Climatic Circumstances Research Articles

Application of the Analysis Time Series and Multispectral Images for the Estimation of the Conditions of the Vegetation Covers of the Natural Areas of Southern Spain

It has been scientifically proven that climate change is a reality. In subarid Mediterranean limates, this fact is observed in the irregular distribution of rainfall, resulting in alternating periods of more or less prolonged drought with episodes of torrential rains concentrated in short periods of time. We have selected 11 natural areas in southern Spain, where we will observe these circumstances and where a series of ecosystems composed of vegetation covers of a high ecological value are found. We start from the question of whether these climatic circumstances are really deteriorating them. For this study, we propose a method that combines three analysis techniques: the design of the time series, the application of vegetation indices, and the use of techniques analysis of changes in land use. From the combination of these techniques in the period from 1997 to 2021, we have observed that there have been a dynamic of changes in land use that has maintained its original characteristics by more than 70%, so it is possible to affirm that the adaptation of ecosystems to climatic conditions has occurred satisfactorily. However, this general statement shows some particularities which are those that we will show in this work.

Modelling the impacts of climate change on cereal crop production in East Africa: evidence from heterogeneous panel cointegration analysis.

Climate change has become an issue of concern for sustainable agriculture production. East African nations are heavily reliant on the agriculture sector, which accounts for a substantial amount of their gross domestic product (GDP) and employment. Due to climatic fluctuations, the output of the sector became very unpredictable. Hence, this study investigates the effects of climate change on cereal crop production in nine East African nations between 1990 and 2018. The study implemented pooled mean group (PMG) approach to examine the long-run and short-run dynamic impacts of the varying climatic circumstances on the output of cereal crops. The results reveal that rainfall and carbon emissions have favourable and significant long-run effects on cereal crop output, even though their short-run impacts are negligible. Additionally, cultivated land area and rural population have a constructive role in enhancing agricultural output both in the long-run and short-run. However, average temperatures have negative repercussions on cereal crop production in the long-run and short-run, even though the magnitude of sensitivity is greater in the short-run. Dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) validated the robustness of the long-run findings of the PMG technique. Besides, the Dumitrescu-Hurlin panel causality outcomes indicate that cereal crop output has a bidirectional causality with temperature, carbon emissions, and cropped area. The study further demonstrated unidirectional causation from rural population to cereal crop yield. The study recommends that East African policymakers improve the quality of farm inputs, the adoption of climate-resilient farming practices, the development of water retention facilities and the establishment of crop diversification initiatives.

Predicting the Potential Distribution of Pine Wilt Disease in China under Climate Change.

The primary culprits of pine wilt disease (PWD), an epidemic forest disease that significantly endangers the human environment and the world's forest resources, are pinewood nematodes (PWN, Bursaphelenchus xylophilus). The MaxEnt model has been used to predict and analyze the potential geographic spread of PWD in China under the effects of climate change and can serve as a foundation for high-efficiency monitoring, supervision, and prompt prevention and management. In this work, the MaxEnt model's criteria settings were optimized using data from 646 PWD infestation sites and seven climate variables from the ENMeval data package. It simulated and forecasted how PWD may be distributed under present and future (the 2050s and 2070s) climatic circumstances, and the key climate factors influencing the disease were examined. The area under AUC (area under receiver operating characteristic (ROC) curve) is 0.940 under the parameters, demonstrating the accuracy of the simulation. Under the current climate conditions, the moderately and highly suitable habitats of PWD are distributed in Anhui, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Sichuan, and other provinces. The outcomes demonstrated that the fundamental climate variables influencing the PWD distribution were rainfall and temperature, specifically including maximum temperature of warmest month, mean temperature of driest quarter, coefficient of variation of precipitation seasonality, and precipitation of wettest quarter. The evaluation outcomes of the MaxEnt model revealed that the total and highly suitable areas of PWD will expand substantially by both 2050 and 2070, and the potential distribution of PWD will have a tendency to spread towards high altitudes and latitudes.

