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1
Kröbel, R., W. Smith, B. Grant, R. Desjardins, C. Campbell, N. Tremblay, C. Li, R. Zentner, and B. McConkey. "Development and evaluation of a new Canadian spring wheat sub-model for DNDC." Canadian Journal of Soil Science 91, no. 4 (July 2011): 503–20. http://dx.doi.org/10.4141/cjss2010-059.
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Kröbel, R., Smith, W. N., Grant, B. B., Desjardins, R. L., Campbell, C. A., Tremblay, N., Li, C. S., Zentner, R. P. and McConkey, B. G. 2011. Development and evaluation of a new Canadian spring wheat sub-model for DNDC. Can. J. Soil Sci. 91: 503–520. In this paper, the ability of the DNDC model (version 93) to predict biomass production, grain yield and plant nitrogen content was assessed using data from experiments at Swift Current, Saskatchewan, and St-Blaise, Quebec, Canada. While predicting wheat grain yields reasonably well, the model overestimated the growth of above-ground plant biomass and nitrogen uptake during the first half of the growing season. A new spring wheat sub-model (DNDC-CSW) was introduced with a modified plant biomass growth curve, dynamic plant C/N ratios and modified plant biomass fractioning curves. DNDC-CSW performed considerably better in simulating plant biomass [modeling efficiency (EF): 0.75, average relative error (ARE): 6.0%] and plant nitrogen content (EF: 0.61, ARE: −2.7%) at Swift Current and St-Blaise (EF of 0.75 and ARE of 2.3%), compared with DNDC 93 (biomass SC: EF 0.49, ARE 17.1%, SB: EF 0.02 ARE 33.4%). In comparison with DNDC 93, DNDC-CSW better captured inter-annual variations in crop growth for a range of wheat rotations, increasing the EF from 0.32 to 0.52 for grain and from 0.35 to 0.39 for straw yields. DNDC-CSW also performed considerably better than DNDC 93 in estimating soil carbon changes at Swift Current. Hence, DNDC-CSW has the potential to improve the performance of DNDC 93 in simulating wheat biomass, plant nitrogen, yield and soil carbon at various Canadian sites.
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Jiang, Qianjing, Zhiming Qi, Chandra A. Madramootoo, Ward Smith, Naeem A. Abbasi, and Tiequan Zhang. "Comparison of RZWQM2 and DNDC Models to Simulate Greenhouse Gas Emissions under Combined Inorganic/Organic Fertilization in a Subsurface-Drained Field." Transactions of the ASABE 63, no. 4 (2020): 771–87. http://dx.doi.org/10.13031/trans.13668.
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HighlightsRZWQM2 was compared with DNDC to predict greenhouse gas emissions.RZWQM2 was applied to simulate the greenhouse gas emissions under manure application.RZWQM2 performed better than DNDC in simulating soil water content and CO2 emissions.Abstract. N management has the potential to mitigate greenhouse gas (GHG) emissions. Process-based models are promising tools for evaluating and developing management practices that may optimize sustainability goals as well as promote crop productivity. In this study, the GHG emission component of the Root Zone Water Quality Model (RZWQM2) was tested under two different types of N management and subsequently compared with the Denitrification-Decomposition (DNDC) model using measured data from a subsurface-drained field with a corn-soybean rotation in southern Ontario, Canada. Field-measured data included N2O and CO2 fluxes, soil temperature, and soil moisture content from a four-year field experiment (2012 to 2015). The experiment was composed of two N treatments: inorganic fertilizer (IF), and inorganic fertilizer combined with solid cattle manure (SCM). Both models were calibrated using the data from IF and validated with SCM. Statistical results indicated that both models predicted well the soil temperature, but RZWQM2 performed better than DNDC in simulating soil water content (SWC) because DNDC lacked a heterogeneous soil profile, had shallow simulation depth, and lacked crop root density functions. Both RZWQM2 and DNDC predicted the cumulative N2O and CO2 emissions within 15% error under all treatments, while the timing of daily CO2 emissions was more accurately predicted by RZWQM2 (RMSE = 0.43 to 0.54) than by DNDC (RMSE = 0.60 to 0.67). Modeling results for N management effects on GHG emissions showed consistency with the field measurements, indicating higher CO2 emissions under SCM than IF, higher N2O emissions under IF in corn years, but lower N2O emissions in soybean years. Overall, RZWQM2 required more experienced and intensive calibration and validation, but it provided more accurate predictions of soil hydrology and better timing of CO2 emissions than DNDC. Keywords: CO2 emission, Corn-soybean rotation, Inorganic fertilization, Manure application, N2O emission, Process-based modeling.
