Simulated N2O Emissions Research Articles

Development of a new model for the simulation of N2O emissions: a case-study on wheat cropping systems under humid Mediterranean climate

Improving the quantification of nitrous oxide (N2O) emissions from agricultural land has become an issue of major concern due to its strong contribution to the greenhouse effect and to the fact that N2O is now the most significant ozone-depleting emission to the atmosphere. The aim of this paper is to describe the development of a new field-scale, simple and empirical model that simulates monthly nitrogen (N) flows in cropping systems based on site characteristics and management practices. We explored its sensitivity for a Basque region of Spain growing winter wheat (Triticum aestivum L.) under humid Mediterranean conditions to varied weather conditions and different scenarios of: (i) fertiliser rates, (ii) soil texture, (iii) organic/mineral fertilisation, (iv) slurry injection/no injection and (v) tillage/no tillage. The model showed sensitivity to most of the changes in the tested parameters. On average, simulated N2O emissions decreased: (i) with the decrease in N fertiliser rates, (ii) in lighter textured soils, (iii) with organic fertilisation, (iv) after non-injecting slurry and (v) under no-tillage. The model showed that it could be useful to simulate some of the potential trade-offs that may occur after implementation of specific N pollution mitigation measures (e.g. trade-offs in crop productivity and ammonia (NH3) volatilisation after implementation of measures that target a reduction in N2O emissions). In a validation exercise, simulated and measured yield and soil moisture showed reasonable agreement. Although the model showed discrepancies for monthly-averaged N2O fluxes, the peak after fertilisation application was reasonably well simulated. These results and the simplicity and user-friendliness of the model suggest that its structure is appropriate and, if properly calibrated for different soil types and weather conditions, it could be a useful model to be used in carbon footprint studies or to develop site-specific emission factors for current or future climatic scenarios.

Parameter-induced uncertainty quantification of soil N<sub>2</sub>O, NO and CO<sub>2</sub> emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Abstract. Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes.

Modelling terrestrial nitrous oxide emissions and implications for climate feedback

Ecosystem nitrous oxide (N2O) emissions respond to changes in climate and CO2 concentration as well as anthropogenic nitrogen (N) enhancements. Here, we aimed to quantify the responses of natural ecosystem N2O emissions to multiple environmental drivers using a process-based global vegetation model (DyN-LPJ). We checked that modelled annual N2O emissions from nonagricultural ecosystems could reproduce field measurements worldwide, and experimentally observed responses to step changes in environmental factors. We then simulated global N2O emissions throughout the 20th century and analysed the effects of environmental changes. The model reproduced well the global pattern of N2O emissions and the observed responses of N cycle components to changes in environmental factors. Simulated 20th century global decadal-average soil emissions were c. 8.2-9.5 Tg N yr(-1) (or 8.3-10.3 Tg N yr(-1) with N deposition). Warming and N deposition contributed 0.85±0.41 and 0.80±0.14 Tg N yr(-1), respectively, to an overall upward trend. Rising CO2 also contributed, in part, through a positive interaction with warming. The modelled temperature dependence of N2O emission (c. 1 Tg N yr(-1) K(-1)) implies a positive climate feedback which, over the lifetime of N2O (114 yr), could become as important as the climate-carbon cycle feedback caused by soil CO2 release.

How Effective is Reduced Tillage–Cover Crop Management in Reducing N2O Fluxes from Arable Crop Soils?

