DPIs must be co-created with the broader private/social ecosystem, going well beyond joint presence in governmental committees, through inclusive, “non-compete” spaces and entrepreneur friendly policies for governance and frictionless collaboration. It is very important to involve states who ‘own’ agriculture in their portfolio. Instead of each state developing its own stack, creating disaggregated silos, there should be a collaborative national approach to DPI creation, agreed by all states and political parties.

Chances of buy-in from smallholder farmers exponentially increase when we involve local communities, intermediaries (such as farmer cooperatives and NGOs) and start-ups while co-creating and implementing DPIs.

b) Focus on Use Cases

We suggest adopting a farmer-centric approach, while solving complex Agri problems for long term sustainable impact while building DPIs – instead of a “technology first” approach – with the use cases prioritized by the Government for maximum societal impact.

c) Reuse/Repurpose

Before building Agri DPIs, we must reuse/repurpose functionality and DPIs built for other sectors including well established ones like Digi Locker, UPI, Aadhaar, as well as upcoming ones like ONDC, OCEN etc.

d) Agri Sandbox

A Sandbox is a “safe playground” – to test policies and technologies. An Agri Sandbox suitably designed can improve coworking across Centre and States and be a catalyst to prioritize and test DPIs with real data and synthesize the often conflicting regulatory and Innovation requirements.

Conclusions

DPI is very critical for transforming Indian agriculture. However, it must be a collaborative effort between multiple stakeholders including central and state governments, private sector and the start-up ecosystem. Basic principles of data interoperability, data governance including data privacy and security must be the basis of architecting the DPI. This is the right time to go for a comprehensive strategy towards this objective.

Making Gender Visible in Agtech:

Nipun Mehrotra had participated in the webinar hosted by Digital Greens wherein Academia, Non Profit NGO’s had shared thoughts. As Empowering women farmers is vital for India’s economic progress, and for reducing poverty and malnutrition. To foster gender and economic equality, focus should be to institutionally address fundamental building blocks:

Access to Credit

Access to Market

Access to Content

Rural Women Collectives: A Step Towards an Able Future

Ram Kaundinya
Director General, Federation of Seed Industry of India,
Board Member, Axis Finance, NICR and Kalgudi,
Co-chair, SVP Hyderabad, & Co-founder,ThinkAg

Even as the agricultural value chains undergo a massive transition with the introduction of collectives and technological advances, it may be a missed opportunity if not for the active participation of its female labour force. This may just be the opportunity to diminish the glaring gender gap, and build sustainable ecosystems by empowering women farmers. Ram Kaundinya, a veteran in agri-space, shares his insights on collectives-farmer producer organisations (FPOs) and self-help groups (SHGs), and how certain policy level imperatives may pave the way for efficient long-term agricultural practices.

Although nearly half of the Indian population consists of women, their representation in several economic activities is not in the same proportion. While certain sectors and geographies have higher percentages of females engaged in economic activities, it is nowhere near the halfway mark Rural women are at a considerable disadvantage due to age-old practices and prejudices, alongside skewed inheritance laws and property rights. With agriculture being the primary occupation in rural areas, it becomes important to understand the reality in that sphere. As per a recent report¹, about 33% of farmers are now women, and this number is constantly on the rise. Even though women are engaged in almost 80% of farming activities, a meagre 13% actually own land. This wide gulf has further amplified the need for an institutional mechanism to bring rural women into the economic mainstream and foster gender equality. It also highlights the crucial role women farmers can play in the emerging agricultural value chains.

There are different routes to achieve this. One is the system of self-help groups (SHGs) to improve the collective strength and livelihoods of women farmers-these are informal groups with no legal structure. Another method is through producer companies (PCs), which are collectives of producers under a legal structure. They could be farmer producer organisations (FPOs) for crop-related activities or PCs for non-crop based products. In my opinion, FPOs can go a long way in addressing this need by giving small and marginal farmers access to resources, inputs and better bargaining power, thereby enabling them to reap the benefits of scale and go beyond labour-intensive activities. FPOs can be formed under the Company Law or the Cooperative Act in India. The number of shareholders can be as small as seven, and there is no upper limit. They function like small companies whose aim is to generate business for the company and profits for the shareholders. They also provide several services and capacity building activities for the members. Usually, an external professional manager is recruited to manage the day to day commercial and other activities. Recognising the merit, the government is pushing forward a target of 10,000 FPOs² to be formed in the country in order to leverage technology, effectively market the produce, and optimise farmers’ incomes. Government organisations like the Society for Elimination of Rural Poverty (SERP) in Andhra Pradesh and Telangana are helping in this process. However, we need more emphasis on building women-led FPOs in agriculture and allied fields.

