Weather Parameters Research Articles

Seasonal incidence of sucking pests and their correlation with weather parameters in Bt cotton

Seasonal incidence of sucking pests and their correlation with weather parameters in Bt cotton

Impact of Weather Parameters and Time of Sowing on Severity of Powdery Mildew in Cluster Bean

Background: Powdery mildew of cluster bean causes considerable yield loss upto 50-55%. Studies carried on cluster bean powdery mildew are very few and as such there is no information related to epidemiology of the disease. Environmental factors decide the epidemic of the disease and are being used to forecast the disease severity. Methods: Five different dates of sowing of cluster bean were evaluated in a randomized complete block design with four replications at Main Agricultural Research Station, College of Agriculture, Dharwad. Observations on the first appearance of the disease and severity were recorded at weekly intervals using disease scoring scale 0-9 and along with pod yield were considered for statistical analysis. The weather data was used to correlate with the disease severity and correlation matrix was worked out. Further, the observations were converted to % disease index to calculate rate of infection and area under disease progress curve (AUDPC) for each date of sowing. Result: Sowing of crop in first fortnight of September recorded the minimum mean disease severity of 19.10% and recorded pod yield of 4.72 t/ha with lower value of area under disease progress curve (1270.50) as against crop sown in first fortnight of November that recorded the 39.39 % of maximum mean disease severity, 3.82 t/ha pod yield and 2583.88 of area under disease progress curve. The mean maximum temperature was positively correlated (0.89) with % disease index whereas, minimum temperature (-0.87), morning relative humidity (-0.81) and evening relative humidity (-0.88) were negatively correlated with % disease index. Disease progression was started at 35 days after sowing and the maximum ‘r’ value of 0.157 was observed during the II fortnight of September followed by an ‘r’ value of 0.142 during the I fortnight of September whereas, a lower ‘r’ value of 0.009 was observed in I fortnight of September sown crop at 63-69 days after sowing.

Optimal place to apply post-processing in the deterministic photovoltaic power forecasting workflow

Is the post-processing of global horizontal irradiance (GHI) forecasts necessary for issuing good photovoltaic (PV) power forecasts? Whenever this question is raised, the instinctive supposition always seems to be “yes,” because GHI is the most important weather parameter governing the amount of PV power generated, and surely, the better the GHI forecasts are, the better the PV power forecasts should result. To attend to this question more scientifically and more formally, two classic deterministic-to-deterministic post-processing methods, namely, the model output statistics and kernel conditional density estimation, are applied at various stages of PV power forecasting, resulting in four distinct workflows. These different workflows are trained and tested on three PV plants in Hungary, using data from a four-year (2017–2020) period. Both ground-based GHI and satellite-retrieved GHI are used as the “truth” with which numerical weather prediction (NWP) GHI forecasts are post-processed. A very thorough deterministic forecast verification exercise is conducted following the best practices. It is found that contrary to the common supposition, post-processing GHI only leads to marginal, if that can be quantified at all, benefits, so long as the PV power forecasts are to be post-processed. This ought to be deemed as a very important finding, as it puts into question the “GHI forecasting + post-processing + irradiance-to-power conversion” workflow that has dominated solar forecasting for decades.

Weather Parameter’s Impact on Natural Parasitization by Campoletis chlorideae Uchida in Chickpea Ecosystem - New Alluvial Zone of West Bengal

Background: A major biotic constraint in chickpea (Cicer arietinum L.) production is gram pod borer, Helicoverpa armigera (Hübner). However, the population of this pest is reduced to some extent by a solitary endo-larval parasitoid, Campoletis chlorideae Uchida under field conditions. Climate change has a greater impact on natural enemies’ effectiveness used in field-level pest management. Therefore, the present study was conducted to understand the impact of weather factors on C. chlorideae parasitization rate of H. armigera in chickpea grown in the new alluvial agro-climatic zone of West Bengal. Methods: Chickpea was grown in a randomized complete block design (RCBD) with three replications during Rabi 2017-18, 2018-19 and 2019-2020, at Mondouri farm, Bidhan Chandra Krishi Visawavidayala, Mohanpur, West Bengal. To know the most significant weather parameter which influences C. chlorideae parasitization or parasitoid incidence, H. armigera larvae (2nd instar) collected from the field experiment were reared until cocoon formation and parasitization % was correlated with weather parameters and subsequently subjected to stepwise regression analysis. Result: The larval endo-parasitoid, C. chlorideae marked its first appearance from 3rd SMW after the appearance of H. armigera and its activity was high (68% to 72% parasitization) during the flowering to pod initiation stage and thereafter declined gradually. Among the weather variables, only temperature (maximum and minimum) had a consistent and significant negative correlation with C. chlorideae incidence and was responsible for nearly 50% C. chlorideae incidence. An increase in 1°C temperature results in a decrease of 4% parasitoid incidence. Thus, climate change in the near future will have a considerable influence on the overall survival, development and rate of parasitization of parasitoids.

