Importance of Site Selection in Model Incubation

By Surabhi Mall, IFMR Rural Finance

This is the third post in our blog series on KGFS Model Incubation. The objective of the series is to methodically conceptualize an approach to build the branch network while incubating a new KGFS entity or expanding to contiguous districts. The posts focus on themes that range from district selection to identification of branch locations and optimization of the distribution network.

The previous blog post of the series discussed the costs and benefits involved in mapping the service area based on the extent of detail sought. Having mapped the area, the next step is to identify an ideal location to set up the branch premise. This post elucidates the factors, importance and possible implications of this activity.

Site selection has long been acknowledged as the key to success for several businesses. Management texts are replete with examples of how a great site selection methodology could be the difference between a successful and unsuccessful business. These businesses are diverse – chains of fast food stores, coffee shops, convenience stores, ATMs among others. Most will argue that such businesses are typically characterised as those wherein the customer convenience is of utmost priority; the client has multiple options to choose from and make impulse purchases by voting quickly with their feet! This post however argues that these are just as important for a rural financier who is often operating in an environment with low(er) competition, serving a not-so-demanding customer with a product like credit that is often demanded but rarely available in the form, quantity and manner as may be desired by them.

What are the location-specific factors & how do they impact business at a branch?

There are several factors at play when it comes to site-selection of any business. These include location demographics, operational feasibility, and competition among others. However, different firms perceive the nexus between these factors differently. For example, while most would agree and avoid positioning the business next to the competitors’ from the threat of market cannibalization, the two most successful fast food joints – McDonalds and Burger King are almost always located next to each other. Similarly, gas stations are often located across the street from each other, rather than being spread out. This behaviour is not irrational, rather coherent and well substantiated by game theory models.

In the KGFS context, site selection activity aids our goal of being customer-centric. Given the service area of every branch is defined with the objective of providing customised products and services, site selection furthers this foundation by putting forth ‘customer convenience’ and ‘access’ as non-negotiable. This is achieved by identifying locations within the village set-up that are natural convergence points and thereby are most likely to be suitable access points for the customers. By positioning the branch in the realm of customer’s day to day activities, it also offers them greater flexibility to manage their daily chores and financial consultations in parallel. For example, a shop owner will find it easy to visit the KGFS branch for a repayment at the lean-hour of business if the branch is proximate to the market area. Unlike a retail business set-up that targets ‘eligible customers’ in its area, KGFS aspires to be relevant to all the customers. This commands great degree of detail to the activities, events and choices they have. A prudently chosen branch premise directly fosters this, thereby enabling high degree of customisation to meet every customer’s need and preference.

Simultaneously, this activity facilitates business decision-making. Like a retail business, setting up a KGFS branch involves hard costs such as construction, equipment, furniture and soft costs such as training and personnel relocation. Site selection aids the ability to justify these costs by projecting revenues as accurately as possible in the given location. By mapping infrastructure and access-related attributes in the area, one gets granular details on estimated footfall at the branch at different points in time, indicative level of proximity and expected market penetration across service villages. This influences decision-making on the scale of investment in the branch infrastructure (e.g. thin branch vs normal branch). It gives insights on questions such as “where to hold KGFS Awareness Meetings (KAMs) to on-board new customers?” If the branch is positioned in high traffic areas, such as next to the bus stop, a school or the panchayat office, it is likely to be more ‘visible’, have more ‘walk-ins’ by inquisitive visitors and add to the KGFS brand recall among villagers. In fact, detailed mapping opens up opportunities for better customisation. For example, by knowing the medical institutions and schools in the area, KGFS branch can be aware of stakeholders it can collaborate with for products such as health insurance and education loan. Finally, if a proposed site scores low of existing financial access and infrastructure, this approach enables discussions to look beyond operational hurdles – such as low population density, poor transport connectivity – if the demand and revenue projections for that location rationalize the associated costs of doing business there.

In the next and concluding post of the series we focus on the nexus between site selection of an individual branch and the larger branch network in the geography and observation techniques to optimise this.


GDP Mapping Exercise – Illustrations from recent studies

By Surabhi Mall, IFMR Rural Finance

In the previous blog post of the KGFS Model Incubation series, we drew out the implications of mapping the GDP of a branch’s service area on strategic decisions related to district selection, branch potential, product suitability and customer centricity. In this context, some of the pertinent follow-up questions that arise are – How much will this activity cost? Should this capability be harnessed indigenously or simply outsourced? What is a statistically approved research design to follow? This post essentially focusses on understanding the ABC of executing this study with relevant elaborations and learning from the past.

