A field experiment was established the Stillwater Research Station (EFAW site, 36.13 N, 97.10 W) to evaluate the combined effect of nitrogen treatments and bioenergy crop species on biomass yield. The nitrogen treatments were: 4 applied N rates of 0, 84, 168 and 252 kgNha-1 and a winter legume [hairy vetch (Vicia villosa Roth) planted in 2011 and crimson clover (Trifolium incarnatum L.) planted in 2012], and two bioenergy crop species; [switchgrass (Panicum virgatum L) “Alamo” established in 2010 and high biomass sorghum (Sorghum
bicolor L) “Blade 5200” planted in June 2011 and April 2012. Site characteristics and cultural
practices are summarized in Table 3.1. The switchgrass and high biomass sorghum were seeded at rates of 5.04 and 9.5 kg ha-1 of pure live seeds using a no-till planter. The winter legume was planted in February each year. All plots were fertilized on 3 June 2011, while in 2012,
switchgrass was fertilized on 19 April and high biomass sorghum on 4 May. The five different N treatments were applied to plots arranged in a split plot randomized design with three
replications to generate plots with varying yield potential. In the split plot design, species was the main plot and fertilizer treatment was the subplot. The experimental plots (30) dimensions were 9 m x 9 m dimension.
Leaf Sampling
To separate N treatments at leaf scale to determine the need for N fertilization, the top most fully expanded green leaf was excised from 6 and 3 random switchgrass and high biomass sorghum plants, respectively, in each plot. The leaves were immediately placed in a sealed plastic bag in an ice chest and transported to the laboratory for spectral measurements. These
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samples were collected between 10:00 and 15:00 local time ([UTC-06:00] Central Time [US & Canada]).
Measurement of Hyperspectral Reflectance
Imaging spectrometer data were collected from switchgrass and high biomass sorghum at leaf and canopy scale throughout the 2011-2012 growing season using a spectroradiometer (FieldSpec Pro FR: Analytical Spectral Devices [ASD], Boulder, Co, USA). The ASD measures spectral reflectance in the 350-2500 nm waveband range and has a spectral sampling of 1.4 nm in the 350-1000 nm range, and 2 nm in the 1000-2500 nm range. The spectral resolution is 3 nm in the 350-1000 nm range, and 10 nm in the 1000 nm range, which were calculated as 1 nm resolution wavelength for the output data using software (RS2 for Windows; ASD). A spectralon (Labsphere, Sutton, NH, USA) white reference panel was used to optimize the ASD instrument prior to taking two canopy reflectance measurements per plot. The white reference was measured at 15-30 minutes intervals to check the stability for 100% reflectance during reflectance
measurement. To reduce the amount of data for analysis, spectral data were averaged at 10-nm wavelength intervals (e.g., a band center at 400 was the averaged value between 395–405 nm) giving a total of 211 spectral bands between 400–2500 nm (Foster et al. 2012). Spectral data at start and end of spectrum due to noise (350–395 nm and 2460–2500) and in the atmospheric water absorption spectral regions (1360–1420 and 1800–1960 nm) were deleted from the data before analysis leaving 185 spectral bands for analysis.
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Leaf Spectral Data
Leaf samples were collected from June to August 2011 and May to September 2012 between 10:00-14:30 hours local time ([UTC-06:00] Central Time [US & Canada]). Spectral reflectance was measured for the leaf samples using the procedure for switchgrass leaf
reflectance that used by Foster et al. (2012). However, to measure the leaf reflectance of the high biomass sorghum, a single leaf rather than two leaves was used due to the larger surface area. The spectral reflectance was obtained by sandwiching a single leaf for the high biomass sorghum or two leaves for the switchgrass between a non-reflecting black body and the light probe
(Kakani et al. 2004). Three replicated spectral measurements were taken on each of the leaf collected from each plot. Each measurement was the average of 25 spectral readings to enable noise reduction within the spectra (Muchovej and Newman 2004; Miphokasap et al. 2012).
