Optimal Plant Research Articles

Making partners in the city: impact of urban soil P enrichment on the partnership between an invasive herb and arbuscular mycorrhizal fungi in a tropical city.

The mutualistic relationship between plants and arbuscular mycorrhizal (AM) fungi is essential for optimal plant nutrition, enabling plants to better withstand biotic and abiotic stressors and enhancing survival, reproduction, and colonization of new environments. Activities, such as soil enrichment or compaction, may decrease the benefits of AM fungi for plants, potentially reducing interactions in urban environments. Here, we examine this prediction by studying how urbanization alters AM interactions with the invasive herb Ruellia nudiflora (Acanthaceae). We collected soil and plants from deep urban sites (DUS; e.g., sidewalks), open urban sites (OUS; parks), and rural sites (RS) to analyse soil nutrient content, plant morphology, AM colonization rates, spore density, richness, and diversity. Contrary to predicted, DUS had the lowest soil nutrient concentration, except for phosphorus, reducing AM colonization. This supports the prediction of reduced AM interactions in urban environments. We also found that potassium affects the AM association. Urban plants had smaller and more compact root systems compared to their rural counterparts, but there were no discernible differences in AM fungi communities between urban and rural environments. Phosphorus enrichment in sidewalks is the main driver of reductionof R. nudiflora-AM fungi interactions in Mérida. More studies are needed to gain a better understanding of how AM fungi contribute to plant colonization in urban environments.

Limiting global warming to 2 °C benefits building climate resilience in rice-wheat systems in India through crop calendar management

CONTEXTRice-wheat rotation system (RWS) is the most important food production system in South Asia and contributes approximately 50% of the total food consumed in India. Building climate resilience of RWS in the context of global warming is critical to sustainably feeding the ever-increasing population in India. Prior research has shown a large potential for climate change adaptation in rice and wheat production through cropping calendar adjustment, but the climate resilience of rice-wheat systems remains elusive. OBJECTIVEThe study aims to project coupled changes in the growing seasons of rice and wheat in RWS under global warming, and evaluate climate resilience of RWS productivity in India through crop calendar adjustment. METHODSWe combined an improved Environmental Policy Integrated Climate model with satellite remote sensing data to investigate the coupling of rice and wheat growing seasons in RWS in India. Then, we shifted crop calendars in RWS to simulate optimal planting dates and potential yield changes of rice and wheat under contrasting global warming scenarios. RESULTS AND CONCLUSIONSWe show that farmers will face a yield trade-off between monsoon rice and dry-season wheat under high greenhouse gas emission scenarios (4 °C warming by the end of the century) in the Indo-Gangetic Plains (IGP) due to a tighter coupling of crop growing seasons in RWS. Early planting for both monsoon rice and dry-season wheat is recommended in the current climate, but the strategy does not apply to future climate change scenarios in the IGP. Limiting global warming below 2 °C by the end of the century is likely to improve RWS productivity by 14% in irrigated systems and 21% in rainfed systems, and keep sufficient maneuvering space for resilient planting strategies in the future. SIGNIFICANCEOur analysis emphasizes that failure to fulfill the pledges of the Paris Agreement will result in not only higher crop damage but also more challenging climate adaptation planning in RWS in India.

Comparative techno-economic assessment and minimization of the levelized cost of electricity for increasing capacity wind power plants by row and angle layout optimization

This work presents techno-economic assessment and optimization results for wind power implementation. The results constitute an attempt to provide recommendations on optimal design configurations and operating conditions for future possibilities of installations in Kuwait, where the wind speed is at maximum levels at high altitudes during the early daytime and late nighttime, whereas in the afternoon, the wind speed reaches its maximum at low altitudes. This cyclic behavior indicates that the wind speed and direction change in a predictable pattern, benefiting wind power generation. The techno-economic assessment and optimization are performed for multi-row design configurations of power plants. The selection using an optimal method comes through evaluating 2220 configurations, from which 60 optimal configurations are determined for different variations of the number of rows in the wind power plant (Nr) based on the minimization criterion of the Levelized Cost of Electricity (LCOE). The optimal configurations have optimal wind power plant layout angle (θplant). Some of the main findings are as follows:(i) the Nr and θplant values impact the LCOE, wake losses, performance ratio, capacity factor, and annual gross energy. (ii) The wind power density is calculated to be 289 W/m2, (iii) June and July have high levels of generation, wind speed, temperature, and humidity, (iv) there exists an exponential relation between the installed cost per watt and Nr. (v) The present value of annual energy, net present value (annual costs), annual energy, and annual gross energy have increasing linear trends as Nr increases, and (vi) the optimal configurations require a power purchase agreement price of at least 7.03 cent/kWh to make a positive return on investment.

