Peak Of Season Research Articles

Exploring the adverse effect of fine particulate matter (PM2.5) on wildland firefighters’ pulmonary function and DNA damage

Chiang Mai encounters severe pollution during the wildfire season. Wildland firefighters encounter various hazards while engaged in fire suppression operations, which encompass significant exposure to elevated concentrations of air pollutants resulting from combustion, especially particulate matter. The adverse effects of wildfire smoke on respiratory health are a significant concern. The objective of this study was to examine the potential adverse effects of PM2.5 exposure on the respiratory function and DNA damage of wildland firefighters. This prospective cohort study conducted in Chiang Mai from January to May 2022 planned to evaluate the health status of wildland firefighters during the pre-peak, peak, and post-peak ambient air pollution seasons. The measurement of PM2.5 was done at every forest fire station, as well as utilizing data from the Pollution Control Department. Participants received general health examinations, spirometry evaluations, and blood tests for DNA damage analysis. Pair t-tests and multiple regression models were used to examine the connection between pulmonary function parameters (FVC, FEV1) and PM2.5 concentration, with a significance level of P < 0.05. Thirty-three peak-season and twenty-one post-peak-season participants were enrolled. Four pre-peak-season wildland firefighters had FVC and FEV1 declines of > 15%. Multiple regression analysis showed a negative association between PM2.5 exposure and FVC% predicted (− 2.81%, 95% CI − 5.27 to − 0.34%, P = 0.027) and a marginally significant negative correlation with FVC (− 114.38 ml, 95% CI − 230.36 to 1.59 ml, P = 0.053). The remaining pulmonary measures showed a statistically insignificant decline. There were no significant changes in DNA damage detected. Wildland firefighters suffered a significant decline in pulmonary function associated with PM2.5 exposure. Spirometry is crucial for monitoring and promptly identifying respiratory issues that occur during wildfire seasons. Further research is recommended to explore DNA damage alterations and their potential association with PM2.5.

Peroxyacetyl nitrate can be used as a comprehensive indicator of air pollution complex

With the acceleration of air cleaning activities in China, air pollution has entered a new stage characterized by seasonal interplay and predominance of fine particulate matter (PM2.5) and ozone (O3) pollutants. However, the differing peak seasons of these two pollution preclude the use of a unified indicator for air pollution complex. Given that peroxyacetyl nitrate (PAN) originates from secondary formation and persists under low-temperature conditions for extended periods, it is vital to determine whether its concentration can be used as an indicator to represent air pollution, not only in summer but also in winter. Here, PAN observational data from 2018 to 2022 for Beijing were analyzed. The results showed that during photochemical pollution events in summer, secondary formation of PAN was intense and highly correlated with O3 (R = 0.8), while during PM2.5 pollution events in winter, when the lifetime of PAN is extended due to the low temperature, the PAN concentration was highly consistent with the PM2.5 concentration (R = 0.9). As a result, the PAN concentration essentially exhibited consistency with both the seasonal trends in the exceedance of air pollution (R = 0.6) and the air quality index (R = 0.8). When the daily average concentration exceeds 0.5 and 0.9 ppb, the PAN concentration can be used as a complementary indicator of the occurrence of primary and secondary standard pollution, respectively. This study demonstrated the unique role of PAN as an indicator of air pollution complex, highlighting the comprehensive ability for air quality characterization and reducing the burden of atmospheric environment management.

