Weather Parameters Research Articles

Determining Effective Factors Regarding Weather and Some Types of Air Pollutants in Seasonal Changes of PM10 Concentration Using Tree-Based Algorithms in Yazd City

Introduction: This study was carried out with the aim of determining weather parameters and air pollutants affecting seasonal changes of particulate matter of less than 10 microns (PM10) in Yazd city using Random Forest (RF) and extreme gradient boosting (Xgboost) models.
 Materials and Methods: The required data was obtained from 2018 to 2022. Levene’s test was applied to investigate the significant difference in the variance of PM10 values in 4 different seasons, and Boruta algorithm was used to select the best predictive variables. RF and Xgboost models were trained using two-thirds of the input data and were tested using the remaining data set. Their performance was evaluated based on R2, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Nash–Sutcliffe Model Efficiency Coefficient (NSE).
 Results: The RF showed a higher performance in predicting PM10 in all the study seasons (R2 > 0.85; RMSE < 22). The contribution of dust concentration and relative humidity in spring PM10 changes was more than other variables. For summer, wind direction and ozone were identified as the most important variables affecting PM10 concentration. In the autumn and winter, air pollutants and dust concentration had the greatest effect on PM10, respectively.
 Conclusion: RF model could explain more than 85% of PM10 seasonal variability in Yazd city. It is recommended to use the model to predict the changes of this air pollutant in other regions with similar climatic and environmental conditions. The results can also be useful for providing suitable solutions to reduce PM10 pollution hazards in Yazd city

Improving winter wheat yield prediction by accounting for weather and model parameter uncertainty while assimilating LAI and updating weather data within a crop model

Accurate crop yield predictions play a crucial role in enabling informed policy-making to ensure food security. Beyond using advanced methods such as remote sensing and data assimilation (DA), it is essential to comprehend the influence of various sources of uncertainty on the overall prediction uncertainty. This study presents a novel approach for enhancing the accuracy of crop yield predictions by assimilating remotely-sensed Leaf Area Index (LAI) and updating weather ensemble data into a crop model (SPASS) while accounting for calibration and weather uncertainty. In addition, we investigated the effect of model calibration prior to DA by four calibration data type scenarios. These scenarios involve calibrating the crop model to different combinations of yield, phenology, and LAI, ranging from minimum (yield only) to maximum (yield, phenology, and LAI) data availability. To address weather uncertainty, we derived weather forecasts downscaled from climate models utilizing the MarkSim weather generator. Our results demonstrate that the assimilation LAI and updating weather data significantly reduces the overall uncertainty in crop yield predictions. Notably, the uncertainty associated with weather ensembles has a more substantial influence compared to the uncertainty resulting from calibration. This finding highlights the significance of accounting for variations and discrepancies in weather predictions when assessing yield uncertainty. Additionally, given the set of SPASS model parameters used for winter wheat calibration, additional field-based LAI data does not improve the calibration quality.

Short-Term Changes in Weather Conditions and the Risk of Acute Coronary Syndrome Hospitalization with and without ST-Segment Elevation: A Focus on Vulnerable Subgroups.

Background and Objectives: Acute coronary syndrome (ACS), a prevalent global cardiovascular disease and leading cause of mortality, is significantly correlated with meteorological factors. This study aims to analyze the impact of short-term changes in meteorological factors on the risk of ACS, both with and without ST-segment elevation, and to identify vulnerable subgroups. Materials and Methods: Daily ACS admissions and meteorological variables were collected from October 2016 to December 2021. A generalized linear model (GLM) with a Poisson distribution was employed to examine how short-term fluctuations in meteorological parameters influence ACS hospitalizations. Subgroup analyses were conducted to identify the populations most vulnerable to climate change. Results: Multiple regression analyses showed that short-term fluctuations in atmospheric pressure (≥10 mbar) and air temperature (≥5 °C) seven days prior increased the number of ACS hospitalizations by 58.7% (RR: 1.587; 95% CI: 1.501-1.679) and 55.2% (RR: 1.552; 95% CI: 1.465-1.644), respectively, notably impacting ST-segment elevation myocardial infarctions (STEMIs). The least pronounced association was observed between the daily count of ACS and the variation in relative air humidity (≥20%), resulting in an 18.4% (RR: 1.184; 95% CI: 1.091-1.286) increase in the risk of hospitalization. Subgroup analysis revealed an increased susceptibility among men and older adults to short-term variations in weather parameters. Conclusions: The findings indicate that short-term changes in weather conditions are associated with an increased risk of ACS hospitalizations, particularly STEMIs. Male and older adult patients exhibit heightened susceptibility to variations in climatic factors. Developing effective preventive strategies is imperative to alleviate the adverse consequences of these environmental risk factors.

