42 NOVEMBER / DECEMBER 2021 Agricultural Data and Technology Brady Brewer, Ph.D. Assistant Professor, Agricultural Economics Purdue University brewer94@purdue.edu AG BANKING A 2016 study by the McKinsey Global Institute looked at the state of digitization of various sectors across the U.S. economy, examining three ways digitization can impact a sector: labor, data usage and assets. What was found was that certain economic sectors lagged drastically compared to others. Of the 22 industries examined, the agricultural sector was dead last in terms of digitization. In fact, agriculture ranked last or near the bottom for each of the three areas of digitization examined in the study. Why do I start with this metric? For starters, I have heard a lot about the digital advancements that the agricultural sector has seen over the past few years. Indeed, many of these have been topics I have written about in this column. Farmers now have online options for buying their agricultural inputs. Additionally, there has been increased pressure from consumers to know where their food comes from, and which farming practices are used in the production of those food products. And now we have the new item everyone is talking about: carbon credits. Depending on which markets farmers choose to participate in, they may be required to provide five years of data on production practices. In summary, the agricultural sector may rank last in terms of digitization, but the adoption curve for data products and the use of this data is steep. Farmers and agribusinesses are learning quickly. In an attempt to explore data and technology usage, the Center for Food and Agricultural Business at Purdue University surveyed farmers from across the United States on what technologies they are currently using. This survey targeted large commercial farmers, specifically those that have gross revenue higher than $1 million. The majority of farmers surveyed reported using various data technologies on their farms such as yield mapping, variable rate technology, drones and unmanned aerial vehicles, and precision irrigation. As an example, 87% of farmers in our survey reported using yield mapping technology on their farms. This may seem high based on the farmer customers at your bank and other data you may have seen. The U.S. Department of Agriculture reported only 31% usage of yield mapping technology nationwide in 2016, according to its Agricultural Resource Management Survey. Another example of this difference is that 73% of farmers who responded to our survey use variable rate technology, but the USDA shows only 25% of farmers nationwide use variable rate technology. This difference was similar for all data technologies we surveyed farmers on. What explains this difference between our recent survey and the USDA data? The answer lies in what I mentioned above: We targeted large commercial farmers, while the USDA is representative of all farmers, both small and large. This difference is important. We intentionally target large commercial farmers, because we want our survey to be indicative of the future of agriculture. Given farm consolidation trends, we over-sample farms that are larger-than-average to get a view of what may be in store in the future. Larger farmers are likely to use data technologies on their farms at a much higher rate. Questions that bankers will need to answer are, “What is the cost of this technology for the farm, and what is the benefit?” These technologies are not cheap, but it is clear that large farms see a benefit to adoption of these technologies. In a follow-up survey question, we asked farmers the top benefits of using these technologies. Farmers
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