Field management is expected to influence nitrous oxide (N2O) production from arable cropping systems through effects on soil physics and biology. Measurements of N2O flux were carried out on a weekly basis from April 2008 to August 2009 for a spring sown barley crop at Oak Park Research Centre, Carlow, Ireland. The soil was a free draining sandy loam typical of the majority of cereal growing land in Ireland. The aims of this study were to investigate the suitability of combining reduced tillage and a mustard cover crop (RT–CC) to mitigate nitrous oxide emissions from arable soils and to validate the DeNitrification–DeComposition (DNDC) model version (v. 9.2) for estimating N2O emissions. In addition, the model was used to simulate N2O emissions for two sets of future climate scenarios (period 2021–2060). Field results showed that although the daily emissions were significantly higher for RT–CC on two occasions (p 0.05) on the cumulative N2O flux, compared with the CT treatment, was found. DNDC was validated using N2O data collected from this study in combination with previously collected data and shown to be suitable for estimating N2O emissions (r 2 = 0.70), water-filled pore space (WFPS) (r 2 = 0.58) and soil temperature (r 2 = 0.87) from this field. The relative deviations of the simulated to the measured N2O values with the 140 kg N ha−1 fertiliser application rate were −36 % for RT–CC and −19 % for CT. Root mean square error values were 0.014 and 0.007 kg N2O–N ha−1 day−1, respectively, indicating a reasonable fit. Future cumulative N2O fluxes and total denitrification were predicted to increase under the RT–CC management for all future climate projections, whilst predictions were inconsistent under the CT. Our study suggests that the use of RT–CC as an alternative farm management system for spring barley, if the sole objective is to reduce N2O emissions, may not be successful.

Validation of the DNDC-Rice model by using CH4 and N2O flux data from rice cultivated in pots under alternate wetting and drying irrigation management

The DNDC (DeNitrification-DeComposition)-Rice model, one of the most advanced process-based models for the estimation of greenhouse gas emissions from paddy fields, has been discussed mostly in terms of the reproducibility of observed methane (CH4) emissions from Japanese rice paddies, but the model has not yet been validated for tropical rice paddies under alternate wetting and drying (AWD) irrigation management, a water-saving technique. We validated the model by using CH4 and nitrous oxide (N2O) flux data from rice in pots cultivated under AWD irrigation management in a screen-house at the International Rice Research Institute (Los Baños, the Philippines). After minor modification and adjustment of the model to the experimental irrigation conditions, we calculated grain yield and straw production. The observed mean daily CH4 fluxes from the continuous flooding (CF) and AWD pots were 4.49 and 1.22 kg C ha−1 day−1, respectively, and the observed mean daily N2O fluxes from the pots were 0.105 and 34.1 g N ha−1 day−1, respectively. The root-mean-square errors, indicators of simulation error, of daily CH4 fluxes from CF and AWD pots were calculated as 1.76 and 1.86 kg C ha−1 day−1, respectively, and those of daily N2O fluxes were 2.23 and 124 g N ha−1 day−1, respectively. The simulated gross CH4 emissions for CF and AWD from the puddling stage (2 days before transplanting) to harvest (97 days after transplanting) were 417 and 126 kg C ha−1, respectively; these values were 9.8% lower and 0.76% higher, respectively, than the observed values. The simulated gross N2O emissions during the same period were 0.0279 and 1.45 kg N ha−1 for CF and AWD, respectively; these values were respectively 87% and 29% lower than the observed values. The observed total global warming potential (GWP) of AWD resulting from the CH4 and N2O emissions was approximately one-third of that in the CF treatment. The simulated GWPs of both CF and AWD were close to the observed values despite the discrepancy in N2O emissions, because N2O emissions contributed much less than CH4 emissions to the total GWP. These results suggest that the DNDC-Rice model can be used to estimate CH4 emission and total GWP from tropical paddy fields under both CF and AWD conditions.

An inventory of global N2O emissions from the soils of natural terrestrial ecosystems

Abstract We develop an inventory of global N2O emissions from natural ecosystem soils using an artificial neural network approach and field observational data of N2O fluxes. We estimate that the global soil N2O source strength from natural ecosystems is 3.37 Tg (1 Tg = 1012 g) N per year with an uncertainty ranging from 1.96 to 4.56 Tg N per year in 2000. While our global estimate is lower than other existing estimates, the spatial patterns of our simulated N2O emissions agree with other existing studies. There was a large spatial and seasonal variability in the soil N2O emissions due to the variation in soil type, vegetation and climate conditions. Consistent with other studies, we confirm that warm and moist tropical soils are the major source of atmospheric N2O. As a result of the low temperatures, the high latitude ecosystems have significantly low emission rates and contribute little (less than 0.10 Tg N per year) to the global N2O source. The simulated annual global N2O emissions are found to be most sensitive to variation in precipitation. This study uses the most current available data for N2O fluxes and their associated environmental variables to quantify the global N2O emissions, and provides an independent global inventory of this important trace gas, which will facilitate future studies of atmospheric chemistry and climate feedbacks at the global scale.

