Plant Growth Stages Research Articles

Bioassessment of Cd and Pb at Multiple Growth Stages of Wheat Grown in Texturally Different Soils Using Diffusive Gradients in Thin Films and Traditional Extractants: A Comparative Study.

The bioavailability of heavy metals in soil is a crucial factor in determining their potential uptake by plants and their subsequent entry into the food chain. Various methods, including traditional chemical extractants and the diffusive gradients in thin films (DGT) technique, are employed to assess this bioavailability. The bioavailability of heavy metals, particularly cadmium (Cd) and lead (Pb), is also influenced by soil texture and their concentrations in the soil solution. The primary objectives of this experiment were to compare and correlate the assessment of the Cd and Pb bioavailability using the DGT technique and traditional extractants across two soil textural classes: sandy clay loam (SCL) and clay loam (CL) at two contamination levels: aged contaminated (NC) and artificially contaminated (AC). The specific objectives included assessing the bioavailability of Cd and Pb at different growth stages of the wheat plant and correlating the DGT-based bioassessments of Cd and Pb with their concentrations in various plant parts at different growth stages. This study also compared the effectiveness of the DGT method and traditional extraction techniques in assessing the bioavailable fractions of Cd and Pb in soil. The regression analysis demonstrated strong positive correlations between the DGT method and various extraction methods. The results showed that the wheat plants grown in the AC soils exhibited lower root, shoot, and grain weights compared to those grown in the NC soils, indicating that metal contamination negatively impacts plant performance. The concentrations of Cd and Pb in the wheat tissues varied across different growth stages, with the highest levels observed during the grain filling (S3) and maturity (S4) stages. It is concluded that the in situ assessment of Cd and Pb though DGT was strongly and positively correlated with the Cd and Pb concentration in wheat plant parts at the maturity stage. A correlation and regression analysis of the DGT assessment and traditional extractants showed that the DGT method provides a reliable tool for assessing the bioavailability of Cd and Pb in soils and helped in developing sustainable soil management strategies to ensure the safety of agricultural products for human consumption.

Probiotics in Eco-efficient Plant Growing Systems

Abstract: The understanding of plant-associated bacterial communities has attracted a lot of attention because of their crucial function in improving plant physiology and productivity. These benefits include bioremediation, nutrient solubilization, and stress tolerance. Notably, the use of bacteria as probiotics in eco-efficient plant growing systems (EPGS), including hydroponics and aquaponics, is emerging. These systems are ecologically friendly, productive, resource-efficient, and hold excellent promises for a more sustainable agriculture. This review explores the use of probiotics in the unique stages of plant growth in EPGS, highlighting the significance of managing nutrients and conditions for the plant growth. Moreover, it clarifies how important bacteria are to the nitrogen cycle in EPGS, particularly nitrifying and nitrogen-fixing species. Furthermore, it discusses the methods for bioprospecting new probiotics, highlighting the importance of the combination of metagenomics and conventional microbiology methods. It also highlights how crucial it is to understand the dynamics of probiotics in EPGS for environmentally friendly plant production and preservation in the face of climate change.

Data-driven determination of plant growth stages for improved weather index insurance design

PurposeThis paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.Design/methodology/approachUsing the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.FindingsOur results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.Originality/valueThis research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

Biosynthesis of Ascorbic Acid and Its Metabolism across Plant Growth Stages, Fruit Maturation, and Postharvest Physiology: A Review

Ascorbic acid (AsA), generally known as vitamin C, is essential for horticulture crops to grow, develop, and maintain themselves after harvest. It is essential for plant health, fruit quality, and human nutrition because of its role in enzymatic processes, hormone production, and antioxidant activity. Important plant hormones, including auxins and gibberellins, which control the growth and development of fruit, are involved in its production. The maintenance of optimal AsA levels in horticultural crops can help ensure their nutritional value and quality for consumers. This is done by utilizing suitable postharvest practices and by understanding the variables like temperature, light exposure, and oxygen that influence AsA content. To fully comprehend the function of ascorbic acid in horticulture crops throughout plant growth, fruit development and ripening, and postharvest physiology, more study is required. This information may be used to increase the ascorbic acid content of crops and improve their nutritional value. Key intermediates in the AsA biosynthesis pathway have been shown in a simplified schematic diagram. The control of plant AsA production and subsequent responses to horticulture crops throughout plant growth, fruit development and ripening, and postharvest physiology may now be better understood through future study.

Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles

Optimizing cannabis crop yield and quality necessitates accurate, automated leaf disease classi-fication systems for timely detection and intervention. Existing automated solutions, however, are insufficiently tailored to the specific challenges of cannabis disease identification, struggling with robustness across varied environmental conditions and plant growth stages. This paper introduces a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), utilizing a comprehensive dataset reflective of the diversity in cannabis leaf diseases and their progression. Our approach combines non-population-based decision fusion in image prepro-cessing with population-based decision fusion in classification, employing multiple CNN archi-tectures. This integration facilitates a significant improvement in performance metrics: Hy-AMIS-DLE achieves an accuracy of 99.58 %, outperforming conventional models by up to 4.16 %, and exhibits superior robustness and an enhanced Area Under the Curve (AUC) score, effectively distinguishing between healthy and diseased leaves. The successful deployment of HyAMIS-DLE within our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) demonstrates its practical value, contributing to increased crop yields, reduced losses, and the promotion of sus-tainable agricultural practices.

Effects of organic amendments on nutrient and quality characteristics of Salvia fruticosa Mill at different plant growth stages

ABSTRACT Salvia (Salvia fruticosa Mill) is used in traditional medicine and in food and pharmacological industries, and as organically grown plants are preferred it is important to understand how organic soil fertilisation regimes affect the nutritional status and quality of the plant. To address this, an experiment was conducted testing the direct (in the first year) and residual (in the second year) effects of different organic nutrient inputs; control (C: untreated plants), farmyard manure (FYM), chicken manure (CM), vermicompost (VC), and spent mushroom compost (SMC). In the first year, N concentrations in the salvia were increased by 15%–46% and 8%–16%, at beginning of flowering and at full flowering, respectively, indicating a direct effect of the organic amendments. In the second year, the residual effects of the organic amendments increased N concentrations by 8%–37% at beginning of flowering. The organic amendments also affected concentrations of P at full flowering and caused increases of 6%–23% in the first year and by 6%–18% in the second year. Soil organic matter increased by 13%–16% as direct effects of the organic amendments and by 14%–24% as residual effects. Antioxidant activity, total flavonoid and phenolic concentrations of the salvia increased with applications of organic amendments. Total phenolic concentration and antioxidant activity were positively correlated with N, Mn and Mg and negatively correlated with K, Ca and P in the plant leaves. VC and SMC were the most effective amendments for enhancing total flavonoid content, antioxidant activity and plant nutrient concentrations.

Plant phenology modulates and undersown cover crops mitigate N2O emissions

Mitigation of N2O emissions, a potent greenhouse gas, remain challenging due to knowledge gaps in plant-mediated nitrogen (N) transformation pathways, which limits ability to identify optimal approaches for efficient N utilization. We set up mesocosms with barley, Italian ryegrass, and barley in combination with Italian ryegrass to assess role of cover crop in N2O emission mitigation. Soil emitted N2O was collected simultaneously from the pots with plants at three growth stages: namely, vegetative, canopy expansion, and grain filling. The gas sample N2O contents, N in microbial biomass (MBN), mineral N content, and phospholipid fatty acid (PLFA) analysis in soils were determined at the three growth stages. Cumulatively, highest N2O was emitted from soil under Italian ryegrass (0.056 mg N g−1 soil) followed by barley (0.0051 mg N g−1 soil) and the least under barley and Italian ryegrass combination (0.0014 mg N g−1 soil). The high emissions under Italian ryegrass occurred at vegetative stage due to high reactive N availability. Strong emissions were observed at canopy expansion stage under barley and were linked to access to the large mineral N proportion redistributed to the lower depth as depicted by highest MBN (0.025 mg N g−1 soil) and decreased extractable N (0.0068 mg N g−1 soil). The high emissions under barley correlated with high fungal/bacterial ratio, pointing towards a fungal role in the emissions. The least soil N2O emissions under barley and Italian ryegrass combination were accompanied by elimination of variations induced by the plant growth stages. Absence of 18:2ω6,9 fungal PLFA biomarker under barley and Italian ryegrass combination indicates a potential inhibition and corresponds with reduced N2O emissions. Together, these results broaden our understanding on how plant-soil interactions drives N2O emissions processes and improves our ability to identify optimal plant-based emission mitigation approaches.

