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Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature 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 research aimed to quantify concentrations of ammonia (NH3), carbon dioxide (CO2) and methane (CH4), estimate emissions, and analyze the factors influencing them during warm periods in an open dairy barn equipped with two cooling systems in a Mediterranean climate zone. Gas distribution within the barn was observed to vary both vertically and horizontally, with the highest gas concentrations observed in the central area of the barn. NH3, CH4 and CO2 ranged in 1.7--7.4, 7--18, 560--724 μg·g-1, respectively. Natural ventilation through openings and the operation of cooling systems induced changes in indoor microclimate conditions, influencing cow behavior and, consequently, gas production. Gas concentrations were the highest at air velocities below 0.5 m·s-1humidity index (THI) was > 72 and ≤ 78; and CO2 and CH4 concentrations were the highest with THI ≥ 72 and decreased with THI ≤ 72. NH3 concentrations when barn management included three daily milkings were higher than those measured when barn management was based on two daily milkings, and lower for CH4 and CO2. NH3 and CH4 emissions were the highest during barn cleaning, while the lowest NH3 emissions occurred during activity of the cows (i.e., feeding, walking). [ABSTRACT FROM AUTHOR]

In this work, the inter-relationship among 10 morphological characters, 8 climatic parameters and the content of total anthocyanins in the cob core of PMV 581 purple maize, cultivated and produced in three different places in Huanuco–Peru region, has been reported. This study of morphological characters was carried out using standard descriptors, both for the plant and the cob. Data on climatic parameters were obtained from three meteorological stations near the test locations. The total anthocyanin content (Acy), expressed as the glucoside-3-cyanidin concentration, has been determined by the differential pH method. From the statistical treatment of the data obtained, the following descriptors were found to be the most representative, given that they are poorly correlated with each other, but in general, depending on the localities: i/ (morphological) grain weight per cob GWC, plant length PL and cob core weight CCW; ii/ (climatic) minimum temperature Tmin, wind speed v and relative humidity RH. Between both types of descriptors, the best correlations occur for (CCW vs. Tmin) and (GWC vs. v). On the other hand, the total anthocyanin content Acy correlates very well with the CCW and Tmin descriptors. So, the highest concentration of Acy (684.2 mg/100 g) and also the highest CCW (38.6 g/cob) have been obtained in cobs of Winchuspata (W-Q), the coldest (Tmin = 7.89 °C) of the considered localities. On the contrary, the lowest concentration of Acy (603.7 mg/100 g) and also the lowest CCW 25.4 g/cob) have been obtained in cobs of Pistaloli (P-SA), the warmest (Tmin = 19.96 °C) of the three locations. The highest GWC value (139.4 g/cob) has been obtained in cobs of Marabamba (M-Y) where the wind speed v (4.13 m/s) was the highest of the locations considered. On the contrary, the lowest value of GWC (79.6 g/cob) has been obtained for cobs of Pistaloli (P-SA) where v was the lowest (1.19 m/s). In this context, it is important to propose studies on climatic variations' impact on different crop cycles, investigating how different agronomic management practices and the use of genetic identification/expression tools can optimize the anthocyanin production of purple maize, in order to facilitate the selection of new varieties for specific climatic conditions. [ABSTRACT FROM AUTHOR]

Introduction. In view of recent trends in melting of permafrost strata, the state of cryolithozone is noted as unstable, so such exogenous geological processes as landslides are widespread.Materials and methods. The landslide process in the Vorkuta River valley near the residential area under the conditions of seasonally frozen soils was analyzed. Based on the results of vertical electrical sounding, a geoelectric section at the landslide failure edge is presented. Statistical calculations based on geodetic data, transformation of landslide displacement time series to stationary form by detrending method, correlation analysis were carried out.Results. Trends of time series on displacement points were determined. They had a quadratic distribution. All investigated series had strong determinacy. The components were checked for stationarity and distribution according to the normal law by estimation of mathematical expectations of the detrended series parameters. Short-term forecast of landslide process for one period was given by the criteria of standard quadratic regression model, the forecast graph is given. According to Kolmogorov–Smirnov criterion, the hypothesis of homogeneity of single series distribution for climatic parameters for the observation period was accepted. The correlation coefficients of the parameters of landslide scarp displacement and some climatic factors on the Chaddock scale ranged from inverse weak (–0.18) to significant (0.58) correlation.Conclusions. Two possible cases of the landslide movement were presented: passive stage and the slope flattening or the beginning of a new landslide cycle. Geodetic monitoring is necessary to establish a reliable forecast. The average annual air temperature increased slightly during the observation period. A close relationship between landslide displacements and the positive average monthly air temperature values was revealed, Kcor = 0.58. A moderate connection (Kcor = 0.5) — with the average annual precipitation. Engineering recommendations for slope stabilization are proposed: installation of heat-insulating screens to the depth of seasonal soil freezing, arrangement of drainage wells in the body of the landslide and in soils up the slope.

