Solving agri’s data puzzle can end in tears of joy – or despair

Market insiders warn of a bubble as the majors rush to build businesses capable of integrating data across the supply chain.


Market insiders warn of a bubble as the majors rush to build businesses capable of integrating data across the supply chain. 

No more than 15 years ago, bookmakers around the world used to price odds manually by filling in Excel spreadsheets. No longer: they have since developed much more sophisticated models, using algorithms that digest multiform data from hundreds of sources, often under the lead of newly hired ex-financial traders.

Institutional investors looking to bet on farming opportunities often complain that agriculture has yet to modernize in a similar way. “I’ve seen tense LP meetings where there was a high level of concern that the investors didn’t know what they didn’t know,” a US-based advisor recently told us. “That’s going to be a significant barrier to entry for new capital to flow into agriculture unless we solve this.”

Many large operations still copy and paste information into Excel spreadsheets; the potential for mistakes is “huge,” our source noted.

Yet where frustration exists, investors smell opportunity. And lately it’s especially been the case among strategics, which have gone on a buying spree to try to find a lucrative answer to agri’s paucity of credible reporting. Most promising, in their minds, is their potential role in building platforms capable of integrating the data produced by discrete agtech devices, most of which have so far focused on solving point-to-point problems. Arguably, this is indeed where rent can be best captured, in much the same way that Google or Apple are earning a profit by hosting apps or integrating content.

That probably was the main rationale behind DuPont’s $300 million acquisition of Granular, an agriculture software company, earlier this month. The latter designs farm management tools and runs a valuation platform for land, in each case compiling lumps of data to produce reports. Another major, Cargill, just invested in the Series B round of Descartes Lab, which applies machine learning to geospatial data. With the financing, the start-up plans to improve its “data refinery,” the cloud-based supercomputing platform it uses to draw insights from disparate datasets.

The motives of such investments are not immediate profit: sources tell us DuPont paid nearly a hundred times Granular’s annual revenue to secure ownership of the business. Rather, one should see it as strategic positioning, catering for a more demanding base of farmland and agribusiness investors. It also comes at a time when the quality of public databases is declining, according to some operators, as the market becomes increasingly controlled by large, private entrepreneurs who don’t share their data. On paper, majors thus stand to win big; those who invested early in chosen ventures, such as Granular, have already pocketed hefty rewards.

Yet market insiders warn of a bubble. “Agri majors are writing massive checks for very small businesses. And the only reason that works is that public markets are so goofy that they don’t penalize strategics for making stupid acquisitions,” an industry veteran told us last week. In his view, central bank-fueled liquidity is to blame for this; unsustainable business models may rapidly find themselves naked once the money-printing recedes.

“Everyone wants to play the lottery. You have three-four players that are acquisitive; in most verticals they’re going to buy one player, maybe two. But the end of that game is near.”

Indeed, as he predicted a future where quantitative easing would play a lesser role, UK central bank chief Mark Carney in February announced that central bankers’ 15 minutes of fame “would soon be over.”

The need for tech platforms will surely outlast QE, whenever monetary policy eventually tightens for good. But investors looking to bank on specific firms should place their chips carefully.

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