Optimal Plant Research Articles

Hydroponics and Synergetic Technologies: A Deep Dive into Small and Medium-Scale Applications

Hydroponics, the practice of soilless plant cultivation, has undergone significant transformation over the years, evolving from its roots in ancient civilizations to its modern, high-tech systems. This paper explores this journey, focusing on technological advancements that have not only refined the principles of hydroponics but have also democratized its accessibility. Early accounts of hydroponic-like practices can be traced back to the Hanging Gardens of Babylon, the Floating Gardens of the Aztecs, and historical records in China and Egypt. The formalization of hydroponics, however, took place in the 20th century, although the concept had been introduced much earlier by Sir Francis Bacon in 1627. Technological leaps in nutrient solutions have been pivotal; German botanists Julius von Sachs and Wilhelm Knop's work in 1860 set the scientific basis for soilless culture. Modern hydroponic systems such as Nutrient Film Technique (NFT), Aeroponics, and Deep Water Culture (DWC) employ pumps, timers, and nutrient reservoirs for efficient cultivation. These systems have scaled hydroponics from a lab-scale practice to a commercial-scale operation, making it accessible to both hobbyist gardeners and large-scale agriculturalists. Another transformative development is in the area of hydroponic lighting. High-Pressure Sodium (HPS) and Light Emitting Diode (LED) lights offer sustainable and efficient alternatives to natural light, thus enabling year-round, indoor hydroponic farming. The automation of hydroponic systems via computer-controlled environments has drastically reduced the manual labor required, making it accessible to individuals without specialized horticultural skills. Lastly, the paper delves into synergistic technologies like smart monitoring systems, Internet of Things (IoT), and renewable energy sources that are setting new paradigms for hydroponics. Smart sensors and analytics provide real-time data for nutrient and environmental control, while IoT devices offer remote management capabilities. Renewable energy sources like solar and wind power bring sustainability to hydroponic setups, thereby reducing the carbon footprint. These technologies collectively create an environment that is conducive not just for optimal plant growth, but also for operational sustainability and efficiency.

Optimal sizing and design of renewable power plants in rural microgrids using multi-objective particle swarm optimization and branch and bound methods

As energy prices rise, optimizing renewable power plant sizing is vital, especially in areas with unreliable electricity supply due to distant transmission lines. This study addresses this issue by optimizing a renewable power plant portfolio for a Spanish municipality facing such challenges. The presented approach involves a systematic method. Firstly, energy demand is thoroughly analyzed. Next, available renewable resources are explored and optimal plant placements are determined. A multi-objective particle swarm optimization algorithm is then used to size each plant, minimizing annualized costs and grid energy imports. The most suitable feasible optimum is selected from theoretical configurations using branch and bound techniques, prioritizing practicality. In the specific case analyzed, the results show a 20-year Internal Rate of Return of 8.33 %. This is achieved with the following capacities for each plant: 750 kW of photovoltaic solar energy, 160 kW of turbine-based generation, 180 kW of hydroelectric pumping, 160 kW for the biomass plant, and 200 kW from the wind turbine. This study offers an innovative solution to energy challenges, providing practical insights for cost-efficient, sustainable projects.

Productivity of soybean varieties of different maturity groups depending on plant density under drip irrigation in the South of Ukraine

Soybeans are a leading leguminous crop for universal use, therefore, measures aimed at increasing their productivity, including the reasonable selection of varietal resources and optimisation of planting density, will make a significant contribution to the creation of sustainable food production systems. The research aims to determine the effect of plant density on the productivity of soybean varieties of different maturity groups. Field laboratory and statistical (correlation and regression analysis) methods were used in the study. The field experiments were conducted during 2018-2020 on dark chestnut soil of a private agricultural enterprise of the agricultural company “Syvash” in Novotroitsk district of Kherson region. Its maximum values for the cultivation of early ripening varieties were ensured by a plant density of 700 thousand/ha, medium early varieties – 500-700 thousand/ha, and medium ripening varieties – 500 thousand/ha. A strong and very strong correlation between yield and soybean yield index was established. Increasing the length of the growing season contributed to the growth of yields. In the group of early maturing soybean varieties, the Monarch variety was determined to be the most productive. There was no significant difference in the level of yield of varieties within other maturity groups. For each variety, the optimum planting density was determined, at which the maximum grain yield was formed. Longer vegetation contributed to a greater accumulation of protein and fat in the grain. In terms of protein content, the difference between varieties of the same maturity group did not exceed 0.2%, but varieties differed in terms of conditional protein yield per hectare of crops. There was no difference in the fat content of mid-season soybean varieties. Among the early-ripening varieties, the Monarch variety was distinguished by higher oil content, and among the mediumearly varieties – by Aratta. The maximum conditional fat yield per hectare of crops was also determined for these varieties. A very strong and strong correlation was found between soybean grain yield and protein and fat content, respectively. A very strong correlation was also found between the protein and fat content of the grain. To ensure sustainable production and efficient use of soybeans and soybean by-products, it is advisable to take the results of the study into account in production conditions

