International Journal of Biometeorology最新文献

Irrigation water requirements are commonly estimated based on the estimated crop evapotranspiration (ET_c) as determined by the reference evapotranspiration (ETr) and crop coefficient (K_c). Recent studies show that, at high evaporative demand (high ETr), K_c tends to decrease, creating an inverse ETr-K_c relationship. The focus of this long-term study is to, if at high atmosphere demand, there is the same inverse ETr-K_c relationship in Nebraska, USA, one of the most intensely irrigated regions in the world, and as a result, propose an adjustment to the Kc–ETr approach. The study was carried out in eastern Nebraska for maize-soybean rotation fields for the period 2002–2012. The Kc was estimated based on energy balance data from eddy covariance flux towers installed in the field and a nearby automated weather station throughout the growing seasons. We found that average Kc values varied depending on the year under high ETr; measured ETc agreed reasonably well with the FAO-56 manual predicted values, but in years with high ETr such as 2012 and 2002 affecting ETc values over the growing season. It was observed that Kc decreased as ETr increased, mainly when ETr reaches values greater than 6 mm d⁻¹ (P values < 0.001). This most likely was due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high atmospheric demands. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETr to improve the required irrigation depth and irrigation management.

As crop productivity is greatly influenced by weather conditions, many attempts have been made to estimate crop yields using meteorological data and have achieved great progress with the development of machine learning. However, most yield prediction models are developed based on observational data, and the utilization of climate model output in yield prediction has been addressed in very few studies. In this study, we estimate rice yields in South Korea using the meteorological variables provided by ERA5 reanalysis data (ERA-O) and its dynamically downscaled data (ERA-DS). After ERA-O and ERA-DS are validated against observations (OBS), two different machine learning models, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), are trained with different combinations of eight meteorological variables (mean temperature, maximum temperature, minimum temperature, precipitation, diurnal temperature range, solar irradiance, mean wind speed, and relative humidity) obtained from OBS, ERA-O, and ERA-DS at weekly and monthly timescales from May to September. Regardless of the model type and the source of the input data, training a model with weekly datasets leads to better yield estimates compared to monthly datasets. LSTM generally outperforms SVM, especially when the model is trained with ERA-DS data at a weekly timescale. The best yield estimates are produced by the LSTM model trained with all eight variables at a weekly timescale. Altogether this study shows the significance of high spatial and temporal resolution of input meteorological data in yield prediction, which can also serve to substantiate the added value of dynamical downscaling.

Pollen production is one plant characteristic that is considered to be altered by changes in environmental conditions. In this study, we investigated pollen production of the three anemophilous species Betula pendula, Plantago lanceolata, and Dactylis glomerata along an urbanization gradient in Ingolstadt, Germany. We compared pollen production with the potential influencing factors urbanization, air temperature, and the air pollutants nitrogen dioxide (NO₂) and ozone (O₃). While we measured air temperature in the field, we computed concentration levels of NO₂ and O₃ from a land use regression model. The results showed that average pollen production (in million pollen grains) was 1.2 ± 1.0 per catkin of Betula pendula, 5.0 ± 2.4 per inflorescence of Plantago lanceolata, and 0.7 ± 0.5 per spikelet of Dactylis glomerata. Pollen production was higher in rural compared to urban locations on average for B. pendula (+ 73%) and P. lanceolata (+ 31%), while the opposite was the case for D. glomerata (− 14%). We found that there was substantial heterogeneity across the three species with respect to the association of pollen production and environmental influences. Pollen production decreased for all species with increasing temperature and urbanization, while for increasing pollutant concentrations, decreases were observed for B. pendula, P. lanceolata, and increases for D. glomerata. Additionally, pollen production was found to be highly variable across species and within species—even at small spatial distances. Experiments should be conducted to further explore plant responses to altering environmental conditions.

