Climate Variance Research Articles

Analysis and Exploration of Carbon Emission Dataset using Machine Learning Techniques

One of the primary environmental climate issues in metropolitan cities is that most people are faced with low air quality. Climate variance is impacting the world, triggering drought, storms, and extreme weather events. The primary contributor to climate change is affected greenhouse carbon gas emissions, of which carbon dioxide (CO2) and carbon monoxide (CO) make up the majority. The carbon emissions are expected to rise steadily worldwide. Many factors, including the burning of fossil fuels in transportation and manufacturing sectors, cause climate variance. Fast urbanization has used a high rate of motor vehicles as compared to a rural area. In metropolitan cities across the world, automobiles are the primary cause of air pollution. The use rate of vehicles keeps increasing, and that results in traffic congestion. This work is focusing on collecting two real-time vehicle emission datasets from two different devices to predict and compare the emissions of different light-duty vehicles by using the machine learning techniques. This research also analyzes the Canadian government emission dataset It shows the results of emission by fuel type and vehicle model. It shows the comparison graph of, mean square error, root mean square error and accuracy comparison of different machine learning algorithms. The work suggests some policies for reducing the carbon emissions. Even the government can adapt certain policies for mitigating the carbon emissions of individual vehicles. In this research work issues for reducing the emission of individual vehicles have been addressed. This work helps the automobile sector to reduce the emission and play a part to save the environment.

Chile Niño/Niña in the coupled model intercomparison project phases 5 and 6

The north and central coast of Chile is influenced by El Niño-Southern Oscillation (ENSO) through oceanic and atmospheric teleconnections. However, it also experiences episodic oceanic warmings off central Chile (30°S) lasting a few months that are not necessarily associated with ENSO. These episodes, called “Chile Niño” events, besides their ecological and socio-economical impacts, have also the potential to influence tropical Pacific variability. Here, we investigate how realistically the models in the Coupled Model Intercomparison Project (CMIP, Phases 5 and 6) simulate Chile Niño/Niña (CN) events, and quantify their changes under anthropogenic forcing. Despite limitations of the global models in simulating realistically coastal upwelling dynamics, we show that they simulate reasonably well the observed spatial pattern, amplitude and seasonal evolution of CN events. They however fail to properly represent the positive skewness from observations. The analysis of a sub-group of models (36) that simulate ENSO realistically reveals that CN events increase in amplitude and variance in the future climate with no changes in their frequency of occurence. This is interpreted as resulting from compensating effects amongst changes in remote drivers and local feedbacks. In particular, ENSO variance increases while that of the South Pacific Oscillation decreases. Conversely, we found that while the Wind-Evaporation-SST feedback tends to increase and the coupling between mixed-layer depth and SST weakens, favoring the development of CN events, the thermocline and wind-SST feedbacks decrease. However, only the change in the thermocline feedback is correlated to changes in CN variance amongst the models, suggesting a dominant role of local oceanic stratification changes in constraining the sensitivity of CN to global warming.

Drivers of municipal water security and vulnerability in Pakistan: A case study of Mardan, Khyber Pakhtunkhwa

Tehsil Mardan has experienced enormous shifts in land use and cover (LULC), growth in population, climate variance, and a drop in the groundwater table. This study sought to evaluate and quantify the vulnerability status of municipal water security in Tehsil Mardan, by incorporating LULC, population, and climate indicators using remote sensing and statistical techniques. LULC changes were quantified from Landsat imageries using Envi from 1990 to 2021. Field and questionnaire surveys were conducted to estimate groundwater depletion and municipal water demand. The Mann-Kendall trend test was applied to temperature and precipitation data. Analysis of correlation was performed to measure the relationship among all the variables. To get a deeper understanding of the vulnerability of municipal water security caused by physical, climatic, and social factors, the analytical hierarchy process (AHP) was utilized. LULC results showed a fivefold rise in built-up class from 37 km2 to 188 km2 and a substantial decline in bare land from 437 km2 to 252 km2 from 1990 to 2021. The average groundwater table depletion was one foot per year and showed a significant positive correlation with the number of tube wells, water supply and demand, and population growth. An increase of 0.008 °C per year in average temperature while a declining trend is observed in mean precipitation during the last 30 years. The results showed that municipal water security vulnerability changed from “invulnerable relatively” to “very vulnerable” stage during the last three decades. The study contributed valuable insights to facilitate policymakers, and urban planners for management of the current and future municipal water works, land use planning, and associated environmental opportunities and threats.