The Effect of Plant and Row Configuration on the Growth and Yield of Multiple Cropping of Soybeans in Southern Xinjiang, China

To study the optimal plant row configuration of the multiple cropping of soybeans that are suitable for planting in southern Xinjiang, a field experiment using soybean variety SN35 was carried out employing different plant row designs. Three row spacing treatments of 15 cm (H1), 30 cm (H2), and 45 cm (H3) and three density treatments of 52.56 million (M1), 55 million (M2), and 60 million (M3) plants per hectare were set up in this experiment to explore the effects of different plant row spacing configurations on agronomic traits, photosynthetic characteristics, dry matter accumulation, and the soybean yield of the multiple cropping of soybeans. The results showed that the soybeans’ plant height, diameter, main stem node number, leaf shape index, leaf area index (LAI), leaf area duration (LAD), and pod dry matter distribution ratio increased gradually with the growth process. In contrast, the stem dry matter distribution ratio decreased gradually, and the leaf dry matter distribution ratio first increased and then decreased. The plant height of the soybeans treated with H2M3 was the highest, reaching 67.38 cm. The number of primary stem nodes of the soybeans treated with H1M3 was the highest, reaching 12.7 nodes. The stem diameter of the soybeans treated with H1M1 was the highest, reaching 0.64 cm. The leaf shape index of the soybeans treated with H3M1 was the highest, reaching 2.72. Intercellular CO2 concentration closely affects the final yield; the correlation coefficients with the pod number per plant, seed number per plant, and yield reached 0.75, 0.78, and 0.87, respectively. The theoretical maximum hundred-grain weights under the H1M1 and H2M1 treatments were higher, reaching 20.33 g and 17.98 g, respectively. The H3M3 treatment had the most significant one-hundred-grain weight, reaching 21.27 g. The soybean yield of each density treatment was M3 > M1 > M2. With the increase in row spacing, the average pod number per plant, grain number per plant, grain weight per plant, and yield of soybeans decreased gradually, and the hundred-grain weight increased gradually. The yield of the density treatment with 60 million plants per hectare under 15 cm row spacing was the highest, reaching 6155.8 kg·hm−2, followed by the density treatment with 60 million plants per hectare under 30 cm row spacing, reaching 5850.6 kg·hm−2.

Optimizing maize planting density based on soil organic matter to achieve synergistic improvements of yield, economic benefits, and resource use efficiency

With the development of the economy, the contradiction between population, resources, and the environment has become more and more prominent. How to make full use of limited cultivated land resources to increase food production while reducing damage to the environment is an important issue facing agricultural production. Maize plays an essential role in ensuring global food security. Furthermore, planting density is a key agronomic factor affecting maize yield. Although soil organic matter (SOM) is an important indicator of soil fertility. Whether there are different agronomic optimal planting densities of maize under varying SOM contents remains unknown. Furthermore, there is limited understanding on whether optimizing maize planting density based on SOM further improves grain yield and resource use efficiency. Therefore, this study investigates the influence of SOM and planting density on maize grain yield. We also determine the relationship between SOM and agronomic optimal planting density (AOPD) and compare the grain yield, economic benefits, and resource use efficiency of sowing under uniform conventional planting density (SUD) versus optimized planting density based on SOM (SOD). The results showed that AOPD and its corresponding yield increased linearly with the increase in SOM. Compared with SUD, the yield of the two experimental sites under SOD increased by 2.3 % and 5.5 %, respectively, and the economic benefits increased by 0.5 % and 4.9 %, respectively. The average energy use efficiency, energy mass productivity, and energy economic productivity of the two experimental sites under SOD were all higher than those of SUD. These results demonstrate that it is theoretically feasible to optimize maize planting density based on the spatial heterogeneity of SOM. SOD is a potentially sustainable maize production method that can fully utilize the resources of cultivated land to increase grain yield and economic benefits.

