Leaf Level Research Articles

Stomatal Responses of Two Drought-Tolerant Barley Varieties with Different ROS Regulation Strategies under Drought Conditions.

Drought stress is a major obstacle to agricultural production. Stomata are central to efforts to improve photosynthesis and water use. They are targets for manipulation to improve both processes and the balance between them. An in-depth understanding of stomatal behavior and kinetics is important for improving photosynthesis and the WUE of crops. In this study, a drought stress pot experiment was performed, and a transcriptome analysis of the leaves of three contrasting, cultivated barley genotypes Lumley (Lum, drought-tolerant), Golden Promise (GP, drought-sensitive), and Tadmor (Tad, drought-tolerant), generated by high-throughput sequencing, were compared. Lum exhibited a different WUE at the leaf and whole-plant levels and had greater CO2 assimilation, with a higher gs under drought stress. Interestingly, Lum showed a slower stomatal closure in response to a light-dark transition and significant differences compared to Tad in stomatal response to the exogenous application of ABA, H2O2, and CaCl2. A transcriptome analysis revealed that 24 ROS-related genes were indeed involved in drought response regulation, and impaired ABA-induced ROS accumulation in Lum was identified using ROS and antioxidant capacity measurements. We conclude that different stomatal ROS responses affect stomatal closure in barley, demonstrating different drought regulation strategies. These results provide valuable insight into the physiological and molecular basis of stomatal behavior and drought tolerance in barley.

Analysis of durum wheat photosynthetic organs during grain filling reveals the ear as a water stress-tolerant organ and the peduncle as the largest pool of primary metabolites

Main conclusionThe pool of carbon- and nitrogen-rich metabolites is quantitatively relevant in non-foliar photosynthetic organs during grain filling, which have a better response to water limitation than flag leaves.The response of durum wheat to contrasting water regimes has been extensively studied at leaf and agronomic level in previous studies, but the water stress effects on source–sink dynamics, particularly non-foliar photosynthetic organs, is more limited. Our study aims to investigate the response of different photosynthetic organs to water stress and to quantify the pool of carbon and nitrogen metabolites available for grain filling. Five durum wheat varieties were grown in field trials in the Spanish region of Castile and León under irrigated and rainfed conditions. Water stress led to a significant decrease in yield, biomass, and carbon and nitrogen assimilation, improved water use efficiency, and modified grain quality traits in the five varieties. The pool of carbon (glucose, glucose-6-phosphate, fructose, sucrose, starch, and malate) and nitrogen (glutamate, amino acids, proteins and chlorophylls) metabolites in leaf blades and sheaths, peduncles, awns, glumes and lemmas were also analysed. The results showed that the metabolism of the blades and peduncles was the most susceptible to water stress, while ear metabolism showed higher stability, particularly at mid-grain filling. Interestingly, the total metabolite content per organ highlighted that a large source of nutrients, which may be directly involved in grain filling, are found outside the blades, with the peduncles being quantitatively the most relevant. We conclude that yield improvements in our Mediterranean agro-ecosystem are highly linked to the success of shoots in producing ears and a higher number of grains, while grain filling is highly dependent on the capacity of non-foliar organs to fix CO2 and N. The ear organs show higher stress resilience than other organs, which deserves our attention in future breeding programmes.

Zinc fertilizers for Citrus production: assessing nutrient supply via fertigation or foliar application

