Maximum Relative Humidity Research Articles

One piece of the puzzle: Modeling vector presence and environment reveals seasonality, distribution, and prevalence of sandflies and Leishmania in an expansion area

The recent geographic spread of Leishmania infantum along the borders of Argentina, Brazil and Paraguay has been highlighted. In our previous study, Lutzomyia longipalpis was found in 55 of 123 patches surveyed, and in some patches, sandflies were found at higher densities, forming hotspots. Based on the One Health approach, we investigated the seasonality of the vector, the presence of parasite DNA, and the environmental factors that contribute to vector and parasite dispersal in these previously described hotspots in Foz do Iguaçu, Brazil. Entomological surveys were conducted monthly for one year. Fourteen hotspots peridomicile and six intradomicile were sampled. PCR was used to assess the prevalence of Leishmania DNA in sandflies. Zero-inflated negative binomial regression was used to determine the association of micro- and mesoscale environmental variables with the occurrence and abundance of the three most abundant sandfly species sampled. A total of 3543 species were captured, with Lutzomyia longipalpis being the predominant species (71.78%) of the 13 species found. Evandromyia edwardsi, Expapillata firmatoi, Micropygomyia ferreirana and Pintomyia christenseni were reported for the first time in the region. NDVI, distance to water, precipitation, west-to-east wind, wind speed, maximum and minimum relative humidity, and sex were significant variables associated with vector presence/abundance in the environment. Vector presence/abundance in the peridomicile was associated with precipitation, altitude, maximum temperature, minimum and maximum relative humidity, west-to-east wind, wind speed, and sex. Leishmania DNA was detected in an average of 21% of Lu. longipalpis throughout the year. Vector abundance is concentrated in urban and peri-urban areas, with some specimens present in different parts of the city and some sites with high vector abundance. This distribution suggests that the risk of actual contact between humans and parasite vectors in urban areas during the epidemic period is associated with patches of peri-urban vegetation and then extends into urban areas.

Morpho-molecular characterization and bioecology of leaf folder, Pycnarmon cribrata (Fabricius) on nirgundi (Vitex negundo): an aromatic medicinal shrub from India

The occurrence of leaf folder, Pycnarmon cribrata (Fabricius, 1794) (Lepidoptera: Crambidae) on the medicinal plant nirgundi, Vitex negundo Linn. has been observed in West Bengal, India. The pest damaged the foliage by folding the leaflets, feeding within and ultimately making the foliage dry and papery. However, the identity of the pest species was confirmed through morphological characteristics of male and female genitalia as well as sequence amplified product of Cytochrome c oxidase subunit I (COI) gene (GenBank No. ON545929). Biological studies revealed that the entire life cycle of the moth completed in 29.90 ± 2.42 days on average, while the range was between 25-34 days. The peak larval incidence was noticed in 16th SMW (Standard Meteorological Week) and 24th SMW with population of 12 and 15 larvae per plant during 2021 and 2022, respectively. The percent plant incidence levels ranged from 15 to 100% during the first year i.e. 2021 and 30 to 100% during the second year i.e. 2022. The correlation of mean larval population (r = −0.66) and percent plant incidence (r = −0.71) with weather parameters indicated a highly significant negative association with the maximum temperature and a significant positive correlation with maximum and minimum relative humidity. The parasitization of Choeras sp. (Braconidae: Hymenoptera) (GenBank No. OP293230) was observed on old age larvae of P. cribrata and the natural parasitization was recorded as 26% during the study period. This study provides a comprehensive information on the occurrence, nature of damage, biology, morphometry, seasonal incidence and natural enemy fauna associated with P. cribrata which will be helpful for implementation of Integrated Pest Management (IPM) strategies to curtail the pest problem on V. negundo, a valuable medicinal plant.