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Smith, W. N., R. L. Desjardins, B. Grant, C. Li, R. Lemke, P. Rochette, M. D. Corre, and D. Pennock. "Testing the DNDC model using N2O emissions at two experimental sites in Canada." Canadian Journal of Soil Science 82, no. 3 (August 1, 2002): 365–74. http://dx.doi.org/10.4141/s01-048.
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Measured data from two experimental sites in Canada were used to test the ability of the DeNitrification and DeComposition model (DNDC) to predict N2O emissions from agricultural soils. The two sites, one from eastern Canada, and one from western Canada, provided a variety of crops, management practices, soils, and climates for testing the model. At the site in eastern Canada, the magnitude of total seasonal N2O flux from the seven treatments was accurately predicted with a slight average over-prediction (ARE) of 3% and a coefficient of variation of 41%. Nitrous oxide emissions based on International Panel for Climate Change (IPCC) methodology had a relative error of 62% for the seven treatments. The DNDC estimates of total yearly emissions of N2O from the field site in western Canada showed an underestimation of 8% for the footslope landscape position and an overestimation of 46% for the shoulder position. The data input for the DNDC model were not of sufficient detail to characterize the moisture difference between the landscape positions. The estimates from IPCC guidelines showed an underestimation of 54% for the footslope and an overestimation of 161% for the shoulder. The results indicate that the DNDC model was more accurate than IPCC methodology at estimating N2O emissions at both sites. Key words: Nitrous oxide, DNDC, soil model, greenhouse gas, testing
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Li, Hu, Jianjun Qiu, Ligang Wang, and Li Yang. "Advance in a terrestrial biogeochemical model—DNDC model." Acta Ecologica Sinica 31, no. 2 (April 2011): 91–96. http://dx.doi.org/10.1016/j.chnaes.2010.11.006.
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Deng, J., Z. Zhou, B. Zhu, X. Zheng, C. Li, X. Wang, and Z. Jian. "Modeling nitrogen loading in a small watershed in Southwest China using a DNDC model with hydrological enhancements." Biogeosciences Discussions 8, no. 4 (July 6, 2011): 6383–413. http://dx.doi.org/10.5194/bgd-8-6383-2011.
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Abstract. 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's 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.
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Deng, J., Z. Zhou, B. Zhu, X. Zheng, C. Li, X. Wang, and Z. Jian. "Modeling nitrogen loading in a small watershed in southwest China using a DNDC model with hydrological enhancements." Biogeosciences 8, no. 10 (October 28, 2011): 2999–3009. http://dx.doi.org/10.5194/bg-8-2999-2011.
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Abstract. 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's 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.
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Smith, W. N., B. B. Grant, R. L. Desjardins, P. Rochette, C. F. Drury, and C. Li. "Evaluation of two process-based models to estimate soil N2O emissions in Eastern Canada." Canadian Journal of Soil Science 88, no. 2 (May 1, 2008): 251–60. http://dx.doi.org/10.4141/cjss06030.
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Process-based models play an important role in the estimation of soil N2O emissions from regions with contrasting soil and climatic conditions. A study was performed to evaluate the ability of two process-based models, DAYCENT and DNDC, to estimate N2O emissions, soil nitrate- and ammonium-N levels, as well as soil temperature and water content. The measurement sites included a maize crop fertilized with pig slurry (Quebec) and a wheat-maize-soybean rotation as part of a tillage-fertilizer experiment (Ontario). At the Quebec site, both models accurately simulated soil temperature with an average relative error (ARE) ranging from 0 to 2%. The models underpredicted soil temperature at the Ontario site with ARE from −5 to −7% for DNDC and from −5 to −13% for DAYCENT. Both models underestimated soil water content particularly during the growing season. The DNDC model accurately predicted average seasonal N2O emissions across treatments at both sites whereas the DAYCENT model underpredicted N2O emissions by 32 to 58% for all treatments excluding the fertilizer treatment at the Quebec site. Both models had difficulty in simulating the timing of individual emission events. The hydrology and nitrogen transformation routines need to be improved in both models before further enhancements are made to the trace gas routines. Key words: Nitrous oxide, process-based model, DNDC, greenhouse gas emissions, soil
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Zhang, Wei, Zhisheng Yao, Siqi Li, Xunhua Zheng, Han Zhang, Lei Ma, Kai Wang, et al. "An improved process-oriented hydro-biogeochemical model for simulating dynamic fluxes of methane and nitrous oxide in alpine ecosystems with seasonally frozen soils." Biogeosciences 18, no. 13 (July 14, 2021): 4211–25. http://dx.doi.org/10.5194/bg-18-4211-2021.