Modelling the effect of aggregates on N<sub>2</sub>O emission from denitrification in an agricultural peat soil

Abstract. Nitrous oxide (N2O) emissions are highly variable in time, with high peak emissions lasting a few days to several weeks and low background emissions. This temporal variability is poorly understood which hampers the simulation of daily N2O emissions. In structured soils, like clay and peat, aggregates hamper the diffusion of oxygen, which leads to anaerobic microsites in the soil, favourable for denitrification. Diffusion of N2O out of the aggregates is also hampered, which leads to delayed emissions and increased reduction of N2O to N2. In this model simulation study we investigate the effect of aggregates in soils on the N2O emissions. We present a parameterization to simulate the effects of aggregates on N2O production by denitrification and on N2O reduction. The parameterization is based on the mobile-immobile model concept. It was implemented in a field-scale hydrological-biogeochemical model combination. We compared the simulated fluxes with observed fluxes from a fertilized and drained peat soil under grass. The results of this study show that aggregates strongly affect the simulated N2O emissions: peak emissions are lower, whereas the background emissions are slightly higher. Including the effect of aggregates caused a 40% decrease in the simulated annual emissions relative to the simulations without accounting for the effects of aggregates. The new parameterization significantly improved the model performance regarding simulation of observed daily N2O fluxes; r2 and RMSE improved from 0.11 and 198 g N2O-N ha−1 d−1 to 0.41 and 40 g N2O-N ha−1 d−1, respectively. Our analyses of the model results show that aggregates have a larger impact on the reduction than on the production of N2O. Reduction of N2O is more sensitive to changes in the drivers than production of N2O and is in that sense the key to understanding N2O emissions from denitrification. The effects of changing environmental conditions on reduction of N2O relative to N2O production strongly depend on the NO3 content of the soil. More anaerobic conditions have hardly any effect on the ratio of production to reduction if NO3 is abundant, but will decrease this ratio if NO3 is limiting. In the first case the emissions will increase, whereas in the second case the emissions will decrease. This study suggests that the current knowledge of the hydrological, biogeochemical and physical processes may be sufficient to understand the observed N2O fluxes from a fertilized clayey peatland. Further research is needed to test how aggregates affect the N2O fluxes from other soils or soils with different fertilization regimes.

Assessing Spring Thaw Nitrous Oxide Fluxes Simulated by the DNDC Model for Agricultural Soils

Large N2O emissions from agricultural soils have been reported during winter and spring thaw. The objective of this study was to assess the ability of the DNDC model to simulate N2O emissions resulting from freeze–thaw cycles, particularly the timing of flux events. The DNDC model was tested against micrometeorological fluxes measured during 5 yr in Ontario, Canada. There was a very large discrepancy between simulated and observed fluxes in terms of magnitude and timing. The simulated event occurred, on average, 38 d later than observed, and N2O fluxes were up to 3.5 times larger than the highest measured flux. Examination of simulated soil conditions indicated that the mechanism underlying freeze–thaw‐induced N2O flux in the DNDC model, release of ice‐trapped N2O, was not correct. This misconception had not been identified before, possibly because cold conditions in previous studies were not as extreme as observed in our data set or because continuously measured N2O fluxes were not available for model assessment. As a result of this analysis, DNDC 9.1 was revised by removing the release of ice‐trapped N2O and adding N2O newly produced by denitrification in the surface layer as the main mechanism for N2O production (DNDC 9.3). Comparison between simulated N2O fluxes using DNDC 9.3 and our data indicated improved timing to within 1 d of observed events. The magnitude of simulated flux differed from measurements by more than a factor of two, however, suggesting that an improved algorithm for N2O production and diffusion under soil freezing and thawing is needed.

