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SUMMARY Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant‐environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9–48%), year (4–52%), and genotype‐by‐year interactions (14–36%) to varying extents throughout development. Genome‐wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits. [ABSTRACT FROM AUTHOR]

The evaluation and prediction of reliability and testability of mining machinery and equipment are crucial, as advancements in mining technology have increased the importance of ensuring the safety of both the technological process and human life. This study focuses on developing a reliability model to analyze the controllability of mining equipment. The model, which examines the reliability of a mine cargo-passenger hoist, utilizes statistical methods to assess failures and diagnostic controlled parameters. It is represented as a transition graph and is supported by a system of equations. This model enables the estimation of the reliability of equipment components and the equipment as a whole through a diagnostic system designed for monitoring and controlling mining equipment. A mathematical and logical model is proposed to calculate availability and downtime coefficients for different structures within the mining equipment system. This analysis considers the probability of failure-free operation of the lifting unit based on the structural scheme, with additional redundancy for elements with lower reliability. The availability factor of the equipment for monitoring and controlling the mine hoisting plant is studied for various placements of diagnostic systems. Additionally, a logistic concept is introduced for organizing preventive maintenance systems and reducing equipment recovery time by optimizing spare parts, integrating them into strategies aimed at enhancing the reliability of mine hoisting plants. [ABSTRACT FROM AUTHOR]

Canopy foliar traits serve as crucial indicators of plant health and productivity, forming a vital link between plant conditions and ecosystem dynamics. In this study, the use of hyperspectral data and foliar traits for white pine needle damage (WPND) detection was investigated for the first time. Eastern White Pine (Pinus strobus L., EWP), a species of ecological and economic significance in the Northeastern USA, faces a growing threat from WPND. We used field-measured leaf traits and hyperspectral remote sensing data using parametric and non-parametric methods for WPND detection in the green stage. Results indicated that the random forest (RF) model based solely on remotely sensed spectral vegetation indices (SVIs) demonstrated the highest accuracy of nearly 87% and Kappa coefficient (K) of 0.68 for disease classification into asymptomatic and symptomatic classes. The combination of field-measured traits and remote sensing data indicated an overall accuracy of 77% with a Kappa coefficient (K) of 0.46. These findings contribute valuable insights and highlight the potential of both field-derived foliar and remote sensing data for WPND detection in EWP. With an exponential rise in forest pests and pathogens in recent years, remote sensing techniques can prove beneficial for the timely and accurate detection of disease and improved forest management practices. [ABSTRACT FROM AUTHOR]

Bioassay with an insect herbivore is a common approach to studying plant defense. While measuring insect growth rate as a negative indicator of plant defense levels is simple and straightforward, analysing more detailed feeding behavior parameters of insects, such as feeding rates, leaf area consumed per feeding event, intervals between feeding events, and spatio-temporal patterns of feeding sites on leaves, is more informative. However, such observations are generally time consuming and labor-intensive. Here, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves. Automated photo scanners record the time-course development of feeding marks on leaves. An image analysis pipeline processes the scanned images and extracts leaf area. By analysing changes in leaf area over time, it detects insect feeding events and calculates the leaf area consumed during each feeding event, providing quantitative parameters of the feeding behavior of insects. In addition, it visualizes spatio-temporal changes in feeding sites, providing a measure of the complex behavior of insects on leaves. Using this analysis pipeline, we demonstrate that Arabidopsis trichomes reduce insect feeding rate, but not feeding duration or intervals between feeding events. Our image acquisition system requires only a photo scanner and a laptop computer and does not require any specialized equipment. The analysis software is provided as an ImageJ macro and R package and is available at no cost. Taken together, our work provides a scalable method for quantitative assessment of the feeding behavior of insects on leaves, facilitating understanding of plant defense mechanisms. [ABSTRACT FROM AUTHOR]

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R² values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds. [ABSTRACT FROM AUTHOR]

Mechanically transmissible and stable viruses such as tobamoviruses, which include Tobamovirus fructirugosum (syn. tomato brown rugose fruit virus (ToBRFV), will continue to pose major challenges for farmers. Consequently, holistic hygiene concepts are being implemented to prevent the introduction and spread of these viruses. The decontamination of tires and castors was previously a weak point in many industrial hygiene concepts. For this reason, the ProfilGate clean-off zone was tested in combination with the disinfectant MENNO Florades for the decontamination of ToBRFV-contaminated tires. In total, 478 tire segments were sampled to evaluate the contamination of ToBRFV and the following decontamination of the tires. This treatment reliably removed high (4.5 µg/cm2), medium (0.45 µg/cm2), and low concentrations (0.045 µg/cm2) of ToBRFV from the tires, as shown by a bioassay. The reduction in necrotic local lesions on susceptible indicator plants N. tabacum cv. Xanthi NN was between 91.9 and 97.6%. The reduction in ToBRFV contamination largely depended on the length of the rollover distance, i.e., the number of tire rotations. For transport trolleys with polyamide and rubber tires, depletions of 97.4 and 97.6%, respectively, was determined after 16 rotations. For transport wagons with tires twice the size and polyurethane tread, the depletion was still at least 91% after eight wheel turns. Even in the case of gross soiling of the tires, the mean reduction from the different tread materials was 80.9 to 98.9%. Subsequent analysis of the clean-off zone revealed that ToBRFV did not accumulate, even when the contaminated tires were driven over several times, but was safely inactivated completely in the disinfectant solution. This provides growers with an effective tool for preventing the introduction and spread of ToBRFV. [ABSTRACT FROM AUTHOR]

A multiplicity of substantial disruptions in the physiological systems of the wheat crop (Triticum aestivum L.) are the result of a potassium deficiency.The foliar application of potassium improves the quality of wheat and the production of grains for end-use purposes. This study was carried out during the growing seasons of 2021-22 to investigate the response of winter wheat cultivars to foliar application of potassium (0, 1, and 2 g/L), at two growth stages (green up and heading) on the grain yield (t/ha), protein content (%), specific weight and plant vegetative indicators (chlorophyll content, flag leaf area and plant height). Foliar applications of potassium fertilizer were implemented at concentrations of (0, 1 and 2 g/L). The treatments were applied at both the tillering and flowering stages. Potassium treatments, wheat cultivars, and their interactions significantly differed in most measured parameters. Application of 2g/L of K increases grain yield, protein content, and plant height than 0, 1 g/L by 5.51 t/ha, 10.52% and 95.60 cm, respectively. Foliar application of nano-potassium timed at both tillering and flowering stages would be beneficial in improving the yield and its component. Furthermore, the various trait responses of cultivars to K application can be attractive to wheat breeders for selection. [ABSTRACT FROM AUTHOR]

Bell pepper (Capsicum annuum L.); an important spice crop of the region is a rich source of vitamins and antioxidants having many health benefits. Many biotic and abiotic factors contribute towards growth and yield losses of this crop. Arsenic (As) toxicity is a global issue, but it is particularly critical in developing countries. The current study was designed to evaluate the efficacy of selenium (Se) in mitigating the toxic effects of As in two varieties (HSP-181 A and PS09979325) of Capsicum annuum L. Different concentrations of As (0, 50, and 100 µM) and Se (0, 5, and 10 µM) were tested using 14 days old seedlings of C. annuum L. The As stress caused a significant (P ≤ 0.001) reduction in growth, uptake of nutrients, and eco-physiological attributes in both varieties however, the response was specific. While the overproduction of osmo-protectants and antioxidants intensified the symptoms of oxidative stress. The maximum reduction in shoot length (45%), fresh weight (29%), and dry weight (36%) was observed in under 100 µM As stress. The organic acids exudation from the roots of both cultivars were significantly increased with the increase in As toxicity. The Se treatment significantly (p ≤ 0.001) improved growth, nutrient uptake, gas exchange attributes, antioxidant production, while decreased oxidative stress indicators, and As uptake in the roots and shoots of all the subjects under investigation. It is concluded from the results of this study that Se application increased photosynthetic efficiency and antioxidant activity while decreasing As levels, organic acid exudation, and oxidative stress indicators in plants. Overall, the var. PS09979325 performed better and may be a good candidate for future pepper breeding program. [ABSTRACT FROM AUTHOR]