Abstract 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.

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]

Based on our own field data and using modeling of modern potential ranges (in the MaxEnt program), an assessment of the spatial distribution of the rare representative of the longhorn beetle family, Callipogon (Eoxenus) relictus Semenov 1899, was conducted in northeast Asia (220 geographic locations). The generated maps of the potential range of C. relictus demonstrate a high likelihood of the species' presence in the upland areas of southern far east Russia, the provinces of northeastern China, and the Korean Peninsula. Field data also indicate the extensive association of the species with undisturbed broadleaf and coniferous–broadleaf forests in northeast Asia. Maps of the potential distribution of C. relictus in northeast Asia have been compiled based on four climate change scenarios from the present time to 2070. Under all of the climate scenarios used, it is shown that suitable habitats for the species will persist in certain areas of Primorsky Krai, as well as neighboring provinces of the People's Republic of China and a small enclave on the Korean Peninsula in Gangwon-do province. Significant reduction in suitable conditions for the rare longhorn beetle will occur in the rest of its distribution range. [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.)

Copyright of Acta Agriculturae Slovenica is the property of Biotechnical Faculty of the University of Ljubljana 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.

In recent decades, severe climate change, decreased precipitation, temperature rise, and increased evapotranspiration (ET) have significantly reduced waterbodies. Furthermore, governments have prioritized the study of water level fluctuations of lakes to protect them from degradation nationally and regionally. The present study investigated the physical changes in lakes Bakhtegan and Tashk due to climatic parameters. To this end, Landsat satellite imagery and the NDWI were employed to calculate the area of the waterbodies from 1986 to 2018. The results showed that the area had decreased during the study period-- since 2009, Lake Bakhtegan had dried up completely. In 2008 and 2010, the lowest precipitation was 127.82 and 107.7 mm, respectively. During the study period (1986 to 2018), the average temperature was 19.44 °C, with an increase of 0.6 °C. Among the climatic parameters, precipitation, with a correlation coefficient of 0.55, and potential evapotranspiration (PET), with a correlation coefficient of about −0.68, were more strongly correlated with changes in the area of the waterbodies. To predict temperature and precipitation in the study area in the coming decades (2020–2050), the HadCM2 model of the CORDEX Project -WAS (South Asia) was used under two scenarios: RCP4.5 and RCP8.5. These scenarios revealed the decrease in precipitation and increase in temperature trends. As a result, the waterbodies’ areas were estimated using the projected precipitation and PET for the period 2050–2020, indicating a decrease in the areas of the waterbodies.

In this work, the inter-relationship among 10 morphological characters, 8 climatic parameters and the content of total anthocyanins in the cob core of PMV 581 purple maize, cultivated and produced in three different places in Huanuco–Peru region, has been reported. This study of morphological characters was carried out using standard descriptors, both for the plant and the cob. Data on climatic parameters were obtained from three meteorological stations near the test locations. The total anthocyanin content (Acy), expressed as the glucoside-3-cyanidin concentration, has been determined by the differential pH method. From the statistical treatment of the data obtained, the following descriptors were found to be the most representative, given that they are poorly correlated with each other, but in general, depending on the localities: i/ (morphological) grain weight per cob GWC, plant length PL and cob core weight CCW; ii/ (climatic) minimum temperature Tmin, wind speed v and relative humidity RH. Between both types of descriptors, the best correlations occur for (CCW vs. Tmin) and (GWC vs. v). On the other hand, the total anthocyanin content Acy correlates very well with the CCW and Tmin descriptors. So, the highest concentration of Acy (684.2 mg/100 g) and also the highest CCW (38.6 g/cob) have been obtained in cobs of Winchuspata (W-Q), the coldest (Tmin = 7.89 °C) of the considered localities. On the contrary, the lowest concentration of Acy (603.7 mg/100 g) and also the lowest CCW 25.4 g/cob) have been obtained in cobs of Pistaloli (P-SA), the warmest (Tmin = 19.96 °C) of the three locations. The highest GWC value (139.4 g/cob) has been obtained in cobs of Marabamba (M-Y) where the wind speed v (4.13 m/s) was the highest of the locations considered. On the contrary, the lowest value of GWC (79.6 g/cob) has been obtained for cobs of Pistaloli (P-SA) where v was the lowest (1.19 m/s). In this context, it is important to propose studies on climatic variations’ impact on different crop cycles, investigating how different agronomic management practices and the use of genetic identification/expression tools can optimize the anthocyanin production of purple maize, in order to facilitate the selection of new varieties for specific climatic conditions.