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Abstract. The hydro-biogeochemical model Catchment Nutrient Management Model – DeNitrification-DeComposition (CNMM-DNDC) was established to simultaneously quantify ecosystem productivity and losses of nitrogen and carbon at the site or catchment scale. As a process-oriented model, this model is expected to be universally applied to different climate zones, soils, land uses and field management practices. This study is one of many efforts to fulfill such an expectation, which was performed to improve the CNMM-DNDC by incorporating a physically based soil thermal module to simulate the soil thermal regime in the presence of freeze–thaw cycles. The modified model was validated with simultaneous field observations in three typical alpine ecosystems (wetlands, meadows and forests) within a catchment located in seasonally frozen regions of the eastern Tibetan Plateau, including observations of soil profile temperature, topsoil moisture, and fluxes of methane (CH4) and nitrous oxide (N2O). The validation showed that the modified CNMM-DNDC was able to simulate the observed seasonal dynamics and magnitudes of the variables in the three typical alpine ecosystems, with index-of-agreement values of 0.91–1.00, 0.49–0.83, 0.57–0.88 and 0.26–0.47, respectively. Consistent with the emissions determined from the field observations, the simulated aggregate emissions of CH4 and N2O were highest for the wetland among three alpine ecosystems, which were dominated by the CH4 emissions. This study indicates the possibility for utilizing the process-oriented model CNMM-DNDC to predict hydro-biogeochemical processes, as well as related gas emissions, in seasonally frozen regions. As the original CNMM-DNDC was previously validated in some unfrozen regions, the modified CNMM-DNDC could be potentially applied to estimate the emissions of CH4 and N2O from various ecosystems under different climate zones at the site or catchment scale.
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Hutchinson, J. J., B. B. Grant, W. N. Smith, R. L. Desjardins, C. A. Campbell, D. E. Worth, and X. P. Vergé. "Estimates of direct nitrous oxide emissions from Canadian agroecosystems and their uncertainties." Canadian Journal of Soil Science 87, Special Issue (March 1, 2007): 141–52. http://dx.doi.org/10.4141/s06-066.
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Using a revised Intergovernmental Panel on Climate Change (IPCC) methodology and the process-based model DeNitrification and DeComposition (DNDC), we estimated N2O emissions from agroecosystems in Canada for each census year from 1981 to 2001. Based on the IPCC methodology, direct emissions of N2O ranged from 12.9 to 17.3 with an average of 15.1 Tg CO2 equivalents, while the DNDC model predicted values from 16.0 to 24.3 with an average of 20.8 Tg CO2 equivalents over the same period, and showed a large interannual variation reflecting weather variability. On a provincial basis, emissions estimated by IPCC and DNDC methods were highest in Alberta, Saskatchewan and Ontario, intermediate for Manitoba and Quebec and lowest in British Columbia and the Atlantic provinces. The greatest source of emissions estimated by the IPCC method was from N fertilizer (avg. 6.32 Tg CO2 equiv. in Canada), followed by crop residues (4.24), pasture range and paddocks (PRP) (2.77), and manure (1.65). All sources of emissions, but especially those from fertilizers, increased moderately over time. Monte Carlo Simulation was used to determine the uncertainty associated with the 2001 emission estimates for both IPCC and DNDC methodologies. The simulation generated most likely values of 19.2 and 16.0 Tg CO2 equivalents for IPCC and DNDC, respectively, with uncertainties of 37 and 41%, respectively. Values for the IPCC estimates varied between 28% for PRP and manure and 50% for N fertilizer and crop residues. At the provincial level, uncertainty ranged between 15 and 47% with higher values on the prairies. Sensitivity analyses for IPCC estimates showed crop residues as the most important source of uncertainty followed by synthetic N-fertilizers. Our analysis demonstrated that N2O emissions can be effectively estimated by both the DNDC and IPCC methods and that their uncertainties can be effectively estimated by Monte Carlo Simulation. Key words: Nitrous oxide, IPCC, DNDC model, Uncertainty analysis, Monte Carlo Simulation
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Horák, Ján, and Bernard Šiška. "EVALUATION OF N2O EMISSIONS BY DNDC MODEL FOR SANDY LOAM SOILS OF DANUBIAN LOWLAND." JOURNAL OF ENVIRONMENTAL ENGINEERING AND LANDSCAPE MANAGEMENT 14, no. 4 (December 31, 2006): 165–71. http://dx.doi.org/10.3846/16486897.2006.9636894.