Impact of average daily rate on hotel’s newest atmosphere

PurposeThis study aims to create actionable guidelines for pricing decision-making by employing game a theory matrix to forecast the correlation between the average daily rate and the latest ambiance of hotels.Design/methodology/approachUtilizing a vector error correction model, the research employs game theory to assess the influence of the average daily rate on the hotel's newest atmosphere during both peak season (April–September) and valley season (October–March).FindingsFindings indicate that during the peak season, when the average daily rate rises in resorts and falls in suburban areas, the hotel’s newest atmosphere is at its best in both types of accommodations. During the off-peak season, the hotel’s newest atmosphere is achieved when both resorts and suburban accommodations increase their average daily rates.Research limitations/implicationsThere are two study constraints. One is the assumption that hotel guests in both parties prefer not to change hotels, but in fact they would. Two is a limited sample of two resort and suburban markets.Practical implicationsThis suggests that the hotel’s newest atmosphere can draw both leisure and business travelers to suburban areas during the low season and more leisure travelers to resorts during the high season.Social implicationsThe study’s findings have implications for revenue related to the hotel’s newest atmosphere and cleanliness for both suburban and resort hotels, particularly when promoting tourism collaboratively.Originality/valueThe study provides valuable insights for hotel managers in analyzing pricing strategies using matrices.

Prediction of daily river water temperatures using an optimized model based on NARX networks

Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.

Extended ozone depletion and reduced snow and ice cover-Consequences for Antarctic biota.

Stratospheric ozone, which has been depleted in recent decades by the release of anthropogenic gases, is critical for shielding the biosphere against ultraviolet-B (UV-B) radiation. Although the ozone layer is expected to recover before the end of the 21st century, a hole over Antarctica continues to appear each year. Ozone depletion usually peaks between September and October, when fortunately, most Antarctic terrestrial vegetation and soil biota is frozen, dormant and protected under snow cover. Similarly, much marine life is protected by sea ice cover. The ozone hole used to close before the onset of Antarctic summer, meaning that most biota were not exposed to severe springtime UV-B fluxes. However, in recent years, ozone depletion has persisted into December, which marks the beginning of austral summer. Early summertime ozone depletion is concerning: high incident UV-B radiation coincident with snowmelt and emergence of vegetation will mean biota is more exposed. The start of summer is also peak breeding season for many animals, thus extreme UV-B exposure (UV index up to 14) may come at a vulnerable time in their life cycle. Climate change, including changing wind patterns and strength, and particularly declining sea ice, are likely to compound UV-B exposure of Antarctic organisms, through earlier ice and snowmelt, heatwaves and droughts. Antarctic field research conducted decades ago tended to study UV impacts in isolation and more research that considers multiple climate impacts, and the true magnitude and timing of current UV increases is needed.

Determinants of Productivity and Problems Associated With Betel Leaf Cultivation in Sylhet Region of Bangladesh

The study aimed at evaluating the determinants of productivity of betel leaf cultivation among the farmers of two districts namely Sylhet and Moulvibazar administered under Sylhet division, Bangladesh. A total of 80 respondents were selected as the sample using multistage random sampling technique for achieving objectives of the study. Data were collected through a structured questionnaire during the month of March, 2020. Based on the objective’s descriptive statistics, ANCOVA and stepwise regression model were employed to analyze the data. The study found that, most of the farmers of betel leaf were followed their indigenous knowledge and technology to cultivate betel leaf. ANCOVA and stepwise regression model were employed to identify the factors as well as their impact on betel leaf production. The study identified the factors that significantly influenced the productivity of betel leaf farming i.e. the involvement of marketing agreement, access to training, access to irrigation, amount of cow dung applied and land area under betel leaf cultivation. The study also confronted some problems associated with the betel leaf cultivation like excess rainfall during peak season, insufficiency of irrigation water, disease attacks and low price of betel leaf. Therefore, this study provide essential information that can be used by the growers, scientists, exporters, policy makers to increase the productivity of betel leaf cultivation in the future. This study will build a foundation for further research based on the present background.

