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Agriculture marketing in Maharashtra is set for a major transformation on the back of technological innovations and carefully crafted policies to further boost agriculture’s contribution to India’s economy. Nawin Sona Natesan, Secretary to Government of Maharashtra, and Managing Director, Maharashtra State Cotton Growers’ Marketing Federation Ltd, Mumbai, presents a comprehensive perspective on the existing challenges for agriculture marketing in the state as also the way forward to tackle them in light of emerging technologies that are set to bring a paradigm shift to the sector.

Nawin Sona Natesan, Secretary to Government of Maharashtra, and Managing Director

The agro market in Maharashtra adds Rs 75,000 crores each year to the economy through a network of governmentbacked Agricultural Produce Market Committees (APMCs), private APMCs and Direct Marketing Licensees (DMLs). Each commodity has its own value chain, thus adding a multiplier effect to the economy of the state and the country. The cotton market itself trifurcates into 400 lakh quintal of seed cotton per annum valued at Rs 20,000 crores, cotton seed valued at Rs 6,000 crores and 88 lakh bales per annum totalling Rs 46,000 crores. However, presently the challenge is how to leverage existing and emerging technologies (BIGFAB 6 – BigData, IoT, GIS, FinTech, AI and ML, and Blockchain), and ‘ReThink’ known business scenarios to steer the economy towards higher growth.

Commodities – Cotton and Others

Maharashtra generates a huge quantity of cotton, a key global commodity, and various other agriculture produce of national and international significance. The state has 39 lakh hectares under cotton with an annual output of close to 88 lakh bales out of the 340 lakh bales per annum of India. Besides countless farm labourers, more than 22 lakh farmers are engaged in cotton production in the state. The state hosts 860 ginning pressing factories, which produce the bales consumed in various parts of the country, largely by the spinning mills in South India. Maharashtra cotton is also exported to countries like China and Bangladesh.

The vibrant APMC network and the pioneering reforms in the sector have created avenues for good price discovery, improved physical infrastructure, reduced transportation costs, and the state is gradually moving towards better services to farmers. Maharashtra’s encouragement of private marketing licenses and DMLs are creating the much needed competition to enable long-term cost efficiencies, and better services, incentives to farmers and intermediaries.

It is a matter of pride that Maharashtra Department of Marketing’s constant and pioneering efforts have been duly recognised by NITI Aayog, which has ranked the state No. 1 in the country in its Agricultural Marketing and Farmer Friendly Reforms Index (AFFMRI) with a commendable score.

Building on this foundation, there is a need to map out the next steps, a road map for plugging the policy gaps, identifying the business needs, the industry demands, and setting them in the context of the larger Indian economy.

Layers of Commodity Markets:

The Produce, The Consumer and The Derivative Commodities are transacted upon in many layers:

• Farm level: As “produce” is unclassified, mixed, fragmented, contaminated, and varying in location and time (harvest), and ownership
• Segregated, assayed and graded produce at market, of better uniformity, amenable to price assignation, collective handling, packing, etc.
• Intermediate consumable: where the raw produce with some value addition is converted into a raw material for the industry up the value chain. For example, raw cotton (seed cotton) is not a global commodity, but the cotton bale is. It is the raw material for spinning units and is exported.
• Trade-able commodity: near fungible assets which can be traded physically or electronically, say, packaged and processed agro produce
• Commodity derivatives: forward, futures, ETCs, swaps, etc.

Hence, there are many “layers” of the same economy based on the same products.

The simplistic notion of a value chain is too linear and sequential to appreciate the vastness and complexity of each layer. There is very little in common between the market at the farm gate, the market at the mandis, the market at the pre-processing stage, and most of them have little relation to the derivatives that drive the economy at the higher end.

Seeing this market stratification, one realises the presence of a highly sophisticated, top end Commodity derivatives market, which is enormous, highly regulated, monitored, techenabled, and integrated into the banking system. If there are physical settlements, then there is backward integration into logistics, warehousing and other services like collateral management, assaying, etc.