Zinc (Zn) deficiency is widespread in citrus orchard around the world, which impairs plant growth and fruit yield. Zn fertilization is usually recommended in field orchards, and application strategies toward nutrient use efficiency are key for successful crop management. Field studies were carried out with sweet orange trees for four growing seasons, testing Zn (nitrate, sulfate or EDTA) applied either via fertigation or foliar spray compared to a control without Zn. The Zn concentrations in the soil increased with nutrient supply by both application methods. Zn-EDTA via fertigation increased soil nutrient availability compared to the control. Likewise, Zn-nitrate via foliar application also increased soil Zn levels. Regarding the plant, Zn-EDTA via fertigation increased leaf nutrient levels only after the third year of fertilization, whereas Zn-nitrate via foliar spray increased leaf levels in the short term, up to 120 mg kg−1. To a lesser extent, Zn-EDTA or sulfate also increased leaf Zn compared to the control. Accumulated fruit yield was ~20% higher in trees with Zn-EDTA via fertigation and ~ 20% higher in trees with Zn-nitrate or sulfate via foliar application compared to the control, with these latter exerting greater responses. Trees supplied with Zn exhibited lower H2O2 and higher CAT activity compared to the control, which correlated with a fruit yield increase. In conclusion, Zn-EDTA via fertigation or nitrate or sulfate via foliar application improved the horticultural performance of trees, supporting the establishment of best nutrient management practices in fruit production.

Melatonin alleviates chromium toxicity by altering chromium subcellular distribution and enhancing antioxidant metabolism in wheat seedlings.

The endogenous stimulating molecule melatonin (N-acetyl-5-methoxytryptamine, MT) has an important function in mitigating the impact of multiple abiotic stressors. However, the ameliorating effect of MT on chromium (Cr) stress and its mechanisms remains unclear. Therefore, the present study aimed to clarify the mitigating effect of exogenous MT (0μM and 100μM) on wheat seedlings under Cr (0μM and 50μM) stress stemming from the growth and physiological characteristics, phytochelatin (PC) biosynthesis, Cr subcellular distribution, and antioxidant system of the plants in these treatments. The results showed that endogenous MT application significantly promoted plant growth and improved root morphology of wheat seedlings under Cr stress due to decreased Cr and reactive oxygen species (ROS) accumulation in both roots and leaves. Accumulation and transport of Cr from roots to leaves were reduced by MT, because enhanced vacuolar sequestration via upregulated PC accumulation, took place, derived from the fact that MT upregulated the expression of key genes for PC synthesis (TaPCS and Taγ-ECS). Furthermore, MT pre-treatment alleviated Cr-induced oxidative damage by diminishing lipid peroxidation and cell apoptosis, profiting from the enhanced scavenging ability of ROS as a result of the MT-induced increase in the activities of superoxide dismutase, catalase, ascorbate peroxidase, and glutathione reductase, and the related encoding gene expression levels of TaSOD2, TaCAT, TaAPX, and TaGR. In conclusion, endogenous MT application improved the growth traits, antioxidant system, and decreased Cr accumulation especially at the leaf level in wheat seedlings under Cr stress mainly through enhancing antioxidant enzyme activities and altering Cr subcellular distribution via strengthening PC biosynthesis. The mechanisms of MT-induced plant tolerance to Cr stress could help develop new strategies for secure crop production in Cr-polluted soils.

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.

Improved chlorophyll and water content estimations at leaf level with a hybrid radiative transfer and machine learning model

