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In 2025, when China allowed export registrations for over a thousand U.S. meat plants to lapse, it sent a jolt through the nation’s agricultural economy. Billions of dollars in trade were suddenly at risk. It was a stark reminder that global food markets can shift in just weeks – from a diplomatic delay, a new tariff, or an outbreak of avian influenza.
'U.S. producers already use precision tools in the field to analyze soil, optimize fertilizer, and track genetics. Our research brings that same precision to international trade, helping agriculture anticipate demand and respond to shifts.'
In a world where a single disease or policy change can ripple through livestock prices across continents, anticipating change has become increasingly crucial.
Conventional forecasting tools, based on simplified economic assumptions and annual data, cannot accommodate these shocks. They explain what happened yesterday but offer little guidance for tomorrow. That is where artificial intelligence (AI) becomes transformative. AI models can capture complex patterns in millions of data points that human analysts might otherwise miss.
Our project, Next Generation Trade Intelligence for U.S. Beef, Pork, and Poultry Producers, led by Modhurima Dey Amin, Syed Badruddoza, and Davis College associate dean for strategic initiatives and assessment, Darren Hudson at Texas Tech, uses AI to forecast international trade and give U.S. producers and policymakers clearer foresight into shifting global demand.
Using more than three decades of monthly trade records, our team is building models that learn how global markets behave under varying conditions — income growth, infrastructure, currency shifts, policy changes, or public health events.
U.S. producers already use precision tools in the field to analyze soil, optimize fertilizer, and track genetics. Our research brings that same precision to international trade, helping agriculture anticipate demand and respond to tariffs, disease or political shifts.
Think of it as a recommendation engine: algorithms suggest which countries are most likely to increase U.S. meat imports based on past behavior and socioeconomic factors. Early results are promising; AI models have reduced forecast errors by 16% to 32% and uncovered “unrealized markets” where exports lag conventional predictions.
We are also developing an interactive global map projecting U.S. meat trade in 2035, categorizing countries by growth, risk and untapped opportunity. These insights can guide trade negotiations, strategic investments, and help producers and federal agencies make informed, data-driven decisions in a volatile global market.
CONTACT: Mary Moenning, Director of Strategic Marketing & Communications, Davis College of Agricultural Sciences & Natural Resources, Texas Tech University at (806) 742-2808 or Mary.Moenning@ttu.edu
0612NM26 | Interested in learning more about Amin’s and Badruddoza’s research? Listen to the episode on the Deep Roots Podcast, available on the Deep Roots website, Spotify, Apple, and YouTube