A Systematic Review of Artificial Intelligence: A Future Guide to Sustainable Agriculture

The population is expected to grow rapidly and reach 10 billion people by the year 2050. As a result, there will be a greater need for food. The conventional techniques employed by the farmers proved insufficient to meet these demands. For this, new automated techniques were unveiled. Along with other cutting-edge computer science applications, farming has long made use of technologies like artificial intelligence. The focus has shifted in recent years to consider the applications of this new technology. A significant percentage of humanity's nutrition has come from agriculture for thousands of years, with the most significant contribution being the broad adoption of efficient farming techniques for a variety of crops. The application of artificial intelligence (AI) in agriculture has sparked a revolution in the field, and AI technology has made the agro-based commercial sector operate more profitably. Artificial intelligence (AI) technologies have the power to transform the future and address problems. This will make it easier for farmers to learn about climate variance and pests that lower crops. The use of AI focuses on identifying damaged crops and enhancing the ability of healthy crops to provide higher yields. This paper gives a thorough analysis of AI models used in agricultural applications. It also examines the use of AI models to specify sustainable goals. This article explores the challenges and opportunities for utilizing AI to develop future generations of sustainable agriculture through this comprehensive review.

Tree-ring based summer temperature variability since 1790 CE in the Hindu Kush region of northern Pakistan.

The Hindu Kush high-altitude regions of Pakistan are currently experiencing severe consequences as a result of global warming. In this sense, increasing soil erosion and the quick melting of glaciers are two particularly evident effects. In such a scenario, understanding long-term temperature changes is crucial for making accurate forecasts about how the Hindu Kush region may experience regional temperature changes in the future. In this study, the climate tree-ring width (TRW) analysis designated a positive and significant correlation (r = 0.622, p < 0.001) between the TRW chronology and the June to September (summer) mean maximum temperature (MMT). Using the tree-ring width of Pinus wallichiana A. B. Jackson, we reconstructed summer temperatures in the Hindu Kush region from 1790 CE. Statistical analysis showed that the reconstruction model has explained 38.7% of the climate variance during the instrumental period of 1967 to 2018 CE. Five extremely warm summer periods (≥ 4 years; before the instrumental period 1967-2018 CE) of 1804-1830, 1839-1862, 1876-1879, 1905-1910, 1923-1935 CE, and six cold summer periods of 1790-1803, 1832-1838, 1863-1875, 1880-1904, 1911-1922, and 1936-1945 CE have been observed during the past 229 years. Individually, the year 1856 CE experienced severe warmth (31.85°C), whereas 1794 CE was relatively cooler (29.60°C). The spectral multi-taper method (MTM) shows significant (p < 0.05) cycles, which take place about every 9.3, 5.7, 4.2, and 3.6 years. In particular, the 9.3-year cycle, which closely aligns with the 11-year solar activity cycle, suggests a potential correlation between solar activity and local temperature fluctuations. Moreover, our reconstruction demonstrates a significant degree of consistency when compared to actual climate data and regional temperature reconstruction series, reporting a strong logic of trust in the reliability and accuracy of our findings. This evidence reaffirms that our reconstruction shows significant and dependable regional temperature signals, notably being representative for the Hindu Kush region.

Reconstruction of July-September Standardized Precipitation-Evapotranspiration Index (SPEI) for the Hindu Kush region of Northern Pakistan

