Date of Award
12-2022
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Division of Agriculture (SAFES)
Committee Chair/Advisor
Dr. Bulent Koc
Committee Member
Dr. Matias Aguerre
Committee Member
Dr. Aaron Turner
Committee Member
Dr. John P. Chastain
Committee Member
Dr. Joseph Bible
Abstract
A large increase in productivity could be realized by small increases in efficiency and utilization on grasslands due to their large area. Pre-harvest biomass estimations can help forage producers make better informed management decisions. Producers of dry hay and hay silage can better identify plant growth stages and the optimal time to harvest. Grazers can better determine the most efficient number of animals and stocking density. The goal of this study was to create a novel pre-harvest biomass estimation method utilizing compressed height combined with an unmanned ground vehicle (UGV). A compression plate named a “compression ski” was constructed and mounted on an UGV to take continuous compressed height measurements. Compression ski measurements were made before and after harvest to analyze change in compressed height (ΔCH) as an alternative to pre-harvest compressed heights (CH1) alone. A rising plate meter (RPM) was used to make discrete compressed height measurements and calibrated for the same trial plots. The methods were tested on trial plots of tall fescue (Schedonorus phoenix) and alfalfa (Medicago sativa) which were measured and harvested at approximately 10, 20, and 30-day intervals. Compressed height indicators were correlated with wet yield (kg/ha) and estimation models were made. Methods of estimating dry matter fraction (DMF) were created independently. Wet yield estimation models were combined with DMF estimations to create dry matter yield (DM yield) prediction models. The best compression ski DM yield prediction models had standard errors of 291 kg DM/ha for tall fescue and 491 kg DM/ha for alfalfa.
Recommended Citation
Erwin, Curtis, "Unmanned Ground Vehicle Proximal Sensing for Forage Biomass Production Estimations" (2022). All Theses. 3916.
https://tigerprints.clemson.edu/all_theses/3916