Analysis of Artificial Intelligence Induced Machines to Improve the Fiscal Advancement of agronomy

Horticulture became a key player in the development of UT. The total land area of Jammu and Kashmir under horticulture is 332704, among which 214162 in the Kashmir valley and 118542 in the Jammu region. The district Baramulla has achieved first rank in production in apples with 404089 metric tons, and also covers an area of 25231 hectares. Horticulture of UT of Jammu and Kashmir contributes 8-10% contribution to the SGDP (DHK, 2021). In 48 years, the area under horticulture shows a growth rate of 25%.The growth of production of horticulture has also increased 10 thousand metric to 25 Lakh metric tons during 1950-2022.In this paper we will analysis growth in production, productivity, area and State Gross Domestic Product through the Compound Growth Rate from 2012 to 2022. Artificial Intelligence will become a game changer for horticulture sector, which is facing loses consistently from the last few years. Need of the hour to improve the technique like artificial intelligence and procedures for the betterment of horticulture, especially open national, use of drones in farms, highway 44 during the peak season.

Comprehensive Analysis of Salmonella Species Antibiogram and Evolving Patterns in Empirical Therapy: Insights From Tertiary Care Hospitals in Peshawar, Pakistan.

Background Typhoid fever presents a significant challenge in developing nations, exacerbated by the emergence of antibiotic-resistant strains due to widespread prevalence and overuse of antibiotics. This study seeks to assess the antibiogram profiles of Salmonella species isolated from blood cultures of patients hospitalized at two prominent tertiary care hospitals in Peshawar, Pakistan: Khyber Teaching Hospital (KTH) and Hayatabad Medical Complex (HMC). By examining these profiles, the research aims to provide valuable insights into the evolving landscape of antibiotic resistance in the context of typhoid fever management. Materials and Methods This retrospective cross-sectional study utilized data gathered from two hospitals in Peshawar, KTHandHMC. Cases of enteric fever were identified based on positive blood cultures for Salmonella species. The study encompasses demographic information, seasonal prevalence, and antibiogram profiles of 3,137 cases that were presented between 2017 and 2023. Results Among the total 3,137 cases, males accounted for the majority, comprising 63% (2,044 cases). Particularly notable was the clustering of cases among children and adolescents aged one to 24 years. The incidence peaked during the months of summer and spring, from April to September. In terms of Salmonella Typhi isolates, considerable resistance was noted against first-line antibiotics such as amoxicillin/clavulanate (80.1%), co-trimoxazole/trimethoprim-sulfamethoxazole (66.6%), and chloramphenicol (86.9%), as well as against ceftriaxone (79.7%) and ciprofloxacin (51.6%). Conversely, certain antibiotics displayed higher sensitivity patterns, including meropenem (97.8%), doripenem (99.5%), imipenem (97.7%), ertapenem (96.5%), polymyxin B (99.4%), colistin (98.1%), and tigecycline (97.3%). Despite a limited sample size of 214 specimens, fosfomycin demonstrated a remarkable sensitivity of 93.4%. Sensitivities of amikacin and gentamicin were 90.7% and 81.5%, respectively. However, the sensitivity of azithromycin was concerning, standing at 66.5%. The antibiogram pattern for Salmonella exhibited significant and drastic changes. Conclusion In conclusion, this study sheds light on a higher prevalence of typhoid fever among males, with a notable seasonal peak observed during the summer and spring months. The age group most affected spans from one to 24 years. Salmonella isolates displayed significant resistance to conventional first-line antibiotics, alongside ciprofloxacin and third-generation cephalosporins. Azithromycin exhibited lower sensitivity compared to amikacin, gentamicin, and fosfomycin. The research advocates for the empirical use of amikacin, gentamicin, fosfomycin, and meropenem in the treatment of typhoid fever in Pakistan. Urgent measures, including regular Salmonella antibiogram surveillance, antibiotic stewardship, public health education, and Salmonella vaccination programs, are deemed crucial for primary disease prevention.

