Use of spectral radiance for correcting nitrogen deficiencies and estimating soil test variability in an established bermudagrass pasture

Abstract

The use of variable rate technology has become increasingly popular for applying plant nutrient elements. The most widely used method for determining variable fertilizer rates is presently based on soil testing and yield mapping. Three field studies (Bumeyville 1995, Burneyville 1996, and Ardmore 1996 were initiated in established Midland bermudagrass [Cynodon dacrylon (L) Pers.] pastures to determine the relationship between spectral radiance at specific wavelengths with forage nitrogen (N) removal and biomass, and to determine field variability of soil test parameters. Variable N (applied to 1.5 × 2.4 m subplots within 2.4 × 45.7 m main plots), fixed N and check treatments were evaluated at each location. Spectral radiance readings were taken in the red (671±6 nm), green (570±6 nm), and near infrared (NIR) (780±6 nm) wavelengths. The normalized difference vegetation index (NDVI) was calculated as NIR-red/NIR+red. Variable N rates were applied based on NDVI. The highest fixed variable N rate was set at 224, 336, and 672 kg N ha -1 for samples were collected in all variable rate plots (1.5 × 2.4 m) and analyzed for various soil test characteristics. NDVI, red, green, and NIR spectral radiance readings were correlated with bermudagrass forage N removal and yield. Correlation of forage yield and N removal with red, NIR, and NDVI were best with maximum forage production, however, when forage production levels were low correlation decreased dramatically for the red wavelength compared with NIR and NDVI. Forage yield and forage N removal in variable rate treatments increased when compared to the check while being equal to the half-fixed and fixed rates where higher N rates were applied. Also, variability about the mean in variable rate plots was significantly lower than half-fixed and fixed rates which supports adjusting N rates based on indirect NDVI measurements. Variable N rate plots reduced fertilizer inputs by 60% and produced the same yield as fixed rate plots, while fixed and half-fixed rates did not increase N content in the forage over that of the variable rate treatment. Soil sample data collected from small consecutive plots (<4 m 2 ) was extremely variable indicating that intense sampling would be needed if variable fertilizer application were to be based on soil test results.

Utilization of color parameters to estimate moisture content and nutrient levels of peanut leaves

“…Utilizing a reflectance sensor, Sui et al (1998) correctly classified 91% of cotton plants as nitrogendeficient. Taylor et al (1998) reported 60% fertilizer savings in a variable-rate nitrogen application based on readings of Bermuda grass with a reflectance-based sensor. Keskin et al (2004) used 4 wavelength bands at 550, 680, 770, and 810 nm to predict nitrogen content of golf course turfgrass clippings (R 2 > 0.82).…”

Development of an algorithm for relating pasture nitrogen status to yield response curves

“…For example, Taylor et al. () used the normalized difference vegetation index (NDVI) calculated from spectral reflectance to adjust N fertilizer application rates in pastures according to spatial or temporal variability. This spatially adjusted fertilization reduced fertilizer inputs by 60%, but provided the same yield as fixed‐rate fertilizer plots.…”

“…Remote sensing is increasingly being used as a timely and nondestructive tool for mapping the nitrogen nutrition status of plants and to rapidly assess the spatial variability within a field based on the canopy reflectance. For example, Taylor et al (1998) (Botha, Leblon, Zebarth, & Watmough, 2007;Darvishzadeha et al, 2008;Lamb et al, 2002), which are generally well correlated with leaf N concentrations. Sanches et al (2013) used hyperspectral data to infer pasture concentrations of N, phosphorus and potassium for NZ pastures in various dairy, and sheep and beef farms around NZ.…”

Improving productivity of warm-season pastures by interseeding legumes

“…Site-specific farm planning has great potential for improving crop production and grazing management at the farm-scale. Spatial variation maps of yield using yield monitors (Doerge, 1999) and nutrient deficiencies using spectral radiance (Taylor et al, 1998) can be developed relatively quickly and easily along with GIS. Brock 1997 Variability is apparent at very small scales (less than 1.5 m) (Taylor et al, 1998;Teutsch et al, 1998).…”

“…Spatial variation maps of yield using yield monitors (Doerge, 1999) and nutrient deficiencies using spectral radiance (Taylor et al, 1998) can be developed relatively quickly and easily along with GIS. Brock 1997 Variability is apparent at very small scales (less than 1.5 m) (Taylor et al, 1998;Teutsch et al, 1998). suggested that it is important to look at plant interactions and variability at multiple scales, patch-scale, between patch-scale, and field-scale.…”