transdisciplinary graduate training in predictive plant phenomics

Clicks: 224
ID: 164735
2018
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Novel methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. It is becoming feasible to use modern sensors and data analysis techniques for predicting plant growth and productivity based on genomic, phenotypic, and environmental data. To design and construct crops that deliver desired traits requires trained personnel with scientific and engineering expertise as well as a variety of “soft” skills. To address these needs at Iowa State University, we developed a graduate specialization called “Predictive Plant Phenomics” (P3). Although some of our experiences may be unique, many of the specialization’s principles are likely to be broadly applicable to others interested in developing graduate training programs in plant phenomics. P3 involves transdisciplinary training and activities designed to develop communication, teambuilding, and management skills. To support students in this demanding and unique intellectual environment, we established a two-week boot camp before their first semester and founded a community of practice to support students throughout their graduate careers. Assessments show that P3 students understand the transdisciplinary training concepts, have formed a beneficial and supportive community, and interact with diverse faculty outside of their home departments. To learn more about the P3 program, visit www.predictivephenomicsinplants.iastate.edu.
Reference Key
lawrence-dill2018agronomytransdisciplinary Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Carolyn J. Lawrence-Dill;Theodore J. Heindel;Patrick S. Schnable;Stephanie J. Strong;Jill Wittrock;Mary E. Losch;Julie A. Dickerson
Journal drinking water engineering and science
Year 2018
DOI
10.3390/agronomy8050073
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.