Study of the embryonic period of female crayfish egg development in different species

Freshwater crayfish farming is a promising area of production in the food market. This aquaculture industry is currently gaining popularity around the world, but given the high consumer demand, it requires modernisation. The efficiency of aquaculture production is based on the introduction of innovative methods into production processes, which are based on knowledge of biological characteristics, including reproductive capacity and the embryonic period of caviar development. The study aims to assess the reproductive quality of females and determine the incubation period of caviar, its quantity, weight, and survival rate. The study presents the results of research on the reproductive qualities and embryonic period of crayfish eggs of four species (Cherax quadricarinatus, Procambarus clarkii, Procambarus fallax forma virginalis, Cherax destructor) kept in separate closed water supply tanks at an optimal planting density of 4 individuals per 0.45 m2 . Studies have shown that the colour range of caviar and the duration of the incubation period for its maturation varies among different crayfish species. Thus, the first stage of incubation was the shortest and ranged from 2.80 (Florida crayfish) to 3.55 (destructor crayfish) days, while the second stage lasted the longest – from 13.17 to 15.80 days for Australian and broad-toed crayfish, respectively. Based on the analysis of the physiological characteristics of the female crayfish micropopulation, they were divided into quality categories, considering the following indicators: female weight, egg weight, total number of eggs, and number of eggs per 1 g of female. Females of the Australian red snapper of the excellent category was the best among all species in terms of live weight (63.0±0.67 g), egg weight (5.0±0.05 g) and total number of eggs (376.8±16.34 pcs.), the lowest similar indicators were in the marble species (16.8±0.15 g, 1.6±0.03 g, 124.5±2.66 pcs.), respectively. In terms of the number of eggs per 1 g of female weight, Florida crayfish prevailed (7.8±0.19). Instead, the destructor crayfish had intermediate results in these characteristics. The study results can be used in the economic activities of enterprises producing aquatic organisms

Enhancing Rhizobium–Legume Symbiosis and Reducing Nitrogen Fertilizer Use Are Potential Options for Mitigating Climate Change

This review article explores the impact of nitrogen fertilizers on the symbiotic relationship between Rhizobium bacteria and legume plants. Nitrogen fixation has the potential to address the global protein shortage by increasing nitrogen supply in agriculture. However, the excessive use of synthetic fertilizers has led to environmental consequences and high energy consumption. To promote sustainable agriculture, alternative approaches such as biofertilizers that utilize biological nitrogen fixation have been introduced to minimize ecological impact. Understanding the process of biological nitrogen fixation, where certain bacteria convert atmospheric nitrogen into ammonia, is crucial for sustainable agriculture. This knowledge helps reduce reliance on synthetic fertilizers and maintain soil fertility. The symbiotic relationship between Rhizobium bacteria and leguminous plants plays a vital role in sustainable agriculture by facilitating access to atmospheric nitrogen, improving soil fertility, and reducing the need for chemical fertilizers. To achieve optimal nitrogen fixation and plant growth, it is important to effectively manage nitrogen availability, soil conditions, and environmental stressors. Excessive nitrogen fertilization can negatively affect the symbiotic association between plants and rhizobia, resulting in reduced soil health, altered mutualistic relationships, and environmental concerns. Various techniques can be employed to enhance symbiotic efficiency by manipulating chemotaxis, which is the ability of rhizobia to move towards plant roots. Plant-specific metabolites called (iso)flavonoids play a crucial role in signaling and communication between legume plants and rhizobia bacteria, initiating the symbiotic relationship and enhancing nitrogen fixation and plant growth. Excessive nitrogen fertilizer application can disrupt the communication between rhizobia and legumes, impacting chemotaxis, root exudation patterns, nodulation, and the symbiotic relationship. High levels of nitrogen fertilizers can inhibit nitrogenase, a critical enzyme for plant growth, leading to reduced nitrogenase activity. Additionally, excessive nitrogen can compromise the energy demands of nitrogen fixation, resulting in decreased nitrogenase activity. This review discusses the disadvantages of using nitrogenous fertilizers and the role of symbiotic biological nitrogen fixation in reducing the need for these fertilizers. By using effective rhizobial strains with compatible legume cultivars, not only can the amounts of nitrogenous fertilizers be reduced, but also the energy inputs and greenhouse gas emissions associated with their manufacturing and application. This approach offers benefits in terms of reducing greenhouse gas emissions and saving energy. In conclusion, this paper provides a comprehensive overview of the current understanding of the impact of nitrogen fertilizers on the symbiotic relationship between Rhizobium and legume plants. It also discusses potential strategies for sustainable agricultural practices. By managing nitrogen fertilizers carefully and improving our understanding of the symbiotic relationship, we can contribute to sustainable agriculture and minimize environmental impact.