A hybrid model based on STL with simple exponential smoothing and ARMA for wind forecast in a Brazilian nuclear power plant site

In the event of a nuclear accident with release of radioactive effluents into the environment, the Environmental Control System (ECS) at the Central Nuclear Almirante Álvaro Alberto (CNAAA) nuclear complex estimates the movement of the radioactive plume produced and provides forecast for one and two hours ahead. The prediction is based on the current accident and atmospheric data collected from meteorological stations spread across the site. In this way, the ECS estimates the spatial dose and dose rates distribution at ground level. The one- and two-hour forecast made by the ECS use the naïve method, which repeats the last known value. Its results are satisfactory if the next value to be collected does not differ substantially from the last known value. However, observing real data, acquired in loco, this fact does not occur in practice, which can make the forecast considerably erroneous.The purpose of this work is to investigate new approaches to improve ECS forecasting method, making it more accurate in real situations. To this end, the Seasonal and Trend decomposition using Loess (STL) was used together with the Simple Exponential Smoothing (SES) method and the Autoregressive Moving Average (ARMA) model to forecast weather parameters at the Angra site. The STL method was chosen since it has a robust mathematical decomposition method, capable of satisfactorily dealing with time series that have a high frequency of acquisition, the so-called intraday series. This model uses the Loess method, which is a smoothing technique based on locally adjusted regressions. The SES method and ARMA model are the techniques that most adequately fit the trend and remainder components generated by the STL decomposition, respectively. In the scope of this work, the method is applied to forecast wind speed measurements made at one of the meteorological towers used by the ECS. Using the hybrid model developed, an increase the of 101.39% in the Mean Absolute Percentage Error (MAPE), was achieved when compared to the ECS forecast method.

Knowledge Evaluation on Weather Forecast and Agro-advisory Services to Farmers in Srikakulam District of Andhra Pradesh, India

Weather is the major factor for crop growth and yield. Farmers were more interested on the current real time weather parameters such as rainfall, humidity in air , air temperature, direction and speed of the wind, Knowing of weather forecast is useful to reduce the yield loss and to obtain more yield with high remunerative price. The precise real time data at exact time allows the farmers to plan field management practices like choosing of correct crops, land preparation, field operations like spraying of pesticides and herbicides, irrigation, fertilizer application, harvest, drying and post harvest operations to reduce the crop damage and economic losses. The District Agromet unit (DAMU) located at KVK, Amadalavalasa is sending timely weather advisories to farmers of Srikakulam district based on the forecast issued by IMD. A total number of 1080AgroAdvisory Service (AAS) bulletins were prepared by using DSS software and disseminated to the registered farmers through whatsapp and other social media during 2022-2023. A random sample survey was conducted to study the impact of agro-advisory services to farmers based on weather forecast issued by DAMU, Srikakualm. The sample size was 60 farmers belong to different villages of Amadalavalasa and Srikakulam blocks of Srikakulam district. The study Results revealed that spotlight on illiterate farmers is much more significant and dispersal of advisories in audio or visual format is more comfortable and easy to understand. The results of the survey exposed that among the total farmers the farmers follow for sowing are 45 percent, for spraying operations are 11 percent, for harvest and drying are 62 per cent. Finally this study confirms that the farmers were more concentrate on sowing, harvest and drying related to weather forecast. Hence finally realized that farmers should follow forecast data of weather from choice of crop and its management practices to after harvest operations to get enhanced productivity and also superior net returns.

