DeNitrification And DeComposition Research Articles

Predicting Soil Carbon Sequestration and Harvestable C-Biomass of Rice and Wheat by DNDC Model

Several biogeochemical models have been applied to understand the potential effects of management practices on soil organic carbon (SOC) sequestration, crop growth, and yield. In this study, the denitrification and decomposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice–wheat rotation under organic/inorganic fertilization with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC underpredicted harvestable grain C-biomass of rice where percent difference (PD) varied from 29.22% to 42.14%, and over-simulated grain C-biomass of wheat where PD was −55.01% with 50% nitrogen–phosphorus–potassium (NPK) and 50% animal manure applied under the CT treatment. However, after calibration by adjusting default values of soil and crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (e.g., average PD ranged from −2.81% to −6.17%). DNDC also predicted the effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha−1). DNDC simulated SOC trends for rice–wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice–wheat systems.

Greenhouse gas emissions following biosolids application to farmland: Estimates from the DeNitrification and DeComposition model

Municipal wastewater sludge may be processed into biosolids and applied to farmland for crop production, rather than be disposed of in landfills. Biosolids supply plant nutrients and increase soil organic carbon but also contribute to the production of greenhouse gases (GHGs). Computational models must therefore be refined to estimate the contribution of these gases to national GHG inventories. The DeNitrification and DeComposition (DNDC) model was evaluated for processes regulating crop growth, GHGs and soil C&N dynamics to determine its suitability for informing policy decision-making and advancing Canada's GHG inventory. Three years (2017–2019) of data were collected from replicated corn (Zea mays L.) plots in Quebec, Canada. The plots received 120 kg of available N ha−1 y−1 in mesophilic anaerobically digested biosolids, composted biosolids, alkaline-stabilized biosolids, urea, or combinations of these, while control plots were left unfertilized. Treatments receiving digested biosolids emitted more nitrous oxide (N2O) during the growing season than other treatments, while carbon dioxide (CO2) emissions were similar between treatments. After calibration, DNDC estimates were within the 95% confidence interval of the measured variables. Correlation coefficients (r) indicated discrepancies in trends between the estimated and measured values for daily CO2 and N2O emissions. These emissions were underestimated in the early and mid-growing season of 2018. They were more variable from plots fertilized with composted or alkaline-stabilized biosolids than from those with digested biosolids. Annual N2O emissions (r = 0.8), crop yields (r = 0.5), and soil organic carbon (r = 0.4) were modelled with higher accuracy than cumulative CO2 emissions (r = 0.3) and total soil N (r = 0.1). These findings suggest that DNDC is suitable for estimating field-scale N2O emissions following biosolids application, but estimates of CO2 emissions could be improved, perhaps by disaggregating the biosolids from the soil organic matter pools in the decomposition subroutines.

Predicting methane emissions from paddy rice soils under biochar and nitrogen addition using DNDC model

Methane (CH4) is a second largest contributor of global warming after carbon dioxide (CO2), and it is crucial to understand how management practices affect CH4 emissions. Among field crops, paddy rice alone has accounted for about 10–12% of the total CH4 emission in 2010. The process-based DeNitrification and DeComposition (DNDC) model can be applied to quantify greenhouse gas (GHG) emissions from agricultural soils. Capturing both the daily time-scale and cumulative growing season CH4 fluxes by DNDC may help to devise appealing mitigation approaches for better rice management. In this study, DNDC was calibrated with a parameter-adjustment approach under two treatments: 140 kg N ha−1 without biochar and 140 kg N ha−1 with 2% biochar. Simulation results show that the model predicted the daily CH4 fluxes in good agreement with measurements under both treatments. Next, DNDC was validated with the adjusted parameters against the remaining biochar and N treatments, and the model performed well in prediction of CH4 fluxes as indicated by several statistical indexes: RMSE ranged from 6.74 to 7.62 g CH4 ha−1d − 1, the d-index varied between 0.92 to 0.98, and MPD and nRMSE were at 10.94–17.43% and 17.54–24.52%, respectively. In terms of cumulative growing season CH4 efflux, DNDC under-simulated all treatments except the control. Further DNDC predicted above-ground dry weights and volumetric water contents in good agreement with the measurements. Moreover, model poorly predicted soil temperature, pH, and soil moisture content. The value of the d-index varied from 0.12 to 0.38 for both soil temperature and pH. Subsequently, DNDC successfully identified the significant impact of biochar on CH4 emission, and model error was strongly correlated with pH of soils with no biochar amendments. In conclusion, the DNDC model can capture the daily time-scale as well as annual-scale CH4 fluxes, though DNDC must be validated by intensive measurements of additional soil variables, including dissolved organic carbon (DOC) and microbial composition under different biochar types.

