Weather Parameters Research Articles

The effect of novel mosquito bite prevention tools on Anopheles minimus landing and key secondary endpoints: semi-field evaluations in Thailand

BackgroundThe Greater Mekong Subregion (GMS) aims to eliminate all human malaria by 2030 and is making substantial progress toward this goal, with malaria increasingly confined to forest foci. These transmission foci are predominantly inhabited by ethnic minorities, local populations, and rural mobile and migrant populations working in mining and agriculture. The recommendations of the World Health Organization (WHO) on malaria elimination states that small population groups which constitute a large proportion of the malaria transmission reservoir should benefit from targeted strategies to reduce transmission overall. These population groups are exposed to malaria vector bites during the day due to Anopheles daytime biting, and during the night, due to low bed net use and open sleeping structures. Such characteristics limit the effectiveness of the WHO core vector control strategies [indoor residual spraying (IRS), insecticide-treated nets (ITNs)], which target indoor resting and indoor feeding mosquitoes. Interventions that target daytime and outdoor resting or biting mosquitoes, and which complement IRS and ITNs and drug strategies, may hasten a decline in the malaria burden.MethodsThis study evaluated two transfluthrin- and one metofluthrin-based volatile pyrethroid spatial repellents (VPSRs), and etofenprox insecticide-treated clothing (ITC) with and without a topical repellent in a semi-field system (SFS) at two research sites in Thailand, across two trial rounds. The study estimated the protective efficacies of the vector control tools against two pyrethroid-susceptible Anopheles minimus strains in the form of 15 interventions, including a combined VPSR and ITC intervention. The interventions’ modes of action were studied by measuring their impact on mosquito landing, and on key life history traits known to affect vectoral capacity (knockdown, post-exposure blood feeding, and 24-h mortality) using a block-randomized crossover design. The odds ratio (OR) for each intervention compared to the control on each outcome was estimated.ResultsAll interventions substantially reduced An. minimus landings and prevented more than 50% mosquito landings when new (VPSRs) or unwashed (treated clothing). In addition to landing reduction, all interventions decreased post-exposure blood feeding, induced knockdown and increased mortality at 24 h. The VPSR interventions were generally more protective against landing than the treated clothing intervention. The combined intervention (VPSR + ITC) provided the greatest protection overall.ConclusionThis SFS evaluation indicates an effect of these VPSR and ITC interventions in reducing An. minimus landing for the user, and indicates their potential for community protection by secondary modes of action. This study demonstrates the utility of SFS trials in the evaluation of bite prevention tools and emphasizes the need for multiple evaluations at different sites. It also highlights possible sources of biases observed, including the measuring of mosquito landing rather than biting, weather parameters, and low mosquito recapture.

Development of multistage crop yield estimation model using machine learning and deep learning techniques.

In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of five districts of Punjab. Wheat yield data and weather parameters over 34years were collected from the study area and the model was developed using stepwise multi-linear regression (SMLR), artificial neural network (ANN), support vector regression (SVR), random forest (RF) and deep neural network (DNN) techniques. Wheat yield estimation was done at the tillering, flowering, and grain-filling stage of the crop by considering weather variables from 46 to 4th, 46 to 8th, and 46 to 11th standard meteorological week. Weighted and unweighted Meteorological variables and yield data were used to train, test, and validate the models in R software. The evaluation results showed a consistent and promising performance of RF, SVR, and DNN models for all five districts with an overall MAPE and nRMSE value of less than 6% during validation at all three growth stages. These models exhibited outstanding performance during validation for the Faridkot, Ferozpur, and Gurdaspur districts. Based on accuracy parameters MAPE, RMSE, nRMSE, and percentage deviation, the RF model was found better followed by SVR and DNN models and, hence can be used for district-level wheat crop yield estimation at different crop growth stages.

