Fine root lignin content is well predictable with near-infrared spectroscopy

However, measuring lignin content is labor-intensive and therefore not well-suited to handle the large sample sizes of most ecological studies. To overcome this bottleneck, we explored the applicability of high-throughput near infrared spectroscopy (NIRS) measurements to predict fine root lignin content.

We measured fine root lignin content in 73 plots of a field biodiversity experiment containing a pool of 60 grassland species using the Acetylbromid (AcBr) method. To predict lignin content, we established NIRS calibration and prediction models based on partial least square regression (PLSR) resulting in moderate prediction accuracies. Combining PLSR with spectral variable selection considerably improved model performance and enabled us to identify chemically meaningful wavelength regions for lignin prediction.