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 nitrogen loading in a small watershed in southwest China using a DNDC model with hydrological enhancements

The degradation of water quality has been observed worldwide, and inputs of nitrogen (N), along with other nutrients, play a key role in the process of contamination. The quantification of N loading from non-point sources at a watershed scale has long been a challenge. Process-based models have been developed to address this problem. Because N loading from non-point sources result from interactions between biogeochemical and hydrological processes, a model framework must include both types of processes if it is to be useful. This paper reports the results of a study in which we integrated two fundamental hydrologic features, the SCS (Soil Conservation Service) curve function and the MUSLE (Modified Universal Soil Loss), into a biogeochemical model, the DNDC. The SCS curve equation and the MUSLE are widely used in hydrological models for calculating surface runoff and soil erosion. Equipped with the new added hydrologic features, DNDC was substantially enhanced with the new capacity of simulating both vertical and horizontal movements of water and N at a watershed scale. A long-term experimental watershed in Southwest China was selected to test the new version of the DNDC. The target watershed\u27s 35.1 ha of territory encompass 19.3 ha of croplands, 11.0 ha of forest lands, 1.1 ha of grassplots, and 3.7 ha of residential areas. An input database containing topographic data, meteorological conditions, soil properties, vegetation information, and management applications was established and linked to the enhanced DNDC. Driven by the input database, the DNDC simulated the surface runoff flow, the subsurface leaching flow, the soil erosion, and the N loadings from the target watershed. The modeled water flow, sediment yield, and N loading from the entire watershed were compared with observations from the watershed and yielded encouraging results. The sources of N loading were identified by using the results of the model. In 2008, the modeled runoff-induced loss of total N from the watershed was 904 kg N yr−1, of which approximately 67 % came from the croplands. The enhanced DNDC model also estimated the watershed-scale N losses (1391 kg N yr−1) from the emissions of the N-containing gases (ammonia, nitrous oxide, nitric oxide, and dinitrogen). Ammonia volatilization (1299 kg N yr−1) dominated the gaseous N losses. The study indicated that process-based biogeochemical models such as the DNDC could contribute more effectively to watershed N loading studies if the hydrological components of the models were appropriately enhanced

Modeling nitrogen loadings from agricultural soils in southwest China with modified DNDC

Degradation of water quality has been widely observed in China, and loadings of nitrogen (N) and other nutrients from agricultural systems play a key role in the water contamination. Process‐based biogeochemical models have been applied to quantify nutrient loading from nonpoint sources at the watershed scale. However, this effort is often hindered by the fact that few existing biogeochemical models of nutrient cycling are able to simulate the two‐dimensional soil hydrology. To overcome this challenge, we launched a new attempt to incorporate two fundamental hydrologic features, the Soil Conservation Service curve and the Modified Universal Soil Loss Equation functions, into a biogeochemistry model, Denitrification‐Decomposition (DNDC). These two features have been widely utilized to quantify surface runoff and soil erosion in a suite of hydrologic models. We incorporated these features in the DNDC model to allow the biogeochemical and hydrologic processes to exchange data at a daily time step. By including the new features, DNDC gained the additional ability to simulate both horizontal and vertical movements of water and nutrients. The revised DNDC was tested against data sets observed in a small watershed dominated by farmlands in a mountainous area of southwest China. The modeled surface runoff flow, subsurface drainage flow, sediment yield, and N loading were in agreement with observations. To further observe the behaviors of the new model, we conducted a sensitivity test with varied climate, soil, and management conditions. The results indicated that precipitation was the most sensitive factor determining the rate of N loading from the tested site. A Monte Carlo test was conducted to quantify the potential uncertainty derived by variations in four selected input parameters. This study demonstrates that it is feasible and effective to use enhanced biogeochemical models such as DNDC for quantifying N loadings by incorporating basic hydrological features into the model framework

Clinical significance of obstructive sleep apnea in patients with acute coronary syndrome in relation to diabetes status.

Objective: The prognostic significance of obstructive sleep apnea (OSA) in patients with acute coronary syndrome (ACS) according to diabetes mellitus (DM) status remains unclear. We aimed to elucidate the association of OSA with subsequent cardiovascular events in patients with ACS with or without DM. Research design and methods: In this prospective cohort study, consecutive eligible patients with ACS underwent cardiorespiratory polygraphy between June 2015 and May 2017. OSA was defined as an Apnea Hypopnea Index ≥15 events/hour. The primary end point was major adverse cardiovascular and cerebrovascular events (MACCEs), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. Results: Among 804 patients, 248 (30.8%) had DM and 403 (50.1%) had OSA. OSA was associated with 2.5 times the risk of 1 year MACCE in patients with DM (22.3% vs 7.1% in the non-OSA group; adjusted HR (HR)=2.49, 95% CI 1.16 to 5.35, p=0.019), but not in patients without DM (8.5% vs 7.7% in the non-OSA group, adjusted HR=0.94, 95% CI 0.51 to 1.75, p=0.85). Patients with DM without OSA had a similar 1 year MACCE rate as patients without DM. The increased risk of events was predominately isolated to patients with OSA with baseline glucose or hemoglobin A1c levels above the median. Combined OSA and longer hypoxia duration (time with arterial oxygen saturation22 min) further increased the MACCE rate to 31.0% in patients with DM. Conclusions: OSA was associated with increased risk of 1 year MACCE following ACS in patients with DM, but not in non-DM patients. Further trials exploring the efficacy of OSA treatment in high-risk patients with ACS and DM are warranted

Modeling soil organic carbon change in croplands of China

Using 1990 conditions, we modeled carbon (C) and nitrogen (N) biogeochemical cycles in croplands of China (and, for comparison, the United States) to estimate the annual soil organic-carbon (SOC) balance for all cropland. Overall, we estimate that China\u27s croplands lost 1.6% of their SOC (to a depth of 0.3 m) in 1990, and that U.S. cropland lost 0.1%. A key element in this difference was that ∼25% of aboveground crop residue in China was returned to the soil, compared to ∼90% in the United States. In China, SOC losses were greatest in the northeast (∼103 kg C·ha–1·yr–1), and were generally smaller (\u3c0.5 × 103 kg C·ha–1·yr–1) in regions with a longer cultivation history. Some regions showed SOC gains, generally \u3c103 kg C·ha–1·yr–1. Reduced organic-matter input to China\u27s cropland soils, and lower overall SOC levels in those soils, led to lower levels of N mineralization in the simulations, consistent with higher rates of synthetic-fertilizer application in China. C and N cycles are closely linked to soil fertility, crop yield, and non-point-source environmental pollution

Changes in the soil organic carbon balance on China’s cropland during the last two decades of the 20th century

Agro-ecosystems play an important role in regulating global changes caused by greenhouse gas emissions. Restoration of soil organic carbon (SOC) in agricultural soils can not only improve soil quality but also influence climate change and agronomic productivity. With about half of its land area under agricultural use, China exhibits vast potential for carbon (C) sequestration that needs to be researched. Chinese cropland has experienced SOC change over the past century. The study of SOC dynamics under different bioclimatic conditions and cropping systems can help us to better understand this historical change, current status, the impacts of bioclimatic conditions on SOC and future trends. We used a simulation based on historical statistical data to analyze the C balance of Chinese croplands during the 1980s and 1990s, taking into account soil, climate and agricultural management. Nationwide, 77.6% of the national arable land is considered to be in good condition. Appropriate farm management practices should be adopted to improve the poor C balance of the remaining 22.4% of cropland to promote C sequestration