Why an Intelligent Future for Farm Policy Matters

On his way out the door as U.S. Secretary of Health and Human services years ago, Tommy Thompson made a rather shocking admission. “For the life of me,” he said, “I cannot understand why the terrorists have not attacked our food supply because it is so easy to do.”

 

It was an uncomfortable truth when he said it in 2004, and it’s even more disturbing that it’s still true today. Given the importance of the nation’s food supply, it only makes sense to have an effective early warning system to detect threats.

 

Perhaps it was not so easy to build such a system sixteen years ago when artificial intelligence systems were less advanced. Humans are terrible at dealing with the tedium of monitoring the endless streams of data you’d have to watch around the clock for such a system to function. Fortunately, “watching grass grow” is exactly what tireless AI algorithms are made to do. They can detect the subtle anomalies and provide an alert so that a human can determine whether or not to do something about it.

 

Most often, the system wouldn’t be detecting the activities of Bond villains and terror masterminds. Rather, they’d see threats that are just as destructive. The spread of invasive species and disease costs our industry billions.[1] But imagine what would happen if we could identify these threats well before they had a chance to spread. Surely Florida growers would have celebrated a well-timed quarantine that would have stopped the plague of citrus greening in its tracks.

 

On top of the value of an early warning system, such a system would provide accurate, up-to-the-minute information on supply, allowing farmers to better plan for the next harvest. Policymakers could also evaluate where their regulations are helping or hurting. In short, they’d be able to make better decisions based on better information.

For such a system to work, a few things are needed. First, farmers need to use growing tools that gather the data needed to provide actionable intelligence to the grower — like IntelinAir’s AgMRI.

 

USDA would also have to be in a position to use the data. USDA aspires to a data-driven decision-making model,[2] but they’re still thinking small. Like most federal agencies,[3] USDA has been struggling with modernizing its multibillion-dollar infrastructure for more than a quarter-century.[4]

 

Including mandatory programs, the U.S. Department of Agriculture will spend $150 billion this year.[5] Shouldn’t a fraction of this money be invested in technologies that can increase yields and boost our GDP while enhancing sustainability?

 

Our next installment will take a closer look at what those investments might look like.

 

 


[1] https://doi.org/10.1016/j.ecolecon.2004.10.002

[2] https://www.ocio.usda.gov/strategic-plan

[3] https://federalnewsnetwork.com/reporters-notebook-jason-miller/2019/12/gaos-report-on-the-technology-modernization-fund-puts-omb-on-the-offensive/

[4] https://www.gao.gov/archive/1997/ai97090t.pdf

https://www.gao.gov/products/AIMD-98-168
[5] https://www.obpa.usda.gov/budsum/fy2020budsum.pdf


About Al:

Al is passionate about leveraging data to solve problems. At IntelinAir, that means feeding a growing population, driving efficiency and improving grower profitability. Before IntelinAir, Al co-founded IconApps, Integrien Corporation, and CreationPoint Systems—all with successful exits, and held leadership roles at LowerMyBills and Minebea.

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