Farmers’ Perception Survey and Evaluation of Management Practices for Mango Fruit Borer (Citripestis Eutraphera)

Mango fruit borer (MFB) is a significant pest that damages mango fruits and leaves. This study explores farmers’ perceptions of the mango fruit borer and evaluates management practices. Survey findings indicated that 48% of mango growers believed MFB attacks occur during April-May, while 30% identified May-June as the peak season. Additionally, 84% of mango growers believed MFB affects fruits, while only 2% think it also targeted leaves. Most mango growers (78%) preferred chemical methods to manage MFB, with 40% using bifenthrin, 28% using emamectin benzoate, and only 8% applying imidacloprid. Experiments evaluating MFB’s preference for different mango varieties revealed that nearly all commercial varieties had varying infestation levels. Sindhri was the most infested (29.9%), while Doseri was the least infested (12.6%). Light trap data suggested that the maximum moth population occurred during the last week of April. Evaluations of chemical pesticides showed that bifenthrin (250 ml / 100 L of water) was the most effective, while Thiamethoxam + Chlorantraniliprole (80 ml / 100 L of water) was the least effective for MFB. Emamectin benzoate, Chlorantraniliprole, and Lufenuron were better options than synthetic pyrethroids (Bifenthrin) because they were safer for natural enemies and pollinators. Integrated management strategies, such as installing one light trap per hectare and monitoring MFB on mango leaves and weeds in March/April for early detection, were recommended. Additionally, collecting fallen infested fruits and judicious use of insecticides can help mitigate losses caused by MFB.

A Combination of Human Activity and Climate Drives Forest Fire Occurrence in Central Europe: The Case of the Czech Republic

Central Europe is not a typical wildfire region; however, an increasingly warm and dry climate and model-based projections indicate that the number of forest fires are increasing. This study provides new insights into the drivers of forest fire occurrence in the Czech Republic, during the period 2006 to 2015, by focusing on climate, land cover, and human activity factors. The average annual number of forest fires during the study period was 728, with a median burned area of 0.01 ha. Forest fire incidence showed distinct spring (April) and summer (July to August) peaks, with median burned areas of 0.04 ha and 0.005 ha, respectively. Relationships between the predictors (climate data, forest-related data, socioeconomic data, and landscape-context data) and the number of forest fires in individual municipality districts were analyzed using Generalized Additive Models (GAM) on three time scales (annually, monthly, and during the summer season). The constructed GAMs explained 48.7 and 53.8% of forest fire variability when fire occurrence was analyzed on a monthly scale and during the summer season, respectively. On an annual scale, the models explained 71.4% of the observed forest fire variability. The number of forest fires was related to the number of residents and overnight tourists in the area. The effect of climate was manifested on monthly and summer season scales only, with warmer and drier conditions associated with higher forest fire frequency. A higher proportion of conifers and the length of the wildland–urban interface were also positively associated with forest fire occurrence. Forest fire occurrence was influenced by a combination of climatic, forest-related, and social activity factors. The effect of climate was most pronounced on a monthly scale, corresponding with the presence of two distinct seasonal peaks of forest fire occurrence. The significant effect of factors related to human activity suggests that measures to increase public awareness about fire risk and targeted activity regulation are essential in controlling the risk of fire occurrence in Central Europe. An increasing frequency of fire-conducive weather, forest structure transformations due to excessive tree mortality, and changing patterns of human activity on the landscape require permanent monitoring and assessment of possible shifts in forest fire risk.

Changes to Peroxyacyl Nitrates (PANs) Over Megacities in Response to COVID‐19 Tropospheric NO2 Reductions Observed by the Cross‐Track Infrared Sounder (CrIS)

AbstractThe COVID‐19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non‐methane volatile organic compounds in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder (CrIS) over eight of the world's megacities. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID‐19 lockdowns. Statistically significant changes to PANs occurred over four megacities: with decreases over Los Angeles and Delhi, and increases over Mexico City and Beijing in the winter. Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export potential of megacities.

Cross-sectional study of influenza trends and costs in Malaysia between 2016 and 2018.

