Data Standards in Agrifood Systems Help Create the World You Want to Live in R. Andres Ferreyra tandards are important. If every company used its The lack of common standards for data formats, code own proprietary sizes of nuts and bolts when lists, and so forth makes interoperability at scale very diffi-designing machinery, then manufacturing would be cult in this context. This may be the natural result of industry hugely complicated, and repairing equipment standardization efforts that have grown from the bottom up, would be next to impossible. Even replacing a lost lug nut along with the industry itself. might be prohibitively expensive. Fortunately, ag and bio Gathering momentum: TC 347 engineering has plenty of examples of successful standardi-Standardization efforts have been underway for some time zation of mechanical and electrical elements, such as the in various segments of the agrifood industry (e.g., agribusi-three-point hitch, hydraulic couplers, safety standards, and ness, machinery, dairy). Here are three examples of nonprofit many more. organizations that serve the data needs of the industry: Recently, more attention has been paid to the topic of • AgGateway is dedicated to implementing (and develop-data standards. Farmers, retailers, advisors, and other actors ing, when necessary) data standards in agriculture, in the agrifood industry exchange increasing amounts of data emphasizing both supply chain and field operations data. as they conduct business. As the world’s food, feed, fiber, and fuel production systems strain under multiple pressures, such as climate change, supply chain disruptions, and political instability, using these data to drive principled, multi-objective (e.g., profitability, sustainability, regulatory compliance) decisions is more impor-tant than ever. The idea of harnessing “big data” revives the promise, long associated with precision agriculture, of optimiz-ing production in agrifood systems. There are difficulties, though, because big data has such great variety. For example, data sources are very hetero-geneous, different vendors use their own schemas and API query patterns when delivering data over the web, and they use their own names for variables and their own code lists for categorical An example of data flows among actors in the agrifood industry. This limited example variables such as crops and active does not include renderers, or data flows between agricultural producers, animal feed mills, and retailers. Note how small producers are shown apart from the system. While ingredients. they would benefit from more interaction with the supply chains, small producers S usually have barriers to participation, such as not having good mobile data plans. 4 May/June 2024 RESOURCE