Master of Science / Department of Biology / Loretta Johnson / Plant epicuticular wax (ECW) isolates internal tissues from harsh external conditions increasing drought tolerance. Beta-diketone-rich ECW reflect light and result in a glaucous phenotype that may ameliorate the thermal environment of the leaf. The overall goal is to characterize the form and function of ECW in two closely related, but phenotypically divergent grasses. [italicized]Andropogon gerardii ssp. [italicized]gerardii, big bluestem, is a non-glaucous, agronomically and ecologically dominant grass in the United States while [italicized]Andropogon gerardii ssp. [italicized]hallii, sand bluestem, is a glaucous subspecies restricted to dry, sandy soils. The objectives are to contrast sand and big bluestem ECW chemistry, morphology, and physiology to determine the distinctions in ECW resulting in the glaucous phenotype and determine the effect this has on leaf optical qualities and permeability. Gas chromatography mass spectrometry (GC-MS) and scanning electron microscopy (SEM) were used to examine ECW chemistry and micromorphology. It was hypothesized that beta-diketones and beta-diketone tubules where present only in leaves of sand bluestem and that the ECW was more reflective and abundant and the cuticle was less permeable. Beta-diketones and tubular ECW were absent in big bluestem and common on sand bluestem’s surface, although less than 20% of ECW was beta-diketones. Functional implications of ECW phenotypes were investigated by comparing minimum conductance (G[subscript]min), wax load, reflectance, and transmittance. Reflectance, with and without ECW, and G [subscript]min were measured with an infrared gas analyzer and a spectroradiometer, respectively. Sand bluestem had twice the ECW in mg cm[superscript]2 (P=.01) and three times lower G [subscript]min in ms[superscript]-1 10[superscript]-5 (P=.02). Partial least squares (PLS) models were trained to predict subspecies from reflectance spectra and were able to distinguish the subspecies. These experiments indicate that in comparison to big bluestem, increased reflectance is a property uniquely imparted to sand bluestem by ECW and the presence of beta-diketones determines the distinction. Glaucous crop species have shown higher yield under drought and extreme weather, including drought, is expected to become more common. Therefore, this study of glaucous waxes, may be applied in engineering drought tolerance.

<p>The thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.</p>

The thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.

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<p>  </p> <p>Sudden death syndrome (SDS) caused by <em>Fusarium virguliforme</em> is one of the top yield-reducing diseases of soybean. This disease results in a two-stage symptom development, root rot followed by foliar interveinal chlorosis and necrosis. Foliar symptoms typically appear late in the growing season [full pod to full seed (R4 to R6) reproductive growth stages]. Prior to foliar symptoms, a destructive technique is usually carried out to identify the root rot phase of SDS. This technique requires intensive crop scouting and an expert for accurate diagnosis. Therefore, a nondestructive technique is needed to diagnose SDS disease in the absence of visible foliar symptoms. Additionally, no soybean cultivar is completely resistant to SDS and no single method can completely manage this disease. So, an improved integrated approach is needed for SDS disease management. </p> <p>Foliar fungal diseases such as frogeye leaf spot (<em>Cercospora sojina</em> Hara), Septoria brown spot (<em>Septoria glycines</em> Hemmi), and Cercospora leaf blight (<em>Cercospora</em> spp.) are also economically important diseases of soybean. To limit the losses caused by these diseases, several management methods can be used including the application of foliar fungicide. However, due to the low foliar disease pressure that is observed most years, fungicide applications may not be warranted to be applied annually in Indiana. </p> <p>The objectives of this research were: 1) to assess the effectiveness and economic impact of integrated management strategies that include cultivar selection, seed treatment, and seeding rate on SDS in Indiana; 2) to pre-symptomatically and non-destructively detect SDS disease using hyperspectral measurements; and 3) to evaluate foliar fungicides on soybean foliar diseases and yield in Indiana. </p> <p>Results from this research support the use of a seed treatment to protect soybean roots from SDS infection and the use of a moderately resistant cultivar planted at a seeding rate of 346,535 seeds/ha to protect yield and maximize on net returns. This research also demonstrated the ability of hyperspectral reflectance to discriminate healthy from <em>F. virguliforme</em> infected soybean roots in the absence of foliar symptoms. In addition, results show that fungicide applications can reduce foliar disease over the nontreated control, but under low foliar disease risk, these fungicides did not significantly increase yield over the nontreated control. Altogether, these results will contribute to improved soybean disease management approaches in Indiana.</p>

Breeding of coniferous trees in the Czech Republic is undergoing an important development during last decades, especially thanks to molecular-genetic methods, which refine and simplify mapping of tree genotypes and the selection of superior genotypes. Recently, in the Czech Republic superior genotypes are selected based on forestry parameters (tree height, trunk diameter, and timber quality) what does not always correlate with the ability of a tree to resist abiotic and biotic stresses. Recently, there is an effort to include in the breeding also physiological parameters and select superior genotypes using nonspecific stress indicators, which are able to correspond better to tree fitness than the forestry growth parameters. The present thesis deals with genotypes of Scots pine (Pinus sylvestris L.) growing in seed orchards Doubrava and Silov in the Pilsen region in the Czech Republic. Seed orchards are tree plantations, which serve as a reserve of the genetically valuable reproduction material, they are parts of breeding programs. Pine needles were collected in July 2015 and analyzed for non-specific stress indicators - photosynthetic pigments, phenolics, lignin, cellulose and proline contents and leaf reflectance and fast chlorophyll fluorescence measurements. First objective of the present thesis...

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<p dir="ltr">Reflectance spectroscopy has been increasingly used in forestry due to its ability to rapidly, efficiently, and non-destructively detect tree stress, enabling timely and cost-effective forest management decisions. This dissertation synthesizes three studies and five experiments to understand and improve our ability to use spectral data to estimate a variety of foliar physiochemical traits and identify spectral responses in multi-stress environments, thus, advancing our understanding and application of hyperspectral data in forest management.</p><p dir="ltr">The first study seeks to refine the hyperspectral approach to monitoring tree stress by selecting optimal wavelength ranges to enhance the estimation of foliar traits, such as CO₂ assimilation rate, specific leaf area, leaf water content, and concentrations of foliar nitrogen, sugars, and gallic acid. The study revealed that model performance varied significantly across the different wavelength ranges tested and consistently, including longer wavelength regions improved trait estimation for all traits modeled. This research also established a framework for discovering novel or previously unknown absorption features associated with functional traits, thereby laying the groundwork for expanded spectral applications. This advancement enables the estimation of diverse foliar traits and facilitates detailed stress detection in trees.</p><p dir="ltr">The second study focuses on assessing the effectiveness of hyperspectral data in estimating foliar functional trait responses to various biotic and abiotic stressors and to differentiate those stressors in black walnut (<i>Juglans nigra </i>L.) and red oak (<i>Quercus rubra</i> L.) seedlings. We demonstrated that spectral data can reliably estimate a wide range of foliar traits, highlighting its potential as a surrogate for reference data in understanding plant responses to stress. This research revealed that spectral leaf predictions can effectively provide stress-specific insights into tree physiochemical responses to biotic and abiotic stressors.</p><p dir="ltr">The third study explores the application of hyperspectral reflectance to identify drought-induced foliar responses in black walnut seedlings during their initial field establishment. Chemometric models developed from greenhouse experiments were applied to spectral data collected in the field to assess their transferability and accuracy in predicting various leaf traits under drought stress. Using only spectral data, we demonstrated that seedlings show distinct spectral responses to past and ongoing drought stress, with varying degrees depending on seed provenances. This research aims to provide practical insights for utilizing spectral analysis in real-world conditions and understanding the challenges of using spectral tools in the field.</p><p dir="ltr">Collectively, this dissertation demonstrates the robust potential of hyperspectral reflectance technology in advancing the monitoring of tree health. By optimizing spectral range selection, reliably estimating tree foliar traits under stress conditions, differentiating various stressors in controlled environments, and effectively detecting current and past drought stress in field conditions, this research offers valuable insights for improving forest health monitoring and management strategies in response to environmental challenges.</p>