Leaf Parameters Research Articles

Influence of weather conditions on the variability of physiological leaf parameters in pear cultivars

The results of the study of the adaptation potential of perennial fruit crops depending on climatic changes are presented. The physiological state of some pear varieties during summer periods in 2021, 2022 in the fruit plantations of the Krasnodar Territory were evaluated. Two Russian varieties (Flamenko, Luberskaya) and two European varieties (Conference, Williams) were studied. An experiment on artificially drying freshly harvested pear leaves at constant room temperature was conducted to amplify the negative effects of summer stressors. The relative water content of leaves and electrolyte leakage were determined before and after the stress according to generally accepted methods. It was found that relative humidity is an important factor for normal leaf development, and the change in its values in the summer months correlated with the variability of values of the stress parameter of pear leaves - electrolyte leakage. It was found that the widespread European variety Conference was the most susceptible to the effects of artificial stress. After a short drying period, this variety was characterized by a sharp increase in the electrolyte leakage to ~36 % on the background of a significant decrease in the relative water content in the leaves (71–73 %), due to the development of oxidative processes, leading to a violation of the integrity of cell membranes. Russian varieties Flamenko and Luberskaya, as well as the European variety Williams, on the contrary, by the studied physiological parameters had no strong differences (the change of physiological parameters was from 1.1 to 1.3 times), which allowed them to be identified as more resistant varieties to this type of stress exposure.

PROSPECULAR: A model for simulating multi-angular spectral properties of leaves by coupling PROSPECT with a specular function

Understanding the optical properties of leaves is of great importance for characterizing the relationships between leaf traits and remote sensing signals. The relationship between biochemical parameters and leaf spectral reflection has been widely established through PROSPECT, while surface structural parameters can be linked to leaf multi-angular reflection through Bidirectional Reflectance Distribution Function (BRDF) models. However, it's still challenging to characterize how leaf spectra response changes with different illumination and viewing geometries, in connection to both biochemical and surface structural traits. To address this challenge, we propose the model PROSPECULAR, which combines PROSPECT with a specular function determined by illumination-viewing geometry parameters, wavelength-dependent refractive index, and roughness. This model simulates spectral and multi-angular BRF and retrieves both leaf biochemical and surface structural parameters. The PROSPECULAR accounts for the wavelength dependence of the leaf surface refractive index in the specular function and separates the specular reflection from PROSPECT in the diffuse component calculation. The accuracy of PROSPECULAR's spectral simulation was assessed using a multi-angular dataset measured in the hemispherical space (42 viewing zenith angles (VZAs) at 20–50° source zenith angles (SZAs)). Then, the inversion accuracy of PROSPECULAR was validated using a multi-angular dataset measured in the principal plane (13 VZAs and 20–50° SZAs). The results showed that PROSPECULAR was able to simulate multi-angular BRF (RRMSE = 16.27%) when SZA was <50° and retrieve a unified set of biochemical parameters for each leaf sample using multi-angular BRF (RMSE of chlorophyll, water, dry matter, protein, and carbon-based constituents were 11.31 μg/cm2, 0.0029 g/cm2, 0.0022 g/cm2, 0.00096 g/cm2, and 0.0028 g/cm2, respectively). The agreement between the calculations and measurements of multi-angular BRF for various plant species indicated that PROSPECULAR is an effective leaf optical model. In contrast, the existing radiative transfer models (PROSPECT, PROCWT, PROCOSINE, and PROSDM) showed varying levels of accuracy in inverting biochemical parameters, depending on the VZA. The advantage of PROSPECULAR is its potential for characterizing both spectral and directional properties of leaves at arbitrary given illumination-viewing geometry and inverting leaf parameters using BRF data from several VZAs in the hemispherical space.