The Actual Situation of Covid-19 Infection at High Altitudes in Perú

This study aimed to reveal the evolution and characteristics of the COVID-19 pandemic in high-altitude areas of Perú. An observational, descriptive, retrospective and longitudinal study based on information from the Peruvian Ministry of Health, COVID-19 Situational Room, warning from the National Epidemiology Center, Prevention and Disease Control, and the Panamerican Health Organization (PAHO) was conducted to analyze the occurrence of the COVID-19 pandemic in Perú from the beginning of the pandemic until March 7, 2022. In this period, 31,635,319 people were examined, 3,637,529 were infected, and 213,551 died from the disease, with a lethality index of 5.87%. Perú is now the country most affected by the pandemic in South America. The data showed heterogeneity in prevalence across the country, with higher altitudes having lower prevalence than coastal and jungle areas, which was related to climatic circumstances and social factors. The situation was complicated by the severe spread of the pandemia, which caused a surge in demand for health services and intensive care units, loss of workplaces and increased levels of poverty. A notable result was a decrease in the prevalence and mortality of the COVID-19 pandemic at high altitudes in Perú compared to the coastal and jungle areas.

Performance assessment of SM2RAIN-NWF using ASCAT soil moisture via supervised land cover-soil-climate classification

The SM2RAIN algorithm developed a simple analytical relationship by inverting the soil-water equation to estimate rainfall through the knowledge of soil moisture. The recently developed SM2RAIN-NWF algorithm offers an improvement in estimating rainfall by integrating the SM2RAIN algorithm and the net water flux (NWF) model. The Advanced Scatterometer (ASCAT) soil moisture products were used to estimate rainfall and evaluate the reliability of the SM2RAIN-NWF algorithm compared to the SM2RAIN on a national scale. Besides, the impact of Land cover-Soil texture-Climate (LSC) characteristics and the intensity of rainfall (four classes of intensity) on the performance of algorithms were discussed. Five performance metrics, including Correlation Coefficient (R), Kling–Gupta (KGE), Root Mean Square Error (RMSE), False Alarm Ration (FAR), and Probability of Detection (POD) were used to validate the estimated cumulative 5-, 14-, and 30-day rainfall. Furthermore, the effect of evapotranspiration (ET) and drainage terms were investigated in the performance of rainfall estimation through the SM2RAIN-NWF algorithm for the first time on a national scale. Results showed the rainfall estimations through the SM2RAIN-NWF algorithm improved approximately up to 7.5% in each accumulation (e.g. rainfall aggregation intervals (AGGR) 5 to 14 and 14 to 30) based on R and KGE indices. In addition, the SM2RAIN-NWF improved rainfall estimations up to 50% based on the KGE index in the southern half of Iran (arid and semi-arid climate) compared to the SM2RAIN estimates. The comprehensive evaluation and uncertainty analysis of rainfall estimations under the supervised classification of 11 LSC and 4 rainfall classes also showed the calibration of the SM2RAIN-NWF was highly affected by environmental and climatic circumstances. Uncertainty analysis showed the SM2RAIN-NWF algorithm can estimate rainfall more consistently in the five LSC classes namely 1) barren-clay loam-arid-desert, 2) barren-loam-arid-steppe, 3) barren-clay loam-arid-steppe, 4) urban-clay loam-arid-desert, and 5) urban-loam-arid-steppe. Similarly, estimating rainfall in the region with precipitation under 267 mm/year can be retrieved more reliably through the SM2RAIN-NWF algorithm. Results obtained from the ET analysis revealed an insignificant (<4%) effect of this term in improving the performance of the SM2RAIN-NWF algorithm. Also, the NWF equation confirmed the paramount role of the drainage term in enhancing the accuracy of rainfall estimates compared to the SM2RAIN method.

