Leveraging Leaf Spectroscopy to Identify Drought-Resistant Soybean Cultivars

Abstract

Abstract Understanding cultivars' physiological traits variations under abiotic stresses, such as drought, is critical to improve phenotyping and selections of resistant crop varieties. Traditional methods in accessing physiological and biochemical information in plants are costly and time consuming, which prevent their use on phenotyping breeding strategies.Spectroscopy data and statistical approaches such as partial least square regression could be applied to rapidly collect and predict several physiological parameters at leaf-level, allowing the phenotyping of several genotypes in a high-throughput manner. We collectedspectroscopy data of twenty cultivars planted under well-watered and drought conditions during the reproductive phase in a controlled environment condition. At 20 days after drought was imposition, we measured leaf pigments content (chlorophyll a and b, and carotenoids), specific leaf area, electrons transfer rate, and photosynthetic active radiation. At 28 days after drought imposed, we measured leaf pigments content, specific leaf area, relative water content, and leaf temperature. Partial least square regression modelsaccurately predicted leaf pigments content, specific leaf area, and leaf temperature (cross-validation R2 ranging from 0.56 to 0.84). Discriminant analysis using 50 wavelengthswas able to select thebest-performance cultivars regarding all evaluated physiological traits. The results showed the great potential of usingspectroscopy as a feasible, non-destructive, andaccurate method to estimate physiological traits and screening of superior genotypes.