Population Dynamics of Natural Enemies of Stem Borer Complex and their Correlation with Weather Parameters in Rice Ecosystem

A field experiment was carried out at the Murjhad Research Farm, College of Agriculture Balaghat, during Kharif 2022 and Kharif 2023 to ascertain the population dynamics of natural enemies of stem borer complex, their relative abundance and correlation with abiotic factors in order to propose ecologically and financially feasible measures. Results reveal that spiders, mirid bugs, coccinellids, odonata, staphylinids, cicindelids and carabids were the most prevalent natural enemies in the rice ecosystem. The pooled results revealed that the population of spiders was first observed during the 31st SMW (0.10 spiders/hill) with its peak during the 40th SMW (1.21 spiders/hill). Population of mirid bugs was first observed during the 31st SMW (0.45 mirid bugs/ hill) with its peak during the 39th SMW (2.45 mirid bugs/ hill). The population of adult beetles of coccinellids was first observed during the 31st SMW (0.92 ladybird beetles/sq m) with its peak during the 40th SMW (13.51 ladybird beetles/sq m). The population of adult dragonflies and damselflies was first observed during the 30th SMW (0.40 dragonflies and damselflies/sq m) with its peak during the 38th SMW (8.28 dragonflies and damselflies/sq m). The population of adult staphylinids was first observed during the 30th SMW (1.50 adults/sq m) with its peak during the 38th SMW (18.00 adults/ sq m). The population of adult cicindelids was first observed during the 32nd SMW (0.24 adults/sq m) with its peak during the 37th SMW (0.70 adults/sq m). The population of adult beetles of carabids was first observed during the 30th SMW (0.23 adults/sq m) with its peak during the 39th SMW (3.69 adults/sq m). The pooled data on relative abundance revealed that a total of 385.55 adults of natural enemies were observed throughout the crop growth. Of them, 2.39% were spiders, 3.06% were mirid bugs, 26.78% were ladybird beetles, 18.80% were odonata, 40.98% were staphylinids, 1.14% were cicindelids, 6.85% were carabid beetles. The pooled data on correlation studies revealed that population of spiders was found significantly negative correlated with evaporation and wind velocity. (r= -0.677 and -0.568, respectively) The adult population of mirid bugs found significantly positive correlated with morning RH and evening RH (r= 0.658 and 0.594, respectively). The adult population of coccinellids, odonata and staphylinids was not found significant with any whether parameters. The adult population of cicindelids found significantly positive correlated with morning RH and evening RH (r= 0.666 and 0.644, respectively). The adult population of carabids found significantly positive correlated with morning RH and evening RH (r= 0.587 and 0.545, respectively).

Influence of Abiotic Factors on the Population Dynamics and Species Composition of Stem Borer Complex in Rice Ecosystem

A field experiment was carried out at the Murjhad Research Farm, College of Agriculture, Balaghat in Madhya pradesh during Kharif 2022 and Kharif 2023 to ascertain the population dynamics of stem borer complex, their relative abundance and correlation with abiotic factors in order to propose ecologically and financially feasible measures. Results pertaining to the dynamics of species composition of stem borers of rice reveal that all the three species of stem borer of rice viz., yellow stem borer, Scirpophaga incertulas (Walker), pink stem borer, Sesamia inferens (Walker), white stem borer, Scirpophaga innotata (Walker) were prevalent during the crop season. The pooled data of the total population of stem borer complex observed during the 30th SMW was 8.0 larvae/ 20 hills attaining two peaks, the first peak during the 36th SMW (69.0 larvae/ 20 hills) and the second peak during the 42nd SMW (96.0 larvae/ 20 hills) and thereafter, the population started decreasing and reached 65.0 larvae/ 20 hills during the 43rd SMW and it disappears at the time of harvest. The per cent dead heart incidence was first observed during the 30th SMW (2.43% dead hearts) with its peak during the 36th SMW (14.84% dead hearts) and thereafter, it started decreasing and reached 5.06 per cent dead hearts during the 38th SMW and further, it disappears. The per cent white ears incidence was first observed during the 37th SMW (1.30 % white ears) with its peak during the 42nd SMW (21.52% white ears) and thereafter, it started decreasing and reached 9.24 per cent white ears during the 43rd SMW and further, it disappears. The pooled data revealed that a total of 654.5 larvae stem borer complex were observed throughout the crop growth. Among them, 467 larvae of Scirpophaga incertulas were observed comprising 71.35 per cent, Sesamia inferens population was 110.0 larvae comprising 16.81 per cent and Scirpophaga innotata population was 77.5 larvae comprising 11.84 per cent among the stem borer complex. The data revealed that population of Scirpophaga incertulas was found significantly positive correlated (r= 0.617) with sunshine hours and significantly negative correlated with minimum temperature (r= -0.553). The population of Sesamia inferens was found significantly positive correlated (r= 0.613) with sunshine hours. The larval population of Scirpophaga innotata was not found significant with any weather parameter. The per cent dead heart was found significantly positive correlated with minimum temperature, morning relative humidity and evening humidity (r= 0.550, 0.662 and 0.633, respectively). The per cent white ear was found significantly positive correlated with maximum temperature and sunshine hours (r= 0.535 and 0.627, respectively) and significantly negative correlated with minimum temperature, morning relative humidity, evening relative humidity and evaporation (r= -0.658, -0.689, -0.703 and 0.819, respectively).