Field-scale verification of nitrous oxide emission reduction with DCD in dairy-grazed pasture using measurements and modelling

Nitrous oxide (N2O) from agricultural soils is a major source of greenhouse gas emissions in New Zealand. Nitrification inhibitors are seen as a potential technology to reduce these N2O emissions from agricultural soils. In previous studies on the effect of dicyandiamide (DCD) on N2O emissions from animal excreta, DCD was directly applied to urine. However, farmers apply DCD to grazed pastures shortly before or after grazing rather than applying it specifically to the urine patches. Accordingly, the objectives of this study were: (1) to test, using chamber measurements, whether the same level of N2O reduction is achieved under grazed conditions where excretal N is non-uniformly deposited, (2) to apply the process-based NZ-DNDC model to simulate the effect of DCD on emission reductions, and (3) to perform a sensitivity analysis on the NZ-DNDC model to investigate how uncertainties in the input parameters affect the modelled N2O emissions. Two circular 1260-m2 treatment plots were grazed simultaneously for 5 h, by 20 cattle on each plot. The following day, DCD was applied in 800 L of water to one of the plots at 10 kg/ha and N2O emissions were measured periodically for 20 days. The cumulative N2O emissions were 220 ± 90 and 110 ± 20 g N2O-N/ha for the untreated and DCD-treated plots, respectively (based on the arithmetic mean and standard error of the chambers). This suggests a reduction in N2O emission from DCD application of ~50 ± 40% from a single grazing event. However, this result should be treated with caution because the possibility of sampling error due to the chamber distribution cannot be excluded. NZ-DNDC simulated N2O emissions of 169 and 68 g N2O-N/ha for the untreated and DCD-treated areas, respectively, corresponding to a reduction of 60% in N2O emissions from DCD application. This level of reduction is consistent with that found in experiments with individual urine patches. N2O emissions found through use of NZ-DNDC were sensitive to uncertainties in the input parameters. The combined effect of varying the initial soil NO3– and NH4+, soil moisture, soil organic carbon, bulk density, clay content, pH, and water-filled pore-space at field capacity inputs within plausible ranges was to change the simulated N2O emissions by –87% to +150%.

On sustainability of bioenergy production: Integrating co-emissions from agricultural intensification

Biomass from cellulosic bioenergy crops is seen as a substantial part of future energy systems, especially if climate policy aims at stabilizing CO2 concentration at low levels. However, among other concerns of sustainability, the large-scale use of bioenergy is controversial because it is hypothesized to increase the competition for land and therefore raise N2O emissions from agricultural soils due to intensification. We apply a global land-use model that is suited to assess agricultural non-CO2 GHG emissions. First, we describe how fertilization of cellulosic bioenergy crops and associated N2O emissions are implemented in the land-use model and how future bioenergy demand is derived by an energy-economy-climate model. We then assess regional N2O emissions from the soil due to large-scale bioenergy application, the expansion of cropland and the importance of technological change for dedicated bioenergy crops. Finally, we compare simulated N2O emissions from the agricultural sector with CO2 emissions from the energy sector to investigate the real contribution of bioenergy for low stabilization scenarios. As a result, we find that N2O emissions due to energy crop production are a minor factor. Nevertheless, these co-emissions can be significant for the option of removing CO2 from the atmosphere (by combining bioenergy use with carbon capture and storage (CCS) options) possibly needed at the end of the century for climate mitigation. Furthermore, our assessment shows that bioenergy crops will occupy large shares of available cropland and will require high rates of technological change at additional costs.