High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions

Measuring plant traits with high throughput allows breeders to monitor and select the best cultivars for subsequent breeding generations. This can enable farmers to improve yield to produce more food, feed, and fiber. Current breeding practices involve extracting leaf parameters on a small subset of the leaves present in the breeding plots, while still requiring substantial manual labor. To automate this process, an important step is the precise distinction between separate leaves, which is the problem we address in this paper. We exploit recent advancements in 3D deep learning to build a convolutional neural network that learns to segment individual leaves. As done in current breeding practices, we select a subset of leaves to be used for phenotypic trait evaluation as this allows us to alleviate the influence of segmentation errors on the phenotypic trait estimation. To this extent we propose to use an additional neural network to predict the quality of each segmented leaf and discard inaccurate leaf instances. The experiments show that our network yields higher segmentation accuracy on sugar beet breeding plots planted under the supervision of the German Federal Office for Plant Varieties. Furthermore, we show that our neural network helps in filtering out leaves with lower segmentation accuracy.

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN.

Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.

LEAF AREA IN GRASS CEREALS IN RELATION TO LEAF PARAMETERS; CASE STUDY IN WHEAT

The study determined values of the correction factor (CF) for four wheat cultivars, in order to determine with greater accuracy the leaf surface by the non-destructive method, based on leaf parameters (length, width). The wheat cultivars Ciprian, Dacic, Glosa and Padureni were considered in the study. The plant samples came from cultures made within SCDA Lovrin, Romania. For each wheat cultivar, 100 leaf samples were taken (flag leaf). The dimensions of the leaves (length – L, width – w) were obtained by measurement (±0.5 mm). By scanning, the scanned leaf surface (SLA) was obtained. The values of the correction factor were obtained by calculation (CF=0.79 for Ciprian cultivar; CF=0.80 for Dacic cultivar, CF=0.78 for Glosa cultivar; CF=0.79 for the Padureni cultivar). The leaf area was calculated with high precision for each wheat cultivar, as a relationship between L, w and CF (RMSEP=1.32042 for Ciprian cultivar; RMSEP=2.41317 for Dacic cultivar; RMSEP=2.21665 for Glosa cultivar; RMSEP=1.30141 for Padureni cultivar). The fit between MLA (measured leaf area) and SLA (scanned leaf area) was described by linear equations, with the highest precision in the case of the Padureni wheat cultivat (R2= 0.933), a cultivar in whose case the RMSEP value also confirmed the higher precision for calculating the MLA. The correlation analysis on the series of data related to each wheat cultivar studied, highlighted various levels of correlations, in conditions of statistical safety. The correlation analysis on the entire data series (400 values) highlighted very strong negative correlations between the calculated ratios (r=-0.977 between L/w and w/L) and very strong positive correlations at the leaf area level (r=0.920 between MLA and SLA). High variability was recorded in the case of MLA for the Ciprian cultivar (CVMLA=19.61861) and for the Padureni cultivar (CVMLA=27.07106), in the case of SLA for the Dacic cultivar (CVSLA=22.72838), and Glosa cultivar (CVSLA=18.52030).

Stionic interaction on leaf parameters and survival of grafted pear saplings

The present study was conducted to find the effect of different rootstock-scion combinations on multiplication of Pear (Pyrus communis) saplings. There were four scion cultivars and five rootstocks, constituting twenty treatment combinations. Data revealed that maximum leaf length and width were recorded in cv. Chinese Sandy Pear grafted on BA-29 and Pear sucker rootstocks. However, the minimum leaf length and width were recorded in cv. Carmen grafted on Quince and cv. Abate Fetel on BA-29 rootstocks. The highest leaf area (23.46 cm2) was recorded in cv. Chinese Sandy Pear on BA-29, while it was minimum (14.19 cm2) in cv. Abate Fetel on BA-29. Leaf chlorophyll content was highest (3.71 mg/g) in Chinese Sandy Pear grafted on Quince. Length (27.11 μm) and width (9.96 μm) of stomata were recorded maximum in cv. Abate Fetel grafted on Kainth and cv. William Bartlett grafted on Quince, respectively. In contrast, the stomatal density was maximum (20.63/μm2) in cv. Carmen grafted on Quince. Grafted saplings of cv. Abate Fetel on Quince , cv. Chinese Sandy Pear on BA 29 and cv. William Bartlett on Quince C rootstocks recorded 86.67 per cent survival compared to other grafts. More number of saleable plants was recorded when cv. William Bartlett was grafted on Quince (86.33%). The investigations concluded that pear varieties raised on Quince rootstock had higher values for all studied parameters, followed by Quince C rootstock.