This study presents a comprehensive investigation into the prediction of biogas production (BP) using meteorological parameters and organic waste (OW) data through feed-forward artificial neural network (ANN) and traditional machine learning (ML) techniques. BP is a crucial renewable energy source, derived from the anaerobic digestion of OW, and its efficient prediction holds immense importance for sustainable waste management and energy production. The dataset used in the study comprised approximately 728 sample points, encompassing records of meteorological parameters, OW, and biogas yields for the period 2015–2021. Additionally, autoregressive integrated moving average (ARIMA) models have been employed to enhance long-term forecasting of BP. The results of the model validation were highly promising. The MLP model demonstrated remarkable performance with a root mean-squared error (RMSE) of 2.76 and an R-squared (R2) value of 0.94, indicating its accuracy in predicting BP rates. Moreover, for forecasting future data, the ARIMA-MLP model outperformed others, yielding a standard error of 5.1152 and a biogas yield of approximately 90 m3. These findings underscore the limitations of traditional empirical approaches and highlight the potential of ML algorithms as effective reconstruction techniques. By developing robust and accurate region-specific BP rate models, these ML methods offer valuable support in fostering integrated circular agricultural systems for a sustainable global future. The integration of ML in waste-to-energy processes can play a significant role in addressing environmental challenges, advancements in sustainable waste management and clean energy production. [ABSTRACT FROM AUTHOR]

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]

This study aims to investigating the effect of climatic parameters on the concentration of suspended particles changes (less than micrometers) in Zabul and its relationship with the health of military forces. To conduct this research, climate parameters and PM concentration of Zabul weather station were used during a ۱۰-year period (۲۰۰۹-۲۰۱۹). Then, the air quality index was checked seasonally and monthly, and Pearson's coefficient was used to check the climatic parameters on changes in the concentration of suspended particles (less than ۱۰ micrometers). The results showed that the significant level obtained between PM۱۰ concentration and average relative humidity (-٠٠٤٢١), total annual rainfall (-٠,٤٥), average annual temperature (۰٫٥٩٦), maximum wind speed (۵۹۲۰۰۰) and with the number of days with dust and the number of visibility days less than km for Zabul station is ۰۰۸۰۱ and ٠٠٧٦٩ respectively. The investigation of the air quality index showed that the months of July and August have the most air pollution and December has the least air pollution. Considering the impact of PM concentration on health in many studies, it is suggested that the commanders should take the necessary steps in the correct use of shift work, periodic division of people in polluted areas and reduction of working time. [ABSTRACT FROM AUTHOR]

In recent decades, severe climate change, decreased precipitation, temperature rise, and increased evapotranspiration (ET) have significantly reduced waterbodies. Furthermore, governments have prioritized the study of water level fluctuations of lakes to protect them from degradation nationally and regionally. The present study investigated the physical changes in lakes Bakhtegan and Tashk due to climatic parameters. To this end, Landsat satellite imagery and the NDWI were employed to calculate the area of the waterbodies from 1986 to 2018. The results showed that the area had decreased during the study period-- since 2009, Lake Bakhtegan had dried up completely. In 2008 and 2010, the lowest precipitation was 127.82 and 107.7 mm, respectively. During the study period (1986 to 2018), the average temperature was 19.44 °C, with an increase of 0.6 °C. Among the climatic parameters, precipitation, with a correlation coefficient of 0.55, and potential evapotranspiration (PET), with a correlation coefficient of about −0.68, were more strongly correlated with changes in the area of the waterbodies. To predict temperature and precipitation in the study area in the coming decades (2020–2050), the HadCM2 model of the CORDEX Project -WAS (South Asia) was used under two scenarios: RCP4.5 and RCP8.5. These scenarios revealed the decrease in precipitation and increase in temperature trends. As a result, the waterbodies' areas were estimated using the projected precipitation and PET for the period 2050–2020, indicating a decrease in the areas of the waterbodies. [ABSTRACT FROM AUTHOR]

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]