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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.
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Deng, J., C. Li, S. Frolking, Y. Zhang, K. Bäckstrand, and P. Crill. "Assessing effects of permafrost thaw on C fluxes based on multiyear modeling across a permafrost thaw gradient at Stordalen, Sweden." Biogeosciences 11, no. 17 (September 9, 2014): 4753–70. http://dx.doi.org/10.5194/bg-11-4753-2014.
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Abstract. Northern peatlands in permafrost regions contain a large amount of organic carbon (C) in the soil. Climate warming and associated permafrost degradation are expected to have significant impacts on the C balance of these ecosystems, but the magnitude is uncertain. We incorporated a permafrost model, Northern Ecosystem Soil Temperature (NEST), into a biogeochemical model, DeNitrification-DeComposition (DNDC), to model C dynamics in high-latitude peatland ecosystems. The enhanced model was applied to assess effects of permafrost thaw on C fluxes of a subarctic peatland at Stordalen, Sweden. DNDC simulated soil freeze–thaw dynamics, net ecosystem exchange of CO2 (NEE), and CH4 fluxes across three typical land cover types, which represent a gradient in the process of ongoing permafrost thaw at Stordalen. Model results were compared with multiyear field measurements, and the validation indicates that DNDC was able to simulate observed differences in seasonal soil thaw, NEE, and CH4 fluxes across the three land cover types. Consistent with the results from field studies, the modeled C fluxes across the permafrost thaw gradient demonstrate that permafrost thaw and the associated changes in soil hydrology and vegetation not only increase net uptake of C from the atmosphere but also increase the annual to decadal radiative forcing impacts on climate due to increased CH4 emissions. This study indicates the potential of utilizing biogeochemical models, such as DNDC, to predict the soil thermal regime in permafrost areas and to investigate impacts of permafrost thaw on ecosystem C fluxes after incorporating a permafrost component into the model framework.
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Deng, J., C. Li, S. Frolking, Y. Zhang, K. Bäckstrand, and P. Crill. "Assessing effects of permafrost thaw on C fluxes based on a multi-year modeling across a permafrost thaw gradient at Stordalen, Sweden." Biogeosciences Discussions 11, no. 3 (March 11, 2014): 3963–99. http://dx.doi.org/10.5194/bgd-11-3963-2014.
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Abstract. Northern peatlands in permafrost regions contain large amount of organic carbon (C) in the soil. Climate warming and associated permafrost degradation are expected to have significant impacts on the C balance of these ecosystems, but the magnitude is uncertain. We incorporated a permafrost model, Northern Ecosystem Soil Temperature (NEST), into a biogeochemical model, DeNitrification-DeComposition (DNDC), to model C dynamics in high-latitude peatland ecosystems. The enhanced model was applied to assess effects of permafrost thaw on C fluxes of a sub-arctic peatland at Stordalen, Sweden. DNDC simulated soil freeze/thaw dynamics, net ecosystem exchange of CO2 (NEE), and CH4 fluxes across three typical land cover types, which represent different stages in the process of ongoing permafrost thaw at Stordalen. Model results were compared with multi-year field measurements and the validation indicates that DNDC was able to simulate observed differences in soil thaw, NEE, and CH4 fluxes across the three land cover types at Stordalen. Consistent with the results from field studies, the modeled C fluxes across the permafrost thaw gradient demonstrate that permafrost thaw and the associated changes in soil hydrology and vegetation increase net uptake of C from the atmosphere, but also increase the radiative forcing impacts on climate due to increased CH4 emissions. This study indicates the potential of utilizing biogeochemical models, such as DNDC, to predict soil thermal regime in permafrost areas and to investigate impacts of permafrost thaw on ecosystem C fluxes after incorporating a permafrost component into the model framework.
