Date of Graduation

Spring 2023


Master of Science in Plant Science (Agriculture)


College of Agriculture

Committee Chair

Michael Goerndt


Silvopasture systems are becoming increasingly popular among sustainable agriculture ranchers, due to the increase in knowledge of benefits to the cattle and ability to grow cool season grasses beneath the canopy. This project focuses on the forest crop aspect of silvopasture systems from monitoring of the health of the trees over time to recommendations for thinning management to keep it functioning as viable silvopasture. The study site consists of five acres of upland hardwood forest area in Southern Missouri with 18 monumented fixed area plots. Arial and ground data was collected at each plot throughout the growing season, along with data from a weather station located in the stand. Multispectral indices and cover percentages were extracted from aerial data through Metashape and ArcGIS processing. The data was used to develop prediction models for chlorophyll and water potential as well as canopy cover percentages. An additional analysis was conducted, comparing data from our drone operations with publicly accessible data through Landsat and local weather stations. It was determined that with multiple years of data collected from the same month, strong prediction models are achievable for all three of the variables in question. While ground metrics do improve the models, they can be removed when modeling over multiple years, with little decrease in model predictive power. Climate metrics had little influence on any of the models and were therefore not used for most final predictions. Similarly, Landsat multispectral indices are also able to predict strong models without the inclusion of weather station or ground metrics.


silvopasture, ArcGIS, Landsat, prediction models, multispectral, R Studio

Subject Categories

Agricultural Science | Applied Statistics | Environmental Sciences | Forest Biology | Forest Management | Multivariate Analysis | Other Forestry and Forest Sciences | Statistical Models


© Bailee N. Suedmeyer

Open Access