For generations, nitrogen management has been a constant guessing game in agriculture. As one of a farmer’s most expensive and volatile inputs, the stakes are high: Under-fertilizing risks a hit to the bottom line at harvest, while
overfertilizing leads to wasted money and environmental concerns for the watershed. At CFAES, science is finding balance with technology to give farmers more certainty.
Luke Waltz, a graduate student in the CFAES Department of Food, Agricultural and Biological Engineering, is working to eliminate that uncertainty. By leveraging AI and Knowledge-Guided Machine Learning (KGML), Waltz is turning field-specific data into a precision science that respects a farmer’s finances and the health of Ohio’s soil.
Tailored research for the win
Traditional land-grant university recommendations are often based on statewide averages from decades of field trials. Waltz recognized that farmers are rightly skeptical of a “one-size-fits-all” number.
“The optimum rate for a given field can vary by more than 50 pounds from year to year based on weather,” Waltz explained. “Land-grant recommendations are based on averages, but biological and chemical interactions are different from field to field.”
His research moves beyond static charts. By using the Ohio Supercomputer Center and resources from ICICLE, an AI institute focused on digital agriculture, he is developing dynamic models. These systems combine process models, which understand the physics and chemistry of the soil, with the predictive power of AI to provide recommendations tailored to a specific field and year.
Source : osu.edu