Katherine Frels1, Mary Guttieri2, Laura Dotterer3, Fares Al-Aboud3, Rick Perk3, Bryan Leavitt4, Brian Wardlow4 and Peter Baenziger5, (1)1991 Upper Buford Circle, University of Minnesota-Twin Cities, St. Paul, MN (2)Hard Winter Wheat Genetics, USDA-ARS, Manhattan, KS (3)University of Nebraska - Lincoln, Lincoln, NE (4)CALMIT, University of Nebraska - Lincoln, Lincoln, NE (5)362D Plant Science Building, University of Nebraska - Lincoln, Lincoln, NE
As nitrogen fertilizer costs and environmental concerns rise, the need to breed nitrogen use efficient (NUE) crops is increasing. However, traditional phenotyping methods for NUE traits are labor intensive and destructive. Numerous publications have highlighted the use of remote sensing of canopy spectral reflectance (CSR) as a proxy for physical sampling. Hyperspectral CSR measures reflectance of incident light reflected by the plant canopy. Reflectance values for specific wavelengths are selected and used to calculate vegetation indices such as Normalized Vegetation Differential Index (NDVI) or Chlorophyll Index (CI). These indices correlate with physical plant characteristics such as biomass and chlorophyll content and indicate plant nitrogen status. While less labor intensive than physical sampling, ground based CSR is slow and gives a narrow, though finely detailed, window of observations at the plot level. These detailed data sets are useful for relatively small studies, but are difficult to manage in larger breeding nurseries. Airborne hyperspectral imaging systems such as AISA Eagle provide lower spectral and spatial resolution and generate a broader view of a large trial at a specific time point. However, small plot breeding nurseries reach the limits of aerial imagery spatial resolution. During the 2012 growing season, the USDA-NIFA Triticeace Coordinated Agricultural Project (TCAP) supported ground based CSR phenotyping in the 299-genotype hard winter wheat association mapping panel grown near Ithaca, NE. The trial was imaged by the AISA Eagle airborne system of the University of Nebraska’s Center for Advanced Land Management Information Technologies’ (CALMIT). Moderate correlation between aerial and proximal indices were found, and the aerial data showed predictive ability for traits such as biomass, grain protein yield, and nitrogen content. With improvements to the data capture and extraction methodology, it is expected that airborne hyperspectral imagining can become a useful tool for evaluating NUE in breeding nurseries.