Comprehensive Dataset on Electrical Load Profiles for Energy Community in Ireland

This paper describes a comprehensive energy-related dataset, collected from residential electricity households within an energy community in Ireland, as part of StoreNet project. The data includes local weather parameters and per household power (W) and energy (Wh) components for various aspects such as active power consumption, PV generation, grid import and export, charging and discharging, and the state of charge of energy storage. Additionally, it provides weather data for the location at a 1-minute temporal resolution for the year 2020. The dataset has been validated by comparing measurements that should yield identical results to standard load profiles, with no significant inconsistencies discovered. Validation examples have also been given through the published articles where this dataset has been used to analyse peer-to-peer energy trading benefits for the energy community and decision-making support for aggregators. The dataset aids in understanding patterns in electrical load curves and the duty cycle of energy storage within an energy community. Furthermore, it can assist in comprehending the impact on distribution networks caused by distributed energy storage.

Seasonal Incidence of Fall Army Worm <i>Spodoptera Frugiperda</i> (J E Smith) on Maize

Investigation on seasonal incidence of fall army worm Spodoptera frugiperda on maize was undertaken at the Department of Entomology, VNMKV, Parbhani. The studies were done during kharif on the maize variety Komal. Laval incidence was observed from 31st SMW (Standard Meteorological Week) i.e. fifth week of July and to till 43rd SMW i.e. fourth week of October with a peak incidence observed (4.05 larvae/plant) during 39th SMW. Thereafter larval count showed declining trend (1.9 larvae/plant) during 42nd SMW. Larval infestation also recorded in %age and under observation highest infestation 38.98% during 39th SMW i.e. fourth week of September. The correlation between larval incidence and weather parameters was showed negatively non-significant with minimum temperature, evening relative humidity and rainfall, whereas positive relationship was observed with maximum temperature and bright sunshine hours. The correlation with morning relative humidity evaporation with FAW population wee positively and negativity significant respectively evaporation was negative significant.

Population Dynamics of Gram Pod Borer <i>Helicoverpa armigera</i> (Hubner)

The population dynamics of gram pod borer Helicoverpa armigera (Hubner) on chickpea was evaluated at the Agricultural Research Farm of B R D P G College, Deoria, Uttar Pradesh during rabi 2022-23. The larval incidence was initially recorded in 6th SMW (first week of February) (2.3 larvae/ plant) with a peak in 11th SMW (third week of March) (10.53 larvae/ plant). Larval incidence on chickpea variety Pusa-261 showed significant positive correlation with maximum temperature (0.837) and minimum temperature (0.804), whereas relative humidity (maximum and minimum) revealed non-significant negative correlation (-0.410), (-0.588), respectively. Correlation between rainfall and larval incidence was found positive and significant (0.734). However, all the weather parameters together contributed 94.18% towards larval incidence.

SURVEY OF LEPIDOPTERAN PESTS OF RICE ORYZA SATIVA L. FROM NAGALAND

A field survey was carried out in the farmer?s field located at Chutsolie colony, Chumukedima, Nagaland, from July to November 2022 using light traps to study the incidence of lepidopteran pests occurring in rice, Oryza sativa L. The survey was conducted from the vegetative till harvesting stages of paddy. Fortnightly, observations of the lepidopteran pests were done and correlated with the weather parameters. During the survey, four lepidopteran pests, namely, Cnaphalocrocis medinalis (Guenee), Leucania loreyi (Duponchel), Melanitis leda (Linnaeus), and Scirpophaga incertulas (Walker) were observed. The highest peak population of C. medinalis (Guenee), M. leda (Linnaeus), and S. incertulas (Walker) were observed on the 37th standard mean week (SMW) (i.e., vegetative stage) whereas the highest population of L. loreyi on 41st SMW. (i.e., reproductive stage). The species M. leda constituted the highest population (75) and persisted in the field till the harvest, followed by S. incertulas (51), and C. medinalis (47). The species L. loreyi was the least in numbers (16). Among the species recorded, L. loreyi was the new record from Nagaland.. KEYWORDS :Cnaphalocrocis medinalis, Incidence, Leucania loreyi, Melanitis leda, Pests, Rice, Scirpophaga incertulas