The GDP exercise can serve different purposes and based on the objective use and nature of requirement the scope of the study can be defined. The following two studies conducted in two different districts in Tamil Nadu attempt to illustrate this.

The objective of the first study was to compare the gross potential of a branch in the Krishnagiri district, Tamil Nadu, to that of a model KGFS branch[1] wherein ‘comparable’ was defined as a range with an acceptable degree of error from the estimates of the model branch. In this case, focus of the study was to get the aggregate number and not necessarily its constituents. Simply put, this means that while sector-wise distribution of the branch economy could be insightful, it was not the focus of the study per se. Resources used as part of this included a full-time staff that spent one day in assimilating secondary data, three working days on the field to collect primary data as well as to validate the secondary data. The staff was actively supported by a Wealth Manager at the branch location.

The second study at Ellakuruchi village in Ariyalur district, Tamil Nadu, was done with an objective of profiling the district as well as the branch service area. District profiling required a thorough review of the district’s demography, geography, economic status, main crops, enterprises and occupations. Profiling the branch service area required field insights on aspects such as different occupations that thrive in the area so as to map each economic activity with its volume; cash-flows associated with the occupation so as to map business potential; formal and informal financial providers so as to understand current and potential gaps in the financial landscape among others. This objective required one data analyst responsible for secondary data collection, methodology design, primary data collection, data collation and presentation of the findings. Primary data collection was actively supported by 1-2 Wealth Managers in the field for 6-8 working days. The entire exercise was executed in one month. In order to add greater rigour and sanctity to the estimates, a similar study was executed in another branch in the district.

Below are pie-charts depicting the findings from both GDP studies in Krishnagiri and Ariyalur districts respectively.

GDP Mapping Exercise

One of the big challenges in initiating such a study is that the data records of this kind are not methodical, very contextual and mainly absent from conventional databases for any triangulation. The other concern may be related to the design of the research methodology for the intended purpose of the study. Often, simple doubts such as the size of a representative sample directly impact the resource requirements and rigour of the study. To address these issues simple project management tools such as defining the objective, scope and research design a priori through a clear project plan will be quintessential. In the first study, since the objective was clearly identified as “compare the gross potential of the branch to that of a model KGFS branch,” an exhaustive sector-distribution map of the branch’s economy was not required. Conversely, in the second study, to “profile the district and branch service area”, there was need for profound understanding of the demographic and economic constitution of the area. This in turn required information about the share of each activity in the total economic pie.

In cases wherein the objective of the exercise is to design a customer engagement strategy or an optimal capacity plan for the branch by projecting lean and peak cash requirement periods, activity mapping will need to be extremely exhaustive. This would imply that aspects such as the ‘crop net income’ component of the GDP pie be further broken down to list the main crops, their seasonality, and cash flow projections related to each crop.

It is also important to acknowledge the homogeneity in variables during the study. For example, if a service area constitutes of four villages of 300 households each[2], the economic map of one village can be multiplied to give the macroeconomic map of the branch. This adds to operational efficiency in the execution whilst minimizing scope for error.

Since the KGFS model is designed to entrench itself in the community it serves by developing a deep understanding of the geography, the local culture, the economic activities and dominant customer segments, the GDP exercise is perfectly tailored for the KGFS model. Understanding its benefits as a principal and inaugural step in model incubation and thereby budgeting for the costs involved will lead towards deepening the very foundation of the model.

The next blog post in the series will discuss the heuristics of site selection in a rural village context. By illustrating the KGFS’ experience across diverse geographies, it will attempt to showcase the various components that may play a foundational role in the science of site selection.

[1] The model KGFS branch is conceived and defined based on the KGFS mission, the business requirements and past learning experience.
[2] Unit of aggregation may vary from a household, a village, a panchayat or a block.


District selection and estimating the potential of the KGFS entity through GDP Mapping

By Surabhi Mall, IFMR Rural Finance

This is the first blog post in the KGFS (Kshetriya Gramin Financial Services) Model Incubation series. The objective of the series is to methodically conceptualize an approach to build the branch network while incubating a new KGFS entity or expanding to contiguous districts. The posts focus on themes that range from district selection to identification of branch locations and optimization of the distribution network.

