Securing Soybean Yields from the Bottom Up

As climate change continues to impact weather events, farmers and agricultural researchers are facing the challenge of declining and variable crop productivity. One of the main goals of my Ph.D. research at Purdue University is to better understand how plants respond to changing environments by exploring the interaction between plant genotypes and the environments in which plants are grown. I use tools such as crop models and remote sensing to understand these interactions. The ultimate goal is to help plant breeders select the best crop varieties for specific environments.

I am focusing on soybeans, a major crop, particularly in the Midwest, where it is often called the “Queen of the Midwest.” Soybeans are beneficial in terms of protein, oil, animal food and biofuel production and are a major source of farm income. According to the USDA agricultural statistics, high productivity and extensive planting area make soybean a major crop in Midwest’s agricultural economy, contributing billions of dollars in revenue annually. I am particularly interested in comparing wild soybean varieties with varieties that have been developed over time by plant breeders to get a picture of what traits might be further developed.

Some of my research includes collecting information not only on above-ground traits, such as plant height and leaf area, but also below-ground traits such as the root length and volume of roots and number of nodules. Roots are often neglected in research studies due to their complex and hidden nature. However, they play the most crucial role in getting water and nutrients from the soil. By utilizing 2D and 3D image analysis techniques, we can get detailed data on root traits including surface area and branching patterns. We can see how, for example, soybeans grown in drought prone areas often develop longer roots to access water from deeper in the soil. This is one simple example of an adaptation mechanisms that varies depending on environment and variety of the soybean.

While snapshots at one point in time of plants both above and below-ground can be informative, these are not enough to capture the broader picture. To truly understand how different plants perform in different environments we need to collect data across time. For this reason, I am conducting field experiments where soybeans are grown in different growth stages and different environments with different soil types and climate. The data collection is carried out in the vegetative, reproductive and maturity stage.

In addition to root traits, we also collect a number of other physical measurements through handheld sensors and remote sensing technologies. This information is used in crop computer models. These are very powerful tools that can simulate the growth of the plants based on current and past data. They act something like “fortune tellers” for the plants, allowing us to estimate how crops could grow in different timepoints and environments. These predictions help plant breeders to make informed decision about which varieties to choose in a specific environment.

It’s not just the research that matters, but how we disseminate our findings to relevant stakeholders, including farmers. The FFAR Fellows program has been incredibly valuable in this aspect of my research, providing me with opportunities to learn how to effectively communicate my findings while also enhancing other professional skills such as collaborating productively with teammates. I am extremely grateful to the FFAR Fellows program, its director Dr. Rebecca Dunning, Purdue University, my advisor Dr. Diane Wang and all my industry and academic mentors: Dr. Laura Potter (Corteva) Dr. Jose Tovar (Bayer, St. Louis), Dr. James Camberato (Purdue, IN), Dr. Hans Schepers (Bayer, Netherlands) and Dr. Jeremie Lecoeur (Syngenta, Switzerland) for their wonderful guidance and support.