Response of terrestrial N2O and NOx emissions to abrupt climate change

Being a potent greenhouse gas, N2O emitted by the terrestrial biosphere during abrupt climate change events could have amplified externally forced warming. To investigate this possibility, we tested the sensitivity of terrestrial N2O emissions to an abrupt warming event by applying the ARVE-DGVM in combination with a novel scheme for process-based simulation of terrestrial N2O and NOx emissions at the Gerzensee site in Switzerland. In this study, we aim to quantify the magnitude of change in emissions for the abrupt climate change event that occurred at the transition from Oldest Dryas to Bøl-ling during the last deglaciation. Using high-resolution multiproxy records obtained from the Gerzensee that cover the Late Glacial, we apply a prescribed vegetation change derived from the pollen record and temperature and precipitation reconstructions derived from δ18O in lake sediments. Changes in soil temperature and moisture are simulated by the ARVE-DGVM using the reconstructed paleoclimate as a driver. Our results show a pronounced increase in mean annual N2O and NOx emissions for the transition (by factor 2.55 and 1.97, respectively), with highest amounts generally being emitted during summer. Our findings suggest that summertime emissions are limited by soil moisture, while temperature controls emissions during winter. For the time between 14670 and 14620 cal. years BP, our simulated N2O emissions show increase rates as high as 1% per year, indicating that local reactions of emissions to changing climate could have been considerably faster than the atmospheric concentration changes observed in polar ice.

A process-based model of N2O emission from a rice-winter wheat rotation agro-ecosystem: Structure, validation and sensitivity

In order to numerically simulate daily nitrous oxide (N2O) emission from a rice-winter wheat rotation cropping system, a process-based site model was developed (referred to as IAP-N-GAS) to track the movement and transformation of several forms of nitrogen in the agro-ecosystem, which is affected by climate, soil, crop growth and management practices. The simulation of daily N2O fluxes, along with key daily environmental variables, was validated with three-year observations conducted in East China. The validation demonstrated that the model simulated well daily solar radiation, soil temperature and moisture, and also captured the dynamics and magnitude of accumulated rice aboveground biomass and mineral nitrogen in the soil. The simulated daily N2O emissions over all three years investigated were generally in good agreement with field observations. Particularly well simulated were the peak N2O emissions induced by fertilizations, rainfall events or mid-season drainages. The model simulation also represented closely the inter-annual variation in N2O emission. These validations imply that the model has the capability to capture the general characteristics of N2O emission from a typical rice-wheat rotation agro-ecosystem. Sensitivity analyses revealed that the simulated N2O emission is most sensitive to the fertilizer application rate and the soil organic matter content, but it is much less sensitive to variations in soil pH and texture, temperature, precipitation and crop residue incorporation rate under local conditions.

Simulation of Nitrous Oxide Emissions from Dairy Farms to Assess Greenhouse Gas Reduction Strategies

Farming practices can have a large impact on the net emission of greenhouse gases, including carbon dioxide (CO2), methane, and nitrous oxide (N2O). The primary sources of N2O from dairy farms are nitrification and denitrification processes in soil, with smaller contributions from manure storage and barn floors. Emissions from all greenhouse gas sources are interrelated, so strategies to reduce emissions from one source can affect emissions from another. Therefore, a comprehensive whole-farm evaluation is needed, which can be cost-effectively achieved through computer simulation. The Integrated Farm System Model (IFSM), a process-based whole-farm model, was extended to simulate sources of N2O and other greenhouse gas emissions. A module was added to simulate N2O emissions from croplands using relationships from the previously established DAYCENT model, and relationships were incorporated to predict emissions from slurry storage and free-stall barn floors. The new module was found to predict N2O emissions consistent with reported values from specific experiments and previously estimated whole-farm emissions. The model also predicted sensitivity to soil texture and soil water content similar to experimental data and DAYCENT model predictions, which further verified this most important component of N2O emissions. Simulations illustrated the impact of management practices on a representative farm in central Pennsylvania. Reducing the use of inorganic fertilizer by accounting for manure nitrogen (N) reduced N2O emission from the farm by 6% and reduced the net farm emission of all greenhouse gases in CO2-equivalent units by 1%. Adding a mulch cover crop to corn land reduced N2O emission by 34% with a 7% reduction of all greenhouse gases. Use of a top-loaded manure storage tank prevented formation of a surface crust, which eliminated storage N2O emission with little effect on net farm emission of all greenhouse gases. This extended whole-farm model provides a tool for evaluating proposed N2O reduction strategies along with their effects on other greenhouse gas emissions, other N and phosphorus losses, and farm profitability.