Exploring the Role of Environmental Factors on the Emergence of Leaf Blight of Mungbean in Maharashtra State, India

Leaf blight disease (LBD) of mungbean caused by Macrophomina phaseolina is one of the major yields reducing factor in mungbean (Vigna radiata (L.) Wilczek). Temperature and relative humidity play a significant role in development of this disease. In the present studies which was carried out at Agricultural Research Station, Badnapur during kharif 2017. The factors were found to have a temperature showing non-significant and humidity showing significant role in disease development on leaf blight under field conditions. Maximum temperature (31.40C), minimum temperature (25.610C) and relative humidity (84.04%) favored high disease development. Positive and non-significant correlation was observed between disease and temperature while significant, positive correlation was noticed between disease development and relative humidity. This is the first-time study on the impact of weather parameter on leaf blight of mungbean in Maharashtra State.

Using random forest to improve EMEP4PL model estimates of daily PM2.5 in Poland

Long-term exposure to poor air quality is responsible for many diseases and increased mortality worldwide. European Environmental Agency reports that Poland is one of the most polluted countries in Europe due to high emissions associated with large coal and wood consumption and specific weather conditions. Exceedances of WHO-recommended PM2.5 thresholds are still common in Poland, so further action is needed to protect the health of the population. Atmospheric chemical transport models (CTMs) provide information on air quality to the public and are used to regulate air pollutant emissions. However, uncertainties associated with CTMs, related to e.g. physical/chemical processes and input data quality often lead to underestimation of pollutant concentrations, especially for PM2.5, and limits the applicability of CTMs e.g. for health impact studies.A hybrid approach combining the EMEP4PL chemical transport model for Poland with the Random Forest (RF) machine learning algorithm was applied to address the limitations of CTM and reduce its underestimation. We used EMEP4PL-modelled PM2.5 concentrations for period 2016–2019 as a predictor and measured daily PM2.5 from 71 monitoring stations in Poland as a dependent variable in three RF scenarios, which differed in terms of the selected predictors. The impact of different additional variables for the area was revealed, including population and emission data, dominant type of land use, Weather and Research Forecast (WRF) meteorological parameters, and temporal patterns of PM2.5 concentrations across the years. The models were evaluated in a random 5-fold and spatial leave-one-station-out cross-validations (LOSOCV), as well as for an independent test set. Our final model achieved a test set R2 of 0.71, compared to 0.38 for EMEP4PL, along with a reduction in negative bias (0.25 μg m−3 for the final RF, compared to −11 μg m−3 for EMEP4PL) and an improved ability to detect severe PM2.5 episodes. Enhanced coefficients of determination were observed in all seasons and at all monitoring sites included in the study, for both types of cross-validation and for the test set. We estimated the contribution of each group of variables separately and discovered, that the most impactful predictors for the area of Poland included temporal patterns of PM2.5 calculated based on the averages of EMEP4PL outcome (such as day of the year, week number, etc.) and meteorological modelled factors related to temperature, planetary boundary layer height, wind speed, and atmospheric pressure. The developed approach provides a basis for improved spatiotemporal estimates and forecasting of PM2.5 in the region, which is an important step toward better understanding the impact of air pollution on the local population well-being.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Seasonal Incidence of Major Insect Pests of Chilli and Their Correlation with Weather Parameters

Study Design: Randomised Block Design (RBD) with three (3) replications. Place and Duration of Study: Biswanath College of Agriculture, Assam Agricultural University, Assam,India. The experiment was conducted during Rabi 2022-23. Methodology: Suryamukhi variety of chilli from packages of practices for Organic crops of Assam was used for this investigation. Three sucking pests viz, Aphis gossypii, Scirtothrips dorsalis, Amrasca biguttula biguttula and one fruit borer Helicoverpa armigera was found infesting the chilli plants. Weather parameters such as maximum temperature, minimum temperature, average relative humidity, rainfall and bright sunshine hours were correlated with the insect populations. Results: Aphids were positively correlated with sunshine hours (r=0.503*), thrips showed negative correlation with average relative humidity (r= -0.620*), jassids had a negative correlation with minimum temperature (r=-0.701*) and fruit borer showed negative correlation with sunshine hours (r= -0625**). Rainfall had no significant effect on incidence of pest population in chilli. Conclusion: Insect pest populations are highly influenced by weather parameters. Through this research we can develop new pest management strategies according to their appearance and peak activity period.