18 O enrichment of sucrose and photosynthetic and nonphotosynthetic leaf water in a C3 grass-atmospheric drivers and physiological relations.

The 18 O enrichment (Δ18 O) of leaf water affects the Δ18 O of photosynthetic products such as sucrose, generating an isotopic archive of plant function and past climate. However, uncertainty remains as to whether leaf water compartmentation between photosynthetic and nonphotosynthetic tissue affects the relationship between Δ18 O of bulk leaf water (Δ18 OLW ) and leaf sucrose (Δ18 OSucrose ). We grew Lolium perenne (a C3 grass) in mesocosm-scale, replicated experiments with daytime relative humidity (50% or 75%) and CO2 level (200, 400 or 800 μmol mol-1 ) as factors, and determined Δ18 OLW , Δ18 OSucrose and morphophysiological leaf parameters, including transpiration (Eleaf ), stomatal conductance (gs ) and mesophyll conductance to CO2 (gm ). The Δ18 O of photosynthetic medium water (Δ18 OSSW ) was estimated from Δ18 OSucrose and the equilibrium fractionation between water and carbonyl groups (εbio ). Δ18 OSSW was well predicted by theoretical estimates of leaf water at the evaporative site (Δ18 Oe ) with adjustments that correlated with gas exchange parameters (gs or total conductance to CO2 ). Isotopic mass balance and published work indicated that nonphotosynthetic tissue water was a large fraction (~0.53) of bulk leaf water. Δ18 OLW was a poor proxy for Δ18 OSucrose , mainly due to opposite Δ18 O responses of nonphotosynthetic tissue water (Δ18 Onon-SSW ) relative to Δ18 OSSW , driven by atmospheric conditions.

Soybean leaf estimation based on RGB images and machine learning methods

BackgroundRGB photographs are a powerful tool for dynamically estimating crop growth. Leaves are related to crop photosynthesis, transpiration, and nutrient uptake. Traditional blade parameter measurements were labor-intensive and time-consuming. Therefore, based on the phenotypic features extracted from RGB images, it is essential to choose the best model for soybean leaf parameter estimation. This research was carried out to speed up the breeding procedure and provide a novel technique for precisely estimating soybean leaf parameters.ResultsThe findings demonstrate that using an Unet neural network, the IOU, PA, and Recall values for soybean image segmentation can achieve 0.98, 0.99, and 0.98, respectively. Overall, the average testing prediction accuracy (ATPA) of the three regression models is Random forest > Cat Boost > Simple nonlinear regression. The Random forest ATPAs for leaf number (LN), leaf fresh weight (LFW), and leaf area index (LAI) reached 73.45%, 74.96%, and 85.09%, respectively, which were 6.93%, 3.98%, and 8.01%, respectively, higher than those of the optimal Cat Boost model and 18.78%, 19.08%, and 10.88%, respectively, higher than those of the optimal SNR model.ConclusionThe results show that the Unet neural network can separate soybeans accurately from an RGB image. The Random forest model has a strong ability for generalization and high accuracy for the estimation of leaf parameters. Combining cutting-edge machine learning methods with digital images improves the estimation of soybean leaf characteristics.

A New Approach Combining a Multilayer Radiative Transfer Model with an Individual-Based Forest Model: Application to Boreal Forests in Finland

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional methods, e.g., remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forest states. In this work, we combine the new-generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results and explore the influence of different calculation methods of mean leaf parameters. For the majority of the forest stands, we generated good reflectances with all levels of forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R2=0.62). This work provides a forward modeling approach for relating forest reflectance to forest characteristics. With this tool, it is possible to analyze a large set of forest stands with corresponding reflectances. This opens up the possibility to understand how reflectance is related to succession and different forest conditions.