Solutions in Sight-Are SHGs and PCs the Answer?

A few not-for-profit organisations and corporates have be working to collectivise rural women under the SHG structure and/or the FPO structure and making them self-reliant. Cooperative values and principles formed the bedrock of these institutions. The SHG movement, essentially of rural women, has seen tremendous success in some parts of the country. It started as an effort to create savings-oriented collectives of rural women but has now transformed into one of the world’s largest platforms for poor rural women to access financial services. Project Shakti of Hindustan Unilever (HUL)³ is an example of successfully leveraging the rural women collectives (SHGs) to empower this underprivileged group by creating opportunities and capacity building. The women (called Shakti Ammas) were trained on the basic principles of distribution management and were given the opportunity to sell HUL products to small-time retailers in the neighbourhood or directly to households in rural areas. By the end of 2020, they had nearly 1,36,000 Shakti entrepreneurs spread across 18 states. Impact was felt on the income and livelihoods of these women, apart from helping them build their self-esteem and confidence, and nurturing an entrepreneurial mindset.

Similarly, Andhra Pradesh Mahila Abhivrudhi Society (APMAS), Hyderabad, has done outstanding work with SHGs in the last two decades and created a tremendous impact. They developed guidelines, resource materials, conducted capacity building exercises, and several other actions through which they helped in scaling up the SHG movement in India.

An industry report⁴ highlights that at the national level, by March 2021, a staggering 1.12 crore SHGs with a membership of almost 12 crore rural women having savings linkages with banks, and 58 lakh SHGs having credit linkage with banks were working with a total savings of about INR l lakh crores (INR 1 trillion) and an outstanding loan of INR 1.12 lakh crores (INR 1.12 trillion).

Women-led FPOs: Thriving and Leading By Example

Ensuring that women farmers are a part of the agricultural revolution and contribute systematically to the evolving value chains, various pockets within the country have done well in promoting women-led FPOs.

National Institute of Agricultural Extension Management (MANAGE)⁵ has documented stories of five women FPOs in agriculture, six in dairy, and five in the veterinary sector, spread across Bihar, Uttar Pradesh, Rajasthan, Maharashtra, and Gujarat. A total of 2,66,215 women farmers are members of these 16 FPOs-about 1,88,900 being dairy farmers who have benefitted from the economic activity they could generate through this collective approach.

Mahila Abhivruddhi Society, Andhra Pradesh (APMAS) has incubated a women’s FPO called Dheesali, promoted in 2018 by local NGO Grameena Mahila Mandali (GMM) located at Bommalaramaram with funding support from NABARD. In the process of recognition, the FPC has 586 women shareholders (farmers, agricultural labourers, landless and marginalised farmers) at present. The FPC has been running an input centre to sell seeds and fertilisers to both member farmers and others, benefitting them immensely. The company is marketing farmers produce, training the interested organic farmers on sustainable agricultural practices, raising the demo plots in sustainable methods, leveraging the government benefits in the form of PSS and Pandal system⁶ of cultivation for the production of vegetables. Farmers have steadily gained confidence in the company activities. Through input shop, this FPO⁷has an average of INR 42 lakh (INR 4.2 million) annual turnover. On the output front, they have clocked a turnover of over INR 38 lakh (INR 3.8 million), cumulatively since 2018.
Access Livelihoods (ALC) helps farmers realise the power of collectivisation by organising them through producer companies and ensuring that they achieve benefits of scale. The primary stakeholders are small and marginal women farmers, and the secondary stakeholders are the consumers. As part of its efforts, ALC incubates women-owned enterprises (producer companies), helping them in skill development (technical, managerial, and entrepreneurial), raising finance for livelihoods initiatives, providing a range of sustainable production services, providing inputs and marketing linkages through their own brand, and creating cutting-edge technology improving traceability and decision-making.

Tata Power Community Development Trust (TPCDT) entered into a partnership with ALC in 2015 to promote a producer company near Pune, comprising 1,475 women dairy farmers as members and a total of INR 22 lakhs (INR 2.2 million) in investments. The dairy had reached a peak procurement of 5,900 litres milk per day within two months of its operations in 32 villages and had become the single largest milk collection agency in these villages. The dairy provided employment to 76 local youth (47 women and 29 men), and became EBITDA positive in February and March 2020.

Overcoming Roadblocks-Policy Imperatives for Equitable Growth

FPO promotion is a complicated story. FPOs are usually formed by landowners, sharecroppers, or those taking land on lease.

Since a vast majority of the rural women farmers aren’t landowners, it places them at a considerable disadvantage from participating in the FPO movement and in accessing bank credit for agriculture right at the beginning. I see this as the primary reason for women-led FPOs not coming up on a large scale.