Analysis of temperature stress and critical heating temperature for hydronic airport pavement

The hydronic heated pavement system has been widely used for snow and ice removal in airports and other transportation infrastructure facilities because of its good snow melting efficiency and environment friendliness. However, the study of the temperature stress distribution in hydronic pavement and the corresponding critical operation method shows many insufficiencies, which allows security problems during system operation. This paper investigated the temperature stress of the hydronic pavement near the pipes with a 3D finite element model. This model was validated by the measured temperature stress in a full-scale hydronic snow melting experiment system. The locations of the maximum compressive and the time-varying tensile stress in the hydronic pavement were reported. The maximum temperature stresses of the hydronic pavement with various design and operation parameters were compared, and the sensitive parameters were proposed. In particular, the critical fluid temperature with various design and weather parameters was presented by comparing the maximum temperature stress in the pavement with the failure strength of concrete material. The findings show the maximum compressive stress appears near the hydronic pipe, and the maximum tensile stress appears at the midpoint between two adjacent pipes after heating for 2 h. Maximum tensile stress increases with pipe embedded depth, pipe diameter, fluid temperature, and pavement capacity, but decreases with air temperature and pipe spacing. To ensure the safety of hydronic pavement, the critical fluid temperature under different conditions is controlled within the range from 38 °C to 77 °C.

Ecological and comparative analysis of the influence of the weather factor under the combined effect of heliomagnetic storms (HMS) on the vegetative balance of boys of different ages

Despite the extensive scientific data available on the impact of meteorological and heliogeophysical factors on the health and physiological parameters of the organism, the ecological role and biological nature of the mechanisms of their interaction remain unclear. The need to choose an integrated approach to the study of the Space–Earth–Human system is obvious and justified because it is based on the idea of using modern technologies and channels for obtaining and disseminating information about the impact of space weather, variability of the atmosphere, electromagnetic and gravitational fields, anthropogenic pollution and other environmental parameters that requires effective and rapid solution. It has been established that the body’s response to various stressful influences is largely determined by the ratio of the tone of the sympathetic and parasympathetic divisions of the autonomic nervous system. It is the autonomic nervous system (ANS) and, above all, its sympathetic division that plays a special role in the formation of adaptive reactions of the body, the development of meteorological reactions, i.e. the autonomic nervous system is the primary site of meteorological influences that cause shifts in its balance. Determination of vegetative status and direction of vegetative activity in boys’ organism was carried out by means of functional and vegetative diagnostics by V. Makats’ method. We examined 151 boys, including 49 boys aged 7–11 years and 102 — aged 12–15 years, FVD was carried out in the morning at 10.00–11.00. Since the state of the autonomic nervous system is decisive in the development of meteotropic reactions, the data of the study of vegetative tone in sunny and cloudy weather under the combined influence of heliomagnetic storms (HMS) were analysed, favourable and unfavourable weather conditions for the body were determined and the state of adaptive health of the examined children was determined. It was found that under the influence of heliometeorological factors on the organism of practically healthy young boys, agerelated peculiarities of meteorological variability are observed. The most sensitive to changes in weather and climatic conditions under the combined influence of HMS was the group of primary school age boys (PSA) aged 7–11 years, who showed maximum deviations in the activity of functional systems from the age norm, and the least sensitive was the group of boys aged 12–15 years. When analysing the influence of changes in weather conditions on the body of boys by the vegetative coefficient (kV), it was found that at the norm of kAv 0.95–1.05, it increased maximally when exposed to sunny weather and amounted to 1.12 in the revealed state of vegetative balance. In a healthy organism, changes in physiological processes and functional activity of systems under the influence of such a factor as changes in boys’ PSA, which indicates a pronounced sympathicotonia, and in boys’ ASA 0.93, respectively, which indicates a slight parasympathicotonia. The effects of cloudy weather on the body in both groups, even with the combined effect of HMB, are easily compensated for, and a special role in this belongs to the autonomic nervous system, whose activity ensures an adequate response of the body to the effects of environmental factors. Knowledge about meteosensitivity and meteorological reactions is necessary for the development of both preventive and therapeutic measures aimed at increasing nonspecific resistance and adaptation mechanisms of the body, normalising the function of organs and systems as a basis for normal reactions to adverse environmental changes. Environmental factors, including meteorological factors, are directly related to human life and health. Each of the meteorological elements has its own biological significance. This raises the question of the impact of solar activity, weather, atmospheric conditions, and, above all, anthropogenic pollution and other environmental parameters on human health, which naturally requires a quantitative approach to risk assessment and the development of necessary management decisions.