To investigate the influence and lag effect of atmospheric pollen concentration on daily visits of patients with allergic rhinitis (AR), we collected the AR data during the pollen seasons from 2018 to 2019 from the outpatient and emergency department of Beijing Shijitan Hospital. The distributed lag non-linear model (DLNM) was used to analyze the correlation and the lag effect between pollen concentration and the incidence of AR. R4.1.2 was used to generate the Spearman correlation coefficients and plot the lag response curves of relative risk specific and incremental cumulative effects. In 2018 and 2019, the number of AR visits was moderately positively correlated with pollen concentration. The peak value of the overall specific cumulative effect for every 10 grains/1000 mm² increase in atmospheric pollen concentration occurred on day 0 (2018, 2019), and the lag disappearance time was day 6 (2018) and day 7 (2019), and the specific cumulative effect duration was respectively 6 days (2018) and 7 days (2019), with the curve showing a downward trend with time increase. In 2018, the peak value of the overall incremental cumulative effect was on day 7, the lag disappearance time was day 13, and the duration of the incremental cumulative effect was 13 days, forming a curve pattern of rising first and then falling. In 2019, the peak value time of the overall incremental cumulative effect was on day 8, and the curve went down afterwards until it showed the trend of ascending again after day26.

The purpose of this study was to determine climatologically suitable places to raise feedlot cattle in Türkiye. The Comprehensive Climate Index (CCI), a model that enables one to quantify beef cattle performance based on environmental conditions (temperature, relative humidity, wind speed, solar radiation) at any time in the year, was used to predict dry matter intake (DMI), average daily gain (ADG), and feed efficiency (FE) of feedlot cattle. Thirty years of daily average temperature, relative humidity, and wind speed values were obtained for 15 cities, namely, Antalya, Balikesir, Çorum, Diyarbakir, Edirne, Elazig, Erzincan, Erzurum, Eskisehir, Isparta, Izmir, Kayseri, Konya, Sivas, and Van. Measured daily solar radiation values were not available and values were calculated based on a formula that takes hemisphere, latitude, and day of the year into account. Since mostly dairy breed calves are placed into a feedlot in Türkiye, the Holstein option in the CCI model was chosen to calculate the maintenance energy requirement. Based on previous feedlot feeding studies conducted in Türkiye, it was assumed that calves would be placed on feed at 250 kg and be marketed at 520 kg, that the diet would have 2600 kcal/kg metabolic energy, and that DMI would be 2.31% of the body weight. Results indicate that cattle raised in Antalya (the hottest place) and Erzurum (the coldest place) had the lowest and highest DMI, respectively (P<0.05). Summer months depressed the DMI of cattle in hotter cities and winter months increased the DMI of cattle in colder cities (P<0.05). Feedlot cattle raised in hotter and colder regions of Türkiye had lower ADG than other places having a more temperate climate (P<0.05). In general, cattle raised in a hotter climate had better FE than those raised in a cold climate (P<0.05).

Even though the scientific documentation is limited, microbiome of healing clay is gradually gaining attention of the scientific community, as a therapeutic force playing an indispensable role in skin disease management. The present study explores the metatranscriptome profile of the Chamliyal clay, widely known for its efficacy in managing various skin problems, using Illumina NextSeq sequencing technology. The gene expression profile of the clay microbial community was analyzed through SEED subsystems of the MG-RAST server. Due to the unavailability of metatranscriptomic data on other therapeutic clays, Chamliyal’s profile was compared to non-therapeutic soils, as well as healthy and diseased human skin microbiomes. The study identified Firmicutes, Proteobacteria, and Actinobacteria as the primary active microbial phyla in Chamliyal clay. These resemble those abundant in a healthy human skin microbiome. This is significant as lower levels of these phyla in the skin are linked to inflammatory skin conditions like psoriasis. Interestingly, pathogenic microbes actively metabolizing in the clay were absent. Importantly, 6% of the transcripts annotated to sulfur and iron metabolism, which are known to play a major role in skin disease management. This study provides the most comprehensive and a novel overview of the metatranscriptome of any of the healing clay available worldwide. The findings offer valuable insights into the clay microbiome’s potential in managing skin disorders, inspiring future endeavors to harness these insights for medical applications.