The success of FPOs has been higher with perishables like fruits, vegetables, and milk because of the frequent production cycles, direct consumer marketing opportunities, and a very focussed approach by the organisations. However, the success of any FPO depends on how much business orientation is brought into its operations. Adoption of modern technology-based solutions, value addition, progressive marketing arrangements, branding and financial discipline are also critical for their success. In their book ‘Making Farmer Producer Organizations Achieve Viability’, Dr Sanjiv Phansalkar and Dr Avinash Paranjpe recommend many measures, including:

financial viability through recovery of all costs and adequate margin through price
larger scale of operations and scope of work
need for capital formation within the FPO
delicate management of interests of members in multiple commodity based FPOs
good governance standards

In my opinion, some of the key policy initiatives that may help empower women farmers and lead the transformation of women-led rural economy and society are:

The RBI may prescribe separate lending guidelines to the banks to facilitate the free flow of credit to women-led FPOs, as was done for SHGs earlier. This will help FPOs access bank finance for their working capital needs without the requirement for large scale collaterals. This should also facilitate capital investments in processing plants, cold storage, warehousing, transportation, etc., by the FPOs.
A certain level of aggregation of the produce of women-led FPOs, especially the perishable commodities, may have to be facilitated by state governments by encouraging private investment into secondary level processing, branding and marketing. Private investments in sourcing perishables from rural aggregation centres will help scale up as it happened in dairy. Daily transportation from the aggregation centres directly to markets, setting up cold storage facilities, processing facilities and digital architecture for ease and speed of transactions can be undertaken by the private sector through large investments.
Agricultural and Processed Food Products Export Development Authority (APE DA) may set up value chain clusters for both domestic and export markets through a network of women-led FPOs and SHGs. 22 values chains were identified by the High Level Expert Group (HLEG)⁸ to promote agricultural exports from India. Women FPOs can be made a part of the formation of export clusters for these 22 value chains, with financial and capacity building support.
Empowering women by enhancing their property rights may help remove gender discrimination in land ownership and facilitate better access to bank finance for agricultural operations. Other parts of the country may follow in the steps of the five states-Kerala, Andhra Pradesh, Tamil Nadu, Maharashtra, and Karnataka-who took active steps to strengthen women’s position following amendments in property rights.⁹

Ensuring Sustainable Ecosystems in the Long Run

The success stories discussed earlier show that the FPOs followed some or all of the above practices to achieve success. However, the challenge lies in scaling up these islands of success to create a robust network of women-led SHGs and FPOs through a federation structure. Women have demonstrated that they can run them efficiently.

It is crucial to ensure that the mindset for the women-led FPO is firmly focussed on making it a business enterprise and not exclusively a service organisation. Going beyond livelihood creation, these FPOs should help women create wealth for themselves and de-risk their lives.

The support of men is essential to make it a success at the household level. Promoter organisations have to build the capacity of these women in business, governance, team building, leadership skills, and most importantly, digital proficiency. They should also train the men to support these initiatives.

Women-led PCs dealing in perishables seem to be doing better and are touching several lives. This line may be pursued to help with scaling up. In my view, associated rural businesses, like handlooms, cottage industries, etc., have a huge opportunity for collectivisation. Corporates have to play a pivotal role by diverting their CSR funds into supporting women-led FPOs and SHGs.

Re-modelling gender roles in the agri-space, in my view, will not only maximise productivity and improve food security, but will be more sustainable and ecologically friendlier in the long run. Ultimately, such successful women have the power to get into our legislatures and contribute to building a strong nation from the grassroots levels.

Ram Kaundinya is an author, strategic management consultant, and a policy analyst with expertise in the field of agriculture management. In a corporate career spanning 36 years, he has held several top positions and worked closely with the Ministry of Agriculture, Government of India. He co-founded ThinkAg, India’s first agritech platform, with an aim to build India’s largest agri-stakeholder network, nurturing partnerships and creating knowledge that will accelerate investments in the sector. Currently, he serves as co-chair of the Hyderabad chapter of Social Venture Partners, a global philanthropic organisation.

1 Oxfam India.(2018, November 15). Moveover ‘Sons of the soil’: Why you need to know the female fanners that are revolutionizing agriculture in India. Retrieved from https://www.oxfamindia.org/women-empowerment-india-farmers

2 Ministry of Agriculture & Farmers Welfare (2020). (Release ID: 1619391) Retrieved from https://pib.gov.in/PressReleasePage.aspx?PRID=l619391

3 Hindustan Unilever Limited. Retrieved from
https://www.hul.eo.in/planet-and-society/case-studies/enhancing-livelihoods-through-project-shakti/

4 NABARD.(2021). Status of Microfinance in India 2020-21. https://www.nabard.org/authjwritereaddata/tender/SoMFl-2020-21.pdf

5 National Institute of Agricultural Extension Management (MANAGE). (2021). Success Stories on Women Farmer Producer Organizations. Retrieved from https://www.manage.gov.in/publicationsjknowledgeseriesjwomenFPOs.pdf

6 Pandal cultivation is an effective and efficient technological improvement to grow gourd crops like ridge gourd, bitter gourd, bottle gourd etc. keeping them pest-free and healthy.

