Introducing Unmagic Quadrant on Digital Agronomy

Why Unmagic? Because I cannot promise good things to those who find themselves in the quadrant.

I've never considered myself a quadrantologist.

Although I've had my share of good and bad consulting karma, I've faithfully followed the gonzo style of deep immersion and hated the idea of chopping reality into neat and legible 4 x 4 quadrants.

Until now.

I want to build a quadrant here, because, when we are facing a late-to-digitalize domain like agritech with its share of high contextual ambiguity and fuzziness, it helps to lay down the broad map of the land with neat phenomenological clusters that would be of help to anyone who feels lost in the #agritech woods.

But why call it unmagic quadrant?

I can't help but have some fun at Gartner's expense, especially when I am drawing a quadrant after being the bête noire for critiquing Gartner's business model. Leaving aside my bias, if you think about it, it's obvious.

Magic Quadrant is rightfully called so for a reason.

When technology vendors want to stand out as "cool vendors" in a crowded market place, they are desperate to do anything to get the attention of Gartner, who promises positional scarcity in a world of abundance.

When technology vendors find themselves in the most desired side of the magical quadrant, good things happen to them. It doesn't matter whether the technology buyers really had a good experience with the technology vendors or not. Just the latter's presence inside the right side of the quadrant could do things of influence and fat revenue only a magician could dream of.

No wonder, every vendor residing inside is happy to flaunt those Magic Quadrant reports along with their marketing wares. After all, they have purchased those reprint rights for us, the customers, to download for free.

There is an irony waiting to unfold here.

Magic Quadrant, in principle, is meant to help technology buyers cut through the marketing fluff and get to the real story behind the company's offering, which ironically, becomes the same marketing fluff for the vendor who wants to sell his offering.

Now you know why I am calling this Unmagic quadrant. It's simple.

I cannot promise good things to happen to those who find themselves in not just the right side, but any side of the quadrant. When you observe the quadrant below, try and go beyond the usual Gartnerian mental conditioning to view those in the right-hand side of the quadrant with shiny lens and those in the left-hand side of the quadrant with not so shiny lens. My intent here is to simply show different gameplays emerging in the market without necessarily showing any player in a positive or negative lens.

There are interesting players in all four sides of the quadrant (and I can assure you, far more interesting players whom I haven't included in this) and all their positions are changing as we speak.

Needles to say, I don't have any stakes/business relationship with the vendors listed in the quadrant. I picked the vendors as archetypes, based on my subjective understanding at a Fingerspitzengefühl (finger-tips feeling) level, to delineate the dynamics emerging in the Indian digital agronomy domain.

With that disclaimer in place, shall we get to work?


I want to map the supply and demand levers for digital agronomy services. The supply levers can be broadly categorized based on the level of interaction provided by the agronomy player to its customers.

Broadly speaking, at one end, there is the low-touch mode of interaction, where the farmer voluntarily shares his data - sowing calendar, which crop he is growing, and which disease he is interested to know about (based on his photos/videos, click patterns, cookies etc.) and at the other end, naturally, there is the high-touch mode.

This is currently happening through IoT sensors being deployed on the farm premises correlated along with external forecast data to measure the error margins. This will improve the model, as more farming data comes along. Primarily, we are seeing models emerge for high-export value horticultural crops.

Let me give you a concrete example to illustrate this polarity between high-touch and low-touch.

When Jayalaxmi Agrotech came to the spotlight a few years back for running an agritech firm from Bellary, Karnataka, their approach was a classic low-touch model. By focusing on educating farmers on various everyday agronomy questions asked by a farmer viz.,

a) How do I differentiate diseases from micro-nutrient deficiency?

b) How do I irrigate appropriately without wasting water?

They were able to successfully design a digital agronomy model in which they collected valuable information pertaining to a) location of the farmer, b) crop which was accessed from their crop-specific app portfolio, and c) phone number which was used to access the application.

Using this data, they were able to build a hypothesis by correlating a) Crop variety and disease accessed and Location. b) Prevalence of a specific disease and location

I am aware that as we speak, Jayalaxmi Agrotech has moved beyond this low-touch mode and have gone ahead with an "Agri app", where farmers can share their crop calendar, check out videos, a package of practices and get expert advice. Unfortunately, the new Agri app, in my opinion, has been badly designed, especially when considering how neat and elegant their design was when they were content to be in low-touch mode.

For this quadrant, I am deliberately focusing on their older version here, because it highlights interesting gameplay that is not often seen in the market. It serves a simple message: It is not necessary for everyone to follow a one-size-fits-all approach when it comes to digital agronomy business model.

Coming back to the polarity, Fasal, on the other hand, operates in a high-touch interaction mode to serve its customers through its Libelium-Fasal smart agriculture solution kit. Here is a small glimpse of the sensors and probes which make up the smart agriculture solution kit.

Now that we have a good understanding of the supply levers, let's move on to demand levers. I have broadly mapped the demand levers based on whether the agronomy model is predicated on a free-to-use or pay-to-use model. Which means that even though the advice is free, if the agronomy advice exchange is happening inside an e-commerce platform, it comes under the pay-to-use model.

Here is the quadrant, as promised.

Let me list down a few questions that might pop up when you read this quadrant.

1) Why is Agrostar listed in Low-Touch, Paid quadrant when any farmer can download their app to get free agronomy advice?

In the case of Agrostar, they are currently in low-touch mode (while probably bidding their time to forge partnerships in order to go deep ala high-touch mode), as they offer agronomy advice based on the photo/videos shared by the farmer, which becomes an opportunity to sell agri-inputs which can address the situation in hand. Even though they offer free agronomy advice, the underlying model is that of an e-commerce platform.

2) Why are Bharat Agri and Agrostar in the same quadrant, when their business models are different?

Bharat Agri is betting on a direct-pay-for-services model when compared to Agrostar's indirect-pay-for-services model through free agronomy advice. Indirect pay has more risks and skin-in-the-game issues if you go by Nassim Nicholas Taleb's golden advice.

You can't be serious about advising change when you have a technology to sell. You either give advice, or you sell technology. It's unethical to think you can do both.

And so, one might argue, what is the difference between Agrostar (or any agri-input e-commerce player offering free agronomy advice) and traditional agri-input retailers?

Both the old and the new give agronomy advice to farmers in order to sell agri-inputs.

In contrast to this indirect pay model, I find BharatAgri's direct-pay model refreshing. It gives them huge leverage to play the data transparency positioning game. Here is the tariff of the services they offer in the market, as I picked up from their website.

Net net, even though their business models may be different, essentially, they are betting on a low-touch, paid gameplay.

3) Why is Agri-Central in High-Touch-Free quadrant? Shouldn't they be in Low-Touch quadrant?

I would have been tempted to place them in Low-touch quadrant, if not for this powerful feature.

By giving farmers the geofencing capability, they are building the necessary infrastructure to offer contextual agronomy advice to farmers, based on satellite data. Wait until they forge partnerships with players like Satsure/Esri. Their gameplay would be very interesting to watch.

Do you have any more questions to ask about this quadrant? Feel free to ask in the comment section below. There are plenty of lessons that can be drawn from these diverse game plays, especially from the farm data standpoint and what it means for the future of agriculture. We will explore this further in the upcoming articles.