Exploring genetic diversity and trait associations with foliar blast disease among parental lines in pearl millet [Pennisetum glaucum (L.) R Br

Thirty-seven pearl millet genotypes were evaluated for morphometric traits and disease incidence and severity during summer and kharif, 2022. Pooled ANOVA revealed significant variation were present in all genotypes across different season. Association studies identified high positive correlations between grain yield and traits such as single earhead weight, single ear head threshed weight, and test weight, with direct and indirect effects on grain yield through key characters. E2 (kharif – 2022) showed favourable conducive weather parameters for disease infection throughout the growing season compared to E1. The higher PCV relative to GCV for disease incidence underscores the environmental influence in disease resistance programs. Negative correlations between disease metrics and yield traits highlight blast disease’s detrimental effect on grain yield. Disease severity indirectly suggests environmental factors may enhance its impact. Disease incidence exhibited a direct negative impact on yield, supported by negative genotypic correlations. The line, PT 6679, exhibit both high yield and highly resistant to blast. Restorer lines (PT 6029, PT 6067, PT 6300, PT 6707, PT 7068) and B lines (ICMB 01666, ICMB 02777) showed promising yield attributes with high to moderate disease resistant for future breeding programs. In D2 analysis, five clusters revealed distinct genetic diversity with Clusters II and V indicating strong hybrid vigor, while Clusters IV (PT 6946, ICMB 06111) and V (ICMB 93111, ICMB 95444) excelled in disease resistance. Clusters I (PT 6029, PT 7068) and II (GMR 58) exhibited superior grain yield, particularly Cluster I, had potential restorer lines for future breeding. Clear differentiation between B and R lines underscored genetic distinctions in trait expression, validating the use of morphological data for assessing genetic diversity.

Effect of meteorological parameters on the dynamics of wilt and pod borer severity in pigeonpea of Bundelkhand region of Uttar Pradesh

The weather parameters play a predominant role in determining the course and severity of insect pests and pathogens in different crops. Wilt and pod borer are the most devastating biotic stresses, causes huge losses from the vegetative stage until harvest. The present study was aimed to analyze the seasonal dynamics of wilt and pod borer incidence in relation to critical weather parameters in pigeonpea from the Bundelkhand region, a pulses basket of Uttar Pradesh. The fusarium wilt (Fusarium udum) and pod borer (Helicoverpa armigera) incidence were found to be major biotic stresses in pigeonpea from 2017-18 to 2020-21. The wilt disease commenced at 48 MSW and the mean incidence ranged from 0.0 to 25.5 percent. The pod borer incidence started at 01 MSW, and the larval population ranged from 0.5 to 1.40 larvae/plant. Though the correlation between pest incidence and weather parameters was found to be non-significant. The wilt and pod borer incidence were positively correlated with Tmax (r= 0.35 and 0.33), Tmin (r= 0.39 and 0.37), rainfall (r= 0.43 and 0.41) and negatively correlated with relative humidity (r= -0.32 and -0.26). The regression analysis indicated variability in wilt and pod borer incidence due to temperature, humidity and rainfall. The information generated on the pest dynamics of wilt and pod borer in pigeonpea under the influence of weather parameters may be helpful in devising the forecasting models as well as the management practices well in advance so as to avoid yield losses.