7 Achieving Gender Equality Through Empowerment of Women in Agriculture (AGEEWA). Retrieved from http:/jwww.apmas.org/ageewa/

8 Finance Commission. (2020). (Release ID: 1642591. Retrieved from https://pib.gov.in/PressReleasePage.aspx?PRID=l642591#:-:text=The%20 High%20Level%20Group%20(HLEG,report%20to%20the%20Commission%20today

9 Gupta, A (2006, August 20). Property rights of women. The Economic Times. Retrieved from https://economictimes.indiatimes.com/bangalore/property-rights-of-women/articleshow(l910002.cms?from=mdr

Agri Data Interoperability

This kind of domain and application integration, requires seamless data interoperability across the Agri eco-system, with due conformance to Data privacy and usage policies.

Currently there is a vacuum with respect to Agri Data standardization, calibration and certification. Disaggregated and non-standardized data is deemed un-trustworthy and rendered ineffective for further processing. Standardization will help improve “data-trust” furthering automation using AI models,

India Agricultural Platform will incubate new “Data Partnerships”, Business models and Revenue streams:

Innovation impacts the entire Agricultural Value-chain:

Soil Testing
Crop selection
Sowing
Irrigation

Yield estimation
Harvesting
Farm equipment

Farm operations & Management
incl. weed control
& pesticide application

Price discovery
Sorting and Food Processing

This value-chain is rapidly becoming digital, leading to an increasing amount of live and real-time data being generated. Physical maps being digitized is an example, while another is through the multiplicity of payment and Agri trading platforms. In addition, there is a rapid increase in “machine-data” generated by precision farming applications using Drones, Satellites, Robotics, Farm sensors, Mobile cameras etc.

This mountain of data, once anonymised, aggregated and processed can be re-purposed using AI on the IAP, for different use cases, raising yields, optimising national resources and doubling farmer income.

The IAP enables a technical framework to harness this dataflow and facilitate “data partnerships” between Govt, Start-ups, Corporates, Research and Academia based on either direct or indirect business benefit. New innovative business models and partnerships will emerge across the value-chain (insurance, market access, assaying etc.) that help monetise data contributing to improved productivity and profitability of Agriculture and other sectors as well.

Given that similar challenges exist across the developing world, India will establish itself as a globally recognised Agri innovator.

Key benefits of the India Agricultural Platform (IAP): / Agri Data Interoperability

“Market Facing”: Agricultural marketplace, for produce and raw materials, equipment sales, rentals etc. Availability of several aggregators on the IAP offers the farmer transparency enabling real time deal making.
“Advisory”: Information a farmer needs: (weather, crop-selection, pricing, etc), available through several advisory channels (govt or private; free or charged) on the IAP will compete for farmer’s consumption, based on credibility and usefulness.
“Decision Making”: Multi-year, diverse data-sets, aggregated from farms up to district, state, national levels, interoperable across the eco-system, aided by data bandwidth and AI, improves decision making by everyone and strategic policy intervention by the Govt.
“Integration”: The IAP uses “Open APIs” to enable software application interoperability thereby creating a seamless Agri framework, allowing end-users freedom of choice and service providers to scale exponentially.

India Agricultural Platform will incubate new “Data Partnerships”, Business models and Revenue streams:

Innovation impacts the entire agricultural value-chain: (Soil testing, Crop selection, Sowing, Irrigation, Yield estimation, Harvesting, Farm equipment, Farm operations & management incl. weed control & pesticide application, Price discovery, Sorting and Food processing). This value-chain is rapidly becoming digital, leading to an increasing amount of live and real-time data being generated. Physical maps being digitized is an example, while another is through the multiplicity of payment and Agri trading platforms. In addition, there is a rapid increase in “machine-data” generated by precision farming applications: field sensors (for soil moisture etc.), satellites (for yield estimation, early pest warning etc.), robotics (for water and nutrient injection, harvesting, high precision weed removal etc.), mobile cameras (for pest attack, nutrient deficiencies), and drones (for real-time farm monitoring, surveying, 3D modelling etc.).