Extended abstract 1-Introduction Soil and air are two essential elements in the life of creatures on the earth. Their interaction in certain conditions can cause many risks; among these dangers, dust storms can be mentioned. Under the conditions of dust storms, a large amount of dust is emitted in the air and the horizontal visibility is reduced to less than 1000 meters. Therefore, due to the important role of the dust phenomenon, it is highly necessary to understand the spatial-temporal changes and to analyze their longterm variations. Jazmurian region, located in the southeast of Iran, between the two provinces of Kerman and Sistan and Baluchistan, has become completely dry and turned into a desert due to drought and construction of numerous dams, and has turned this region into one of the key areas of dust production in the country. Therefore, the purpose of this study is to investigate the temporal-spatial changes of dust storms in relation to climatic parameters in Jazmurian wetland, which is of particular importance in order to properly manage this area to face the problems caused by dust storms. 2-Methodology In order to carry out the present research, the meteorological data related to the synoptic stations located in the Jazmurian wetland area for a period of 20 years (2000-2020) were received from the Iranian Meteorological Organization. Climatic data used in this research include temperature, precipitation, relative humidity, wind speed and direction, horizontal visibility, and remote sensing data including Aerosol Optical Depth (AOD) and Normalized Difference Vegetation Index (NDVI). The research method uses a combination of statistical analysis, observation and remote sensing. In order to check the AOD and NDVI, the monthly data of MODIS sensor was used. In this study, 12 synoptic stations that had the longest and most complete statistical periods were used in order to identify the temporal-spatial changes of dust occurrence in Jazmurian wetlat. The Mann-Kendall test was used to examine the trend of time changes. The slope of the Sen estimator was used to check and confirm the accuracy of the trend changes. In order to better understand the spatial distribution pattern of dust events in the Jazmurian wetland basin, the inverse distance interpolation method (IDW) was used. Also, Pearson's spatial correlation analysis was used to investigate the mutual effects between the indicators. 3- Results The 20-year average review of the indicators showed that the maximum value of aerosol optical depth in the studied area was 0.3, which is seen in the central part of the wetland. The vegetation index also showed that the maximum value of this index was 0.2, which covers most of the northern, northwestern and western parts. Examining the average rainfall trend showed that the maximum and minimum rainfall in this period are about 219 and 85 mm, respectively. Meanwhile, the 20-year average temperature survey showed that the maximum and minimum temperatures were 28.7 and 17.2 degrees Celsius, respectively. The fact that the maximum rainfall is in the northern and western part is in harmony with the minimum temperatures in these areas. The examination of the average wind speed showed that the maximum speed was 3.6 m/s, which was mostly in the central, west and northwest, south and southwest parts. The results of the wind direction index showed that the lowest wind direction is in the northern parts of the region and the highest wind direction is in other parts of the Jazmurian wetland basin. The maximum humidity in the studied area is 43.73%, which is mostly seen in the north, northwest, west, south, and southwest parts. Examination of the horizontal visibility index showed that the maximum amount of horizontal visibility was in the northeastern and northwestern parts of the region. Also, the process of changes in the MannKendall test showed that the annual averages of the indicators of wind direction, wind speed and horizontal visibility have been increasing with a positive slope, and the trend of changes in the relative humidity index has a negative slope and indicates a decrease in the period of 20 years. The results of the correlation analysis (at a significance level of 5%) showed that the highest correlation of the optical depth index was with the wind direction parameter and the lowest correlation was with the relative humidity index. 4- Discussion & Conclusions The growing trend of the emission of dust particles caused by the phenomenon of wind erosion in recent decades has caused major concerns at different regional, national and global levels. Therefore, it is necessary to understand the temporal-spatial changes of the dust caused by these events in order to reduce their adverse consequences in different regions. Accordingly, in this research, with the help of this knowledge, information related to the optical depth of particles in the air (AOD) and the vegetation cover index (NDVI) were investigated using the MODIS sensor satellite data. Using the meteorological data received from the website of the National Meteorological Organization, the climatic data of rainfall, temperature, relative humidity, horizontal visibility, wind direction and wind speed were evaluated in the period of 2000-2020 and finally the correlation between the AOD index and other climate parameters was evaluated. The results of the optical depth investigation showed that the maximum optical depth is located in the central parts of Jazmurian wetland. The trend of changes in rainfall and temperature indicators also showed that the trend of these two parameters was increasing; this increase in temperature is more noticeable. Correlation investigation between optical depth and other parameters showed that the highest correlation of optical depth index was with the wind direction parameter and the lowest correlation was with the relative humidity index. In general, we can conclude that the optical depth of airborne particles is highly dependent on environmental factors, which is more evident in arid and semi-arid areas, especially in Iran and the Jazmurian wetland area. In recent years, the area of the wetland has acted as a source of dust. Therefore, by using the remote sensing data obtained from the MODIS sensor and the climate data, it is possible to examine and analyze the trend of dust changes in the units of time and space. [ABSTRACT FROM AUTHOR]