Effect of Ecoenzim Solution on Balsam Plant (Impatiens balsamina L.) Growth

Balsam plant is widely known as plants with a myriad of benefits. This plant includes plants that are easily propagated and cared for. However, plant maintenance will be important to ensure the continued growth and development of these plants. One of the environmentally friendly liquid organic fertilizers is ecoenzyme. Ecoenzyme is widely recognized as a multipurpose solution made from fermented sugar with vegetable and or fruit residue. This study aims to determine the effect of ecoenzyme solutions on the growth of Impatiens balsamina L. plants. This study followed a completely randomized design (CRD) with one factor. The data obtained were then analyzed by means of analysis of variance (ANOVA) in a one-factor completely randomized design and followed by the Duncan Multiple Range Test (DMRT) with a 95% confidence level. The results showed that on the parameters of leaf and stem length, watering with the addition of ecoenzyme had a significant effect on the growth of balsam plants starting at 30-day after culturing (DAC) and 51-DAC. Anatomical analysis of the root, stem and leaf of balsam plant treated with watering using the addition of ecoenzyme solution showed that there were significant differences with the control. The results of this study are expected to increase knowledge related to the propagation of balsam plants and the use of ecoenzyme as liquid organic fertilizer.

Seaweed fertilizer improved drought tolerance of tomato seedlings in sandy soil

Global climate change has increased atmospheric transpiration and evaporation and decreased rainfall, and these changes have exacerbated water deficit-induced restrictions on agricultural production in arid and semi-arid areas. To test the influence of seaweed fertilization, a product of Qingdao Bright Moon Seaweed Group Co., Ltd®., on tomato drought response in sandy soils, on tomato drought response in sandy soils, a pot experiment was conducted in a greenhouse to measure biomass and leaf parameters, including chlorophyll concentration, relative water content, and carbohydrate and nitrogen content, under two water-availability scenarios including 85 ± 5% field water content and without irrigating treatment (chemical fertilization under well-watered conditions (WW), chemical fertilization under drought-stressed conditions (WD), seaweed fertilization under well-watered conditions (WW + SF), and seaweed fertilization under drought-stressed conditions (WD + SF)). The results showed that under water deficit stress, seaweed fertilization increased soil urease activity (429%) and soil ammonium nitrogen content, thereby increasing the nitrogen content available to the plants. Seaweed fertilization also enhanced the leaf proline content and water content of the plants, and decreased oxidative damage (low malondialdehyde content, which is lower 43.8% compared with WD). In addition, seaweed fertilization increased the leaf nitrogen content and reduced the nonstructural carbohydrate contentto maintain a relatively low total nonstructural carbohydrate/nitrogen (C/N) ratio. Taken together, seaweed fertilization-induced changes improved drought resistance in tomato seedlings and these results suggested that seaweed fertilization has the potential to improve crop drought resistance in sandy areas.

Assessment of variation for leaf parameters among different sources of Terminalia arjuna (Roxb.)Wight &amp;Arn. in Uttara Kannada district of Western Ghats

Terminalia arjuna is one of the medicinally important native tree species of India and Sri Lanka and well knownfor its superlative cardio-protective properties. As bark is the most economical part of the tree, only a handful of informationis available on leaves, but leaves act as good morphological markers to assess morphometric superiority of the trees. Presentinvestigation was carried out to study variation of quantitative and qualitative parameters of leaf in four different girthclasses (G1: 0-50 cm, G2: 50-100 cm, G3: 100-150 cm and G4: &gt;150 cm) of four different sources of Uttara Kannada district{Hosakambi (S1), Manchikeri (S2), Yellapura (S3), and Mundgod (S4)}. Significant variation was observed in quantitativeleaf parameters i.e., leaf length, leaf width and leaf area; among the sources highest leaf length (15.10 cm), leaf width (4.80cm) and leaf area (70.90 cm2) was recorded for the trees of Hosakambi source (S1). Among the girth classes, highest leaflength (17.00 cm), leaf width (5.10 cm) and leaf area (81.70 cm2) was recorded for leaves of G1 girth class. Significantvariation was observed in qualitative leaf parameters i.e., leaf shape, leaf tip shape and leaf margin. Among the sources, moreoblong (38.33%) and elliptical (38.33%) shaped leaves; more leaves with crenate-serrate margin (76.67%) were recorded inMundgod source (S4). Whereas, Hosakambi (S1) source recorded more number of leaves with sub-acute leaf tip (56.67%).Overall, among the sources and girth classes, more number of trees exhibited elliptical shaped leaves (38.00%), more numberof leaves with crenate-serrate (72.00%) margin and higher proportion of sub-acute (53.00%) leaf tip shapes. The currentstudy revealed that the leaf parameters are reliable morphological characters to study the source variation.