Recent Advances in Methods Detecting the Dengue Virus

Dengue fever, caused by the Dengue virus (DENV), has emerged as a mild to lethal human infection. Globally, an estimated 400 million patients have been infected with DENV over the past 10 years, and this number is expected to increase in the coming years. The DENV, possessing a single positive- stranded RNA with five serotypes, is transmitted by mosquitos of the Flaviviridae family—particularly, the Flavivirus genus and Aedes species. The DENV genome encodes three structural and seven non- structural proteins. In the Kingdom of Saudi Arabia (KSA), serotype 2 of the dengue virus (DENV-2) emerged in 1994 and caused a major epidemic in Jeddah, KSA. Dengue outbreaks first appeared in Makkah in 2004, according to the characteristics studied in hospitalized patients. Major factors causing the wide and rapid distribution of the virus include increased urbanization, migration, global commerce, weather variation, inadequate mosquito control, the development of pesticide resistance in mosquitos, irregular use of insecticides, and shifting climatic circumstances. Detection methods currently used for DENV include the detection of viral antigens (Ag) (virus extraction and purification, immunofluorescence test, and NS1 detection assay), serological assays (plaque reduction neutralization titers (PRNT), IgM/IgG immunological assays), and RNA detection using RT-PCR. Low sensitivity, specificity, and accessibility of the detection protocols represent major challenges necessitating the advent of more amenable protocols. The Aedes mosquito is the primary vector for horizontal transmission of DENV. DENV-infected mosquitos infect people, and DENV passes from one human to another through this vector. Once acquired, the virus requires 5–7 days of incubation before the patient exhibits various symptoms of dengue fever; subsequently, uninfected mosquitoes that come in contact with infected patients feed on their blood and become infected. The DENV may also be spread through the mating of male and female Aedes mosquitoes. The reverse transcription loop-mediated isothermal application (RT-LAMP) has emerged as one of the most adaptable viral detection procedures. This method could prove to be an excellent pathogen detection tool because it is cheap, simple, sensitive, cost-effective, accessible, and fast. The method relies on the use of 4–6 primers to recognize eight different loci in the target sequence contained in the DENV clinical isolates with a 100% success rate and a sensitivity of about 93%. We strongly recommend the use of LAMP in detecting spots of virus spread, especially in urban regions where accessibility to detection methods is scarce.

Seasonal variation of lupus nephritis in a cohort of Egyptian patients

IntroductionSystemic lupus erythematosus is an autoimmune multisystem disease; renal affection is one of its most common manifestations. The effect of environmental factors on lupus nephritis flares is not fully understood.MethodsThis is a retrospective study that included 200 patients with lupus nephritis flares. All patients had confirmed diagnosis of lupus nephritis on histopathological examination. Lupus nephritis flares were defined by either (1) nephritic flare: defined as increased proteinuria or serum creatinine concentration; abnormal urinary sediment or a reduction in creatinine clearance, or (2) proteinuria flare defined as persistent increase in proteinuria > 0.5–1.0 g/day after achieving complete remission; doubling to > 1 g/day after achieving partial remission. The time of renal flare (month of the year) was recorded to determine the effect of seasonal variation on lupus nephritis flares.ResultsThe median age for the patients was 33 years (IQR = 13); 92% of patients were females. The median duration of lupus was 7 years (IQR = 6). The median serum creatinine was 1.4 mg/dl, median serum urea level was 32, and median UPCR was 2.4 gm/dl. The highest incidence of flares occurred in June (14%) and July (12.5%) (p = 0.003).ConclusionSeasonal pattern of LN flare was observed in our study in Egyptian cohort of patients, with most flares observed during meteorological summertime. Larger studies are needed to confirm this seasonal pattern.Key Points• Flares of lupus nephritis are common in patients with systemic lupus erythromatosus.• A seasonal pattern of flares of lupus nephritis was observed in our study. This seasonal pattern has been observed by previous studies in variable ethnicities and variable climatic circumstances.

Effects of Negarim Micro Catchment on Growth and Survival of Two Provenances of Moringa oleifera Under Different Spacing in a Semi-Arid Area of Kitui County, Kenya

Kenya has a large population of multifunctional tree species like Moringa oleifera (Lamark). Two provenances of Moringa oleifera from areas of Gede and Machakos were used in a study to examine how Negarim micro catchment affects their growth and survival traits under various spacings. An experiment was set up in a Randomised Complete Block Design with three replicates. Root collar diameter, branches development, survival rate and increase in height were the variables of interest and data was collected for 16 months (January 2018/April 2019). For Gede provenance under catchment, plant height ranged from 83 to 123 cm, root collar diameter from 1.7 to 2.5 cm, number of branches from 13 to 15, and survival rate of 51 to 74 %. Under no catchment, plant height varied from 127 and 191 cm, root collar diameter between 2.7 and 4.4 cm, number of branches between 15 and 19, and survival rate between 70 and 75 %. The Machakos provenance results under catchment revealed that plant height varied from 62 to 174 cm, root collar diameter ranged from 0.9 to 4.7 cm, the number of branches varied from 8 to 15, and survival rate ranged from 48 to 69 %. Under no catchment, it reached a height of between 43 and 171 cm, root collar diameter between 1.5 and 3.6 cm, 10 to 16 branches, and survivability of between 57 and 78 %. Provenance and Negarim micro catchment had a significant effect on survivability (p ≤ 0.05), height (p ≤ 0.05), and branch development (p ≤ 0.05). The significant differences between plants under Negarim micro catchment were because the catchment provided more moisture advantages to plants than those that were under no catchment. This demonstrates their appropriateness and the possibility of recommending them to be included in tree cultivation systems in the area and other places with comparable climatic circumstances. This research is noteworthy in promoting soil erosion management and environmental preservation by providing information and methods for effectively using runoff in valuable crops/tree production

