A growing global population drives the need to increase agronomic output using less land and agricultural inputs. At the same time, increases in climate variability require crops of the future be more resilient. Integrated research and development efforts that seek to address these needs involve expertise in engineering to develop novel sensing devices that can measure environmental parameters and plant traits; genetics to identify causal genes; and data sciences to predict genetic combinations that will result in crop improvement.

The Predictive Plant Phenomics (P3) NSF Research Traineeship (NRT) created an integrated interdisciplinary training program to fill this gap. The P3 NRT directly trained a diverse group of 30 scientists and engineers to address complex problems in modern agriculture. These ?T-shaped? students differ from most students in STEM graduate programs that produce students with deep disciplinary knowledge in a limited area. This depth represents the vertical bar of the "T". The horizontal bar of the ?T? represents their ability to effectively collaborate across a variety of different disciplines.

The plant phenomics degree specialization draws students from various departments and majors across the campus and involves faculty members working in the Colleges of Agriculture and Life Sciences, Liberal Arts and Sciences, and Engineering. The graduate specialization requires students to complete a set of focused course requirements as part of their existing Ph.D. Program known as the 3-2-1 program. The trainees take 3 courses in their main discipline, 2 in a second and 1 in the third. For example, a student make take three courses in plant breeding, two in data science and statistics, and one in engineering such as image processing. This structure was designed to fit within existing departmental guidelines for electives and minimize any requirements for additional coursework.

A learner-centered training program was created that included: 1) a two-week long immersive Boot Camp to build camaraderie and provide foundational technical and professional skills; 2) a core leveling course in the fundamentals of plant phenomics ranging from plant biology to data science to engineering sensor systems; 3) a graduate learning community; 4) research rotations in labs across different disciplines; 5) team mentoring on interdisciplinary team research projects; 6) support for trainees to present at research conferences; and 7) support for independent research projects in plant phenomics.

Four cohorts of students have now completed two years of training in the graduate program in plant phenomics. The internal evaluation focused on metrics such as student recruitment and retention, program outcomes, and student performance as students take classes in diverse areas.  External evaluation provided quantitative assessments of how well the program developed scientists and engineers with broad skillsets to address the research needs to increase understanding of crop plant and agricultural production.

Evaluations demonstrated increased technical and professional skills for trainees entering research and research-related careers focusing on crop plants and agricultural production: The graduates increased technical skills through Boot Camp training in computational environments, coursework in all three disciplines, unmanned aerial vehicle (UAV) training, and hands-on work in the Fundamentals of Predictive Plant Phenomics course and lab. The most recent cohort used new sensor boxes to perform collaborative work in the course. Students have used technical skills outside their main discipline in their dissertation research.

In addition to broad technical training, the trainees gained competence in key areas including communication and industrial application of concepts/skills:  Communication training occurred in multiple venues (i.e., Boot Camp, presentation practices and workshops in Learning Community meetings, and in the core course). The trainees in all cohorts perceive themselves as highly capable of pursuing careers and applying skills in industrial settings.

Institutional impacts include integration of P3?s novel training activities into courses across the campus, a distance learning version of the P3 core course, and wide dissemination of courses for data analysis and machine learning.

Last Modified: 02/15/2022
Modified by: Julie A Dickerson