Leaf Clip Research Articles

Density‐dependent influence of ribbed mussels on salt marsh nitrogen pools and processes

Abstract Bivalves are becoming an increasingly popular tool to counteract eutrophication, particularly in vegetated coastal ecosystems where synergistic interactions between bivalves and plants can govern important N sequestration pathways. In turn, new calls to evaluate how bivalve densities modify N pools and processes across multiple scales have surfaced. Ribbed mussels, Geukensia demissa, and their relationship with smooth cordgrass present a classic demonstration of positive bivalve‐plant interactions and offer a useful model for assessing density dependence. We measure porewater ammonium concentrations, N stable isotope signatures in cordgrass tissue, and sediment N fluxes in mussel aggregations and in cordgrass‐only plots across a southeastern U.S. salt marsh. In addition to measuring the effect of mussel presence, we evaluate mussel density dependence through a multiscale approach. At the patch scale, we quantify mussel density effects within their aggregations (individuals m−2) while at a larger landscape scale, we quantify mussel density effects on the cordgrass‐only areas they neighbour (individuals ~30 m−2). Porewater ammonium concentrations were halved in mussel biodeposits relative to sediments in cordgrass‐only areas and negatively related to mussel density within aggregations. Leaf clip ẟ15N signatures were nearly 2‰ higher in cordgrass growing among mussel aggregations and increased with increasing patch mussel density. Microcosm incubations showed that mussels enhanced N2 flux (i.e., nitrogen removal) and DIN flux (i.e., N regeneration) into the water column, where only nitrogen removal increased with increasing patch‐scale mussel density. Across the marsh landscape, mussel coverage drove ammonium accumulation and N2 flux in sediments. Synthesis. Our results suggest that, at the patch scale, mussels stimulate the microbial metabolism of N, the assimilation of this bioavailable N by cordgrass, and nitrogen removal in a positive, density‐dependent manner. Tidal currents redistribute mussel biodeposits from mussel aggregations to surrounding areas, influencing biogeochemical transformations at scales beyond their physical footprint. We emphasize that the N regeneration potential of bivalve populations is a significant metric contributing to their mitigation potential and that bivalve density effects may be non‐linear, vary across patch to ecosystem scales, and have differing implications for the plants with which they interact.

Quantitative monitoring of salt stress in rice with solar-induced chlorophyll fluorescence

Under salt stress, rice leaves usually accumulate sodium and chloride ions, causing water loss. The consequent nutrition imbalance and damage to photosynthetic systems ultimately affects growth and development. Therefore, early identification of rice salt stress is important for timely amelioration to reduce rice yield loss. Solar-induced chlorophyll fluorescence (SIF) shows substantial effectiveness for its quick and sensitive response to early-stage stress, suggesting its potential for early crop stress detection. To further assess the capacity of SIF for salt stress detection, this study conducted field plot experiments with a single rice cultivar across two growth stages – jointing and booting. The leaf reflectance spectrum of rice was obtained using an ASD FieldSpec Pro FR portable field spectrometer (Analytical Spectral Devices, Boulder, Colorado, USA) and a leaf clip holder. The leaf solar-induced chlorophyll fluorescence (SIF) spectrum was obtained from an ASD coupling filter and FluoWat, which filters out incident sunlight beyond 650 nm. We calculated nine SIF yield indices (FYs), including ↑FY687 (upward fluorescence yield at 687 nm), ↑FY739 (upward fluorescence yield at 739 nm), ↓FY739 (downward fluorescence yield at 739 nm), totFY687 (total of upward and downward fluorescence yield at 687 nm), totFY739 (total upward and downward fluorescence yield at 739 nm). We also explored the adaptive regulation of three biological rice parameters under salt stress: the net photosynthetic rate (Photo), chlorophyll content (Chl), and maximum photochemical efficiency of photosystem II (Fv/Fm). Photo was the most sensitive to salt stress, and showed significant differences on the first day under salt stress. A salt stress response index (SSRI), which combines physiological and biochemical parameters, was constructed to describe the salt stress level. SSRI showed a significant difference on the first day of salt stress, and the dynamics of SSRI were consistent with that of Photo. In addition, we clarified the change rule of SIF for rice leaves under the salt stress. The gray correlation analysis identified five SIF fluorescence yield indices (FYs) that are highly correlated with SSRI: totFY739, ↓FY739, ↑FY739, totFY687, and ↑FY687. Based on the results of gray correlation analysis, a support vector regression model of SSRI was established, which gave an excellent result as follow: R2 of the calibration for the all dataset, booting dataset, and jointing dataset were 0.74, 0.74, and 0.70, respectively, and R2 of the validation data were 0.71, 0.73, and 0.68. In summary, the proposed method to quantitatively detect rice leaf salt stress based on SIF technology allows the early monitoring of rice leaf salt stress.