An Intensified Marine Predator Algorithm (MPA) for Designing a Solar-Powered BLDC Motor Used in EV Systems

Recently, due to rapid growth in electric vehicle motors, used and power electronics have received a lot of concerns. 3ϕ induction motors and DC motors are two of the best and most researched electric vehicle (EV) motors. Developing countries have refined their solution with brushless DC (BLDC) motors for EVs. It is challenging to regulate the 3ϕ BLDC motor’s steady state, rising time, settling time, transient, overshoot, and other factors. The system may become unsteady, and the lifetime of the components may be shortened due to a break in control. The marine predator algorithm (MPA) is employed to propose an e-vehicle powered by the maximum power point tracking (MPPT) technique for photovoltaic (PV). The shortcomings of conventional MPPT techniques are addressed by the suggested approach of employing the MPA approach. As an outcome, the modeling would take less iteration to attain the initial stage, boosting the suggested system’s total performance. The PID (proportional integral derivative) is used to govern the speed of BLDC motors. The MPPT approach based on the MPA algorithm surpasses the variation in performance. In this research, the modeling of unique MPPT used in PV-based BLDC motor-driven electric vehicles is discussed. Various aspects, which are uneven sunlight, shade, and climate circumstances, play a part in the low performance in practical scenarios, highlighting the nonlinear properties of PV. The MPPT technique discussed in this paper can be used to increase total productivity and reduce the operating costs for e-vehicles based on the PV framework.

Issues and Challenges of the Indian Handloom Sector: A Legal Perspective

One of the most significant unorganized industries in the Indian economy is the handloom industry. According to some estimates, the handloom industry employs about forty lakh people. However, weavers involved in handloom production are unable to thrive in the face of fierce global competition and predatory tactics by multinational textile corporations, which threaten the survival of traditional handloom weavers through technical innovation. Due to these factors, the Indian handloom is always on the verge of hunger and starvation. The handloom industry’s strength comes from the fact that it is built on human talent, which is a component of traditional knowledge. Traditional knowledge cannot be adequately protected by the IP system in place. However, GI has developed as a legal tool for the defence and advancement of traditional knowledge. This is especially true in the case of the Indian GI Act, which covers a wide range of handloom products that are solely the result of human talent and independent of natural variables like typical area climatic circumstances. To preserve our rich historical and cultural legacy, it is imperative to safeguard and protect traditional methods, knowledge, and handloom products. Handloom weavers can benefit from Intellectual Property Rights protection under the existing worldwide regime. Some of the problems the handloom industry is currently facing and which give rise to legal issues include the socioeconomic circumstances of the weavers, the profitability of the handloom industry, the difficulties the weavers face, the accessibility of financial support, and innovative marketing strategies. Therefore, it is imperative that we consider these challenges and develop the kinds of plans that could aid the handloom business and weavers in improving their current situation. As a result, the paper looks at the current legal system for intellectual property rights to effectively safeguard the personal rights of handloom weavers as well as the craft itself.

Rice plant disease classification using dilated convolutional neural network with global average pooling