While influenza circulates year-round in Malaysia, research data on its incidence is scarce. Yet, this information is vital to the improvement of public health through evidence-based policies. In this cross-sectional study, we aimed to determine the trends and financial costs of influenza. Data for the years 2016 through 2018 were gathered retrospectively from several sources. These were existing Ministry of Health (MOH) influenza sentinel sites data, two teaching hospitals, and two private medical institutions in the Klang Valley, Malaysia. Expert consensus determined the final estimates of burden for laboratory-confirmed influenza-like illness (ILI) and severe acute respiratory infection (SARI). Economic burden was estimated separately using secondary data supplemented by MOH casemix costing. Altogether, data for 11,652 cases of ILI and 5,764 cases of SARI were extracted. The influenza B subtype was found to be predominant in 2016, while influenza A was more prevalent in 2017 and 2018. The distribution timeline revealed that the highest frequency of cases occurred in March and April of all three years. The costs of influenza amounted to MYR 310.9 million over the full three-year period. The study provides valuable insights into the dynamic landscape of influenza in Malaysia. The findings reveal a consistent year-round presence of influenza with irregular seasonal peaks, including a notable influenza A epidemic in 2017 and consistent surges in influenza B incidence during March across three years. These findings underscore the significance of continuous monitoring influenza subtypes for informed healthcare strategies as well as advocate for the integration of influenza vaccination into Malaysia's national immunization program, enhancing overall pandemic preparedness.

Human bocavirus in Saudi Arabia: Molecular epidemiology and Co-infections among children with acute respiratory tract infections during 2014–2016

Respiratory tract infections due to a variety of viruses continue to threaten the human population worldwide, particularly in developing countries. Among the responsible viruses, Human Bocavirus (HBoV), a novel discovered virus, causes respiratory tract and gastroenteritis disorders in young children. In Saudi Arabia, data regarding virus molecular epidemiology and evolution and its implication in respiratory tract infection are scarce. In the current study, genetic diversity and circulation pattern of HBoV-1 among hospitalized children due to acute respiratory tract infection (ARTI) during two consecutive years were charted. We found that 3.44% (2014/2015) and 11.25% (2015/2016) of children hospitalized due to ARTI were infected by HBoV-1. We have shown that HBoV was detected year-round without a marked seasonal peak. HBoV-1 also was co-detected with one or multiple other respiratory viruses. The multisequence analysis showed high sequence identity (∼99%) (few point mutation sites) between strains of each genotype and high sequence variation (∼79%) between HBoV-1 and the other 3 genotypes. Phylogenetic analysis showed the clustering of the study's isolates in the HBoV-1 subclade. Our data reveal that genetically conserved HBoV-1 was circulating among admitted children during the course of the study. Further epidemiological and molecular characterization of multiple HBoV-1 strains for different years and from all regions of Saudi Arabia are required to understand and monitor the virus evolution.

Wastewater-Based Epidemiology for Viral Surveillance from an Endemic Perspective: Evidence and Challenges.

Wastewater-based epidemiology (WBE) is currently used to monitor not only the spread of the viral SARS-CoV-2 pandemic but also that of other viruses in endemic conditions, particularly in the absence of syndromic surveillance. The continuous monitoring of sewage requires high expenditure and significant time investments, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Tuscany (Italy) and targeted human adenovirus (HAdV), norovirus genogroup II (NoVggII), enterovirus (EV), and SARS-CoV-2. Samples were collected at the entrances of treatment plants and concentrated using PEG/NaCl precipitation, and viral nucleic acids were extracted and detected through real-time reverse transcription qPCR. NoVggII was the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. Compared with data that were previously collected in the same study area (from February 2021 to September 2021), the results for SARS-CoV-2 revealed a shift from an epidemic to an endemic pattern, at least in the region under investigation, which was likely due to viral mutations that led to the spreading of new variants with increased resistance to summer environmental conditions. In conclusion, using standardized methods and an efficient monitoring strategy, WBE proves valuable for viral surveillance in pandemic and epidemic scenarios, enabling the identification of temporal-local distribution patterns that are useful for making informed public health decisions.