Groth’s algorithm to detect the possible presence of landmines using changes in the reflection of plants

Abstract. Post-conflict reconstruction includes the removal of land mines and remnants of war. The CSIR conducted field experiments to determine the impact of TNT in the soil on plants as a possible means to detect landmines. All the leaf clip readings were done using a spectrometer to determine reflection and absorption of light at one micrometre intervals between 350 to 2500 micrometre. Laboratory analysis such as UPLC qTOF MS indicated that the TNT have an influence on the plants. Several indices such as Modified Red Edge Normalized Difference Vegetation Index (mNDVI705), Red Edge Position (REP) and Moisture Stress Index (MSI) did not show any significant differences between control plants and experimental plants with different TNT concentrations. Groth’s pattern matching algorithm designed to match several photographs of the same part of the universe. A set of triangles using dominant stars are created for each photograph and matched using an error band. If the selected triangles from the two photographs fit within the error band, then they are from the same section of the universe. Bands for the Pléiades instruments were simulated using the data from the spectrometer for each plant. The reflectance value of the band and the normalised midpoint wavelength of each Pléiades band were used to construct the triangles. The control plant triangle is then matched with the experimental plant and if the triangles do not match, then the effect of TNT on the plant is significant. The initial results with the control plants and experimental plants are positive.

Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought

Due to the mechanistic coupling between solar-induced chlorophyll fluorescence (SIF) and photosynthesis, SIF has an advantage over greenness-based vegetation indices in detecting drought. Since photosystem I (PSI) contributes very little to red SIF, red SIF is assumed to be more responsive to environmental stress than far-red SIF. However, in addition to affecting photosynthesis, drought also has an impact on vegetation chlorophyll concentration and thus affects the reabsorption process of red SIF. When these responses are entangled, the sensitivity of SIF in the red and far-red regions in response to drought is not yet clear. In this study, we conducted a water stress experiment on maize in the field and measured the upward and downward leaf SIF spectra by a spectrometer assembled with a leaf clip. Simultaneously, leaf-level active fluorescence was measured with a pulse-amplified modulation (PAM) fluorometer. We found that SIF, after normalization by photosynthetically active radiation (PAR) and dark-adapted minimal fluorescence (Fo), is a better estimation of SIF yield. By comparing the wavelength-dependent link between SIF yield and nonphotochemical quenching (NPQ) across the range of 660 to 800 nm, the results show that red SIF and far-red SIF have different sensitivities in response to drought. SIF yield in the far-red region has a strong and stable correlation with NPQ. Drought not only reduces red SIF due to photosynthetic regulation, but it also increases red SIF by reducing chlorophyll content (weakening the reabsorption effect). The co-existence of these two contradictory effects makes the red SIF of leaf level unable to reliably indicate NPQ. In addition, the red:far-red ratio of downward SIF and the ratio between the downward SIF and upward SIF at the red peak can be good indicators of chlorophyll content. These findings can help to interpret SIF variations in remote sensing techniques and fully exploit SIF information in red and far-red regions when monitoring plant water stress.

Grapevine leaf physiology and morphological characteristics to elevated CO2 in the VineyardFACE (Free air Carbon dioxide Enrichment) experiment.