Numerical Study on the Heating Effect of a Spring-Loaded Actuator—Part Ⅰ: Temperature and Humidity Distribution Characteristics

Inappropriate distributions of temperature and humidity will cause the failure of the spring-loaded actuators. Therefore, it is essential to understand the temperature and humidity distribution characteristics in typical spring-loaded actuators, to guarantee the safe operation of the spring-loaded actuators. In this work, a numerical simulation study on the temperature and humidity distribution characteristics in a spring-loaded actuator was conducted. The influence laws of ambient temperature, heater power, and heater size on the temperature and humidity distributions inside the spring-loaded actuator were analyzed. The practical empirical correlations for the spring-loaded actuators were fitted. The results show that the air temperature around and directly above the heater is the highest and the corresponding relative humidity is the lowest. Then, the air temperature gradually decreases, and the relative humidity increases with the lateral flow of air. When the ambient temperature increases from 233.15 K (−40 °C) to 313.15 K (40 °C), the minimum temperature inside the actuator is increased by 34%, the maximum humidity first increases and then decreases, and the maximum temperature on the heater surface is increased by 30%. When the heating power increases from 10 W to 150 W at ambient temperatures of 273.15 K and 298.15 K, the minimum temperature inside the actuator is increased by 3.40% and 3.61%, the maximum humidity is decreased by 51.97% and 58.63%, and the maximum temperature on the heater surface is increased by 30.33% and 33.25%, respectively. The influence of heater length, width, and height on the minimum temperature and maximum relative humidity inside the spring-loaded actuator is relatively small. Within the study range, the increase in heater length, width, and height makes the maximum temperature on the heater surface decrease by 9.15%, 7.59%, 4.63% at ambient temperatures of 273.15 K, and 10.74%, 9.01%, 4.73% at ambient temperature of 298.15 K, respectively. The results may provide a reference for predicting temperature and humidity distributions inside general spring-loaded actuators and provide a calculation basis for the design of their heaters.

Potential variables forcing litterfall in a lower montane evergreen forest using Granger and superposed epoch analyses

AbstractThe interplay between long‐term environmental variability and litterfall is complex and through this study, we quantified the response of peak leaf, flower, and fruit litterfall production to such variability in a lower montane evergreen forest located in Doi Suthep‐Pui National Park, Thailand. We observed seasonality in litterfall accumulation with peak leaf and flower litterfalls occurring mostly during the cool and hot dry seasons, while fruit litterfall occurs mostly during the wet season. Probable associations with environmental drivers (barometric pressure, temperature, relative humidity, number of sunlight hours, evaporation, wind speed, and vapor pressure deficit) were tested on a 5‐year litter trap dataset using superposed epoch analysis and Granger causality tests. The tests indicated that significant deviations in barometric pressure, maximum temperature, maximum relative humidity, and wind speed were the most plausible precursors to peak litter production of all components. Additionally, we observed a lag in peak litterfall production relative to climate variability by up to 4 months for most of the climate variables. Changing environmental conditions can affect both the timing and amount of litterfall production, which in turn can alter the nutrient recycling rate and other essential ecosystem services of such forests.

Prevalence of tick-borne encephalitis virus in questing Ixodes ricinus nymphs in southern Scandinavia and the possible influence of meteorological factors.

Ixodes ricinus ticks are Scandinavia's main vector for tick-borne encephalitis virus (TBEV), which infects many people annually. The aims of the present study were (i) to obtain information on the TBEV prevalence in host-seeking I. ricinus collected within the Øresund-Kattegat-Skagerrak (ØKS) region, which lies in southern Norway, southern Sweden and Denmark; (ii) to analyse whether there are potential spatial patterns in the TBEV prevalence; and (iii) to understand the relationship between TBEV prevalence and meteorological factors in southern Scandinavia. Tick nymphs were collected in 2016, in southern Scandinavia, and screened for TBEV, using pools of 10 nymphs, with RT real-time PCR, and positive samples were confirmed with pyrosequencing. Spatial autocorrelation and cluster analysis was performed with Global Moran's I and SatScan to test for spatial patterns and potential local clusters of the TBEV pool prevalence at each of the 50 sites. A climatic analysis was made to correlate parameters such as minimum, mean and maximum temperature, relative humidity and saturation deficit with TBEV pool prevalence. The climatic data were acquired from the nearest meteorological stations for 2015 and 2016. This study confirms the presence of TBEV in 12 out of 30 locations in Denmark, where six were from Jutland, three from Zealand and two from Bornholm and Falster counties. In total, five out of nine sites were positive from southern Sweden. TBEV prevalence of 0.7%, 0.5% and 0.5%, in nymphs, was found at three sites along the Oslofjord (two sites) and northern Skåne region (one site), indicating a potential concern for public health. We report an overall estimated TBEV prevalence of 0.1% in questing I. ricinus nymphs in southern Scandinavia with a region-specific prevalence of 0.1% in Denmark, 0.2% in southern Sweden and 0.1% in southeastern Norway. No evidence of a spatial pattern or local clusters was found in the study region. We found a strong correlation between TBEV prevalence in ticks and relative humidity in Sweden and Norway, which might suggest that humidity has a role in maintaining TBEV prevalence in ticks. TBEV is an emerging tick-borne pathogen in southern Scandinavia, and we recommend further studies to understand the TBEV transmission potential with changing climate in Scandinavia.