In this post we start with a brief discussion on the choice of a district and later discuss geography-specific questions that influence the economics of the model. Finally, the post stresses on GDP mapping as a heuristic solution to those questions.

Among the early steps of setting up a KGFS model is identifying a district. District selection is mostly a matter of organisational strategy and choice. The choice may be based on parameters such as district’s rural population, its population density, credit to GDP ratio, forest cover, road density, among others. Reviewing these attributes prior to finalising the district provides an elementary sense of the KGFS’s business potential, composition of the product basket in the branches as well as the degree of customisation that may be required to set up the model. Following this, reconnaissance (Recce) of the district provides insights to questions such as – Does the model need any fundamental changes or customisation in the given geography? What is the degree of competition it is likely to face? What is the current status of customers – i.e. their access to credit, income earning potential, and savings or repayment behaviour. This step serves as a final validation to the district selection strategy.

Recce gives field-level insights on the geography and competition in the district while secondary data helps understand the demography and infrastructure availability. Since geography, demography and the availability of credit in a place have significant influence on how the local economy develops, this should provide reasonable understanding of village-level economics and perhaps its influence on the branch’s business. It may even be leveraged to formulate the entity’s business plan, its competitive strategy and the network of branches. However, is this sufficient to understand the requirements and provide suitable comprehensive financial services? Is this information adequate for each branch to realise and achieve its potential volume of business at every life-stage? In fact, how does one quantify potential of a typical service branch in the chosen district? Finally, can the impact that the branch or the entity would have in the economic well-being of people in the long-run be measured? Estimating the branch service area’s Gross Domestic Product (GDP) provides powerful insights in unpacking such questions.

Figure 1: Three steps prior to setting up a new KGFS branch

Why the GDP?

To start with, what do we mean by estimating the GDP of a branch service area? GDP of an area is essentially the summation of different economic activities that thrive and contribute to the economy of that area.

Now, a KGFS stands to ensure the financial well-being of ‘every’ customer and enterprise in the area. This dictates that each customer’s needs be identified, acknowledged and serviced. In this context, the GDP exercise provides valuable insights on the composition of different sectors that thrive in the area. This can then be used to segment customers, identify their respective needs, customise the product basket at the branch and design an apt pricing as well as marketing strategy for them. A deep dive into the village economics through this exercise enables the branch to identify and prioritize customers from occupations with high degree of cash-flow mismatches, i.e. customers who are most likely to benefit from financial services. In effect, all this reinforces the KGFS’ geographic commitment by accounting for all possible households and economic drivers in the area.

The exercise gives insights on the share of existing financial players (from the interest income generated in the area), the median profile of a customer’s household and her debt servicing capacity. At the very outset, these can serve as a filter (post the Recce) to re-assert the choice of the district and the working estimates of the business plan. These estimates can then be built into the annual and monthly business targets for the branch. The activity can also be designed to give a rich sense of actual business that the branch is expected to do, i.e. its market share1. By capturing the share of other formal and informal financial institutions in the area, one can assess the volume of competition. This can then be netted off from the estimated demand for credit, thereby giving the potential market share of the branch.

From an operational point of view, data from GDP can be used to add greater granularities to the branch’s customer management database. It also enables scope for new business development2. For the product development team, it helps estimate occupation-based credit requirement and decisions regarding risk-exposure limits.

More generally, the GDP map helps visualise what kind of financial services are required to increase the size of the pie in the first place. It is a quantifiable measure that may be used as baseline for a ‘village profile’ in order to assess the financial viability and/or impact of a branch.

When – Ex-ante or ex-post?

While the above arguments advocate executing this prior to branch opening, this may not be a binding proposition. One can customize the scope of the exercise based on the objective sought at different life stages of a branch. For example, for an existing, low-performing branch where the enrolled database isn’t representative of the area’s population, the study’s objective can be to capture the economic drivers, re-estimate the branch’s potential and identify untapped business avenues. It may also aid to gauge the share of competition of other players’ vis-à-vis that of the KGFS branch.