Firming Technologies to Reach 100% Renewable Energy Production in Australia’s National Electricity Market (NEM)

Australia has committed to reducing its greenhouse gas emissions in a manner consistent with limiting anthropogenic climate change to no more than 2 degrees Celsius. One of the ways in which this commitment is being realised is through a shift towards variable renewable energy (VRE) within Australia’s National Electricity Market (NEM). Substituting existing dispatchable thermal plant with VRE requires consideration of long-term energy resource adequacy given the unpredictability of solar and wind resources. While pumped hydro and battery storage are key technologies for addressing short-term mismatches between resource availability and demand, they may be unable to cost effectively address ‘energy droughts’. In this article, we present a time sequential solver model of the NEM and an optimal firming technology plant mix to allow the system to be supplied by 100% VRE. Our conclusion is that some form of fuel-based technology (most likely hydrogen) will probably be required. This has important implications for Australian energy policy.

Effect of Spacing on Survival and Growth Performance of Eucalyptus grandis Hill ex Maiden at Holeta Research Site, Central Ethiopia

Application of optimum initial plant spacing is one of the most important silvicultural practices to maximize the survival and growth performance of established plants at field. A field experiment was conducted to evaluate the effect of different plant spacing on survival and growth parameters of E. grandis at the ages of 4, 17, 34, 56, 66, and 79 months after establishment at Holeta Research Site. The experimental trail was planted at the initial plant spacings of 1.5 m × 1.5 m, 2 m × 2 m, 2.5 m × 2.5 m, and 3 m × 3 m using a randomized complete block design (RCBD) in three replicates. Other than regular management intervention including regular spot hoeing, weeding, and cleaning, the experiment did not receive any other treatment applications. Evaluation was made on survival and growth performance (plant RCD, height, and DBH) at different ages from 4 to 79 months after planting. Except survival percentage, all other variables (plant RCD, height, and DBH) were significantly affected by initial plant spacing. At the age of 56, 66, and 79 months after planting, the closer plant spacing (2 m × 2 m) was most promising in producing optimum plant height and DBH. Overall, our finding confirmed that initial plant spacing has a significant effect on growth performance of E. grandis at Holeta trail site. Evaluation of basic wood properties in relation with spacing or stand density management for quality wood and wood-based production and clear bole formation is suggested.

Revegetasi Lahan Bekas Tambang dengan Tanaman Multifungsi di Karang Panganten, Desa Gunungmasigit, Kabupaten Bandung Barat

The uncontrolled sand, stone, limestone, and marble mining activities have caused damage to Karang Panganten in the Rajamandala karst area, West Bandung Regency, which is a geologically protected area in the form of a nature reserve. The resulting damage has had adverse effects on the community, such as the loss of clean water sources, environmental degradation, decreased air quality, and disruptions to the livelihoods of people dependent on natural resources in the area. Therefore, a Community Service Activity (PKM) was conducted by the Bandung Energy and Mining Polytechnic (PEP) from June to December 2022 with approximately 50 to 75 participants. The method used involved providing materials on organic fertilizer production using stacked buckets, greening the mining area by distributing and planting 140 multifunctional plant seedlings, namely moringa, bamboo, and balsa, renovating some slippery and steep footpaths, and installing plaques as a sign that the activity had been carried out. The results of this PKM activity include conveying an understanding of the importance of greening to achieve a balance between mining activities and environmental sustainability management, as well as increasing the community's knowledge of organic fertilizer production for optimal plant growth. This PKM activity indirectly contributes to the Citarum River Basin (DAS) conservation program, specifically the "Citarum Harum" program. Subsequent PKM activities can replicate this initiative in other villages in the Cipatat Subdistrict or other karst areas in West Bandung, as these areas are in great need of rehabilitation. The entire process can be modularized as a guide for future educational activities.