Summary Soil moisture shapes ecological patterns and processes, but it is difficult to continuously measure soil moisture variability across the landscape. To overcome these limitations, soil moisture is often bioindicated using community‐weighted means of the Ellenberg indicator values of vascular plant species. However, the ecology and distribution of plant species reflect soil water supply as well as atmospheric water demand. Therefore, we hypothesized that Ellenberg moisture values can also reflect atmospheric water demand expressed as a vapour pressure deficit (VPD). To test this hypothesis, we disentangled the relationships among soil water content, atmospheric vapour pressure deficit, and Ellenberg moisture values in the understory plant communities of temperate broadleaved forests in central Europe. Ellenberg moisture values reflected atmospheric VPD rather than soil water content consistently across local, landscape, and regional spatial scales, regardless of vegetation plot size, depth as well as method of soil moisture measurement. Using in situ microclimate measurements, we discovered that forest plant indicator values for moisture reflect an atmospheric VPD rather than soil water content. Many ecological patterns and processes correlated with Ellenberg moisture values and previously attributed to soil water supply are thus more likely driven by atmospheric water demand. [ABSTRACT FROM AUTHOR]

Background Results Conclusion Leaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV‐RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI.The results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = −0.783). Furthermore, the visible atmospheric resistance index, the green–red vegetation index, the modified green–red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables.This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer‐level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth. [ABSTRACT FROM AUTHOR]

Phenological shifts across plant species is a powerful indicator to quantify the effects of climate change. Today, mobile applications with automated species identification open new possibilities for phenological monitoring across space and time.Here, we introduce an innovative spatio‐temporal machine learning methodology that harnesses such crowd‐sourced data to quantify phenological dynamics across taxa, space and time. Our algorithm links individual phenological responses across thousands of species and geographical locations, using a similarity measure. The analysis draws on nearly ten million plant observations collected through the AI‐based plant identification app Flora Incognita in Germany from 2018 to 2021.Our method quantifies changes in synchronisation across the annual cycle. During the growing season, synchronised behaviour can be encoded by a few characteristic macrophenological patterns. Nonlinear spatio‐temporal changes of these patterns can be efficiently quantified using a data compressibility measure. Outside the growing season, the phenological synchronisation diminishes introducing noise into the patterns.Despite biases and uncertainties associated with crowd‐sourced data, for example due to human data collection behaviour, our study demonstrates the feasibility of deriving meaningful indicators for monitoring plant macrophenology from individual plant observations. As crowd‐sourced databases continue to expand, our approach holds promise to study climate‐induced phenological shifts and feedback loops. [ABSTRACT FROM AUTHOR]

Biomass serves as a crucial indicator of plant productivity, and the development of biomass models has become an efficient way for estimating tree biomass production rapidly and accurately. This study aimed to develop a rapid and accurate model to estimate the individual aboveground biomass of Salix suchowensis. Growth parameters, including plant height (PH), ground diameter (GD), number of first branches (NFB), number of second branches (NSB) and aboveground fresh biomass weight (FW), were measured from 892 destructive sample trees. Correlation analysis indicated that GD had higher positive correlations with FW than PH, NFB and NSB. According to the biological features and field observations of S. suchowensis, the samples were classified into three categories: single-stemmed type, first-branched type and second-branched type. Based on the field measurement data, regression models were constructed separately between FW and each growth trait (PH, GD, NFB and NSB) using linear and nonlinear regression functions (linear, exponential and power). Then, multiple power regression and multiple linear regression were conducted to estimate the fresh biomass of three types of S. suchowensis. For the single-stemmed plant type, model M1 with GD as the single parameter had the highest adj R2, outperforming the other models. Among the 16 constructed biomass-estimating equations for the first-branched plant type, model M32 FW = 0.010371 × PH1.15862 × GD1.250581 × NFB0.190707 was found to have the best fit, with the highest coefficient of determination (adj R2 = 0.6627) and lowest Akaike Information Criterion (AIC = 5997.3081). When it comes to the second-branched plant type, the best-fitting equation was proved to be the multiple power model M43 with PH, GD, NFB and NSB as parameters, which had the highest adj R2 value and best-fitting effect. The stability and reliability of the models were confirmed by the F-test, repeated k-fold cross-validation and paired-sample t-tests. The models developed in this study could provide efficient tools for accurately estimating the total aboveground biomass for S. suchowensis at individual tree levels. The results of this study could also be useful for optimizing the economic productivity of shrub willow plantations. [ABSTRACT FROM AUTHOR]

Plant height is an important parameter of plant phenotype as one indicator of plant growth. In view of the complexity and scale limitation in current measurement systems, a scaleless method is proposed for the automatic measurement of plant height based on monocular computer vision. In this study, four peppers planted side by side were used as the measurement objects. Two color images of the measurement object were obtained by using a monocular camera at different shooting heights. Binary images were obtained as the images were processed by super-green grayscale and the Otsu method. The binarized images were transformed into horizontal one-dimensional data by the statistical number of vertical pixels, and the boundary points of multiple plants in the image were found and segmented into single-plant binarized images by filtering and searching for valleys. The pixel height was extracted from the segmented single plant image and the pixel displacement of the height was calculated, which was substituted into the calculation together with the reference height displacement to obtain the realistic height of the plant and complete the height measurements of multiple plants. Within the range of 2–3 m, under the light condition of 279 lx and 324 lx, this method can realize the rapid detection of multi-plant phenotypic parameters with a high precision and obtain more accurate plant height measurement results. The absolute error of plant height measurement is not more than ±10 mm, and the absolute proportion error is not more than ±4%. [ABSTRACT FROM AUTHOR]

In modern agricultural systems, high crop yields depend on the high input of orthophosphate-based fertilizers. Owing to the looming scarcity of rock phosphate in the near future, utilization of arbuscular mycorrhizal (AM) symbiosis for the efficient uptake of phosphorus is a necessary approach to maintain crop yield. Plant responsiveness to AM fungi is assessed based on the difference in biomass between plants with and without AM fungal inoculation. Soybean is a plant which establishes both mycorrhizal and rhizobial symbiosis. Therefore, it is difficult to eliminate the symbiotic effects of rhizobial nitrogen fixation during long-term cultivation until soybean harvest. In this study, Bradyrhizobium diazoefficiens USDA110 was inoculated at the same time as soybean seeds were sown, and soybean was grown under Rhizophagus irregularis inoculated and non-inoculated conditions to verify whether the mycorrhizal inoculation effect was observed at the yield level. In 2013, two soybean cultivars Tachinagaha and Williams 82, were grown at five levels of phosphate availability (0, 5, 10, 20, and 40 kg P 10a−1) in an open greenhouse in Tsukuba, Japan. Among the several growth indices analyzed, seed weight per plant was selected as a reliable indicator of plant response. Mycorrhizal inoculation showed a positive effect on soybean under 20 kg P 10a−1 phosphorus fertilization for Williams 82, while Tachinagaha showed almost no effect. This trend was replicated in 2014 and 2016, indicating that Tachinagaha has a lower AM responsiveness than Williams 82. [ABSTRACT FROM AUTHOR]

