FACULT Y SPOTLIGHT
Song Cui
Associate Professor, School of Agriculture
MTSU’s 2019 Distinguished Research Award Winner
Song Cui is a broadly trained agronomist with expertise in big data algorithms, crop physiology, forage production, simulation modeling, soil and boundary layer flux (CO2, water vapor, and greenhouse gases) measurements, and large-scale agroecosystem studies addressing issues such as water sustainability and climate change. Cui landed a three-year $714,000 grant, the largest ever to MTSU by the U.S. Department of Agriculture (USDA), for collaborative research with Texas A&M and Sam Houston State universities to discover novel ways of land management and solve important ecological problems. He earned his Ph.D. in Crop Science from Texas Tech University and a B.S. in Agricultural Science from Lanzhou University (China). You were the project leader on the 2016 USDA grant. Where has that agroecological research project taken you?
Historically, this kind of cross-state, multi-institutional agroecological project would typically take place at land-grant institutions. With the support from the USDA’s National Institute of Food and Agriculture, a group of researchers at MTSU was actually leading the team this 16 MTSU Magazine
time. We have quantified carbon footprint and hydrological dynamics from two major forage pasture ecosystems on the MTSU farm, we have evaluated the possibility of using unmanned aircraft systems (UAS) as a way of estimating forage yield and nutritive value, and several students in the School of Agriculture gained invaluable research experience and eventually went to graduate school. As a researcher, I used the large quantity of carbon flux data collected from this project and derived new methods and data analytical routines for processing carbon flux data, which extends the impacts of the project to the entire agroecological society. How do you see machine-learning technologies and precision agriculture techniques changing the future of agriculture around the world?
Precision agriculture (PA), a farming technology proposed in the early 1980s, utilizes high-resolution, remotesensing techniques to study crop and soil variations and to apply farm inputs at the right place, at the right time, and in the right amounts. Most recently, the PA concept has evolved into digital agriculture, which integrates the use of traditional PA approaches to collect, integrate, and transmit data into cloud-based decision-making