Flash drought monitoring using diurnal-provided evaporative demand drought index

Flash drought, as a relatively new concept, represents a rapidly evolving drought with sudden onset, short-term duration, and great destructiveness. With the warming of the planet in recent decades, the frequent occurrences of flash droughts have resulted in considerable losses to personnel and properties on a global scale. Nowadays, the emerging Evaporative Demand Drought Index (EDDI) has revealed its significant potential and adaptability in the early warning and quantification of flash droughts. In this study, an advanced method for improving the accuracy and temporal resolution of EDDI is proposed. The new set of EDDI estimates is calculated using diurnal-provided potential evapotranspiration values that have been calibrated with weather parameters and Global Navigation Satellite Systems (GNSS) atmospheric products. Then, the monitoring performances of flash droughts resulting from the diurnal-provided EDDIs are evaluated over the period 2010–2021 in Hong Kong. Results showed that the Probability Of Detection (POD) and False Alarm Rate (FAR) were 87.1 % and 16.7 %, respectively. By comparing with their counterparts obtained from the use of monthly EDDIs, it was found that the improvements made by the new method in the metrics of POD and FAR were 24.6 % and 11.2 %, respectively. In addition, with the use of diurnal-provided EDDI, the mean lead time for flash drought detection has been prolonged to 37.74 days, offering more time to prevent and mitigate the detrimental effects brought by flash drought events.

92 Rearing system influenced the performance, GI-parasite burden, and immune profile of pregnant does during winter

Abstract Small ruminants in the Southeast are predominantly raised outdoors, regardless of seasons and associated weather conditions. Severe weather in winter can decline the performance of pregnant animals and compromise their performance and immunological states. Indoor raising of pregnant animals during winter may minimize the adverse effect of cold stress; however, such potential has not been evaluated. This study hypothesized that performance, GI-parasite burden, and immune response would differ between pregnant does raised outdoor and indoor during winter. The objective of this study was to evaluate the performance, GI-parasite burden, and hematological, biochemical, and immunological parameters of pregnant does raised outdoor and indoor in winter. The study was conducted from late January to early April 2023. Sixteen pregnant Kiko does were divided into outdoor and indoor groups. Outdoor group was kept in one grazing plot consisting of mobile shelters, mineral feeders, hay feeders, and watering troughs. Indoor group was kept in individual pens consisting of containers for water, minerals, hay, and grain. Both groups were given corn and soybean mixed in 3:2 ratio (0.8% of animal body weight), ad libitum hay, minerals, and water. Daily ambient temperature and relative humidity (RH) data were collected for both sites, and temperature-humidity index (THI) was calculated. Animal performance data [body weight, body condition score (BCS), and FAMACHA score] and fecal samples were collected on d 1, weekly during the study, and at the end of the study. Fecal samples were analyzed for the type and quantity of GI parasites using the McMaster technique, and parasite eggs per gram of animal feces (EPG) was calculated. Blood samples were collected on d 1, 34, and 69 and analyzed for hematological, biochemical, and immunological parameters. Data on weather, animal performance, and hematological, biochemical, and immunological parameters were analyzed using the GLM procedure and EPG data were analyzed using the Wilcoxon Rank-Sum test in SAS 9.4. Indoor temperature was higher (7%, P < 0.01), RH was lower (8%, P < 0.05), and THI was higher (5%, P < 0.01) vs. outdoor. Overall, indoor does had better FAMACHA by 9% (P < 0.05) and greater EPG by 35% (P < 0.05). Eosinophils were reduced by 73% (P < 0.05), calcium was increased by 11% (P < 0.01), and total bilirubin was greater by 86% (P < 0.05) in indoor does at the end of the study (P < 0.05) vs. outdoor does. Results showed that performance, GI-parasite burden, and biochemical and immunological parameters of pregnant does were influenced by the rearing system during winter.

