An A-maize-ing Internship

About the Program

The Data Driven Agriculture Research Experience and Extension for Undergraduates (REEU) is a 9-week summer internship funded by the USDA. In 2023, I was accepted into this program along with nine other students. This program was unique in that it taught participants about plant, data, and computer sciences, and helped participants gain career development experience. Over the course of the program, I got to participate in classroom learning, research activities, and field trips.

The program was broken up into three major segments:

• The first few weeks consisted of gathering data and learning about statistics. Each student had a set of 20 maize plants to grow, monitor, and measure. After a certain level of plant growth was reached, each student terminated their samples and collected final measurements.
• Over the next few weeks, we began our analyses. I helped compile and clean the data to ensure that it was accessible to everyone.
• During the last few weeks of the internship, we broke into subgroups to finalize our research and prepare for our final presentation. Once the data was compiled, we began coding in R to see what conclusions we could draw from analyzing it. As my group’s sole computer science student, I was the main coder, but every member participated in coding and contributed equally to the project as a whole.
• On the last day of the internship, my partner and I presented our final poster at the 2023 UHM SURE Symposium. It was great to see how our cohort’s hard work paid off and culminated into something tangible.

Overall, this internship was a great experience for me. I learned a lot about many different subjects and tried things that were outside of my comfort zone. For me, this internship served as a great introduction to R programming, machine learning, and academic research. Along the way, I also developed my skills in teamwork, communication, presentation, problem solving, experimental design, and project management.

Project Description

Title: Corn conundrum: the differential growth of maize genotypes adapted to different geographic locations

Authors: Ethan S. Morrell*, Danielle Jaden K. Yamagata-Santos*, Abigail H. Ana, Joseph Carmelo M. Averion, Roma Amor B. Malasarte, Zeus Gean Paul Miguel, Amanda K. Nitta, Stephenie Andriana Santos, Kayla-Marie A. Torres, Keilah C. Wilkes, Rishi Prasadh, Michael Kantar, Tai Maaz, Michael Muszynski, Nhu Nguyen *contributed equally

Abstract:

Geographical location plays a crucial role in the growth and development of agricultural crops. Maize (Zea mays) is a staple crop that serves multiple uses, such as livestock feed and food production for human consumption. Thus, it is important to investigate how different maize genotypes perform when grown outside the location to which they were originally adapted. We seek to understand whether maize genotypes that are adapted to the same regions share similar shoot and root traits. Data for shoot and root traits were collected from a genetically diverse collection of maize genotypes and analyzed according to their region of adaptation. We will use an analysis of variance to explore phenotype differences and then employ machine learning techniques to classify genotypes by region of adaptation. We expect that maize genotypes that were originally adapted to the same regions will exhibit similar growth patterns and characteristics when grown in the same controlled setting. An understanding of the similarities and differences between maize genotypes from the same regions can be used to select traits of interest that may increase the ability of maize to succeed in different environmental conditions.

To see more, here is a link to the final poster.