The Indian economy is thought to be primarily dependent on agriculture. In plants with various climatic circumstances, illness is highly prevalent and natural. As a result, the quality of crop deteriorates. Getting the best quality and quantity of harvest is farmers' most challenging task due to recent changes in weather cycles. Crop diseases must be identified and prevented as soon as possible to improve productivity. Deep learning is an artificial intelligence branch. It has been actively discussed in academic and industrial circles in recent days, because of the advantages of autonomous learning and feature extraction. The use of learning-based algorithms in plant leaf disease recognition can help to strengthen the objectivity of plant leaf disease feature extraction, minimize the limitations of intentionally selecting disease spot features, and speed up the study. In this paper, we examine existing approaches to detecting plant leaf disease using deep learning and high-end imaging methods, as well as their challenges. We anticipate that our research will be useful to researchers interested in plant disease identification. The traditional CNN has the issue of using up too much computational power. To address this issue, this research developed a DCNN (Dilated Convolutional Neural Network) model with Global Average Pooling (GAP), which will be constructed by changing regular CNN convolution kernels with dilated convolution kernels and the fully connected layer in traditional CNN replaced by Global Average Pooling. The dilated convolution gives the advantages of less computational cost and reduced memory usage then GAP avoids overfitting. These two new concepts are implemented with CNN and the results of this method is compared with other learning and hybrid learning methods using performance metric such as precision, recall, f1-score and accuracy. The classification includes four classes such as bacterial blight, blast, brown spot and turgo. The performance metrics shows that, in the same experimental setup, the DCNN model with GAP improves training accuracy by 5.49 percent on average, compared to the classic CNN model and the results are compared with other learning and hybrid learning methods.

Global warming pushes the distribution range of the two alpine 'glasshouse' Rheum species north- and upwards in the Eastern Himalayas and the Hengduan Mountains.

Alpine plants’ distribution is being pushed higher towards mountaintops due to global warming, finally diminishing their range and thereby increasing the risk of extinction. Plants with specialized ‘glasshouse’ structures have adapted well to harsh alpine environments, notably to the extremely low temperatures, which makes them vulnerable to global warming. However, their response to global warming is quite unexplored. Therefore, by compiling occurrences and several environmental strata, we utilized multiple ensemble species distribution modeling (eSDM) to estimate the historical, present-day, and future distribution of two alpine ‘glasshouse’ species Rheum nobile Hook. f. & Thomson and R. alexandrae Batalin. Rheum nobile was predicted to extend its distribution from the Eastern Himalaya (EH) to the Hengduan Mountains (HM), whereas R. alexandrae was restricted exclusively in the HM. Both species witnessed a northward expansion of suitable habitats followed by a southerly retreat in the HM region. Our findings reveal that both species have a considerable range shift under different climate change scenarios, mainly triggered by precipitation rather than temperature. The model predicted northward and upward migration for both species since the last glacial period which is mainly due to expected future climate change scenarios. Further, the observed niche overlap between the two species presented that they are more divergent depending on their habitat, except for certain regions in the HM. However, relocating appropriate habitats to the north and high elevation may not ensure the species’ survival, as it needs to adapt to the extreme climatic circumstances in alpine habitats. Therefore, we advocate for more conservation efforts in these biodiversity hotspots.

Semantic sensor network ontology based decision support system for forest fire management

The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for various applications in industries such as medical, environmental monitoring, home automation, etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and the converted dataset into Resource Description Framework (RDF). This research describes the various steps in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation and supports reasoning and the general semantics of the open world, followed by all the ontologies.

An experimental study of solar receiver under cloudy weather conditions

The performance of the central solar receiver should be properly examined for an effective design of a solar power or solar heating system. The performance of solar receiver is affected by three parameters. These are the amount of solar radiations, the state of the heating medium and the temperature of the surrounding air. Also, while evaluating the performance of a solar receiver, the ambient weather conditions should be taken into account. As a result, when a large amount of direct light strikes a solar receiver, the power generated increases as well. In contrast, as the amount of direct light falling on the solar receiver diminishes, so does the amount of electricity generated. As a result, it is generally understood that on sunny days, the performance of the central solar receiver will be higher, while on overcast days, the performance will be reduced. The solar receiver collects heat from diffused or reflecting light (when the sun is obscured by clouds) and absorbs reflective light from glossy or light-colored surfaces. The experimental results reveal that solar radiation collection performance is dependent on climatic circumstances; thus, when installing a solar receiver, it should be suitable for the specific region depending on weather conditions.

