Adaptation of Technological Methods to Climatic Conditions in Agrotechnologies in South Ural

This study aimed to define an optimal sowing date of winter wheat in the steppe zone of South Ural and West Siberia. The effects of climatic factors were determined by analyzing modern climatic resources and experimentally testing in the conditions of the central zone in Orenburgskaya oblast. Research from the All-Russian ScientificResearch Institute of Hydro-meteorological Information – World Data Center (RSRIHIWDC) served as a source of archival meteorological data for 2009-2019. Experimental data were collected through field work on the south chernozem in the Central zone of Orenburgskaya oblast for 2019-2020. Digital material was processed using statistical analysis. It was confirmed that in the Central zone of Orenburgskaya oblast under modern climatic conditions, the period between 25-30 August is the most acceptable date to sow winter wheat. If sowing occurs at later dates, there is a risk of not obtaining the required amount of effective temperatures, which can result in disunited sparse shoots, bushes that are not fully formed, and low phytometric parameters, and therefore a low realization of climatically secured productivity. These results could be more widely tested in other steppe regions of Ural and West Siberia with a prospect to introduce the results into zonal recommendations for production. Keywords: climatic resources, productivity reserves, winter whea

Ice Nucleating Particles in Northern Greenland: annual cycles, biological contribution and parameterizations

Ice nucleating particles (INPs) can initiate ice formation in clouds at temperatures above &minus;38 &deg;C through heterogeneous ice nucleation. As a result, INPs affect cloud microphysical and radiative properties, cloud life time and precipitation behavior and thereby ultimately the Earth&rsquo;s climate. Yet, little is known regarding the sources, abundance and properties of INPs especially in remote regions such as the Arctic. In this study, two-year-long INP measurements (from July 2018 to September 2020) at Villum 5 Research Station (VRS) in Northern Greenland are presented. A low-volume filter sampler was deployed to collect filter samples for off-line INP analysis. An annual cycle of INP concentration (NINP) was observed and the fraction of biogenic INPs was found to be higher in snow-free months and lower in months when the surface was snow-covered. Samples were categorized into three different types based only on the slope of their INP spectra, namely into summer, winter and mix type. For each of the types a temperature dependent INP parameterization was derived, clearly different depending on the time 10 of the year. Winter and summer type occurred only during their respective seasons and were seen 60 % of the time. The mixed type occurred in the remaining 40 % of the time throughout the year. April, May and November were found to be transition months. A case study comparing April 2019 and April 2020 was performed. The month of April was selected because a significant difference in NINP was observed during these two periods, with clearly higher NINP in April 2020. NINP in the case study period revealed no clear dependency on either meteorological parameters or different surface types which were passed 15 by the collected air masses. Overall, the results suggest that the coastal regions of Greenland were main sources of INPs in April 2019 and 2020, most likely including both local terrestrial and marine sources. In parallel to the observed differences in NINP, also a higher cloud ice fraction was observed in satellite data for April 2020, compared to April 2019.</p

Potential impact of seasonal forcing on a SARS-CoV-2 pandemic

A novel coronavirus (SARS-CoV-2) first detected in Wuhan, China, has spread rapidly since December 2019, causing more than 100,000 confirmed infections and 4000 fatalities (as of 10 March 2020). The outbreak has been declared a pandemic by the WHO on Mar 11, 2020. Here, we explore how seasonal variation in transmissibility could modulate a SARS-CoV-2 pandemic. Data from routine diagnostics show a strong and consistent seasonal variation of the four endemic coronaviruses (229E, HKU1, NL63, OC43) and we parameterise our model for SARS-CoV-2 using these data. The model allows for many subpopulations of different size with variable parameters. Simulations of different scenarios show that plausible parameters result in a small peak in early 2020 in temperate regions of the Northern Hemisphere and a larger peak in winter 2020/2021. Variation in transmission and migration rates can result in substantial variation in prevalence between regions. While the uncertainty in parameters is large, the scenarios we explore show that transient reductions in the incidence rate might be due to a combination of seasonal variation and infection control efforts but do not necessarily mean the epidemic is contained. Seasonal forcing on SARS-CoV-2 should thus be taken into account in the further monitoring of the global transmission. The likely aggregated effect of seasonal variation, infection control measures, and transmission rate variation is a prolonged pandemic wave with lower prevalence at any given time, thereby providing a window of opportunity for better preparation of health care systems

Changes in environment cause dietary shifts in the Svalbard Arctic fox: A stable isotope study

In this thesis, shift in diet of Arctic foxes on Svalbard over a long time frame was analyzed. The Svalbard Arctic fox is a generalist who links the terrestrial and marine ecosystems. The objectives were 1) investigate whether there are spatial and temporal trends in Arctic fox diet on Svalbard, 2) determine how important the changes in the environmental variables are for dietary shifts of the Arctic fox in different seasons and 3) determine whether other parameters like the distance to the coast and age class have any impact on the diet as inferred from stable isotopes. Stable isotope values of carbon and nitrogen from Arctic fox muscle (representing winter diet) and fur (representing autumn diet) samples over the winter seasons 1997/1998 to 2019/2020 were used to assess dietary shifts. Arctic fox isotope values fitted mainly linearly between marine and resident terrestrial prey (reindeer and ptarmigan). Both negative temporal trends and differences between regions were found, signifying a shift towards more use of terrestrial resources in both winter and autumn. This was also found when analyzing environmental effects, where the number of geese and year to year fluctuations in reindeer carcass number were significant in shifting diet towards more use of terrestrial resources in winter. The distance to the coast also showed significant difference in the diet between coastal and inland Arctic foxes. The value of long time data series was shown in this thesis as these gave significant results, while short time data series usually did not. Continued monitoring and sampling, as well as including other parameters like seabird population estimates and the fox’s preference of marine invertebrates are of interest to improve models and give more accurate estimates