Epidemiological Studies on Various Weather Parameters for Development of Brown Spot Disease in Sugarcane

Availability of susceptible host and prevalence of favorable weather condition play important role in the process of disease development, dissemination of diseases and favour epidemics. Epidemiological studies on brown spot disease of sugarcane will gave an idea about the time of occurrence, development, survival of the pathogen in nature and intensity of particular disease on specific location based on various weather parameters which may help us to formulate the management strategy. A field trial on epidemiological studies was conducted during Suru-2023 at Central Sugarcane Research Station, Padegaon farm, Tal. Phaltan Dist. Satara on highly susceptible cv. CoM 0265 in natural condition. Different weather parameters were explored by correlation and regression analysis to find out their contribution to infection and development of the brown spot disease of sugarcane. Epidemiological studies under field conditions revealed that, the first appearance of brown spot disease of sugarcane on susceptible cultivar CoM 0265 was recorded during third week of July. During 2023, correlation matrix showed that, maximum temperature, relative humidity (morning), and bright sunshine were the three significant factors that showed a significantly positive correlation. Rainfall also showed a positive correlation. However, minimum temperature, relative humidity (evening) and wind speed showed significantly negative correlation with correlation coefficient.

Impact of Abiotic Factors on the Incidence of Brown Plant Hopper Nilaparvata lugens Stal in Rice Crop at Cuddalore District, Tamil Nadu, India

Rice is a dietary staple of more than half of world’s population, and more than 90% of world’s rice is grown and consumed in Asia alone but faces significant threat from insect pests most importantly Brown plant hopper (BPH), Nilaparvata lugens Stal, consider to be a major insect pest of rice. This study showcases the correlation between weather parameters and BPH infestation in rice crop grown in two Kharif (rainy) and two Rabi (winter) seasons of 2022 and 2023 in Chidambaram, Tamil Nadu, India. Non-resistant rice varieties (ADT 43 and ADT 54) were used. BPH population and weather data (maximum and minimum temperature, relative humidity, sunshine hours, rainfall, and wind speed) were collected weekly. In all the seasons the BPH population ranged from 0.0 to 8.2 at its peak in our study area. Results showed that BPH populations were higher during kharif seasons compared to rabi seasons. Maximum temperature and sunshine hours exhibited a positive correlation with BPH population, while rainfall and minimum temperature were negatively correlated. By taking into account these weather parameters, farmers can predict and monitor fluctuations in pest populations. This enables them to apply pesticides strategically, only when economically justified, and to implement a range of integrated pest management techniques.

Brown patch severity of lawn grass species as influenced by weather parameters in mid-hills of Himachal Pradesh

Brown patch severity of lawn grass species as influenced by weather parameters in mid-hills of Himachal Pradesh

Weather-Wise CropAlert: Predicting and Preventing Plant Diseases

This paper provides a system which consists of three components namely, weather parameter input system, plant disease prediction system and disease prevention system. The system will help the farmers to efficiently use the weather data, and to take preventive measures in time and helps establishing a direct relationship between the weather and plant diseases. The system currently provides information for three crops, namely, wheat, cotton, rice. The proposed system used machine learning to identify the patterns and predict the plant disease. Various clustering and classification models were used in the system such as K means, Random Forest and XGboost.

Forecasting rice blast disease severity using weather-dependent regression and time series models

Rice (Oryza sativa) is a staple food crucial for food security and economic stability, especially in developing countries. However, rice cultivation faces significant challenges, with rice blast disease, caused by the fungal pathogen Magnaporthe oryzae, being one of the most severe threats, potentially leading to yield losses of up to 30 %. This study aims to develop and apply regression models, including stepwise regression, multiple linear regression (MLR) and ARIMA (Autoregressive Integrated Moving Average), to predict the severity of rice blast disease based on weather parameters. Weekly data over 7 years (2017-2023) were collected from the Paddy Breeding Station at Tamil Nadu Agricultural University, Coimbatore, encompassing various weather factors such as temperature, relative humidity, rainfall and solar radiation. Data pre-processing included handling missing values, detecting outliers and creating time-lagged variables. The study revealed distinct seasonal patterns in rice blast incidence, with peak occurrences observed from mid-November to late January. Among the regression models, the ARIMA model incorporating weather variables as external regressors demonstrated superior performance with an R-squared value of 0.92, compared to 0.55 for stepwise regression and 0.57 for MLR. Accurate predictions of rice blast outbreaks could enable farmers and agricultural managers to implement timely and targeted disease management strategies, reducing dependence on broad-spectrum fungicides and minimizing crop losses. This study contributes to data-driven agriculture and disease management, potentially leading to more effective, economically viable and environmentally sustainable rice cultivation practices.