Implications of Russia-Ukraine war on land surface temperature and air quality: long-term and short-term analysis.

The world is currently witnessing the military operations of Russia invading Ukraine, leading to missile bombing and shelling on various parts. Although the economic ill effects are more conspicuous and much talked about, the environmental impacts are grimmer and more devastating but ironically are less in the news. Hence, in this work, we focused on the environmental impact of the Russia-Ukraine war by quantifying the long-term(2001 to 2023) and short-term temperature changes using land surface temperature (LST) and air temperature (AT) as proxies and monitoring changes in air quality, mainly methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2), between 2021 and 2022. We used NASA MODIS FIRMS fire points from 24th February 2022 to 08th September 2023 to prepare the heat map for identifying the regions heavily devastated by bombing. Thus, parts of Kiev, Lviv, Luhansk, Odesa, Donetsk, Kherson, etc., in Ukraine were chosen for assessing the LST, AT variations during the peak season of war along with analysis of long-term and short-term changes. We used MODIS Terra LST and Emissivity, ERA 5 AT, CH4, CO2 from AIRS and CO from Sentinel 5P. The results of the LST showed an average increase of around 2.32°C (2022-2023), 3.44°C (2021 and 2022) in parts of Ukraine and an increase of about 2°C from COVID time, whilst a decrease of about 1°C during COVID. This increase in LST will cause enhanced warming, thus changing the regional climate in a shorter time frame. A consistent upward trend in CH4, CO and CO2 is seen from 2019 to 2023. These heat waves and pollution will grip Ukraine and cause menace due to the cumulative effect of heat waves, changing climate and the aftermaths of war. This would be catastrophic as it might lead to a widespread impact on human health, agricultural yield and infrastructure, to name a few.

Assessment of Water Resources under Climate Change in Western Hindukush Region: A Case Study of the Upper Kabul River Basin

This study aims to estimate the surface runoff and examine the impact of climate change on water resources in the Upper Kabul River Basin (UKRB). A hydrological model was developed using the Soil and Water Assessment Tool (SWAT) from 2009 to 2019. The monthly calibration was conducted on streamflow in six stations for the period from 2010 to 2016, and the results were validated from 2017 to 2018 based on available observed data. The hydrological sensitivity parameters were further prioritized using SWAT-CUP. The uncertainty of the model was analyzed by the 95% Prediction Uncertainty (95PPU). Future projections were analyzed for the 2040s (2030–2049) and 2090s (2080–2099) compared to the baseline period (1986–2005) under two representation concentration pathways (RCP4.5, RCP8.5). Four Regional Climate Models (RCMs) were bias-corrected using the linear scaling bias correction method. The modeling results exhibited a very reasonable fit between the estimated and observed runoff in different stations, with NS values ranging from 0.54 to 0.91 in the calibration period. The future mean annual surface runoff exhibited an increase in the 2040s and 2090s compared to the baseline under both RCPs of 4.5 and 8.5 due to an increase in annual precipitation. The annual precipitation is projected to increase by 5% in the 2040s, 1% in the 2090s under RCP4.5, and by 9% in the 2040s and 2% in the 2090s under RCP8.5. The future temperature is also projected to increase and consequently lead to earlier snowmelt, resulting in a shift in the seasonal runoff peak to earlier months in the UKRB. However, the shifts in the timing of runoff could lead to significant impacts on water availability and exacerbate the water stress in this region, decreasing in summer runoff and increasing in the winter and spring runoffs. The future annual evapotranspiration is projected to increase under both scenarios; however, decreases in annual snowfall, snowmelt, sublimation, and groundwater recharge are predicted in the UKRB.