Simulating carbon sequestration and GHGs emissions in Abies fabric forest on the Gongga Mountains using a biogeochemical process model Forest-DNDC

The process-oriented model Forest-DNDC describing biogeochemical cycling of C and N and GHGs (greenhouse gases) fluxes (CO2, NO and N2O) in forest ecosystems was applied to simulate carbon sequestration and GHGs emissions in Abies fabric forest of the Gongga Mountains at southeastern edge of the Tibetan Plateau. The results indicated that the simulated gross primary production (GPP) of Abies fabric forest was strongly affected by temperature. The annual total GPP was 24,245.3 kg C ha−1 yr−1 for 2005 and 26,318.8 kg C ha−1 yr−1 for 2006, respectively. The annual total net primary production (NPP) was 5,935.5 and 4,882.2 kg C ha−1 yr−1 for 2005 and 2006, and the annual total net ecosystem production (NEP) was 4,815.4 and 3,512.8 kg C ha−1 yr−1 for 2005 and 2006, respectively. The simulated seasonal variation in CO2 emissions generally followed the seasonal variations in temperature and precipitation. The annual total CO2 emissions were 3,109.0 and 4,821.0 kg C ha−1 yr−1 for 2005 and 2006, the simulated annual total N2O emissions from forest soil were 1.47 and 1.36 kg N ha−1 yr−1 for 2005 and 2006, and the annual total NO emissions were 0.09 and 0.12 kg N ha−1 yr−1 for 2005 and 2006, respectively.

Simulation of N2O emissions from rain-fed wheat and the impact of climate variation in southeastern Australia

The Water and Nitrogen Management model (WNMM) was applied to simulate N2O emissions from a rain-fed wheat cropping system on a loam-textured soil for two treatments, conventional cultivation with residue burn (CC + BURN + N) and direct drill with residue retention (DD + RET + N), at Rutherglen in southeastern Australia from January 2004 to March 2005. Both treatments received the same amount of nitrogen (N) fertiliser. The WNMM satisfactorily simulated the soil water content, mineral N contents and N2O emissions from the soil, as compared with the field observations for both treatments. The simulated nitrification-induced N2O emissions accounted for 45% and 34% of total N2O emissions for the treatments CC + BURN + N and DD + RET + N, respectively. The calibrated WNMM was used to simulate N2O emissions from this soil using historic daily weather data from 1968 to 2004 and applying seven scenarios of fertiliser N application. Correlation analysis found that the annual N2O emissions for this rain-fed wheat cropping system were significantly correlated to the annual average of daily maximum air temperature (r = 0.51 for CC + BURN + N and 0.56 for DD + RET + N), annual rainfall (r = −0.56 for CC + BURN + N and −0.59 for DD + RET + N) and fertiliser N application rate (r = 0.43 for CC + BURN + N and 0.31 for DD + RET + N). Based on the 37-year historic simulations, multivariate regression models for estimating annual N2O emissions were developed to account for climatic variation, and explained about 50% of variations of annual N2O emissions estimated by WNMM.