Investigating tropospheric radio refractivity variation with weather parameters in Kampala, Uganda: implications for communication systems

Investigating tropospheric radio refractivity variation with weather parameters in Kampala, Uganda: implications for communication systems

Deployment of intelligent irrigation monitoring system with Android app for machine learning prediction.

Water is a fundamental necessity for humans and a critical resource in agriculture. However, water scarcity poses a significant challenge, especially considering that agriculture accounts for a substantial portion of freshwater usage. The inadequate monitoring resources in agriculture lead to unnecessary wastage of water, affecting crop growth and the water supply. This challenge has spurred us to focus on the agricultural domain, aiming to conserve water by imparting intelligence into irrigation systems by converging IoT and machine learning technologies. Our study has proposed a Smart Irrigation System (SIS), designed to operate remotely, leveraging open-source IoT platforms. Utilizing IoT and machine learning methodologies can effectively optimize water usage in irrigation practices. The designed system comprises hardware and software tools, where comprehensive data is monitored through an application interface. The sensor's interface with a 32-bit microcontroller is used to gather data such as environmental temperature, humidity, and soil moisture and temperature. The user (farmer) continuously receives real-time updates about the condition of the farmland through the Blynk app. The app triggers a notification advising the user to start or stop watering based on pre-set threshold values of soil moisture. Additionally, users can control the water pump remotely through the app. The data collected from the deployed smart irrigation system was used to train a machine learning algorithm incorporating weather parameters and soil temperature to predict soil moisture content. This trained model aims to offer guidance for future assessments of unknown soil samples based on the weather conditions.

OPTIMIZATION IOT TECHNOLOGY IN WEATHER STATIONS FOR IMPROVE AGRICULTURAL SUCCESS DURING EL NIÑO ERA

The El Niño phenomenon is significant to global weather patterns, particularly in Indonesia, which adversely affects the agricultural sector, especially rice production. El Niño causes drastic changes in rainfall patterns, making it difficult for farmers to determine the right planting time. Limited access to accurate weather information is a major obstacle for farmers in planning their agricultural activities. This research aims to develop an Internet of Things (IoT)-based weather station capable of providing real-time and accurate weather data to support farmers' decision-making in their land management. The research method starts with observation in Babakan Jaya Village, Gabuswetan District, Indramayu Regency, to understand the local weather conditions and specific challenges faced by farmers. Next, the construction and implementation of a weather station equipped with sensors to measure various weather parameters such as temperature, humidity, wind direction and speed, and rainfall. The weather data collected by these stations is then processed and presented in real-time through a cloud platform, which allows access from computer devices and smart phones. The observation results from 1 June to 27 July 2024 showed that the air temperature ranged from 29°C to 35°C, humidity between 55% to 90%, and wind speed ranged from 0 to 7 km/h, with sporadic rainfall patterns. The developed IoT weather station successfully provides relevant and accurate weather data, which can be accessed in real-time by farmers. With this data, farmers can make more informed decisions in their land management, hopefully improving the efficiency and success of farming practices, especially in the midst of erratic weather conditions due to El Niño.

Incorporation of weather parameters in MMRC-K model for rainfall disaggregation

Incorporation of weather parameters in MMRC-K model for rainfall disaggregation

Estimation of some Climatological Parameters by WEKA Software for Selective Regions in Iraq

An Estimation is a direct summation that can produce new data from past measurements. In this study, we confirm the possibility of using the (WEKA) program in estimating the monthly values of some climatological parameters and investigate the influence of the time series' length parameters on the accuracy of estimation for selected regions of Iraq. Satellite data were used, which represent the monthly values for each of the minimum and maximum temperature, wind velocity, and relative humidity for the 1981 – 2021 time period, the absolute error rate (MAE), and the square root of the error rate (RMSE) with the correlation ( R2) are also identified to test the confidence of the prediction. Using (WEKA) software, which depends only on the time series of the parameters in the estimation, 12 months were estimated for the mentioned parameters. The estimated values by WEKA were close to the satellite data, thus we can depend on the software as a good source of meteorological data. Also, the study indicates that the time series' length strongly affects the accuracy of the estimation, as the increase in the time series of weather parameters increases the estimate's accuracy, and this increase fluctuates among parameters and varies from one region to another.