Soil Fertility Improvement with Mixtures of Wood Ash and Biogas Digestates Enhances Leaf Photosynthesis and Extends the Growth Period for Deciduous Trees.

In the context of climate change, it is necessary to establish forest management by balancing more products, using less area, and minimizing environmental impacts. The use of different industrial bio-based by-products as soil conditioners in the last few decades has gain more interest, because it leads to an extended use time of these products and supports the circular economy. The aim of this study was to determine the effect of fertiliser made from cattle and pig manure biogas fermentation digestate and wood ash from two cogeneration plants, applied in different mixture ratios, to test its suitability for fertilisation of deciduous trees, using the physiological, morphological, and chemical parameters of the leaves as an indicator. We selected two poplar clones: foreign 'OP42' (syn. Hybrid 275) and local 'AUCE' annual shoot stem cuttings as planting materials. A negative control group with acidic forest mineral soil as substrate and four fertilised groups with different applied digestate and wood ash ratio mixtures to forest soil was established (ash:digestate 0:0 (Control), 1:1, 2:1, 3:1, 4:1). Mixture application improved growing conditions because all fertilised group poplars had longer growth periods and photosynthetic rates in August than the control group. Both local and foreign clones showed a good response to fertilisation in terms of leaf parameters. Poplar is a suitable culture to fertilise with bio-waste biogenic products, because of its capacity to absorb nutrients and fast response to fertilisation.

Proline Accumulation and Growth of Bean Leaf (Phaseolus vulgaris L.) With Biochar Application in The Shallow Water Table Environment

Agronomic constraints for vegetable cultivation in riparian wetlands are low soil quality and shallow water table conditions. This study aims to identify the effect of biochar application and shallow water table on proline accumulation and plant growth of bean leaf (Phaseolus vulgaris L.) at the generative stage. This study was carried out from April to August 2018 in factorial randomized block design. The first factor was doses of biochar (0, 1, and 2 kg.m-2), and the second was shallow water table condition (20 cm and 10 cm below the soil surface) given in the generative stage. The results showed that biochar application and the shallow water table significantly affected leaf parameters; proline accumulation and increases in the sucrose levels occurred on the leaves. Proline content increased started at 3 days after treatment (DAT) and decreased at 6 DAT until recovery day (7 DAT). The biochar application of 2 kg.m-2 provided good aeration in the soil, which enhanced the bean's ability to survive under excess water. Proline accumulation is one of the adaptation mechanisms of beans to abiotic stress in shallow water table conditions.

Nocturnal Transpiration May Be Associated with Foliar Nutrient Uptake.