Agrarian Synthesis and Precision Cultivation Optimization System

The ever-growing demand for food production calls for innovative solutions in agriculture. This research introduces a machine learning-based approach, specifically utilizing logistic regression, to predict optimal crops based on soil and weather conditions. The dataset encompasses crucial attributes including Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, pH, rainfall, with corresponding crop labels. The proposed methodology employs logistic regression, a powerful classification algorithm, to model the relationships between input features and crop types. Through careful feature engineering, the model is fine-tuned to enhance its predictive accuracy. Rigorous evaluation metrics validate the model's performance, ensuring its reliability in real-world applications. Results showcase the logistic regression model's efficacy in accurately predicting suitable crops for given soil and weather parameters. This predictive tool serves as a practical decision support system for farmers, aiding in crop selection and resource allocation. This research contributes to the synergy of machine learning and agriculture, showcasing logistic regression as a valuable tool for crop prediction and resource optimization. As technology continues to transform traditional farming, the integration of logistic regression in precision agriculture offers a practical and efficient approach to crop selection.

Weather Based Forewarning Model for Powdery Mildew Disease of Mustard

Powdery mildew disease is the most important disease of mustard (Brassica Juncea) which is highly sensitive to weather condition particularly temperature and humidity. To ascertain the relationship between weather variables and powdery mildew disease of mustard crop at Sardarkrushinagar, weekly powdery mildew data of 12 years (2010-11 to 2021-22) was collected from Centre for Oilseed Research, S.D. Agricultural University, Sardarkrushinagar. The data were pooled over the year and correlated with corresponding weather parameters recorded in Agromet Observatory, S.D. Agricultural University, Sardarkrushinagar. The forewarning regression equation was developed with the help of weather variables to predict the disease incidence. The results revealed that the maximum and minimum temperature ranged 26.2 to 38.1oC and 8.7 to 18.5oC respectively found congenial for disease incidence. The weather variables like maximum temperature (Tmax) and minimum temperature (Tmin) correlated significantly positive with powdery mildew disease. While morning relative humidity (RH I) and evening relative humidity (RH II) correlated significantly negative with disease severity. The impact of rainfall (RF), sunshine hour (SSH) and wind speed (WS) found positive but non-significant on powdery mildew disease. The weather variables those have significant relationship with powdery mildew disease, were used to develop multiple regression model. The regression equation Y = 62.837 + 8.072*Tmax – 3.919*RH1 developed with maximum temperature and morning relative humidity caused up to 96 % variability in the powdery mildew disease. Hence the combined effect of Tmax and RH1 was more pronounced and most influencing weather parameters on powdery mildew disease. Therefore, model could be used for predicting the powdery mildew disease well in advance.

Influence of weather parameters on rice false smut disease progression in Tamil Nadu, India

False smut of rice is an upcoming menace to rice production in India. In order to understand the intricate relationship between disease incidence and weather parameters, field experiments were conducted for three years (2019, 2020 and 2021) in two cropping seasons viz., late kharif (August to November) and rabi (October to January) at the Agricultural College and Research Institute (AC & RI), Madurai, Tamil Nadu. Results revealed that the disease severity had positive correlation with relative humidity (RH), wind speed (WS) and bright sunshine hours (BSS) and negative correlation with heavy rainfall (RF), evaporation (EP) and temperature. The pooled data analysis (2019 and 2020) for the late kharif and rabi cropping season revealed that disease severity was perfectly showed positive correlation with relative humidity (0.80) and wind speed (0.83) and negatively correlated with weekly maximum temperature (-0.78) and minimum temperature (-0.84). The step wise linear regression analysis was performed which revealed that among the six weather factors minimum temperature influenced the false smut disease severity up to 92%.