Emissions and meteorological impacts on PM2.5 species concentrations in Southern California using generalized additive modeling

The chemical composition of PM2.5 has a significant impact on human health and air quality, and its accurate knowledge can be used to identify contributing emission sources. Assessing and quantifying the impacts of various factors (e.g., emissions, meteorology, and large-scale climate patterns) on the main PM2.5 chemical components can give guidance for implementing effective regulations to improve air quality in the future. In this study, we developed generalized additive models (GAMs) to assess how emissions, meteorological factors, and large-scale climate indices affected ammonium, sulfate, nitrate, elemental carbon, and organic carbon from 2002 to 2019 in the South Coast Air Basin (SoCAB). Concentration trends from three sites in the SoCAB are studied. The statistical results showed that GAMs can capture the variability of these species' daily concentrations (R2 = 0.6 to 0.7) and annual concentrations (R2 = 0.93 to 0.99). Precursor emissions most significantly affect PM2.5 species production, though meteorological factors like maximum temperature, relative humidity, wind speed, and boundary layer height, also influence PM2.5 composition. In the future, these meteorological factors will become more significant in affecting PM2.5 speciation, although emissions will continue to strongly affect formation. Results show that the composition of most PM2.5 species will decrease in the future except for OC, which will become the largest contributor to PM2.5.

Numerical Study on the Heating Effect of a Spring-Loaded Actuator—Part II: Optimization Design of Heater Parameters

Unfavorable temperatures and humidity will cause the failure of spring actuators. In order to ensure the safe operation of the actuator, it is necessary to optimize the design of the built-in heater system of the actuator itself. In this study, an experimental design and a response surface model were used to fit the empirical formulas for the minimum temperature, maximum humidity, and maximum temperature on the heater surface. On this basis, a genetic algorithm was used to establish the optimal size of the heater in the chamber of the spring actuator. The study results show that the air inside the actuator shows a trend of a decrease in temperature and an increase in relative humidity from top to bottom. The empirical equation obtained by fitting the second-order response surface model has high accuracy, and the maximum prediction errors for the minimum temperature, maximum relative humidity, and maximum temperature of the heater surface of the spring actuator are −0.5%, 11.7%, and 4.7%, respectively. When the environmental temperature reduces from 313 K to 233 K, the optimal heating power of the heater increases from 10 W to 490 W, the optimal relative length increases from 3.57 to 6, and the optimal relative width increases from 1 to 5.3. Therefore, the study can act as a reference for the temperature and humidity control system of future actuators.

Spatial Distribution and Trends of Heat Stress in Vietnam

This study investigates the spatial distribution and trend of heat stress in Vietnam using data from 68 meteorological stations across the country’s seven climatic sub-regions between 1979 and 2018. Daily maximum wet-bulb temperature (TWmax), an indicator of heat stress, was computed based on the daily maximum air temperature (Tx) and relative humidity at 13:00 LST (RH13). Results indicate a strong positive correlation (>0.97) between daily TWmax and Tx and a weaker relationship between daily TWmax and RH13. Tx and TWmax experienced a general increasing trend across most stations over the study period, while RH13 displayed both negative and positive trends. Vietnam’s heat stress thresholds were found to be higher than those in many other parts of the world due to the country’s location and its temperature-humidity conditions. The study identifies most parts of Vietnam have experienced an increase in heat stress days, typically ranging from 5 to 12 days per decade. Among the four seasons, summer contributed the most (50-80%) to the annual number of heat stress days in most sub-regions, except for the Central Highlands and the South where spring had the highest contribution (65-80%). Overall, this study provides useful benchmark values for future research on heat stress in Vietnam.