The development of winter ski tourism and characteristics of ski resorts in various regions of Russia are closely related to climatic conditions, the most important of which are the presence and duration of snow cover. For the period 2000–2021, a study of snow cover, availability of “optimal ski days” and climatic indicators necessary for artificial snowmaking at ski resorts located in different regions of Russia was performed, using data of the reanalysis ERA5-Land. The characteristics of snow cover and temperature from the reanalysis data were compared with data of the meteorological network. The ERA5-Land data for temperature, precipitation, and snow cover thickness are well synchronized with the observational data, and estimates of the error of trends in air temperature and snow cover depth according to the reanalysis data relative to the station data give satisfactory results. In the conditions of the current climate, the average and maximum thickness of snow cover in all resorts is sufficient for their functioning, but in 2000–2021, a decrease in both the maximum and average values of snow cover is noted in most resorts. The study shows that in terms of snow and weather conditions, the highest mountain resorts of the North Caucasus and Kirovsk (Murmansk region) are the most prosperous, where thickness of the snow cover and duration of its occurrence as well as a significant number of “optimal ski days” sustains stability of the resorts and creates favorable conditions for their further development.

This study examined the spatiotemporal variations in extreme values of relative humidity (RH), temperature, temperature-humidity index (THI) and the effect of climatic variables on THI in the northern parts of Iran from 1986 to 2016. The THI was generated for each of the 30 meteorological stations. Previously, trend analysis was calculated in different seasons using quantile regression. For the most part, the results indicate a declining and rising trend in RH and temperature, respectively. The highest percentage of decreasing trend slopes was for maximum RH (≤-50 %), but for maximum temperature, the highest percentage of increasing slopes in summer and winter (≥30 %) was identified on the southern coast of the Caspian Sea. However, a decreasing trend (%≤-15) was detected for the maximum temperature in autumn. The highest increasing (decreasing) trend slope of THI (30 % and −30 %) was for low values of THI in spring and winter (autumn and winter). The minimum and maximum temperatures positively affected THI in all seasons, and the value of the effect coefficient was higher in the coastal areas. Also, high values of minimum RH in the spring, summer, autumn, and winter seasons had a positive (negative) effect in 73, 100, 33, and 0 % (26, 0, 66, and 100 %) of the stations, respectively. But the maximum RH had a positive (negative) effect on 80, 100, 33, and 6 % (20, 0, 66, and 93 %) of stations, respectively. The findings of this study are generally helpful in identifying changes in weather extremes and their effects on human health.

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.

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]

The problem of modeling the impact of the future climate on the yield of agricultural crops (on the example of spring wheat) is considered. The sources of climatic data and values of agrometeorological indicators for past and future periods were analyzed. A mathematical model of the dependence of the median of the spring wheat yield distribution function on climatic changes was developed using agroclimatic parameters. The calculations were carried out according to this model, and the obtained results were analyzed. The analysis of the results 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 in relation to historical data. [ABSTRACT FROM AUTHOR]

