PSX-23 Use of unmanned aerial vehicle technologies to assess nutrient landscape of cattle pens managed with energy dense vs. low energy feeding programs.

An experiment was conducted to determine if feedlot pen conditions can be detected using unmanned aerial vehicle (UAV) technologies. Steers fed an energy-dense limit-fed diet or low-energy ad-libitum-fed diet were used to create a gradient in pen nutrient landscapes. The objectives were (1) characterize direct measurements to describe the landscape of cattle pens managed with energy-dense or low-energy feeding programs, and (2) evaluate the association of thermal images taken using UAV technologies with the nutrient landscape of feedlot pens. Dirt floor feedlot pens (n=6) (9.1 m x 15.2 m) stocked with 14 dairy-beef crossbred steers (avg. BW = 453.6 kg) at the Kansas State University Stocker Unit (Manhattan, KS) were used in the study. Two feeding programs were applied to the pens: 1) energy-dense 64 NEg limit-fed ration (n=3) and 2) low-energy 50 NEg ad libitum fed ration (n=3). Prior to data collection, pens were gridded into 15 quadrants (3 m x 3 m). Pen quadrants were further classified into pen regions. During the first daily feeding, a UAV (DJI M300) equipped with a thermal sensor (H20T) was flown over each pen (30 m altitude) to capture images. Images were processed using Pix4D, visualized using ArcGIS, and Photoshop was used to obtain color values (luminance) for each quadrant using 3 random sampling points. Within each pen, 3 pen floor grab samples (250 g) and 3 direct temperature measurements were taken from random locations within each quadrant and analyzed for moisture, ash, NDF, and ADF. Linear mixed-effects models were used to evaluate the effect of feeding program, pen region, and feeding program by pen region for each direct measurement (moisture, ash, moisture:ash, NDF, ADF, direct temperature, luminance). Differences in direct measurements across physical locations within feedlot pen by feeding program were identified for moisture (P< 0.01), moisture:ash (P< 0.01), direct temperature (P< 0.01), luminance (P< 0.01), ADF (P< 0.01), and NDF (P< 0.01). A trend (P=0.10) for an association between pen region and feeding program for ash was observed. Direct measurements with identified differences (P< 0.05) across the pen regions by feeding program were then tested for association by pen region to luminance using linear mixed-effects models. Results indicate a significant relationship between luminance and all direct measurement variables (P< 0.05). Lin’s concordance correlation coefficient was used to understand precision and accuracy between direct measurement variables and luminance. Moisture demonstrates good accuracy and precision (CCC=0.82) when predicted from luminance values. Results indicate moisture can be used to characterize pen landscape from UAV thermal images.