In silico and in vitro Assessment of Indigenous Organic Practices on Germination and Seedling Metrics of Compact Cotton using GerminaR R Software Package

Background: Cotton crop is sensitive in its young stages i.e. stand establishment. Cotton seeds are fuzzy and messy in handling and field sowing. Delinting is a necessary process done commonly using acids and are comprehensively not protective. Optimal plant population, growth and persistence are strongly influenced by germination and establishment. In line with this, it is necessary and needful of an hour to find an alternative, eco-friendly, ecologically safe and low cost seed treatment method. Methods: The experiment was conducted in two methods viz., a petridish method and roll-towel method with seed treatment methods viz., cowdung and red earth mix treated with and without biofertilizer and compared with acid-delinted and fuzzy seeds. The germino-metrics were determined using GerminaR package, an interactive web application and statistical analysis was performed using R software package v. 4.2.1. Result: The summative effect of seed treatment with fresh cow dung, red earth and biofertilizer (Azospirillum and Phosphobacteria) per kg of seeds at respective quantities (0.5:0.25:0.12) recorded significantly maximal germination indices and seedling growth determinants. This present study would be a complementary to comprehensively promote the use of indigenously available natural materials as an alternative to acids in cotton seed delinting.

ZmNRT1.1B (ZmNPF6.6) determines nitrogen use efficiency via regulation of nitrate transport and signalling in maize.

Nitrate (NO3 - ) is crucial for optimal plant growth and development and often limits crop productivity under low availability. In comparison with model plant Arabidopsis, the molecular mechanisms underlying NO3 - acquisition and utilization remain largely unclear in maize. In particular, only a few genes have been exploited to improve nitrogen use efficiency (NUE). Here, we demonstrated that NO3 - -inducible ZmNRT1.1B (ZmNPF6.6) positively regulated NO3 - -dependent growth and NUE in maize. We showed that the tandem duplicated proteoform ZmNRT1.1C is irrelevant to maize seedling growth under NO3 - supply; however, the loss of function of ZmNRT1.1B significantly weakened plant growth under adequate NO3 - supply under both hydroponic and field conditions. The 15 N-labelled NO3 - absorption assay indicated that ZmNRT1.1B mediated the high-affinity NO3 - -transport and root-to-shoot NO3 - translocation. Transcriptome analysis further showed, upon NO3 - supply, ZmNRT1.1B promotes cytoplasmic-to-nuclear shuttling of ZmNLP3.1 (ZmNLP8), which co-regulates the expression of genes involved in NO3 - response, cytokinin biosynthesis and carbon metabolism. Remarkably, overexpression of ZmNRT1.1B in modern maize hybrids improved grain yield under N-limiting fields. Taken together, our study revealed a crucial role of ZmNRT1.1B in high-affinity NO3 - transport and signalling and offers valuable genetic resource for breeding N use efficient high-yield cultivars.

Biometric parameters and yield of maize hybrids in dependence on agricultural technology elements

Modern innovative corn hybrids of the intensive type have a significant individual response of morphometric and photosynthetic indicators to the density of the coenosis. Establishing correlations of these indicators with the level of grain yield of different genotypes of corn hybrids and determining the optimal parameters for the manifestation of these characteristics allows technological measures to ensure the realization of productive potential. The purpose of the research was to establish the peculiarities of the formation of biometric and photosynthetic indicators of innovative corn hybrids depending on the genotype, and the density of the coenosis, and to determine the correlationregression dependence of these characteristics. Field experiments were conducted during 2019-2021 in the agroecological zone of the Central Forest Steppe. The soil of the experimental site is typical chernozem, the precursor is soybean. With the help of field, morphometric, laboratory, and statistical (dispersion and correlation analysis) it was established that the height of the plant, the height of attachment of the upper (productive) cob, the area of the assimilation surface of one plant, and the photosynthetic potential depended on the studied factors – the genotype of the hybrid and the density of the coenosis. A medium and strong positive correlation was established between productivity and these characteristics, which indicates the need to technologically ensure optimal plant growth and development at various stages of the development of corn plants of all hybrids. The mid-ripe hybrid Zedan 32 (FAO 320) showed maximum productivity at a density of 80,000 plants/ha and sharply reduced productivity at a density of up to 100,000 plants/ha. Mid-early hybrids showed maximum yield at a density of 90,000 plants/ha, while an increase or decrease in plant density from the optimum led to a decrease in grain yield. Therefore, each hybrid has its optimal coenosis density for obtaining the maximum grain yield, and increasing the photosynthetic potential of crops by agrotechnical measures does not always guarantee a parallel increase in the grain yield of corn hybrids