Aerosols can contribute to plant nutrition via foliar uptake. The conditions for this are best at night because the humidity is high and hygroscopic, saline deposits can deliquesce as a result. Still, stomata tend to be closed at night to avoid unproductive water loss. However, if needed, nutrients are on the leaf surface, and plants could benefit from nocturnal stomatal opening because it further increases humidity in the leaf boundary layer and allows for stomatal nutrient uptake. We tested this hypothesis on P-deficient soil by comparing the influence of ambient aerosols and additional foliar P application on nocturnal transpiration. We measured various related leaf parameters, such as the foliar water loss, minimum leaf conductance (gmin), turgor loss point, carbon isotope ratio, contact angle, specific leaf area (SLA), tissue element concentration, and stomatal and cuticular characteristics. For untreated leaves grown in filtered, aerosol-free air (FA), nocturnal transpiration consistently decreased overnight, which was not observed for leaves grown in unfiltered ambient air (AA). Foliar application of a soluble P salt increased nocturnal transpiration for AA and FA leaves. Crusts on stomatal rims were shown by scanning electron microscopy, supporting the idea of stomatal uptake of deliquescent salts. Turgor loss point and leaf moisture content indicated a higher accumulation of solutes, due to foliar uptake by AA plants than FA plants. The hypothesis that deliquescent leaf surface salts may play a role in triggering nocturnal transpiration was supported by the results. Still, further experiments are required to characterize this phenomenon better.

Exploring the potential of transmittance vegetation indices for leaf functional traits retrieval

ABSTRACT Leaf functional traits are key indicators of plant functions useful for inferring complex plant processes, including their responses to environmental changes. Vegetation indices (VIs) composed of a few reflectance wavelengths hold the advantages of being relatively simple and effective and have been widely used within remote sensing to estimate leaf traits. However, the difference between the reflectance from the upper and lower part of the leaf suggests that leaf reflectance mainly provides one-sided information, constraining its ability to estimate leaf functional traits. Leaf transmittance, on the other hand, gives information about the whole leaf and has more potential to be sensitive to changes in leaf biochemistry. As transmittance-based VI is rare, this study aims to propose new transmittance-based VIs for accurate estimations of leaf traits. Three forms, i.e. the normalized difference VI, the simple ratio VI, and the difference VI were employed, and wavelength selection for transmittance-based and reflectance-based VIs were conducted, respectively. The applicability of these VIs for estimating four leaf functional traits (leaf chlorophyll (Cab), leaf carotenoids (Car), equivalent water thickness (EWT), and leaf mass per area (LMA)) were evaluated. Cross-validation using three datasets of field observations and sensitivity analysis showed that the VIs constructed using transmittance were relatively less affected by interferences from other leaf parameters, improving the estimation accuracy of Car, EWT, and LMA compared to their optimal reflectance counterparts (RMSE reduced by 2% to 15%, and MAE reduced by 7% to 20% for the pooled dataset). Our study revealed that the normalized difference VI based on transmittance showed considerable sensitivity to Car, EWT, and LMA, whereas the difference VI based on reflectance was effective in indicating Cab. The proposed transmittance-based VIs will aid remote monitoring of leaf traits and thereby plant adaptations and acclimation to changes in environmental conditions.

Evaluation of the Relationship Between Growth Parameters for Spring-sown Onion Cultivation in Tohoku, Japan

Onion is one of the most productive vegetables in Japan, and onion growth models have been developed to predict its yield. However, onion growth is complex, and the selection of simple parameters is required to estimate bulb weight. This study utilized a 2-year dataset from a sequential growth study of spring-sown onions in the northeastern region of Japan to evaluate the correlation among growth parameters and to determine suitable parameters for estimating the dry weight of bulbs via multiple regression analysis. The results indicated that when bulb dry weight was the objective variable, the coefficients of determination of the single regression equation with plant height and leaf area as explanatory variables were 0.553 and 0.508, respectively, and the leaf parameter explained more than 50% of variance in bulb dry weight. Furthermore, the coefficient of determination was 0.912 when the leaf sheath basal diameter (ball width of the bulb) was used as an explanatory variable to explain the bulb dry weight. This indicates that there is a gap between the top and bulb growth. This study revealed the important parameters for predicting onion yield and demonstrated the switch in onion development from tops to bulbs.