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Nihayah, Badi'atun, Bayu Dwi Apri Nugroho, and Nur Aini Iswati Hasanah. "Denitrification Decomposition (DNDC) Model for Estimation CH4 Emissions in Rice Cultivation through SRI Method." Jurnal Ilmiah Rekayasa Pertanian dan Biosistem 10, no. 1 (March 24, 2022): 116–28. http://dx.doi.org/10.29303/jrpb.v10i1.278.
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Budidaya padi merupakan salah satu sektor pertanian yang menyumbang emisi gas rumah kaca terutama CH4. Upaya penurunan emisi gas CH4 pada penelitian ini yaitu mengintegrasikan komponen teknologi antara varietas, penggunaan pupuk dan irigasi berselang melalui metode budidaya System of Rice Intensification. Penelitian bertujuan untuk mengetahui pengaruh varietas dan pemupukan terhadap emisi gas CH4 selama satu musim tanam serta melakukan pemodelan simulasi untuk estimasi emisi CH4. Model yang digunakan adalah Denitrification-Decomposition (DNDC) berdasarkan parameter data input kondisi iklim, sifat tanah dan praktik manajemen pertanian (termasuk pemupukan, irigasi, pengolahan tanah, produksi biomassa). Rancangan yang digunakan yaitu Nested Design dengan dua faktor perlakuan yaitu pemupukan yang terdiri 1) Pupuk kandang dan MOL (P1) ; 2) Pupuk kandang, ZA, SP36 dan KCl (P2), dan perlakuan varietas diantaranya 1) Ciherang dan 2) IR-64. Hasil observasi menunjukkan bahwa perlakuan pemupukan P2-C menghasilkan total emisi CH4 13.41% lebih rendah daripada P1-C dan perlakuan P2-IR 39.43% dibanding P1-IR. Begitu pula hasil simulasi DNDC yang menunjukkan bahwa perlakuan pemupukan (P2) menghasilkan total emisi CH4 dari kedua varietas yaitu ciherang 53.57% dan IR-64 sebesar 58.74% lebih rendah dibanding permupukan (P1). Evaluasi model antara hasil observasi dan simulasi DNDC menunjukkan nilai R2 dan RMSE setiap perlakuan yaitu P1-C ; P1-IR ; P2-C dan P2-IR berturut-turut sebesar (R2 = 0.65 ; RMSE = 13.19) ; (R2 = 0.003 ; RMSE = 3.55) ; (R2 = 0.17 ; RMSE = 32.06) dan (R2 = 0.35 ; RMSE = 12.25). Sehingga dapat dikatakan bahwa hasil simulasi DNDC belum cukup memuaskan dan dibutuhkan kalibrasi.
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Deng, Lu, Xianyong Meng, Ruide Yu, and Qian Wang. "Assessment of the Effect of Mulch Film on Crops in the Arid Agricultural Region of China under Future Climate Scenarios." Water 11, no. 9 (August 31, 2019): 1819. http://dx.doi.org/10.3390/w11091819.
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Plastic mulch film is widely used in agricultural production. However, there are very few studies on degradable mulch film. In order to investigate the effects of using degradable mulch film in arid regions on crop yield and water use efficiency, we used fully biodegradable mulch films on both maize and bare land cultivation experimental areas. The DeNitrification-DeComposition (DNDC) model was used to analyze changes in maize biomass in the future under different climate scenario models. We found that using fully biodegradable mulch film in an arid region had a positive effect on biomass yields. In 2015–2017, the annual maize biomass yield increased by 24.5%, 28.9%, and 32.9%, respectively. Hence, this method has expansion and promotion value. A comparison of the DNDC model simulated biomass yields and actual measured values found that the ranges of R2, root mean square error (RMSE), and model efficiency (ME) were 0.98–0.99, 0.38–0.86 mg C ha−1, and 0.80–0.98. This result shows that the DNDC model can accurately simulate changes in maize biomass in this region. Under the premise of a good model fit, future climate scenario model data were used to drive the DNDC model. The results showed that the possible range of maize biomass yields in the future is −6.5% to 10.3%, with the most probable range being 0.2–1.5%. Using future climatic conditions, our work suggests that degradable mulch films can increase water use efficiency by an average of 9.5%. The results of this study can be used to promote the use of degradable mulch films in arid regions, significantly improving sustainable agricultural development.
