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

Abstract

1. Root lignin is a key driver of root decomposition, which in turn is a fundamental component of the terrestrial carbon cycle and increasingly in the focus of ecologists and global climate change research. 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. 2. 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 (RPD = 1.96, R 2 = 0.74, RMSE = 3.79). 3. In a second step, we combined PLSR with spectral variable selection. This considerably improved model performance (RPD = 2.67, R 2 = 0.86, RMSE = 2.78) and enabled us to identify chemically meaningful wavelength regions for lignin prediction. 4. We identified 38 case studies in a literature survey and quantified median model performance parameters from these studies as a benchmark for our results. Our results show that the combination Acetylbromid extracted lignin and NIR spectroscopy is well suited for the rapid analysis of root lignin contents in herbaceous plant species even if the amount of sample is limited.

Plant phenotyping relevance

近赤外分光とPLSRによる植物細根リグニン含量の高速推定法を開発・検証し、性能比較とベンチマークも行っており、植物形質取得が中心である。

Code and data availability

The paper's fine root lignin/NIR spectral dataset is publicly deposited in PANGAEA. The carspls, pls, baseline, and prospectr R packages are generic third-party libraries, not authors' analysis code, and no author code or trained model is deposited.