Temperature adjustment was used to buffer against changes in external environmental temperature by a heating device. Two groups of honey bee colonies Apis mellifera anatoliaca were used: one as experimental (20) and the other control (10), which were equalized in terms of the number of brood frames, frames with bees, food, hive materials, and health treatments. The experimental colonies were heated during the cold season, and the temperature and humidity inside the hive were recorded. The number of frames of brood, frames of bees, and Varroa destructor mite numbers were also determined for all hives. Results suggest that the number of brood frames and frames with bees increased significantly in the hives with the heating treatment. However, varroa mite numbers also increased significantly in the heated colonies in comparison to the control colonies. Colony losses were significantly less (15%) in heated colonies compared to a 40% loss in control colonies. This is the first account demonstrating that using simple heating devices can support weak colonies during the cold season in general, and colony losses can be reduced significantly from this heating treatment in cold seasons.

Kacang (K) and Kacang Etawah (KE) cross goats are the major goat breeds that are important for farming livelihood and income and recognized for their tolerance to hot and humid tropical climates. As global warming progresses, the daily maximum temperature (T_max) is predicted to be continuously increased, which will challenge goat production in the future. The objective of this study was to evaluate the physiological and behavioral responses of the goats to the elevated T_max. Six K and six KE female goats were housed in a normal environment (average T_max: 33°C; temperature humidity index (THI): 76 to 86) for 6 weeks and then in a hot environment (average T_max: 38°C; THI: 76 to 92) for 7 weeks. During hot conditions, rectal, rectal surface, and skin temperature, respiration rate, hemoglobin, and cholesterol increased (p < 0.05), whereas glucose blood levels decreased (p < 0.01). Dry matter (DM) intake was lowered (p < 0.01), and DM digestibility was elevated (p < 0.01), whereas drinking water and body water retention were lowered but varied (p < 0.05) during hot weeks. Lying time increased during the hot weeks in both breeds (p < 0.01), whereas lying and ruminating as well as total ruminating time was longer (p < 0.05) in KE goats compared to K goats, which explain the greater (p < 0.05) DM digestibility in KE goats. The effect of the elevated T_max might be less severe since it also depends on the duration of the T_max and the variation of daily THI.

The modern unambiguous climate change reveals in a rapid increase of air temperature, which is more distinctly expressed in the Arctic than in any other part of the world, affecting people health and well-being. The main objective of the current research is to explore the inter- and intra-annual changes in thermal stress for people in the Arctic, specifically for two parts of Beringia: Alaska, USA, and Chukotka, Russia, using climatology of the universal thermal climate index (UTCI). Data for 39 locations are taken from the ERA5-HEAT reanalysis for the period 1979–2020. Climatologically, the study area is divided into four subregions in Alaska: North, Interior, West and South, and two in Chukotka: Interior and Coast. The extreme coldest UTCI categories (1 and 2) are most common in coastal locations of northern Alaska and Chukotka, where strong winds exacerbate the low temperatures during winter. The results show that the frequency of category 1 (UTCI<−40°C) varies spatially from a quarter of all hours annually in Alaska North to almost zero in Alaska South. On the other hand, the warmest categories are rarely reached almost everywhere in Alaska and Chukotka, and even categories 7 and 8 (UTCI between +26 and +38°C) are found occasionally only at interior locations. Category 6 with no thermal stress (UTCI between +9 and+26°C) has frequencies up to 3% and 25% in Alaska North and Interior, respectively. The extremely cold thermal stress frequencies have substantially decreased over the 1979–2020 period, especially in Alaska North and Chukotka Coast. At the same time, the number of hours with UTCI in the comfortable category of thermal perception has increased depending on subregion, from 25 to 203 h/year. Overall, a decrease in the UTCI categories of extremely cold stress is coupled with an increase in the comfortable range in both Alaska and Chukotka. The salient conclusion is that, from the point of view of comfort and safety, global warming has a positive impact on the climatology of thermal stress in the Arctic, providing advantages for the development of tourism and recreation.

Abstract