Effects of Nutritional and Culture Medium-based Approaches for Aquaponics System with Bio-floc Technology on Pak Choi and Catfish Growth Rates

Aquaponics with bio-floc can potentially offer sustainable food production with zero waste as it allows farming plants and fish simultaneously, whereby the former use fish waste as their nutrient sources, while the latter receives cleaner water from the plants in a closed-loop system. In the aquaponics system, additional nutrients are usually added to support optimal plant growth, but it is suggested that the amount of such chemical nutrients should be controlled to prevent any harm to the fish. Furthermore, the plant and fish growth rates are influenced by the nutrients and the culture media used. This study aims to examine the effect of nutrition (full-nutrient and half-nutrient treatments) and different types of culture mediums (rockwool, rockwool-perlite, and rockwool-husk) on the growth rates of pak choi (Brassica rapa) and catfish (Clarias gariepinus) using split-plot design. Findings show that the half-nutrient treatment yielded a 17.12% higher plant growth rate and 23.87% heavier catfish weight than the full-nutrient treatment, but these treatments did not affect the fish survival rate. It was also observed that using different culture mediums did not result in any significant difference.

Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions

Optimization of crop structure is an efficient way to reduce greenhouse gas (GHGs) from agriculture production. However, carbon footprint have rarely been incorporated into previous planting structure optimization models due to the challenges of assessing the spatial and temporal distribution of agricultural carbon footprint for multiple crops in irrigated districts. In addition, previous planting structure models suffered from strong subjectivity in objective function determination, and the obtained non-dominated solution set offered difficulties to decision-makers in selecting specific implementation options. To fill such gaps, an integrated accounting-assessment-optimization-decision making (AAODM) approach was proposed, which remedies the shortcomings of previous crop planting structure optimization models in carbon footprint mitigation, and overcomes the subjectivity of objective function determination and the difficulty in selecting specific implementation options. Firstly, life cycle assessment (LCA) method was used to account for the multi-year agricultural carbon footprints of multiple crops in the irrigation district. The optimization objective functions of planting structure optimization models can then be determined based on the assessment method of carbon footprint influencing factors. Next, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to generate a non-dominated solution set of the optimization model. The optimal planting structure can be finally obtained based on decision making methods by determining the maximum harmonic mean (HM) and knee points (KPs) of the non-dominated solution set. The developed AAODM approach was then applied to a case study of agricultural crop management in Bayan Nur City, China. The results showed that the level of economic development was a key factor influencing the increase in carbon footprint in Bayan Nur City over the past 20 years. The regulation of the level of economic development would significantly influence the agricultural carbon footprint in Bayan Nur City. Moreover, two optimal crop cultivation patterns were provided for decision-makers by selecting solutions from the Pareto front with decision making methods. The comparison results with other methods showed that the solutions obtained by NSGA-II were superior to MOPSO in terms of carbon reduction. The developed AAODM approach for agricultural GHG mitigation could help agricultural production systems in achieving low carbon emissions and high efficiency.

Influence of varietal characteristics of winter wheat and weather conditions on lodging resistance and productivity

Growing technologies and variety selection are crucial for increasing yields and improving the quality of winter wheat grain. The area under winter wheat is the largest in Ukraine, which is associated with the production of high-quality grain. The aim of the study was to determine the influence of weather conditions and variety characteristics on winter wheat plant height, internode length, lodging resistance, productivity factor and yield during the years under study. In the course of the study, the following generally accepted methods were used: systematic approach, system analysis approach, analytical synthesis approach, field approach and statistical approach. The article presents data on the results of research with 20 varieties of soft winter wheat in the conditions of the Training and Practical Centre of Mykolaiv National Agrarian University from 2017 to 2023. Agricultural technology for growing winter wheat varieties is widespread in the steppes of southern Ukraine. The influence of weather conditions and varietal characteristics on lodging resistance and productivity of winter wheat was investigated. The results showed that the optimum plant height of winter wheat varieties ranged from 82.1 to 84.5 cm, and the highest yield was 6.32 t/ha in Staleva and 6.68 t/ha in Duma Odeska. A significant effect of varietal traits on stem length, second and last internode, number of stems formed per 1 m2 , grain weight in the ear and lodging resistance of winter bread wheat was established. Plants of the tested winter soft wheat varieties Staleva, Dyvo, Katarina, Felix, Ozerna, PONTICUS, Faust, Glaucus have a very high (9.0 points) resistance to lodging regardless of the weather conditions of the year. Ukrainian varieties Duma Odeska (6.68 t/ha) and Staleva (6.32 t/ha) achieved higher productivity. The obtained scientific results of the research contribute to the widespread use of the studied winter wheat varieties of this reasonably climatic zone and contribute to further improvement of grain production

