Replication Data for: Determination of plant silicon content with near infrared reflectance spectroscopydoi:10.18710/4PZFHQDataverseNO2019-03-082Bråthen, Kari Anne; Ancin-Murguzur, Francisco Javier, 2019, "Replication Data for: Determination of plant silicon content with near infrared reflectance spectroscopy", https://doi.org/10.18710/4PZFHQ, DataverseNO, V2, UNF:6:PvRUDKMp2Uh3Ynd+ef3knw== [fileUNF]Replication Data for: Determination of plant silicon content with near infrared reflectance spectroscopydoi:10.18710/4PZFHQBråthen, Kari AnneAncin-Murguzur, Francisco JavierUiT The Arctic University of Norway2013-06Finnmark, northern NorwayRDataverseNOUiT The Arctic University of NorwayBråthen, Kari AnneAncin-Murguzur, Francisco JavierLongva, Leif2017-02-17Earth and Environmental SciencesNear Infrared Reflectance Spectroscopy (NIRS)plant silica concentrationcalibrationFennoscandiaecosystem researchgraminoidsDeschampsia cespitosaOrthosilicic acidplant defense mechanismSilicon (Si) is one of the most common elements in the earth bedrock, and its continental cycle is strongly biologically controlled. Yet, research on the biogeochemical cycle of Si in ecosystems is hampered by the time and cost associated with the currently used chemical analysis methods. Here, we assessed the suitability of Near Infrared Reflectance Spectroscopy (NIRS) for measuring Si content in plant tissues. NIR spectra depend on the characteristics of the present bonds between H and N, C and O, which can be calibrated against concentrations of various compounds. Because Si in plants always occurs as hydrated condensates of orthosilicic acid (Si(OH)4), linked to organic biomolecules, we hypothesized that NIRS is suitable for measuring Si content in plants across a range of plant species. We based our testing on 442 samples of 29 plant species belonging to a range of growth forms. We calibrated the NIRS method against a well-established plant Si analysis method by using partial least-squares regression. Si concentrations ranged from detection limit (0.24 ppmSi) to 7.8% Si on dry weight and were well predicted by NIRS. The model fit with validation data was good across all plant species (n = 141, R2 = 0.90, RMSEP = 0.24), but improved when only graminoids were modeled (n = 66, R2 = 0.95, RMSEP = 0.10). A species specific model for the grass Deschampsia cespitosa showed even slightly better results than the model for all graminoids (n = 16, R2 = 0.93, RMSEP = 0.015). We show for the first time that NIRS is applicable for determining plant Si concentration across a range of plant species and growth forms, and represents a time- and cost-effective alternative to the chemical Si analysis methods. As NIRS can be applied concurrently to a range of plant organic constituents, it opens up unprecedented research possibilities for studying interrelations between Si and other plant compounds in vegetation, and for addressing the role of Si in ecosystems across a range of Si research domains.2012-06-012013-09-10NorwayFinnmarkExperimental<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Smis, Adriaan (et.al.): "Determination of plant silicon content with near infrared reflectance spectroscopy", Frontiers in Plant Science (2014), Volume 5, Article 496.10.3389/fpls.2014.00496Smis, Adriaan (et.al.): "Determination of plant silicon content with near infrared reflectance spectroscopy", Frontiers in Plant Science (2014), Volume 5, Article 496.00_README.txtREADME filetext/plainModeling_script.RScript to process the spectral data and obtain a partial least squares model to prdict Si in plantstype/x-r-syntaxSpectral_data.txtSpectral data obtained from scanning plant samples with a FieldSpec3 spectrometertext/plain