Background and objectives Wind erosion and dust phenomenon are considered as one of the important processes of land degradation and a serious challenge in Iran. This phenomenon occurs due to the interaction between weather and terrestrial processes. The purpose of this research is to investigate and analyze the trend of temporal and spatial changes in the number of dusty days in Iran and to investigate the role of climate in its spread. Methodology In this research, after receiving hourly and daily statistics of dust events (through 148 synoptic and climatology stations) and parameters of precipitation, evaporation, temperature as key climatic elements (from 171 stations) in 22 provinces located in arid and semi-arid regions of Iran, The spread of dust was investigated and the correlation between climate and dust was determined. After calculating the number of dust days in Iran, the information was called in the GIS environment and a map of the spatial changes of the parameter of the number of dust days in the three decades of 1987-1996, 1997-2006, 2007-2016 was prepared. In the following, the spatial analysis of this parameter and its expansion over three decades were investigated. Next, the climate parameter that had the highest correlation with the number of dusty days was selected and after preparing a map of its spatial changes in the GIS software environment, the spatial compatibility of the desired parameter with the expansion of the number of dusty days was investigated. In the next step, after performing the homogeneity test on the dust occurrences, the partial square regression model was used to determine the contribution of the three mentioned climatic elements as independent variables and the number of dust days as a dependent variable. Results The results showed that the number of dusty days and the area of its occurrence have increased in Iran, so that in the last decade under study (2007-2016) compared to the previous two decades, the dustiest event occurred in Iran. This increase has been more intense especially in the western and southwestern regions of the country, which were also affected by extra-local dust events. These dust events have spread to the central regions of the country with greater frequency. The homogeneity test in many provinces showed a common change point (year 2007-2008) in the number of dusty days, which could be related to the beginning of severe droughts and climate changes from this year in Iran. The study of climatic parameters (temperature, precipitation and evaporation) on changes in the number of dust days in Iran showed that during recent years, temperature had the highest significant correlation level (0.58) with the occurrence of dust. The temperature gradient map in the last decade (2007-2016) compared to the previous decade (1997-2006) also showed the increasing trend of temperature, especially in the western and southwestern regions as well as the southeastern regions of Iran. Modeling the effect of climatic parameters of temperature, precipitation and evaporation on the parameter of the number of dusty days showed that the contribution of the studied climatic parameters to the occurrence of dusty days is about 33% and other effects can be related to things such as human factors or soil characteristics, type of vegetation, solar radiation, etc. Conclusion In general, the results of examining the trend of changes in the number of dust days and climatic indicators for nearly 3 decades indicate a significant increase in the number of dust days in most parts of Iran and the conditions for this increase have been provided and if the trend of changing these indicators in the coming years is according to the current trend, the risk of environmental problems in the country can be expected to increase. [ABSTRACT FROM AUTHOR]

Tree distribution has been considered crucial to assess the relationships between species occurrence and changing climate across the globe, and accordingly in designing the conservation action plans. Therefore, it becomes imperative to understand the most influential parameters for tree species distribution and its regeneration pattern. We used National Forest Inventory data collected in Western Himalaya (WH) and environmental parameters to reveal the aforesaid relationship and pattern. Three topographical factors (altitude, aspect, and slope), four major climatic variables (temperature, temperature seasonality, precipitation and its seasonality) and four edaphic factors (soil depth, soil humus, soil consistency and soil coarse fragments) were considered. The results indicated that the altitude and temperature were the major determining factors followed by temperature seasonality in shaping the distribution of major tree species in WH. The study also indicated an upward shift of regenerating individuals of these tree species toward higher altitude. These information about the extent of current tree distribution and their regeneration over the last one and half decade might help in systematic conservation planning and monitoring tree range dynamics under climate change scenario. [ABSTRACT FROM AUTHOR]

Aromatic and medicinal plants in ecosystems are subject to various climatic disturbances that impact their morphological and physiological processes. Although plants have mechanisms to adapt to their climatic conditions, such as periods of drought and lack of precipitation, their metabolism is still affected. This study aimed to predict and evaluate the behavior of Salvia officinalis under climatic disturbances. Over a period of 4 years in a controlled environment, three treatments were applied to the plant: Treatment 1 with normal monthly average temperature and precipitation in the first year; Treatment 2 with a temperature increase of 5°C and a 50% reduction in water supply in the second year; and Treatment 3 with a temperature increase of 10°C and a 75% reduction in water supply in the fourth year. The results show that the percentage of primary metabolites, including nutritional values, changed with increasing temperature and decreasing precipitation. Treatment 1 had 7.13% protein, 6.21% carbohydrate, 1.35% lipid, and 4% dietary fiber, while Treatment 2 had 7.05% protein, 5.12% carbohydrate, 1.01% lipid, and 3.01% dietary fiber, and Treatment 3 had 6.86% protein, 3.02% carbohydrate, 0.52% lipid, and 2.34% dietary fiber. The mineral composition of the plant also changed with each treatment, with Mg decreasing from 10.02 to 8.55 to 0.05%, Fe decreasing from 8.18 to 8 to 7.62%, K decreasing from 5.55 to 5.05 to 4.02%, Mn decreasing from 5.54 to 5.11 to 3.48%, Ca decreasing from 4.65 to 2.75 to 1.23%, and P decreasing from 3.37 to 3.05 to 2.25%. Regarding secondary metabolites, the percentage of alkaloids, flavonoids, saponins, coumarins, tannins, and essential oil yield changed as well. Treatment 2 showed an increase in secondary metabolites, while Treatment 3 showed a decrease. Alkaloids increased from 9.56 to 13.68 to 11.3%, flavonoids increased from 7.53 to 13.48 to 10.49%, saponins increased from 5.23 to 7.44 to 6%, coumarins increased from 3.35 to 4.85 to 3.99%, tannins increased from 2.26 to 3.22 to 2.62%, and essential oil yield increased from 0.53 to 0.80 to 0.62%. Gas chromatography analysis revealed that the major compounds of the essential oils of Salvia officinalis, such as α-thujone, manool, β-caryophyllene, α-humulene, viridiflorol, 1,8-cineol, and camphor, were also modified by temperature and water stress.