Accurate and robust quantifications of leaf chlorophyll and water contents facilitate a better understanding of plant water and nutrient needs. Besides simplified spectral indices, other existing methods primarily use machine learning models to estimate plant traits from spectroscopy data. A powerful machine learning model usually relies on a large number of training samples. However, in-situ training data sampling in agriculture is often constrained by time and workforce. To cope with the issue of limited in-situ samples, this study proposed and evaluated a new hybrid method (Spiking-Hybrid) that coupled mechanistic model simulation with machine learning to estimate leaf chlorophyll a + b content (Cab) and equivalent water thickness (Cw). In addition, Spiking-Hybrid leveraged a ‘spiking’ technique that added in-situ data with extra replications into the simulated data to enhance modeling performance. We evaluated the Spiking-Hybrid method on two sets of in-situ data: 299 sorghum samples grown in the greenhouse and 566 maize samples grown in the field. Leaf VIS-NIR-SWIR reflectance data (400–2500 nm) were measured concurrently with Cab and Cw sampling. We used a leaf-level radiative transfer model (PROCOSINE) to simulate leaf reflectance as the function of leaf biochemical and internal structural parameters. The machine learning models included partial least squares regression, Gaussian process regression, and gradient boosting regression. The proposed method was further compared with three other common methods in plant trait estimation: the PROCOSINE inversion with numerical optimization, the conventional hybrid method, and the empirical machine learning method. Results showed that the proposed and the empirical methods outperformed the other two methods for estimating Cab and Cw, indicating the importance of involving in-situ data in model training. Moreover, we explored the effect of in-situ sample size. When the number of available in-situ samples was limited (i.e., less than 5 % of the total training samples), the Spiking-Hybrid method obtained consistently higher accuracy and better robustness than the empirical machine learning method. These results imply that the Spiking-Hybrid method can achieve accurate and robust estimations for Cab and Cw by harnessing the free simulated data and a significantly reduced number of in-situ samples. This method has great potential for agricultural research with limited in-situ samples.

Study of Genetic Variation in Bermuda Grass along Longitudinal and Latitudinal Gradients Using Spectral Reflectance

Genetic variation among populations within plant species can have huge impact on canopy biochemistry and structure across broad spatial scales. Since canopy spectral reflectance is determined largely by canopy biochemistry and structure, spectral reflectance can be used as a means to capture the variability of th genetic characteristics of plant species. In this study, we used spectral measurements of Bermuda grass [Cynodon dactylon (L.) Pers.] at both the leaf and canopy levels to characterize the variability of plant traits pertinent to phylogeographic variation along the longitudinal and latitudinal gradients. An integration of airborne multispectral and hyperspectral data allows for the exploitation of spectral variations to discriminate between the five different genotypic groups using ANOVA and RF models. We evaluated the spectral variability among high-latitude genotypic groups and other groups along the latitudinal gradients and assessed spectral variability along longitudinal gradients. Spectral difference was observed between genetic groups from the northern regions and those from other regions along the latitudinal gradient, which indicated the usefulness of spectral signatures for discriminating between genetic groups. The canopy spectral reflectance was better suited to discriminate between genotypes of Bermuda grass across multiple scales than leaf spectral data, as assessed using random forest models. The use of spectral reflectance, derived from remote sensing, for studying genetic variability across landscapes is becoming an emerging research topic, with the potential to monitor and forecast phenology, evolution and biodiversity.

Photosynthesis Characteristics of Tomato Plants and Its’ Responses to Microclimate in New Solar Greenhouse in North China

With the increasing demand for vegetable fruits, vegetable plants are moved to protected structures for achieving high production and economic revenue, especially in undesirable seasons. In North China, tomato crops, as widely consumed vegetables, are now increasingly planted in solar greenhouses (GH), especially in the winter period. To improve the microclimate inside GH in winter, a sunken solar greenhouse was used recently. This study was to evaluate the photosynthetic characteristics of tomato plants and its responses to the inside microclimate in this new GH. In this experiment, the plant transpiration (E) and photosynthesis (Pn) rates of healthy and diseased plants were measured from July to December for three growth seasons in a commercial GH in North China. Results show both E and Pn were positively related to inside radiation and vapor pressure deficit. The stomata conductance to E (gsw) and Pn (gtc) performed relatively constant during daytime, and weakly related to inside microclimate. The parameters of E, Pn, gsw and gtc were greatly reduced for diseased plants in summer because of the heat shock. The water use efficiency at the leaf level, the ratio of Pn to E, was higher for solar radiation of 400–500 W m−2, temperature of 20–30 °C, relative humidity of higher than 80%, and vapor pressure deficit of less than 2.0 kPa. The results of this study could help farmers in the region of 30 to 40 degrees north latitude to enhance the growth of tomato crops in winter by using this sunken solar greenhouse.