Analysis of a Segmented Axial Active Magnetic Bearing for Multi-MW Compressor Applications

In high-speed compressor and turbine applications, active magnetic bearings are employed to increase reliability, reduce maintenance, and provide contact-free and oil-free operation. Evidently, the dynamic performance of active magnetic bearings must correspond to application-specific events like compressor surge or thrust disturbances. This article presents a segmented stator as one of the solutions to increase the bandwidth of axial active magnetic bearings (AMBs) and further improve the stability of the shaft system. We analyze the effects and discuss the tradeoffs of axial active magnetic bearing stator segmentation and slitting to alleviate bandwidth limitations. Finite element method design optimization and control plant modeling methods, open- and closed-loop tests, and identification of the overall thrust AMB–rotor control plant are studied.

STUDY OF PLANTING PERIODS IN THE CULTIVATION OF EARLY, MEDIUM AND LATE VARIETIES OF KHOREZM MELONS

In the article: study of optimal planting, germination, flowering and fruit ripening for the cultivation of varieties of "Tarnak", "Khandalak", "Gurvak", medium-sized sardine. Aq novvot, Karikiz, late Amudarya, Sakhavat, Gulobi Khorazmi grown in Vokhada. Based on the obtained results, varieties with high productivity were shown.

Leaf shape, planting density, and nitrogen application affect soybean yield by changing direct and diffuse light distribution in the canopy

When attempting to maximize the crop yield from field-grown soybean (Glycine max (L.) Merr.) by means of improving the light conditions for photosynthesis in the canopy, it is crucial to find the optimal planting density and nitrogen application rate. The soybean plants that were the subject of our experiment were cultivated in N-dense mutual pairs, and included two cultivars with different leaf shapes; one cultivar sported ovate leaves (O-type) and the other lanceolate leaves (L-type). We analyzed the results quantitatively to determine the amount of spatial variation in light distribution and photosynthetic efficiency across the canopy, and to gauge the effect of the experimental parameters on the yield as well as the photosynthetic light and nitrogen use efficiency of the crop. Results indicate that the different leaf shapes were responsible for significant disparities between the photosynthetic utilization of direct and diffuse light. As the nitrogen fertilizer rate and the planting density increased, the soybean plants responded by adjusting leaf morphology in order to maximize the canopy apparent photosynthetic light use efficiency, which in turn affected the leaf nitrogen distribution in the canopy. Despite the fact that the light interception rate of the canopy of the L-type cultivar was lower than that of the canopy of the O-type cultivar, we found its canopy apparent photosynthetic nitrogen and light use efficiency were higher. It was interesting to note, however, that the nitrogen and light use efficiency contributions associated with exposure to diffuse light were greater for the latter than for the former.

Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice

Real-timely monitoring of the crop water status can improve irrigation scheduling to increase water saving and enhance agricultural sustainability, whereas the canopy temperature measured by thermal imaging is an essential indicator for determining the rice water status. The primary goal of this study was to propose a new temperature index based on the temporal variance of the daily temperature and to develop the rice water status estimation model. Two field experiments involving two rice varieties and multi-irrigation regimes were conducted from 2019 to 2020. A thermal imaging camera was used to measure the canopy temperature from 8:00–16:00 at 2-hour intervals across all growth stages. Three plant water parameters, namely plant water content (PWC), canopy water content (CWC), and canopy equivalent water thickness (CEWT), were collected simultaneously. The results showed that canopy temperature and plant water parameters differed obviously among different irrigation treatments. The relative canopy temperature velocity (RCTV) was developed based on the temporal variance of the daily temperature, and the RCTV8–12 performed well in distinguishing different irrigation treatments and quantifying the rice water status. The coefficient of determination (R2) values of the exponential relationships between the optimal RCTV and plant water parameters reached 0.47 (PWC), 0.39 (CWC) and 0.18 (CEWT). The random forest model, which integrates the multi-temperature indices, achieved a good estimation for PWC (R2 = 0.78), CWC (R2 = 0.77), and CEWT (R2 = 0.64) across all growth stages. In summary, combining the multi-temperature indices derived from the thermal infrared imagery and machine learning algorithm can facilitate the non-destructive estimation of the rice water status and improve the precision irrigation schedule.