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Chen, Can, Deli Chen, Jianjun Pan, and Shu Kee Lam. "Application of the Denitrification-Decomposition Model to Predict Carbon Dioxide Emissions under Alternative Straw Retention Methods." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/851901.
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Straw retention has been shown to reduce carbon dioxide (CO2) emission from agricultural soils. But it remains a big challenge for models to effectively predict CO2emission fluxes under different straw retention methods. We used maize season data in the Griffith region, Australia, to test whether the denitrification-decomposition (DNDC) model could simulate annual CO2emission. We also identified driving factors of CO2emission by correlation analysis and path analysis. We show that the DNDC model was able to simulate CO2emission under alternative straw retention scenarios. The correlation coefficients between simulated and observed daily values for treatments of straw burn and straw incorporation were 0.74 and 0.82, respectively, in the straw retention period and 0.72 and 0.83, respectively, in the crop growth period. The results also show that simulated values of annual CO2emission for straw burn and straw incorporation were 3.45 t C ha−1 y−1and 2.13 t C ha−1 y−1, respectively. In addition the DNDC model was found to be more suitable in simulating CO2mission fluxes under straw incorporation. Finally the standard multiple regression describing the relationship between CO2emissions and factors found that soil mean temperature (SMT), daily mean temperature (Tmean), and water-filled pore space (WFPS) were significant.
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Mikhalchuk, Alexander, Ludmila Borilo, Elena Burnashova, Yulia Kharanzhevskaya, Ekaterina Akerman, Natalia Chistyakova, Sergey N. Kirpotin, Oleg S. Pokrovsky, and Sergey Vorobyev. "Assessment of Greenhouse Gas Emissions into the Atmosphere from the Northern Peatlands Using the Wetland-DNDC Simulation Model: A Case Study of the Great Vasyugan Mire, Western Siberia." Atmosphere 13, no. 12 (December 7, 2022): 2053. http://dx.doi.org/10.3390/atmos13122053.
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The peatlands of Western Siberia occupy an area of about 1 million km2 and act as important regulator of carbon exchange between the earth and the atmosphere. Extrapolation of the results of discrete field measurements of CO2 fluxes in bog ecosystems to such a territory is a difficult task, and one of the ways to overcome it is to use a simulation model such as DNDC. However, using this model with a specific territory requires ground verification to confirm its effectiveness. Here, we tested the DNDC model on the largest pristine bog ecosystem of the world, the Great Vasyugan Mire (GVM). The GVM of western Siberia is virtually undisturbed by anthropogenic activity and is the largest bog of Northern Eurasia (53,000 km2). Based on various ground-based observations, the performance of the Wetland-DNDC model was demonstrated (Thale coefficient 0.085 and R2 = 0.675 for CO2). Model input parameters specific to the GVM were constrained and model sensitivity to a wide range of input parameters was analyzed. The estimated annual terrestrial carbon fluxes in 2019 from the GVM test site are mainly controlled by plant respiration (61%) and forest floor degradation (38%). The net CO2 emission flux was 8600 kg C ha−1 year−1, which is in line with estimates from other independent studies.
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Zhang, Yajie, and Haishan Niu. "The development of the DNDC plant growth sub-model and the application of DNDC in agriculture: A review." Agriculture, Ecosystems & Environment 230 (August 2016): 271–82. http://dx.doi.org/10.1016/j.agee.2016.06.017.
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Gilhespy, Sarah L., Steven Anthony, Laura Cardenas, David Chadwick, Agustin del Prado, Changsheng Li, Thomas Misselbrook, et al. "First 20 years of DNDC (DeNitrification DeComposition): Model evolution." Ecological Modelling 292 (November 2014): 51–62. http://dx.doi.org/10.1016/j.ecolmodel.2014.09.004.
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Abdalla, Mohamed, Xiaotong Song, Xiaotang Ju, and Pete Smith. "Evaluation of the DNDC Model to Estimate Soil Parameters, Crop Yield and Nitrous Oxide Emissions for Alternative Long-Term Multi-Cropping Systems in the North China Plain." Agronomy 12, no. 1 (January 2, 2022): 109. http://dx.doi.org/10.3390/agronomy12010109.