Atmospheric carbon dioxide (CO2) concentration has continuously increased since pre-industrial times and has currently reached an average growth rate of 2.3 ppm per year. For the majority of plant species elevated CO2 (eCO2) improves photosynthesis and thus plant biomass production. To investigate the effects of eCO2 on leaf physiology and morphological leaf characteristics two Vitis vinifera L. cultivars, Riesling and Cabernet Sauvignon, grown in the VineyardFACE (Free Air Carbon dioxide Enrichment) system were used. The VineyardFACE is located at Geisenheim, Rheingau comparing future atmospheric CO2-concentrations (eCO2, predicted for the mid-21st century) with current ambient CO2-conditions (aCO2). Experiments were operated under rain-fed conditions for two consecutive years (2015 and 2016). For both varieties and CO2 treatments, leaf gas exchange measurements were performed as well as measures of epidermal flavonoid (Flav) and leaf chlorophyll (Chl) indices by using a portable leaf clip. Furthermore, leaves were sampled for spectrophotometric analysis of the leaf pigments chlorophyll a (Chl a), chlorophyll b (Chl b) and carotenoid (Car). Additionally, leaf cross-sections were produced as permanent preparations to investigate morphological characteristics of the leaf structure. Both cultivars did not differ in leaf chlorophyll meter readings or leaf pigments between the two CO2 treatments while net assimilation was highly stimulated under elevated CO2 for both seasons. Differences found in leaf cross-sections were detected in palisade parenchyma and epidermal thickness of Cabernet Sauvignon under eCO2, whereas Riesling net assimilation increased by 40% under a 20% CO2 enrichment while remaining unaffected in different leaf layer thickness. The observed results within grapevine leaf tissues provide insights to seasonal adaptation strategies of grapevines under elevated CO2 concentrations predicted in future.

A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants

This research investigates the capability of field-based spectroscopy (350–2500 nm) for discriminating banana plants (Cavendish subgroup Williams) infested with spider mites from those unaffected. Spider mites are considered a major threat to agricultural production, as they occur on over 1000 plant species, including banana plant varieties. Plants were grown under a controlled glasshouse environment to remove any influence other than the imposed treatment (presence or absence of spider mites). The spectroradiometer measurements were undertaken with a leaf clip over three infestation events. From the resultant spectral data, various classification models were evaluated including partial least squares discriminant analysis (PLSDA), K-nearest neighbour, support vector machines and back propagation neural network. Wavelengths found to have a significant response to the presence of spider mites were extracted using competitive adaptive reweighted sampling (CARS), sub-window permutation analysis (SPA) and random frog (RF) and benchmarked using the classification models. CARS and SPA provided high detection success (86% prediction accuracy), with the wavelengths found to be significant corresponding with the red edge and near-infrared portions of the spectrum. As there is limited access to operational commercial hyperspectral imaging and additional complexity, a multispectral camera (Sequoia) was assessed for detecting spider mite impacts on banana plants. Simulated multispectral bands were able to provide a high level of detection accuracy (prediction accuracy of 82%) based on a PLSDA model, with the near-infrared band being most important, followed by the red edge, green and red bands. Multispectral vegetation indices were trialled using a simple threshold-based classification method using the green normalised difference vegetation index (GNDVI), which achieved 82% accuracy. This investigation determined that remote sensing approaches can provide an accurate method of detecting mite infestations, with multispectral sensors having the potential to provide a more commercially accessible means of detecting outbreaks.

Assessment of crop traits retrieved from airborne hyperspectral and thermal remote sensing imagery to predict wheat grain protein content

Wheat (Triticum spp.) is among the world’s most widely grown crops and receives large quantities of nitrogen (N) fertiliser. Grain protein content (GPC) is influenced by genetic, agronomic and weather conditions affecting crops’ physiological status and stress levels; accurate GPC prediction has potential to reduce N losses and improve profit. Success in GPC estimation from remotely sensed plant traits has been limited. For progress to be made, it is necessary to robustly identify imaging spectroscopy-based physiological traits most closely associated with GPC in both experimental and commercial contexts. We present results from piloted hyperspectral flights and ground campaigns at two dryland field experiments with divergent water supply and wide-ranging N treatments, and from two years’ flights over 17 commercial fields planted to either bread (T. aestivum) or durum (T. durum) wheat, in the southern Australian wheat belt. Imagery was acquired with airborne hyperspectral and thermal sensors, with spatial resolutions of approx. 0.3 m and 0.5 m for experimental plots and 1 m/1.7 m in commercial fields. Leaf clip measurements, leaf and grain samples were collected and, in commercial fields, ∼40,000 records obtained from harvester-mounted protein monitors. Crop Water Stress Index (CWSI), solar-induced fluorescence (SIF), reflectance indices and PRO4SAIL radiative transfer model inverted parameters were retrieved for each plot and GPC record location. The photochemical reflectance index (PRI) related to xanthophyll pigments was consistently associated with GPC at both leaf and canopy scale in the plots and transect. In the commercial crops, a gradient boosted machine learning algorithm (GBM) ranked CWSI as the strongest indicator of GPC under severe water stress, while SIF, PRI and inverted biochemical constituents anthocyanins and carotenoids were consistently important under more benign conditions. Structural parameters inverted from the hyperspectral reflectance imagery were not prominent except under severe drought. We attained statistically significant results estimating GPC in unseen samples, with best relationships between predicted and observed GPC of r2 = 0.80 in a model built with thermal and physiological traits obtained from the hyperspectral and thermal imagery.

An advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations

As a key phenolic pigment concentrated in the surface tissues of leaves, flavonoids (Flav) are the major bioactive ingredients in Ginkgo leaf extracts. Flav are also marked natural antioxidants and significant indicators of biotic and abiotic stresses, critical for determining cultivation quality and enhancing Flav yield. In particular, area-based Flav (Flavarea) is related to the shortwave-blue light interaction within leaves per unit leaf area, whereas mass-based Flav (Flavmass) is useful for the quantitative assessment of Flav yield. In order to accurately estimate the contents of Flavarea and Flavmass in leaves of Ginkgo plantations, in this study, we developed an advanced bidirectional reflectance factor (BRF) spectra-based approach by reducing the effects of specular reflection and enhancing the absorption signals of Flav (in the shortwave-blue region of spectrum), using a suite of new spectral indices (SIs) (i.e., flavonoid index (FI), modified flavonoid index (mFI) and double difference index (DD)) calculated from the leaf clip equipped spectrometers-collected data. The results demonstrated that most of the SIs derived from the developed BRF spectra-based approach obtained relatively high performance for Flav estimation by alleviating adverse effects of specular reflection to different extents (CV-R2 = 0.60–0.76). In specific, DDnir434,421 selected from DD-type indices performed (CV-R2 = 0.76 for Flavarea; CV-R2 = 0.69 for Flavmass) better than other indices. These findings represent marked potentials of the developed BRF spectra-based approach for non-destructively estimating leaf Flav content, as well as improving the understanding of the mechanisms of specular effects on Flav estimations in leaves of Ginkgo plantations.

Xanthomonas indica sp. nov., a Novel Member of Non-Pathogenic Xanthomonas Community from Healthy Rice Seeds.

Xanthomonas is a major group of pathogenic bacteria infecting staple food crops like rice. Increasingly it is being recognized that non-pathogenic Xanthomonas (NPX) are also important members of a healthy plant microbiome. However, the vast majority of the species described in this genus are of pathogenic nature, and only a few NPX species have been reported till now. Genomic and taxonogenomic analysis of NPX is needed for the management of this important group of bacteria. In this study, two yellow-pigmented bacterial isolates were obtained from healthy rice seeds in Punjab, India. The isolates designated PPL560T and PPL568 were identified as members of the genus Xanthomonas based on biochemical tests and 16S rRNA gene sequence analysis retrieved from the whole-genome sequences. Isolates formed a distinct monophyletic lineage with Xanthomonas sontii and Xanthomonas sacchari as the closest relatives in the phylogenetic tree based on core gene content shared by the representative species of the genus Xanthomonas. Pairwise ortho Average Nucleotide Identity and digital DNA-DNA hybridization values calculated against other species of Xanthomonas were below their respective cut-offs. In planta studies revealed that PPL560T and PPL568 are non-pathogenic to rice plants upon leaf clip inoculation. The absence of type III secretion system-related genes and effectors further supported their non-pathogenic status. Herein, we propose Xanthomonas indica sp. nov. as novel species of the genus Xanthomonas with PPL560T = MTCC 13185 = CFBP 9039 = ICMP 24394 as its type strain and PPL568 as another constituent member.