This study investigated the growth of Euphorbia tithymaloides L. in soil contaminated with Cr (VI), focusing on plant development, Cr (VI) accumulation in the plant, and associated rhizosphere bacterial communities. Plants were cultivated in pots for a period of 123 days with varying Cr (VI) levels (0, 10, 20, 30, and 40 mg-kg−1) in the soil. As Cr (VI) concentration increased, plant growth indicators such as fresh weight, dry weight, root length, and shoot length were observed to be decreasing. The Cr (VI) concentration peaked in plant tissues under the 40 mg-kg−1 treatment, with concentrations of 397.5 mg-kg−1, 98.12 mg-kg−1, and 62.32 mg-kg−1 in roots, stems, and leaves respectively. Whereas, the total Cr (VI) accumulation was maximum in the 30 mg-kg−1 treatment due to higher plant biomass. Further, the phytoremediation efficiency of the plant was evaluated through the bio-concentration factor (BCF) and translocation factor (TF). The BCF indicated soil-to-plant heavy metal uptake capacity exceeding 1, while the TF showed root-to-shoot translocation below 1 for all treatments, implying effective phytostabilisation potential. Metagenomic analysis of rhizospheric soil highlighted dominant bacterial phyla of Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Acidobacteria. Many observed bacterial taxa are metal-tolerant and commonly found in heavy metal-contaminated soil, suggesting a potential contribution to mitigating metal toxicity and enhancing plant growth. However, a comprehensive investigation into microbial communities' roles during phytoremediation requires further in-depth research. [ABSTRACT FROM AUTHOR]

The article presents the results of two-year research on the dependence of a complex of quantitative and qualitative indicators of oil flax seed yield of Southern Night and Vodohray varieties, as well as seed sowing conditions on the methods and regimes of combine harvesting of the crop. The direct single-phase method of combine harvesting did not lag behind the control in terms of seed productivity per plant or M1000 indicator. Harvesting according to the two-phase scheme had a significant negative impact on the formation of these indicators: the M1000 indicator decreased by 0.5 g or 7.1% compared to the control, and the seed mass per plant decreased by 0.06 g or 15.4%. Pre-harvest desiccation in the single-phase method also negatively affected the formation of these indicators: when using Reglone Super, the M1000 indicator decreased by 0.7 g, Roundup by 1.0 g compared to the control; seed productivity per plant decreased by 0.07 and 0.1 g, respectively. The oil flax yield with direct single-phase harvesting was lower than the control by 0.03 t/ha or 2.1% (Southern Night variety) and by 0.04 t/ha or 3.0% (Vodohray variety). Additional desiccation of crops significantly reduced seed yield. When using Reglone Super desiccant, this reduction was 0.06-0.09 t/ha (4.5-6.4%), Roundup - 0.10-0.13 t/ha (7.5-9.2%). The two-phase harvesting method yielded lower seed yield compared to the control by 0.10 t/ha (7.1%) when growing the Southern Night variety and by 0.08 t/ha (6.0%) when growing the Vodohray variety. The oil content of seeds of both flax varieties did not differ from the control with single-phase harvesting. Desiccation of crops during single-phase harvesting led to a significant decrease in crude fat content in seeds - by 0.7-0.9% (Southern Night variety) and 0.5-0.9% (Vodohray variety). Two-phase harvesting when growing the Southern Night variety contributed to a decrease in seed oil content by 0.5%, while it did not affect this quality indicator when growing the Vodohray variety. Laboratory germination similarity and energy reached maximum values in the control and in variants of direct singlephase harvesting without desiccation of crops - 96.2 and 88.5-88.7%, respectively. Other studied methods and regimes of harvesting worsened the sowing conditions of oil flax seeds. [ABSTRACT FROM AUTHOR]

The effective management of water and nitrogen is crucial in the artificial cultivation of medicinal plants. Sophora alopecuroides, a perennial herbaceous plant in the Fabaceae family, is extensively used in medicine, with alkaloids as its primary bioactive constituents. Nevertheless, there remains a significant knowledge gap regarding how rhizospheric microbial communities respond to varying water and nitrogen conditions and their intricate relationships with soil environments and the growth of S. alopecuroides. In this study, two-year-old S. alopecuroides were used in a two-factor, three-level water-nitrogen interaction experiment. The irrigation levels included W1 (30–35% of maximum water holding capacity), W2 (50–55%), and W3 (70–75%), while nitrogen levels comprised N1 (32 mg/kg), N2 (64 mg/kg), and N3 (128 mg/kg). The study assessed plant growth indicators, total alkaloid content, and rhizospheric soil physicochemical parameters of S. alopecuroides. High-throughput sequencing (16S rRNA and ITS) was employed to analyze variations in rhizospheric microbial community composition and structure. The results showed that Proteobacteria and Ascomycota are the predominant bacterial and fungal phyla in the rhizosphere microbial community of S. alopecuroides. The highest biomass and alkaloid accumulation of S. alopecuroides were observed under the N1W3 treatment (50% nitrogen application and 70–75% of maximum water holding capacity). Specifically, six bacterial genus-level biomarkers (TRA3_20, MND1, env_OPS_17, SBR1031, Haliangium, S0134_terrestrial_group) and six fungal genus-level biomarkers (Pseudeurotium, Rhizophagus, Patinella, Pseudeurotium, Patinella, Rhizophagus) were identified under the N1W3 treatment condition. In the partial least squares path modeling (PLS-PM), water and nitrogen treatments demonstrated markedly positive direct effects on soil physicochemical parameters (p < 0.01), while showing significant negative direct impacts on alkaloid accumulation and plant growth indicators (p < 0.05). Soil physicochemical parameters, in turn, significantly negatively affected the rhizosphere fungal community (p < 0.05). Additionally, the rhizosphere fungal community exhibited highly significant negative direct effects on both the plant growth indicators and total alkaloid content of S. alopecuroides (p < 0.01). This study provides new insights into the interactions among rhizosphere soil environment, rhizosphere microbiota, plant growth, and alkaloid accumulation under water and nitrogen regulation, offering a scientific basis for the water and nitrogen management in the cultivation of S. alopecuroides. [ABSTRACT FROM AUTHOR]

Diameter and height are crucial morphological parameters of banana pseudostems, serving as indicators of the plant's growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a threedimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudostems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers. [ABSTRACT FROM AUTHOR]

SUMMARY: Food security is threatened by climate change, with heat and drought being the main stresses affecting crop physiology and ecosystem services, such as plant–pollinator interactions. We hypothesize that tracking and ranking pollinators' preferences for flowers under environmental pressure could be used as a marker of plant quality for agricultural breeding to increase crop stress tolerance. Despite increasing relevance of flowers as the most stress sensitive organs, phenotyping platforms aim at identifying traits of resilience by assessing the plant physiological status through remote sensing‐assisted vegetative indexes, but find strong bottlenecks in quantifying flower traits and in accurate genotype‐to‐phenotype prediction. However, as the transport of photoassimilates from leaves (sources) to flowers (sinks) is reduced in low‐resilient plants, flowers are better indicators than leaves of plant well‐being. Indeed, the chemical composition and amount of pollen and nectar that flowers produce, which ultimately serve as food resources for pollinators, change in response to environmental cues. Therefore, pollinators' preferences could be used as a measure of functional source‐to‐sink relationships for breeding decisions. To achieve this challenging goal, we propose to develop a pollinator‐assisted phenotyping and selection platform for automated quantification of Genotype × Environment × Pollinator interactions through an insect geo‐positioning system. Pollinator‐assisted selection can be validated by metabolic, transcriptomic, and ionomic traits, and mapping of candidate genes, linking floral and leaf traits, pollinator preferences, plant resilience, and crop productivity. This radical new approach can change the current paradigm of plant phenotyping and find new paths for crop redomestication and breeding assisted by ecological decisions. Significance Statement: While climate change threatens food production through affecting plant physiology and pollinating ecosystem services, plant phenotyping platforms aim at identifying traits of resilience mostly based on vegetative indexes. However, since flowers are better indicators of the plant well‐being and provide food for pollinators, we propose to use the senses of animal pollinators and their preferences to assist in the identification of flower traits of resilience to abiotic stresses for plant breeding based on ecological decisions. [ABSTRACT FROM AUTHOR]