Solar power forecasting using domain knowledge

Integrating renewable energy into the existing energy market is crucial. Solar power forecasting is essential since it depends on weather parameters and must integrate with the central grid to use the produced solar power effectively. Contemporary studies indicate that machine learning has the potential to predict the future generation of solar energy based on past data. This research demonstrates a broad range of solar power forecasting, combining the one-year time series solar power generation data, solar panel physical features, and weather information with the help of machine learning and deep learning tools with domain knowledge. The dataset is curated and preprocessed. We propose a deep learning ensemble model based on BI-LSTM. The proposed model can forecast well regardless of geographical position and is able to predict both short-term and long-term time horizons. We compared the results of the proposed model with the existing dataset and multiple standard deep learning models and found that our model produced better performance than traditional models. We also validated our model using different solar plants in Durgapur, India. For long-term forecasting, our model also outperformed the base model.

APPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROP YIELDS

Analysis and improvement of crop productivity is one of the most important areas in precision agriculture in the world, including Kazakhstan. In the context of Kazakhstan, agriculture plays a pivotal role in the economy and sustenance of its population. Accurate forecasting of agricultural yields, therefore, becomes paramount in ensuring food security, optimizing resource utilization, and planning for adverse climatic conditions. In-depth analysis and high-quality forecasts can be achieved using machine learning tools. This paper embarks on a critical journey to unravel the intricate relationship between weather conditions and agricultural outputs. Utilizing extensive datasets covering a period from 1990 to 2023, the project aims to deploy advanced data analytics and machine learning techniques to enhance the accuracy and predictability of agricultural yield forecasts. At the heart of this endeavor lies the challenge of integrating and analyzing two distinct types of datasets: historical agricultural yield data and detailed daily weather records of North Kazakhstan for 1990-2023. The intricate task involves not only understanding the patterns within each dataset but also deciphering the complex interactions between them. Our primary objective is to develop models that can accurately predict crop yields based on various weather parameters, a crucial aspect for effective agricultural planning and resource allocation. Using the capabilities of statistical and mathematical analysis in machine learning, a Time series analysis of the main weather factors supposedly affecting crop yields was carried out and a correlation matrix between the factors and crops was demonstrated and analyzed. The study evaluated regression metrics such as Root Mean Squared Error (RMSE) and R2 for Random Forest, Decision Tree, Support Vector Machine (SVM) algorithms. The results indicated that Random Forest generally outperformed the Decision Tree and SVM in terms of predictive accuracy for potato yield forecasting in North Kazakhstan Region. Random Forest Regressor showed the best performance with an R2 =0.97865. The RMSE values ranged from 0.25 to 0.46, indicating relatively low error rates, and the R2 values were generally positive, indicating a good fit of the model to the data. This paper seeks to address these needs by providing insights and predictive models that can guide farmers, policymakers, and stakeholders in making informed decisions.

LSTM Deep Learning Techniques for Wind Power Generation Forecasting

Wind power generation forecasting is crucial for the optimal integration of renewable energy sources into power systems. Traditional forecasting methods often struggle to accurately predict wind energy production due to the complex and nonlinear relationships between wind speed, weather parameters, and power output. In recent years, deep learning techniques have emerged as promising alternatives for wind power forecasting. This conference paper provides a comprehensive review of deep learning techniques, with a specific focus on Long Short-Term Memory (LSTM) networks, for short-term wind power generation forecasting. Leveraging insights from recent research and empirical evaluations, this paper explores the effectiveness of LSTM networks in capturing temporal dependencies in wind data and improving prediction accuracy. The review highlights the potential of LSTM-based models to enhance the integration of wind energy into power systems and provides guidance for future research in this area.

