Modeling ammonia emissions from dairy production systems in the United States

Dairy production systems are hot spots of ammonia (NH3) emission. However, there remains large uncertainty in quantifying and mitigating NH3 emissions from dairy farms due to the lack of both long-term field measurements and reliable methods for extrapolating these measurements. In this study, a process-based biogeochemical model, Manure-DNDC, was tested against measurements of NH3 fluxes from five barns and one lagoon in four dairy farms over a range of environmental conditions and management practices in the United States. Results from the validation tests indicate that the magnitudes and seasonal patterns of NH3 fluxes simulated by Manure-DNDC were in agreement with the observations across the sites. The model was then applied to assess impacts of alternative management practices on NH3 emissions at the farm scale. The alternatives included reduction of crude protein content in feed, replacement of scraping with flushing for removal of manure from barn, lagoon coverage, increase in frequency for removal of slurry from lagoon, and replacement of surface spreading with incorporation for manure land application. The simulations demonstrate that: (a) all the tested alternative management practices decreased the NH3 emissions although the efficiency of mitigation varied; (b) a change of management in an upstream facility affected the NH3 emissions from all downstream facilities; and (c) an optimized strategy by combining the alternative practices on feed, manure removal, manure storage, and land application could reduce the farm-scale NH3 emission by up to 50%. The results from this study may provide useful information for mitigating NH3 emissions from dairy production systems and emphasize the necessity of whole-farm perspectives on the assessment of potential technical options for NH3 mitigation. This study also demonstrates the potential of utilizing process-based models, such as Manure-DNDC, to quantify and mitigate NH3 emissions from dairy farms

Modeling carbon biogeochemistry in agricultural soils

An existing model of C and N dynamics in soils was supplemented with a plant growth submodel and cropping practice routines (fertilization, irrigation, tillage, crop rotation, and manure amendments) to study the biogeochemistry of soil carbon in arable lands. The new model was validated against field results for short-term (1–9 years) decomposition experiments, the seasonal pattern of soil CO2 respiration, and long-term (100 years) soil carbon storage dynamics. A series of sensitivity runs investigated the impact of varying agricultural practices on soil organic carbon (SOC) sequestration. The tests were simulated for corn (maize) plots over a range of soil and climate conditions typical of the United States. The largest carbon sequestration occurred with manure additions; the results were very sensitive to soil texture (more clay led to greater sequestration). Increased N fertilization generally enhanced carbon sequestration, but the results were sensitive to soil texture, initial soil carbon content, and annual precipitation. Reduced tillage also generally (but not always) increased SOC content, though the results were very sensitive to soil texture, initial SOC content, and annual precipitation. A series of long-term simulations investigated the SOC equilibrium for various agricultural practices, soil and climate conditions, and crop rotations. Equilibrium SOC content increased with decreasing temperatures, increasing clay content, enhanced N fertilization, manure amendments, and crops with higher residue yield. Time to equilibrium appears to be one hundred to several hundred years. In all cases, equilibration time was longer for increasing SOC content than for decreasing SOC content. Efforts to enhance carbon sequestration in agricultural soils would do well to focus on those specific areas and agricultural practices with the greatest potential for increasing soil carbon content

Modeling impacts of changes in temperature and water table on C gas fluxes in an Alaskan peatland

Northern peatlands have accumulated a large amount of organic carbon (C) in their thick peat profile. Climate change and associated variations in soil environments are expected to have significant impacts on the C balance of these ecosystems, but the magnitude is still highly uncertain. Verifying and understanding the influences of changes in environmental factors on C gas fluxes in biogeochemical models are essential for forecasting feedbacks between C gas fluxes and climate change. In this study, we applied a biogeochemical model, DeNitrification-DeComposition (DNDC), to assess impacts of air temperature (TA) and water table (WT) on C gas fluxes in an Alaskan peatland. DNDC was validated against field measurements of net ecosystem exchange of CO2 (NEE) and CH4 fluxes under manipulated surface soil temperature and WT conditions in a moderate rich fen. The validation demonstrates that DNDC was able to capture the observed impacts of the manipulations in soil environments on C gas fluxes. To investigate responses of C gas fluxes to changes in TA and soil water condition, we conducted a series of simulations with varying TA and WT. The results demonstrate that (1) uptake rates of CO2 at the site were reduced by either too colder or warmer temperatures and generally increased with increasing soil moisture; (2) CH4 emissions showed an increasing trend as TAincreased or WT rose toward the peat surface; and (3) the site could shift from a net greenhouse gas (GHG) sink into a net GHG source under some warm and/or dry conditions. A sensitivity analysis evaluated the relative importance of TA and WT to C gas fluxes. The results indicate that both TA and WT played important roles in regulating NEE and CH4 emissions and that within the investigated ranges of the variations in TA and WT, changes in WT showed a greater impact than changes in TA on NEE, CH4 fluxes, and net C gas fluxes at the study fen