Seasonal Incidence of Sucking Pests and Their Natural Enemies on Moth Bean [Vigna aconitifolia(Jacq.) Marechal

A field experiment was conducted at the College of Agriculture, NAU, Bharuch (Gujarat) to study the seasonal incidence of sucking pests and their natural enemies and their correlation with weather parameters on moth bean during kharif -2023. The studies on seasonal incidence indicated that jassid infestation began in the 1st week of August (31st SMW) and peaked during the 1st week of September (35th SMW). Whitefly infestation also began in the 1st week of August (31st SMW) achieved its peak during 2nd week of September (36th SMW). The population of thripscommenced from 1st week of August (31st SMW) reached its peak during2nd week of September (36th SMW). The population of ladybird beetle appeared from 2nd week of August (32th SMW) and reached its peak during 2nd week of September (36th SMW). The population of chrysoperla appeared from 2nd week of August(32nd SMW) and reached its peak population during 2nd week of September (36th SMW). Among the different environmental factors, morning relative humidity had a significant positive impact on the jassid and whitefly population. Bright sunshine hours showed significant positive influence on thrips and whitefly population. However no significant correlation was observed between ladybird beetle and Chrysoperla population with weather parameters.

Survey and molecular characterization of begomovirus, and assessment of yield losses caused by leaf curl disease of sunflower (Helianthus annuus)

A comprehensive survey was conducted during winter (rabi) and rainy (kharif) seasons of 2019–20 to 2021–22 in the Kurnool, Nandyal, and Prakasam districts of Andhra Pradesh, focusing on the incidence of leaf curl disease in hybrid sunflower (Helianthus annuus L.) crop. The results revealed a high incidence of the disease, ranging from 40–94.5% across most of the surveyed hybrid varieties. Leaf curl-infected samples collected from the surveyed fields were analyzed by PCR using specific primers for the DNA-A component of the virus to confirm the presence of the pathogen. The PCR-amplified fragments were cloned and sequenced. Sequence analysis revealed that the sunflower isolate (Snf-AP) shared 99.2% nucleotide identity with the tomato leaf curl Karnataka virus (ToLCKV), which infects sunflower crops in Karnataka. This indicates a strong geographical and genetic connection between the viral strains affecting sunflowers in Andhra Pradesh and Karnataka. This genetic similarity is significant, as it suggests that the same or closely related viral strains are responsible for sunflower leaf curl disease across broader regions. The disease was found to affect sunflowers at all growth stages, with the highest incidence (42.3%) observed at the star bud stage. Infection at the star bud stage leads to substantial seed yield losses, with reductions of up to 82.8%. This level of damage underscores the economic impact of early infections, as yield losses of this magnitude can severely affect the profitability of sunflower farming. Additionally, a positive correlation was found between the whitefly population, weather parameters, and disease development.

Impact of Key Insect Pests on Cowpea and their Relationship with Weather Parameters

Introduction: Cowpea (Vigna unguiculata L.) is a vital pulse crop with substantial nutritional value in India that faces production challenges due to various biotic and abiotic factors. Among the biotic factors that affects cowpea, pests such as Maruca vitrata and Riptortus pedestris significantly impact cowpea yields. This study aimed to evaluate the seasonal incidence of these pests and analyze their correlations with weather parameters in Thiruvananthapuram, Kerala. Methodology: Field observations were conducted over the cropping season to record pest populations across Standard Meteorological Weeks (SMWs). The population of the pod bugs and pod borers were taken from five plants. Statistical analyses were performed to identify correlations between pest incidence and key weather factors. Key Findings: The incidence of R. pedestris peaked in the 16th SMW, reaching 7.54 bugs per five plants. This pest displayed a significant positive correlation with maximum temperature (r = 0.637) and minimum temperature (r = 0.559) and a negative correlation with evening relative humidity (r =-0.480). Similarly, M. vitrata larval populations peaked during the same period at 2.65 larvae per five plants, showing a similar temperature-dependent correlation. Implications: The temperature-sensitive incidence patterns observed underscore the need for integrated pest management (IPM) strategies that address climatic factors to enhance cowpea sustainability. This data provides valuable insights for future pest forecasting and management efforts.

