188 - Comparison of Nutritive Value Parameters to Normalized Difference Vegetation Index by Means of Unmanned Aerial Vehicle

Abstract: Remote sensing has opened doors for grazing management by allowing producers to monitor forage in a more efficient way. The objective of this study was to observe the relationship between the normalized vegetation index (NDVI) from images taken by an unmanned aerial vehicle (UAV) to nutritive value parameters. This project took place at the H. H. Leveck Animal Research Center located in Starkville, MS where 9, 2-hectare pastures planted in Marshall annual ryegrass (Lolium multiflorum) were utilized. Each 2-hectare pasture was subdivided into 0.20-hectare sections where forage samples were collected from 3, 1/4 of a meter quadrat every 14-days. Images were taken with UAV over a 76-day grazing period every 14-days. Images from the UAV were captured at 121.9 m above ground level using a DJI inspire 2 rotor wing fitted with a MicaSense RedEdge camera (MicaSense; Seattle, WA). Images were processed using Pix4D (Pix4D SA; Prilly, Switzerland) and analyzed in QGIS (QGIS; OSGeo; Finland). Raster calculator in QGIS was utilized to calculate NDVI values. Forage samples were analyzed using NIR for crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), relative forage quality (RFQ), and invitro dry matter digestibility (IVDMD) and correlated to NDVI values. Regression analysis was conducted in R (RStudio, PBC) using the package easynls (easynls-package) procedure within package ggplot2 (ggplot2-package). Regression models were obtained through a backward-elimination technique. Neutral detergent fiber (r² = 0.27) had the best correlated relationship to NDVI compared with CP (r² = 0.21), ADF (r² = 0.25), RFQ (r² = 0.18), and IVDMD (r² = 0.22). As NDVI values increased, CP, RFQ, and IVDMD increased while ADF and NDF decreased. There was a weak correlation between all nutritive value parameters and NDVI values. In conclusion, weak relationships indicate that NDVI values may not be the best predictor of nutritive value.