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]

Aerosols' influences on Earth's climate have been documented by several authors. This ranges from scattering and reflecting of shortwave radiation (direct effect) which is also regarded as the "Whitehouse Effect," to the ability to act as condensation nuclei (indirect effect) which results in cloud droplet formation. This broad summary of aerosol's effect on earth's climate has in turn affected some other weather variables either positively or negatively depending on people's perspectives. This work was done in a view to ascertaining some of these claims by determining the statistical significance of some certain aerosol's relationships with some selected weather variables. This was done over six (6) stations across the West African region to represent the climatic zones from the rainforest around the coasts to the desert of the Sahel. Data used consist of aerosol types (biomass burning, carbonaceous, dust, and PM2.5) and climatic types (convective precipitation, wind speed, and water vapor) over a period of 30 years, with the python and ferret programs explicitly used for the graphical analyses. Climatologically, locations close to the point source seem to record more of the presence of the pollutants than the farthest ones. Results indicated that aerosols were more pronounced in the dry months of NDJF over the rainforest region depending on the latitudinal position of the location. The relationship result showed a negative correlation between convective precipitation and aerosols, except carbonaceous. But the strongest relationship can be found between water vapor and the selected aerosol types. [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]

Pistachio is an export product with very high economic value in different regions of the world, including Iran. Since each agricultural product requires specific climatic and environmental conditions, land management based on appropriate models, identification, and evaluation is important to determine suitable places for its cultivation to be used with maximum yield. This study aims to identify the ecological potential of pistachio cultivation areas in the south of Fars province, Iran, with an emphasis on climatic parameters. In this regard, the ecological needs of pistachio such as topographic indicators (altitude, slope), geological features, and climatic characteristics (precipitation, relative humidity, and temperature) were prepared. Then, using near and small fuzzy membership functions, a fuzzy map of each of the effective parameters in cultivating this crop was prepared. Also, using the opinions of experts and a pairwise comparison of each parameter, the importance of each parameter for the cultivation of this crop was quantified. Using the weights prepared by the AHP method, land suitability maps were prepared for the cultivation of this crop. Finally, by preparing a map of cold needs from April to October, suitable areas for growing 8 different cultivars of pistachio (Kerman, Peters, Kaleghoochi, Ouhadi, Ahmad Aghaei, Akbari, Fandoghi Ghafouri, Choruk) were identified. The results of AHP and fuzzy methods showed that the central parts of the study area (about 50%) have more potential for the cultivation of this plant. [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]

This study explores how a vegetation cover (VC) index can be employed as a pollution warning tool in gold mining areas in the Northwest of Iran. The analysis included the following: (a) the extraction of normalized difference vegetation index (NDVI) maps from Landsat images in three zones, i.e., mining operations, upstream areas without any exploration, and the downstream area of the mining activities, (b) calculation of the zones' VC, (c) investigation of transformation trends in each pixel of VC time series using the Mann–Kendall trend test, (d) determination of the pixels with significant VC reduction and the significant starting points of the trend using the sequential Mann–Kendall test, (e) assessment of the correlation between the zones with significantly reduced VC, and (f) a correlation test between average monthly and annual climate parameters and VC. Our results indicate that although 51 ha of VC has been demolished around the mining activities areas (i.e., zone 1), an overall upward trend in vegetation with no chemical leakage is observed into the downstream area of the basin (i.e., zone 3). This upward trend can be mostly attributed to the increasing precipitation and decreasing temperature in the study period. The fact that the area downstream of the mine shows that the heap leaching method for gold mining in Andaryan mine is currently not damaging the vegetation, this likely means that there is no leakage to the surrounding environment from the mine. Our results further show that using NDVI in a pixel-based scale and statistical methods has a high potential to quantify the effects of human activities on surface biophysical characteristics. [ABSTRACT FROM AUTHOR]