The IAP enables a technical and commercial framework to harness this dataflow and facilitate “data partnerships” between Govt, Start-ups, Corporates, Research, Academia based on either direct or indirect business benefit. Once AI processed data is available to be leveraged, new, innovative business models will emerge that help monetise data contributing to improved productivity and profitability of Agriculture and other sectors as well.

This kind of application integration, requires seamless data interoperability across the Agri eco-system, with due conformance to Data privacy and usage policies. Currently there is a vacuum with respect to Agri Data standardization, calibration and certification. Disaggregated and non-standardized data is deemed un-trustworthy and rendered ineffective for further processing. Standardization will help improve “data-trust” furthering automation using AI models, also avoiding real-world biases creeping into AI prediction.

Advance Crop Price Forecasting

Being able to accurately predict crop pricing two months before harvest, at a district level plays a huge role in ensuring the small & marginal farmer gets fair remuneration and in mitigating farmer stress and suicides.

Picture this real-life decision-making scenario:

A Bihar Government bureaucrat is trying to forecast tomato’s post-harvest prices to avoid the heartbreaking crisis last season when prices crashed due to a sudden glut. Tomatoes dumped on the road and farmer suicides attracted bad press.

He pores over submissions from each district, showing crop-wise acreage and sowing week. From experience, he knows this data could be 2-3 months old, while tomato’s harvest in 3-4 months. He observes that gross crop acreage varies 15-25% across submissions and he suspects some districts fill in data sheets without stepping out of the office.

Based on this, how can he forecast prices or take action?

Should he believe the input suggesting Tomato acreage is 15% lower than the previous cycle? How would the forecasted winter rains and colder weather impact tomato yield?

He wishes he could advise farmers better because staggering sowing and harvesting by 1-2 weeks play a big role in smoothening market price swings.

But for that, he needs automated, real-time data.

To solve this problem, an enabling framework like the IAP will use multi-year Agri Data-sets from multiple sources (government, enterprises, start-ups), seamlessly translating it into information and then into precisely actionable insight.

For instance, digital crop signature, from satellites, combined with AI, can reveal crop-wise acreage under plantation, by district within 4-6 weeks of planting. As the crop matures, it estimates crop yield, and then combined with acreage and processing capacity in proximity – likely post-harvest prices. The IAP also provides an alternate forecast by analysing aggregated seed sales data, district wise, from suppliers to predict acreage under tomatoes. Both estimates are correlated for accuracy

Early price forecasts enable faster tactical actions avoiding price crashes, like helping stagger harvesting, or tying-up additional quantities with processing plants in advance. Powered by Artificial Intelligence (AI) & Data Analytics, IAP helps tactical and strategic decision making, leveraging aggregated insights from the farms to state/national levels.

Small & Marginal Farmer Credit Assessment

Availability of institutional credit for farmers at reasonable rates, is probably the most vital element of making the Indian farmer profitable and resilient.

More than half our farmers – an estimated 60-100 Million – cannot get institutional credit easily, because of incorrect or missing land records and are hence unable to provide land as collateral that the lenders seek. Some farmers can provide alternate collateral such as gold or jewellery, but most are unable to, and hence have to resort to informal lending sources including the middle men, to raise the 50,000 – 90,000 rupees on average needed for seed, fertiliser, etc. Even if lenders were willing to lend without collateral, there is limited information for credit-risk assessment. They sometimes rely on a local revenue official’s confirmation on farm revenues, but this documents’ quality is suspect due to corruption and incorrect data.

Clearly there is a need to think differently and this concept note describes an attempt to use data flows from disparate external sources, using an Agri Data Exchange, AI and “confidential computing frameworks” – real time and at scale, to relook at solving this problem in a very transformative manner for India’s 120 Million farmers. This will need extensive testing, policy tweaks and willing participation from the financial community. But we believe that the time is ripe for providing transformative impact to the Indian farmer – similar to how Aadhaar with UPI transformed digital payments. We cannot afford to delay this any further for the benefit of the Indian small and marginal farmer and for the development of Indian agriculture.

Year 2024. Our Vision – Credit assessment for the Small & Marginal Farmer.

The below scenario describes how farm credit could be assessed in the near future

This data flows in, completing Selvam’s application and providing visibility of his farming history to all potential lenders. To evaluate credit risk, they use the combination of geo-location along with Aadhar to extract Selvam’s farm credit history and details of all existing and completed loans. The automated process allows a majority of the lenders to approve/reject the loan online within minutes. Artificial Intelligence flags issues needing a clarifying phone call.

Selvam chooses a financial institution who pays the seed supplier directly, crediting the balance loan into Selvam’s regular bank crediting the balance loan into Selvam’s regular bank using the UPI interface and creating an auto-debit for the month after harvest. The entire process is digital, with no paperwork. The bank also remits a small fee to the start-up(s) for providing Selvam’s cropping history.