N2O emission from conventional and minimum-tilled soils

In this study, we investigated N2O emissions from two fields under minimum tillage, cropped with maize (MT maize) and summer oats (MT oats), and a conventionally tilled field cropped with maize (CT maize). Nitrous oxide losses from the MT maize and MT oats fields (5.27 and 3.64 kg N2O-N ha−1, respectively) were significantly higher than those from the CT maize field (0.27 kg N2O-N ha−1) over a period of 1 year. The lower moisture content in CT maize (43% water-filled pore space [WFPS] compared to 60–65%) probably caused the difference in total N2O emissions. Denitrification was found to be the major source of N2O loss. Emission factors calculated from the MT field data were high (0.04) compared to the CT field (0.001). All data were simulated with the denitrification decomposition model (DNDC). For the CT field, N2O and N2O + N2 emissions were largely overestimated. For the MT fields, there was a better agreement with the total N2O and N2O + N2 emissions, although the N2O emissions from the MT maize field were underestimated. The simulated N2O emissions were particularly influenced by fertilization, but several other measured N2O emission peaks associated with other management practices at higher WFPS were not captured by the model. Several mismatches between simulated and measured \({\text{NH}}_4^ + \), \({\text{NO}}_3^ - \) and WFPS for all fields were observed. These mismatches together with the insensitivity of the DNDC model for increased N2O emissions at the management practices different from fertilizer application explain the limited similarity between the simulated and measured N2O emissions pattern from the MT fields.

EVALUATION OF N2O EMISSIONS BY DNDC MODEL FOR SANDY LOAM SOILS OF DANUBIAN LOWLAND

Except for food production the sector of agriculture contribute significantly to emissions of some Greenhouse gases (GHGs), especially N2O. Agricultural practices (especially increase of N consumption in the sector) are now recognized as a major factor influencing increase of N2O emissions into the atmosphere. Estimates of greenhouse gas emissions from the agricultural sector both at a local and regional level are necessary to create possible mitigation strategies with respect to environmental efficiency and economic possibility. We used the DNDC (DeNitrification and DeComposition) model that simulates a full carbon (C) and nitrogen (N) balance, including different C and N pools, and the emissions of all relevant trace gases from soils as NH3, N2O, NO, NO2 and N2. However, for this study only N2O was considered. Intergovernmental Panel on Climate Change (IPCC, 1997) includes methodologies for calculating both direct and indirect emissions of N2O related to agricultural production. Finally, the modeled emissions by DNDC were compared with those estimated according to IPCC methodology at a regional level. The rules of a good practice in GHGs inventory in agriculture were taken into account. The N2O emissions estimated by DNDC model ranged 0,09–0,68 kg N2O‐N/ha yr with an average value of 0,28 kg N2O‐N/ha yr. The N2O emissions estimated according to IPCC methodology ranged 0,46–2,86 kg N2O‐N/ha yr with an average value of 1,66 kg N2O‐N/ha yr. Simulated N2O emissions were lower than the N2O emissions estimated by IPCC methodology (1997). The simulated N2O emissions ranged 0,04–0,51 % of the total N applied to a field as a mineral N‐fertilizer. If DNDC and IPCC emissions are compared in this study, it can be concluded that simulated (DNDC) emissions are in the range of default emission factors (1,25 ±1 %) defined by IPCC methodology (1997), except for 2002.

Field Validation of DNDC Model for Methane and Nitrous Oxide Emissions from Rice-based Production Systems of India

The DNDC (DeNitrification and DeComposition) model was tested against experimental data on CH4 and N2O emissions from rice fields at different geographical locations in India. There was a good agreement between the simulated and observed values of CH4 and N2O emissions. The difference between observed and simulated CH4 emissions in all sites ranged from −11.6 to 62.5 kg C ha−1 season−1. Most discrepancies between simulated and observed seasonal fluxes were less than 20% of the field estimate of the seasonal flux. The relative deviation between observed and simulated cumulative N2O emissions ranged from −237.8 to 28.6%. However, some discrepancies existed between observed and simulated seasonal patterns of CH4 and N2O emissions. The model simulated zero N2O emissions from continuously flooded rice fields and poorly simulated CH4 emissions from Allahabad site. For all other simulated cases, the model satisfactorily simulated the seasonal variations in greenhouse gas emission from paddy fields with different land management. The model also simulated the C and N balances in all the sites, including other gas fluxes, viz. CO2, NO, NO2, N2 and NH3 emissions. Sensitivity tests for CH4 indicate that soil texture and pH significantly influenced the CH4 emission. Changes in organic C content had a moderate influence on CH4 emission on these sites. Introducing the mid-season drainage reduced CH4 emissions significantly. Process-based biogeochemical modeling, as with DNDC, can help in identifying strategies for optimizing resource use, increasing productivity, closing yield gaps and reducing adverse environmental impacts.