Performances Analysis of Three Grid-Tied Large-Scale Solar PV Plants in Varied Climatic Conditions: A Case Study in Algeria

Currently, for the determination of the suitable and optimal PV power plant according to the climate conditions of the concerned region, researchers focus on the estimation of certain performance factors, which are reported to be the key parameters for the analysis of the performances of grid-connected photovoltaic (PV) power systems. In this context, this paper focuses on on-site real-time analysis of the performance of three solar photovoltaic plants: Sidi-bel-Abbés (12 MWp), Laghouat (60 MWp), and Ghardaïa (1.1 MWp). These plants are located in different regions experiencing diverse climatic conditions in Algeria. The analysis was carried out by the standardized norms of IEC 61724, using monitoring data collected over one year. The photovoltaic power plants were evaluated in terms of performance factors, such as the reference yield (Yr), final yield (Yf), performance ratio (PR), and capacity factor (CF). On the other side, based on real data collected at the concerned sites, two linear functions depending on solar irradiance and the PV module temperature for each site are proposed for the evaluation of the generated alternative power output (PAC) for the three PV plants. The obtained results based on the study presented in this paper can help designers of PV power plants of different technologies and different climate conditions to precisely decide the convenient technology that allows the best production of the electrical energy for grid-tied PV systems. Furthermore, this study can contribute in giving a clear vision of the implementation of upcoming large-scale solar PV power plants in Algeria within the studied area and other areas.

Growth , yield and quality of medicinal plant Chia (Salvia hispanica) as influenced by planting methods and density

An experiment was conducted at Central Research Farm, Gayeshpur, Nadia under BCKV, West Bengal to assess the impact of plant density and planting methods on growth, yield and quality attributes of chia (Salvia hispanica L.) grown during winter (rabi) seasons of 2019–20 and 2020–21. Treatments were distributed in split-plot design, with 2 establishment methods (M1, direct sowing and M2, Transplanting) in main plots and 4 planting density (S1, 60 cm × 50 cm = 33,333 plants/ha; S2, 50 cm × 50 cm = 40,000 plants/ha; S3, 50 cm × 25 cm = 80,000 plants/ha; S4, 50 cm × 20 cm = 1,00,000 plants/ha) in sub-plots. Crop under direct sowing with closer spacing (50 cm × 20 cm) had the greater number of primary branch (15.7/plant), primary branch length (38.4 cm) and main inflorescence length (25.9 cm), thereby reflecting superiority of this treatment combination over others. The same treatment combination produced significantly the highest grain (671.7 kg/ha), stem (1109.4 kg/ha) and husk yield (375.2 kg/ha), which led to increase harvest index (31.2). The greatest economic response in terms of gross return (228.4 × 103 ₹/ha), net return (166.3 × 103 ₹/ha) and B: C ratio (3.67) was recorded in crop under the same treatment combination. Based on quadratic regression equation, the optimum plant density of chia was calculated as 71,428 plants/ha. Thus, direct seed sowing at a density of 71,428 plants/ha (spacing ~ 55 cm × 25 cm) can be done for chia cultivation during rabi season in West Bengal condition.