Effect of abiotic factors on progress and severity of sheath rot (Sarocladium oryzae) in rice

AbstractSheath rot, caused by Sarocladium oryzae, is an emerging serious threat in rice cultivating regions especially in Union Territory of Jammu & Kashmir, causing yield losses of 20%–85%. Little and scattered information is available on the epidemiology of the disease. Hence, the field experiments were conducted during the cropping seasons of 2019 and 2020 to find out the effect of weather factors on the development of the disease as well as computation of predictive model for its initiation and spread in the field in five popular and commercially grown rice varieties viz. PB‐1509, PB‐1401, PR‐114, PB‐1121 and Arize 6444 Gold. The higher mean percent disease index (PDI) of 37.3% was recorded during 2019, whereas it was 33.8% during 2020. The effect of cropping seasons (years) on PDI, area under disease progress curve (AUDPC) and infection rate (r) was significant. Significant and negative correlation was observed between PDI and selected meteorological parameters, that is maximum and minimum temperature and relative humidity in the evening during both the cropping seasons The PDI had positive and significant correlation with relative humidity in the morning during the cropping season 2020. Rainfall showed significant and negative correlation with PDI in all the varieties during 2019 whereas it was non‐ significant and positive during 2020 in all the varieties except PR‐114 where, it was positive and significantly correlated. Minimum temperature, morning and evening relative humidity and sunshine hours resulted in ~92%–95% and 97%–99% variation for PDI in all the varieties during the cropping seasons 2019 and 2020, respectively. Sheath rot was the maximum recorded at minimum temperature (11.5–20.0°C), high morning relative humidity (74.0%–94.0%) and moderate evening relative humidity (26.0%–75.0%) and sunshine hours of 2.8–9.3 h, respectively. All the assumptions related to the developed model were acceptable as the global stat, skewness, kurtosis, link function and heteroscedasticity tests had the least p‐values in all the varieties during both the cropping seasons. Hence the model may be used for the prediction of sheath rot, which would be helpful in the early adoption, precise and judicious management practices.

Historic evolution of population exposure to heatwaves in Xinjiang Uygur Autonomous Region, China

Heatwaves have pronounced impacts on human health and the environment on a global scale. Although the characteristics of heatwaves has been well documented, there still remains a lack of dynamic studies of population exposure to heatwaves (PEH), particularly in the arid regions. In this study, we analyzed the spatio-temporal evolution characteristics of heatwaves and PEH in Xinjiang using the daily maximum temperature (Tmax), relative humidity (RH), and high-resolution gridded population datasets. The results revealed that the heatwaves in Xinjiang occur more continually and intensely from 1961 to 2020. Furthermore, there is substantial spatial heterogeneity of heatwaves with eastern part of the Tarim Basin, Turpan, and Hami been the most prone areas. The PEH in Xinjiang showed an increasing trend with high areas mainly in Kashgar, Aksu, Turpan, and Hotan. The increase in PEH is mainly contributed from population growth, climate change and their interaction. From 2001 to 2020, the climate effect contribution decreased by 8.5%, the contribution rate of population and interaction effects increased by 3.3% and 5.2%, respectively. This work provides a scientific basis for the development of policies to improve the resilience against hazards in arid regions.

Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil).

Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil's essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson's algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.

Research on Provincial-Level Soil Moisture Prediction Based on Extreme Gradient Boosting Model

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate the distribution of water resources for crop growth in various regions. However, the spatial variability of soil moisture is dramatic, and its time series data are highly noisy, nonlinear, and nonstationary, and thus hard to predict accurately. In this study, taking Jiangsu Province in China as an example, the data of 70 meteorological and soil moisture automatic observation stations from 2014 to 2022 were used to establish prediction models of 0–10 cm soil relative humidity (RHs10cm) via the extreme gradient boosting (XGBoost) algorithm. Before constructing the model, according to the measured soil physical characteristics, the soil moisture observation data were divided into three categories: sandy soil, loam soil, and clay soil. Based on the impacts of various factors on the soil water budget balance, 14 predictors were chosen for constructing the model, among which atmospheric and soil factors accounted for 10 and 4, respectively. Considering the differences in soil physical characteristics and the lagged effects of environmental impacts, the best influence times of the predictors for different soil types were determined through correlation analysis to improve the rationality of the model construction. To better evaluate the importance of soil factors, two sets of models (Model_soil&atmo and Model_atmo) were designed by taking soil factors as optional predictors put into the XGBoost model. Meanwhile, the contributions of predictors to the prediction results were analyzed with Shapley additive explanation (SHAP). Six prediction effect indicators, as well as a typical drought process that happened in 2022, were analyzed to evaluate the prediction accuracy. The results show that the time with the highest correlations between environmental predictors and RHs10cm varied but was similar between soil types. Among these predictors, the contribution rates of maximum air temperature (Tamax), cumulative precipitation (Psum), and air relative humidity (RHa) in atmospheric factors, which functioned as a critical factor affecting the variation in soil moisture, are relatively high in both models. In addition, adding soil factors could improve the accuracy of soil moisture prediction. To a certain extent, the XGBoost model performed better when compared with artificial neural networks (ANNs), random forests (RFs), and support vector machines (SVMs). The values of the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), Nash–Sutcliffe efficiency coefficient (NSE), and accuracy (ACC) of Model_soil&atmo were 0.69, 11.11, 4.87, 0.12, 0.50, and 88%, respectively. This study verified that the XGBoost model is applicable to the prediction of soil moisture at the provincial level, as it could reasonably predict the development processes of the typical drought event.