Abstract The Prolonged drought resulting from global warming is considered an important factor affecting the Asia's socioeconomic growth of West Asia, with a significant impact on the dynamic forecasting of water supply and forest ecosystems. In such a scenario, Understanding future long-term drought (SPEI) changes is crucial for accurately forecasting regional drought shifts in the Hindukush region. In this study, a 517-year (1506-2022 CE) long tree-ring width chronology of the Himalayan Cedar (Cedrus deodara D. Don) from the eastern Hindukush has been developed. The July-September SPEI has revealed a positive and significant relationship (r = 0.633, p &lt; 0.001) with tree growth, which leads to SPEI reconstruction from AD 1626 in the Hindu Kush Region. Our reconstruction model has explained 40.01% of the climate variance during the instrumental period from AD 1965 to 2018. Fourteen wet periods (≥ 3 years) were observed before the instrumental period, specifically in C.E. 1629–1635, 1638–1658, 1666–1674, 1680–1701, 1715–1724, 1770–1776, 1794–1797, 1802–1810, 1822–1846, 1850–1857, 1872–1881, 1883–1890, 1906–1914, and 1921–1937. Similarly, twelve dry summer periods were also observed in the past 339 years, such as C.E. 1659–1665, 1675–1679, 1702–1714, 1725–1769, 1777–1793, 1798–1801, 1811–1821, 1847–1849, 1858–1871, 1891–1905, 1915–1920, and 1938–1963. Nevertheless, AD 1663 was individually the wettest (with a value of 2.13), while AD 1754 was the driest (-0.99) year. The spatial correlation analysis and its comparisons with Karakoram-Himalayan drought and precipitation reconstructions have convincingly confirmed the reliability of our SPEI reconstruction. Consequently, this reconstruction can effectively serve as a proxy for large-scale drought variability in the Hindu Kush region of northern Pakistan. Our findings strongly suggest the considerable dendrochronological potential for further climatological studies in the western Hindu Kush Mountains System.&amp;#xD;

Impact of SumiLarv® 2MR on Aedes aegypti larvae: a multicenter study in Brazil

BackgroundAedes aegypti is associated with dengue, Zika, and chikungunya transmission. These arboviruses are responsible for national outbreaks with severe public health implications. Vector control is one of the tools used to prevent mosquito proliferation, and SumiLarv® 2MR is an alternative commercial product based on pyriproxyfen for larval/pupal control. In this study, the residual effectiveness of SumiLarv® 2MR in different regions of Brazil was evaluated in simulated field conditions.MethodsWe conducted a multicenter study across four Brazilian states—Amapá, Pernambuco, Rio de Janeiro, and São Paulo—given the importance to the country’s climatic variances in the north, northeast, and southeast regions and their influence on product efficiency. The populations of Ae. aegypti from each location were held in an insectary. Third-instar larvae (L3) were added every 2 weeks to water containers with SumiLarv® 2MR discs in 250-, 500- and 1000-l containers in Amapá and Rio de Janeiro, and 100-l containers in Pernambuco and São Paulo, using concentrations of 0.04, 0.08, and 0.16 mg/l.ResultsAdult emergence inhibition over 420 days was observed in all tests conducted at a concentration of 0.16 mg/l; inhibition for 308–420 days was observed for 0.08 mg/l, and 224–420 days for 0.04 mg/l.ConclusionsSumilarv® 2MR residual activity demonstrated in this study suggests that this new pyriproxyfen formulation is a promising alternative for Aedes control, regardless of climatic variations and ideal concentration, since the SumiLarv® 2MR showed adult emergence inhibition of over 80% and residual activity greater than 6 months, a period longer than that recommended by the Ministry of Health of Brazil between product re-application in larval breeding sites.Graphical abstract

Climates and associated factors for evidence-based practice implementation among nurses: a cross-sectional study

BackgroundThe organizational climate that fosters and supports the implementation of evidence is a key factor influencing the effective implementation of evidence-based practice (EBP). Nurses, being the largest group of medical staff, play a crucial role in EBP implementation. The perception of the climate for EBP implementation among nurses in their organizations is unknown, especially among Chinese nurses.AimsTo clarify the implementation climate of EBP among Chinese nurses and identify the factors associated with the implementation and development of evidence-based nursing practices.MethodsThis study employed a descriptive cross-sectional study design. From March 2023 to April 2023, a sample of nurses (n = 1260) from two Tertiary care hospitals in central China were selected and surveyed by self-designed social-demographic questionnaire and Implementation Climate Scale. Multiple linear stepwise regression analysis was conducted to determine the predictors of implementation climate.ResultsThe nurses achieved a mean ICS score of 59.10 ± 11.22, with mean scores exceeding 3 points for each sub-dimension and item. In the results of multiple linear regression, income satisfaction, implementation of evidence-based nursing practice project(s) within the unit, experience of evidence-based nursing practice during working life, and specific training or courses in evidence-based nursing practice were predictors of ICS. Of these, income satisfaction was the most significant predictor. These factors could explain 17.5% of the total variance in implementation climate.ConclusionThe climate for implementing EBP in Chinese nursing organizations was relatively strong. Nursing managers can enhance the climate for implementing EBP in their organizations by actively improving salary and enhancing EBP-related trainings and practices.Relevance to clinical practiceUnderstanding nurses’ perceptions of the EBP implementation climate in their organizations can help to identify specific barriers and facilitators to the development of EBP and facilitate its successful implementation.Patient or public contributionClinical nurses were involved in data collection and completed the questionnaires on EBP implementation climate.