Basil is a culinary herb in high demand all year round, either fresh, dried, or frozen. Field basil crops are commercially predominant, while greenhouse crops can cover the demand for fresh basil during the off-season. The leaf aspect of basil has great importance for the food industry, and a great diversity of genotypes has been created. The aim of this research was to conduct a comparative characterization of 12 basil genotypes (green and purple leaf) that present interest for breeding programs from a national germplasm collection. The study examines the accumulation of photosynthetic pigments in basil cultivated in field versus greenhouse conditions as indicators of plant performance and herb quality from the perspective of ensuring fresh plant material all year round. The experimental median indicated that photosynthetic leaf pigments accumulated in higher concentrations in the field relative to the greenhouse conditions, in descending order: β-carotene, lutein, chlorophyll a, and chlorophyll b. The trend was not as consistent for chlorophyll b, since four out of twelve genotypes presented higher levels in the greenhouse than in the field, while the overall values were not much lower in the greenhouse than the field (16.82%). All genotypes accumulated much higher carotenoid contents in field conditions relative to greenhouse conditions (>200%) and could also provide better nutritional advantages given their demonstrated health benefits. The differences in photosynthetic leaf pigments have both nutritional (the carotenoids), shelf-life, and processing relevance (chlorophylls) and serve as quality markers. [ABSTRACT FROM AUTHOR]

Plant water use efficiency (WUE) is a comprehensive physiological indicator of plant growth and ability to adapt to drought. However, research on the mechanisms controlling WUE during plant growth and development remains weak. Here, we studied Pinus koraiensis as a typical evergreen conifer species in Northeast China. After collecting 80 tree samples with varying diameters at breast height (DBH), we measured δ13C and δ18O as an indicator of WUE, leaf morphology (volume, dry weight, and total epidermal area), ecological stoichiometry (carbon, nitrogen, and phosphorus content), and abiotic factors (light environment, soil pH, soil water content, and soil nutrient content). Correlational analysis of these variables revealed distinct differences between smaller/younger and larger/older plants: (1) In plants with DBH less than 52 cm, δ13C was positively related to DBH, and δ18O was negatively related to DBH. Plants with DBH greater than 52 cm showed no relationship between δ13C and DBH, and δ18O was positively related to DBH. (2) In plants with DBH less than 52 cm, there was a negative correlation between δ13C and δ18O and between δ13C and leaf phosphorus content (LP), but a positive correlation between δ13C and DBH, leaf mass per area (LMA), and leaf density (LD). The slopes of DBH-δ13C, δ18O-δ13C, leaf nitrogen content (LN)-δ13C, and LMA-δ13C correlations were greater in smaller plants than large plants. (3) Structural equation modelling showed that in smaller plants, DBH had a direct positive effect on δ13C content and a direct negative effect on δ18O, and there was a direct positive effect of light environment on δ18O. In larger plants, there was a direct negative effect of light environment on δ13C and a direct positive effect of DBH on light environment, as well as a negative effect of soil nitrogen content on leaf nitrogen. In smaller plants, DBH was the most important factor influencing δ13C, followed by δ18O and soil moisture, with light and soil pH showing minimal influence. In larger plants, light environment influenced δ13C the most, followed by soil nitrogen content and soil moisture content, with leaf nitrogen and DBH contributing little. The results suggest that water use efficiency strategies of P. koraiensis vary according to growth stage, and the effects of abiotic factors and functional traits vary at different growth stages. [ABSTRACT FROM AUTHOR]

This study is dedicated to the development of an Internet of Things (IoT)-based water-fertilizer coupling optimization model for landscape plant cultivation using big data analysis technology to solve the problems of resource wastage and unstable plant growth quality in traditional water-fertilizer management. Through realtime monitoring of soil, climate and plant physiological indicators, combined with autoregressive moving average model and other prediction means, to achieve accurate prediction of plant water and fertilizer demand and dynamic coupling management. The model is able to customize personalized water and fertilizer supply strategies according to plant species, growth stages and environmental conditions, thus enhancing the efficiency of water and fertilizer utilization, promoting healthy plant growth, and enhancing the effect of urban greening. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy. [ABSTRACT FROM AUTHOR]

The work analyzes the performance assurance of mine hoisting machines, including the problem of the quality of performance of the functions. The quality of functioning allows evaluation of a set of properties of the process of lifting loads, designed to meet the given requirements in accordance with the purpose and evaluated performance indicators. In this case, the quality of the function depends not only on the elements that worked properly or failed during system functioning but also on the moments involving certain changes in the states of the system. The considered system of power supply of mine hoisting installations is rather complex with respect to reliability. The proposed approach allows this rather complex system to lead in terms of the form of a serial connection of elements, allowing for determining the influence of the functioning of its subsystems and electrical equipment on the technological process of cargo lifting in a coal mine. The presented mathematical concept of increasing the reliability and failure-free operation of mine hoisting plants with the help of the developed mathematical model of the mine hoisting plant allowed studying the reliability indicators of the hoisting plant operation and reserving the equipment most effectively to increase reliability. The determination of coupling coefficients in this study made it possible to analyze the impact of the reliability of electrical equipment and power supply systems on the operation of technological machines to improve the reliability of mining equipment and the efficiency of technical systems of mining equipment. [ABSTRACT FROM AUTHOR]

Plant viruses are famed for serious economic losses to most of the agricultural and horticultural crops all around the world. The losses caused by plant viruses are estimated to be several billion bucks per year and are therefore a major threat to global crop production and food security. Traditional diagnostic methods based on biological and physical properties of virus such as appearance of symptoms, bioassay on indicator plants, vector borne and virus particle morphology by electron microscopy are cumbersome, time-consuming and expensive. Recognition of disease based on the symptoms indicated by virus affected plants, can invariably be the first process in several cases, however it's confusing additionally as a result of the symptoms are changeable and depends on several factors like abiotic conditions. There are several sensitive and effective diagnostic methods developed during the last four decades based on virus protein e.g., immunosorbent electron microscopy (ISEM), enzyme-linked immunosorbent assay (ELISA) and nucleic acid e.g., nucleic acid hybridization, isothermal and thermostable PCRs, for plant virus detection, each having its own advantage and disadvantage. This review provides an update on the progress made on the techniques that are useful for early, sensitive, accurate and reliable detection and diagnosis of plant viruses. [ABSTRACT FROM AUTHOR]