However, the maximum utility of the exercise would lie in leveraging it as a diagnostic tool to acclimatize with the new geography of business. Such information, when captured at the very inception stage of branch set-up can greatly aid in understanding the branch’s gross potential and scale of operations, relevant needs of potential customer and perhaps even insights into strategies to thwart current or budding competition. If the GDP study in a branch is conducted at the proposed stage of branch, re-estimation and scoping for new business development through primary studies may then become redundant. Another added advantage of doing this prior is that this can be a part of the branch staff’s training track aimed at familiarizing them to the landscape of the area.

In the long run, a time-series GDP exercise process– prior to branch opening and ‘x years’ post branch opening is perhaps going to be the only real indicator of branch performance. Branch performance in this case will not mean the business at the branch but perhaps the change in (composition of) GDP of the service area and the impact of the KGFS branch on the lives of the people3.

KGFS Impact Long Run = f (actual business and type of customers served by the branch, increase in GDP share contributed by those served in the area that can be exclusively attributed to KGFS’ operations between KGFS0 & KGFS1).
[KGFS0 – is the GDP estimate from the study in time period 0 (prior to branch opening) & KGFS1 – Is the GDP estimate from the study in time period 1 (post x years of operation)]

The next blog in the series is on the Activity-based Costing of the GDP exercise. Through the lens of GDP studies done in the past, it will attempt to provide indicative answers to questions such as time and resource costs of the exercise.


  1. Market share of a branch estimates the actual served market/sales of a branch. This addresses concerns such as market cannibalization.
  2. Based on forward and backward linkage of value chains in the area, loan purpose and occupation type, unexplored segments, etc,.
  3. This estimation needs to account for fixed effects.

We recently hosted a series of knowledge management sessions (Spark Spring Edition 2015), as part of which Surabhi presented on this topic. In her session titled “Gross Domestic Product Mapping”, she spoke on how GDP Mapping can provide an intensive diagnosis of a defined location and how it can be used in a research process aimed at providing richer, robust and relevant information about markets, variables and potential.

View the presentation from her session below:


Extending the third-party aggregator model from ATMs to Business Correspondents

Guest post: By Ignacio Mas and Abhishek Sinha

When you have two systems running in parallel, the hardest part is always managing the interface between the two. Customers don’t usually all migrate to the new system entirely and at the same time, so there is a need for the new system to offer backward compatibility with the older, more established system. Without that, the stakes to migrating to the new system may be too large, and adoption will lag.

So it is with money. The new electronic form of money must integrate as seamlessly as possible with legacy paper money if new-to-banking people are going to be at all comfortable in experimenting with and using it. That’s why the retail agent or Business Correspondent (BC) bit is the cornerstone of any branchless or mobile banking proposition.

But that is where the economics and operations of branchless banking ventures get really tricky. The technical bit is easy: as long as it’s about managing electrons and bits, scalable, low-cost systems can be put in place. But a cash in/out channel is an entirely different story: it needs to be carefully constructed bit by bit as a patchwork of stores. Each store needs to be vetted, supervised and supported. Each store needs to be fed with enough revenue day in and day out to justify setting liquidity aside and running to the bank to rebalance when liquidity runs out.

You don’t see many large-scale, branded retail chains in India in any sector (think groceries, pharmacies, agricultural inputs, whatever), and that’s for a reason: managing retail channels is really hard business in a country with such a disperse geography and where there are already so many local shops running on razor thin margins.

If that’s the case, why should banks, who have no experience operating beyond their own branches, be expected to succeed in creating their own networks of BCs? Some are trying hard, but how many can claim to have a sufficiently dense network of stores doing brisk cash in/out business every day?

You should let specialists manage retail networks, and let banks ride over them. Specialist store aggregators, operating under specific rules and guidelines issued by the RBI, could then offer BC service for any and all banks.

Think how such specialized shared BC networks could transform the problem. Entrepreneurs with experience in managing indirect channels would develop business where banks themselves are not willing to go. Banks would simply sign up whichever shared BC networks they feel are in relevant locations and offer good service to their clients. Individual stores would be able to offer service to all their clients, whichever bank they happen to bank with, in the same way as they sell a range of toothpastes to suit their clients’ various toothpaste preferences. Banks wouldn’t each need to deploy essentially the same systems to reach pretty much the same stores – a costly duplication that makes the already precarious economics of cash in/cash out altogether unachievable.