Leaf-level chlorophyll fluorescence and reflectance spectra of high latitude plants

Little is known about the chlorophyll fluorescence spectra for high latitude plants. A FluoWat leaf clip was used to measure leaf-level reflectance and chlorophyll fluorescence spectra of leaves of common high latitude plants to examine general spectral characteristics of these species. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut-off for the FluoWat low pass filter (either 650 or 700 nm). The Fyield spectra grouped into distinctly different patterns among different plant functional types. Black spruce (Picea mariana) Fyield spectra had little red fluorescence, which was reabsorbed in the shoot, but displayed a distinct far-red peak. Quaking aspen (Populus tremuloides) had both high red and far-red Fyield peaks, as did sweet coltsfoot (Petasites frigidus). Cotton grass (Eriophorum spp.) had both red and far-red Fyield peaks, but these peaks were much lower than for aspen or coltsfoot. Sphagnum moss (Sphagnum spp.) had a distinct Fyield red peak but low far-red fluorescence. Reindeer moss lichen (Cladonia rangiferina) had very low fluorescence levels, although when damp displayed a small red Fyield peak. These high latitude vegetation samples showed wide variations in Fyield spectral shapes. The Fyield values for the individual red or far-red peaks were poorly correlated to chlorophyll content, however the ratio of far-red to red Fyield showed a strong correlation with chlorophyll content. The spectral variability of these plants may provide information for remote sensing of vegetation type but may also confound attempts to measure high latitude vegetation biophysical characteristics and function using solar induced fluorescence (SIF).

Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech

The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica. We first use fabrics as standard material to quantify measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over one growing season (2.7 ± 1.7%), or between individuals sampled over one week (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual at the time of the measurement. However, measurements of variation within individuals at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches toward a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance.

Retrieving potassium levels in wheat blades using normalised spectra

This work explored potassium nutrient retrieval in wheat blades using reflectance spectra. Spectral data were collected from wheat blades at different growth stages, in different cultivars, and following different fertilisation treatments from 2016 to 2019 using a leaf clip and halogen bulb with an ASD spectrometer. Reflectance data from 350 to 2500 nm were collected, and data of 400 to 2400 nm were used in the retrieval. Using a leaf clip to measure the reflectance of a narrow blade can cause bias, which can be corrected using a normalisation method, i.e. the reflectance of each band was divided by the average reflectance of all bands. Three such methods were employed: vegetation index (VI), partial least squares (PLS), and random forest (RF). The approach yielded leaf potassium content (LKC, %) and leaf potassium per area (LKA, g/m2). The results showed that newly developed VIs outperformed previously published indices. The model using a modified ratio spectral index, mRSI(2275, 1875), yielded LKC with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 0.57%. Normalisation methods can eliminate multiplicative error in blade spectra, thereby correcting the underestimated reflectance of narrow blades, and improving the accuracy of potassium retrieval models. Among the three methods, PLS achieved the highest accuracy. The retrieval of LKC and LKA based on normalised spectra and the PLS method yielded R2 values of 0.74 and 0.65, respectively, and their corresponding RMSE values were 0.46% and 0.21 g/m2. LKC retrieval models had higher R2 values than LKA models. This comprehensive analysis of different methods revealed the importance of reflectance at 1883 nm and 2305 nm. In conclusion, it is feasible to retrieve wheat leaf potassium levels using spectral data.

Application of a Combined Transmittance/Fluorescence Leaf Clip Sensor for the Nondestructive Determination of Nitrogen Status in White Cabbage Plants.

The correct fertilization of vegetable crops is commonly determined on the basis of soil and plant costly destructive analyses, demanding more sustainable non-invasive optical detection. Here, we tested the ability of the combined transmittance/fluorescence leaf clip Dualex device for determining the nitrogen (N) status of cabbage plants. Fully developed leaves from plants grown under different N rates of 0; 100; 200; 300 kg N ha−1 in 2018 and 2019 were measured in the field by the Dualex sensor twice a year in July and October. The chlorophyll (Chl) and nitrogen (nitrogen balance index, NBI) indices and the flavonols (Flav) index of the sensor were positively and negatively correlated to leaf nitrogen, respectively. Merging the two-years data, the NBI versus leaf N correlation was less point dispersed in October than July (R2 = 0.76 and 0.64, respectively). NBI was also correlated to cabbage yield, better in July than October. Our results showed that the multiparametric Dualex device can be used as precision agriculture tool for the early prediction of plant N and cabbage yield with economic advantage for the growers and reduced environmental contamination due to nitrate leaching.