Effects of planting date, environments and their interaction on grain yield and quality traits of maize hybrids

Maize is one of the most important staple food crops for most low-income households in the Southern African region. Erratic and inconsistent rainfall distribution across maize-growing areas is a major threat to maize production. Late rains in recent years have forced farmers to plant later than the optimal planting dates, leading to poor maize quality being reported by industry, which raised the question of the influence of later planting dates on grain yield and quality traits of maize. Three yellow and three white maize hybrids were evaluated at three planting dates in three different production environments for three consecutive seasons using a randomized complete block design with three replications. The second and third planting dates caused a significant yield decrease of 23.37 % and 53.73 % from the first planting date across environments, respectively. Planting date three was associated with decreased grain yield, starch content, and increased protein but no significant change in fat and fiber content. Some hybrids yielded relatively well at all planting dates. In conclusion, the early planting date was the most suitable for maize grain yield and starch production in the maize-growing areas of the country. However, planting in January should be avoided at all costs, as it leads to very low yield and poor grain quality.

Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches

The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision making, and streamline power production planning. This paper explores the novel application of machine learning techniques to predict the performance of hybrid thermal power plants, specifically the integrated solar combined cycle power plant (ISCCPP). These plants combine conventional thermal power generation with renewable energy sources, making them crucial in the context of carbon reduction and enhanced efficiency. We employ three machine learning approaches: the adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized via particle swarm optimization (ANFIS-PSO), and ANFIS optimized through a genetic algorithm (ANFIS-GA). These methods are applied to the complex ISCCPP, comprising steam and gas turbine sections and a concentrated solar power system. The results highlight the accuracy of ANFIS-based models in evaluating and predicting plant performance, with an exceptional overall correlation coefficient of 0.9991. Importantly, integrating evolutionary algorithms (PSO and GA) into ANFIS significantly enhances performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA, with ANFIS-GA outperforming the others. This research provides a robust tool for designers, energy managers, and decision makers, offering valuable support in assessing the performance of hybrid thermal power plants. As the world transitions to cleaner energy sources, the insights gained here are poised to have a significant impact on the growing number of these thermal power plants globally.

PURIFICATION AND CHARACTERIZATION OF AMYLASE EXTRACTED FROM LOCAL WHEAT

This study was aimed to test four types of different plants for the purpose of selecting the optimal plant as a source of the amylase enzyme, including wheat, barley, rice, and potatoes. Among these plants, wheat was selected as the best plant source for enzyme extraction because it possessed the highest effectiveness of the enzyme specific activity (3.0 U/mg protein). Furthermore, sodium acetate buffer (0.2 M, pH 6.0) was determined to be the optimal extraction buffer with a 1:5 (w:v) ratio after 75 min, and it provided 68 U/mg protein. The enzyme was concentrated with sucrose before being purified by gel filtration chromatography using Sephadex G-150. The results demonstrated a 1.8-fold increases in final purification folds with a 332% yield of enzymes. At pH 6.0, the purified enzyme showed its highest activity and stability. The activity of the purified enzyme was most effective at 45 °C and remained stable up to 37 °C. At concentrations of 5 and 10 M, various ionic and chemical substances had an impact on amylase.