But as we dive deeper down the value chain, the picture becomes murkier. Each one of the product presents a very staggering variety of market sophistication. Let me illustrate. Market Assessment ToolKit :

In my opinion, a market maybe characterised by some key indicators:

1. Size of the market/value: the amount of produce it throughputs, its carrying capacity, and associated market-capitalisation

2. Geographical Spread

3. Buyer-seller profile: numbers, location, limits on trading, qualifications/registration, and taxation requirement

4. Market entry characteristics of sellers-buyers: easy or difficult to enter the market, resistance

5. Market information: availability and access

6. Product differentiation: choice for buyers

7. Product standardisation, quality assurance and assaying

8. Trading mechanisms: physical or electronic, primary or secondary (resale possibilities of same produce in same or different market).

9. Transaction settlement systems: formal or informal, credit-based or cash and carry.

10. Level of Intermediation: can produce be bought without brokerage, or other intermediaries?

11. Dispute resolving mechanisms: formal, informal or non-existent.

12. Risk profile of the market: are there inherent risk mitigation mechanisms in the market—informal or formal?

13. Regulation ecosystem: for derivatives it is SEBI, for example

14. Technology presence vs. human intervention: how much automation and what level of dependence is on human (subjective judgment )?

15. Variety and availability of supporting services: financial like insurance, or physical like assaying

16. Network connectivity: integration with other markets – regional, national and global, is the market isolated or networked?, or how demand and supply characteristics are correlated to global markets or national markets

17. Free and fair trade: degree of transparency, reliability, neutrality and accountability and ethics of the market – how much the market is immune to manipulation of information, to adulteration, to cartelisation, to insider trading and other malpractices.

18. Economic efficiency of the market: does it promote optimum value recovery for the produce, reduce negative externalities through its transactions, and use economic resources inversely commensurate to value it generates?

The list can be endlessly extrapolated, including taxation, aspects of political economy, etc.

Applying this toolkit to each level of the so-called value chain, starting from the farm gate to commodity derivatives, we see that there is lot of work to do. Some levels have no transparent market information, most levels have no dispute settlement, lot of the trade is unregulated, and there is lot of risk and intermediation. The produce standards are generic or governmental. Grading and sorting as a service is still to reach the farmer; the farmer is still hesitant in turning from a producer of primary good to getting it processed and then selling it.

Also Read: Future ready IoT for futuristic cities: Nawin Sona Natesan

The markets are highly intermediated; brokerage is not the only economic cost. The brokers or arhatias are also quality assayers, dispute settlers, credit and risk managers, transaction advisors and often transaction mechanisms themselves. So, to have a level-playing field with free and fair trade having quality-based price discovery, there is scope for improvements in the existing scheme of things. Using this tool kit, one can assess the institutional gaps at each layer.

Innovation: Under the Hood

What is Innovation by the way? Is it ‘solutions looking for problems’ scenario? Is it rearrangement of a few parts? Is it the much celebrated ‘jugaad’? Or is it what is called bricolage, assemblage of whatever is at hand, just to do something new? It doesn’t seem to be so simple an answer; at least to have a lasting and huge impact.

Or is it dismantling and re-working at the components? Is it a systems thinking approach to the problem world? Is it deep insight? Or is it a naked hunch clothed later in theory and armed with modern tools? The answer really depends on who the innovator is and what the system is. There are as many approaches as are problems. What is certain is that India has a long way to go to reach the smoother levels that the West has reached. We are not talking about incremental innovations, but paradigm shifts, i.e., the huge systemic change that is possible.

For example, cotton spot trade has disappeared in Australia—entire fields are forward sold, often pre-sowing. The collective selling method works on a reference rate of the International Cotton Exchange ICE Index, and US dollar/Aus dollar exchange rate basis. The basis is best discussed in USD as US cents per pound (US cents/lb). Basis is the difference in price between the value of a physical bale and the underwriting futures contract price. The basis value for a physical bale is a function of quality, location, availability, demand and competition (other growths and subsidies). ICE is the International Index of spot prices.

The entire country has accepted this formula, and replaced spot markets with forward sale. Pre-sowing sale is possible, because of standardisation in seed selection, and production methods.

BPR 2.0: Business Process ReThinking

What business processes can be better seems to be the logical question to ask while undertaking to lay the roadmap for the future. The large “free market” scenario teeters on being an “unorganised” market – with often no market information. For example, cotton bale trade in India is a case in point. The global product that is Cotton Bale is traded in a highly unorganised way: highly intermediated through brokers, with very high market information asymmetry – with the seller and product information being in the hands of brokers only, the product information itself being a source of business – akin to the erstwhile real estate market. Product differentiation is minimal if any.

