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Title:   Author  item
                                            BILGILI, ALI - CORNELL UNIVERSITYitem
                                            VANES, HAROLD - CORNELL UNIVERSITYitem
                                            AKBAS, FEVZI - U. GAZIOSMANPASA, TURKEYitem
                                            DURAK, ALPER - U. GAZIOSMANPASA, TURKEYitem
                                            Hively, Wells

                                                         - *Dean*item
                                            DEGLORIA, STEPHEN - CORNELL UNIVERSITYitem
                                            OWIYO, THOMAS - CORNELL UNIVERSITY
  Submitted to: Soil Science Society of America Annual Meeting
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 10/1/2005
                                    Publication  Date: 11/7/2005
                                    Citation: Bilgili, A.V., Vanes, H.M., Akbas, F., Durak, A., Hively, W.D., DeGloria, S.D., Owiyo, T. 2005. Near-infrared reflectance spectroscopy for assessment of soil properties in semi-arid Turkey [abstract]. Soil Science Society of America Annual Meeting. Available:  http://crops.confex.com/crops/2005am/techprogram/P6996.HTMInterpretive Summary:
                                Technical Abstract: Reflectance spectroscopy   has been adopted as a nondestructive method for material characterization in a wide range of applications. In this study,  near infrared reflectance (NIR) spectroscopy  was used to evaluate diverse soil properties for five different soil series of  Entisol, Mollisol and Aridisol soil groups in  northern  Turkey, using 512 soil samples collected in a 25x25 m sampling grid  over a 32 ha (800x400m) area . Air-dried soil samples were scanned at 1-nm resolution from 350 to 2500 nm (near-infrared wavelengths) and calibrations between soil properties and soil reflectance were  developed by using  a cross validation procedure under partial least squares (PLS) regression and multivariate adaptive regression splines (MARS). The two data analysis methods were evaluated and compared by using raw reflectance data and first derivative reflectance data, separately and combined.  The strongest correlations (r2 values) between reflectance and soil chemical properties were observed using PLS regression and first derivative data, for exchangeable Ca (0.85), cation exchange capacity (0.88), exchangeable Mg (0.78), organic C concentration (0.84), clay content (0.93), sand content (0.90), and CaCO3 (0.82).  Weaker correlations were observed for pH, EC, exchangeable K and Na (0.55 - 0.65).  Overall, PLS regression provided larger r2 values than MARS.  Also, no improvement was obtained by using both first derivative and raw data combined, nor by increasing the number of soil samples processed from 250 to 512.  In conclusion, cross-validated regression models were able to predict soil properties with variable predictive ability, with the best results provided by PLS regression on first derivative reflectance data.


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