A modeling framework to assess the crop production potential of green roofs

The increase in food demand and limited opportunities to expand agricultural land pose a threat to local and global food security. Producing food in urban areas such as green roofs can help satisfy urban food demand and thus alleviate pressure on agricultural land. However, a modeling framework that simulates crop growth and production potential on green roofs at a city scale is missing. Here, we adapt the Aquacrop model to explore the growth potential of various types of crops on green roofs and apply it to suitable roof areas in the city of Amsterdam. Our modeling framework includes irrigation methods for water use on green roofs that are optimized according to various climate-driven scenarios of water availability. We find that cabbage has the maximum achievable crop yields ranging from 30.8 to 75.9 t ha−1 yr−1, while pea has the minimum achievable crop yields ranging from 1.7 to 6.4 t ha−1 yr−1. The potential suitable green roof area (i.e., roofs with a certain slope and bearing capacity) for Amsterdam is roughly 400 ha for crop production. This represents 16 % of the total rooftop areas of Amsterdam and can produce up to a total of 28 kt of crops on an annual basis. Our modeling framework can be easily applied to other cities to identify the crop growth potential of green roofs. Our results can help policymakers and urban planners find optimal planting strategies and contribute to shorter food supply chains.

Decision-theoretic rough set model and spatial analysis-based waste-to-energy incineration plant site selection: a case study in first-tier cities of China.

Selecting a sustainable waste-to-energy (WTE) incineration plant site is important for handling huge challenges created by on-going municipal solid waste. However, many studies with WTE incineration plant site problems fail to determine alternative evaluation criteria and cities beforehand, which may increase decision costs and evaluation risks. This paper proposes a novel methodology based on decision-theoretic rough set model and suitable analysis for selecting the optimal WTE incineration plant site. Firstly, from the features of cities, alternative evaluation criteria are determined by three-phase method. Considering different geographical features, a geographical index system is established. Secondly, subjective and objective criteria weights are determined by an improved DEMATEL (Decision Making Trial and Evaluation Laboratory) method and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method-based linear programming model under the hesitant fuzzy linguistic context, respectively. Subjective and objective criteria weights are combined to form the final criteria weights by building an optimization model. Thirdly, the decision-theoretic rough set model is utilized to select alternative WTE incineration plant sites. We utilize spatial analysis adopting Geographic Information System technology to rank all alternative cities to build facilities. Finally, a numerical case is performed to illustrate the feasibility of the proposed methodology. The sensitivity analysis with the parameter [Formula: see text] ranking from 0 to 1 is performed, the result confirms that the proposed methodology has better robustness. Compared with the multi-criteria decision-making methods, the effectiveness and superiority of the proposed methodology are demonstrated.

The Influence of Mineral NPK Fertiliser Rates on Potassium Dynamics in Soil: Data from a Long-Term Agricultural Plant Fertilisation Experiment.

A fertilisation experiment, with the aim to determine the effects of different potassium fertiliser rates and their interactions with nitrogen and phosphorus on field-rotation productivity, potassium balance, fertiliser utilization, and changes in the content of potassium in soil, was carried out in Lithuania between 1971 and 2020. The multi-factorial scheme with 45 treatment plots, where seven rates (including zero) of nitrogen, phosphorus, and potassium fertilisers were studied. The experimental treatments during the study period were carried out on winter wheat, spring wheat, spring barley, sugar beet, spring rapeseed, and annual and perennial grasses. It was found that potassium fertilisers were the most effective on agricultural crops when used in combination with other major plant nutrients-i.e., nitrogen and phosphorus. The required balance of potassium (K2O) in the soil was measured, when nitrogen and phosphorus fertilisers were applied together to compensate for potassium removal; when applying low nitrogen (N) (72 kg ha-1) and phosphorus (P2O5) (64 kg ha-1) fertiliser rates, 128 kg ha-1 of potassium fertilisers are required. When using high nitrogen (180 kg ha-1) and phosphorus (160 kg ha-1) fertiliser rates, 160 kg ha-1 of potassium is needed. The highest potassium uptake, reaching 51.6%, was achieved when plants had been fertilised with nitrogen (108 kg ha-1), phosphorus (96 kg ha-1), and potassium (96 kg ha-1). When fertilising with potassium fertilisers alone, the content of plant-available K2O content in the soil increased, whereas with fertilisation with nitrogen and phosphorus combined K2O content is decreased, except in the plots where the plants had been fertilised with potassium fertiliser at rates of 128 kg ha-1 and higher. Due to the influence of fertilisers, the amount of non-exchangeable potassium in the soil also increased, but relatively little compared to the amount of available potassium content. Thus, one of the main conditions for the effective use of potassium fertilisers is ensuring optimal plant nutrition with other nutrition elements, especially nitrogen and phosphorus.

