The overall purpose of this project is to improve breeding of plant crops by using modern remote sensing and high throughput phenotyping methods to gather important information about plant traits. We collect high resolution phenomic data and make these publicly available, which can be combined with weather and genomic data to produce insights about plant structure and function. We also aim to provide the tools and training needed to effectively leverage TERRA REF data. Visit terraref.org for more information.
The Predictive Ecosystem Analyzer (PEcAn) is a framework for running ecosystem models. Our mission is to "Develop and promote accessible tools for reproducible ecosystem modeling and forecasting". It provides a workflow and standard inputs and outputs to support modeling, as well as a Bayesian approach to integrate information contained in biophysical models and data across multiple sources and scales.
We are currently using PEcAn to predict the physiology, growth, and ecology of genetically modified crops before they can be grown in field trials. Previously, we have used PEcAn to predict the productivity and yield stability of bioenergy crops at regional to global scales.
- LeBauer DS, Wang D, Richter KT, Davidson CC, Dietze MC. Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs. 2013 May;83(2):133-54. (web) (pdf)
- Dietze MC, LeBauer DS, Kooper RO. On improving the communication between models and data. Plant, Cell & Environment. 2013 Sep;36(9):1575-85. (web) (pdf)
The goals motivating the drone pipeline are (in no particular order):
- common processing: provide components that are reusable in multiple environments
- dynamic work flows: mix common and unique processing components to create meaningful processing pipelines
- scalable work flows: use scalable architecture as needed, in the right places, to return results faster
The drone pipeline effort is derived from the larger TERRA REF project, and we are working to adapt these tools to facilitate the use of drones and other platforms for agricultural research. Additional code and information can be found on our project page.
The purpose of the Sentinel project is to design plants to sense and detect biological compounds. We are using a grass species that will have varying traits, including height and fluorescence, in response to exposure to certain compounds.