Furthermore, if woody annual increments were considered this proportion would be even lower. Likewise the observed 1.7 Mg ha−1 in fruit represents ~14% of total biomass , which is within 10% of other studies in the region at similar vine densities. More importantly, this study reports the fraction of C that could be recovered from wine making and returned to the soil for potential long term storage. However, this study is restricted to the agronomic and environmental conditions of the site, and the methodology would require validation and potential adjustment in other locations and conditions. Few studies have conducted a thorough evaluation of below ground vine biomass in vineyards, although Elderfield did estimate that fine roots contributed 20–30% of total NPP and that C was responsible for 45% of that dry matter. More recently, Brunori et al. studied the capability of grapevines to efficiently store C throughout the growing season and found that root systems contributed to between 9 and 26% of the total vine C fixation in a model Vitis vinifera sativa L. cv Merlot/berlandieri rupestris vineyard. The results of our study provide a utilitarian analysis of C storage in mature wine grape vines, including above and below ground fractions and annual vs. perennial allocations. Such information constitutes the basic unit of measurement from which one can then estimate the contribution of wine grapes to C budgets at multiple scales— fruit, plant or vineyard level—and by region, sector, or in mixed crop analyses. Our study builds on earlier research that focused on the basic physiology, development and allocation of biomass in vines. Previous research has also examined vineyard-level carbon at the landscape level with coarser estimates of the absolute C storage capacity of vines of different ages, pe grow bag as well as the relative contribution of vines and woody biomass in natural vegetation in mixed vineyard-wild land landscapes.
The combination of findings from those studies, together with the more precise and complete carbon-by-vine structure assessment provided here, mean that managers now have access to methods and analytical tools that allow precise and detailed C estimates from the individual vine to whole-farm scales. As carbon accounting in vineyard landscapes becomes more sophisticated, widespread and economically relevant, such vineyard-level analyses will become increasingly important for informing management decisions. The greater vine-level measuring precision that this study affords should also translate into improved scaled-up C assessments . In California alone, for example, there are more than 230,000 ha are planted in vines. Given that for many, if not most of those hectares, the exact number of individual vines is known, it is easy to see how improvements in vine-level measuring accuracy can have benefits from the individual farmer to the entire sector. Previous efforts to develop rough allometric woody biomass equations for vines notwithstanding, there is still a need to improve our precision in estimating of how biomass changes with different parameters. Because the present analysis was conducted for 15 year old Cabernet vines, there is now a need for calibrating how vine C varies with age, varietal and training system. There is also uncertainty around the influence of grafting onto root stock on C accumulation in vines. As mentioned in the methods, the vines in this study were not grafted—an artifact of the root-limiting duripan approximately 50 cm below the soil surface. The site’s location on the flat, valley bottom of a river floodplain also means that its topography, while typical of other vineyard sites per se, created conditions that limit soil depth, drainage and decomposition. As such, the physical conditions examined here may differ significantly from more hilly regions in California, such as Sonoma and Mendocino counties. Similarly, the lack of a surrounding natural vegetation buffer at this site compared to other vineyards may mean that the ecological conditions of the soil communities may or may not have been broadly typical of those found in other vineyard sites.
Thus, to the extent that future studies can document the degree to which such parameters influence C accumulation in vines or across sites, they will improve the accuracy and utility of C estimation methods and enable viticulturists to be among the first sectors in agriculture for which accurate C accounting is an industry wide possibility. The current study was also designed to complement a growing body of research focusing on soil-vine interactions. Woody carbon reserves and sugar accumulation play a supportive role in grape quality, the main determinant of crop value in wine grapes. The extent to which biomass production, especially in below ground reservoirs, relates to soil carbon is of immediate interest for those focused on nutrient cycling, plant health and fruit production, as well as for those concerned with C storage. The soil-vine interface may also be the area where management techniques can have the highest impact on C stocks and harvest potential. We expect the below ground estimates of root biomass and C provided here will be helpful in this regard and for developing a more thorough understanding of below ground C stores at the landscape level. For example, Williams et al. estimated this component to be the largest reservoir of C in the vineyard landscape they examined, but they did not include root biomass in their calculations. Others have assumed root systems to be ~30% of vine biomass based on the reported biomass values for roots, trunk, and cordons. With the contribution of this study, the magnitude of the below ground reservoir can now be updated.Wine is a commodity of worldwide importance, and vineyards constitute a significant land use and contribution to economies across Mediterranean biome and beyond. Like orchards and tree plantations, grapevines are a perennial crop that stores C long-term in woody tissue, thereby helping to mitigate GHG emissions. Our study provides estimates of C in grape vines by vine component, as well as a simple measurement tool kit that growers can use to estimate the C in their vines and vineyard blocks. The equations presented here represent some of the first allometric models for estimating grapevine C from berries to blocks, with the hope that widespread use and refinement of these techniques may lead to recognition and credit for the C storage potential of vineyards and other perennial woody crops, such as orchards. The successful implementation of these methods, if applied widely to multiple cropping systems, could improve the precision of measurement and the understanding of C in agricultural systems relative to other human activities.Ultra-high-energy neutrino astronomy expands the opportunity to learn more about the fierce processes of astronomical objects. Neutrinos are ideal messengers because they have negligible mass, are neutral in charge, and, due to the fact that they only interact through the weak force, have a low interaction probability. Once created, these properties allow them to travel through space unhindered by intervening matter or radiation such as dust, gas, and electromagnetic fields. The same properties also make them challenging to detect. Even at the extreme energies relevant to radio neutrino detectors, neutrinos rarely interact with matter. When this feature is combined with the low expected fluxes, and stringent experimental upper limits have been published by the IceCube Collaboration, the detector architecture must incorporate large volumes of target material.