Flowering time is a crucial determinant of both the yield and oil quality of rapeseed as well as a key indicator of plant maturity. We performed a genome-wide association study and transcriptome analysis to identify key genes/loci regulating flowering time in Brassica napus L. Forty-six haplotype regions harboring candidate genes were determined to be significantly associated with flowering time, and 28 of these haplotype regions overlapped with previously reported quantitative trait loci. A further investigation of these haplotype regions revealed nucleotide variations in the genes BnaFT-A02, BnaFRI-A10, and BnaFPA-A09 that correlated with phenotypic variations in flowering time. Furthermore, the co-expression network analysis indicated that BnaFT-A02 is directly linked to BnaFRI-A10 and BnaFPA-A09, in a subnetwork and also associated with 13 vernalization, 31 photoperiod, 33 autonomous pathway, and 10 gibberellin pathway genes, forming a potential network regulating flowering time in rapeseed. These results provide valuable haplotype markers for the breeding of early maturing rapeseed varieties. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the effect of adding wheat straw fermented with the oyster mushroom Pleurotus ostreatus on the growth and productivity of tomato plants grown in mini-plots in the greenhouse. The results showed that the treatment of soil + animal manure + plant waste + wheat straw mushroom compost (sterilized) (T9) was better than the other treatments, as it led to a significant increase in the growth indicators of tomato plants, especially the plant height and the number of leaves, flowers, and fruits, compared to the lowest values of the indicators compared to the treatment of wheat straw mushroom compost only (T6). On the other hand, the treatment of soil + animal manure + plant waste + unsterilized wheat straw mushroom compost (T8) outperformed the other treatments in terms of the biochemicals, vitamins, and other nutrients in the tomato fruits. [ABSTRACT FROM AUTHOR]

Landfills and waste disposal sites in the Basque Country are summarized in the inventory of soils that either currently support or have supported potentially polluting activities or facilities (Law 4/2015). Notably, "Landfill 17," located in Gernika-Lumo, has been receiving, for decades, sewage sludges from the local wastewater treatment plant (WWTP) as agricultural amendment. In order to decontaminate and recover soil functionality, a combination of bioremediation (which involved bioagumentation and phyto- and vermitechnologies) and complementary bioremediation strategy (i.e., promotion and maintenance of the native vegetation) was implemented in situ. Physicochemical and ecotoxicological characterization were achieved. Furthermore, an ecotoxicological assessment of the soils upon flora and fauna was carried out through the application of different bioassays and biomarkers. Additionally, an integrative biomarker response (IBR/n) index was calculated to provide a holistic view of the soil general status. Critical pollutants [Cd, Cr, Ni, Pb, benzo(a)pyrene, and dieldrin] were observed in most of the treated sites. Microbial parameters did not present remarkable differences among sites. However, plant indicators pointed the non-treated site (MN8) as the unhealthiest. This was also observed in earthworms' immune system, where cytotoxicity appears when exposed to non-treated soils. In conclusion, this field study showed that the combination of bioaugmentation, phytoremediation with native species, and vermiremediation is highly useful in eliminating mixed contamination, improving soil health, and ultimately restoring ecosystem functionality and biodiversity. [ABSTRACT FROM AUTHOR]

Plot‐scale experiments indicate that functional diversity (FD) plays a pivotal role in sustaining ecosystem functions such as net primary productivity (NPP). However, the relationships between functional diversity and NPP across larger scale under varying climatic conditions are sparsely studied, despite its significance for understanding forest–atmosphere interactions and informing policy development. Hence, we examine the relationships of community‐weighted mean (CWM) and functional dispersion (FDis) of woody plant traits on NPP across China and if such relationships are modulated by climatic conditions at the national scale. Using comprehensive datasets of distribution, functional traits, and productivity for 9120 Chinese woody plant species, we evaluated the distribution pattern of community‐weighted mean and functional dispersion (including three orthogonal trait indicators: plant size, leaf morphology, and flower duration) and its relationships with NPP. Finally, we tested the effects of climatic conditions on community‐weighted mean/functional dispersion–NPP relationships. We first found overall functional diversity–NPP relationships, but also that the magnitude of these relationships was sensitive to climate, with plant size community‐weighted mean promoting NPP in warm regions and plant size functional dispersion promoting NPP in wet regions. Second, warm and wet conditions indirectly increased NPP by its positive effects on community‐weighted mean or functional dispersion, particularly through mean plant size and leaf morphology. Our study provides comprehensive evidence for the relationships between functional diversity and NPP under varying climates at a large scale. Importantly, our results indicate a broadening significance of multidimensional plant functional traits for woody vegetation NPP in response to rising temperatures and wetter climates. Restoration, reforestation actions and natural capital accounting need to carefully consider not only community‐weighted mean and functional dispersion but also their interactions with climate, to predict how functional diversity may promote ecosystem functioning under future climatic conditions. [ABSTRACT FROM AUTHOR]

The purpose of the study was to study the adaptive ability to drought of collection varieties of cotton of the species G.hirsutum L. and G.barbadense L. with subsequent assessment of the technological properties of the fiber. An analysis of stress-depression of seed germination in a sucrose solution was used as an indicator of plant resistance to drought. The technological properties of the fiber were analyzed using the HVI (High Volume Instrument) electronic system in accordance with the unified international classification. It was established that, with the same intensity of the extreme factor, varieties of the same type of cotton differed significantly in the amplitude of changes in the physiological indicator, which made it possible to identify drought-resistant samples. Varieties Agdash-3, Ganja-2, AF-16, Karabakh-11, Zafar, Akala 1517 of the species G.hirsutum L. and varieties S-6002, 5230-V, Aspero, S-6022, AP-154, Agdash-21, Termez-74 of the species G. barbadense L. were characterized by drought resistance. All drought-resistant varieties of the species G. barbadense L. are characterized by a complex of positive fiber quality indicators. The variety Zafar within the species G.hirsutum L. was superior to other genotypes in this indicator. These cotton varieties are recommended to breeders for use in various breeding programs. [ABSTRACT FROM AUTHOR]

As carriers of direct contact between plants and the atmospheric environment, the microbiomes of phyllosphere microorganisms are increasingly recognized as an important area of study. Salt secretion triggered by salt-secreting halophytes elicits changes in the community structure and functions of phyllosphere microorganisms, and often provides positive feedback to the individual plant/community environment. In this study, the contents of Na+ and K+ in the rhizosphere, plant and phyllosphere of Tamarix chinensis were increased under 200 mmol/L NaCl stress. The increase in electrical conductivity, Na+ and K+ in the phyllosphere not only decreased the diversity of bacterial and fungal communities, but also decreased the relative abundance of Actinobacteriota and Basidiomycota. Influenced by electrical conductivity and Na+, the bacteria–fungus co-occurrence network under salt stress has higher complexity. Changes in the structure of the phyllosphere microbial community further resulted in a significant increase in the relative abundance of the bacterial energy source and fungal pathotrophic groups. The relative abundance of Actinobacteriota and Acidobacteriota in rhizosphere showed a decreasing trend under salt stress, while the complexity of the rhizosphere co-occurrence network was higher than that of the control. In addition, the relative abundances of functional groups of rhizosphere bacteria in the carbon cycle and phosphorus cycle increased significantly under stress, and were significantly correlated with electrical conductivity and Na+. This study investigated the effects of salinity on the structure and physicochemical properties of phyllosphere and rhizosphere microbial communities of halophytes, and highlights the role of phyllosphere microbes as ecological indicators in plant responses to stressful environments. [ABSTRACT FROM AUTHOR]