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Optimizing crop rotations is one of the proposed sustainable management strategies for increasing carbon sequestration. The main aim of this study was to evaluate the DeNitrification-DeComposition (DNDC) model for estimating soil parameters (temperature, moisture and exchangeable NO3− and NH4+), crop yield and nitrous oxide (N2O) emissions for long-term multi-cropping systems in Hebei, China. The model was validated using five years of data of soil parameters, crop yields and N2O emissions. The DNDC model effectively simulated daily soil temperature, cumulative soil nitrogen and crop yields of all crops. It predicted the trends of observed daily N2O emissions and their cumulative values well but overestimated the magnitude of some peaks. However, the model underestimated daily water filled pore space, especially in dry seasons, and had difficulties in correctly estimating daily exchangeable NO3− and NH4+. Both observed and simulated cumulative N2O results showed that optimized and alternative cropping systems used less nitrogen fertiliser, increased grain yield and decreased N2O emissions compared to the conventional cropping system. Our study shows that although the DNDC model (v. 9.5) is not perfect in estimating daily N2O emissions for these long-term multi-cropping systems, it could still be an effective tool for predicting cumulative emissions.
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Yu, Shujie, and Wencong Yue. "Analysis of Reactive Nitrogen Emissions from Maize Ethanol Production Based on the DNDC Model." IOP Conference Series: Earth and Environmental Science 1011, no. 1 (April 1, 2022): 012002. http://dx.doi.org/10.1088/1755-1315/1011/1/012002.
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Abstract Agricultural system is an important source of reactive nitrogen (Nr) emissions. In this study, DNDC model was established for analyzing Nr emissions in maize planting and maize ethanol production under climate change scenarios (i.e., RCPs 4.5 and 8.5). The DNDC model was applied in maize fields of Shandong Province. The Nr emissions in 2025 and 2030 under the RCPs 4.5 and 8.5 scenarios would range from 2869.24 to 2969.18 kg N/ha. An inventory of Nr emissions in maize ethanol production was obtained in this study. The results showed that compared with maize planting in other cities, maize fields in Linyi would release the biggest amount of N2O and NO, as well as the smallest amount of NH3. The study can support decision making for Nr emissions reduction in agricultural systems.
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Lembaid, Ibtissame, Rachid Moussadek, Rachid Mrabet, and Ahmed Bouhaouss. "Modeling Soil Organic Carbon Changes under Alternative Climatic Scenarios and Soil Properties Using DNDC Model at a Semi-Arid Mediterranean Environment." Climate 10, no. 2 (February 9, 2022): 23. http://dx.doi.org/10.3390/cli10020023.
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Soil organic carbon (SOC) is one of the central issues in dealing with soil fertility as well as environmental and food safety. Due to the lack of relevant data sources and methodologies, analyzing SOC dynamics has been a challenge in Morocco. During the last two decades, process-based models have been adopted as alternative and powerful tools for modeling SOC dynamics; whereas, information and knowledge on the most sensitive model inputs under different climate, and soil conditions are still very limited. For this purpose, a sensitivity analysis was conducted in the present work, using the DeNitrification-DeComposition (DNDC) model based on the data collected at a semi-arid region (Merchouch station, Morocco). The objective is to identify the most influential factors affecting the DNDC-modeled SOC dynamics in a semi-arid region across different climatic and soil conditions. The results of sensitivity analysis highlighted air temperature as the main determinant of SOC. A decrease in air temperature of 4 °C results in an almost 161 kg C ha−1 yr−1 increase in C sequestration rate. Initial SOC was also confirmed to be one of the most sensitive parameters for SOC. There was a 96 kg C ha−1 yr−1 increase in C sequestration rate under low initial SOC (0.005 kg C ha−1). In the DNDC, air temperature in climatic factors and initial SOC in variable soil properties had the largest impacts on SOC accumulation in Merchouch station. We can conclude that the sensitivity analysis conducted in this study within the DNDC can contribute to provide a scientific evidence of uncertainties of the selected inputs variables who can lead to uncertainties on the SOC in the study site. The information in this paper can be helpful for scientists and policy makers, who are dealing with regions of similar environmental conditions as Merchouch Station, by identifying alternative scenarios of soil carbon sequestration.
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Shin, Min Hwan, Jeong Ryeol Jang, Chul Hee Won, Young Hun Jung, Su In Lee, Kyoung Lim, and Joong Dae Choi. "Simulation of GHG Emission from Paddy Field using DNDC Model." Journal of The Korean Society of Agricultural Engineers 56, no. 2 (March 31, 2014): 47–57. http://dx.doi.org/10.5389/ksae.2014.56.2.047.