Peculiarities of plant adaptation of interspecific hybrid Betula ex vitro

Microclonal propagation as one of the methods of biotechnology allows obtaining genetically homogeneous plants during the year from a minimum amount of donor material. Adaptation of plants ex vitro to environmental conditions is the final and important stage of microclonal propagation. The purpose of the study is to determine the optimal mode of plant adaptation of triploid interspecific hybrid Betula ex vitro to environmental conditions. Biotechnological (microclonal propagation) and statistical (arithmetic mean, standard error, one-way analysis of variance (ANOVA)) methods were used for research. The study was carried out in the research of Plant Biotechnology Laboratory of the Separated Subdivision of National University of Life and Environmental Sciences of Ukraine “Boyarka Forest Research Station” during 2019-2023. According to research, the MS nutrient medium with the addition of 0.25 mg∙l -1 of kinetin stimulated the active proliferation of microshoots and root system. Optimum plant development took place on the nutrient medium vitro for a 25-30- day cycle of cultivation, which contributed to successful adaptation to environmental conditions. The proportion of adapted Betula ex vitro hybrid plants on agroperlite substrate was more than 60% for 30 days. Significant plant viability (more than 80%) was obtained under the conditions of preliminary exposure of the plant root system in auxin solution (1.0 mg/lIAA, 1.0 mg/l NAA, 1.0 mg/lIBA) for 25-30 minutes followed by daily spraying of leaves with 20% glycerin for 15-20 days. Adapted plants had typical pigmentation without signs of chlorosis and vitrification. In the spring, the survival rate of plants under environmental conditions was more than 80% and indicated a high level of adaptability after in vitro. The developed protocol for the adaptation of the Betula ex vitro hybrid allows obtaining high plant survival in environmental conditions. The practical value of the research is obtaining plants adapted to environmental conditions ex vitro, which in the future can be used to qualitatively enrich the range of artificial plantings in settlements

Use of interpretive machine learning and a crop model to investigate the impact of environment and management on soybean yield gap

Context Management and environmental conditions are the main factors influencing yield of soybean (Glycine max (L.) Merr.). Despite an increase in average soybean yield in recent years in Iran, a considerable gap remains between actual yield and potential yield. Aims The objective of this study was to identify critical climate and management factors affecting soybean yield in Iran’s major soybean production area. Methods A combination of machine learning approaches (using gradient boosted decision trees, XGBoost) and the SSM-iCrop2 simulation model was used. Critical management factors affecting soybean yield were determined through interpretive machine learning using information collected from 268 soybean fields over a 5-year period. Potential yield and water-limited potential yield at six weather stations were estimated for 30 years via the SSM-iCrop2 simulation model. Water limitation was determined by considering the ratio of water-limited yield potential to potential yield, and heat stress status was quantified as the number of days with maximum temperature >36°C during the soybean growing season. Key results The XGBoost models adequately described the observed changes in soybean yield. Root-mean-square error and Lin’s concordance correlation coefficient values of the calibrated model were 262 kg ha−1 and 0.96, respectively, which indicated that the predictor variables could describe most of the variation in soybean yield for the studied dataset. Conclusions We identified 15 climatic and management variables that affect soybean yield. A large part of the studied area is under high water stress and low heat stress. Implications Optimal planting date and improved irrigation management are the main options for reducing the yield gap in the study area.

CPDOS: A Web-Based AI Platform to Optimize Crop Planting Density

Increasing crop yield is a significant objective in modern agriculture, with adjusted planting density and rational fertilization strategies standing out as the foremost approaches for attaining such a goal. Through the use of modern artificial intelligence techniques such as genetic algorithms and neural networks, the CPDOS (Crop Planting Density Optimization System), an online intelligent system that can automate the modeling, optimization, and analysis of the two models, was developed in the present study. The goal of the system is to optimize the planting density model and fertilizer application in combination with other computer system development techniques. The CPDOS comprises three main modules: yield density optimization module, optimal planting density range module, and fertilization and planting density optimization module. The three modules are complemented by two modules for data input and result visualization, culminating in the comprehensive process of optimizing planting density and fertilizer allocation through the CPDOS. The CPDOS was tested using potato, corn, and soybean data, and the results show that the optimization effects of planting density and fertilizer application were satisfactory. The CPDOS is an automated crop planting optimization system that integrates algorithms and models and is driven by artificial intelligence technology. The introduction of the CPDOS reduces the barriers to utilizing these algorithms and models, facilitating wider adoption of intelligently optimized planting technology. The platform’s launch will accelerate the swift advancement of this field.