Back in 2020, Selvam recalls filling several loan applications individually, spending money travelling to Madurai and loan sanctions took an average of two months. The IAP has reversed and democratised the process, giving him the power while lenders now bid for Selvam’s loan.

Comparing different offers transparently allows Selvam to choose wisely.

We can facilitate this, as in Selvam’s example, by processing large multi-year, Agri Data-sets, triangulating data real-time, from multiple sources (government, enterprises, start-ups):

Drones / UAV (e.g. real-time monitoring of farms, surveying, 3D modelling)
Satellites and remote sensing (providing past years revenue and yield estimation,
early pest warning etc.)
Farm sensors and management (e.g. for soil moisture, water and nutrient injection)
Mobile cameras (e.g. soil quality and moisture levels and pest, nutrient deficiencies)
Geolocation coordinates
Aadhaar, etc.

Data from several entities is processed seamlessly into precisely actionable insight. This uses a “confidential computing” framework, masking raw data visibility across sources from one another – as well as from the final recipient of the credit assessment, i.e. the financial lender.

The signed loan papers along with the assessment documentation are stored in the Govt’s digital locker for auditability. The lender pays an assessment fee to the farm data providers, aiding their revenue stream. Using tools like video, voice, vernacular translation help facilitate farmer engagement.

Daughters of the Soil: Feminization of Indian Agriculture

The term “Feminization of Agriculture” found its way a few years ago, in the 2017-18 Economic Survey.

The gender shift in agriculture is understandable. The urban migration of men in search for more lucrative opportunities has resulted in women playing the role of entrepreneur, cultivator, and often farm labourer.

As women today lead grape-cultivation in Nashik, or cotton cultivation in Gujarat, the fact remains that women face inordinate challenges in the agricultural sector, despite their contribution.

As per an Oxfam India report, the Agriculture sector employs a whopping 80% of all economically active women in India; additionally – women comprise 33% of the agriculture labor force and 48% of self-employed farmers, collectively producing a bulk of India’s food and dairy output.

Women’s work in agriculture is as expected, in addition to her role as a wife, a daughter-in-law and as a mother.

However, we continue to let our daughters of the soil down, through policies and processes that often don’t recognize them as farmers thereby denying them institutional support from banks, lenders, insurance providers, cooperatives, and government departments. Traditionally, women are used to cultivating land with little to no resources available, often accounting for scarcity of water, fertilizers and pesticides. This was one of the reasons why their contribution during the pandemic was immediately evident. Interestingly, the FAO has estimated that if women had similar access to productive resources as men, there would be a 20-30% increase in agricultural yield. This would ultimately raise national agricultural output by up to 4%.

Only 13% of women farmers, own the land they till, or have formal land rights, due to growing land fragmentation. Persistently low female ownership of farmland occurs in all regions ranging from 28 per cent in the hills, to only around 8 per cent each in the east and west.

Across India, as many as 40 to 60% of women widowed by farmer suicides are yet to obtain rights to the farmland they cultivate. Further, only 35% secured the rights to their family house. What is worse, these farm widows are burdened with outstanding debts against loans they didn’t take, consequently becoming ineligible for fresh loans that they do need. The numerous barriers faced in land ownership include lack of legal awareness about their inheritance rights and the skewed implementation of laws fuelling gendered social discrimination.

As land is used as collateral for credit loans, lack of property rights by the women creates an inability to access credit resulting in inadequate investment for the land they cultivate and also in women being excluded from contract farming arrangements.

The way forward:

In 2011, M S Swaminathan proposed the Women Farmers Entitlement Bill, to alleviate women from a set of challenges they face. These range from access to resources to land ownership.

Outlined below are a few recommendations:

Introduce women-only benefits and schemes. Earmark at least 30% of budget allocation for women beneficiaries in all ongoing schemes/programs and development activities.

Expand provision of credit without collateral under the micro-finance initiative of the National Bank for Agriculture and Rural Development (NABARD). In this regard the transformational work being done by “Agri Alliance for Innovation” in partnership with entities like Cropin, IISc, Nasscom CoE, Samunnati, ITC, Govt of Telangana etc. would be of direct benefit to raise credit for women farmers with no titles to their farms.

Self-help groups to be extended as a social enterprise for women, to help access micro-credit.

Improve female landholding patterns by including them in land records.

Enhance female representation in different Agri decision-making bodies and in Farmer Producer Organizations (FPO) to ensure access to better quality inputs, like seeds and fertilizers. Encourage formation of female FPOs.

Train women in modern, sustainable agricultural techniques tailored to local conditions, through Krishi Vigyan Kendras (KVKs) and State Government departments.