Agricultural Greenhouse Gas Emissions in a Data-Scarce Region Using a Scenario-Based Modeling Approach: A Case Study in Southeastern USA

Climate change may impact agricultural greenhouse gas emissions (GHGs) and yields under higher temperatures, higher atmospheric CO2 concentrations, and variable precipitations. This calls for adaptation strategies to optimize agricultural productions with minimal GHGs. This study aimed to identify these optimum agricultural managements in response to current and projected climatic scenarios for the Choctawhatchee Basin in Southeastern USA, an experimentally unexplored data-scarce region lacking validation data. This scenario-based modeling study analyzed a total of 1344 scenarios consisting of four major crops, eight managements (varying tillage, manuring, and residue), and forty climatic combinations under current as wells as two representative concentration pathways with process-based Denitrification and Decomposition (DNDC) model. The results indicated that the region’s GHGs and yields were most affected by higher temperatures (≥+3 °C) and extreme precipitation changes (≥±40%), while high atmospheric CO2 concentrations exerted positive fertilization effects. The manure-related and higher residue incorporation scenarios were found to be better options in varying climates with minimal present global warming potentials (GWP) of 0.23 k to −29.1 k MT equivalent CO2. As such, the study presented climate change impacts and potential mitigation options in the study region while presenting a framework to design GHG mitigation in similar data-scarce regions.

GDNDC: An integrated system to model water-nitrogen-crop processes for agricultural management at regional scales

Agroecosystem modelling has increasingly focused on the integration of soil biogeochemical processes and crop growth. However, few models are available that offer high computing efficiencies for region-scale simulations, integrated decision support tools, and a structure that allows for easy extension. This paper introduces a new modelling tool to fill this gap: the GDNDC (Gridded DNDC) system for gridded agro-biogeochemical simulations. Based on the established DeNitrification and DeComposition (DNDC) model version-95, its main advancements include (i) implementation of parallel computation to significantly reduce computation time across multiple scales; (ii) a built-in parameter optimization algorithm to improve the predictive accuracy, and (iii) several decision support tools. We demonstrate each of these for county-level maize growth simulations in Liaoning Province (China) and reveal the potential of this new modelling tool to guide both long-term policy decisions regarding optimal fertilizer application and near-term crop yield forecasting for reactive decisions required in times of drought.

Modeling a long-term effect of rice straw incorporation on SOC content and grain yield in rice field

ABSTRACTThe long-term effects of rice straw incorporation on soil organic carbon (SOC) content and rice yield were evaluated from rice cultivation with different treatments: no rice straw (control), rice straw (RS), and rice straw compost (RSC) as a main-plots; five levels of nitrogen (N) fertilizer application: 0, 100, 150, 200, and 250 of N (kg ha−1) as a sub-plots. The denitrification and decomposition (DNDC) model was employed to simulate changes in SOC content and rice grain yield over 35 years. Additionally, scenario analysis on continuous or discontinuous RS incorporation in rice fields was conducted using the DNDC model. The long-term results indicated that RS and RSC treatments played a crucial role in increasing grain yields by 9% and 11% due to the increased SOC contents compared to control treatment. The validated DNDC model on SOC contents and yields showed a good agreement between the observed and simulated values based on the normalized root mean square errors. The model predicted a rapid decline of SOC contents without RS incorporation. Results suggested that incorporating rice straw or amending manure to paddy soils is a preferred practice for maintaining SOC contents.

Reduced nitrate leaching in a perennial grain crop compared to maize in the Upper Midwest, USA