From Wasteland to Bloom: Exploring the Organizational Profiles of Occupational Health and Well-Being Strategies and Their Effects on Employees' Health and Well-Being.

Based on the signaling and conservation of resources theories, this study aims to identify different strategic organizational profiles related to occupational health and well-being (OHWB). Additionally, this study explores how these various organizational profiles impact employees' well-being, specifically in relation to absenteeism, emotional exhaustion, work overload, intention to quit, and job satisfaction. Data were collected from 59 organizations and 2828 employees. The first phase of this study presents the latent profile analysis carried out to identify OHWB organizational profiles. This analysis reveals four organizational profiles that are metaphorically named according to the growth stages of plants (i.e., wasteland, sprouting, budding, and blooming OHWB profiles). The second phase of this study investigates the associations between the latent profiles assigned to the organizations with absenteeism, intention to quit, emotional exhaustion, feelings of work overload, and job satisfaction among their employees using MANOVA. The results show that organizational profiles influence employees' health and well-being. Employees working in organizations with a low OHWB profile, known as the "wasteland profile", tend to report more days of absenteeism, higher levels of emotional exhaustion, greater work overload, and lower job satisfaction. Employees are also more likely to express a greater intention to quit their jobs than those working in organizations with a higher OHWB profile (a "blooming profile"). This study is useful for organizations and practitioners seeking to understand how investing in a health and well-being strategy can benefit their employees.

Predicting Plant Growth Stages Using Random Forest Classifier: A Machine Learning Approach

The optimization of plant growth through predictive modelling is a crucial aspect of modern agricultural practices. This study investigates the application of a Random Forest Classifier to predict plant growth stages based on various environmental and management factors. The dataset, sourced from Kaggle, includes variables such as soil type, sunlight hours, water frequency, fertilizer type, temperature, and humidity. The research involves extensive data pre-processing, including encoding categorical variables, scaling data, and splitting it into training (80%) and testing (20%) sets. The Random Forest Classifier is implemented with 5-fold cross-validation, and its performance is evaluated using accuracy, precision, recall, and F1-score metrics. The model exhibits robust performance with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. Visualization techniques such as correlation heatmaps, PCA plots, t-SNE plots, and violin plots are used to provide insights into the data structure and feature relationships. The results confirm the hypothesis that machine learning can effectively predict plant growth stages, offering significant implications for precision agriculture. By accurately identifying growth stages, farmers and greenhouse managers can optimize resource allocation and management practices, leading to enhanced crop yields and sustainability. The study's limitations include the specificity of the dataset and the sole use of the Random Forest Classifier. Future research should explore additional machine learning models and incorporate more diverse datasets to improve generalizability. The findings contribute to the growing body of knowledge on the application of machine learning in agriculture and suggest practical applications for improving agricultural productivity

Effect of Different Zinc Sources and Application Methods on Content and Uptake of Micronutrients of Basmati Rice

Zinc (Zn) is an essential trace element for plants, animals and human’s health. Zn deficiency is widely spread in paddy soils of India and has negative impact on national rice production. Field experiment on rice crop was conducted in sandy loam soil at crop research center Chirodi of SVBP University of Agriculture and Technology, Meerut., during Kharif season of 2011 and 2012 toevaluate the “Effect of different sources and application methods of zinc on content and uptake of micronutrients in basmati rice crop in sandy loam soil. The experiment was laid out in randomized block design with three replications. The experiment comprised of twelve treatments viz.; T1 (control), T2 (recommended NPK @120:60:60 kg ha-1) T3 (5 kg Zn through ZnSO4.7H2O+RDF), T4(5 kg Zn through mono ZnSO4.7H2O),T5 (0.1% Zn spray through ZnSO4.7H2O+RDF ),T6 (0.1% Zn spray through ZnSO4.7H2O + RDF),T7 (0.012% Zn spray through chelated Zn at tillering + RDF),T8 (0.05% Zn spray through ZnSO4.7H2O at tillering +0.05% at panicle initiation+ RDF), T9 (0.05% Zn spray through mono ZnSO4.7H2O at tillering + 0.05% at panicle initiation +RDF),T10 (0.006% Zn spray through chelated Zn at tillering + 0.006% at panicle initiation+ RDF ), T11 (micronutrient mixture@ 25kg ha-1 + RDF), T12 (vermicompost @ 3tha-1+ RDF).The experimental soil was low in organic carbon and available nitrogen and medium in phosphorus and higher in potassium with slightly alkaline in pH. The status of DTPA extractable Zn 1.23 mg Kg-1,Fe 14.85 mg Kg-1 Cu 2.43 mg Kg-1 Mn 10.91 mg Kg-1 in the surface soil. Nutrient assimilation at different stages by the rice crop varied significantly due to application of different treatments in the study. Maximum zinc content 85.78 and 93.57 ppm and uptake 288.60 and 341.85 g/ha at 30 DAT during 2011 and 2012 found in T12 which was significantly higher than the rest of the treatments while minimum zinc content recorded in T1 was significantly lower than the rest of the treatments during both the years. Similar trends were also recorded at 60 DAT, grain and straw growth stages of rice plant. The zinc content of plant sample in T11 and T3 was also higher and statistically at par to the level of zinc content recorded in T12. The Cu, Fe, and Mn content and uptake at different growth stages of rice plant sample in T11 and T3 was also higher and like the level in T12. Among the method of Zn application, soil application resulted in higher biomass, micronutrient content and uptake in the grain and straw. Foliar application caused greater effect on zinc content and uptake and as well as content and uptake of Cu, Fe and Mn in rice plant at different growth stages during both the years. Among the sources of Zinc, ZnSO4.7H2O proved to be the most efficient source of Zn for rice production.

