s:1788:"%T Prediction of wood density and carbon-nitrogen content in tropical agroforestry tree species in western Kenya using infrared spectroscopy 
%A Olale, K 
%X The global debate on climate change needs to be furnished with accurate and precise
 measurement of biomass in agricultural landscapes. Wood density is a supporting
 parameter for biomass estimation; however, empirical methods for wood density
 determination are destructive and complex, as are conventional wet chemistry analyses
 of carbon and nitrogen. Thus a low cost and non-destructive method of estimation is
 required. Infrared Spectroscopy coupled with chemometrics multivariate techniques
 offers a fast and non-destructive alternative for obtaining reliable results without
 complex sample pre-treatments. This study sought to develop a prediction model for
 estimation of wood density, carbon and nitrogen across species using Infrared
 Spectroscopy.
 Empirical data for determination of these parameters were obtained from coring 77 trees
 sampled from three benchmark sites (Lower, Middle and Upper Yala blocks) along Yala
 basin in Western Kenya. Samples from cored holes in the tree (branch, stem and roots)
 were used to estimate wood biovolume and density. Models for estimation of these
 parameters were derived from scanning 404 cores using diffuse reflectance Infrared
 Spectroscopy and reference values for carbon and nitrogen obtained using a CarbonNitrogen
 analyzer. Partial least squares regression, using first derivative spectra pretreatment,
 was used to develop a model based on different calibrations sets. Models were
 compared on the basis of the accuracy of prediction using the coefficient of
 determination (R2), Standard Error of Calibration (SEC) and Standard Error of Prediction
 (SEP). 
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