Knowledge of the health condition of urban trees is fundamental for making decisions regarding management and resource allocation. The objective of this study was to determine the current health status of the trees at the National Institute of Astrophysics, Optics, and Electronics (INAOE) in San Andres Cholula, Mexico. Five health indicators previously used in urban forests were assessed. The structure and diversity indicator indicated the existence of 2210 trees, mostly (58.8 %) of small size (< 20 cm average diameter at breast height), 20 botanical families, and 32 tree species. The crown condition and its variables indicated high foliage transparency (> 40 %) in some of its species and low percentages of dieback. Live crown ratio and crown density were moderate in eight and six species, respectively. The tree damage indicator showed the existence of 44 damaging agents in 956 trees (43.3 % of the total), highlighting the ball moss (Tillandsia recurvata), the felt fungus of evergreen ash (Septobasidium sp.) associated with an armored scale, the same fungus also affecting white cedar (Hesperocyparis lusitanica) in association with another scale (a new record), and two bark beetles, Hylesinus aztecus in evergreen ash, and Phloesinus sp. in white cedar. Regarding the mortality indicator, 75 trees (3.3 %) were recorded. Finally, the soil condition indicated that the pH was close to neutral, with electrical conductivity values above 2 dS m-1 and a low beneficial mycoflora population. All these factors, together with the imbalance in tree diversity, high planting density, compacted soils, and the presence of risky trees, are negatively affecting the health of INAOE's tree community. [ABSTRACT FROM AUTHOR]

The purpose of this study was to identify the most important physiological and biological effects of green synthesis ZnO nanoparticles at a size of 65 nm, biostimulant (Folcare) and interaction biostimulant ZnO NPs on plant growth and metabolism. As our understanding of biostimulants' preventive and restorative modes of action has increased, it is critical to maintain the best crop output and quality possible. The reduction of fertilizers must be substituted by strategies that improve the nutrients uptake or their utilization by the plants. New processing methods are required as an efficient green process or an integrated (hybrid) process for different new technologies of interest. The effects of NPs, biostimulant, and combination ZnO NPs biostimulant on plant cell metabolism were examined in cytosol, chloroplast, and mitochondria of cells from the stems, roots, and leaves. The interaction NPs/biostimulant had a beneficial effect on the morphological and physiological indicators of plant health than when nanoparticles and biostimulant are applied separately. Folcare biostimulant coupled with zinc oxide nanoparticles improved pea crops growth. The improved of the quality of pea plants can be explained at least, in part, by increase in antioxidant activities during plant growth phenophase. 1. Highlighting the environmental effect the risk management of biostimulants based on ZnO bionanoparticles on the growth of pea - Identification of specific responses of plants to nano stresses: Analyzes of metabolites Antioxidants enzymes 2. Folcare coupled with zinc oxide bionanoparticles has a significant favorable influence on environmental conservation management by assisting plants in more effectively using nutrients. [ABSTRACT FROM AUTHOR]

Norway launched a national action plan on wetland restoration in 2016. So far, 90% of the restoration effort has been on peatland restoration, with about 140 mires restored so far. There are three main restoration goals stated in the action plan: 1) limit greenhouse gas (GHG) emission, 2) climate adaptation and 3) improved ecological condition. Quantifying the outcome of the restoration actions is necessary to evaluate whether the goals of the action plan are met. A vegetation monitoring protocol was suggested before restoration started and has been implemented at five restoration sites. As the peatland restoration effort in Norway is increasing, it is timely to evaluate if the data currently collected can measure peatland restoration outcome. We evaluate the monitoring protocol based on statistical analyses of the data collected at two sites, describe how indicator species can be identified using generalized composition data used as the basis for classifying habitats in Norway (EcoSyst framework), and suggest the way forward for peatland restoration monitoring in Norway. Data collected according to the monitoring protocol can document changes in species composition at restoration sites, but has limitations when the ecological complexity at the sites increases and reference sites are unavailable. We argue that adjusting the monitoring protocol will: 1) facilitate alignment with existing peatland research; 2) connect better with monitoring programs where data is collected applying EcoSyst framework principles; and 3) enable upscaling to cover the wide variation emerging in peatland restoration. [ABSTRACT FROM AUTHOR]

Plant growth indicators (GIs) are important for evaluating how different genotypes respond to normal and stress conditions separately. They consider both the morphological and physiological components of plants between two successive growth stages. Despite their significance, GIs are not commonly used as screening criteria for detecting salt tolerance of genotypes. In this study, 36 recombinant inbred lines (RILs) along with four genotypes differing in their salt tolerance were grown under normal and 150 mM NaCl in a two-year field trial. The performance and salt tolerance of these germplasms were assessed through various GIs. The analysis of variance showed highly significant variation between salinity levels, genotypes, and their interaction for all GIs and other traits in each year and combined data for two years, with a few exceptions. All traits and GIs were significantly reduced by salinity stress, except for relative growth rate (RGR), net assimilation rate (NAR), and specific leaf weight (SLW), which increased under salinity conditions. Traits and GIs were more correlated with each other under salinity than under normal conditions. Principal component analysis organized traits and GIs into three main groups under both conditions, with RGR, NAR, and specific leaf area (SLA) closely associated with grain yield (GY) and harvest index, while leaf area duration (LAD) was closely associated with green leaf area (GLA), plant dry weight (PDW), and leaf area index (LAI). A hierarchical clustering heatmap based on GIs and traits organized germplasms into three and four groups under normal and salinity conditions, respectively. Based on the values of traits and GIs for each group, the germplasms varied from high- to low-performing groups under normal conditions and from salt-tolerant to salt-sensitive groups under salinity conditions. RGR, NAR, and LAD were important factors determining genotypic variation in GY of high- and low-performing groups, while all GIs, except leaf area duration (LAR), were major factors describing genotypic variation in GY of salt-tolerant and salt-sensitive groups. In conclusion, different GIs that reveal the relationship between the morphological and physiological components of genotypes could serve as valuable selection criteria for evaluating the performance of genotypes under normal conditions and their salt tolerance under salinity stress conditions. [ABSTRACT FROM AUTHOR]

Herbaceous layers in second-growth forests are shaped by past land use. Disturbances such as agriculture may impact populations of mycoheterotrophs, non-photosynthetic mycorrhizal plants that obtain carbon from fungal networks by altering mycorrhizal communities or removing trees they derive carbon from. I tested the hypotheses that two mycoheterotrophic forest herbs increase in abundance during succession and become most common in older forests as plant communities reassemble through time. Distributions of Hypopitys monotropa and Monotropa uniflora were sampled in Athens County, Ohio, USA. I surveyed populations in a 40-site post-agricultural forest chronosequence with five upland and five valley sites in each of four age classes: 40–60, 61–80, 81–100, and >130 years since canopy closure. Aspect and elevation were measured to assess environmental influence. Both H. monotropa and M. uniflora were most common in older stands with EM tree-rich canopy composition and west- or south-facing aspects, indicating influence of historical, biotic, and edaphic factors. Hypopitys was exclusive to forests >80 years old, while M. uniflora was present in younger stands. Abundance of both species was also significantly predicted by Fagaceae basal area. Because EM trees were also most abundant in south- and west-facing uplands, environmental influence appears to be mediated through canopy composition. [ABSTRACT FROM AUTHOR]

