Near-infrared spectroscopy-based models correctly classify Abies alba seed origin and predict germination properties

Abstract

Forestry industry requires high-quantity and quality seeds for afforestation and assisted migration programs. Finding reliable non-destructive methods to characterize seeds would significantly enhance efforts to identify climate-adapted populations. This study presents near-infrared (NIR) spectroscopy models to classify seed origin and predict germination characteristics at different temperatures non-destructively. We focus on Abies alba Mill., a key European forest tree with genetic variation along climatic gradients and seeds with shallow physiological dormancy. Seeds from six populations were analyzed using NIR spectroscopy, and germination was tested at 15{degrees}C, 20{degrees}C, and 25{degrees}C after stratification treatments at 4{degrees}C (0 or 3 weeks). Population classification accuracy using Partial Least Squares Discriminant Analysis was 69%, with significant NIR peaks at 1712, 1929, and 2111 nm, linked to moisture content and storage compounds. NIR spectra explained 51% and 65% of the variation in germination probability and timing using Partial Least Squares Regression, with significant peaks at 1712, 1929, 2111, 1632, and 2073 nm. General Linear Mixed-Effects Models showed that a NIR predictor contributed to 39% of the germination probability variance explained by fixed-effects, and the stratification treatment was the most important driver explaining germination time. Our results proved the utility of NIR-based tools to effectively classify bulked seeds and predict germination, opening new perspectives to nursery and forestry sectors and populations adaptation and adjustments to warming climate. This study will facilitate further investigations on the physiological processes that occur during dormancy, a critical process for forest regeneration given the expected impact of shorter and warmer winters on seed behavior.