Nitrous oxide emissions from 2008 to 2012 for agricultural lands in the conterminous United States

The soil N2O emissions data for the conterminous United States were generated by the DayCent ecosystem model using the crop and land-use histories for survey locations in the USDA-NRCS National Resources Inventory (NRI). The model also requires weather and soils data. Daily maximum/minimum temperature and precipitation data are based on gridded weather data from the PRISM Climate Data product. Soils data are obtained from Soil Survey Geographic Database (SSURGO). See Del Grosso et al. (2022) and US-EPA (2020) for more details about the simulations. Atmospheric inversions were conducted using the CarbonTracker Langrage framework (Nevison et al. 2018). These results provide total N2O fluxes for the domain using atmospheric observations and an inverse modeling, and are compared to the DayCent emissions to confirm seasonal patterns, particularly the role of freeze-thaw events in driving pulses of N2O emissions from agricultural lands.Nitrous oxide (N2O) is an important greenhouse gas (GHG) that also contributes to depletion of ozone in the stratosphere. Agricultural soils account for about 60% of anthropogenic N2O emissions. Most national GHG reporting to the UN Framework Convention on Climate Change assumes nitrogen (N) additions drive emissions during the growing season, but soil freezing and thawing during spring is also an important driver in cold climates. We show that both atmospheric inversions and newly implemented bottom-up modeling approaches exhibit large N2O pulses in the northcentral region of the United States during early spring and this increases annual N2O emissions from croplands and grasslands reported in the national GHG inventory by 11%. Considering this, emission accounting in cold climate regions is very likely under-estimated in most national reporting frameworks. Current commitments related to the Paris Agreement and COP 26 emphasize reductions of carbon compounds. Assuming these targets are met, the importance of accurately accounting and mitigating N2O increases once CO2 and CH4 are phased out. Hence, the N2O emission under-estimate introduces additional risks into meeting long term climate goals.US Forest Service 18-CR-11242305-109, US Department of Agriculture (USDA) UV-B Monitoring and Research Program, Colorado State University, under USDA National Institute of Food and Agriculture Grant 2016-34263-25763, and the USDA GHG and DayCent modeling NACA agreements (58-3012-9-012 and 58-3012-1-015

Why future nitrogen research needs the social sciences

Nitrogen management is on the cusp of becoming a major global policy issue — the international community is gradually acknowledging that the feasibility of an array of environmental, health and food security goals hinges on how humanity manages nitrogen as a resource and a pollutant over the coming decades. As a result, the nitrogen research agenda should expand to consider more policy-relevant questions, such as the power dynamics of the broader food system and the many influences on farmer decision-making. Doing so demands much closer collaboration between the natural and social sciences, from problem formulation to research execution, which requires overcoming a range of ideological, institutional and knowledge barriers

Approaches and concepts of modelling denitrification: increased process understanding using observational data can reduce uncertainties

Denitrification is a key but poorly quantified component of the Ncycle. Because it is difficult to measure the gaseous (NO$_{x}$, N$_{2}$O, N$_{2}$)and soluble (NO$_{3}$) components of denitrification with sufficientintensity, models of varying scope and complexity have beendeveloped and applied to estimate how vegetation cover, landmanagement and environmental factors such as soil type andweather interact to control these variables. In this paper we assessthe strengths and limitations of different modeling approaches,highlight major uncertainties, and suggest how differentobservational methods and process-based understanding can becombined to better quantify N cycling. Representation of howbiogeochemical (e.g. org. C., pH) and physical (e.g. soil structure)factors influence denitrification rates and product ratios combinedwith ensemble approaches may increase accuracy withoutrequiring additional site level model inputs

Chapter 18. DAYCENT Simulated Effects of Land Use and Climate on County Level N Loss Vectors in the USA

We describe the nitrogen (N) gas (NH3, NOx, N2O, N2) emission and NO3 leaching submodels used in the DAYCENT ecosystem model and demonstrate the ability of DAYCENT to simulate observed N2O emission and NO3 leaching rates for various sites representing different climate regimes, soil types, and land uses. DAYCENT simulated seven major crops, grazing lands, and potential native vegetation at the county level for the United States. At the national scale, NO3 leaching was the major loss vector, accounting for 86%, 66%, and 56% of total N losses for cropped soils, grazed lands, and native vegetation, respectively. NH3 volatilization + NOx emissions made up the majority of national N gas losses, accounting for 58%, 89%, and 86% of N gas losses from cropped soils, grazed lands, and native vegetation, respectively. However, there was considerable spatial variability in the N loss vectors, with leaching accounting for less than 20% of total N losses and NOx + NH3 emissions accounting for less than 50% of N gas losses in some counties. Land use area weighted mean annual N losses were 43.9 (SD = 26.8) and 12.3 (SD = 22.2)kg N/ha for cropped/grazed and native systems, respectively. Area weighted mean annual N gas losses were 11.8 (SD = 4.8) and 5.4 (SD = 2.1)kg N/ha for cropped/grazed and native systems, respectively. Total N losses and NO3 leaching tended to increase as N inputs and precipitation increased, and as soils became coarser textured. Total N gas losses also increased with N inputs and as soils became coarser textured, but N2O and N2 made up a larger portion of N gas losses as soils became finer textured and as precipitation increased

Assessing precipitation, evapotranspiration, and NDVI as controls of U.S. Great Plains plant production

Productivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation-related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi-year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite-derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April-to-July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April-to-July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May-to-September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county-level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county-level NRCS ANPP. Accounting for how different precipitation-related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision-making by land managers and use of grassland ANPP for biofuels

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC

The low-noise operation of readout electronics in a liquid argon time projection chamber (LArTPC) is critical to properly extract the distribution of ionization charge deposited on the wire planes of the TPC, especially for the induction planes. This paper describes the characteristics and mitigation of the observed noise in the MicroBooNE detector. The MicroBooNE's single-phase LArTPC comprises two induction planes and one collection sense wire plane with a total of 8256 wires. Current induced on each TPC wire is amplified and shaped by custom low-power, low-noise ASICs immersed in the liquid argon. The digitization of the signal waveform occurs outside the cryostat. Using data from the first year of MicroBooNE operations, several excess noise sources in the TPC were identified and mitigated. The residual equivalent noise charge (ENC) after noise filtering varies with wire length and is found to be below 400 electrons for the longest wires (4.7 m). The response is consistent with the cold electronics design expectations and is found to be stable with time and uniform over the functioning channels. This noise level is significantly lower than previous experiments utilizing warm front-end electronics.Comment: 36 pages, 20 figure