Investigating the Diurnal Radiative, Turbulent, and Biophysical Processes in the Amazonian Canopy‐Atmosphere Interface by Combining LES Simulations and Observations

AbstractWe investigate the atmospheric diurnal variability inside and above the Amazonian rainforest for a representative day during the dry season. To this end, we combine high‐resolution large‐eddy simulations that are constrained and evaluated against a comprehensive observation set, including CO2 concentrations, gathered during GoAmazon2014/15. We design systematic numerical experiments to quantify whether a multilayer approach in solving the explicit canopy improves our canopy‐atmosphere representation. We particularly focus on the relationship between photosynthesis and plant transpiration, and their distribution at leaf and canopy scales. We found the variability of photosynthesis drivers like vapor pressure deficit and leaf temperature to be about 3 times larger for sunlit leaves compared to shaded leaves. This leads to a large spread on leaf stomatal conductance values with minimum and maximum values varying more than 100%. Regarding the turbulent structure, we find wind‐driven stripe‐like shapes at the canopy top and structures resembling convective cells at the canopy. Wind‐related variables provide the best spatiotemporal agreement between model and observations. The potential temperature and heat flux profiles agree with an observed decoupling near the canopy top interface, although with less variability and cold biases of up to 3 K. The increasing complexity on the biophysical processes leads to the largest disagreements for evaporation, CO2 plant assimilation and soil efflux. The model is able to capture the correct dependences and trends with the magnitudes still differing. We finally discuss the need to revise leaf and soil models and to complete the observations at leaf and canopy levels.

Sodium Lignosulfonate Effect on Physiological Traits of Cucumis sativus L. Seedlings

The application of pulp and paper mill sludge to agricultural soils is commonly considered as a strategy to improve soil properties, promote plant growth, and reduce the demand for costly chemical fertilization. The aim of this study was to evaluate if sodium lignosulfonate (sLS), one of the sludges of pulp production, may affect the biomass production, the respiration (R) and net CO2 assimilation rate (An) at the leaf level, and the content and accumulation of trace elements in the leaves of cucumbers grown under a sufficient nutrient supply or soil nutrient deficit. A pot culture experiment was conducted using sLS application rates of 0, 1.0, 2.5, 5.0, and 10 vol% to sandy loam soil. The decline in nutrient availability caused an increase in the R/An ratio and dramatically depressed biomass accumulation. The leaf Fe, Ni, Cr, Co, Al, and Pb contents were lower under low nutrient availability than under sufficient nutrient supply. Although sLS was not very effective in lessening the negative effect of nutrient deficiency on biomass accumulation, it reduced respiratory carbon losses and cell membrane permeability in the leaves of cucumbers grown under nutrient deficit. The reduction in the toxic level of leaf Mn in seedlings grown under sufficient nutrient availability and the toxic level of leaf Fe under a nutrient deficit might also be considered as a positive effect of the sLS application to sandy soil.

Divergent adaptations of leaf functional traits to light intensity across common urban plant species in Lanzhou, northwestern China.

Leaves are the most important photosynthetic organs in plants. Understanding the growth strategy of leaves in different habitats is crucial for elucidating the mechanisms underlying plant response and adaptation to the environment change. This study investigated the scaling relationships of the laminar area (LA), leaf fresh mass (LFM), leaf dry mass (LDM), and explored leaf nitrogen (N) and phosphorus (P) content in leaves, and the relative benefits of these pairwise traits in three common urban plants (Yulania denudata, Parthenocissus quinquefolia, and Wisteria sinensis) under different light conditions, including (full-sun and canopy-shade). The results showed that: the scaling exponent of LDM vs LA (> 1, p < 0.05) meant that the LDM increased faster than LA, and supported the hypothesis of diminishing returns. The LFM and LDM had isometric relationships in all the three species, suggesting that the leaf water content of the leaves was nearly unaltered during laminar growth. Y. denudata and W. sinensis had higher relative benefit in full-sun habitats, while the reverse was observed in P. quinquefolia. The N and P content and the N:P ratio in full-sun leaves were generally higher than those of canopy-shade leaves. The leaves of the three urban plants exhibited a shift in strategy during transfer from the canopy shaded to the sunny habitat for adapting to the lower light conditions. The response of plant leaves to the environment shapes the rich variations at the leaf level, and quantification of the relative benefits of plants in different habitats provides novel insights into the response and adaptation strategies of plants.