A multi-angular invariant spectral index for the estimation of leaf water content across a wide range of plant species in different growth stages

Plant leaf water content plays a key role in several biogeochemical processes, such as photosynthesis, evapotranspiration, and net primary production. Yet, the accurate estimation of leaf water content using multi-angular reflectance measurements across different plant species is still challenging. This study aims to propose a generic spectral index for accurately estimating equivalent water thickness (EWT) when multi-angular spectral reflection is considered. The index was selected to have the format of a difference ratio using three reflectance factors. The reflectance factor at 410 nm was used to reduce the specular reflection from the leaf surface in the 400–2500 nm range, and the ratio of the wavelengths in the 1268–1285 nm range (at non-water absorption wavelengths) to the wavelengths in the 1339–1346 nm range (at water absorption wavelengths) strengthened the relationship with EWT for all of the sampled plant species. The modified difference ratio (MDR) index, (R1271-R410)/(R1342-R410), was linearly proportional to EWT, with R2 > 0.90, when the leaves reflection data were collected from various viewing angles. However, the relationships between some existing indices (simple difference, simple ratio, normalized difference, double difference index and difference ratio indices) and EWT at the leaf level were weak and unstable for all of the 18 plant species (including 14 broadleaf, 3 shrub, and 1 liana species) at different angles under laboratory and field conditions. Moreover, validation results from six independent datasets (n = 1800) and one modeled dataset (n = 2375) further confirmed that the algorithm derived from the proposed index (based on multi-angular reflectance factors of leaves) was not only effective for EWT estimation across a diverse set of plant species with widely variable leaf structure and water content, but also insensitive to different measurement conditions (leaf clip, integrating spheres or multi-angle measurements). The algorithm developed from this new index is generic, does not require reparameterization for each species, and can be accurately used for nondestructive EWT estimations using a simple handheld laboratory or field instrument, and thus, is convenient for agricultural and ecological studies.

Application of Proximal Optical Sensors to Assess Nitrogen Status and Yield of Bell Pepper Grown in Slab

Nitrogen (N) is an essential element frequently associated with environmental issues, and the utilization of leaf clip sensors to adjust N fertilization is a technology of interest. This study aimed to evaluate the indices measured with SPAD and Dualex through the bell pepper crop cycle cultivated under different N doses, to identify the ability of the SPAD and Dualex optical sensors to assess N status and establishing the accuracy of the chlorophyll indices as yield estimators. Bell pepper plants were cultivated for 150 days after transplanting (DAT) in slabs fertigated with 6 different N doses divided into 10 bi-weekly applications. The indices SPAD, chlorophyll (CHL), flavonoids (FLV), and nitrogen balance index (NBI) were measured over the cycle on the uppermost mature leaves of each treatment. We determined the N content of these leaves and the final commercial yield. An interaction effect was found between N dose and growth stage. The SPAD and CHL indices were not significantly affected by N dose up to 60 DAT. The FLV and NBI indices show an opposite pattern in response to N dose. We found a strong correlation between the SPAD, CHL, and NBI and the N content in the leaves and between these indices and commercial yield in several evaluated dates. However, we did not observe a significant correlation between commercial yield and FLV. Both leaf clip sensors could predict the N status of the leaves and the commercial yield, improving the correlation as the crop development progressed.

Tissue Tolerance Coupled With Ionic Discrimination Can Potentially Minimize the Energy Cost of Salinity Tolerance in Rice.

