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Data as Agriculture’s New Currency: Advancing to the Next Level

May 29, 2017

Editor’s Note: Joseph Byrum is senior R&D and strategic marketing executive in Life Sciences – Global Product Development, Innovation, and Delivery at Syngenta. This last in a three-part series explores the use of data by non-farming organizations and the need to improve data collection for the benefit of the whole agriculture industry.

Government and private economists around the globe keep close tabs on estimates for the yields of internationally-traded crops such as corn, oats, soybean, rice and wheat. The US Department of Agriculture, for example, publishes monthly reports and forecasts that keep the markets up-to-date on the progress of these commodities throughout the season.

As with all commodities, prices rise and fall with the global level of supply and demand. That makes accurate and timely information about crop supplies extremely valuable.

When it comes to agricultural commodities, forecasting supply levels is a bit trickier than it is with commonly traded energy commodities like crude oil or precious and industrial metals. Tapping into proven oil reserves or extracting copper and silver from a mine is more straightforward than planting, growing and harvesting something that’s living. Weather conditions, disease outbreaks, insect infestations and farming techniques all have an impact on plant yield.

Growers keep a close eye on current commodity prices, the price of inputs and outputs, and the environmental conditions that affect crop development. They adjust the decisions they make on the farm based on this information — whether they can turn a profit or not, for example. These decisions, in turn, impact the global supply of that commodity.

Agricultural production data measure the supply of various agricultural commodities throughout the various stages of plant growth. Pre-planting estimates use the prior season’s harvest, field observations, and surveys to track what farmers intend to plant. Crop emergence estimates track the plants that have survived several weeks after planting. Crop condition estimates rate the local environment’s suitability for growing from “very poor” to “excellent.” Post-harvest estimates track the number of crops that have reached the final phase in the supply chain — information that comes from surveys of farmers as well as from the millers, traders and others who process or resell the final product of the harvest.

Information from thousands of farmers in each country is gathered and processed on a regular schedule, creating data that have value not just for those growers, but likely for every player in agriculture. Farmers might look at the numbers and weigh mid-season risks that could affect the final harvest. Seed producers will have an obvious interest in monitoring how well their varieties perform against the competition given the weather scenarios in each geographic location. Downstream partners will want to know how much of a given crop moves from production areas to central trading locations. Academic and industry researchers will want to monitor the geographic and environmental trends affecting crops. Governments will be primarily interested in bigger picture issues, such as stockpile levels and how they affect the balance of trade with other nations.

So there’s no question that data are valuable, but as it stands, agricultural data lack the systemization and validation needed to become a finite commodity with the ability to function as a currency.

Raw farm data have limited value. As with a barrel of crude oil, the true value comes in the refinement. Various scientific studies[ have identified the problem of “poor quality” or the “coarse-scale” of agricultural data. So how do we fix that problem?

One example of how we can do better would be to move toward faster data capture, using real-time information technology systems rather than regular surveys. Currently, the USDA asks farmers to fill out questionnaires on Friday or early Monday morning so that they can send the documents to the nearest field office via “mail, telephone, fax, e-mail, or through a secured internet website.” Legacy methods of communication will have to go if we want to see a faster, more accurate flow of information. Sharing data from remote sensors in real time should be the goal.

Better data collection will help everyone involved in agriculture, but farmers have reason to be skeptical about broadly releasing vital data from their farms, which they might see as offering greater benefit to the rest of the value chain than to the individual famers themselves. The industry needs to commit to data privacy, accuracy and ownership principles.

It’s also essential for industry to involve farmers in the process of improving data collection so that they will see a benefit to participation in the flow of information. With more refined data, they’ll see better products, receive better advice and know with greater certainty how their crops will perform. Refined data can be used by farmers to make better decisions about growing, and those better decisions will lead to better harvests and, ultimately, more profit. Data are the most potent weapon against the risk and uncertainty inherent in farming.

Open data initiatives are also working on standards that will facilitate the flow of data and encourage transparency among industry players. This is exactly what’s needed to broadly enhance product innovation and transform data into a commodity.

It’s in everyone’s best interests to work toward the collection and processing of timely and accurate, commoditized agricultural data that will drive the industry toward greater success at every level. As the foundation of the industry, ensuring the farmer’s needs are met is essential to harnessing data’s potential as the next big currency.

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