Prediction of Grassland Biodiversity Using Measures of Spectral Variance: A Meta-Analytical Review

Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess the test results of the hypothesis across studies using several moderating variables relating to the botanical and spectral sampling strategies and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatments due to their dynamic phenology and the taxonomic complexity of their canopies on a small scale. Across studies, the results suggest an overall positive relationship between spectral variation and species diversity (mean correlation coefficient = 0.36). However, high levels of both within-study and between-study heterogeneity were found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf-level studies displaying a stronger relationship compared to canopy-level studies. We highlight the challenges facing the synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multitemporal assessments to resolve the controversy in this field.

Exploring plant responses to abiotic stress by contrasting spectral signature changes.

In this study, daily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes (Triticum aestivum L.) susceptible to adverse conditions. Four genotypes were grown in pots experiments under semi-controlled conditions in Chile and Spain. Three treatments were applied: i) control (C), ii) water stress (WS), and iii) combined water and heat shock (WS+T). Spectral reflectance, gas exchange and chlorophyll fluorescence measurements were performed on flag leaves for three consecutive days at anthesis. High canopy temperature ( H CT ) genotypes showed less variability in their mean spectral reflectance signature and chlorophyll fluorescence, which was related to weaker responses to environmental fluctuations. While low canopy temperature ( L CT ) genotypes showed greater variability. The genotypes spectral signature changes, in accordance with environmental fluctuation, were associated with variations in their stomatal conductance under both stress conditions (WS and WS+T); L CT genotypes showed an anisohydric response compared that of H CT , which was isohydric. This approach could be used in breeding programs for screening a large number of genotypes through proximal or remote sensing tools and be a novel but simple way to identify groups of genotypes with contrasting performances.

The Effect Of Administration Of Salam Leaf Extract (Eugenia Polyantha) On Hdl And Ldl Colesterol Levels In Prolanis Patients At Iman Clinic

Lipids are a component of micro-substance molecules in the body that function as structural components in cell membranes and can also be used as a source of energy for the body. Lipid levels in the blood can be measured by examining the total cholesterol, triglycerides, HDL, LDL and VLDL. This study analyzed the effect of bay leaf extract on HDL and LDL cholesterol levels in Prolanis patients at Iman clinic. Its an experimental pretest-posttest study using 38 elderly (&gt;45 years) divided into the experimental and control group. The results showed that the average effect on HDL cholesterol levels was 40.37 mg/dL pretest and 40.11 mg/dL posttest, LDL cholesterol levels found an average value of 119.58 mg/dL pretest and posttest 130.63 mg/dL in the experimental group, whereas in the control group it was found that the effect of giving the extract on HDL and LDL cholesterol levels was not significant. The significance value based on the Wilcoxon statistical test was 0.461 for HDL cholesterol levels and 0.953 for LDL cholesterol levels. Conclusion: There is no effect of bay leaf extract ( Eugenia Polyantha ) on HDL and LDL cholesterol levels in prolanis patients at the Iman clinic. Keywords : Lipid, Bay Leaf, HDL and LDL Cholesterol Levels

Twigs of dove tree in high-latitude region tend to increase biomass accumulation in vegetative organs but decrease it in reproductive organs.