Simulation of Effects of Soils, Climate and Management on N2O Emission from Grasslands

Nitrous oxide (N2O) is a potent greenhouse gas with a high contribution from agricultural soils and emissions that depend on soil type, climate, crops and management practices. The N2O emissions therefore need to be included as an integral part of environmental assessments of agricultural production systems. An algorithm for N2O production and emission from agricultural soils was developed and included in the FASSET whole-farm model. The model simulated carbon and nitrogen (N) turnover on a daily basis. Both nitrification and denitrification was included in the model as sources for N2O production, and the N2O emissions depended on soil microbial and physical conditions. The model was tested on experimental data of N2O emissions from grasslands in UK, Finland and Denmark, differing in climatic conditions, soil properties and management. The model simulated the general time course of N2O emissions and captured the observed effects of fertiliser and manure management on emissions. Scenario analyses for grazed and cut grasslands were conducted to evaluate the effects of soil texture, climatic conditions, grassland management and N fertilisation on N2O emissions. The soils varied from coarse sand to sandy loam and the climatic variation was taken to represent the climatic variation within Denmark. N fertiliser rates were varied from 0 to 500 kg N ha−1. The simulated N2O emissions showed a non-linear response to increasing N rates with increasing emission factors at higher N rates. The simulated emissions increased with increasing soil clay contents. N2O emissions were slightly increased at higher temperatures, whereas increasing annual rainfall generally lead to decreasing emissions. Emissions were slightly higher from grazed grasslands compared with cut grasslands at similar rates of total N input (fertiliser and animal excreta). The results indicate higher emission factors and thus higher potentials for reducing N2O emissions for intensively grazed grasslands on fine textured soils than for extensive cut-based grasslands on sandy soils.

Disaggregated greenhouse gas emission inventories from agriculture via a coupled economic-ecosystem model

Abstract Estimates of regional greenhouse gas emissions from agricultural systems are needed to evaluate possible mitigation strategies with respect to environmental effectiveness and economic feasibility. Therefore, in this study, we used the GIS-coupled economic-ecosystem model EFEM–DNDC to assess disaggregated regional greenhouse gas (GHG) emissions from typical livestock and crop production systems in the federal state of Baden-Wurttemberg, Southwest Germany. EFEM is an economic farm production model based on linear programming of typical agricultural production systems and simulates all relevant farm management processes and GHG emissions. DNDC is a process-oriented ecosystem model that describes the complete biogeochemical C and N cycle of agricultural soils, including all trace gases. Direct soil emissions were mainly related to N2O, whereas CH4 uptake had marginal influence (net soil C uptake or release was not considered). The simulated N2O emissions appeared to be highly correlated to N fertilizer application (R2 = 0.79). The emission factor for Baden-Wurttemberg was 0.97% of the applied N after excluding background emissions. Analysis of the production systems showed that total GHG emissions from crop based production systems were considerably lower (2.6–3.4 Mg CO2 eq ha−1) than from livestock based systems (5.2–5.3 Mg CO2 eq ha−1). Average production system GHG emissions for Baden-Wurttemberg were 4.5 Mg CO2 eq ha−1. Of the total 38% were derived from N2O (direct and indirect soil emissions, and manure storage), 40% were from CH4 (enteric fermentation and manure storage), and 22% were from CO2 (mainly fertilizer production, gasoline, heating, and additional feed). The stocking rate was highly correlated (R2 = 0.85) to the total production system GHG emissions and appears to be a useful indicator of regional emission levels.