Prediction of wood density and carbon-nitrogen content in tropical agroforestry tree species in western Kenya using infrared spectroscopy

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).