Response of Yield and Agronomic Output of Bangabandhu dhan100 under Varying Sowing Window in Cold Prone Rangpur Region

Background: Selection of a suitable planting time is one of the key factors for obtaining a desirable rice grain yield. This study focuses on the effect of planting date on the key agronomic traits and provides useful information on optimal planting window to maximize grain yield of Bangabandhu dhan100 in the cold-prone Rangpur region, Bangladesh. Methods: The study was conducted at the Rangpur regional station farm of the Bangladesh Rice Research Institute from October 2021 to June 2022. The selected cultivar was seeded on eight different dates at a 15-day interval, starting on October 16. Time series data on agronomic parameters including seedling biomass, leaf number of seedlings and tiller number per plant were collected at a 15-day interval. Result: A good relationship was found between accumulated GDD (growing degree days) and the associated leaf number per plant and the highest leaf number was observed with early seeding. Similarly, seedling biomass yield depended on accumulated GDD and it was found to be higher at early seeding than late seeding. The tillering pattern of Bangabandhu dhan100 was influenced by planting dates and reached its peak at 60 days after planting for all seeding dates. The highest tiller number (368/plants) was found when the cultivar was seeded on December 1. Total biomass showed an increasing trend after planting and reached its maximum of 13.9 t/ha with December 1 seeding. Planting time significantly influenced the grain yields and the highest yields were observed during seeding between 1 and 16 December; yields gradually decreased with the advancement of seeding dates. This finding provided a good idea for the selection of an optimal planting time for Bangabandhu dhan100 to maximize grain in the Rangpur region, Bangladesh.

Lead Toxicity-Mediated Growth and Metabolic Alterations at Early Seedling Stages of Maize (Zea mays L.).

To investigate the toxic effects of lead (Pb) on key metabolic activities essential for proper germination and seedling growth of maize seeds, experiments were carried out with different levels of Pb (0 to 120 mg of Pb L-1 as PbCl2) applied through growth medium to two maize hybrids H-3310S and H-6724. The research findings indicated that growth and metabolic activities were adversely affected by increased Pb contamination in growth medium; however, a slow increase in these parameters was recorded with increasing time from 0 to 120 h. Protease activity decreased with an increase in the level of Pb contamination but increased with time; consequently, a reduction in seed proteins and an increase in total free amino acids were observed with time. Similarly, α-amylase activity decreased with an increase in Pb concentration in growth medium while it increased with increasing time from 0 to 120 h; consequently, reducing and non-reducing sugars increased with time but decreased with exposure to lead. The roots of both maize hybrids had higher Pb contents than those of the shoot, which decreased the uptake of nitrogen, phosphorus, and potassium. All these nutrients are essential for optimal plant growth; therefore, the reduction in growth and biomass of maize seedlings could be due to Pb toxicity that altered metabolic processes, as sugar and amino acids are necessary for the synthesis of metabolic compounds, rapid cell division, and proper functioning of enzymes in the growing embryo, but all were dramatically reduced due to suppression of protease and α-amylase by toxicity of Pb. In general, hybrid H-3310S performed better in Pb-contaminated growth medium than H-6724.

Assessment of planting soil temperature and growing degree day impacts on silage corn (Zea mays L.) biomass.

The annual forage crop production system, enclosing silage corn (Zea mays L.) and following cool-season annual forage, can enhance forage production efficiency where available land is limited for pasture production. In this forage production system, successful silage corn cultivation has a significant value due to the great yield of highly digestible forage. However, some untimely planting or harvesting of corn due to changing weather often reduces biomass and feeding values. Therefore, a study was conducted to quantify the corn silage biomass reductions by the deviations from optimum planting soil temperature and optimum growing degree day (GDD). The approximations of maximum corn production were estimated based on field trial data conducted between 1978 and 2018 with early, medium, and late-maturity corn groups. Based on weather data, the recorded planting dates and harvest dates were converted into the corresponding trials' soil temperatures at planting (STP) and the GDD. The silage corn biomass data were regressed against STP and GDD using a quadratic function. The maximum biomass point was modeled in a convex upward quadratic yield curve and the optimum STP and GDD were defined as those values at the maximum biomass for each maturity group. Optimized STP was at 16.6°C, 16.2°C, and 15.6°C for early, medium, and late maturity corn groups, respectively, while optimized GDD at harvest was at 1424, 1363, and 1542°C. The biomass reductions demonstrated quadratic functions by the departures of STP or GDD. The 5% reductions were anticipated when STP departed from the optimum temperature by 2.2°C, 2.4°C, and 1.4°C for early, medium, and late maturity corns, respectively; the same degree of reductions were estimated when the GDD departed by 200, 180, and 130°C in the same order of the maturity groups. This result indicates that biomass reductions of late-maturity corn were more sensitive to the departures of STP or GDD than the early-maturity corn. Therefore, early maturing cultivars are more stable in biomass production in a silage corn-winter annual forage crop production system to enhance forage-based livestock production efficiency.