The eastern sarus crane (Antigone antigone sharpii; ESC) is a species related to wetland ecosystems in Southeast Asia. The habitat suitability of the eastern sarus crane in Ayeyarwady Delta was surveyed between March 2018 and February 2019. Eastern sarus cranes were found at 73 locations and Maximum Entropy (MaxEnt) was used to classify the habitat suitability among different seasons. MaxEnt showed the largest total area of highly suitable habitat was in the winter season (2450 km2, AUC = 0.968), while the least amount of available suitable habitat was evident during the rainy season (1028.7 km2, AUC = 0.979). A difference in the assessment of home range areas using the Minimum Convex Polygon (95% MPC) and the Kernel Density Estimate (95% KDE) was found. The total area in the winter season was highest at 95% KDE (13,839.5 km2) and lowest in the rainy season (1238.1 km2), while 95% MCP was highest in the rainy season (7892.9 km2) and lowest in the summer season (7014.6 km2). Analysis of the environmental parameters indicated that low temperature in the summer season and high precipitation in the rainy season and winter season are important for ESC habitat suitability. These climatic parameters were important for ESC in all seasons (AUC > 0.9). Important parameters influencing ESC habitat suitability were elevation, slope, distance to road in the summer season, elevation, distance to road and village and slope in the rainy season, and elevation and slope in the winter season. Annual precipitation was the main parameter influencing ESC habitat suitability in both summer and winter, while in the rainy season it was mean diurnal range (>90%). [ABSTRACT FROM AUTHOR]

Grape is considered as one of the world's most important crop. Climatic and soil parameters can significantly affect the yield and quality of grapes. The aim of this study was to analyse and assess this impact of soil and climatic conditions on table grape yield and quality. The experiments took place on a commercial table grape vineyard in Greece for three successive years (2015, 2016 and 2017). The climatic conditions were found to affect significantly the table grape yield and all the tested quality parameters apart from the berry density and titratable acidity. In addition, the climatic conditions affected the table grape yield and quality but in a different degree per phenological stage. The effect of soil conditions on table grape yield and quality parameters differed significantly per annum due to the climatic conditions variation in terms of temperature and precipitation. The conducted analysis highlighted that the information regarding the combined effect of weather and soil conditions on the spatiotemporal variability of the production of table grapes in terms of their quantity and quality is an essential component of the modern precision viticulture. Based on the contemporary principles of the precision viticulture, appropriate cultivation practices can be planned and implemented, reducing thus the impact of climate change on the vine production efficiency and quality.

Nepal has substantial potential to generate electricity through hydropower projects. Most of the hydropower projects in Nepal are Run-off-River (ROR) types. Significant seasonal variation can be pronounced on its river basins resulting in higher streamflow & higher hydropower generation during the wet/summer season and just reverse scenario in case of the dry/winter season. Thus, ROR-type hydropower in Nepal is more susceptible to Climate Change. This study assesses the impact of variation in climatic parameters on the hydropower generation by implementing WEAP model using the meteorological and hydrological data from 1976 to 2004 under Reference & Climatic Scenarios. The results reveal that the streamflow of Dordi River of Nepal is in increasing trends and can be more pronounced during April, May, June & July of the season under climatic scenarios. The generation of hydropower plant is likely to increase up to 15%, 1%–32% & 1%–51% over the study period under climatic scenario-1, 2 & 3, respectively, as compared to baseline scenario and the increments are observed to be more prominent during April & May of the season which is very crucial finding in current context of Nepal as there is power deficit during the dry season. Therefore, detailed technical and policy level planning can enhance the power generating capability of the future hydropower projects that will be developed in this corridor. This will significantly impacts the national energy planning and implementation.

In this study, relevant variables were determined to predict the probability of sooty mould biofilm (SMB) occurrence on willow leaves in a multivariate model. Despite a large temporal gap between the main sap-feeding insect (SFI) season in spring (May to June) and the main season of SMBs in late summer and fall (August to September), trees infested with SFIs on four or more observation dates had significantly more SMBs (3.7-fold) toward the end of the growing season. In addition, a horizontal leaf position and recessed leaf veins, traits that may increase the wettability of the leaves, positively influenced SMB occurrence (27.8-fold). Among the weather parameters, those that lead to higher moisture on leaf surfaces, namely high morning and evening relative humidity, higher precipitation sums, and lower vapour pressure deficit values, also had the strongest positive effect. In addition to these parameters, higher wind speeds also had a strong positive effect on SMB occurrence. [ABSTRACT FROM AUTHOR]