Salinity is one of the major constraints in rice production. To date, development of salt-tolerant rice cultivar is primarily focused on salt-exclusion strategies, which incur greater energy cost. The present study aimed to evaluate a balancing strategy of ionic discrimination vis-à-vis tissue tolerance, which could potentially minimize the energy cost of salt tolerance in rice. Four rice genotypes, viz., FL478, IR29, Kamini, and AC847, were grown hydroponically and subjected to salt stress equivalent to 12 dS m–1 at early vegetative stage. Different physiological observations (leaf chlorophyll content, chlorophyll fluorescence traits, and tissue Na+ and K+ content) and visual scoring suggested a superior Na+-partitioning strategy operating in FL478. A very low tissue Na+/K+ ratio in the leaves of FL478 after 7 days of stress hinted the existence of selective ion transport mechanism in this genotype. On the contrary, Kamini, an equally salt-tolerant genotype, was found to possess a higher leaf Na+/K+ ratio than does FL478 under similar stress condition. Salt-induced expression of different Na+ and K+ transporters indicated significant upregulation of SOS, HKT, NHX, and HAK groups of transporters in both leaves and roots of FL478, followed by Kamini. The expression of plasma membrane and vacuolar H+ pumps (OsAHA1, OsAHA7, and OsV-ATPase) were also upregulated in these two genotypes. On the other hand, IR29 and AC847 showed greater salt susceptibility owing to excess upward transport of Na+ and eventually died within a few days of stress imposition. But in the “leaf clip” assay, it was found that both IR29 and Kamini had high tissue-tolerance and chlorophyll-retention abilities. On the contrary, FL478, although having higher ionic-discrimination ability, showed the least degree of tissue tolerance as evident from the LC50 score (amount of Na+ required to reduce the initial chlorophyll content to half) of 336 mmol g–1 as against 459 and 424 mmol g–1 for IR29 and Kamini, respectively. Overall, the present study indicated that two components (ionic selectivity and tissue tolerance) of salt tolerance mechanism are distinct in rice. Unique genotypes like Kamini could effectively balance both of these strategies to achieve considerable salt tolerance, perhaps with lesser energy cost.

Seasonal Patterns in Spectral Irradiance and Leaf UV-A Absorbance Under Forest Canopies.

Plants commonly respond to UV radiation through the accumulation of flavonoids and related phenolic compounds which potentially ameliorate UV-damage to crucial internal structures. However, the seasonal dynamics of leaf flavonoids corresponding to epidermal UV absorbance is highly variable in nature, and it remains uncertain how environmental factors combine to govern flavonoid accumulation and degradation. We studied leaf UV-A absorbance of species composing the understorey plant community throughout two growing seasons under five adjacent tree canopies in southern Finland. We compared the relationship between leaf flavonol index (Iflav—repeatedly measured with an optical leaf clip Dualex) and measured spectral irradiance, understorey and canopy phenology, air temperature and snowpack variables, whole leaf flavonoid extracts, and leaf age. Strong seasonal patterns and stand-related differences were apparent in Iflav of both understorey plant communities and individual species, including divergent trends in Iflav during spring and autumn. Comparing the heterogeneity of the understorey light environment and its spectral composition in looking for potential drivers of seasonal changes in Iflav, we found that unweighted UV-A irradiance, or the effective UV dose calculated according to the biological spectral weighting function (BSWF) for plant growth (PG action spectrum), in understorey shade had a strong relationship with Iflav. Furthermore, understorey species seemed to adjust Iflav to low background diffuse irradiance rather than infrequent high direct-beam irradiance in sunflecks during summer, since leaves produced during or after canopy closure had low Iflav. In conclusion, we found the level of epidermal flavonoids in forest understorey species to be plastic, adjusting to climatic conditions, and differing according to species' leaf retention strategy and new leaf production, all of which contribute to the seasonal trends in leaf flavonoids found within forest stands.

Predicting N Status in Maize with Clip Sensors: Choosing Sensor, Leaf Sampling Point, and Timing.

Nitrogen (N) losses from agricultural systems increase air and water pollution, and these losses are highly correlated with the excessive fertilization. An adjusted N fertilization is then a key factor in increasing the N fertilizer efficiency, and leaf clip sensors can help to improve it. This study (combining five different field experiments in Central Spain) tried to identify the ability of the clip sensors in maize N status identification and yield prediction, comparing two different devices (SPAD-502® and Dualex®) and identifying the best protocol for maize leaf sampling. As a result, the study demonstrated that different leaf clip chlorophyll sensors presented similar results, although some differences appeared at larger N concentrations. Complementary polyphenol information (as flavonol) can improve the maize N deficiency prediction. Moreover, valuable information for a proper sampling protocol was obtained with this study. It proved that the sampling position (in the leaf and in the plant) and sampling time were crucial for a better estimation of the maize N status. Proper fertilization recommendations could be achieved based on clip chlorophyll sensor measurements.

Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra

As an important indicator of plant photosynthetic activity, leaf chlorophyll content (LCC) has often been estimated non-destructively in the past decades from reflectance spectra measured with various spectrometers and leaf-holding accessories. Most studies established LCC predictive models with either integrating sphere measured directional-hemispherical reflectance factor (DHRF) spectra or leaf clip measured bidirectional reflectance factor (BRF) spectra. Given the effect of specular reflection on leaf reflectance, it remains unclear how DHRF spectra differ from the corresponding BRF spectra and whether the derived predictive models could be transferred between these two types of spectral data. To fill the gap in spectral examination and model comparison, this study aimed to examine the effect of specular reflection on leaf reflectance properties, the commonly used spectral indices or indicators, and the derived regression models across a broad variety of leaf DHRF and BRF spectra. Specifically, we quantified the difference between leaf BRF and DHRF spectra measurements and evaluated the effect of specular reflection on the estimation of LCC with 20 spectral features in four categories (simple ratio, SR; modified SR, mSR; double difference index, DD and red edge position, REP). Seven measured datasets collected from a combination of species, growing conditions, sites, and years were used to evaluate the model difference and calibrate the unified models across DHRF and BRF spectra. The robustness of those predictive models was validated with independent measured and simulated datasets comprised of both BRF and DHRF spectra.Our results demonstrated that the BRF spectra exhibited systematically higher amplitude than DHRF spectra. The widely used vegetation indices (VIs) in the SR category exhibited the most noticeable sensitivity to specular reflection as a significant proportion in BRF spectra. The adverse effect of specular reflection can be alleviated by the other three categories to different extents. With further assessment of model comparisons, we determined four unified models from the pooled data with two DD indices and two REP metrics. Application of the unified models to the validation spectra yielded low RMSE values up to 4.5 μg/cm2 and would not result in a significant loss in accuracy as compared to the BRF-specific or DHRF-specific LCC predictive models derived from the four spectral features. The assessment of unified models with comprehensive measured and simulated data could help us better understand the mechanism underlying the specular reflection effect on the relationship of LCC with sensitive spectral features. It will also facilitate the direct estimation of LCC with the common types of leaf reflectance spectra, which is beneficial to rapid and non-destructive determination of LCC for the plant science and agronomy communities.

Visible-near infrared spectroradiometry-based detection of grapevine leafroll-associated virus 3 in a red-fruited wine grape cultivar

Grapevine leafroll disease (GLD) is one of the major threats to wine grapes (Vitis vinifera) causing substantial economic losses to the growers. This study was undertaken to evaluate the applicability of visible and near infrared (VIS-NIR) spectroradiometery as a rapid, robust and non–destructive optical sensing method for the detection of Grapevine leafroll-associated virus 3 (GLRaV-3) at different phenological stages in a red-berried wine grape cultivar. Using VIS-NIR spectroradiometer, data was collected from the healthy and GLRaV-3-infected leaf samples from cv. Cabernet Sauvignon for two seasons at specific intervals during asymptomatic and symptomatic stages of the disease. Fiber optic leaf clip was used to collect spectral responses from grapevine leaves under field conditions. Salient feature extraction using stepwise multilinear regression and partial least square regression methods showed significant differences between healthy and virus–infected leaves in the visible (351, 377, 501, 526, 626 and 676 nm) and near infrared (701, 726, 826, 901, 951, 976, 1001, 1027, 1052 and 1101 nm) regions. Spectral wavelengths from near infrared region (1001, 1027 and 1052 nm) were validated at different phenological stages spanning both asymptomatic and symptomatic stages of the disease. Selected spectral wavelengths demonstrated robustness in virus detection with overall classification accuracies in the range of 75–99% using quadratic discriminant analysis (QDA) classifier. QDA based classification accuracies for healthy, infected and overall classes were significantly higher compared to Naïve Bayes classifier. The accuracy for virus detection during asymptomatic stages was not significantly different from the symptomatic phase, indicating reliability of the selected features for early detection of GLRaV–3–infected grapevines.