Meteo-Climatic Conditions of Wind and Wave in the Perspective of Joint Energy Exploitation: Case Study of Dongluo Island, Hainan

Combined wind and wave power generation has advantages such as energy synergy and complementarity and will play a leading role in the integrated development of offshore renewable energy. From the perspective of joint energy development, this study focuses on the meteo-climatic wind and wave conditions in Dongluo Island, Hainan, in the South China Sea. Based on the concurrent measurement from in situ monitoring system, hourly data from June 2020 to September 2021 are used to reveal typical climate characteristics associated with the weak (inverse) correlation between wind and wave. The energy flux density of wind and wave are also assessed to describe the energy pattern. Principal component analysis (PCA) shows the wind parameters contribute a larger variance to the matrix of the wind–wave dataset than the waves, suggesting a lower stability of the wind climate. The first three components via PCA are then classified into five clusters to represent different climatic characteristics. Among them, the dominating cluster symbolizes a climatic circumstance with weaker winds and waves below normal. This cluster, evenly distributed in different seasons, shows the lowest wave–wind correlation, suggesting a favorable condition of the synergy of the two energies throughout the year. The clusters with the second and third largest sample sizes are mainly dominated in spring and winter, respectively. The magnitudes of the wind and wave parameters in these two clusters yield to a relation of “as one falls, another rises”, implying a high interest in complementarity between the two resources to a certain extent. The energy features inferred by meteo-climatic clusters are further verified by direct assessment of energy density. There are generally consistent variations between wind–wave climate and energy, both in magnitude and in seasonality. Based on these results, differentiated exploitation schemes considering the complementarity or synergy of wind and wave according to different seasons are recommended.

Determinant Factors of Teff Production in Lokka Abbaya Woreda of Sidama Regional State: Ethiopia

One of the important sectors in low-income nations like Ethiopia is agriculture. To provide for the needs of its population in terms of food is the foundation of an economy. It is essential to the nation's economy and gives the people of the nation sources of work. Many people on the African continent rely on the agricultural industry. Agriculture employs 85% of the labor force, accounts for 50% of GDP, 90% of total exports, and supplies 70% of the country's raw materials to the agro-industrial sector (World Bank 2011). Teff, a cereal with small grains that was once produced for human consumption. It has been grown for thousands of years in East Africa (D. Andrea 2008). In Africa, it is a necessary food item. It has been growing under certain climatic circumstances, including those that range from sea level to 2800 meters above sea level, the necessary temperature, rainfall, and soil requirements (D. Milkias and A. Abdulahin 2018). The average output of teff grain per hectare in Ethiopia was 1228 kg. It anticipates increasing production by 2500 kg ha-1 with the employment of modern farming techniques and management strategies (Wato T. 2019). Therefore, the goal of this study is to examine determining factors that affect small farmers' ability to produce teff in the Lokka abbaya woreda of Sidama regional state, as well as to look at related difficulties and challenges in order to provide solutions. The researcher surveyed farmers and obtained questionnaires from the participants. The sample size was determined to be 63 out of all household heads from three kebele.Agriculture is a significant sector in low-income countries like Ethiopia. An economy's cornerstone is its ability to provide the food needs of its population. It provides the people of the nation with sources of employment and is crucial to the nation's economy. On the African continent, the agricultural sector supports a large population. 85 percent of the workforce is employed in agriculture, which also generates 50 percent of the country's GDP, 90 percent of its exports, and provides 70 percent of its raw materials to the agro-industrial sector (World Bank 2011). Small-grain cereal called teff, which was formerly created for human consumption. In East Africa, it has been grown for countless years (D. Andrea 2008). It is a necessary food staple in Ethiopia. Keywords : Agriculture, sector, Teff production, Ethiopia, determine factors DOI: 10.7176/JNSR/13-14-03 Publication date: July 31 st 2022

Chikungunya Virus Asian Lineage Infection in the Amazon Region Is Maintained by Asiatic and Caribbean-Introduced Variants.

The simultaneous transmission of two lineages of the chikungunya virus (CHIKV) was discovered after the pathogen’s initial arrival in Brazil. In Oiapoque (Amapá state, north Brazil), the Asian lineage (CHIKV-Asian) was discovered, while in Bahia state, the East-Central-South-African lineage (CHIKV-ECSA) was discovered (northeast Brazil). Since then, the CHIKV-Asian lineage has been restricted to the Amazon region (mostly in the state of Amapá), whereas the ECSA lineage has expanded across the country. Despite the fact that the Asian lineage was already present in the Amazon region, the ECSA lineage brought from the northeast caused a large outbreak in the Amazonian state of Roraima (north Brazil) in 2017. Here, CHIKV spread in the Amazon region was studied by a Zika–Dengue–Chikungunya PCR assay in 824 serum samples collected between 2013 and 2016 from individuals with symptoms of viral infection in the Amapá state. We found 11 samples positive for CHIKV-Asian, and, from these samples, we were able to retrieve 10 full-length viral genomes. A comprehensive phylogenetic study revealed that nine CHIKV sequences came from a local transmission cluster related to Caribbean strains, whereas one sequence was related to sequences from the Philippines. These findings imply that CHIKV spread in different ways in Roraima and Amapá, despite the fact that both states had similar climatic circumstances and mosquito vector frequencies.