CHARACTERIZATION OF THE Hedera helix L. LEAVES GEOMETRY BASED ON SOME DIMENSIONAL PARAMETERS AND CALCULATED RATIOS

The study analyzed leaves of ivy, Hedera helix L., in order to characterize the geometry of the leaves. The leaf samples were taken from the Protected Area "Padurea Cenad", Timis County, Romania. The dimensions of the leaves (L, w) were determined by measurement, with a precision of 0.5 mm. The leaves were scanned, 1:1 ratio. From the analysis of the leaf images, the perimeter (Per) and scanned leaf area (SLA) were determined. The correction factor (CF), specific to ivy leaves, was found (CF=0.69) for the purpose of use in non-destructive methods of measuring the leaf area (MLA) based on a general formula of the type MLA=L·w·CF. The fitting relationship between MLA and SLA was described by a linear equation, under statistical safety conditions (p&lt;0.001). Different ratios between leaf parameters were calculated in order to characterize the leaf geometry (L/w, Per/L, Per/w, SLA/L, SLA/w, SLA/Per, MLA/L, MLA/w). Different levels of correlation between basic leaf parameters, leaf surface and the calculated ratios were identified, with statistical certainty for most cases (p&lt;0.001). Both from the correlation analysis and from the regression analysis, a tighter relationship of MLA with foliar parameters (L, w) was found than in the case of SLA (based on r, R2, F test, and RMSEP values). From the analysis of the values obtained for the coefficient of variation (CV), the highest variability was found in the case of SLA (CVSLA=38.6726), followed by MLA (CVMLA=36.8300), and the lowest variability in the case of the ratio Per/w (CVPer/w=10.1515). The regression analysis facilitated the finding of some equations that described the variation of SLA and MLA with leaf parameters (L, w, Per) in conditions of statistical safety (p&lt;0.001). 3D graphic models and in the form of isoquants were also generated, which represented the MLA variation in relation to the dimensional parameters of the ivy leaves studied.

Photosynthetic parameters of winter pea sowings under the effect of nitrogen fertilization and seed inoculation in the Right Bank Forest-Steppe of Ukraine

Purpose. To evaluate the photosynthetic parameters of winter pea sowings under the effect of seed inoculation and nitrogen fertilization in the Right Bank Forest-Steppe of Ukraine. Methods. The research was carried out in the years 2019–2021 in the fields of the Agronomic Research Station (a division of NULES of Ukraine, Kyiv region). Winter pea ‘NS Moroz’ was sown in the second half of September at the rate of 1.2 million seeds/ha. The width of the rows was 15 cm, and the seed sowing depth was 4–4.5 cm. The design of the experiment provided for the combined use of such technological factors as seed inoculation (without inoculation; Optimize Pulse, 3.3 l/t) and nitrogen fertilizer [main application + early spring top dressing (N0; N15 + N15; N15 + N30; N15 + N45; N30 + N0; N30 + N15; N30 + N30; N45 + N0; N45 + N15; N60 + N0)]. P45K45 was also applied in the primary fertilization as a background. Results. Seed inoculation and nitrogen fertilizers positively affect the photosynthetic activity of winter pea; however, the efficiency of their action largely depends on the application doses and timing, as well as the stage of crop development. It was established that in the autumn period of winter pea vegetation (complete sprouting–stalking), the influence of the studied agrotechnical measures on the leaf area index (LAI), photosynthetic potential (PP), and net photosynthetic productivity (NPP) was mostly insignificant. In the period of spring-summer vegetation, the effect of the factor of seed inoculation, as well as early spring fertilization with nitrogen, increased significantly, while the main fertilization decreased. The highest LAI (on average according to the experiment 31.4 thousand m2/ha) was formed in the flowering stage, PP during the stages of bean formation – ripening of seeds (0.75 million m²/ha per day). The use of seed inoculation compared to options without it contributed to the increase of these indicators by 5.33 and 0.17 million m2/ha per day, respectively. In the experiment, the maximum values of both LAI (35.77–36.45 thousand m²/ha) and PP (0.91–0.93 million m2/ha per day) were obtained when applying N15 + N30–45 and N30 + N30 on the background of seed inoculation, while the minimal in control treatments (N0). The regularities of the formation of the NPP were radically different. Its highest indicators during the period of the beginning of vegetation – budding (as well as in the following interphase stages) were observed primarily with the minimal application of fertilizers (N30 + N0, N15 + N15) or in the control treatments (N0), 8.1–9.2 g/m2 per day. Seed inoculation had no significant effect on the formation of NPP. The maximum content of chlorophyll a in plants (5.90 mg/g) was formed in the budding stage, gradually decreasing to 5.74 and 5.05 mg/g in the flowering and bean formation stages, respectively. Instead, the content of chlorophyll b was the lowest in the budding stage (12.37 mg/g), reaching its maximum in the flowering stage (19.71 mg/g) and slightly decreasing in the stage of bean formation – 16.37 mg/g of raw mass. Seed inoculation helped to increase the content of chlorophyll a by an average of 0.9–1.0 mg/g over growth stages and chlorophyll b by 1.4–2.2 mg/g. The best fertilization treatment was N15 + N30–45. Conclusions. Seed inoculation and nitrogen fertilization are significant and effective factors to intensify the photosynthetic activity of winter pea sowings. The patterns of the parameters of leaf s area, photosynthetic potential, net productivity of photosynthesis, and the content of photosynthetic pigments revealed in the study may be used to improve the cultivation technology to fully realize the crop potential productivity under the conditions of the Right Bank Forest‑Steppe of Ukraine.