If this seems far-fetched, that is essentially what is happening today under the RBI’s new policy allowing third-party ATM networks. Why should banks themselves have to run with the operational hassles of installing and managing hardware and replenishing cash boxes? That can easily be delegated, as long as certain ground-rules are met, especially on aspects of technology platform and consumer protection. These are properly identified and clearly laid out in the RBI’s guidance on the topic.

A BC is functionally equivalent to an ATM. An ATM is essentially a point-of-sale (POS) terminal (the card reader, the screen, the keyboard) with a cash box attached. In a BC setting, the only difference is that the cash box is physically separated from the POS terminal (which might be as simple as a mobile phone), and the cash gets dispensed or accepted manually by the shopkeeper rather than automatically. But the transaction is governed electronically through a banking technology platform, because the POS informs the customer how much cash to hand over to or to take from the shopkeeper. What is important is that transactions always occur on a technology platform controlled by a bank (not the third party network manager) and hence under the clear supervision of the RBI, and that the network manager be bound by clear, specific consumer protection rules.

Under a shared BC model, each store might operate from a single bank account using the technology platform provided by one bank (we could term this the “acquiring” bank). Any transactions it performs on behalf of customers from other banks could be settled through the NPCI’s real-time mobile switch.

Creating third-party networks of shared BCs is the logical next step in the process of enabling scalable branchless banking services. Free up banks from the burden of managing the operational aspects of cash-in/out networks. Aggregate the cash in/out transactional volumes across all banks to make the business case easier for individual stores. Flash forward to the inevitable step of infrastructure sharing, like telcos have come to accept with tower sharing and banks with ATMs. And let banks concentrate on their core mission: developing, selling and managing a variety of electronic financial services that solve people’s broad financial needs.


Improving Competitiveness in Agri-commodity Markets – Part 3: Recommendations for Policy and Market Framework


By Sreya Ray and Bama Balakrishnan, IFMR Capital, with inputs from Kshama Fernandes.

This post is the concluding part of the three-part series on the competitiveness of agri-commodity markets in India.

How can policy enable competitive agri-commodity markets thereby also improving the conditions and incentives for farmers and others? Once again, we use the three key fundamentals as a guideline:

  1. Access to Credit: As compared to a target of INR 575,000 crore credit for the agriculture sector, only INR 308,025 crore was disbursed to the farming sector (as on 31st October 2012)1. Not only is the quantum insufficient to meet the needs of the farmers, but also the credit arrives too late in the cycle (whether at the pre-harvest, harvest, or post-harvest stage), and the repayment schedules can be inflexible or too short and not accommodating of the length of the cycle. Policy reforms should be put in place to encourage and expand lending at each of the three stages of the cycle.
  2. Hedging of risks: The government at both the state and national level should incentivize the usage of hedging measures by both the farmer and others in the value chain, whether it be by promoting the use of micro-insurance products, giving farmers access to commodity derivatives or keeping transaction costs low for risk hedgers in commodity markets (OTC, spot, futures, and derivatives). As an illustration, the proposed CTT would increase transaction costs for traders and key risk hedgers that purchase commodity futures. This could constrain trading volumes, which in turn would hamper liquidity and price discovery and result in inefficient risk hedging, all of which would affect parties exposed to the commodity – farmers, traders, and wholesalers. A lot of commodities underlying traded futures contracts that are classified as industrial commodities are often used by farmers for price discovery (price of sugar as a proxy for sugarcane price). A drop in the underlying volumes of these contracts could harm hedgers as well as farmers who use these contracts to price their produce.
  3. Price discovery: Having in place established and widespread commodity exchanges that deal in not only spot but also futures and derivatives will, as explained earlier in the post, enhance fair and transparent pricing of agricultural produce. Towards that, the government should encourage the setting up of exchanges whether physical or virtual, and allot a public sector body to regulate such markets. Apart from exchanges, direct markets can also be encouraged on a smaller scale, such as Rythu bazaar cooperative model, the ITC e-choupal model. This will require a dedicated awareness campaign that will reach out into the rural hinterland and educate the farmers about the options available to them. Suitable amendment of the Food Safety Standards Act and/or the promulgation of laws to standardize quality at a wholesale level may be required to protect the players in the agri-commodity market at a wholesale level. Besides this, policies that promote the linkages between spot and futures market and thereby aid price discovery in a fair, transparent and reliable manner will go a long way.