A rough estimate suggests that instrumented volumes must reach of order one teraton to observe a few neutrinos per year for commonly discussed theoretical models of neutrino production. Radio based neutrinos experiments have been successfully explored in the past with pilot arrays such as the ARA experiment and the ARIANNA experiment, the latter being the focal point of this paper. These efforts helped focus in on the radio techniques required to operate in extremely cold and harsh conditions. While these experiments showed the technical feasibility, they were too small to measure the low neutrino flux. Undeterred, several radio-based experiments in development are further illustrating the capabilities of this detection method, such as ARIANNA-200, the radio component of IceCube-Gen2 , the Radio Neutrino Observatory in Greenland, Giant Radio Array for Neutrino Detection, Taiwan Astroparticle Radiowave Observatory for Geo-synchrotron Emissions, growing bags and Payload for Ultrahigh Energy Observations, a successor to ANITA. These experiments exploit various target materials such as ice, water, mountains, and air. The challenge for experimenters is to reach the teraton detection volumes at a reasonable cost. One of the most promising methods for observing UHE neutrinos in large target volumes exploits radio detection in ice. For this reason, locations such as Greenland and Antarctica are popular sites for radio detection experiments. Ice is transparent to radio signals, with field attenuation lengths ranging from 0.5 km at Moore’s Bay to more than a kilometer in colder ice found at the South Pole or the Greenland ice sheet. Radio pulses are created via the Askaryan effect when interacting neutrinos create particles showers in ice, which in turn generate a time-varying negative charge excess that produces radio emission in the 50 MHz to 1 GHz range. The radio technique enables cost-efficient instrumentation for monitoring large detection volumes. However, because of the low flux of UHE neutrinos, event rates are still small even for the large array of hundreds of radio detector stations that is foreseen for the next-generation neutrino observatory at the South Pole, IceCube-Gen2. Thus, improving the sensitivity of the detector is one of the primary objectives. The easiest way to increase the sensitivity — but also the most expensive way — is to build more radio detector stations. A more efficient way is to increase the sensitivity of each radio detector station and a lot of work has been made towards this goal. The sensitivity can be increased by simply lowering the trigger threshold which records additional neutrino interactions that produce smaller signal strengths in the radio detector. The problem with this is that the trigger thresholds are already set close to the thermal noise floor such that the trigger rate is dominated by unavoidable thermal noise fluctuations. For example, an amplitude threshold trigger with a two out of four antenna coincidence logic has a trigger rate increases by about six orders-of-magnitude if the trigger threshold is lowered from four times the RMS noise, RMS noise, to just three time RMS noise. Therefore, the trigger threshold is limited by the maximum data rate a radio detector can handle which is typically on the order of 1 Hz if a high-speed communication link exists. If the communication relies on Iridium satellite communication, the maximum data rate is limited to 0.3 mHz. However, if thermal noise fluctuations are identified and rejected in real time, the trigger thresholds can be lowered while maintaining the same data rate, thus increasing the sensitivity of the detector. The sensitivity can be improved by up to a factor of two with the intelligent trigger system presented here . In this paper it is demonstrated that deep learning can be used to reject thermal noise in real time by implementing these techniques in the current ARIANNA data acquisition system. Deep learning, a modern rebranding of neural networks, has been shown to outperform other methods in a variety of scientific and engineering areas, including in physics. The significant amount of data that need to be classified in real time with low latency in high energy physics experiments makes deep learning an ideal tool to use. By rejecting thermal events, the trigger rate can be increased dramatically while maintaining the required low rate of event transmission over the communication links from the remotely located ARIANNA stations. Overall, lower thresholds increase the effective volume of ice observed by each station, which is proportional to the sensitivity of the detector. This paper is organized as follows. Additional details on the ARIANNA detector are provided, along with the expected gain in sensitivity for this study. Next the trade off between network efficiency and processing time is assessed to find the optimal deep learning models for a representative sample of microprocessor platforms. The deep learning method is then compared to a template matching study to determine how well the more common approach performs. Then the current ARIANNA data acquisition system is evaluated to determine the suitability for a deep learning filter. Moreover, the specific predictions for the optimal deep learning model are experimentally verified for the current microprocessor hardware. Lastly, the deep learning filter is tested on measured cosmic rays to verify that they are classified similar to neutrino signal and not rejected as thermal noise.