Adaptive traits are an important dimension for studying the interactions between rare plants and environment. Although the endangered mechanism of rare plants has been reported in many studies, how their twigs adapt to heterogeneous environments associated with latitude is still poorly known. Dove tree (Davidia involucrata Baill.), a monotypic rare species in China, was employed as a model species in our study, and the differences in functional traits, growth relationships and resource allocation among components of annual twig were investigated in three latitudinal regions (32°19' N, 30°08' and 27°55') in the Sichuan, Southwest China. Compared with low- and middle-latitude regions, the twig diameter in high-latitude region decreased by 36% and 26%, and dry mass decreased by 32% and 35%, respectively. Moreover, there existed an allometric growth between flower mass and stem mass or leaf mass in high-latitude region but an isometric growth in low- and middle-latitude regions. At the flower level, an isometric growth between bract area and flower stalk mass was detected among in three latitudinal regions, and the flower stalk mass in the low-latitude region was higher than in the middle- and high-latitude regions for a given bract area and flower mass. At the leaf level, the growth rate of petiole mass was significantly higher than those of leaf area, lamina mass and leaf mass among three latitudinal regions, and the petiole mass in the low-latitude region was higher than in the other two regions for a given leaf mass. Our research demonstrated that the twigs of dove tree in high-latitude region tend to become smaller, and resource input increase in stems and leaves but decrease in flowers, which reflects that dove tree can adapt to the environmental changes across different latitudes by adjusting phenotypic traits growth and biomass allocation of twigs.

PhenoBot: an automated system for leaf area analysis using deep learning.

A low-cost dynamic image capturing and analysis pipeline using color-based deep learning segmentation was developed for direct leaf area estimation of multiple crop types in a commercial environment. Crop yield is largely driven by intercepted radiation of the leaf canopy, making the leaf area index (LAI) a critical parameter for estimating yields. The growth rate of leaves at different growth stages determines the overall LAI, which is used by crop growth models (CGM) for simulating yield. Consequently, precise phenotyping of the leaves can help elucidate phenological processes relating to resource capturing. A stable system for acquiring images and a strong data processing backend play a vital role in reducing throughput time and increasing accuracy of calculations, compared to manual analysis. However, most available solutions are not dynamic, as they use color-based segmentation, which fails to capture leaves with varying shades and shapes. We have developed a system that uses a low-cost setup to acquire images and an automated pipeline to manage the data storage on the device and in the cloud. The system is powered by virtual machines that run multiple custom-trained deep learning models to segment out leaves, calculate leaf area (LA) for the whole set and at the individual leaf level, overlays important information on the images, and appends them on a compatible file used for CGMs with very high accuracy. The pipeline is dynamic and can be used for multiple crops. The use of open-source hardware, platforms, and algorithms makes this system affordable and reproducible.

Short term variation in leaf level water use efficiency in a tropical forest.

The representation of stomatal regulation of transpiration and CO2 assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance (gs ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g1 (stomatal slope, inversely proportional to water use efficiency) and g0 (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf level response curves of six tropical broadleaf evergreen tree species. g1 estimated from ex-situ response curves averaged 50% less than g1 estimated from survey data. While g0 and g1 varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g1 and g0 that significantly improved model projections of diurnal trends in transpiration. The accuracy of modelled gs can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g1 will be required to account for this phenomena in land surface models.