Canopy Spectral Data
Canopy reflectance measurements were made on clear-sky days from June to August 2011 and May to September 2012 between 10:00-14:30 hours local time ([UTC-06:00] Central Time [US & Canada])using an ASD spectroradiometer To measure the canopy reflectance the sensor head was held approximately 60 cm above the canopy at the nadir position at each sampling interval. The radiometer was mounted on the back of pickup and raised to a height of 200 to 290 cm above the ground (Figure 1).Table 3.3.2 shows height of radiometer, canopy height and height of the sensor from ground at each sampling date. The radiometer had a 25° field of view (FOV), producing a view area of 88-128 cm diameter at ground level.
Hyperspectral reflectance was collected from 30 plots of switchgrass and high biomass sorghum with varied rate of nitrogen fertilizer to create variation in biomass and quality within the plots.
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Two replicated spectral measurements were taken from each plot, with each measurement being an average of 25 spectral readings to enable noise reduction within the spectra.
Vegetation Indices
Vegetation indices are designed to either detect vegetation structural parameters such as LAI, biomass, or chlorophyll/pigment concentrations (Pinter et al. 2003). The information generated from vegetation indices are dependent upon the phenological stage and plant
parameter to which the index is closely related (Hatfield and Prueger 2010). Therefore, indices used in this study were selected from the most common VIs and grouped into three categories, structural indices, chlorophyll/pigment related indices and red edge indices that are related to pigments (Table 3.3).
Data Analysis
The principal component analysis (PCA) was performed using the PROC PRINCOMP procedure in SAS to identify the optimal wavebands, while stepwise discriminant analysis (SDA) was performed to find the best indices and wavebands that could distinguish the nitrogen treatments at different sampling intervals throughout the growing season. The SDA was
performed using the PROC STEPWISE procedure in SAS. To determine the optimal wavebands that best described the vegetation characteristics at different time throughout the growing season a comprehensive analysis using PCA was performed. The PCA was used as a method because of its reliability and ease in determining selecting best wavebands to model biophysical and
biochemical quantities. While, the SDA was carried out to identify the best vegetation indices (Table 3.3) and wavebands (from 186 bands) at each sampling intervals in the switchgrass and
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the high biomass sorghum for separation of the N treatments. Stepwise discrimination (SDA) was used because it provides the most rapid and straight forward results in discriminating among multiple groups (Thenkabail et al. 2004).
The PCA is a method that transformed the original data into a set of new uncorrelated variables called principal components (PC), thereby reducing the number of variables. The value of the PCA is that the importance of each wavebands in each PC can be determined by the magnitude of the eigenvectors or factor loading as the higher the eigenvector the greater the importance of the waveband in relation to the switchgrass and the high biomass sorghum N status. Therefore, the magnitude of the eigenvector in each PC was used to determine the
wavebands with the greatest influence in PC1, PC2 and PC3. The PCA also provides the percent variability explained by each PC (eigenvalues). This approach allows for the selection of the best wavebands associated with the switchgrass and high biomass sorghum N status.
The SDA is a method that reduces the data set to those variables that maximize between statistical group variability while minimizing within group variability. The Wilk’s lambda
statistics was used to select the best indices and wavebands for differentiating the N treatments at the different sampling intervals and at the leaf and canopy scale. In addition to the Wilk’s
lambda, there are other SDA methods for discrimination such as Pillai’s trace and canonical correlation (SAS 2009). However, the Wilk’s lambda is the most commonly used and reported (Thenkabail et al. 2004; Thenkabail, Lyon, and Huete 2012; Thenkabail et al. 2013). Low Wilk’s lambda value suggests a great degree of separation (Thenkabail et al. 2004; Thenkabail, Smith, and De Pauw 2002). Therefore, indices or wavebands identified at each sampling date and at leaf and canopy scale with the lowest Wilk’s lambda value resulted in the greatest degree
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of separation among the N treatments. The difference between the PCA and SDA is that the PCA creates new set of uncorrelated variables that defines the axes of greatest variability in the data and the SDA identifies from amongst the original variables the best variable that describes differences between given groups.