UPDATING ABOVEGROUND BIOMASS AT A PAN-EUROPEAN SCALE THROUGH SATELLITE DATA AND ARTIFICIAL INTELLIGENCE

Abstract. In this work, an ensemble of machine learning algorithms was trained using stratified sampling from an existing European-scale biomass map from 2018 to predict an updated version for 2020. The objective of stratification is to make sure that the full range of biomass values is represented. The sampled biomass values from 2018 were filtered to remove areas that did were subject to forest disturbances between 2018 and 2020. This information was available from forest cover/loss/gain maps derived from satellite imagery. We train using a total of 49 features derived from the following sources: bioclimatic data, maps of land-cover, tree cover, tree height, annual composites of vegetation indices per pixel (EVI and NDVI) obtained from Sentinel-2, radar backscatter median annual values from Sentinel-1 and ALOS-2, and the ALOS DSM (3D) elevation grid. A model was created dividing Europe into 19 tiles to limit variability due to very different bioclimatic zones. The result is a raster with 100 m × 100 m resolution and an estimated value of biomass (Mg ha−1) at each node. Overall results on validation data over Europe report a root mean square error (RMSE) of 32.4 Mg ha−1 and a mean absolute error (MAE) of 21.5 Mg ha−1; when considering single tiles, the largest RMSE was 54.7 Mg ha−1 in tile D2, which can be explained by the very high variance of climate, environment, terrain topography and biomass values as the tile enclosed the Alpine region and the western part of Eastern Europe.

Impacts of large-scale climatic circulation on floods through precipitation and temperature in the Lancang-Mekong River Basin

The importance of large-scale climatic circulation impacts on precipitation and floods in the Lancang-Mekong River Basin (LMRB) has been widely acknowledged. However, the mechanisms related to the impacts of circulation on floods are not yet fully understood. To address this issue, circulations were characterized by using the climate indices, and floods were represented by the flood volume and simulated using an improved hydrological-hydrodynamic model. The linear regression model was used to assess the impacts of circulation on precipitation and temperature, and also to quantify the impacts of precipitation, temperature, and circulation on floods. The results show that circulation affects precipitation with large spatial variability, while affects temperature with small spatial variability. On average, a unit change in different climate indices can cause a 2.5 %–6.1 % change in precipitation and a 0.3 %–1.2 % (0.09 °C–0.16 °C) change in temperature. Further, precipitation has a positive impact on floods, while temperature mainly has a negative impact. On average, a unit increase in precipitation and temperature can cause a 22.6 % increase and a 9.3 % decrease in floods, respectively. Consequently, circulation affects floods mainly through its impact on precipitation, while its impact on temperature also plays an important role. On average, a unit change in different climate indices can cause a 3.1 %–8.7 % change in floods. In addition, a multi-linear regression model was used to discuss the explained variance and relative contribution of climate. The results show that circulation can explain 12 %–23 % of the precipitation, temperature, and flood variances, while temperature and precipitation can explain 69 % of the flood variance. Among all circulations, the monsoon systems, North Atlantic Oscillation, and El Niño–Southern Oscillation dominate precipitation, temperature, and floods in the LMRB with the largest contributions and the most affected regions.

Voting Restrictions and Public Health: An Analysis of State Variation 1996–2020

The study examines the relationship between voting restrictions and public health in the 50 American states. Using a recently developed Cost of Voting Index (COVI) as a surrogate for variance in state electoral climate the research tests for a statistical relationship while controlling for other considerations that notably influence community health outcomes. The research uses the America’s Health Rankings composite indicator of state health as the dependent variable and examines the relationship at seven points of time over a 25-year period (1996–2020). In both bivariate and multivariate tests, a robust relationship is uncovered between the COVI and overall state health. In the bivariate tests, it is possible to see changes in state COVI values, overtime, corresponding with movement in the state public health scores.