Some more steps towards Competitive and Sustainable Commodity Markets: Addressing Current Challenges in Regulation and Governance

The main goals of a commodity exchange are to provide a platform for risk hedging and price discovery. Both these are important aspects of the agricultural cycle affecting all stakeholders. We review the existing landscape of commodity exchanges (NCDEX, MCX, NSEL, NSPOT) to identify the issues that hamstring the agri-commodities market.

Strong linkages between the exchange and warehouse regulators: If the commodity exchange is to honour contracts based on physical delivery of commodities, it must ensure that the warehouse meets minimum standards of quality, security, climate control, staffing, waterproofing, et cetera. This is particularly important as warehouse receipts can be used to raise financing for the warehouse itself to expand or invest in facilities such as pest control, fumigation, fireproofing, and standardized weighing equipment. The Warehouse Development and Regulatory Authority can act as a third party verifier of scientific warehousing standards and issue receipts that are treated as negotiable instruments and accepted by the Indian government and banks. In order to register with the WDRA, the warehouse must comply with strict rules on the terms of storage and construction and make penal provisions for stock shortfalls from the amount on the receipts. The 350 warehouses across the country that are registered with WDRA are subject to regular inspections by empanelled agencies. Although WDRA has been in existence since 2010, most of the warehouses in employ under the current exchanges –NSEL, NCDEX, MCX, NSPOT- are not registered with WDRA. In light of the NSEL debacle, it has been reported that the FMC is considering a proposal for compulsory registration of all warehouses used by spot and futures exchanges with the WDRA. Such a move would be a positive step for the market. Registration would also incentivize warehouse operators to improve the quality of their construction and facilities and therefore reduce wastage in the long run.

Price linkage to external markets: Since commodities are traded globally, it would make sense to start thinking about linkages of the Indian commodity exchanges with global markets. If there is a bumper corn harvest in West Africa that drives global corn prices down, the Indian farmer should be aware of these global movements. Removing the barriers to market information will ensure more transparency and information symmetry and lead to a more efficient exchange particularly in commodities where linkages with the global markets are increasing.

Price distortion caused by the government being a large player in markets for those commodities under MSP: Since the government is a key high-volume buyer of the commodities under the MSP umbrella at pre-fixed prices, it will invariably cause price distortion. More efficiency in procurement, storage and stock management by the Government may in turn minimize the degree of distortion in the market.

Need to differentiate between industry crops and food crops: Policy makers should understand the distinction in the impact measurement of movements in prices for industry crops and food crops. A supply shock in rice will directly affect the consumer who will have to pay much higher prices for his daily staple. However, a similar magnitude of supply shock in guar, which is an input into many different processed food and non-food products and for which there exist alternatives, will not affect the end-customer to the same extent in the same window of time. This distinction has implications for product design and policymaking (for example, applying the Essential Commodities Act differently to industry vs. food crops). In the context of commodity exchanges, the distinction could come into play in the pricing of derivatives, in hedging measures, as well as transaction costs.

FCRA Act: It has been nearly a year since the Cabinet approved the Forwards Contracts Regulation Amendment Bill and it awaits passage by the Parliament. Passing this bill will ensure a boost to the commodity exchange industry through introduction of new products like options and new generation commodity derivatives to expand risk management opportunities beyond the usual futures and forward instruments, and will provide more autonomy and accountability in the operations of the FMC (the regulator of forwards markets). An analysis of the NSEL crisis currently unfolding may reiterate the urgent need to pass this bill for empowered supervision of this important component of agri-commodity markets.

Even if the FCRA Act is passed, still needs a few more tweaks for FMC: FMC currently reports to Ministry of Consumer Affairs while all other financial market regulators are under the Ministry of Finance. In order to serve as an effective regulator of commodity markets, it should be independent and supervised by the more relevant Ministry of Finance and held accountable for its critical supervisory activities.

A better business model for exchanges in line with the top global commodity exchanges: Across the world, commodity exchanges use turnover fee based on volume rather than value. This encourages exchanges to push for higher volumes of contracts which in turn enables better price discovery – the primary aim of such an exchange. Further such a revenue model would avoid any conflict of interest as the exchange would be indifferent to the direction of price movements.

Correction: FMC has now come under the purview of Ministry of Finance since early September. Thanks to Mr. Ramesh for pointing it out in the comments section.

1 – Farmer’s Access To Agricultural Credit – Department of Agriculture And Cooperation, GoI