Congratulations to our Hatch Grant Award Winners!
Development of a High-Resolution Weather Forecast Database for Digital Agricultural Research and Outreach Applications
Arthur DeGaetano, Professor, CALS, Earth and Atmospheric Sciences (EAS); and Madeleine Udell, Assistant Professor, CIS, Statistics and Data Science (DSDS).
The project will assess weather forecasting capabilities by deploying a forecasting model for the Finger Lakes region, and explore and implement more efficient data storage formats and retrieval algorithms. More accurate forecast information promises to maximize growers’ ability to use existing and future agricultural tools and give them more lead time to proactively manage their operations.
E-Synch: A Tool to Automate and Optimize Cattle Reproductive Management
David Erickson, Professor, College of Engineering (COE), Mechanical and Aerospace Engineering (MAE); and Julio Giordano, Associate Professor, CALS, Animal Science (ANSC).
E-synch, or electronic synchronization, is a project aimed at developing a device to automate synchronization of ovulation and monitor the physiology of cows before insemination. A prototype device and controlling software will be created to increase farm sustainability by reducing labor needs and improving cow welfare through better reproductive performance and reduced cow manipulation.
Improving Vineyard Management Using Touch-Sensitive Soft Robots
Kirstin Petersen, Assistant Professor, COE, Electrical and Computer Engineering (ECE); and Justine Vanden Heuvel, Associate Professor, CALS, SIPS.
This project will apply inexpensive robots that can touch, sense and manipulate fragile agricultural products to collect data on grape yield and quality. The researchers envision a series of inexpensive platforms in the vineyard that will communicate to a base station, providing the grower with real-time cluster data to facilitate decisions about canopy management, crop control, potentially pest management, harvest and streaming in the winery.
A New Foundation for Digital Irrigation Scheduling in Apple Orchards
Lailiang Cheng, Professor, CALS, SIPS; Alan Lakso, Professor Emeritus, CALS, SIPS; and Abraham Stroock, ’95, Professor, COE, Chemical and Biomolecular Engineering (CBE).
This project will develop a framework of measurements as well as water-use models for use in apple orchards to provide precise feedback control of irrigation and water stress in orchards. One goal of this project is to translate findings into commercial practice, giving growers access to such data for their apples and other high-value horticultural crops.
The government publishes a variety of remotely sensed (satellite) data, but these are not widely used in operational policy contexts in agriculture. Given the continued difficulty of collecting and processing farm level production, conservation, and economic information for policy analysis, and there is increased interest in using remotely sensed data to drive policy designs. This project will integrate remotely sensed data with process-based models using adaptive optimization and machine learning to identify conservation practice use in agriculture (e.g., the use of cover crops) and design new policies.