Integration with Commodity Markets:

Maharashtra being the home of more than 2000 FPO/FPCs, the state is leading in the Prime Minister’s vision of developing 10,000 FPCs. Giving a boost to their profits and risk management tools, Maharashtra is already having an MoU with MCX for Cotton Futures integration of FPOs and other farmers into the exchange. Dedicated MCX accredited warehouses are linked, and farmers have been trained for the same. This, when seen with SEBI-led concessions to FPOs in commodity market trades, is a welcome innovation in this sector.

Markets as Network:

A Complexity Science View We are drenched with the word network—social network, networking, etc. The field of network science is a highly formal and mathematical theoretical framework which analyses all networks to unravel, understand, replicate and control (and innovate in and through) them. We can see each agro market, and especially each unorganised market as a network of networks. Each seller and buyer being a node, the brokers being connectors – they build rich graph structures. In fact, to borrow the terminology from field of complexity science; each unorganised market is somewhat a “self-organised” system. Cotton, for example, is sold as it seems through social networks.

How can this be better? (the favourite question of innovators). Technology may have some answers worth exploring.

BIGFAB 6: Big Data, IoT, GIS, Fin Tech, AI and ML, Blockchain

These major technological paradigms, which I anagrammed into a speakable acronym “BIGFAB 6”, represent a virtual arsenal of tools to change civilisation as we know it. And that is not an overstatement. Each of these would in the coming years tear apart and rebuild many systems we have taken for granted. But how would BIGFAB 6 impact agro markets? What is in store? Let us look at some possibilities.

Where is price discovery today? WhatsApp. What is the source of knowledge? YouTube. Who is the dermatologist of choice (read plant entomologist)? Google Images. These simple front ends are the tips of huge icebergs, built on sophisticated Machine Learning and AI algorithms, along with Cloud Computing, NoSQL and NewSQL Databases, Graph Databases, API Call-based agile development and deployment, rapid modular toolkit based developments, etc. Seamless integration across front end applications are created by these very technologies.

But by leveraging each technology for its USP, the sector of agro marketing can phase transform from the 20th Century trade practices into the 21st Century of efficient market economy.

Case Study of Cotton:SMART Cotton

Cotton itself is a vast subject and field. The Government of Maharashtra and the World Bank together are in the process of launching a Rs 2000-crore, seven-year programme—the SMART State of Maharashtra Agriculture and Rural Transformation Project. In this multi-sector project, the flagship sectoral project is that of SMART Cotton, developed by the writer and his organisation (the Maharashtra Cotton Federation).

In this SMART Cotton project we have aimed to anchor some of the growth and learning strategies precisely on these technologies. We aim at providing field level AI-based knowledge support to farmers, introduce traceability, and encourage quality-based market growth in cotton, including process standardisation and product standardisation and market access by using various technologies.

Use Case Rogue’s Gallery:

GIS: All agricultural growth is geographical. GIS can overlay soil condition, weather data, micro weather, seed choice, pest attack, water levels, labour shortage, fertiliser use, credit availability, insurance cover, land holding size, nearest market distance logistics, warehousing logistics, market information, and pricing—all together. But imagine if you are able to project the quality of the bale from the farm seed cotton. Imagine if you are able to predict the quality of the yarn from the bale. Is it possible? Yes it is. But the answer is complex—involving many actors and layers of processing, and variations at each stage of handling aggregation and maintenance of quality controls.

Big Data: That is where Big Data, along with GIS and as standalone can work. Big Data is essentially a NoSQL or NewSQL-based system, which leverages cloud computing backend to offer a variety of data analytics, data integration, visualisation—opening up to large scale insight hitherto unheard of. Velocity, volume, variety, value and veracity—the Big Vs of Big Data—are all combinable. Various sources of data can be used to plan better production, marketing services, better prediction of market trends, locally and globally. And also plan for gap identification, in services, logistics, understand quality demand and build recommendation systems.

AI and ML: AI can not only mimic human judgment but can come up with supra-human understanding of systems, compressing learning across millions of persons and man hours and offer recommendations which would have escaped consciousness. For example, AI systems can be built for grading and sorting, quality assurance ecosystem. High quality visual analytics has been developed for sorting tea leaves.