Current and future geographical distribution of the indoor conditions for high thermal inertia historic buildings across Portugal via hygrothermal simulation

Meteorological conditions play a major influence on the indoor conditions of buildings, especially in southern European countries, where the hygrothermal requirements are systematically neglected, on contrast to northern European countries. Outdoor conditions will greatly differ with a building's location, even more if climate change is considered. Designing or retrofitting should take the climate requirements and, if necessary, adapt appropriately.This paper presents a methodology that can easily assess the variance of the indoor climate or even the performance of a retrofit measure in accordance with location. The methodology encompasses five steps: 1) Set meteorological data; 2) Build outdoor weather files; 3) Obtain interface soil/slab temperature; 4) Obtain simulation outputs; and 5) Build map. This methodology is flexible in terms of location/region, computational model, requirements and weather data.A model of a historic church was used to compute current and future climates’ distribution for Portugal. It was shown that the indoor climates are more moderate on locations near the coast than on those in the interior of the country, and that the buffering effect is higher in the west coast than in the south coast. A significant increase in terms of indoor temperature from scenario RCP 4.5 to RCP 8.5 is expected. In most of the coastal zones, an increase in RH is expected in the near future, whilst in some interior regions, a decrease is expected. Finally, it was shown that when using an adequate interpolation function, the coarser grid can correctly simulate the geographical distribution of the indoor conditions.

Combining genetic and environmental data to map and model regions of provenance for silver fir (Abies alba Mill.) in Italy

Regions of provenance for forest reproductive materials are the basis for wise use of forest resources in a changing climate. In this work a modelling framework is proposed for silver fir (Abies alba Mill.) in Italy where genetic clusters described by nuclear microsatellites were combined with high-resolution climatic data. When the genetic clusters were too large or had an uncertain ecological niche expression, an additional subregion division-was evaluated according to a climatic assessment. Subsequently each genecological group (Region of Provenance, RoP) was projected in geographic space separately using species distribution modelling (SDM) procedure under current (1991–2020) and a future climate scenario derived from the 6th assessment report for the period 2041–2070. The final division into nine RoPs was able to explain 77.41% of the total climatic variance, a good trade-off between statistical significance and practical usability. The modelling steps then showed a large degree of ecological overlap between RoPs with some of them occurring in similar ecological environments but characterized by a different genetic structure. When projected at the continental scale, the Italian RoPs were found to be suitable for almost all the current European range of silver fir, with potential expansion in Nordic countries in the future, beyond the current distribution range. The study showed that the combination of genetic and ecological data can be a robust way to proceed in areas where a strong genetic differentiation between populations occurs, such as in Italy. New markers such as SNPs can then be used to detect adaptive traits and drive the selection of provenances for common garden experiments in areas where the SDM modelscurrently extrapolate potential sites outside the current natural range.

Infrastructure to factorially manipulate the mean and variance of precipitation in the field

AbstractExtensive ecological research has investigated extreme climate events or long‐term changes in average climate variables, but changes in year‐to‐year (interannual) variability may also cause important biological responses, even if the mean climate is stable. The environmental stochasticity that is a hallmark of climate variability can trigger unexpected biological responses that include tipping points and state transitions, and large differences in weather between consecutive years can also propagate antecedent effects, in which current biological responses depend on responsiveness to past perturbations. However, most studies to date cannot predict ecological responses to rising variance because the study of interannual variance requires empirical platforms that generate long time series. Furthermore, the ecological consequences of increases in climate variance could depend on the mean climate in complex ways; therefore, effective ecological predictions will require determining responses to both nonstationary components of climate distributions: the mean and the variance. We introduce a new design to resolve the relative importance of, and interactions between, a drier mean climate and greater climate variance, which are dual components of ongoing climate change in the southwestern United States. The Mean × Variance Experiment (MVE) adds two novel elements to prior field infrastructure methods: (1) factorial manipulation of variance together with the climate mean and (2) the creation of realistic, stochastic precipitation regimes. Here, we demonstrate the efficacy of the experimental design, including sensor networks and PhenoCams to automate monitoring. We replicated MVE across ecosystem types at the northern edge of the Chihuahuan Desert biome as a central component of the Sevilleta Long‐Term Ecological Research Program. Soil sensors detected significant treatment effects on both the mean and interannual variability in soil moisture, and PhenoCam imagery captured change in vegetation cover. Our design advances field methods to newly compare the sensitivities of populations, communities, and ecosystem processes to climate mean × variance interactions.