Global expansion of high-input annual grain crops and associated nitrogen (N) fertilizer use can have negative consequences for the environment and human health. Nitrate nitrogen (NO3-N) leaching from fertilized annual crops can contaminate groundwater and pollute natural aquatic ecosystems and rural drinking water sources. Intermediate wheatgrass (IWG; Thinopyrum intermedium) is a perennial grass being domesticated to serve as the world’s first widely available perennial grain crop. Our objective was to measure water quality variables and crop yields in order to model NO3-N leaching beneath IWG, maize, and switchgrass under three N fertilizer treatments; low N (0 kg N ha−1), medium N (maize = 80, switchgrass and IWG = 40 kg N ha−1), and high N (maize = 160, switchgrass and IWG = 120 or 160 kg N ha−1). The switchgrass and IWG medium N treatments also included alfalfa as an intercrop. The NO3-N concentration in soil solution 50 cm below the surface was one and two orders of magnitude lower in high N fertilized IWG compared to switchgrass and maize, respectively. Soil solution NO3-N increased with N fertilizer in all crops. Soil water content was less at 50 and 100 cm depths in IWG compared to switchgrass and maize but was unaffected by N fertilizer treatment. Using the Denitrification and Decomposition (DNDC) model, average annual NO3-N leaching estimates in the high N treatments were 21.7 kg N ha−1 for maize and 3.7 and 0.2 kg N ha−1 for switchgrass and IWG, respectively. Despite consistent biomass yields through time, IWG grain yields decreased with stand age at all N fertilizer treatments. Alfalfa did not persist in the IWG and switchgrass medium N treatments, thus other legumes should be tested for intercropping with perennial grain crops. Intermediate wheatgrass has great potential to provide food-quality grain and biomass while preventing NO3-N leaching.

A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer

Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.

A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging

Process-based crop models are popular tools to evaluate the impact of climate change and agricultural management on crop growth. Accurate simulation of crop production over large geographic regions using an individual crop model remains challenging due to different sources of uncertainty. We present a Bayesian model averaging (BMA) method for a multiple crop-growth model ensemble to provide more reliable predictions of maize yields in Liaoning Province, northeastern China, which covers an area of 148,000km2 and has 2200,000ha of maize. We apply the photosynthesis-oriented WOFOST (WOrld FOod STudy) model, the water-oriented AquaCrop model and the nitrogen-oriented DNDC (DeNitrification and DeComposition) model to independently generate original predictions of county-level maize yields. The integrated prediction is achieved using a linear combination of the three ensemble members using BMA weights. This integrated approach results in more accurate and precise predictions than any individual model over the entire province. This is because the BMA framework effectively compensates for the uncertainty of individual model simulation and takes advantage of each competing model for reliable prediction. Furthermore, the interpretation of the BMA weight values is also strengthened by comparison with regional precipitation, fertilization and radiation data. We find these values adequately fit the regional limiting factors, e.g., the AquaCrop model generally has a high weight value in counties with frequent droughts, while WOFOST is the dominant member in areas with radiation deficit. Compared with the simple average method and median estimate, the results show that the BMA framework is powerful in computing the ensemble weights and interpreting the mechanism beyond the observed data.

Characterising effects of management practices, snow cover, and soil texture on soil temperature: Model development in DNDC

Agro-ecosystem models, such as the DNDC (DeNitrification and DeComposition) model are useful tools when assessing the sustainability of agricultural management. Accuracy in soil temperature estimations is important as it regulates many important soil biogeochemical processes that lead to greenhouse gas emissions (GHG). The objective of this study was to account for the effects of snow cover in terms of the measured snow depth (mm of water), soil texture and crop management in temperate latitudes in order to improve the surface soil temperature mechanism in DNDC and thereby improve GHG predictions. The estimation of soil temperature driven by the thermal conductivity and heat capacity of the soil was improved by considering the soil texture under frozen and unfrozen conditions along with the effects of crop canopy and snow depth. Calibration of the developed model mechanisms was conducted using data from Alfred, ON under two contrasting soil textures (sandy loam vs. clay). Independent validation assessments were conducted using soil temperatures at different depths for contrasting managements for two field sites located in Canada (Guelph, ON and Glenlea, MB). The validation results indicated high model accuracy (R 2 > 0.90, EF ≥ 0.90, RMSE 2 O emissions during spring thaw and provide a foundation for future studies aimed at improving simulations in DNDC for better representations of other biogeochemical processes.

Dynamic drought risk assessment using crop model and remote sensing techniques

Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

Sensitivity analysis of the DeNitrification and Decomposition model for simulating regional carbon budget at the wetland-grassland area on the Zoige Plateau, china