A model of nitrous oxide evolution from soil driven by rainfall events: 2. Model applications

Simulations of nitrous oxide (N2O) and carbon dioxide (CO2) emissions from soils were carried out with a rain-event model of nitrogen and carbon cycling processes in soils (Li et al., this issue). Model simulations were compared with five field studies: a 1-month denitrification study of a fertilized grassland in England; a 2-month study of N2O emissions from a native and fertilized grassland in Colorado; a 1-year study of N2O emissions from agricultural fields on drained, organic soils in Florida; a 1-year study of CO2 emissions from a grassland in Germany; and a 1-year study of CO2 emissions from a cultivated agricultural site in Missouri. The trends and magnitude of simulated N2O (or N2O + N2) and CO2 emissions were consistent with the results obtained in field experiments. The successful simulation of nitrous oxide and carbon dioxide emissions from the wide range of soil types studied indicates that the model, DNDC, will be a useful tool for studying linkages among climate, land use, soil-atmosphere interactions, and trace gas fluxes

A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity

This paper describes a rain-event driven, process-oriented simulation model, DNDC, for the evolution of nitrous oxide (N2O), carbon dioxide (CO2), and dinitrogen (N2) from agricultural soils. The model consists of three submodels: thermal-hydraulic, decomposition, and denitrification. Basic climate data drive the model to produce dynamic soil temperature and moisture profiles and shifts of aerobic-anaerobic conditions. Additional input data include soil texture and biochemical properties as well as agricultural practices. Between rainfall events the decomposition of organic matter and other oxidation reactions (including nitrification) dominate, and the levels of total organic carbon, soluble carbon, and nitrate change continuously. During rainfall events, denitrification dominates and produces N2O and N2. Daily emissions of N2O and N2 are computed during each rainfall event and cumulative emissions of the gases are determined by including nitrification N2O emissions as well. Sensitivity analyses reveal that rainfall patterns strongly influence N2O emissions from soils but that soluble carbon and nitrate can be limiting factors for N2O evolution during denitrification. During a year sensitivity simulation, variations in temperature, precipitation, organic C, clay content, and pH had significant effects on denitrification rates and N2O emissions. The responses of DNDC to changes of external parameters are consistent with field and experimental results reported in the literature

Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers

In this work, we extend our previously proposed offline SpatialNet for long-term streaming multichannel speech enhancement in both static and moving speaker scenarios. SpatialNet exploits spatial information, such as the spatial/steering direction of speech, for discriminating between target speech and interferences, and achieved outstanding performance. The core of SpatialNet is a narrow-band self-attention module used for learning the temporal dynamic of spatial vectors. Towards long-term streaming speech enhancement, we propose to replace the offline self-attention network with online networks that have linear inference complexity w.r.t signal length and meanwhile maintain the capability of learning long-term information. Three variants are developed based on (i) masked self-attention, (ii) Retention, a self-attention variant with linear inference complexity, and (iii) Mamba, a structured-state-space-based RNN-like network. Moreover, we investigate the length extrapolation ability of different networks, namely test on signals that are much longer than training signals, and propose a short-signal training plus long-signal fine-tuning strategy, which largely improves the length extrapolation ability of the networks within limited training time. Overall, the proposed online SpatialNet achieves outstanding speech enhancement performance for long audio streams, and for both static and moving speakers. The proposed method is open-sourced in https://github.com/Audio-WestlakeU/NBSS.Comment: Accepted by IEEE Signal Processing Letter

Multichannel Speech Separation with Narrow-band Conformer

This work proposes a multichannel speech separation method with narrow-band Conformer (named NBC). The network is trained to learn to automatically exploit narrow-band speech separation information, such as spatial vector clustering of multiple speakers. Specifically, in the short-time Fourier transform (STFT) domain, the network processes each frequency independently, and is shared by all frequencies. For one frequency, the network inputs the STFT coefficients of multichannel mixture signals, and predicts the STFT coefficients of separated speech signals. Clustering of spatial vectors shares a similar principle with the self-attention mechanism in the sense of computing the similarity of vectors and then aggregating similar vectors. Therefore, Conformer would be especially suitable for the present problem. Experiments show that the proposed narrow-band Conformer achieves better speech separation performance than other state-of-the-art methods by a large margin.Comment: submitted to INTERSPEECH 2022. arXiv admin note: text overlap with arXiv:2110.0596