APRENDIZAGEM DE MÁQUINA PARA IDENTIFICAÇÃO DE PLANTAS DE SOJA SOB ATAQUE DE INSETOS USANDO DADOS HIPERESPECTRAIS

The integration between the areas of remote sensing and machine learning has allowed an advance in the way of mapping agricultural fields and monitoring crops. This work investigates the ability of machine learning algorithms to classify soybean plants under insect attack, using reflectance spectroscopy measurements collected at the leaf level. To this end, tests were developed with different algorithms using a set of 991 spectral curves referring to healthy soybean plants under attack by pests, collected in eight consecutive days. These curves were measured by the EMBRAPA team, using a portable spectroradiometer, which records in the range of 350 to 2500 nm. Such curves were, initially, pre-processed to remove the regions of atmospheric absorption by water vapor, and then subdivided into a set of training, validation and testing of the machine learning algorithms. The Google Collabs interpreter was used and the algorithms were written in Python language, using libraries such as Skit Sklearn. Among the algorithms used, there are Random Forest, Decision Tree, Support Vector Machine, Logistic Regression and Extra-Tree. The Extra-tree has better performance (F1-score = 80.40%; precision = 81%; recall = 80%) in the proposed task. It is concluded that it is possible to process reflectance spectroscopy measurements with machine learning algorithms to monitor insect attack on soybean plants. It is recommended that the applied approach be tested in other cultures.

Physiological strategies for handling summer water stress differ among co-existing species and between juvenile and mature trees

Determining tree response to climate stress is critical for predicting changes in forest carbon dynamics as well as tree mortality. In temperate deciduous forests, describing this response is complicated by the complex diversity of leaf and wood characteristics among co-existing species. Furthermore, because of the inherent logistical limitations of measuring mature forest trees, many carbon models and stress-response studies are informed by physiological data collected from juvenile trees (seedlings or saplings). However, the extent to which juvenile and mature trees differ in their physiological responses to water stress is not well documented under natural conditions. The majority of carbon sequestered in a forest is in mature trees; therefore, direct canopy measurements comparing responses to climate in juvenile and mature trees would allow us to more accurately predict changes in ecosystem carbon uptake. Here, we present data describing the physiological responses to summer water stress in juvenile trees of six temperate deciduous species. Our results indicate that species exhibited variation and plasticity in stress hydraulic parameters yet maintained similar rates of carbon uptake. We demonstrate how integrative photosynthetic parameters, such as photosynthetic capacity and quantum efficiency of photosystem II, are beneficial for wholistically displaying physiological responses at the leaf level. We further compared seasonal patterns of leaf water potential during decreasing soil water availability between the juvenile trees and co-existing mature trees of the same species. Our data reveal that while some species remain static in their hydraulic behavior from the juvenile to adult stage, other species are dynamic between life stages. Models, as well as experimental studies examining tree response to stressors, should plan for plasticity in physiological parameters among co-existing species, and should further allow variability between life stages for particular species. The capacity to effectively inform models from data collected in mature trees will inevitably lead to improved predictions of tree mortality and forest carbon trajectories.

A New Method to Estimate the Leaf Chlorophyll Content From Multiangular Measurements: Anisotropy Index

Anisotropy index (ANIX), which is defined as the ratio between the maximum and minimum reflectance factors in the principal plane, has been applied in characterizing the optical properties of vegetation. But it is seldom used to estimate the chlorophyll content in leaf level. In this study, we found the leaf spectral anisotropy index (ANIX) depended on the variation of leaf chlorophyll content (LCC). Newly proposed indices, (ASRI: A750/A720 and ANDI: (A750-A720)/(A750+A720), A is the modified ANIX (mANIX=ANIX-1)), were derived from the format of the existing reflectance-based vegetation indices: simple ratio (SR) and normalized difference (ND) indices. The diffuse reflection, closely related to LCC, can be extracted from the two new indices (or directly obtained as the minimum bidirectional reflectance factor). They have strong linear relationships with LCC (R2=0.88 and 0.89), and have good estimation accuracy using an independent dataset (RMSE=6.39 μg/cm2 and 6.10 μg/cm2). We also found the ASRI (ANDI) had similar LCC estimation accuracy as the range-ASRIs or range-ANDIs (RANDIs or RANDIs), which were calculated by the random combination of reflectance factors in the forward scattering directions (ranged from -30° to -50°) and near the backward scattering directions (ranged from 0° to 20°). The wide and effective ranges of viewing angles strengthen the usability of ASRI and ANDI to estimate LCC in the practical measurements.