Overwintering diapause and survival of western Leucotaraxis argenticollis, a promising biological control agent for Adelges tsugae, in the eastern United States

Successful use of biological control agents can be improved with a thorough understanding of the life history, survival, diapausing strategy, and synchrony with the host in the introduced range. Introduction of a novel organism can be influenced by abiotic factors that can impact the interactions with their target prey. Leucotaraxis argenticollis (Zetterstedt) and Le. piniperda (Malloch) (Diptera: Chamaemyiidae) are two species of predatory fly showing promise in the biological control program to manage the invasive hemlock woolly adelgid, Adelges tsugae (Annand)(Hemiptera: Adelgidae), in eastern North America. Field releases of these Leucotaraxis spp. began in 2015, but little is known about their potential for survival or how they overwinter. In an effort to explore Le. argenticollis establishment potential, laboratory reared puparia were observed at seven sites, across a latitudinal gradient, on the east coast and one site on the west coast (native range) of North America from July 20, 2020, to June 1, 2021. Leucotaraxis piniperda puparia were included at five sites and observed to be diapausing in a different stage or using a different cue. Adult Le. argenticollis emerged in spring 2021, after successfully overwintering as puparia, with a subset emerging in summer-fall 2020 at all observation sites. Results suggest that 1) the interaction of mean daily fall maximum temperatures and relative humidity has the most impact on survival, 2) mean daily minimum fall temperatures influence induction/maintenance of diapause, and 3) mean average daily fall temperatures and relative humidity impact survival to spring emergence. Substantial variation in survival and facultative diapause of Le. argenicollis was observed along a latitudinal gradient, potentially influencing predator establishment, supporting the need for continued research.

Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform

Accurate daily solar radiation prediction is a crucial task for the management and generation of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy of new machine learning methods, wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron artificial neural network (WMLPANN), long short-term memory (LSTM), multi-layer perceptron artificial neural network (MLPANN), and multivariate adaptive regression splines (MARS), was assessed for modeling daily solar radiation using various input combinations of climatic data of maximum and minimum relative humidity, potential evapotranspiration, maximum and minimum temperature, precipitation and wind speed from two stations, Brownstown and Carbondale located in Illinois, USA. For accurate assessment of prediction accuracy of the proposed models, four reliable statistical metrics, root mean squared error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and One-Tailed Wilcoxon Signed-Rank Test were employed. Comparison of results, based on the RMSE values, indicated that the WLSTM method performed better than the WMLPANN, LSTM, MLPANN and MARS methods in the estimation of solar radiation values at both stations. The average RMSE values of WMLPANN, LSTM, MLPANN, and MARS approaches was decreased by 6%, 4%, 7.3%, and 13.5% using WLSTM method at Brownstown Station, by 6%, 5.2%, 13.2%, and 14.3% at Carbondale Station, respectively. The overall results during the testing phase of both stations revealed the successful application of hybridization of the LSTM model with the wavelet transform technique for improving the prediction accuracy of solar radiation based on climatic parameters.

Do the foraging strategies of Odontotermes horni (Wasmann)workers on various organic matter depend on the weather parameters?

