By Seana Day
In a previous post about Mapping the AgTech Adoption Curve, a colleague observed that Big Data in Ag may have reached the “trough of disillusionment” where the promise and hype of the technology fails to deliver and interest wanes. This somewhat broken-hearted view made me wonder, are we too narrowly focused on the value of field level data? To be clear, many in the AgTech industry understand the value of data beyond decision support and farm management, but maybe it’s time to elevate and rationalize the value today, while we wait for the promise of tomorrow.
In this post, I am looking at a few Ag Data value drivers that could have a more prominent place in the discussion, particularly if warmly embraced by the financial sector.
Things You Always Wish You Knew Before You Got Involved…
With an aging farming population and more uncertainty in the farm labor markets, Ag Data will play a critical role in knowledge transfer. According to the 2012 USDA NASS Census of Agriculture, the average age of farm operators has now reached 56-years-old (58 for principal operators). With challenges in recruitment of new farmers and generational transfer, there is an increasingly urgent need to migrate information about soil types, yields, problem areas and unique characteristics of a field from a notebook (or memory) to digital formats.
A favorite adage is that every grower has about 40 chances to get it right, meaning 40 harvests over the course of their lifetime. As many can attest, it can often take years to figure out the particulars of a given field. If that learning process can be shortened, the productivity will accelerate. Furthermore, this knowledge can be invaluable when planting a new field or planning a new irrigation system capable of integrating precision technology.
Knowledge transfer doesn’t only happen at the management level. Many growers (especially in California) will describe their workforce as either “lifers” or those who cycle out seasonally (or more often). With the ever-increasing turnover, valuable knowledge is lost. Capturing the data that informs field decision making, even basic functions, can allow operators to more rapidly onboard new workers and avoid repeating the same mistakes.
Ag Data’s Cinderella Story?
Not only does Ag Data accelerate knowledge transfer, but it also has the potential to transform how we think about farmland values. Now it needs more champions to extol its virtues.
The last decade saw the farmland market in the U.S. change dramatically with small farmers selling their land to larger farm groups. In fact, according to Green Money Journal it is estimated that 400 million acres or 70% of U.S. farmland will change hands by 2030. The information that facilitates these sales is not standardized and tends to create widespread frustration from all sides of the farmland rental and resale markets. AgTech companies like AcreValue (who became a part of Granular in 2015) and Terva are recognizing the opportunity to integrate more transparency and new comparison tools.
Two fundamental drivers generally influence the value of any capital asset, including farmland, future earnings and the expected opportunity cost of funds (or the rate at which future earnings are discounted).
The industry today mostly looks at a valuation model based on capitalization of rents or income. Although dated, CropLife did a nice job summarizing prior to the most recent downturn. As CropLife notes, “the problem is that this broadly applied valuation framework focuses on a snapshot of the value of farmland today, and has no predictive value reflecting future expectations of crop price, profit and interest rate expectations, which will likely be dramatically different from today.”
Historically, the lack of tangible data to form credible assumptions about future profitability and yields meant that the more rudimentary rent/income capitalization model was “good enough.” Given the increasing range of technologies available to growers that capture field level data and integrate with accounting systems it begs the question, when will a more sophisticated, standardized approach to valuing farm land be adopted?
The financial markets are accustomed to valuing a company or other income producing assets using a net present value methodology which incorporates micro (asset specific) and macro (economic) assumptions to assess the present value of future cash flows. In the case of agland, this approach could look at micro (soil, water access, historic crop yields, etc.) and macro factors (interest rates, economy, supply/demand, pricing) to create a predictive model that has the potential to bring more capital markets efficiency into farmland.
The role of the financial sector in communicating the value of Ag Data has never been more important. The call-to-action is for an industry that prides itself on the pursuit of pure information efficiency to recognize that the value of Ag Data in risk and asset value modeling, even succession strategies, is relatively untapped (although the crop insurers have a strong head start). To the financial wizards in the high towers of Wall Street, as well as the ag lender on Main Street, create the right incentives for growers to invest in data collection and monitoring in order to make the connection between sustainable activities and asset performance. Your Valentine will thank you!