Although mathematical models (e.g., DeNitrification and DeComposition (DNDC) provide a powerful tool to study regional carbon budget, it is still difficult to obtain accurate simulation results because there exists large uncertainties in modeling regional carbon budget. Through the investigation on the sensitivity of model output parameters to the input parameters, sensitivity analysis (SA) has been proved to be able to identify the key sources of uncertainties and be helpful to reduce the model uncertainties. However, some input parameters with discrete values (e.g., land use type and soil type) and the regional effect of the sensitive parameters were rarely examined in SA. In this paper, taking the Zoige Plateau as a case area, we combined the one-factor-at-a-time (OAT) with Extended Fourier Amplitude Sensitivity Test (EFAST) to conduct a SA of DNDC for simulating the regional carbon budget, including Gross Primary Productivity (GPP), Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP), autotrophic respiration (Ra), soil microbial heterotrophic respiration (Rh) and ecosystem respiration (Re). The result showed that the combination of OAT and EFAST could test the contribution of the input parameters with discrete values to the output parameters. In DNDC model, land use type and soil type had a significant impact on the regional carbon budget of the Zoige Plateau, and daily temperature was also confirmed to be one of the most important parameters for carbon budget. For the other input parameters, with the change of land use type or soil type at regional scale, the sensitive parameters of carbon budget would vary accordingly. The SA results would provide scientific evidence to optimize DNDC model and they suggested that we should pay attention to the spatial/temporal effect of SA and try to use the appropriate data in simulation of the regional carbon budget.

How does climate variability influence nitrogen loss in temperate agroecosystems under contrasting management systems?

Many recent studies have focused on how climate variability affects crop production and yields, however, little is known about the effects of climate variability on nitrogen (N) loss despite the necessity of mitigating N loss for sustainable agroecosystems. By using the DeNitrification and DeComposition (DNDC) model and measurements collected from two management systems (conventional (CV) and best management (BM)) from 2000 to 2004), our objective was to evaluate N losses under different climate scenarios. Our modelled results support the conclusions from previous work that reductions in N losses were obtained by implementing BM rather than CV management. Under numerous climate scenarios (n=25), greater variation was observed in the CV system compared to the BM system for total N losses accumulated over the 5-yr period and for N lost during each year of the crop sequence. Regardless of the management system, the most dominant N loss pathway was NO3− leaching, followed by NH3 volatilization, and N2O emissions. The BM system reduced the frequency of total N loss events compared to CV system, yet, the capacity of the BM system to reduce the frequency of major NO3− events (>1kgd−1) was reduced under a high N loss scenario compared to a low N loss scenario. Higher losses were associated with higher daily temperatures, greater total precipitation, more frequent and intense precipitation events. Thus, the effectiveness of BM systems in controlling major N loss events via NO3− leaching may decrease in the future as a more variable climate ensues with intense precipitation events. Overall, the effect of management remained greater than the impact of climate in determining accumulated N loss over long-term periods, which emphasizes the importance of implementing conservation practices to control N losses.

Simulation of the Grazing Effects on Grassland Aboveground Net Primary Production Using DNDC Model Combined with Time-Series Remote Sensing Data—A Case Study in Zoige Plateau, China

Measuring the impact of livestock grazing on grassland above-ground net primary production (ANPP) is essential for grass yield estimation and pasture management. However, since there is a lack of accurate and repeatable techniques to obtain the details of grazing locations and stocking rates at the regional scale, it is an extremely challenging task to study the influence of regional grazing on the grassland ANPP. Taking Zoige County as a case, this paper proposes an approach to quantify the spatial and temporal variation of grazing intensity and grazing period through time-series remote sensing data, simulated grassland ANPP through the denitrification and decomposition (DNDC) model, and then explores the impact of grazing on grassland ANPP. The result showed that the model-estimated ANPP while considering grazing had a significant relationship with the field-observed ANPP, with the coefficient of determination (R2) of 0.75, root mean square error (RMSE) of 122.86 kgC/ha, and average relative error (RE) of 8.77%. On the contrary, if grazing activity was not considered in simulation, a large uncertainty was found when the model-estimated ANPP was compared with the field observation, showing R2 of 0.4, RMSE of 211.51 kgC/ha, and average RE of 32.5%. For the whole area of Zoige County in 2012, the statistics of the estimation showed that the total regional ANPP was up to 3.815 × 105 tC, while the total regional ANPP, without considering grazing, would be overestimated by 44.4%, up to 5.51 × 105 tC. This indicates that the grazing parameters derived in this study could effectively improve the accuracy of ANPP simulation results. Therefore, it is feasible to combine time-series remote sensing data with the process model to simulate the grazing effects on grassland ANPP. However, some issues, such as selecting proper remote sensing data, improving the quality of model input parameters, collecting more field data, and exploring the data assimilation approaches, still should be considered in the future work.