The investigation delved into how worker termites of O. horni search for food sources under varying weather conditions in an undisturbed forest ecosystem. The result indicated that the maximum atmospheric temperature had a significant negative correlation with foraging patches < 25 cm. The minimum atmospheric temperature exhibited a significant negative correlation with tree damage and the height of earthen sheeting construction by the termite workers. Moreover, the maximum atmospheric relative humidity showed a significant negative correlation with tree damage and the height of earthen sheeting by the workers. Similarly, the minimum atmospheric relative humidity had a significant negative relationship with tree damage and the height of earthen sheeting construction by the workers, but a positive significant relationship with foraging patches < 25 cm. Lastly, rainfall had a significant negative relationship with tree damage and the height of earthen sheeting on the trees, while sunshine hours showed a significant positive relationship with tree damage by the workers.

Airborne inoculum dynamics of Polystigma amygdalinum and progression of almond red leaf blotch disease in Catalonia, NE Spain

AbstractThe dynamics of airborne ascospores and disease progress of red leaf blotch (RLB) of almond, caused by Polystigma amygdalinum, and their correlations with weather variables were studied from 2019 to 2021 in two almond orchards located in Lleida, NE Spain. Airborne ascospores were detected and quantified by real‐time qPCR using species‐specific primers for P. amygdalinum. Ascospores were detected mainly from April to June, with a high variability between the yearly cumulative concentrations. Positive significant correlations were found between the weekly proportion of airborne ascospores and the number of wet and mild days—either combined or separated— accumulated rainfall, number of rainy days, accumulated low temperatures on wet days, and mean and maximum relative humidity. In contrast, several thermal variables (maximum temperature, VPD, and number of warm days) were negatively correlated with ascospore catches. Positive significant correlations were found between the cumulative proportion of ascospores and RLB incidence and severity. Weekly variations in RLB incidence and severity showed significant positive correlations with the number of warm days while negative with the number of mild days. Severity was also positively correlated with several thermal variables (mean, maximum, and minimum temperature, and VPD), and negatively correlated with the number of cold days and wet and mild days. Stronger correlations were generally found with ascospore catches or disease progress when using concurrent weekly weather data. Gompertz, monomolecular, and logistic growth models were evaluated to describe RLB disease progress.

Effect of Stress on Ovarian Follicular Activity in Postpartum Sahiwal Cows during Hot-humid Season

Present investigation was carried out to study the effect of stress on ovarian follicular activity in postpartum acyclic Sahiwal cows during hot-humid months. Postpartum ovarian activity was monitored using trans-rectal ultrasound scanning on alternate days beginning between day 60 and 90 postpartum in Sahiwal cows (n=16) up to next 21 days.Daily maximum temperature and relative humidity was recorded andtemperature-humidity index (THI)was calculated during study period. Six animals (37.5 %) showed presence of dominant follicle (≥10 mm, cyclic) and 10 animals (62.5 %) were classified as acyclic (ovarian follicle < 10 mm diameter). Present study demonstrated that as the value of THI reduced, the mean follicular diameter proportionately increased and vice versa. Based on the findings of present study, it may be concluded that development of ovarian follicle is highly affected by THI value during hot-humid season in Sahiwal cows and the reproductive performance of animal may be predicted using THI values.

The Impact of the First and Second Waves of COVID-19 Pandemic in Nigeria.

In recent times, the COVID-19 pandemic has been the subject of global concern. It has so far claimed over 5.4 million lives globally, with over 291 million cases recorded worldwide as of 5 January 2022. It is known to have different waves and variants, thus making it difficult to handle/manage. This study investigates the impact of the first and second waves of COVID-19 in Nigeria, West Africa. The data used is for the 36 states of Nigeria archived at the National Centre for Disease Control from February 2020 to April 2021. Results from the study reveal that the highest number of COVID-19 cases during the first/second wave was recorded at Lagos (23,238/34,616), followed by the Federal Capital Territory (FCT) (6,770/12,911) and alternates between Plateau (3,858/5,170) and Kaduna (3,064/5,908). Similarly, the highest number of deaths (during the first/second wave) was also recorded in Lagos (220/219), followed by Edo (112/73), and then FCT (83/81). The Case Fatality Ratio (CFR) was observed to be higher mostly in northern Nigeria during the first wave and the southeast during the second wave of the pandemic. On the average, the number of cases/deaths recorded during the second wave was higher than those of the first wave, but a decrease in the CFR values was observed during the second wave. Higher values of COVID-19 cases/death were mostly recorded in Nigeria during; maximum relative humidity (RH) (>70%) with minimum Temperatures (<25°C), Low temperatures, and low RH which is mostly observed during the cold/dusty periods.

Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions.