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The genetic progress achieved by animal breeding programs may be affected by genotype-by-climate interactions (GxC). This systematic literature review assesses the scientific evidence for GxC on multiple traits of cattle, pigs, and poultry. Two search engines (Scopus, Web of Science) were queried for original peer-reviewed scientific (English full-text) studies. We included (1) observational studies and designed experiments considering dairy or beef cattle, swine, chicken or turkeys, where (2) at least one production, fertility, or health trait was tested for GxC, (3) the existence of GxC was tested directly based on temperature, relative humidity, or climatic indices for heat or cold stress, and (4) genetic effects within a breed or line were investigated. The search resulted in 46 eligible studies, with a (low) risk that the requirement of full-text English studies may have resulted in some work not included in this review. Our review shows an increase in GxC studies from 2015 onwards. Cattle was the most studied species (n = 36; 78.3%), whereas eligible studies on turkeys were lacking. Climatic parameters used in the studies ranged from well-known parameters (e.g., temperature, temperature-humidity index) to more complex indices combining temperature, relative humidity, wind speed and solar radiation. All observational studies (n = 40; 87.0%) used weather station data. In total, 75 traits were studied, which were predominantly production traits regardless of species. Studies on fertility and health traits have been emerging from 2010 onwards, but their numbers still lag far behind those for production traits. Genotype-by-climate interaction was confirmed in 54.0% of the study outcomes. This systematic review shows that little is known about the role of GxC in health and fertility traits in cattle, and for all traits in pigs and poultry in general. As current evidence shows that genotype-by-climate interaction is common across species and traits, we suggest to collect detailed climatic data and use them to assess the presence of GxC in indoor and outdoor production systems, as well. [ABSTRACT FROM AUTHOR]

The paper considers a problem of modeling the impact of the future climate on the yield of crops (in the example of spring wheat). The authors have analyzed the sources of climatic data and values of agrometeorological indicators for past and future periods. Using agroclimatic parameters, they have developed a mathematical model of the dependence of the median of the distribution function of spring wheat yield on climatic changes. The authors performed the calculations using this model and analyzed the obtained results. The analysis indicates a significant increase in the yield level of spring wheat in 2030–2040 and an increase in the risk of losses of its yield compared to historical data. [ABSTRACT FROM AUTHOR]

Outdoor thermal comfort is an essential aspect of sustainable architecture, and it is key to maintaining a safe outdoor environment. Several programs have been developed to predict thermal comfort based on climate parameters, as well as the built environment, and to forecast comfort within the urban context. Solar radiation and wind speed can be manipulated by the constructed environment. This research uses Jabal Al Natheef as a case study. An attempt is made to evaluate the outdoor thermal comfort of the urban environment and to identify the contributing factors that impede or facilitate outdoor thermal comfort in Amman. The goal of this research is to investigate behavioral factors, including perception, in addition to environmental parameters, such as thermal adaptation and solar radiation intensity, as well as the green infrastructure implementation. A comprehensive literature analysis of outdoor thermal comfort over the last decade is conducted in this paper, which included adaptive and rational thermal comfort approaches, from the standpoint of contextualizing the behavioral perspective that is connected to the use of urban space. This research employed a scenario-based approach, enabling site assessment and community participation. The possibility of enhancing the outdoor thermal comfort in Jabal Al Natheef is discussed. Furthermore, we identify the effect of design measures that influence outdoor thermal comfort. Recommendations for improving outdoor thermal conditions in the region to improve urban life and stimulate outdoor activities are provided. The results indicate that vegetation has a significant effect on reducing air temperature by providing shade. Urban areas with more vegetation have a mean radiant temperature that is significantly reduced, by almost 7 °C, especially during the summer. [ABSTRACT FROM AUTHOR]

Reliable identification of realistic biomass at regional level is particularly important in creating a real energy strategy. It must stand for the region in particular in the long-term aspect of its sustainable development. The aim of the paper is to identify and quantify environmental impacts, especially microclimatic impacts, on the possibility of establishing and growing economically and energy-efficient plantations of fast-growing energy plants in Slovakia regions. The NUTS III methodology was used to locate the climatic conditions of the Slovakia regions. Climatic factors in Slovakia regions were expressed as average monthly air temperatures and average monthly precipitation for the vegetation period from April to October. The climatic parameters of the model plantation in the Nitra region were defined on the basis of measurements of the Physicus Meteostation, located in the field conditions of Kolíòany village. Regions in the southern and south-eastern regions of Slovakia (Bratislava, Trnava, Nitra, Košice) are the most climatic friendly for the growing Mustanthus and Populus. The Northern regions (Žilina and Prešov) are less suitable for climatic reasons - the shorter length of vegetation period required for the economically efficient production of above-ground biomass. In the Nitra region, productivity in real climatic conditions is 33.66 t.ha-1 (Miscanthus) and 135.14 t.ha-1 (Populus). [ABSTRACT FROM AUTHOR]

In the context of climate change, the paper presents an analysis of the evolution of grapevine phenophases, in the period 1991-2021, for four grapevine cultivars Selena, Blasius, Rubin and Radames, and two clones Sauvignon blanc 9 Bl, Feteasca alba 29 Bl homologated at SCDVV Blaj. The study is based on the climatic and phenological data from SCDVV Blaj. The climatic data have been processed into fourteen parameters and for the phenological periods the starting date was analyzed. Regarding the average annual temperature, during 2010-2021 there is an increase of 1.1 ºC compared to the multiannual average reference temperature (calculated as the annual average of the years 1975-2010), which means that in the studied area, during the period 2010-2021 there was a warming compared to reference years. The average duration of the vegetation period from 2010-2021 is shorter than the reference period by 3.2 days. In 2011 the annual aridity index denotes a semi-arid dry climate, and in 2012, 2015, 2017 and 2019 a moderately humid climate. The hydrothermal coefficient was higher in the studied period compared to the reference period, registering values between 1.0 (2011) and 2.8 (2016), which has a positive influence on grape production. The Huglin index for the studied period is between 2237.6 in 2021 and 2928.5 in 2012, with an average of 2542.6 which places Tarnave vineyard area in a warm climate zone. Concerning the precipitation, a slight increase of 34.7 mm/m2 compared to the multiannual reference amount was monitored. In these conditions the warming of the climate induced an earlier budburst with an average of 8 days, correlated with an earlier maturation with 9-11 days in average. So far for the blooming and veraison phenophases starting date, no differences of the outrunning were clearly observed. [ABSTRACT FROM AUTHOR]

Nowadays, Surabaya experience Urban Heat Island (UHI) phenomenon because of the increase of surface temperature from 1994 until 2011. The urban structure parameters have a role in decreasing the UHI phenomenon. Rajawali is a historic area where it can be sensitive to climatic condition and urban heat island. This paper aimed to analyze the correlation of urban structure parameters toward climatic variables (as an indicator affecting UHI) in the heritage area of Rajawali. This study focuses on some urban structure parameters that are Building Coverage Ratio (BCR), Floor Area Ratio (FAR), aspect ratio (H/W), Volumetric Compactness (S/V) and land cover in the site. The analysis method is a simulation from Envi-met where simulation performed is run in March 2018. In order to check how far the urban structure affects climatic variable, the urban structure parameters are compared with each variable of climatic using the statistical package, R. For correlation analysis Pierson's r coefficient is used. The result shows that not all urban structure parameters correlate with climatic variables in the Heritage Area. The findings of this study are expected to be the basis for designing the heritage area in Rajawali so that the design can respond to the UHI phenomenon in Surabaya. [ABSTRACT FROM AUTHOR]

Copyright of Periódico Tchê Química is the property of Grupo Tche Quimica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

McArthur's fire danger index (FDI) is an integrated measure of the risk of fire occurrence, spreading speed and containment difficulty. The climatic parameter-dependent McArthur forest and grass fire danger indices were used to categorize fire danger severity. This paper explored the characteristics of four different climatic stations in Victoria, Australia, to determine their spatial and temporal variations impacting FDIs and the risk of fire occurrence. Sensitivity analysis was performed to develop nomograms to relate the variation of FDIs with key climatic parameters. These nomograms combine temperature, relative humidity and wind speed to determine fire risk categories in assisting field-based emergency and fire management authorities to identify danger and response plans to mitigate risks by mobilizing human and mechanical resources. [ABSTRACT FROM AUTHOR]

Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan. [ABSTRACT FROM AUTHOR]

This work investigates the impact of environmental changes on the efficiency of the photovoltaic panel system. To analyze the impact, we first study the effect of the current climatic parameters on the evolution of the output energy of two solar photovoltaic modules, such as irradiance, temperature and wind speed. Meteorological data was acquired using specific sensors for temperature, irradiance, and wind speed; these data have been initially conditioned by using of 34972A LXI data acquisition and thereafter, interfaced to a PC using IEEE 488.2 general purpose interface bus (GPIB). We use the LabVIEW real-time interface to display and store the collected data in the PC. Finally, from the data stored, we have carried out the discussions on the experimental results-based data driven analysis using clustered heatmap which supports the approval of this impact using climate forecasts and strategy to adopt for an efficient and economical used and consequently a solar photovoltaic energy under climatic constraints is proposed. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal of Plant Protection is the property of Arab Society for Plant Protection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the application of regression trees as a versatile alternative to other machine learning and statistical modelling techniques to forecast the power generation at five renewable power plants: one large hydropower plant, two mini hydropower plants, and two wind farms in Sri Lanka. The prediction models for each power station were developed by varying the depth of the regression tree. The regression tree model with the lowest depth that forecasts the output (power) in terms of all the predictor variables was selected for each power station and the accuracy of the models was evaluated by means of the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). According to the degree of the above performance indicators, i.e. very low values of MAE, MAPE, and RMSE supplemented by R2 of 0.95 or more, the regression tree method proved to be a convenient forecasting technique to predict the power generation at both hydro and wind power plants. Further, it could be found that a good correlation between the input and output variables paves the way for a smaller depth in the regression tree. Moreover, regression trees presented here could accurately identify the relationship between the power generated and the most influential weather factors, without being affected by potential outliers or missing values while managing collinearity too. Extension of this study would enable to generalize the prediction of renewable power generation based on the regression tree method, leading towards minimizing the use of fossil fuel.

The mountain forests of the Northwestern Caucasus represent unique refugia for the native biodiversity of flora and fauna. Endemic species are also preserved among soil invertebrates, including the group of earthworms, which are important ecosystem engineers. This study assesses the spatial distribution of the endemic anecic species of earthworms Dendrobaena nassonovi nassonovi Kulagin, 1889 in deciduous, coniferous–deciduous, small-leaved, and conifer forests of the Northwestern Caucasus (a total of 1028 geographical points were surveyed, of which the species was found in 185 points) based on our own field data by modeling the current potential areas using the Maxent software. The D. n. nassonovi potential area maps show a high probability of the species inhabiting mid- and high-mountain forests of the Northwestern Caucasus and being confined to mixed coniferous–deciduous and beech forests. The optimum soil and climatic parameters, as well as a lack of large-scale business operations in the mid- and high-mountain forests, make it possible for these ecosystems to remain suitable refugia, in particular for the endemic anecic species of Caucasus earthworms D. n. nassonovi. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Research, Engineering & Management / Aplinkos Tyrimai, Inžinerija ir Vadyba is the property of Institute of Environmental Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Kemija u Industriji is the property of Croatian Society of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In Romania, foehn winds are short-time atmospheric phenomena of low-to-medium intensity, which do not appear always in the weather station-performed observations. Such situations require additional data to provide a larger and dynamic picture in comparison with the punctual information sent by stations. This paper aims to capture, present and analyze the characteristic parameters of northern Oltenia foehn winds, in the Carpathian-Balkan Internal Curvature. Herein, it is shown a typical foehn situation revealed by weather stations and also by satellite images (MSG), atmospheric radio soundings and numerical modeling. [ABSTRACT FROM AUTHOR]

The uncertainty of climatic or weather variations makes human adaptation a challenging task. Though lots of techno developments have taken place addressing the need of predictions and forecasting of climatic behavior, the uncertainty of atmospheric and geoprocesses poses a severe challenge to the effectivity of efforts in dealing with disasters. It has become very crucial to understand the future atmospheric uncertainty process and further predict and forecast for analysis of different activities based on geographical locations. Climate parameters are mandatorily required to improve system activities and output analysis of acquired data. For a sustainable adaptation, this is very important on how the uncertainty of variations in climatic parameters is dealt and the decisions based upon appropriate forecasting model are taken. The work has primarily focused on the development of an appropriate model, training the model with massive monthly rainfall and temperature data which can be used to analyze any climatic predictions and provide support to deal with adverse future situations. This model determines the appropriate amount of rainfall and temperature varied during the dry season and helps set a relationship with other input variables like humidity and maximum and minimum temperature. The model has been trained and tested using the obtained data and performed well with better accuracy. The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis. Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction. The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size. [ABSTRACT FROM AUTHOR]

Plentiful and diverse Organic wastes such as green, food, pruning and landscaping waste necessitate upon effective and efficient recycling strategies such as composting. In this article, for the first time, machine learning (ML) models have been applied to model compost generation rate as a function of climatic parameters and organic waste content. The data size contained approximately 864 sample points records of the meteorological parameters (Modern-Era Retrospective Analysis), organic waste (Central Pollution Control Board) and compost yields (Open Government Data) data for 2010–2021. The modelling efforts involved the consideration of MLP and traditional ML algorithms namely k-nearest neighbour (kNN), gradient boosting (GB) and random forest (RF) for prediction and autoregressive integrated moving average (ARIMA) model supplemented ML models for long-term forecasting of the compost generation rate. Model validation resulted in an RMSE of 0.757 and R2 of 0.99 for GB model, and a correlation index of 0.68 between observed and predicted values to thereby outperform all other models. However, forecasted data for ten years after the predicted outcomes resulted in the best performance of ARIMA–MLP model with a standard error of 21.1152 and a CP yield of 74,958 kg. Thereby, the findings affirm upon the evidence for the limitations of the broader application of the empirical approaches and the feasibility of ML algorithms as a potential reconstruction technique for developing robust and accurate region-specific compost prediction and forecasting models to assist integrated circular agricultural system development for a sustainable global future. [ABSTRACT FROM AUTHOR]

Artemisia jacutica Drob. (Yakut wormwood) is a biennial plant endemic to Eastern Siberia. This species is distributed across the Republic of Sakha (Yakutia) and southern Central Siberia, colonizing salt meadows and grasslands, steppe areas, saline to sodic surface soil patches, abandoned agricultural lands, and ruderal habitats. The species is rather rare in Buryatia, due to the southeastern frontier of its area. The essential oil of the Yakut wormwood contains chamazulene as a principal compound component with an anti-inflammatory effect. The data generated within the survey and the published statistics indicate that the climatic conditions affect the formation of the essential oil component composition in plants, which is obviously appropriate to provide the best adaptation of the plants to their environmental growing conditions. Therefore, the effects of climatic conditions on the essential oil component composition in the Yakut wormwood plants result in the formation of two chemotypes known as Buryat and Yakut. It has been ascertained that the formation of these chemotypes occurs due to the differences between the rates of heat and moisture supply to the lands favorable for plant growth, expressed by the hydrothermal coefficient (HTC). The temperature–humidity extremeness parameters defining the coefficient based on annual data greatly affect the ratio of components in the essential oil. The climate-related factors (air temperature, precipitation, etc.) and their integrated potential can influence certain constituents and their proportions in the essential oil, affecting their variability. [ABSTRACT FROM AUTHOR]

Forest herb-layer vegetation responds sensitively to environmental conditions. This paper compares drivers of both taxonomic, i.e., species richness, cover and evenness, and functional herb-layer diversity, i.e., the diversity of clonal, bud bank and leaf-height-seed plant traits. We investigated the dependence of herb-layer diversity on ecological determinants related to soil properties, climatic parameters, forest stand characteristics, and topographic and abiotic and biotic factors associated with forest floor structure. The study was conducted in different forest types in Slovenia, using vegetation and environmental data from 50 monitoring plots (400 m2 each) belonging to the ICP Forests Level I and II network. The main objective was to first identify significant ecological predictors and then quantify their relative importance. Species richness was strongly determined by forest stand characteristics, such as richness of the shrub layer, tree layer shade-casting ability as a proxy for light availability and tree species composition. It showed a clear positive relation to soil pH. Variation in herb-layer cover was also best explained by forest stand characteristics and, to a lesser extent, by structural factors such as moss cover. Species evenness was associated with tree species composition, shrub layer cover and soil pH. Various ecological determinants were decisive for the diversity of below-ground traits, i.e., clonal and bud bank traits. For these two trait groups we observed a substantial climatic signal that was completely absent for taxonomy-based measures of diversity. In contrast, above-ground leaf-height-seed (LHS) traits were driven exclusively by soil reaction and nitrogen availability. In synthesis, local stand characteristics and soil properties acted as the main controlling factors for both species and trait diversity in herb-layer communities across Slovenia, confirming many previous studies. Our findings suggest that the taxonomic and functional facets of herb-layer vegetation are mainly influenced by a similar set of ecological determinants. However, their relative importance varies among individual taxonomy- and functional trait-based diversity measures. Integrating multi-faceted approaches can provide complementary information on patterns of herb-layer diversity in European forest plant communities. [ABSTRACT FROM AUTHOR]

This paper investigates the climate change impact on cooling energy consumption of office buildings in four major architectural climate zones in China. The results show that there are apparent differences in the responses of monthly or yearly cooling loads to climatic change in different climates. Dominant factors affecting monthly cooling loads change from dry bulb temperature (DBT) to wet bulb temperature (WBT) with the climate zones changing from severe cold to hot. The yearly cooling load was mainly afftectd by DBT in severe cold climate, whereas the WBT had the dominant effect in the other climates. With the continuous warming climate, the yearly cooling load did not rise significantly for any climate zone except for severe cold climate zone where cooling load had a significant increase ( p  < 0.01). More importantly, the cooling load showed a weak decrease in Guangzhou. These results suggest that different measurements for buildings cooling energy conservation should be made in different climate zones. The humidity effect should be fully considered to improve buildings energy efficiency, especially in the hot climate zones. The continuous increase of temperature in the future does not necessarily increase cooling loads of buildings in the cold, hot summer & cold winter and hot summer & warm winter climate zones, and change trendency of humidity in the future need to be considered to predict cooling energy consumption. [ABSTRACT FROM AUTHOR]

Settlements in forest areas are subject to restrictions and rules that differ quite significantly from those of "regular" buildings. The case study considered in this paper is of a settlement consisting of the administration building, a traditional timber restaurant and an ecotourism hotel situated in Dadia protected wildlife forest, in Greece. The bioclimatic renovation of the settlement in order to reduce energy consumption relies on the collection of climatic data and of data on the building's performance, which depend on in-situ measurements. Low cost instruments were used to measure climatic parameters (air temperature, relevant humidity, solar radiation, wind speed and direction) in the area. Small scale business are lacking adequate funds and specialized scientists. Furthermore, due to economic crisis they cannot attract managers to undertake a formal decision making process. The methodology used provides a simplified approach of decision making that can be applied in building renovation linked to the improvement of energy performance of ecotourism hotel. These actions are based on bioclimatic design techniques and green building concept methods that have been successfully applied in practice in Greece providing a combination of cost effectiveness and optimum energy consumption outcome in compliance to European and national directives actions against climate change. The results revealed a low annual average wind velocity, of 1.90 m/s, an annual monthly average solar insolation of 150 W/m², an annual temperature range of -3.1 to 36.3 °C and of relevant humidity from 20.6 to 100%. The proposed bioclimatic renovation actions focused on the alteration of the HVAC systems proposing the use of biomass for heating, installation of sun thermal systems for hot water and ceiling cooling fans, combined with passive design measures, such as, the enhancement of insulation, the replacement of old windows and exploitation of natural ventilation. [ABSTRACT FROM AUTHOR]

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST5), 15-cm (ST15), and 30-cm (ST30) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash–Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the different parameters that affect solar still productivity when the solar still productivity is very low compared with other desalination systems, such as other thermal processes or membrane processes. These parameters include environmental, design and operational parameters. The results show that productivity was highly affected by environmental parameters due to the unpredictability of metrological factors. When design and operational parameters were varied, increases in productivity were observed. The results indicated that the solar still was inversely affected by evaporation area, water depth, a minimization in the angle of the solar still cover during summer seasons and maximization during winter seasons. Stepped solar still techniques enhanced the productivity, and the addition of wicks in a stepped solar still was proposed. Insulating the sun tracking system positively affected the productivity because the insulation increased the heating capability and evaporative effects inside the still basin. [ABSTRACT FROM AUTHOR]

Climatic parameters obtained from the meteorological measurements over a long period represent a start position for monitoring and forecasting climate changes in the future. The paper observes monthly values of climatic parameters such as minimum and maximum air temperature ( T min , T max ), precipitation, wind speed at 2 m height ( u 2 ) and actual vapor pressure ( e a ) from twelve meteorological stations in Serbia during the period 1981-2010. Parameters T min , T max and e a constantly increase in the observed period. However, precipitation and u 2 decreased during the last decade. Changes and spatial distribution of monthly precipitation, T min and T max were observed during the crop growing season (April-September) in the last decade. [ABSTRACT FROM AUTHOR]

Climate change threatening the word food security including fisheries production. The current study aimed to find the relationship between different climatic variables and fish production by generating spatial maps. The climatic parameters data viz., mean maximum temperature; mean minimum temperature; total rainfall, and mean wind speed were plotted in GIS (Geographical Information System) platform and thematic mapping of it's for all the districts of Maharashtra for the year 1975 and 2017 on quarter basis was done using QGIS 3.6.1. The thematic maps of the percentage change in parameters of all the quarters of the year 2017 and 1975 were done for all the zone of Maharashtra. In addition, the thematic maps of the percentage change in parameters along with fish production of all the quarters of the year 2017-18 for the year 2012 were done for all the zone. The sensitivity analysis of climatic parameters with inland fish production for Maharashtra state and all the seven zones were done using GAM (Generalized Additive Model). The mean maximum temperature (MMAX) and total monthly rainfall (TMRF) were found to the most significant factors in the prediction of inland fish production. The average increase in MMAX during the 2nd and 3rd quarters (April to September) was less than the average increase in MMAX during the 1st and 4th quarters, whereas, in case of rainfall an average decrease was estimated during the 2nd and 3rd quarter. The current study found that Nagpur and Pune were the two highest fish-producing regions, and Konkan and Aurangabad were the two least productive regions. [ABSTRACT FROM AUTHOR]

Abstract: Recently, the International Commission on Illumination (CIE) has adopted a range of 15 standard sky distributions representing the whole probable spectrum of actual skies in the world. Each sky standard has its own well-defined sky luminance pattern which can be conveniently used to calculate the sky radiance and luminance for a given sky patch and the solar irradiance and daylight illuminance on inclined surfaces facing various orientations. The crucial issues are whether the skies could be correctly identified. This paper presents the work on the classification of the CIE Standard General Skies using various climatic parameters and indices. Meteorological variables namely luminance distribution for the whole sky including zenith luminance, global, direct-beam and sky-diffuse illuminance on a horizontal surface and vertical sky illuminance, and horizontal and vertical solar irradiance data are adopted for analysis. The results demonstrate that there are a number of appropriate climatic parameters for sky classification and the selection depends on their availability, accuracy and sensitivity. The approaches could contribute to the estimation of solar irradiance and daylight illuminance which are essential to the renewable and sustainable developments and energy-efficient building designs. [Copyright &y& Elsevier]

Alternate deformations in the expansive clay have crippled civil infrastructure systems in and around the city of Regina that lies in a semi-arid zone. The main objective of this paper was to develop a soil-atmosphere model for predicting the net water flux and the corresponding volume change in the local expansive soil. A one-dimensional hydrologic model was developed, for a homogeneous soil and no ground water table, by coupling material properties with atmospheric parameters. The use of site coordinates ensured material continuity in a strain-independent framework and soil volume changes were calculated using model results in conjunction with laboratory data. Results showed high water absorption and retention capacities for the investigated soil deposit. Conducive atmospheric conditions during summer gradually desatuarted the top 2.5 m layer of the clay that imbibed sporadic rainfall water thereby resulting in volume increases. Cyclic variations in the degree of saturation and the corresponding swelling potential were highest at and near the ground surface and gradually decreased with depth. The highest swelling potential was predicted for late summer and equaled 37%. The model reasonably estimated the soil-atmosphere interactions for the investigated expansive clay and was found to depend on an effective capture of site conditions and material properties. An increased modeling duration can ensure steady state with respect to antecedent moisture. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a dynamic prediction of solar radiation and ambient temperature. Medium term prediction is based on climatic parameters behaviours during the day before and on time distribution models. As for short term prediction, it is ensured by an ARMA predictor using Kalman filter. Herein, we describe our method and present prediction results. Validation is based on measures taken during the year 2005 in the north of Tunisia. The work effectiveness is illustrated by a short term and medium term prediction of the electric energy produced by a 1KWp photovoltaic panel (PVP) installed at the Energy and Thermal Research Centre (CRTEn) in the north of Tunisia. Since our work delivers accurate climatic parameters prediction, the obtained results can be easily adapted to predict any other solar conversion system output. [Copyright &y& Elsevier]

In studying solar energy and daylight availability, particularly the sky luminance distributions, the required data are always analysed under various sky types which are categorised by some climatic parameters. Overcast days are important because they are used in more general sky models and appear quite frequent in some places. This paper studies some common climatic parameters including cloud cover, sunshine hour, solar irradiance, daylight illuminance and sky luminance and their applications in the classification of sky conditions. Frequency of occurrence of each parameter under overcast skies in Hong Kong has been established. It has been found that no single parameter can effectively describe the fully overcast skies and hybrid indices should be adopted. The general luminance distribution formula for the overcast sky with various luminance distribution parameter values has been used to test the selected sky luminance databases. The analysis indicates that the CIE standard overcast sky shows a good agreement with the overcast sky luminance distributions obtained in Hong Kong. [Copyright &y& Elsevier]

Motivation: Leaf traits represent an important component of plant functional strategies, and those related to carbon fixation and nutrient acquisition form the leaf economics spectrum. However, observations of functional leaf traits are underrepresented in tropical regions in comparison with those in temperate areas. Brazil, a country with continental scale and vast biodiversity is a timely example, where many biomes are impacted by human activities and climate change. However, leaf traits relevant to understand vegetation responses to these impacts remain poorly quantified for many species found in the country. We compiled an extensive data set of four functional leaf traits for native woody species occurring in the Brazilian territory. In addition to trait observations, sampling dates and geo‐references were compiled and climatic parameters and soil properties of each sampling site were extracted from several databases. Main types of variables contained: The LT‐Brazil data set contains 3,479, 1,216, 775 and 775 clean observations of leaf mass per area, leaf nitrogen (N) concentration per unit mass, leaf phosphorus (P) concentration per unit mass, and leaf N : P ratio, respectively, from native woody species, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties. Spatial location and grain: We compiled trait observations from 223 sites under native vegetation distributed in all main biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa and Pantanal) across the Brazilian territory. Time period and grain: The data represent information published and/or sampled during the last 25 years. Major taxa and level of measurement: Our compilation was focused on trait data observed for native woody species, excluding monocots, palm trees, herbs, and hemiparasitic plants. Thus, 108, 478 and 1,321 botanical families, genera and species were included, covering c. 9% of the woody angiosperm flora of Brazil. Software format: Data are provided as comma‐separated value (.csv) files. [ABSTRACT FROM AUTHOR]