Climate Change as an Existential Threat to Tropical Fruit Crop Production—A Review

Climate change is an emerging threat to global food and nutritional security. The tropical fruits such as mango, bananas, passionfruit, custard apples, and papaya are highly sensitive to weather changes especially; changes of monsoon onset and elevated temperature are influencing crop growth and production. There is a need for more specific studies concerning individual crops and regional variations. Long-term effects and interactions of weather parameters and increased concentration of greenhouse gases, especially carbon dioxide, with phenological stages of the plant, pests, and diseases remain understudied, while adaptation strategies require further exploration for comprehensive understanding and effective mitigation. Few researchers have addressed the issues on the effect of climate change on tropical fruits. This paper focuses on the impact of abiotic (temperature, rainfall, humidity, wind speed, evaporation, carbon dioxide concentration) and biotic (pest and pathogens dynamics) factors affecting the fruit crop ecosystem. These factors influence flowering, pollination, fruit set, fruit yield and quality. This review paper will help develop adaptive strategies, policy interventions and technological innovations aimed at mitigating the adverse effects of climate change on tropical fruit production and safeguarding global food and nutritional security.

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

An Approach to Multivariable Regression Analysis of a Rooftop PV System in Households’ Sector in Tirana. A Novel Approach using Energy Modeling Tool

Solar power prediction plays an essential role in functioning, mapping, and obtaining energy and climate goals in 2030 and beyond and contributing to real-time balancing of the power system. On the other side, electricity consumption is influenced by Heating Degree Days (HDD), Cooling Degree Days (CDD), average monthly temperature, energy management, human behaviour, architecture, orientation, and many other factors. The simulations are performed based on three consecutive energy consumption data for a typical dwelling in Tirana city. Once the base case scenario is designed, the model is validated based on the monthly electricity bills. In the base case scenario are included some energy efficiency measures (EEM) afterward the optimization of the supply side using PV and Solar Water Heating is part of our simulation, too. The focus of our work is to design a correlation between the relationship between consumption and generation of electricity given as a dependent variable (Y), which is regressed with weather parameters given as independent variables (Xi) under Durbin Watson statistics. The output of the study can help designers to compile a reliable power system, better utilization of energy resources, and forecasting accuracy analysis from both sides of the energy system (demand and supply side). The tested household and EEM applied in the proposed scenario may lead to an electricity reduction level of 8655 kWh per year and 76.6 % of solar fraction is used to meet the hot water demand.

Atmospheric icing meteorological parameter study using field experiments and simulation

AbstractAtmospheric icing on ground structures is a concern from design, operation, and safety perspectives. Supercooled water droplets size and liquid water content (LWC) are important weather parameters to better understand the ice accretion physics on ground structures. Most existing studies are based on measurements at high altitude. The study is based on the field results of a specific event (from 9:30 to 22:27 h on October 29, 2022) in Arctic region of northern Norway. The data from this event are presented and used for analytical validation and simulation. Field measurements of different meteorological weather parameters including the droplet size and LWC are carried out leading to recording of resultant atmospheric ice load and intensity. A comprehensive study is also carried out to validate droplet collision efficiency and ice load using the existing analytical model ISO‐12494 and computational fluid dynamics (CFD)–based numerical simulations. Furthermore, the differences in icing simulation using parameters such as median volume diameter (MVD), Langmuir B –J as alternatives to the actual droplet size distribution (DSD) spectrum are also analyzed. The results show that under natural meteorological conditions, the characteristics of water DSD change in real time. Using MVD alone to calculate the water droplet collision efficiency on circular cylinders can lead to significant errors. Accurately selecting the Langmuir distribution as a substitute for the actual DSD can reduce simulation errors to within 5%. Compared to the analytical model, the numerical simulations result better reflects the collision characteristics of water droplets of different sizes on the cylindrical object.

Impact of weather parameters on groundnut yield in Dharwad: A statistical analysis

Impact of weather parameters on groundnut yield in Dharwad: A statistical analysis