Accelerate farm mechanization by making it affordable with new shared business models and through women-friendly machinery.

While the future of farming in India may be female, we owe it to the millions of Indian women to ensure they receive investment and support from both governmental and private organizations, encouraging their participation in the sector, sustained profitability and help in balancing their role as a women farmer and home maker.

Navya M,
Consultant, The Agri Collaboratory

“India Agricultural Platform” could transform India’s agriculture within a decade.

Let’s face it, Indian Food (Nutrition) & Agriculture is a messy sector but for India to be a 5 trillion-dollar economy, it’s clear that it has to play a much larger role.

A quagmire of complex challenges, policy neglect over decades have resulted in daunting demographics. India’s agriculture sector employs more than half of India’s workforce, consumes around 90% of freshwater resources, uses nearly half the available land area, yet generates barely 13% of GDP and around 10% of exports. With nearly half of all farmers lacking access to credit, with rapid soil degradation and a staggering Rs 92,000 Cr-plus of produce lost to spoilage annually, the path forward appears long and difficult.

All however, is not bleak.

Production growth has outpaced population growth for several decades, catapulting India into becoming amongst the largest global producers for wheat, rice, sugarcane, cotton, milk, pulses, fruits and vegetables. Going forward, India needs to balance volumetric targets with efficiency and sustainability, while moving up the value-chain to enhance farmer and national income.

Encouraged by crucial policy announcements recently, can we now dare to envision a reality wherein led by bold policies, mega-scale innovation and a foundation of new-age skills, Indian Agriculture becomes profitable, resilient and sustainable? By 2030, can India reposition itself as a world-leading agricultural Innovator?

We must. And we can, within the decade.

Given the long term neglect of Agronomics, the only way to make progress within this decade is to “pole-vault” over deficiencies by injecting appropriate technologies and innovation on a massively parallel scale, and adopting holistic, transformative, “platform” thinking as a foundation for a collaborative national approach.

Innovation and precision technologies [e.g.: IoT, sensors, weather forecasting, remote-sensing data from satellites, drones, robotics, mobile cameras, Artificial Intelligence etc] can transform agriculture: raising yields, optimising resources, improving profitability. They yield multi-pronged impact across the complex agricultural value-chain.

The “India Agricultural Platform” (IAP) will accelerate sectoral transformation:

There is a visible need for an open, scalable, integrating platform, that democratises access to Agri information, credit, insurance and markets; incubates innovative business models; and enables better decision making. The Indian Agricultural Platform (IAP) created by the eco-system, governed by the Govt., is envisioned as an “enabling framework of Data and Services (applications) around a data exchange”.

Consider this scenario, leveraging the IAP for a credit use-case in the year 2024:

Selvam, a rice farmer with a two-acre farm near Madurai, logs into the IAP using retina scan, fills in a loan request in Tamil, attaches photos of himself and the farm. He accords consent for his data to be accessed from different entities (Govt, Start-ups, FPOs etc): Aadhaar, geo-location, three year’s crop type, yield and earnings.

This data flows in, completing Selvam’s application and providing visibility of his farming history to all potential lenders. To evaluate credit risk, they use the combination of geo-location along with Aadhar to extract Selvam’s farm credit history and details of all existing and completed loans. The automated process allows a majority of the lenders to approve/reject the loan online within minutes. Artificial Intelligence flags issues needing a clarifying phone call.

Selvam chooses a financial institution who pays the seed supplier directly, crediting the balance loan into Selvam’s regular bank crediting the balance loan into Selvam’s regular bank using the UPI interface and creating an auto-debit for the month after harvest. The entire process is digital, with no paperwork. The bank also remits a small fee to the Start-up for providing Selvam’s cropping history.

Back in 2020, Selvam recalls filling several loan applications individually, spending money travelling to Madurai and loan sanctions took an average of two months. The IAP has reversed and democratised the process, giving him the power while lenders now bid for Selvam’s loan.

Comparing different offers transparently allows Selvam to choose wisely.

The IAP can facilitate this, as in Selvam’s example, by triangulating data real-time, from several entities: a farm management start-up revealing cropping history, satellite data for estimated yield & water source, geolocation coordinates with Aadhaar helps a lender assess credit-risk. Data from several entities is processed in a “confidential computing” framework, which masks visibility of data across sources from one another – as well as from the final recipient of the credit assessment, i.e. the financial lender.

The signed loan papers along with the assessment documentation are stored in the Govt’s digital locker for auditability. The lender readily pays a 0.5% assessment fee to the start-up for its farm data, aiding its revenue stream.