Application of the DNDC model to estimate N2O emissions under different types of irrigation in vineyards in Ningxia, China

Furrow irrigation, which can be combined with fertilisation by dissolving solid fertilisers in irrigation water, is the most common practice in vineyards in the Ningxia Hui Autonomous Region, and drip irrigation and fertigation have been employed in some areas. These irrigation methods and their corresponding fertilisation schemes may affect nitrous oxide (N2O) emissions from the soil. Therefore, it is important to use a model to simulate differences in N2O emissions and identify farmland management methods that limit N2O emissions. During the July–August 2012 and July–September 2013 growing seasons, the denitrification and decomposition (DNDC) model was tested against experimental N2O emissions data from vineyards in Yuquanying, Yongning, Yinchuan, Ningxia. After model validation, the simulated differences in emissions between furrow irrigation and drip irrigation were 9.86±0.845 and 0.966±0.464kgha−1a−1 in 2012 and 2013, respectively. Thus, the emissions were reduced by approximately 72.5% and 52.4% in 2012 and 2013. In the regional simulation, the Global Warming Potential (GWP) of the annual N2O emission reductions reached 27,749,760±5,489,160kg CO2-equivalents after the vineyards in the Ningxia Autonomous Region were converted from furrow irrigation to drip irrigation. In conclusion, the DNDC model, which proved to be a powerful tool for addressing the efficacy of alternative management practices in vineyards, revealed that N2O emissions can be reduced by adopting drip irrigation systems rather than traditional furrow irrigation systems.

Comparing the performance of the STICS, DNDC, and DayCent models for predicting N uptake and biomass of spring wheat in Eastern Canada

Modelling the production and N uptake of spring wheat (Triticum aestivum L.) according to climate and N fertilization in Eastern Canada is important for estimating efficient N application rates and evaluating the sustainability of agricultural practices. The objective of this paper was to examine the response of observed yield, biomass, and plant N to fertilization rates and climate variations and to compare the performance of the STICS (Simulateur mulTIdisciplinaire pour des Cultures Standard), DNDC (DeNitrification and DeComposition), and DayCent (daily version of CENTURY) models for predicting these outcomes. The results indicate that when rainfall was near normal and the recommended N application rates were applied, the three models had good predictions, especially STICS and DNDC (average relative error<10%, root mean square error<24%). Under lower N rates, STICS and DayCent were less effective for predicting biomass, whereas for rainfall excess, DNDC and DayCent overestimated plant N. For simulating plant N, the STICS model was more sensitive to the quantity and timing of water available to the crop. The models showed that mild rainfall deficit or excess early in the season had a negative impact on estimates of biomass and plant N as well as on yield and protein content, and the impact of the N application rate tended to disappear. When rainfall was near normal and close to measured evapotranspiration, the effect of N rates on biomass and plant N was accurately predicted.

Modeling Impacts of Alternative Practices on Net Global Warming Potential and Greenhouse Gas Intensity from Rice–Wheat Annual Rotation in China

BackgroundEvaluating the net exchange of greenhouse gas (GHG) emissions in conjunction with soil carbon sequestration may give a comprehensive insight on the role of agricultural production in global warming.Materials and MethodsMeasured data of methane (CH4) and nitrous oxide (N2O) were utilized to test the applicability of the Denitrification and Decomposition (DNDC) model to a winter wheat – single rice rotation system in southern China. Six alternative scenarios were simulated against the baseline scenario to evaluate their long-term (45-year) impacts on net global warming potential (GWP) and greenhouse gas intensity (GHGI).Principal ResultsThe simulated cumulative CH4 emissions fell within the statistical deviation ranges of the field data, with the exception of N2O emissions during rice-growing season and both gases from the control treatment. Sensitivity tests showed that both CH4 and N2O emissions were significantly affected by changes in both environmental factors and management practices. Compared with the baseline scenario, the long-term simulation had the following results: (1) high straw return and manure amendment scenarios greatly increased CH4 emissions, while other scenarios had similar CH4 emissions, (2) high inorganic N fertilizer increased N2O emissions while manure amendment and reduced inorganic N fertilizer scenarios decreased N2O emissions, (3) the mean annual soil organic carbon sequestration rates (SOCSR) under manure amendment, high straw return, and no-tillage scenarios averaged 0.20 t C ha−1 yr−1, being greater than other scenarios, and (4) the reduced inorganic N fertilizer scenario produced the least N loss from the system, while all the scenarios produced comparable grain yields.ConclusionsIn terms of net GWP and GHGI for the comprehensive assessment of climate change and crop production, reduced inorganic N fertilizer scenario followed by no-tillage scenario would be advocated for this specified cropping system.

