“Why can’t these things just talk to each other?!?”
This has been a common complaint as agriculture has gone digital, and solving the underlying issues is more pressing than ever before. The sense of urgency exists because farming is getting harder. That’s one reason we need to support what’s being called “smart farming,” which is farming that
This idea of smart farming is really nothing new. It’s essentially what good farmers have been doing all along, but consolidation, tighter margins and a more restrictive regulatory context demand doing this accurately and efficiently at scale and documenting it.
Data collection is at the heart of smart farming, but not all data are created equal. Data scientists say that for things to work, the data must be findable, accessible, interoperable and reusable, or FAIR. This is currently next to impossible to do at scale in agriculture because we do not have the standards we need.
Standards are recipes for reality that are agreed upon and adopted by a broad community of practitioners. Standards are especially important for FAIR data. Things would talk to each other if we had standards for how to
But the standards landscape is incomplete and inconsistent. There are multiple standards organizations, there are gaps in the landscape that are not covered by any of them and sometimes there are competing standards to enable the same capability.
The International Organization for Standardization has a portfolio of more than 24,000 standards that simplify many aspects of human activity worldwide. Examples include ISO 9001 for quality management, ISO 14001 for environmental management, ISO 45001 for occupational safety and health, ISO/IEC 27001 for information security, and ISO 22000 for food safety.
ISO recognized the limitations in the agricultural standards landscape; as a result, in September 2021, they chartered a Strategic Advisory Group on Smart Farming. This group of more than 200 international experts is tasked with
An example of specific irrigation-themed data standards is ASABE/ANSI S632 aka Precision Agriculture Irrigation Language or PAIL (See “Implementing an irrigation data standard in the real world” in the Winter 2021 issue of Irrigation Today). This family of three standards emerged from a collaboration between the AgGateway industry consortium and the American Society for Agricultural and Biological Engineers, and it is in the pipeline to become a set of international ISO standards.
In the early days of PAIL’s development, the stakeholders recognized that if PAIL worked as intended, most growers would not even notice. The immediate benefits such as lower cost of development and easier integration would be felt by manufacturers and their partners. The years since then have uncovered some potential immediate benefits for growers, though, provided there is broad adoption across the irrigation industry.
One of those benefits is simplifying regulatory compliance. Some irrigation incentive programs require participants to document their practices. This documentation can be cumbersome for the grower to produce and for the regulators to consume. Farm management information systems software often has data export functions, but each FMIS differs on the format, the way the data are encoded and so forth. A PAIL-compliant FMIS could produce a PAIL file containing all the data needed to document irrigation practices and nothing more. The regulators could, if they are PAIL-friendly, consume these files in their own documentation system. This standard format could make regulatory compliance as simple as a click of a button.
They would also simplify onboarding a new consultant. Starting up with a new irrigation consultant usually requires giving the consultant relevant data about the farm. This data exchange is then followed by meetings where the consultant clarifies questions about the data the grower provided. If all of the information is available in the PAIL format, the consultant’s job is easier and the startup process is faster.
Finally, it can accelerate technology transfer. University researchers are constantly developing new irrigation management methods and refining existing ones. A problem is that much of the data needed by these algorithms is developed around scientific-grade data collection systems, not the kind found on most farm-focused sensing platforms. This creates an additional technical hurdle to transferring the algorithms to the industry. If academics can target the PAIL format, new algorithms will be more readily adoptable by industry and available to growers.
Standards apply to several other important topics that deserve your attention when setting up your smart farming solution, such as data security, quality and scalability. Refer to the Summer 2021 issue of Irrigation Today for the article “Beware of IoT’s hidden monsters” for more information on these critical topics. The Farm Bureau Federation of America, working with commodity groups and other stakeholders, established 10 data security and privacy Principles for Farm Data in 2014. The Data Ownership principle states:
“We believe farmers own information generated on their farming operations. However, it is the responsibility of the farmer to agree upon data use and sharing with the other stakeholders with an economic interest, such as the tenant, landowner, cooperative, owner of the precision agriculture system hardware, and/or ATP [agriculture technology provider], etc.”
This is the closest we currently are to having a broad standard for these matters in the United States, and it puts a burden on the grower to understand what they are getting when they make a technology purchase.
In the meantime, farmers have several options to make the most of data collection and security. When buying or contracting a solution, make sure you understand what the license and usage rights are. Make purchasing decisions based on whether you’ll be paying for FAIR data, and make sure you own the data that you pay for. Stay on the offensive with the interoperability, data security/privacy and scalability concerns. Nobody knows your farm or your needs better than you do, and your data are a huge asset for supporting that knowledge so you can execute full-blown smart farming at scale.