Implementation and Performance Evaluation of a Community-Scale Adobe Evaporative Cooling Chamber for Vegetable Preservation

The construction of evaporative coolers in remote areas can increase the longevity of vegetables, improving food security and the local economy of small farmers in remote, impoverished communities without access to electricity. This work presents a 1:1 scale prototype of an 8 m3 (2.1 × 2.1 × 2.3 m) stabilized adobe evaporative cooler, with a design based on the appropriate technology framework, and it was built as a chamber using double adobe walls, filled with wet sand, to induce evaporative cooling. Furthermore, the paper presents the prototype’s performance evaluation. The tests were carried out in the dry and wet states, with different volumes of water. The results show good performance compared with other prototypes, although the optimum watering volume could not be determined because of the high climate variance (outside temperature and humidity) that prevented the repetition of the experiments in identical operating conditions. Stabilized adobe proved to be a good choice for use in the cooler, even when subject to moisture accumulation, indicating an estimated long lifetime for the cooler. The data obtained about the efficiency of evaporative cooling show that the cooler, as expected, has its best performance on the hottest and driest days, reducing the internal temperature (up to 13.24 °C) and managing to keep the internal humidity. The cost, efficiency, durability, and replicability make the proposed evaporative cooler a feasible solution for food preservation.

Applying the SIMPLE Crop Model to Assess Soybean (Glicine max. (L.) Merr.) Biomass and Yield in Tropical Climate Variation

Soybean Glicine max. (L.) Merr. is one of the most major food crops. In some areas, its responses to different climates have not been well studied, particularly in tropical countries where other crops are more dominant. Accordingly, we adopted the SIMPLE crop model to investigate the responses of soybeans to the climate. We conducted two experiments on crop growth in the Summer–Autumn season of 2020, and Winter–Spring 2021 in the Hoa Binh Commune, in the Mekong Delta, Vietnam, which is an area that is vulnerable to climate change impacts, to obtain data for our model input and assessment. The assessment was concerned with the effects of climate variables (temperature and CO2) on soybean biomass and yield. The results indicated that the SIMPLE model performed well in simulating soybean yields, with an RRMSE of 9–10% overall. The drought stress results showed a negative impact on the growth and development of soybeans, although drought stress due to less rainfall seemed more serious in Spring–Winter 2021 than in Summer–Autumn 2020. This study figured out the trend that higher temperatures can shorten biomass development and lead to yield reduction. In addition, soybeans grown under high CO2 concentrations of 600 ppm gave a higher biomass and a greater yield than in the case with 350 ppm. In conclusion, climate variance can affect the soybean yield, which can be well investigated using the SIMPLE model.

Effect of seasonal variation on yield and leaf quality of tea clone (Camellia sinensis (L.) O. Kuntze) in South West Ethiopia

The tea plant is widely cultivated in southwest Ethiopia. But the impact of seasonal variation on monthly yield, leaf quality, and the long-term yield response potential of clones has not been studied. The objective of the study is to determine the impact of seasonal change and climate variables on the yield and leaf quality of tea plants in southwest Ethiopia. The experiment consisted of five clones and four seasons under a split-plot design and was replicated three times. The results indicated that the yield and leaf quality showed significant variation in the different seasons at P < 0.05. The highest peak yields of 12.68, 12.59, and 11.3 kg plot−1 were recorded in May, June, and April, respectively, and the yield suddenly dropped by 5.1% in July. Then the soft banjhi increased by 5–10% in July. The yield response potential of clones is highly affected by monthly climate variation at P < 0.05. Clone BB-35 recorded the highest (18.8 kg plot−1) yield in June, followed by clones 11/4 (18.3) in May, 11/56 (14.7) in November, 6/8 (11.7) in December, and 12/38 (5.78 kg plot−1) in June. The lowest mean green leaf and a longer shoot replacement cycle were created due to a decrease in rainfall to 760 mm/month and rising temperatures above 26.35 °C in winter. The leaf phenological response of tea clones is strongly governed by the monthly temperature and suitable precipitation pattern of a season. The highlands have two harvesting seasons, i.e., a dry and a wet harvesting season. The dry harvesting season, which exists between the middle of December and March, accounts for 18.3–24.3% of the total annual yield. The wet harvesting season is subdivided further into two peak harvesting seasons. The first harvest is characterized by a short plucking round, and the highest peak yield occurs in April, May, and June, accounting for 40.22–42.2% of the total annual yield. The second wet harvesting season begins in September and ends in the middle of December, contributing to 35.5–40% of the annual yield. Seasonal variation has a direct impact on leaf quality and clone yielding potential. Clones show higher yield and shorter plucking rounds at maximum temperatures above 23.03 °C and below 26.35 °C, but temperatures above 28.34 °C and below 10.38 °C have a negative effect on leaf quality and yield. Over the last two decades, rainfall, maximum, and mean temperatures all increased by 16.09 mm y-1, 0.127 °C, and 0.0566 °C y−1, respectively, and the tea plant showed a strong correlation with maximum temperature (76%), whereas mean temperature (44.6%) and annual rainfall (32.8%) correlated weakly. Green leaf production is well explained by around 85.4% of the observed climate variance, with an increase of 1287.18 tonnes y−1, and highland tea production will exhibit a positive net benefit from expected climate change in the future.