Deep Learning systems can pick up existing knowledge of domain from human “teachers”, and learn from positive feedbacks to build near faultless identification of grades of produce. So the classer, grader or assayer of the far future could be a machine and not a human. ML models can also match market price to inputs, i.e., what conditions of soil, package of practices, etc lead to a particular price band of the produce. Machine Vision, Visual Analytics, combined with sensors are the future of agri mandis.

IoT: Connected devices and objects— products, infrastructure assets, logistics, storage, market information, surrounding information can all be on a network. For example, lots of produce could have RFID or QR coded information, weighment would auto read the bags and update the data base, the storage and movement of the assets could be tracked.

Blockchain, IoT and Smart Contracts:

Blockchain in itself is a large topic. One of the fascinating use cases is that of Smart Contracts. Fit cases can be built in the logistics and warehousing space. Commodities are such a field that the physical asset holding, its quantity, quality certification, quality assurance, and its valuation is very critical. So is the due diligence process of quality inspections, assessment against minimum commodity standards for the price by agreed third party. Also added services are the insurance products based on these assets; credit raised as collateral; settlement in forward or futures based on such holdings. Now with electronic tradeable e-warehouse receipts, the digital documentation of produce is the next step.

This is where Smart Contracts can come into play: imagine a quality assurance ecosystem, seamlessly integrated with IoT based sensors, reading and validating and providing proof of assets being held in warehouses. The IoT sensors would keep track of fumigation, humidity, pest as also about movement, etc. Sensors would trigger events. The quality certificates, inspections and other documentation itself could be on a Blockchain. Such information systems would enable a trust network to be established; the asset could be leased, used as collateral or sold, based on solid trusted certification. When the transaction is settled, Smart Contracts could automatically track the movement and alert the new owner of movements and generate new documentation.

Blockchain is a supreme solution for traceability—farm to fork. It can enable building of better networks and assessing the supply chain.

IoT-based Quality Assurance: Quality assurance and IoT have a long journey. Sensors and actuators network can alert the produce owner, aggregator or the intermediary of loss of quality, say humidity, and alert for critical risks like rain or fire, etc.

FinTech + IoT: The large suite of technologies under the FinTech umbrella allows seamless integration of individual and corporate identity validation, payment systems, KYC, asset agnostic payment solutions. Also FinTech solutions can build farmer credit profiles, help agri business go paperless, faceless and cashless. Faster monetisation of produce is possible. A quality assured, certified, and IoT secured stored lot of say cotton bales may immediately be used as collateral to raise short term loans, while the FPO explore the market or use the commodity exchanges to hedge their produce.

Global Associations: As we speak, huge conglomerates have leveraged these BIGFAB 6 technologies to build global trading networks in various commodities—VAKT in energy trading, Komgo in trade finance, Forcefield in commodity tracking, especially metals. Such systems are yet to be developed in our country.

Innovation: Never say Never With these kinds of tools at hand, it is the need of the day to reach out to the industry, and to the various international organisations to develop partnerships, encourage investment and to develop market led solutions, and to foster growth of free and fair trade with equitable returns to all stakeholders. One of the key projects in cotton sector, SMART Cotton builds on these very foundations for a radical change in approach of quality in the field of cotton. And we are keenly looking forward to taking this project ahead including such developments.

Public-Policy Perspective: The public policy of the 21st Century, in the global, cashless, integrated, free trade, sustainable, equitable, fast and flat world has to be:

a) Highly data oriented

b) Adaptive

c) Flexible in approach and tools, but focused on positive outcomes

d) Leverage private and business perspectives of risk

e) Keep public interest and equitable growth in mind.

With these challenges, the BIGFAB 6 tools and the market analysis toolkit in hand, better market systems, structures and processes are possible. New policies ought to encourage such systems—exactly what SMART Cotton Project the author is heading, aims to do.

The field of agriculture marketing is on the cusp of a huge leap. This growth has to be carefully nurtured and all support has to be given to accelerate towards it. Innovations in this space are needed both from public policy perspective and from private enterprise. This would help Maharashtra reach the $1 trillion economy, with agribusiness taking a leading role.

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