Foragecaster is 4-year research project with leading-edge industry and academia working on creating an accurate livestock supply forecaster using the best of breed domain models with machine learning. As a part of the project, I designed research-driven interfaces that translated complex data into practical, user-friendly tools for farmers. By conducting user interviews and collaborating with research teams, I ensured that advanced models were acessible and actionable for effective farm managament.

In agriculture, accurately predicting pasture growth is essential for managing livestock and ensuring sustainable land use. Farmers need reliable insights into forage availability to make timely decisions on grazing, feed supplements, and herd management.

Without accurate predictions, they risk overgrazing, underfeeding, or having to deal with feed shortages, all of which impact farm productivity and profitability. The challenge lies in the complexity of modeling factors such as weather variability, soil conditions, and plant growth stages, which all influence pasture growth.

In this project, my role involved research and the design of the ForageCaster interface. I was responsible for creating a responsive, intuitive interface that would effectively communicate complex data in a clear and accessible way.

This involved designing interactive graphs to display predictive growth rates and uncertainty intervals, as well as integrating user interactions for filtering and customizing data views. Throughout the project, I collaborated closely with farmers over interviews to ensure the interface met user needs, iterated based on feedback, and tested the tool to ensure accuracy and usability.

By integrating weight gain and pasture growth visuals into the grazing planning flow, ForageCaster provides users with a complete toolkit for managing livestock and pasture health.

These interactive elements allow users to make data-driven grazing decisions, aligning animal growth with pasture availability to optimize land use. This integration empowers users to anticipate outcomes, adjust grazing strategies, and ultimately achieve a balanced, sustainable approach to livestock management.