Developing weather-based biomass prediction equation to assess the field pea yield under future climatic scenario

The present research focuses on the variation of field pea production under different prevailing weather parameters, aiming to develop a reliable forecasting model. For that a field experiment was conducted in New Alluvial Zone of West Bengal during 2018-19 and 2019-20 with three different varieties (VL42, Indrira Matar, Rachana) of this region. Biomass predicting equation based on maximum temperature, minimum temperature and solar radiation was developed to estimate field pea yield for 2040-2099 period under SSP 2-4.5 and SSP 5-8.5 scenarios. It reveals that solar radiation positively influences crop biomass, while high maximum and minimum temperatures have adverse effects on yield. The developed forecasting equation demonstrated its accuracy (nRMSE=17.37%) by aligning closely with historical data, showcasing its potential for reliable predictions. Furthermore, the study delves into future climate scenarios, showing that increasing temperatures are likely to impact field pea yield negatively. Both biomass and yield showed decreasing trend for the years from 2040 to 2099. SSP 5-8.5 scenario, which is more pessimistic one, foresees a substantial reduction in crop productivity. This weather parameter-based biomass prediction equation can be effectively utilized as a method to assess the impact of climate change on agriculture.
 Keywords: Field pea, weather parameters, crop yield prediction, New Alluvial Zone, nRMSE

Impact of Weather Parameters on Population Dynamics of Tobacco Leaf Eating Caterpillar, Spodoptera litura (F.) Infesting Groundnut

Investigations on impact of weather parameters on population dynamics of tobacco leaf eating caterpillar, Spodoptera litura (F.) infesting groundnut was carried out at regional research station, Anand Agricultural University, Anand during kharif 2021. Infestation of the S. litura on groundnut was observed from July to October. The infestation of S. litura started from third week of July (29th SMW) during kharif 2021. Thereafter, population and per cent damaged plants were gradually decreased toward the maturity of the crop up to last week of October (44th SMW). The maximum larval population of S. litura as well as per cent damaged plants were observed during the month of September (35th SMW to 39th SMW). S. litura larvae showed significantly positive correlation with rainfall, morning and evening relative humidity as well as morning and evening vapor pressure. Morning relative humidity contributed 44.5% variation in the larval population of S. litura.

Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning

AbstractThis study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. Our objective is to simulate monthly global PI characteristics using a multilayer 1D‐CNN model trained on 12 space weather and ionospheric parameters. In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. Our findings indicate that non‐equinox months exhibit the highest equatorial PI magnitude over the American‐African longitudinal sector, contrary to the expected higher Rayleigh‐Taylor instability growth rate during equinox months. Winter months display the most intense and widespread vertically and horizontally distributed equatorial PI patterns. We also observe double peaks across geomagnetic latitudes and longitudinally varying wavelike irregularity structures, particularly in May, August, and predominantly in September. Furthermore, north‐south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance‐related factors play a crucial role in predicting global PI variabilities. These findings emphasize the significance of these variables in controlling the strongest PI characteristics observed in the Atlantic sector, which has garnered considerable attention in PI research. The employed 1D‐CNN model demonstrates exceptional predictive capabilities, exhibiting a strong correlation of 0.98 for global PI characteristics across all months and satellites.

Forecasting of Airborne Conidia Quantities and Potential Insect Associations of Cryphonectria parasitica, the Causal Agent of Chestnut Blight, in England.

Sweet chestnut, an Asiatic tree introduced in many parts of Europe including the United Kingdom, is planted for nut production, timber, and amenity. Its major threat is the disease called blight, caused by the fungus Cryphonectria parasitica, which infects through wounds by airborne spores. Field trapping using sticky rods rotating traps was performed in an infected area in Devon (between May 2021 and April 2023). An improved dual hydrolysis Taqman probes real-time PCR was used. The number of spores was calculated by comparing the cycle threshold to the Ct of standards with known amounts of conidia or known target fragment copies cloned into a plasmid. Weekly spore counts were in the range of around 60 to approximately 8.5 × 103, with fluctuations of peaks (mainly in late summer-autumn 2021) and troughs. The effects of weather parameters were modelled, finding correlations between spore numbers and temperature, humidity, dewpoint, rainfall, wind speed, and wind duration. Additionally, an insect trapping was performed to confirm the presence/absence and quantity of C. parasitica conidia potentially phoretic on some insects by using the same molecular approach. None of the ten collected insect species harboured spores of this fungus.