Characterizing Spatiotemporal Dynamics of Methane Emissions from Rice Paddies in Northeast China from 1990 to 2010

BackgroundRice paddies have been identified as major methane (CH4) source induced by human activities. As a major rice production region in Northern China, the rice paddies in the Three-Rivers Plain (TRP) have experienced large changes in spatial distribution over the recent 20 years (from 1990 to 2010). Consequently, accurate estimation and characterization of spatiotemporal patterns of CH4 emissions from rice paddies has become an pressing issue for assessing the environmental impacts of agroecosystems, and further making GHG mitigation strategies at regional or global levels.Methodology/Principal FindingsIntegrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition (DNDC), was utilized to quantify the regional CH4 emissions from the entire rice paddies in study region. Based on site validation and sensitivity tests, geographic information system (GIS) databases with the spatially differentiated input information were constructed to drive DNDC upscaling for its regional simulations. Results showed that (1) The large change in total methane emission that occurred in 2000 and 2010 compared to 1990 is distributed to the explosive growth in amounts of rice planted; (2) the spatial variations in CH4 fluxes in this study are mainly attributed to the most sensitive factor soil properties, i.e., soil clay fraction and soil organic carbon (SOC) content, and (3) the warming climate could enhance CH4 emission in the cool paddies.Conclusions/SignificanceThe study concluded that the introduction of remote sensing analysis into the DNDC upscaling has a great capability in timely quantifying the methane emissions from cool paddies with fast land use and cover changes. And also, it confirmed that the northern wetland agroecosystems made great contributions to global greenhouse gas inventory.

Application of DNDC model to estimate N2O emissions from green tea fields in Japan

Heavy doses of N fertilizers are commonly applied to green tea fields in Japan, and cause large amount of nitrate leaching in ground water and emission of ammonia and nitrous oxide (N2O) to the atmosphere. The Denitrification and Decomposition (DNDC) model was tested against experimental data on N2O emissions from the tea field in Nishio, Aichi, Japan. There were reasonable agreements between the simulated and measured values of N2O emissions for this site. The model was then applied for estimating the environmental impacts as affected by farm management practices, climate change, and soil properties. The model results were assessed with respect to major indicators of agro-ecosystems including crop yield, soil organic carbon sequestration, nitrate leaching loss, and N2O emission. The results indicated that use of compost significantly reduced nitrate leaching and N2O emissions in comparison with N fertilizer. When soil pH and texture shifted to non-acidic and coarser soil, N2O emission increased; and a change in temperature and precipitation affected N2O emission, nitrate leaching, and SOC sequestration. This study thus revealed the biogeochemistry model as a powerful tool in addressing the complex efficacy of the alternative farm management practices in tea fields across various climate and soil conditions.

A tool to link agricultural activity data with the DNDC model to estimate GHG emission factors in Canada

Research is ongoing to develop ways to reduce emissions of greenhouse gases (GHGs) from agricultural sources. A convenient technique to estimate emissions is to develop emission factors for a wide range of management practices. Default emission factors such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often used but these can result in substantial errors when applied to specific geographical regions. In this paper an interface was developed to link soil, climate and agricultural activity data in Canada with the DeNitrification and DeComposition (DNDC) model to create a modeling tool for estimating emission factors for changes in agricultural management. This tool was also designed to calculate country-specific IPCC Tier II emission factors for comparison against modeled results. The DNDC-Management Factor Tool (DNDC-MFT) was developed to automatically generate soil, climate and agricultural management model input data from national databases for estimating emissions factors for any of 462 ecodistricts across Canada. Six ecodistricts were selected across the major climatic regions to test the tool. The emission factors generated by the DNDC model were significantly different from Tier II values. Much variability in N 2O emission estimates exist, partly due to limitations in certain biophysical processes in the model and partly due to quality of input data. The DNDC model is very sensitive to climate, size of initial soil C levels, and fertilizer application rates. We should also keep in mind that there is uncertainly associated with Tier II emission factors. The combined N 2O and soil C factors estimated by the DNDC model are generally comparable to values that are being used to estimate Canada's national inventory (Tier II/III) but only the tillage factor was found to be statistically similar. The DNDC-MFT will be useful for testing the ability of the DNDC model to generate GHG emission factors for many management scenarios across varying climatic regions in Canada. The framework can be extended to include improved versions of DNDC and other ecosystem models.