Both Short-term and Long-term Ozone Pollution Alters the Chemical Composition of rice Grain.

The increasing ground-level ozone (O3) is threatening food security, especially in Asian areas, where rice is one of the most important staple crops. O3 impacts on rice could be exacerbated by its spatiotemporal heterogeneity. To improve evaluation accuracy and develop effective adaptations, direct data is urgently needed. Studies on the short-term effects of O3 on rice grain, however, are lacking. Which may lead to an underestimation of the O3 impacts. Through a field experiment, we studied the responses of grain nitrogen, grain carbon, and grain protein in rice cultivars to elevated concentrations of O3 (40 ppb plus that in background air, eO3), especially examining the effects of short-term eO3 during different plant growth stages. We found that long-term eO3 increased grain nitrogen by 29.29% in a sensitive rice cultivar, and short-term eO3 at the tillering and jointing stages increased grain nitrogen by 19.31%, and the grain carbon to nitrogen ratio was decreased by 14.70%, and 21.14% by short-term and long-term eO3. Here we demonstrate that short-term eO3 may significantly affect the chemical composition of rice grains. Previous evaluations of the effects of eO3 may be underestimated. Moreover, changes in the grain nitrogen and grain protein were greater when the short-term eO3 was added to rice plants during the tillering and jointing stage, compared to heading and ripening stage. These results suggest that to improve the tolerance of rice to eO3 to achieve food security, studies on cultivar screening, as well as developing growth-stage-specific adaptations are needed in future.

Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms

Vertical indoor farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use deep learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s-eye view and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering, and fruiting classes. All the trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Role of Endophytic Entomopathogenic Fungi in Mediating Host Selection, Biology, Behavior, and Management of Tarnished Plant Bug, Lygus lineolaris (Hemiptera: Miridae).

Non-insecticidal control strategies using entomopathogens, nematodes, and endophytes provide sustainable and safer alternatives for managing crop pests. This study investigated the potential of different fungal endophytes, specifically Beauveria bassiana strains, in colonizing cotton plants and their efficacy against tarnished plant bug, Lygus lineolaris. The effect of endophytes on plant growth parameters and cotton yield were measured during different plant growth stages. The entomopathogenicity of these fungi was studied in diet cup bioassays using L. lineolaris adults. The behavior of adult males and females toward endophytic cotton squares was analyzed using olfactometer assays. The experiments showed that the fungal endophytes colonized the plant structures of cotton plants, which resulted in an increase in the number of cotton squares, plant height, and weight compared to control plants. B. bassiana strains/isolates such as GHA, NI-8, and JG-1 caused significant mortality in Lygus adults compared to controls. Also, male and female Lygus adults exhibited repellence behavior towards endophytic cotton squares containing JG-1 isolate of B. bassiana and to other B. bassiana strains such as NI-8, GHA, and SPE-120. No differences were observed in the survival and development of L. lineolaris second-instar nymphs on endophytic cotton, and no yield differences were observed in the field experiments.