• Key message: This document provides valuable environmental information about a triplets' essay of Scots pine and Maritime pine in Spain. The dataset characterizes the soil profile (physicochemical parameters of organic and mineral horizons), climate, physiography, understory and overstory. Dataset access is athttps://doi.org/10.5281/zenodo.4384530and associated metadata is available athttps://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/21cca830-daf9-4913-9b5b-a185d77943d5 [ABSTRACT FROM AUTHOR]

Considering the very important role of climatic parameters on the yield and biomass of plants (especially in rain-fed agriculture), in this research, using data series of 9 stations during 1968–2017 the influence of 6 climatic parameters including the average of annual maximum and minimum temperature (Max-T and Min-T), the average of annual sunshine (Su-Sh), the average of annual relative humidity (H), the average of annual wind speed (Wi) and the average annual precipitation (P) on the yield and biomass of winter wheat (YWW and BWW) and barley (YB and BB) as two important and strategic species to provide the human food and the livestock feed in Iran, was investigated and prioritized. To assess the effectiveness rate of the climatic parameters on the response variables (YWW, BWW, YB and BB), the random forest algorithm (RF) was used. The results indicated that the RF algorithm had a good ability to predict the response variables because (1) the linear regression between simulated and predicted YWW, BWW, YB and BB using the AquaCrop model and the RF algorithm (respectively) had no difference with perfect reliable line (Y = X) in 0.05 or 0.01 significant levels and (2) R2 between simulated and predicted YWW, BWW, YB and BB was significant at 0.01 levels. According to the results, the P, Wi and Min-T parameters were the most influential climatic parameters on the YWW (respectively), the Min-T, Wi and P parameters were the most influential parameters on the YB and the Wi and Min-T parameters were the most influential parameters on the BWW and BB. On the other hand, in all stations, the Su-Sh and Hu were the least influential parameters on the YWW, BWW, YB and BB. [ABSTRACT FROM AUTHOR]

The present study aims to investigate the morphological characteristics of the Blanche de Montagne sheep breed, mainly known by its long white fleece. The morphological characterization was performed on a total of 70 unrelated individuals of both sexes, having between 2 and 8 teeth and belonging to 13 farms located in the Ouarzazate region. The body measurements studied were body weight, body length, chest circumference, height at withers, rump height, chest depth, chest width, hip width, head length, head width, ear length, ear width, horn length, cannon circumference, wool length, and tail length. These measurements averaged 28.2±8.10 kg, 65.8±6.25 cm, 73.5±6.71 cm, 59.5±4.39 cm, 59.5±4.72 cm, 27.4±2.90 cm, 19.8±2.70 cm, 21.0±2.14 cm, 19.2±1.57 cm, 11.0±1.01 cm, 9.71±1.02 cm, 5.47±0.53 cm, 43.2±13.8 cm, 7.44±0.77 cm, 10.9±3.56 cm, and 31.8±4.36 cm, respectively. Descriptive statistics presented an overall coefficient of variation less than 15%, showing a homogeneous morphostructure of the breed. Most characters were small in relation with the low productivity of pastures. Moreover, 77.5% of correlation coefficients among the different body measurements were positive and significant, reflecting the strong morphological harmony of the breed, suggesting a long period of adaptation to its environment. The variance analysis showed that sex influenced the measurements, with males having the highest values. Similarly, individuals with 6 or 8 teeth showed higher values than those with 2 or 4 teeth. Through the comparison with other Moroccan breeds, the variation of some morphological traits was found in relation to some climatic parameters (mainly winter temperatures) and feeding supplementation. [ABSTRACT FROM AUTHOR]