225-4 Choosing the Best Vegetation Index for Use in Nitrogen Use Efficiency Selection in Winter Wheat.

Poster Number 206

See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: General Sensor Based Nutrient Management: II
Tuesday, November 4, 2014
Long Beach Convention Center, Exhibit Hall ABC
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Katherine Frels1, Mary Guttieri1 and Peter Baenziger2, (1)Agronomy and Horticulture, University of Nebraska - Lincoln, Lincoln, NE
(2)362D Plant Science Building, University of Nebraska - Lincoln, Lincoln, NE
Poster Presentation
  • KFrels_ASA2014.pdf (1.9 MB)
  • Nitrogen use efficient (NUE) crops are needed to reduce increasing nitrogen costs and environmental concerns. However selecting for NUE wheat is difficult due to the labor intensive and destructive nature of traditional phenotyping methods. Canopy spectral reflectance (CSR) is non-destructive, quick, and less labor intensive phenotyping method that measures incident light reflected by the plant canopy.  Reflectance values for specific wavelengths are selected and used to calculate vegetation indices such as Enhanced Vegetation Index (EVI). These vegetation indices can be used to estimate specific traits related to nitrogen use efficiency such as biomass, canopy N content at flowering, and yield. During the 2012 and 2013 growing seasons, a 299-genotype hard winter wheat association mapping panel grown near Ithaca, NE was phenotyped weekly from anthesis to physiological maturity using CSR. Biomass samples were harvested at anthesis and physiological maturity. Protein concentration in vegetative tissues and grain was measured using a Perten DA7200 diode array NIR (Hägersten, Sweden). Grain N yield was calculated as (grain yield x grain protein content x 0.01)/5.7. Several vegetation indices were calculated from this data set. The plant productivity traits such as anthesis biomass, grain yield, and grain N yield were compared with the vegetation indices. In 2012, a year with a yield limiting environment, EVI (Enhanced Vegetation Index) was highly heritable and showed high correlation with all plant productivity traits.  In 2013, an optimal yield year, all VI had high heritability but were less sensitive to genotype differences. Alternative VI or analysis methods will be needed for optimal years.
    See more from this Division: ASA Section: Agronomic Production Systems
    See more from this Session: General Sensor Based Nutrient Management: II