Abstract
Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.
Highlights
Due to global climate change, droughts around the world have become more frequent and have increased in severity, which will have a serious impact on the growth of plants and crops (Stocker, 2014; Mazis et al, 2020)
The results showed that (1) chlorophyll content prediction was best determined by using standard normal variate (SNV) combined with the second derivative and SG smoothing spectra (SNV_SSG) preprocessing method as well as genetic algorithm PLS (GA) variable selection method; (2) N content prediction was determined by the first derivative combined with SG smoothing spectra (FSG) preprocessing method and significance multivariate correlation (sMC) variable selection method
Under various drought stress treatments, the T. sinensis leaf chlorophyll content showed a decreasing trend, with the most stable chlorophyll content under the 75% field capacity (FC) treatment
Summary
Due to global climate change, droughts around the world have become more frequent and have increased in severity, which will have a serious impact on the growth of plants and crops (Stocker, 2014; Mazis et al, 2020). Drought impacts vegetation differentially across fields, seasons and species (Douma et al, 2012), and available water ground and rain are the most important factors that significantly influence plant growth and productivity (Hoover et al, 2014; Gao et al, 2019; Khaleghi et al, 2019). More attention has been given to how plants respond to water availability (Khaleghi et al, 2019). Drought stress typically reduces photosynthetic capacity and carbon storage in the form of non-structural carbohydrate (NSC) concentrations and plant respiration rates (Centritto et al, 2009; Bongers et al, 2017)
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