Using Digital Tools to Enhance Farmer-Led On-Farm Experimentation

The purpose of this project is to leverage digital tools (proximal sensors, GIS, remote sensing, big data, etc.) to enhance the on-farm experimentation process for growers. Most farmers experiment on their farms, with or without input from scientists, but the results often rely on limited context and can carry large uncertainty. Our aim is to add contextual data and a causal framework to experiments of the farmers' own design. We will recruit several NY farmers using a survey which asks about their goals and expected outcomes. From these results, sampling plans will be designed and implemented throughout the season. Field data will be combined with additional sources (weather, soil, remote sensing, etc.) to provide insights as to why the experiment was successful or not. Each grower will be provided with an individualized report to summarize the findings.

Roles and responsibilities 

The student intern will be expected to participate in field work throughout the season. This will include soil samples, biomass harvest, and the use of proximal sensors and precision GPS. Students should be comfortable using basic tools (e.g. shovels, pruners) as well as more sophisticated ones (e.g. spectrometer, soil sensors) in the field. The student will also assist in processing data and generating figures for the reports which will be given to the growers. If the student is available early enough, they will have the opportunity to join the interviews with the farmers where the team gathers information to support the experiments.
 

Qualifications and previous coursework

This opportunity is available to non-graduating students in Cornell University's College of Agriculture and Life Sciences.

• Coursework in field crop systems and soil science are beneficial
• Some programming experience would also be helpful
   

Learning outcomes 

The student intern will gain experience in field scouting and sample collection with a variety of tools, such as spectrometers, soil moisture sensors, and soil EC meters. The student will also learn how to use several digital tools to provide additional context to the collected data, including remotes sensing imagery, GIS software, and machine learning models. The student will have the opportunity to participate in the entire process, from sample plan all the way to completed report.