How will Big Data influence U.S. ag?

New report from agricultural economists looks at growing needs and changes that may come with increased use of precision agriculture data.

Precision agriculture — a suite of information technologies used as management tools in agricultural production — has already advanced and will continue to change farm management, from the way farmers consider their commodity mix, scout fields and purchase inputs to how they apply conservation techniques and even how they price their crops and evaluate the long-run size of their operations. Major investments are being made to capture and use Big Data analytics in the agricultural sector. The future of agriculture depends on the adoption of new field technologies that facilitate the gathering of data.

The Council on Food, Agricultural & Resource Economics, a national nonprofit organization that channels information from the agricultural economics profession to policy-makers and the public, has released a report on Big Ag data. The paper adds context to the growth trajectory of Big Ag data technologies, identifies policy and science questions and reviews the limitations of, and opportunities for, greater use of Big Ag Data and Big Ag data analytics.

It remains to be seen how precision agriculture and Big Data technologies will influence the structure of U.S. agriculture. The report noted that, historically, the adoption of new technologies has led to increasing farm size. This may be the case as larger farmers are often early adopters of technology, in part because of their economies of scale and greater access to capital.

However, as Big Data information technologies evolve, they may be recognized as scale-neutral technologies, the report said, adding, “Midsized farms often depend on their off-farm income to sustain them, and efficiencies offered by Big Data may very well afford them extra time to work off the farm.”

Farm management is now being changed by a variety of software and novel decision-making tools utilizing Big Data analytics as well as the speed at which those decisions can be made. The prevalence of spatially referenced data and precision equipment allows more granular management and optimization. “It is anticipated that this area of research will rapidly expand across the farm economy,” the report explains.

The adoption of new data-driven technologies, which are increasingly designed to interact with Big Data analytics, is likely to play a larger role in farmers’ efforts to conserve resources while maximizing net returns.

The report identifies that the widespread use of reliable sensor data can contribute to improved field-level nutrient management and reduced pesticide leaching and runoff. Analysis of data from “smart” irrigation systems can play a crucial role in management strategies designed to conserve irrigation water in drought-prone areas and regions with declining groundwater recharge rates. The use of global positioning system-assisted navigation can contribute to decreases in on-farm energy use, reducing agriculture’s carbon footprint.

Moreover, greater use of “smart” grid technologies or substitution of renewable energy (e.g., solar and wind power) can result in substantial farm energy savings. Similarly, the digitization of farm records and automated data uploads may help confined animal feeding operations achieve implementation of their nutrient management plans. Maintenance of digital records and use of technologies can also assist farmers in more rapidly completing external sustainability certifications.

The report suggests that significant progress will be made in developing more sophisticated nutrient, irrigation and environmental management, farm management approach validation and mechanisms to certify sustainability practices as demanded by changes in private markets.

“Many farmers feel a combination of threatened, empowered and powerless when it comes to their role in producing Big Data,” the report said. Some farmers feel that they can directly profit from selling data generated on their farms to potential aggregators, while others do not hesitate to pay data services to store data from their farms. “Given the wide spectrum of opinions of farmers, a need exists to provide basic education on the risks and rewards of participating in community data systems,” the added.

View the executive summary of the paper here or the full report here.

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