Powered by Artificial Intelligence (AI) & Data Analytics, IAP helps tactical and strategic decision making, leveraging multi-year, multi-source information, aggregated from the farms to state/national levels. It processes huge data flows and using tools like video, voice, vernacular translation, facilitate farmer engagement. The platform is hosted on a Cloud, and reduces duplication by integrating data sources and a vast backend of new and existing applications: Govt’s eNam, ITC’s eChoupal, NCDEX’s NeML, APEDA’s TraceNet etc related to logistics, weather, supply-chain, warehousing, assaying, recommendation engines, etc.

AI and Agri data interoperability across the eco-system will incubate new business models (Agri and non Agri) providing transformative impact to agriculture similar to how Aadhaar with UPI transformed digital payments.

Key benefits of the India Agricultural Platform (IAP):

“Market Facing”: Agricultural marketplace, for produce and raw materials, equipment sales, rentals etc. Availability of several aggregators on the IAP offers the farmer transparency enabling real time deal making.

“Advisory”: Information a farmer needs: (weather, crop-selection, pricing, etc), available through several advisory channels (govt or private; free or charged) on the IAP will compete for farmer’s consumption, based on credibility and usefulness.

“Decision Making”: Multi-year, diverse data-sets, aggregated from farms up to district, state, national levels, interoperable across the eco-system, aided by data bandwidth and AI, improves decision making by everyone and strategic policy intervention by the Govt.

“Integration”: The IAP uses “Open APIs” to enable software application interoperability thereby creating a seamless Agri framework, allowing end-users freedom of choice and service providers to scale exponentially.

India Agricultural Platform will incubate new “Data Partnerships”, Business models and Revenue streams:

Innovation impacts the entire agricultural value-chain: (Soil testing, Crop selection, Sowing, Irrigation, Yield estimation, Harvesting, Farm equipment, Farm operations & management incl. weed control & pesticide application, Price discovery, Sorting and Food processing). This value-chain is rapidly becoming digital, leading to an increasing amount of live and real-time data being generated. Physical maps being digitized is an example, while another is through the multiplicity of payment and Agri trading platforms. In addition, there is a rapid increase in “machine-data” generated by precision farming applications: field sensors (for soil moisture etc.), satellites (for yield estimation, early pest warning etc.), robotics (for water and nutrient injection, harvesting, high precision weed removal etc.), mobile cameras (for pest attack, nutrient deficiencies), and drones (for real-time farm monitoring, surveying, 3D modelling etc.).

The IAP enables a technical and commercial framework to harness this dataflow and facilitate “data partnerships” between Govt, Start-ups, Corporates, Research, Academia based on either direct or indirect business benefit. Once AI processed data is available to be leveraged, new, innovative business models will emerge, that help monetise data contributing to improved productivity and profitability of Agriculture and other sectors as well.

Agri Fintech (Credit use-case): Half our farmers, cannot get credit easily, because of incorrect or lack of land records and financiers have limited information for credit-risk assessment. The IAP can facilitate this, as in Selvam’s example, by triangulating data real-time, from several entities: a farm management start-up revealing cropping history, satellite data for estimated yield & water source, geolocation coordinates with Aadhaar helps a lender assess credit-risk. The lender readily pays a 0.5% assessment fee to the start-up for its farm data, aiding its revenue stream.

Similar partnerships leveraging AI and data analytics will emerge and innovative business models will be created across the value-chain: for insurance, market access, grading of produce etc. Given that similar challenges exist across the developing world, India will establish itself as a globally recognised Agri innovator.

Translating Agri Data into actionable Insight: (Govt. decision making use-case).

Picture this real life scenario:

A Bihar Govt bureaucrat is trying to forecast tomato’s post-harvest prices to avoid the heartbreaking crisis last season when prices crashed due to a sudden glut. Tomatoes dumped on the road and farmer suicides attracted bad press. He pores over submissions from each district, showing crop-wise acreage and sowing week. From experience, he knows this data could be 2-3 months old, while tomato’s harvest in 3-4 months. He observes that gross crop acreage varies 15-25% across submissions and he suspects some districts fill in data without stepping out of the office.

Based on this, how can he forecast prices or take action?

Should he believe the input suggesting Tomato acreage is 15% lower than the previous cycle? How would the forecasted winter rains and colder weather impact tomato yield? He wishes he could advise farmers better because staggering sowing and harvesting by 1-2 weeks play a big role in smoothening market price swings.

But for that, he needs automated, real-time data.

To solve this problem, an enabling framework like the IAP will use Agri Data-sets from multiple sources (government, enterprises, start-ups), seamlessly translating it into Information and then into precisely actionable Insight to be leveraged for varied Agri (and non Agri) use cases.

Early price forecasts enable faster tactical actions avoiding price crashes, like helping stagger harvesting, or tying-up additional quantities with processing plants in advance.