Analysis of the Relationship between Futures Prices and Mandi Spot Prices of Spices in India

For this study, we used secondary data and applied various statistical methods, including the compound growth rate, ADF test, seasonality analysis, Johansen's cointegration test, and the Granger Causality Test. The findings revealed a positive trend in the prices of selected spices, indicating a significant growth rate for both prices and arrivals. Seasonality in prices was observed, particularly during the peak season. The price series were found to be cointegrated at a significance level of 1 per cent, and a uni-directional causality was identified in both spices. It suggested that futures prices played a dominant role in the price discovery process. It was recommended to improve warehouse structures and storage chambers to enhance the retention capacity of farmers, thereby emphasizing the importance of proper storage facilities.

Assessment of Variability and Seasonal Patterns in Lemon Arrivals and Prices across India

The current study examined the variability and seasonal patterns in lemon arrivals and prices across the significant lemon-producing states in India from 2013 to 2022. Six important markets (Tenali, Surat, Pune, Udipi, Attabira, and Lashkar) in the country, one from each of the selected states, were chosen based on their annual market arrivals of lemons. The variability measured through the Cuddy-Della-Valle index showed similar price behaviour in all the selected markets, with the highest price variability during the peak season. The price behaviour of lemons showed a significant positive trend in all selected markets. Seasonal indices for market arrivals were highest during the peak harvesting season of lemons, with the Tenali market demonstrating the highest seasonal variation among all the selected markets. The prices were high during the off period of arrivals.

How climate change and population growth will shape attendance and human-wildlife interactions at British Columbia parks

Protected areas are important for ecological conservation while simultaneously supporting culturally and economically valuable tourism. However, excessive visitor pressure strain operations and risk human-wildlife conflict, threatening the sustainability of nature-based tourism. Thus, park managers need to know what factors underpin attendance and how these might interact to shape future attendance. Using a decade of attendance records from 249 provincial parks in British Columbia (BC), Canada, as well as 12 years of human-wildlife interactions (HWI) records at five national parks in BC, we modelled the impacts of weather conditions and population growth on park attendance and HWIs. We paired these models with climate change and population growth scenarios to generate projections of how attendance and HWIs will change throughout the century. Climate change is projected to result in more precipitation and higher temperatures, and, over this same time span, BC's population is expected to grow substantially. Based on the observed relationship between attendance and weather, parks should anticipate a marked rise in visitors and HWIs, especially during their respective peak seasons. These projections provide park managers with the information required for proactive management, ultimately contributing to the sustainability of recreation and tourism in protected areas.

Consistency in Verreaux's sifaka home range and core area size despite seasonal variation in resource availability as assessed by Enhanced Vegetation Index (EVI).

Primates are adept at dealing with fluctuating availability of resources and display a range of responses to minimize the effects of food scarcity. An important component of primate conservation is to understand how primates adapt their foraging and ranging patterns in response to fluctuating food resources. Animals optimize resource acquisition within the home range through the selection of resource-bearing patches and choose between contrasting foraging strategies (resource-maximizing vs. area-minimizing). Our study aimed to characterize the foraging strategy of a folivorous primate, Verreaux's sifaka (Propithecus verreauxi), by evaluating whether group home range size varied between peak and lean leaf seasons within a seasonally dry tropical forest in Madagascar. We hypothesized that Verreaux's sifaka used the resource maximization strategy to select high-value resource patches so that during periods of resource depression, the home range area did not significantly change in size. We characterized resource availability (i.e., primary productivity) by season at Kirindy Mitea National Park using remotely-sensed Enhanced Vegetation Index data. We calculated group home ranges using 10 years of focal animal sampling data collected on eight groups using both 95% and 50% kernel density estimation. We used area accumulation curves to ensure each group had an adequate number of locations to reach seasonal home range asymptotes. Neither 95% home ranges nor 50% core areas differed across peak and lean leaf resource seasons, supporting the hypothesis that Verreaux's sifaka use a resource maximization strategy. With a better understanding of animal space use strategies, managers can model anticipated changes under environmental and/or anthropogenic resource depression scenarios. These findings demonstrate the value of long-term data for characterizing and understanding foraging and ranging patterns. We also illustrate the benefits of using satellite data for characterizing food resources for folivorous primates.