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- Title
Spectroscopic Determination of Leaf Nitrogen Concentration and Mass Per Area in Sweet Corn and Snap Bean.
- Authors
Mingwei Yuan; Couture, John J.; Townsend, Philip A.; Ruark, Matthew D.; Bland, William L.
- Abstract
Rapid nondestructive measurements at leaf level of nitrogen concentration (%N) and leaf mass per area (LMA) are needed to improve crop simulation model development and calibration, and better understanding of in-season N management. Many contact reflectance-based techniques for %N and LMA estimations require calibration across species, cultivars, growing stages, and cultural practices. Narrowband (hyperspectral) reflectance spectroscopy, in combination with partial least square regression (PLSR) models, offers improved performance over vegetation indices derived from standard linear regression analysis with simple ratios or combined formulas. Little research on the application of contact spectroscopy data and PLSR techniques has been conducted for sweet corn (Zea mays L.) and snap bean (Phaseolus vulgaris L.). In this study, we sought to determine the optimum wavelength ranges for %N and LMA estimations and to develop and evaluate spectroscopic models in estimating %N and LMA in the two species. Best PLSR predictions utilized 1500 to 2400 nm for %N estimations and 450 to 2400 nm for LMA estimations in both species, with high averaged coefficient of determination (R2) values (0.90-0.95 for cross-validation, 0.52-0.71 for external validation) and low root mean square error (RMSE, reported as percentage of sample data range) (4.59-5.67% for cross-validation, 9.50-18.46% for external validation). The results indicate that narrowband reflectance spectroscopy (450-2500 nm) combined with PLSR analysis is a promising method for rapid and nondestructive estimates of %N and LMA at leaf level in sweet corn and snap bean across growth stages, N management, and years.
- Subjects
NITROGEN in agriculture; SWEET corn; KIDNEY bean; BIOMASS; REGRESSION analysis; REFLECTANCE spectroscopy
- Publication
Agronomy Journal, 2016, Vol 108, Issue 6, p2519
- ISSN
0002-1962
- Publication type
Article
- DOI
10.2134/agronj2016.05.0260