ADAPTIVE POTENTIAL AND PHENOTYPIC VARIABILITY OF RIBES SPECIES IN THE LOWER VOLGA REGION

Background. The climate, soil, and hydrology of the Volgograd Region complicate agricultural work. The ever-greater intensification of agriculture has brought substantial anthropogenic change upon the natural terrain in the agricultural areas of the country. Protective afforestation seeking to address soil erosion and drought, as well as the artificial selection of economically valuable species, have been the source of the most prominent change.&#x0D; Purpose. To evaluate the biological potential of Ribes L. species, the authors compared the development and reproductive traits, assessed the ecological flexibility of the species (an effective metric for introduction in arid regions that can be used in practice to muster biological resources and nursery work).&#x0D; Materials and methods. Currant populations were mainly studied by sampling population statistics and biometry, to which end the authors applied analysis of variance.&#x0D; Quantitative varying traits included fruiting, qualitative traits included leaf, fruit, and shoot color, and ordinal traits included bark smoothness. To evaluate the biological potential of the species Ribes aureum Pursh., the authors compared the development and reproductive traits, assessed the ecological flexibility of the species (an effective metric for introduction in arid regions that can be used in practice to muster biological resources and nursery work). Annual maturation rates of Ribes aureum Pursh. Shoots determine whether they will survive at winter. The following visual cues of maturation were used: lignification, coloring, and development of outer covers, budding, shoot growth completion, and leaf fall completion timings.&#x0D; Results. Currant grows in any soil, including alkaline light-chestnut soils, outcrops of bedrock ravines (Kamyshin), washed-away, eroded soils of steep slopes and ravines (Volgograd); winter hardiness depends on the natural range. Currant tends to live longer in soils most suitable for afforestation. Young shoots also differed in color. In Volgograd and its vicinity, such shoots were green (or brown), rugged. In Kamyshin, they were reddish or grayish, finely pubescent. Lamina morphometry revealed pronounced differences in lamina size and color from area to area.&#x0D; Conclusions. Currant species are promising shrubs that are important for forest reclamation and for nurseries. Currant plantations enrich flora and fauna as they form an ecological niche where beneficial insects, birds, mammals, and other animals can disperse, feed, or find a habitat. Currant polymorphism has an important role to play in plant evolution. It is caused by the variability of various traits in individuals of a population, which creates subspecies that are valuable for the purposes of selection and introduction. The genotype of a species determines its lamina parameters. Air temperature, precipitation, and soil moisture affect the modifiability of leaf parameters, as well as the location of leaves in the bush and on the shoots. A stable difference between lamina parameters indicates a species difference. The results hereof could be of use in the context of bioindicators of the ecological status and adaptability of currant.