Impacts of Climate Change on the Mean and Variance of Indica and Japonica Rice Yield in China

The overall goal of this study was to examine the impacts of climate change on the mean and variance of rice yields in China by using historical climate and crop data. An econometric model was established to estimate Just–Pope stochastic production functions and identify the potential impacts of climate change on the mean and variance of rice yields by type, keeping other factors constant. Based on the estimated production functions, the contribution rate of climatic factors to rice yield was then assessed by conducting the growth accounting of yields over the past 30 years. The results showed that both the mean rice yield and the yield variability were influenced by changes in the mean climate conditions and climatic variance. In the future, the impacts of climate change on rice yields will depend on local regions’ present climatic conditions. The results have implications for improving the adaptation capacity of rice production.

Girls’ Rights to Education in Chadereka Muzarabani District, Zimbabwe: A perspective on Sustainable Development Goal Number 5

Abstract The study unearthed embedded factors that hinder Muzarabani District from achieving gender equality and the empowerment of all women and girls by 2030. Main research focus is how far has Zimbabwe’s rural areas such as the Chadereka community achieved gender equality and empowerment of women and girls with respect to the 2030 target. The research further explores reasons why there is a high number of girls of school-going age outside the school system. A human rights-based approach guided the study’s arguments. The research utilised a case study design while a thematic analysis was used for data analysis. A qualitative methodology was employed while key informant interviews, focus group discussions and observations were used to collect data with purposive sampling being adopted in the selection of respondents. A total of 20 participants were chosen and they included; three men (a villager, a chief and a village head); five girls; five women; two officials from the DA’s office who consisted of the DA and an official dealing with women, children and disability, and development; two officials from local NGOs, a school head and two female teachers. The study found that women in Chadereka were still in a perpetual state of domination by their male counterparts due to persistent gender stereotypes, school setting related challenges and climatic variances that impacted on the education and ultimate emancipation of the girl child.

Physico-chemical and Carbon Stock Analysis of Soil under Forest Ecosystem

Temperature and moisture levels in the soil are two of the most important factors that determine the rate of soil respiration. Changes in the microclimate of the soil throughout the year play an essential part in determining seasonal fluctuations in the amount of carbon dioxide that is emitted from the soil at individual locations, and climatic variances because varying rates of soil respiration at distant sites. In forest ecosystems, one essential step in the cycling of carbon is the transfer of carbon dioxide from the soil to the atmosphere. The in-situ measurement of the increase in CO2 concentration at the soil’s surface is often what is meant when people talk about “soil respiration.” At the level of soils, CO2 emission is induced by both plant and microbial activities, including root respiration and the breakdown of organic matter in soil and litter. According to some reports, soils are responsible for between 60 and 80 percent of the overall respiration of an ecosystem. Microorganisms that live in the soil and the litter are responsible for the vast majority of heterotrophic respiration that occurs in forest ecosystems. A measurement of soil respiration in a substrate that is deteriorating has been recognised as a helpful indication of the rate of decomposition and mineralization of organic matter, as well as the cycling of carbon in an ecosystem, and as an index of relative soil biological activity. In order to investigate the physicochemical properties, bacterial and fungal populations, and soil respiration of various land use zones in Indian forest, the present study was carried out.