Artificial neural Network-Based LCOH estimation for concentrated solar power plants for industrial process heating applications

A study was condacted to assess the feasibility of a 5MWt solar-only parabolic trough concentrated solar power (CSP) for industrial process heat (IPH) in various locations across Morocco. Furthermore, the initial plant design has been optimized by considering the number of full-load storage hours and the size of the solar field. Additionally, this study employs a Multi-Layer Perceptron (MLP) model, featuring five input parameters(storage hours, solar multiples,Direct Normal Irradiation, latitude, and longitude), two hidden layers, and one output parameter(levelized cost of heat (LCOH)). The model has been trained using Levenberg-Marquardt (LM) training algorithms. According to the study, the Errachidia site was found to be the most efficient location, generating 21983.56 MWhth at a LCOH of 3.31 ¢/kWth for the initial design. The optimal plant design was determined to have a solar multiple of 1.5 across all investigated sites. The statistical analysis revealed that the MLP model proposed in the study, which had 43 neurons in the hidden layer, demonstrates the lowest errors, including the root mean square error (0.0071) and the coefficient of variance (0.1852), and achieved the highest coefficient of determination value (0.99999) during the training process. Furthermore, the disparities in LCOH values between simulated data and the proposed MLP model across six different Moroccan cities, with errors ranging from 0.01% to −4.07%, confirm the reliability of the MLP model for predicting LCOH.

A Protocol for Activated Bioorthogonal Fluorescence Labeling and Imaging of 4‐Hydroxyphenylpyruvate Dioxygenase in Plants

Abstract4‐Hydroxyphenylpyruvate dioxygenase (HPPD) plays a crucial role in the synthesis of nutrients needed to maintain optimal plant growth. Its level is closely linked to the extent of abiotic stress experienced by plants. Moreover, it is also the target of commercial herbicides. Therefore, labeling of HPPD in plants not only enables visualization of its tissue distribution and cellular uptake, it also facilitates assessment of abiotic stress of plants and provides information needed for the development of effective environmentally friendly herbicides. In this study, we created a method for fluorescence labeling of HPPD that avoids interference with the normal growth of plants. In this strategy, a perylene‐linked dibenzyl‐cyclooctyne undergoes strain‐promoted azide‐alkyne cycloaddition with an azide‐containing HPPD ligand. The activation‐based labeling process results in a significant emission enhancement caused by the change in the fluorescent forms from an excimer to a monomer. Notably, this activated bioorthogonal strategy is applicable to visualizing HPPD in Arabidopsis thaliana, and assessing its response to multiple abiotic stresses. Also, it can be employed to monitor in vivo levels and locations of HPPD in crops. Consequently, the labeling strategy will be a significant tool in investigations of HPPD‐related abiotic stress mechanisms, discovering novel herbicides, and uncovering unknown biological functions.

A Protocol for Activated Bioorthogonal Fluorescence Labeling and Imaging of 4-Hydroxyphenylpyruvate Dioxygenase in Plants.

4-Hydroxyphenylpyruvate dioxygenase (HPPD) plays a crucial role in the synthesis of nutrients needed to maintain optimal plant growth. Its level is closely linked to the extent of abiotic stress experienced by plants. Moreover, it is also the target of commercial herbicides. Therefore, labeling of HPPD in plants not only enables visualization of its tissue distribution and cellular uptake, it also facilitates assessment of abiotic stress of plants and provides information needed for the development of effective environmentally friendly herbicides. In this study, we created a method for fluorescence labeling of HPPD that avoids interference with the normal growth of plants. In this strategy, a perylene-linked dibenzyl-cyclooctyne undergoes strain-promoted azide-alkyne cycloaddition with an azide-containing HPPD ligand. The activation-based labeling process results in a significant emission enhancement caused by the change in the fluorescent forms from an excimer to a monomer. Notably, this activated bioorthogonal strategy is applicable to visualizing HPPD in Arabidopsis thaliana, and assessing its response to multiple abiotic stresses. Also, it can be employed to monitor in vivo levels and locations of HPPD in crops. Consequently, the labeling strategy will be a significant tool in investigations of HPPD-related abiotic stress mechanisms, discovering novel herbicides, and uncovering unknown biological functions.