Submitted by Timothy Bowles on
Crop rotation is a fundamental practice of sustainable agricultural systems and a key management practice for healthy soils. Yet recent analyses at the county level show declines in rotational complexity across the central U.S. as agricultural policy and economics push farmers toward more frequent corn cropping.
In this project, we use large-scale, high resolution spatial analyses to explore spatial patterns of crop rotational complexity . We examine field-level rotational complexity and its drivers in the central U.S., including inherent soil quality, distance to grain elevators, distance to biofuel plants, and precipitation. Having used remotely sensed data from the USDA to make a metric of crop rotational complexity, we compare that rotational complexity to other publically available datasets showing drivers that may influence farmers’ management decisions about rotating their crops.
Up to this point, much of the discussion of soil health in federal policy has revolved around defining soil health metrics. We ultimately hope to shift conversations from defining indicators to tackling the structural factors that undermine farmers’ ability to apply all principles of soil health management.
An undergraduate student will be involved in gathering, cleaning, managing, and analyzing data from large spatial datasets. The student will assist in creating new rasters of rotational complexity, aggregating these and other datasets to a field level, and running spatial regressions on the resulting datasets. The student will also make figures (mostly maps) that communicate these spatial findings. All analysis will be done in R. If interested, the student is encouraged to bring in new datasets and design/answer research questions of personal interest.
This project is an excellent opportunity for students interested in gaining experience in large-scale spatial analysis in R, data management, and science communication within agricultural systems.
Day-to-day supervisor for this project: Yvonne Socolar, Graduate Student (yvonne.socolar@berkeley.edu)
- Completed coursework in data science and/or statistics
- Familiarity with R
- Have interest in agricultural practices and/or soil health
- Maintain a 3.0 GPA or higher
- Be able to work independently and to summarize and communicate findings clearly
- Have experience with spatial data (preferred, but not required)