A Method Based on Artificial Intelligence That Predicts Arabica Coffee Yield by Analyzing Abiotic Factors and the Prevalence of Coffee Leaf Rust

Coffee is a perennial crop that harbors infections throughout the plant, which may worsen illness under favorable circumstances. Coffee leaf rust, a widespread coffee disease and Arabica is susceptible to leaf rust. Disease incidence and severity depend on abiotic variables. Cloudy and constant South-West monsoon weather (June–September) promotes coffee leaf rust growth. In this five-model study, an Extra Tree and Gradient Boosting regression model predicted coffee crop output in Chikamagaluru, Karnataka, with the least error utilizing biotic and abiotic factors. We investigated additional tree, gradient boosting, RF, Decision Tree, and KNN models using biotic and abiotic predictors. Used the independent testing dataset's MSE, MAE, RMSE Root mean square errors, and R-squared errors to compare model performance. The extra tree (R²=0.98 kg/ha ˉ¹ and RMSE = 7.96 kg/ha ˉ¹) and gradient boosting (R²=0.96 kg/ha ˉ¹ and RMSE = 10.96 kg/ha ˉ¹) regression models used Group 1 and 2 characteristics as predictor variables and different parameter fine tuning functions to estimate coffee yield most accurately. Compared to the less precise probabilistic models utilized in this work, such as Random Forest, decision tree, and KNN models, shown in the results section. The optimum weather parameter for coffee production forecasts and biotic-CLR incidence data outperformed random forest, decision tree, and K-Nearest Neighbor models.

Dynamic Succession of Natural Microbes during the Ecolly Grape Growth under Extremely Simplified Eco-Cultivation.

The composition and continuous succession of natural microbial communities during grape growth play important roles in grape health and flavor quality as well as in characterizing the regional wine terroir. This study explored the diversity and dynamics of fruit epidermal microbes at each growth and developmental stage of Ecolly grapes under an extremely simplified eco-cultivation model, analyzed microbial interactions and associations of weather parameters to specific communities, and emphasized metabolic functional characteristics of microecology. The results indicated that the natural microbial community changed significantly during the grape growth phase. The dominant fungal genera mainly included Gibberella, Alternaria, Filobasidium, Naganishia, Ascochyta, Apiotrichum, Comoclathris, and Aureobasidium, and the dominant bacterial genera mainly contained Sediminibacterium, Ralstonia, Pantoea, Bradyrhizobium, Brevundimonas, Mesorhizobium, Planococcus, and Planomicrobium. In summary, filamentous fungi gradually shifted to basidiomycetous yeasts along with fruit ripening, with a decline in the number of Gram-negative bacteria and a relative increase in Gram-positive bacteria. The community assembly process reflects the fact that microbial ecology may be influenced by a variety of factors, but the fungal community was more stable, and the bacterial community fluctuated more from year to year, which may reflect their response to weather conditions over the years. Overall, our study helps to comprehensively profile the ecological characteristics of the grape microbial system, highlights the natural ecological viticulture concept, and promotes the sustainable development of the grape and wine industry.

Infrastructural and operational availability for air taxi operations at German aerodromes

According to Flightpath 2050, door-to-door travel times of less than four hours are aspired for journeys within Europe. To accomplish this goal, on-demand air mobility (ODAM) can improve the conventional means of transportation with short travel times and comfort gains. This paper outlines a methodology for analyzing the availability of German aerodromes for air taxi operations. Analyses are based on the usage of hybrid-electric fixed-wing aircraft under visual flight rules (VFR) for ODAM. This future use-case is applied for the evaluation of infrastructural availability (quality) and operational availability in Germany. An aerodrome database and METAR weather database are developed and evaluated for the availability analyses. The main research finding is the availability of German aerodromes for small fixed-wing aircraft (according to EASA CS-23) operation under VFR for ODAM. The lack of suitable infrastructure for air taxi services (e.g., terminals, paved runways) at many small airfields limits the availability of the service. Furthermore, unsuitable weather conditions reduce operational availability, especially in winter, due to lack of visibility, e.g., snowfall. Current ODAM research has assumed continuous availability, causing an overestimation of availability. This paper provides a holistic approach in which the current state of aerodrome infrastructure and operational constraints are considered by the example of German aerodromes. Minimal infrastructure needed for this use-case is determined, main weather parameters causing operational constraints are detected and possibilities for increasing operational availability at aerodromes are revealed.