Ontogenic Predisposition of Tomato Foliage to Early Blight Caused by Alternaria solani under Mountainous Conditions of Kashmir

This study explores the susceptibility of tomato plants to early blight caused by Alternaria solani concerning their chronological and physiological ages. While previous research has noted higher resistance in younger potato plants and an acropetal increase in resistance within plant leaves, such investigations are limited in tomatoes. Our study aimed to address this gap by investigating the influence of chronological and physiological ages on early blight resistance in tomato plants. Using the Keck Ruth Ageti cultivar, we conducted experiments at various plant growth stages. Chronological age variations were achieved by sowing seeds at 10-day intervals, with inoculation occurring when the youngest seedlings reached 20 days. Leaf sampling from different sections facilitated the evaluation of physiological age effects. Results showed a direct relationship between plant age and disease susceptibility, with older plants exhibiting higher incidence rates. Moreover, resistance increased from lower to upper leaves, with top leaves demonstrating the highest resistance.

Effects of Foliar Selenium Spraying on Yield and Quality Characteristics of Fenugreek (Trigonella foenum graecum L.) at Different Plant Growth Stages

ABSTRACT The study was conducted to determine the effects of foliar selenium spraying on the yield and quality characteristics of fenugreek in different plant growth stages. The experiment was established according to the split-split block experimental design. In the study, the main plots, sub-plots, and sub-sub plots consist of different plant growth stages (seedling, flowering, and seed formation periods), cultivars (Gürarslan, Çiftçi, and Berkem), and selenium doses (Control, 25, 50 and 75 g ha−1), respectively. Applying selenium at different plant growth stages was a statistically significant effect on the number of pods and protein content in the first year and on the number of branches in the second year. According to different plant growth stages, the highest results for the number of pods per plant, protein content and number of branches per plant were obtained during the flowering period. Applied selenium doses were ineffective on all investigated plant parameters except for plant height and number of seeds per pod in the first year. The highest results in the plant height and the number of seeds per pod were obtained from 75 g ha−1 selenium application and the control parcels, respectively. Biological yield and protein content were significant among cultivars only in the second year. The highest results in terms of biological yield and protein content were obtained in Gürarslan and Çiftçi cultivars. This study revealed that more studies are needed to determine the effects of selenium applications on medicinal and aromatic plants.

Close-range imaging for green roofs: Feature detection, band matching, and image registration for mixed plant communities

Green roofs offer ecological benefits but harsh rooftop environment stressors like heat, water scarcity, and wind can limit optimal plant growth. Monitoring plant health is crucial for optimizing green roof performance and remote sensing provides a non-destructive approach, yet challenges persist in tight, urban settings. This allows for close-range ground monitoring as a viable solution in these emerging environments. Recent studies explored automatic image registration techniques, showing promise in homogeneous settings, but faced uncertainties in mixed species systems or early plant growth stages. Feature detection techniques such as Speeded up Robust Features (SURF) using fixed and moving image spaces have been proposed for alignment. This study aimed to assess feature detection, matching, and image registration techniques in mixed species plant communities on green roofs throughout the growing season. This study developed a sophisticated monitoring system using close-range multispectral sensors for aligning bands in mixed species plant communities, contributing to enhanced green roof management and sustainability. The results of the study showed no significant differences in band alignment accuracy between growth stages for mixed species bush bean/sedum and single species sedum modules, but significant differences were found in single species bush bean systems. Additionally, mixed species modules showed better accuracy compared to single species bush bean modules, indicating that heterogenous systems provide better alignment accuracy due to more diverse feature extractions.

Ensemble transfer learning using MaizeSet: A dataset for weed and maize crop recognition at different growth stages