Plant-growth-promoting bacteria are an important economic and environmental resource as biofertilizers that can stimulate plant growth and improve agricultural yields. In this study, potential plant growth-promoting bacteria were isolated from soil samples collected in Russia. Strains that manifested active growth on a nitrogen-free medium, the Pikovskaya medium (with insoluble phosphates) and CAS (Chrome Azurol S) agar, were selected for the study. All bacterial isolates were identified by 16S rRNA gene sequencing analysis. Seventeen bacterial isolates of different species were purified and quantified for their ability to grow on nitrogen-free media; dissolve phosphate; and produce ammonium, indole-3-acetic acid, siderophores, and antifungal activity. Principal component analysis identified three groups of strains: one with the maximum signs of providing "plant nutrition"; one with signs of "antimicrobial activity"; and a group "without outstanding signs". All 17 strains were involved in experiments involving growing inoculated wheat seeds (Triticum aestivum) in pots under natural environmental conditions, and were assessed by their effect on the wheat growth and yield as well as on the chemical composition of the soil. For the "plant nutrition" group, regression analysis revealed a connection between indicators of plant growth, ear length, and ammonium accumulation in the soil. However, in other groups, there were also strains showing a positive effect on plant growth, which suggests the necessity of involving additional factors to predict the ability of strains to affect plants when screened in the laboratory. [ABSTRACT FROM AUTHOR]

Background: Focusing on key indicators of drought resistance is highly important for quickly mining candidate genes related to drought resistance in cotton. Results: In the present study, drought resistance was identified in drought resistance-related RIL populations during the flowering and boll stages, and multiple traits were evaluated; these traits included three key indicators: plant height (PH), single boll weight (SBW) and transpiration rate (Tr). Based on these three key indicators, three groups of extreme mixing pools were constructed for BSA-seq. Based on the mapping interval of each trait, a total of 6.27 Mb QTL intervals were selected on chromosomes A13 (3.2 Mb), A10 (2.45 Mb) and A07 (0.62 Mb) as the focus of this study. Based on the annotation information and qRT‒PCR analysis, three key genes that may be involved in the drought stress response of cotton were screened: GhF6'H1, Gh3AT1 and GhPER55. qRT‒PCR analysis of parental and extreme germplasm materials revealed that the expression of these genes changed significantly under drought stress. Cotton VIGS experiments verified the important impact of key genes on cotton drought resistance. Conclusions: This study focused on the key indicators of drought resistance, laying the foundation for the rapid mining of drought-resistant candidate genes in cotton and providing genetic resources for directed molecular breeding of drought resistance in cotton. [ABSTRACT FROM AUTHOR]

The physiological and biochemical parameters of sorghum (Sorghum bicolor L. Moench) characterizing the reactions of its 3-week-old seedlings to the presence of biotic (bacterial strain Gordonia sp. N7) and abiotic (nickel and n-hexadecane) factors were studied. Such indicators as plant survival, biomass of roots and shoots, tolerance indices, nickel translocation factor (TF), content of photosynthetic pigments, and spectrum and activity of peroxidases in root exudates were studied. It was shown that the responses of sorghum to the combined influence of stressors differed from those under their individual influence. It was shown that n-hexadecane and bacterization, individually and together, had a stimulating effect on the growth of sorghum biomass. On the contrary, nickel at the studied concentration (20 mg/kg) showed pronounced inhibitory impact on the studied indicators. The combination of the studied components changed some indicators relative to the individual ones, but the toxicity of the metal dominated, significantly leveling the stimulating effect of n-hexadecane and strain N7 on plant growth. However, depending on the state of the photosynthetic apparatus and the activity of peroxidases in root exudates, different plant responses to combined stress were noted. Significant changes occurred in the processes of nickel accumulation in plant organs: in the presence of n-hexadecane and, especially, bacteria, the accumulation of nickel in the aboveground part of sorghum increased and TF increased more than two times. The obtained results provide justification for the feasibility of using the strain Gordonia sp. N7 as a technological method facilitating the process of phytoremediation to extract nickel from contaminated soil using Sorghum bicolor. At the same time, it should be noted that the identified effect should be taken into account in agricultural feed production since it indicates an increase in the contamination of plant products with heavy metal. [ABSTRACT FROM AUTHOR]

One of the major insect pests in Pisum sativum L. (is Acyrthosiphon pisum Harris (Hemiptera: pests in Pisum sativum L. (Hemiptera: Aphididae) is Acyrthosiphon pisum Harris (Hemiptera: Aphididae). An effective strategy for aphid control is the resistant host plant use. The current study aimed to identify resistance mechanisms and assess biochemical and morphological markers of pea aphid resistance in pea accessions. Meteorological variables affected the pea aphid density, which positively correlated with temperature, while precipitation amount and humidity negatively impacted. The aphid number was significantly and positively associated with the leaf area and the nitrogen content but negatively correlated with calcium and phosphorus levels. The pea aphid-resistant cultivars L 123-7-11, L 128-1and L 125-5 had small leaf areas, and high phosphorus and calcium content but a low nitrogen level. In the mutual influence of the plant indicators, phosphorus concentration had the highest negative impact on pea aphid density, followed by calcium. The plant marker inclusion in the pea breeding process is an efficient tool for a substantial selection program improvement for aphid resistance. Therefore, resistant host plants are essential tools promoting considerable selection program improvement for aphid resistance in the P. sativum breeding process and helping develop sustainable and environmentally friendly agriculture. [ABSTRACT FROM AUTHOR]

Tugai ecosystems of desert river valleys are one of the most affected and endangered landscape types. The largest areas of tugai vegetation in Uzbekistan are preserved in the lower reaches of the Amu Dar'ya River. The main massifs of poplar forests are there. To assess their current status, 284 relevés, representing tugai plant communities in the lower reaches of the Amu Dar'ya, have been analyzed. For each one, the position on ecological gradients was determined by species indicator values. It is shown that the scatter of relevés is the greatest for moisture gradient: from 17 to 97 grades, and it is noticeably smaller for pasture digression (2.5–6.5) and soil salinity (16.1–19.5). To reveal the relationships between the species composition and environmental factors, CCA ordination was performed. Groups of relevés, representing the native tugai vegetation and three ecological series connected with salinity, soil moisture, and anthropogenic pressure, are identified. According to them, four generalized plant communities, differing in species composition, floristic diversity, and structure, are distinguished. Indicator plants are identified on the basis of a formalized assessment of species constancy and importance values. The changes in species composition and structure of communities (life form spectra) are shown. It has been established that vegetation halophytization is the most active process following progressive desertification. This process is reflected in the replacement of poplar forests by shrub communities dominated by Halostachys belangeriana and Tamarix hispida. As a result of intensive anthropogenic impact, the vegetation is transformed and the importance value of ruderal annual plants becomes higher. [ABSTRACT FROM AUTHOR]

Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf‐level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies‐cytotype relate to leaf‐level temperature. When considered across the whole season, stomatal conductance was negatively, non‐linearly correlated with crown‐level temperature derived from UAS. Subspecies identity best explained crown‐level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf‐level temperature. These results show how fine‐scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate‐change‐induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene‐by‐environment interactions. [ABSTRACT FROM AUTHOR]

Background: For market approval of genetically modified plants (GMPs), the evaluation of agronomic and phenotypic plant traits is a standard requirement and part of the comparative assessment of the GMP and its conventional counterpart. This comparative assessment is a starting point for environmental risk assessment (ERA) and should inform all areas of risk. We scrutinize frequently used approaches to characterize GMPs in EU market applications and discuss their usefulness for drawing conclusions on risks related to the plant's ability to survive, persist or become invasive. Results: Our analysis shows that the agronomic and phenotypic characterization of GMPs, although based on guidelines, is confined to plant traits and test designs that are relevant for the quality control and agronomic performance of genetically modified (GM) crops. We provide evidence of how methodological approaches frequently applied during the agronomic and phenotypic characterization of the GMP could be improved and complemented to better inform on potential phenotypic changes relevant to assessing environmental risks. These approaches refer to (i) the assessment of the survival of GM seeds and plants (e.g., volunteers); (ii) the consideration of environmental exposure and (iii) improved methodological approaches for the assessment of biotic and abiotic stress responses for GMPs. Conclusions: The comparative assessment of agronomic and phenotypic plant traits currently does not provide suitable data to draw conclusions on environmental risks relating to the persistence and invasiveness of the GMP. Ecologically more realistic assessments should be part of the phenotypic characterization of GMPs and need guidance and decision criteria to be implemented in ERA. This is of considerable importance, as new genomic techniques are expected to increase the diversity and complexity of GM plants and traits, particularly stress tolerance, which may affect the survival of GMPs in the environment. [ABSTRACT FROM AUTHOR]