Predicting the Suitable Current and Future Potential Distribution of the Native Endangered Tree Tecomella undulata (Sm.) Seem. in Pakistan

The burgeoning human population exhibited a rapid amplification in demand for timber and fuelwood and as a result, the natural population of the native tree Tecomella undulata reduced rapidly due to its high economic and medicinal significance. The recognition of appropriate regions for threatened plants in the climate change scenario is a fundamental step for the restoration and conservation of biodiversity. The current study predicts the potentially suitable areas in Pakistan for T. undulata restoration. This research identifies the highly appropriate regions for vulnerable T. undulata through the maximum entropy model from MaxEnt software. The model’s Area Under Curve 0.968 suggested its accuracy. The mean temperature of the wettest quarter, precipitation of the warmest quarter, and mean temperature in the driest quarter significantly shaped the T. undulata distribution. Future suitable areas for T. undulata were made by using RCP (4.5 and 8.5) for the years 2050 and 2070 through 19 bioclimatic variables and 66 occurrence points. The current highly suitable area for T. undulata is approximately 135,749 km2 (15.4%) while the unsuitable area identified is approximately 404,917 km2 (45.91%). The highly suitable area for T. undulata increases by 3.6–7% under climate change regimes (RCP 4.5 and RCP 8.5). The Central Punjab (District Faisalabad, Nankana sahib, Jhang, Kasur, and Okara), Salt Range, Western Khayber Pakhtunkhwa (KPK), FATA area, Eastern Balochistan, and Thar and Tharparker in Sindh are the current appropriate habitats for T. undulata. Under all future climatic circumstances, the extremely appropriate area for T. undulata was anticipated to expand, whereas the unsuitable zones would all shrink. The research would be significant for the further development of T. undulata management and conservation techniques.

The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine

The performance of photovoltaic (PV) solar panels is dependent on certain factors, such as dust effects. Even though Palestine’s energy issues are well-known, no research has been undertaken on the soiling effect on solar energy generation in Palestine’s climatic circumstances. The study’s findings can aid Palestine’s efforts to achieve long-term energy sustainability and solar energy use. Outdoor research was conducted in Tulkarm, Palestine, to explore the impact of dust on PV systems. The current study examined the impact of dust accumulation based on the Mediterranean climate. To accomplish this, a one-year experiment was conducted from 1 January to 31 December 2021. An 85-kW PV power plant at Tulkarm was utilized in the study. Knowing the efficiency reduction over time will aid in minimizing cleaning expenses by selecting the most appropriate cleaning interval. The results concluded that in January, February, November, and December, there will be a two-month cleaning period, monthly cleaning in March and October, as well as two weeks of cleaning in April and May. It may also be concluded that the plant should be cleaned weekly throughout the months of June, July, August, and September. This recommendation is necessary to maintain the PV panel plant operating at peak efficiency.

Towards a sustainable food production: modelling the impacts of climate change on maize and soybean production in Ghana.

The Ghanaian economy relies heavily on maize and soybean production. The entire maize and soybean production system is low-tech, making it extremely susceptible to environmental factors. As a result, climate change and variability have an influence on agricultural production, such as maize and soybean yields. Therefore, the study’s ultimate purpose was to analyze the influence of CO2 emissions, precipitation, domestic credit, and fertilizer consumption on maize and soybean productivity in Ghana by utilizing the newly constructed dynamic simulated autoregressive distributed lag (ARDL) model for the period 1990 to 2020. The findings indicated that climate change enhances maize and soybean yields in Ghana in both the short run and long run. Also, the results from the frequency domain causality showed that climate change causes maize and soybean yield in the long-run. These outcomes were robust to the use of the ordinary least squares estimator and the impulse response technique. The findings show that crop and water management strategies, as well as information availability, should be considered in food production to improve resistance to climate change and adverse climatic circumstances.