Examination of a Visualization Tool for Biomass and Crude Protein Estimates on Remotely Sensed Grazed Forages.

Remote sensing technology can provide an accurate and efficient solution in estimating forage biomass and crude protein. However, the need to produce more easily-attainable and practical data outputs warrants further development in post processing methodology. The objective of this research was to produce a visual representation of the data collected utilizing remote sensing technology. The visual representations were created using the Empirical Bayesian Kriging (EBK) geostatistical model tool in ESRI ArcMap 10.3. Forage species involved were 800RR alfalfa (Medicago sativa L.) and bermudagrass (Cynodon dactylon (L.). Pastures and forage involved were subject to different grazing intensities (e.g. stocking rate), grazing systems (e.g. continuous vs. rotational), supplementation, and nitrogen fertilizer treatments. Periodic sensing data collection occurred to estimate forage biomass and crude protein to monitor and estimate response to different management strategies. Surface models were produced to visually illustrate biomass and crude protein and to allow visual comparison of forage condition based on different grazing schemes and intensities, fertilizer rates, and feed supplementation over time. The development and production of these model-based visuals provides a visual data output allowing for an efficient representation of forage quality. Further, visuals produced in chronological order allows for the evaluation of specific management strategies over time and specific timeframes. In conclusion, this effectively delivers a practical output for agricultural producers to manage their forage-based cattle production systems.