Impact of Abiotic Factors on Occurrence of Capsule Borer and Sucking Pests in Sesamum

A field experiment was conducted at Agricultural college, Naira, Srikakulam, Andhra Pradesh, India during early summer (January–April) of 2021 to know about the incidence of leaf webber and sucking pests viz., leafhopper (Hishimonus phycitis Distant) and whitefly (Bemisia tabaci Gennadius) its relation with main weather factors. Results revealed that a peak incidence of mean capsule borer population per plant were recorded in 9th and 11th standard weeks, respectively. The correlation study revealed that the Antigastra larvae had significantly positive correlation with maximum temperature (r=0.48) and significantly negative correlation with relative humidity (r=- 0.71). The regression analysis revealed that these factors accounted for approximately 59.8% of the variation in the capsule borer population (R2=0.598). The incidence of leafhopper (Hishimonus phycitis Distant) and whitefly (Bemisia tabaci Gennadius) were started during 5th and 4th SMW attained their peaks during 11th an 12th SMW respectively. The correlation study revealed that the leafhopper showed significant positive correlation with maximum temperature (r=0.623) and minimum temperature (r=0.283) and significantly negatively correlated with morning and evening relative humidity (r=-0.165, r=-0.122) respectively. While the correlation study between whitefly population and weather parameters revealed that the it showed positive correlation with maximum (r=0.821) and minimum (r=0.120) temperature and negatively correlated with morning and evening relative humidity (r=-0.097, r=-0.182) respectively whereas, rainfall showed non-significant correlation (r=0.00) with the both sucking pests.

Effects of some weather variables on the signal strength of Maloney FM radio, Nasarawa State, Nigeria

This study aims to assess the effects of atmospheric temperature, relative humidity, and atmospheric pressure on the signal strength of Maloney FM Radio, 95.9 MHz, Keffi, Nasarawa State. It is essential to measure the signal strength of the new radio station in relation with some parameters of weather to provide effective service delivery to the critical sectors and the entire populace. The research was conducted using the data of atmospheric temperature, atmospheric pressure, relative humidity, and the strength of the Maloney radio signal acquired from 6.00 a.m. to 6.00 p.m. daily at a 30 min interval, from January 2023 to June 2023, in Karshi, Abuja, a neighbouring town to Nasarawa state where the radio station is located. The data analysis involving cointegration and descriptive statistics of the effects of atmospheric variables on the radio signal strength was implemented using Eviews and Microsoft Excel Software. The results showed daily and monthly variations of the radio signal strength as well as the atmospheric temperature, atmospheric pressure and relative humidity. The variation is such that the lowest monthly signal strength was in June, probably due to higher rainfall in the area in June being the last month in this work (Jan.–Jun.) compared to other months which are dry season (January–March) or when the rain was just starting (April–May), in the study area. It was observed that atmospheric temperature and pressure negatively correlate with signal strength while relative humidity positively correlates with received signal strength in all the six months under study. The cointegration analysis also showed a long-term relationship between the radio signal strength and the atmospheric components, as the probability on the first row on the tables of both Trace Test and Maximum Eigenvalue methods were 0.0016 and 0.0008, which are less than the 5% (0.05) set critical value. In the same way, the critical value results on the same first row in both methods are smaller than the Trace statistics values. This implies that there is a long-term relationship between the atmospheric variables and the Maloney FM radio signal strength. These results will help mitigate the attenuation effect, and attendant signal loss of the radio station's signal strength. By proxy, the results will support the operations and regulations of spectrum management in the design of satellite communications and satellite microwave band specifications in Nigeria. In addition, the pre-determination of the radio station location will depend on this study's result analyses, considering that the effects of atmospheric temperature, pressure, and humidity conditions are inevitable. Governments, policymakers, stakeholders, and other relevant authorities should ensure the domestication of the study by providing the necessary research materials and facilities for wider studies. As regards the Maloney FM radio station, Keffi, studied in this work, there is no similar or related work done on it before now, hence, providing the detailed relationship between the signal strength of the radio station and some weather variables will go a long way in the improvement of the quality of signal transmission from Maloney radio station. The results when provided to the station, will enable the management to also evaluate if their set target of the station such as the radio horizon, radio signal coverage, and the link budget have been met.