Maize holds significant importance as a staple food source globally. Increasing maize yield requires the effective removal of weeds from maize fields, as they pose a detrimental threat to the growth of maize plants. In recent years, there has been a drive towards Precision Agriculture (PA), involving the integration of farming methods with artificial intelligence and advanced automation techniques. In the realm of PA, deep learning techniques present a promising solution for addressing the complex challenge of classifying maize plants and weeds. In this work, a deep learning method based on transfer learning and ensemble techniques is developed. The proposed method is implementable on any number of existing CNN models irrespective of their architecture and complexity. The developed ensemble model is trained and tested on our custom-built dataset, namely MaizeSet, comprising 3330 images of maize plants and weeds under varying environmental conditions. The performance of the ensemble model is compared against individual pre-trained VGG16 and InceptionV3 models using two experiments: the identification of weeds and maize plants, and the identification of the various vegetative growth stages of maize plants. VGG16 attained an accuracy of 83% in Experiment 1 and 71% in Experiment 2, while InceptionV3 showcased improved performance, boasting an accuracy of 98% in Experiment 1 and 81% in Experiment 2. With the proposed ensemble approach, VGG16 when combined with InceptionV3, achieved an accuracy of 90% for Experiment 1 and 80% for Experiment 2. The findings demonstrate that integrating a sub-optimal pre-defined classifier, specifically VGG16, with a more proficient model like InceptionV3, yields enhanced performance across various analytical metrics. This underscores the efficacy of ensemble techniques in the context of maize classification and analogous applications within the agricultural domain.

An Innovative Prototype to Measure Carbon Dioxide, Ethylene, and Nitric and Nitrous Oxides in the Soil Air

ABSTRACT Agricultural intensification has increased the consumption of inputs, mainly fertilizers, which may increase emissions of greenhouse gases (GHG). The improvement of our knowledge on GHG dynamics in the soil is mandatory to optimize the use of fertilizers and reduce environmental impacts. We developed an electronic sensor prototype based on Arduino to monitor concentrations of carbon dioxide (CO2), ethylene (C2H4), ammonia (NH3), and nitrogen oxides (N2O/NOx) in the soil air in a greenhouse experiment with soybean (Glycine max L.) under two treatments: single inoculation (Bradyrhizobium spp.) or triple co-inoculation (Bradyrhizobium spp. Azospirillum brasilense and Pseudomonas fluorescens) in a completely randomized design. One-way analysis of variance, as well as correlation between plant traits at 35 days of emergence and the average daily concentration of gases in the soil air were performed. Although not affecting plant growth or yield-related traits, co-inoculation increased CO2, while decreased NOx concentrations compared with single inoculation, whereas C2H4 emission changed with the plant growth stage. Although co-inoculation increased respiration, decreased N losses by denitrification, mitigating the total emissions of GHG. The prototype showed to be efficient for monitoring GHG across the experiment at a low cost and easy use, contributing for monitoring GHG concentrations in the soil air.

The Utilization and Roles of Nitrogen in Plants

Nitrogen (N) is an essential mineral element for plants and is the main component of protein, nucleic acid, phospholipid, chlorophyll, hormones, vitamins and alkaloids. It is involved in all stages of plant growth and development. Low-N stress seriously hinders plant growth and reduces yield and quality. Plants have evolved a series of elaborate regulatory mechanisms for N uptake and assimilation to cope with different soil N states. After N is absorbed and utilized by plants, it plays important regulatory roles in phytohormones, microRNA (miRNA), root development and mycorrhizal symbiosis to cope with environmental stress. Here, we highlight the research progress on the regulation mechanisms for N absorption and assimilation. Then, we emphasize the regulation roles of N regarding hormone signals, miRNA, lateral root growth, drought resistance, anthocyanin synthesis and mycorrhizal symbiosis. A thorough understanding of N uptake, utilization and interaction with other biological processes is helpful to improve N use efficiency and to breed plants with “less-input-more-output”.

Effects of Hot Water Obtained by Solar Energy on the Weeds Convolvulus arvensis L., Setaria viridis (L.) P. Beauv. and Amaranthus retroflexus L.

This study was carried out to determine the effect of hot water obtained by the solar method (solar energy) on the different plant growth stages of Convolvulus arvensis, Amaranthus retroflexus, and Setaria viridis species, which are problematic in agricultural areas. Hot water at a temperature of 98 °C, obtained using the solar method, was applied at 15:00 pm. The hot water was applied in two different doses depending on the driving speed (1st speed: 4 km h-1, 2nd speed: 2 km h-1) of the tractor. The application was carried out at three stages of plant growth (20, 40, 60 days old plants). According to the BBCH scale, these periods correspond approximately to GS:19, GS:40, and GS:60. In the results of the study; it was found that hot water application was more effective in the of GS:19 2 km h-1 (77%) to the aerial parts of C. arvensis and GS:19 2 km h-1 (68%) to the underground parts of A. retroflexus. In the hot water speed, it was observed that the 2nd speed (2 km h-1) was more effective on weeds than the 1st speed (4 km h-1).