In apple cultivation, the total nitrogen content is an important indicator of plant growth, fruit quality, and yield. Timely monitoring of growth becomes imperative, since an imbalance, either in deficiency or excess nitrogen, can result in physiological disorders, adversely impacting both the quantity and quality of fruit. Leaf nitrogen content can be determined using simple chlorophyll meters or destructive testing; however, these methods are time-consuming. However, by employing spectral imaging technology, it is possible to swiftly predict leaf nitrogen content. This study estimated the total nitrogen content in apple trees via hyperspectral imaging and machine learning-based regression analysis (partial least-squares regression (PLSR), support vector regression (SVR), and eXtreme gradient boosting regression (XGBoost). Additionally, to reduce computational costs and improve reproducibility, spectral binning was divided into three stages (4, 8, and 16 bins), and models were compared with a 2-binning estimation model. The analysis focused on green, red, red edge, and near-infrared (NIR) spectra, with 5–10 selected wavelengths, and the SVR-based prediction model showed a similar or greater performance to that of the full spectrum. At 4- and 8-binning, the selected wavelengths were similar to those at 2-binning, maintaining similar prediction model performance. However, at 16 bp, the performance of the prediction model decreased owing to spectral data loss, leading to a significant reduction in wavelengths for nitrogen content estimation. These results can support informed nitrogen fertilization decisions, enabling precise, real-time monitoring of nitrogen content for enhanced plant growth, fruit quality, and yield in apple trees. Additionally, the selected wavelengths can be considered in the development of new types of multispectral sensors. [ABSTRACT FROM AUTHOR]

In Romania, for plum species, the last registered vegetative rootstock was 'Mirodad 1'. The purpose of the research was to study the behavior of the 'Mirodad 1' vegetative rootstock compared to the 'Saint Julien A' rootstock at propagating by softwood cuttings, at planting and grafting in nursery conditions from the Southern part of Romania. In the nursery, the interaction of the two rootstocks with the plum varieties 'Tuleu gras', 'Jojo' and 'Romanța' was evaluated. The indicators monitored were the rooting percentage of softwood cuttings, viable rootstocks remaining after planting and growth indicators of grafted trees (height, cross-section area of the trunk, number and length of shoots). The results of the propagation showed that the 'Mirodad 1' rootstock has a significantly higher rooting percentage than the 'Saint Julien A' rootstock. After grafting, the 'Mirodad 1' rootstock induces the same vigor as the 'Saint Julien A' rootstock to the 'Tuleu gras' and 'Romanța' cultivars. In the case of the 'Jojo' cv., the 'Mirodad 1' rootstock induces greater vigor than the 'Saint Julien A' rootstock. [ABSTRACT FROM AUTHOR]

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and "three-edge" parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), "three-edge" parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales. [ABSTRACT FROM AUTHOR]

The magnitude of chlorophyll fluorescence emission represents both chlorophyll content and energy quenching processes enabling its application to serve as a proxy of photosynthetic activity. Thus, there is interest in advancing methods for canopy‐scale monitoring of chlorophyll fluorescence. Remotely sensed solar‐induced fluorescence (SIF) retrievals offer daytime monitoring of chlorophyll fluorescence, which can serve as an indicator of photosynthesis. However, it represents an instantaneous measurement during the day, which is strongly influenced by incoming radiation, solar angle, and sun/shade fraction—making it difficult to tease out baseline information on plant health and potential photosynthetic capacity—which could be tracked by changes in fluorescence yield (independent of sunlight). Recent advances have demonstrated the potential for inducing nighttime chlorophyll fluorescence via LED light sources at the canopy‐scale, which can be retrieved as LED‐induced chlorophyll fluorescence (LEDIF), potentially serving as a baseline indicator of plant health and photochemical capacity, independent of daytime conditions. In this study, we explored two methods of LEDIF retrievals: (a) hyperspectral sensor (1.33 nm full‐width half max) and (b) low‐cost Red‐Far‐Red photodiode sensor. LEDIF retrieved by the hyperspectral sensor demonstrated strong correlations with daytime SIF and gross primary productivity during mid to end of season phenology (R2 > 0.70). In contrast, phenological dynamics of LEDIF retrieved by the photodiode sensor was more subtle, likely due to weaker signal‐to‐noise ratio, but still demonstrated some potential. Overall, LEDIF offers a technique to monitor nighttime chlorophyll fluorescence emissions (and changes in its spectral shape with a hyperspectral sensor) to assess canopy‐scale phenology of photosynthetic potential. Plain Language Summary: Monitoring the baseline health status of plants is difficult because of their dynamic responses to the environment over the course of a day to sunlight, temperature, and water availability. Measuring plants at night offers an alternative method to assess a plant's baseline health status. Here we use blue LED lights at night to induce the reemissions of light from leaves via a red‐far‐red chlorophyll fluorescence signal detected by a low‐cost system. We found that this signal closely tracks the seasonal variation of photosynthesis. Because this signal was measured at night, unconfounded by daytime variation of sunlight, temperature, and water availability, our low‐cost system offers an approach to assess a plant's photosynthetic capacity and baseline health. Key Points: Low‐cost hyperspectral and red‐far‐red photodiode sensors with blue LED lights offer nighttime retrieval of chlorophyll fluorescence (LEDIF)LEDIF can track photosynthetic phenology similar to tower‐based solar‐induced fluorescence and gross primary productivityLEDIF may serve as a more direct assessment of baseline plant status unaffected by incoming radiation [ABSTRACT FROM AUTHOR]

Correlation analysis was used to assess the level of correlation between plant resistance to late blight and the content and activity of components of the antioxidant system and protective proteins in plants grown under conditions of lack of moisture and under normal conditions. A direct relationship between the degree of plant damage and the expression level of the proline synthase gene, which, in addition to hydrolase inhibitors, is obviously one of the indicators of plant stress, has been revealed. In general, the expression levels of the proline synthase and PR-6 genes, as well as antihydrolytic activity in leaves, show the most pronounced relationship between themselves and with the degree of plant damage. Also, according to the results of factor analysis, the level of expression of methyltransferase, the activity of peroxidase and cellulase are interrelated. Various treatment options with bacteria and their mixtures with signaling molecules had a similar effect on the activity of protease and amylase inhibitors, as well as the expression level of PR-1, PR-5, PR-6 and proline synthase genes both during phytophthora infection and under conditions of lack of moisture. The expression levels of PR-5, proline synthase, and the activity of amylase inhibitors showed similar changes under the appropriate treatments also under normal conditions; apparently, the change in these indicators is a manifestation of universal plant reactions to treatment with bacteria and signaling molecules. It can be concluded that the expression of genes and the subsequent manifestation of the activity of protease inhibitors, as well as the expression of the gene of the main antimicrobial protein, can be considered as common ways to implement plant resistance, activated under the influence of treatment by bacteria and signaling molecules, both under abiotic and biotic stress. [ABSTRACT FROM AUTHOR]