Theme 1: Rangeland/Grassland Ecology--Oral Sessions

Description

Globally, there is an urgent need of research-based technologies for small ruminant producers to benefit from rapidly growing market demands for ruminant meat and related food products from forage-based operations. However, effective forage-based animal production requires a rapid assessment, and monitoring of grazing management needs to adjust stocking rates and/or spatial animal distribution in a timely manner. Applicability of unmanned aerial vehicles (drones) for grazing management offers opportunities to rapidly estimate biomass build-ups, ground coverage, and monitor animal behaviour. The reliability of drone-based laser scanning (DLS) for monitoring warm-season grass responses to simulated grazing intensities was assessed at Virginia State University. Aerial DLS point-cloud data were collected from pure and mixed native warm-season grass stands at early- and mid-season post-harvest regrowth in early May and June, respectively, along with ground-based estimates of yield and ground cover. Animal behavioural responses to drone activity at different heights were also recorded. The aerial estimates from DLS point cloud characteristics were compared with matching ground-based measurements of forage biomass, sward heights, and ground cover. Discernible correlations between the point cloud-based estimates and actual measurements for forage biomass, ground cover and sward heights were observed. Goats showed no changes in activity due to drone flights at ≥15 m above ground but demonstrated curiosity to drone presence between 10 & 15 m. These preliminary results suggest reliable applicability of DLS for expedited assessment of plant and animal responses to grazing management.

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Enhanced Grazing Management Assessment Using Drone-Based Lidar Measurements

Globally, there is an urgent need of research-based technologies for small ruminant producers to benefit from rapidly growing market demands for ruminant meat and related food products from forage-based operations. However, effective forage-based animal production requires a rapid assessment, and monitoring of grazing management needs to adjust stocking rates and/or spatial animal distribution in a timely manner. Applicability of unmanned aerial vehicles (drones) for grazing management offers opportunities to rapidly estimate biomass build-ups, ground coverage, and monitor animal behaviour. The reliability of drone-based laser scanning (DLS) for monitoring warm-season grass responses to simulated grazing intensities was assessed at Virginia State University. Aerial DLS point-cloud data were collected from pure and mixed native warm-season grass stands at early- and mid-season post-harvest regrowth in early May and June, respectively, along with ground-based estimates of yield and ground cover. Animal behavioural responses to drone activity at different heights were also recorded. The aerial estimates from DLS point cloud characteristics were compared with matching ground-based measurements of forage biomass, sward heights, and ground cover. Discernible correlations between the point cloud-based estimates and actual measurements for forage biomass, ground cover and sward heights were observed. Goats showed no changes in activity due to drone flights at ≥15 m above ground but demonstrated curiosity to drone presence between 10 & 15 m. These preliminary results suggest reliable applicability of DLS for expedited assessment of plant and animal responses to grazing management.