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Investigating the effects of inter-annual weather variation (1968- 2016) on the functional response of cereal grain yield to applied nitrogen, using data from the Rothamsted Long-Term experiments

The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat (Triticum aestivvum L.) and spring barley (Hordeum vulgare L.) was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rothamsted Research. Grain yields of crops from 1968 to 2016 were modelled as a function of N rates using a linear-plus-exponential (LEXP) function. The extent to which inter-annual variation in the parameters of these responses was explained by variations in weather (monthly summarized temperatures and rainfall), and by changes in the cultivar grown, was assessed. The inter-annual variability in rainfall and underlying temperature influenced the crop N response and hence grain yields in both crops. Asymptotic yields in wheat were particularly sensitive to mean temperature in November, April and May, and to total rainfall in October, February and June. In spring barley asymptotic yields were sensitive to mean temperature in February and June, and to total rainfall in April to July inclusive and September. The method presented here explores the separation of agronomic and environmental (weather) influences on crop yield over time. Fitting N response curves across multiple treatments can support an informative analysis of the influence of weather variation on the yield variability. Whilst there are issues of the confounding and collinearity of explanatory variables within such models, and that other factors also influence yields over time, our study confirms the considerable impact of weather variables at certain times of the year. This emphasizes the importance of including weather temporal variation when evaluating the impacts of climate change on crops

Anomalies in the carbonate system of Red Sea coastal habitats

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Baldry, K., Saderne, V., McCorkle, D. C., Churchill, J. H., Agust, S., & Duarte, C. M. Anomalies in the carbonate system of Red Sea coastal habitats. Biogeosciences, 17(2), (2020): 423-439, doi:10.5194/bg-17-423-2020.We use observations of dissolved inorganic carbon (DIC) and total alkalinity (TA) to assess the impact of ecosystem metabolic processes on coastal waters of the eastern Red Sea. A simple, single-end-member mixing model is used to account for the influence of mixing with offshore waters and evaporation–precipitation and to model ecosystem-driven perturbations on the carbonate system chemistry of coral reefs, seagrass meadows and mangrove forests. We find that (1) along-shelf changes in TA and DIC exhibit strong linear relationships that are consistent with basin-scale net calcium carbonate precipitation; (2) ecosystem-driven changes in TA and DIC are larger than offshore variations in >70 % of sampled seagrass meadows and mangrove forests, changes which are influenced by a combination of longer water residence times and community metabolic rates; and (3) the sampled mangrove forests show strong and consistent contributions from both organic respiration and other sedimentary processes (carbonate dissolution and secondary redox processes), while seagrass meadows display more variability in the relative contributions of photosynthesis and other sedimentary processes (carbonate precipitation and oxidative processes). The results of this study highlight the importance of resolving the influences of water residence times, mixing and upstream habitats on mediating the carbonate system and coastal air–sea carbon dioxide fluxes over coastal habitats in the Red Sea.This research has been supported by the King Abdullah University of Science and Technology (KAUST) (grant nos. BAS/1/1071-01-01 and BAS/1/1072-01-01) and the Investment in Science fund at WHOI

Spatial variation in aragonite saturation state and the influencing factors in Jiaozhou Bay, China

Both natural processes and human activities affect seawater calcium carbonate saturation state (Ωarag), while the mechanisms are still far from being clearly understood. This study analysed the seawater surface Ωarag during summer and winter in Jiaozhou Bay (JZB), China, based on two cruises observations performed in January and June 2017. The ranges of Ωarag values were 1.55~2.92 in summer and 1.62~2.15 in winter. Regression analyses were conducted to identify the drivers of the change of Ωarag distribution, and then the relative contributions of temperature, mixing processes and biological processes to the spatial differences in Ωarag were evaluated by introducing the difference between total alkalinity (TA) and dissolved inorganic carbon (DIC) as a proxy for Ωarag. The results showed that biological processes were the main factor affecting the spatial differences in Ωarag, with relative contributions of 70% in summer and 50% in winter. The contributions of temperature (25% in summer and 20% in winter) and the mixing processes (5% in summer and 30% in winter) were lower. The increasing urbanization in offshore areas can further worsen acidification, therefore environmental protection in both offshore and onshore is needed

Research on the performance of radiative cooling and solar heating coupling module to direct control indoor temperature

The energy crisis and environmental pollution pose great challenges to human development. Traditional vapor-compression cooling consumes abundant energy and leads to a series of environmental problems. Radiative cooling without energy consumption and environmental pollution holds great promise as the next generation cooling technology, applied in buildings mostly in indirect way. In this work, a temperature-regulating module was introduced for direct summer cooling and winter heating. Firstly, the summer experiments were conduct to investigate the radiative cooling performance of the module. And the results indicated that the maximum indoor temperature reached only 27.5 °C with the ambient temperature of 34 °C in low latitude areas and the air conditioning system was on for only about a quarter of the day. Subsequently, the winter experiments were performed to explore the performance of the module in cooling and heating modes. The results indicated that indoor temperature can reach 25 °C in the daytime without additional heat supply and about a quarter of the day didn't require heating in winter. Additionally, the transient model of the module and the building revealed that the electricity saving of 42.4% (963.5 kWh) can be achieved in cooling season with the module, and that was 63.7% (1449.1 kWh) when coupling with energy storage system. Lastly, further discussion about the challenges and feasible solutions for radiative cooling to directly combine with the buildings were provided to advance the application of radiative cooling. Furthermore, with an acceptable payback period of 8 years, the maximum acceptable incremental cost reached 26.2 $/m2. The work opens up a new avenue for the application mode of the daytime radiative cooling technology