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Zhang, F., J. Qi, F. M. Li, C. S. Li, and C. B. Li. "Quantifying nitrous oxide emissions from Chinese grasslands with a process-based model." Biogeosciences 7, no. 6 (June 28, 2010): 2039–50. http://dx.doi.org/10.5194/bg-7-2039-2010.
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Abstract. As one of the largest land cover types, grassland can potentially play an important role in the ecosystem services of natural resources in China. Nitrous oxide (N2O) is a major greenhouse gas emitted from grasslands. Current N2O inventory at a regional or national level in China relies on the emission factor method, which is based on limited measurements. To improve the accuracy of the inventory by capturing the spatial variability of N2O emissions under the diverse climate, soil and management conditions across China, we adopted an approach by utilizing a process-based biogeochemical model, DeNitrification-DeComposition (DNDC), to quantify N2O emissions from Chinese grasslands. In the present study, DNDC was tested against datasets of N2O fluxes measured at eight grassland sites in China with encouraging results. The validated DNDC was then linked to a GIS database holding spatially differentiated information of climate, soil, vegetation and management at county-level for all the grasslands in the country. Daily weather data for 2000–2007 from 670 meteorological stations across the entire domain were employed to serve the simulations. The modelled results on a national scale showed a clear geographic pattern of N2O emissions. A high-emission strip showed up stretching from northeast to central China, which is consistent with the eastern boundary between the temperate grassland region and the major agricultural regions of China. The grasslands in the western mountain regions, however, emitted much less N2O. The regionally averaged rates of N2O emissions were 0.26, 0.14 and 0.38 kg nitrogen (N) ha−1 y−1 for the temperate, montane and tropical/subtropical grasslands, respectively. The annual mean N2O emission from the total 337 million ha of grasslands in China was 76.5 ± 12.8 Gg N for the simulated years.
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Sereni, Laura, Bertrand Guenet, Charlotte Blasi, Olivier Crouzet, Jean-Christophe Lata, and Isabelle Lamy. "To what extent can soil moisture and soil Cu contamination stresses affect nitrous species emissions? Estimation through calibration of a nitrification–denitrification model." Biogeosciences 19, no. 12 (June 20, 2022): 2953–68. http://dx.doi.org/10.5194/bg-19-2953-2022.
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Abstract. Continental biogeochemical models are commonly used to predict the effect of land use, exogenous organic matter input or climate change on soil greenhouse gas emission. However, they cannot be used for this purpose to investigate the effect of soil contamination, while contamination affects several soil processes and concerns a large fraction of land surface. For that, in this study we implemented a commonly used model estimating soil nitrogen (N) emissions, the DeNitrification DeCompostion (DNDC) model, with a function taking into account soil copper (Cu) contamination in nitrate production control. Then, we aimed at using this model to predict N2O-N, NO2-N, NO-N and NH4-N emissions in the presence of contamination and in the context of changes in precipitations. Initial incubations of soils were performed at different soil moisture levels in order to mimic expected rainfall patterns during the next decades and in particular drought and excess of water. Then, a bioassay was used in the absence or presence of Cu to assess the effect of the single (moisture) or double stress (moisture and Cu) on soil nitrate production. Data of nitrate production obtained through a gradient of Cu under each initial moisture incubation were used to parameterise the DNDC model and to estimate soil N emission considering the various effects of Cu. Whatever the initial moisture incubation, experimental results showed a NO3-N decreasing production when Cu was added but depending on soil moisture. The DNDC-Cu version we proposed was able to reproduce these observed Cu effects on soil nitrate concentration with r2 > 0.99 and RMSE < 10 % for all treatments in the DNDC-Cu calibration range (> 40 % of the water holding capacity) but showed poor performances for the dry treatments. We modelled a Cu effect inducing an increase in NH4-N soil concentration and emissions due to a reduced nitrification activity and therefore a decrease in NO3-N, N2O-N and NOx-N concentrations and emissions. The effect of added Cu predicted by the model was larger on N2-N and N2O-N emissions than on the other N species and larger for the soils incubated under constant than variable moisture. Our work shows that soil contamination can be considered in continental biogeochemical models to better predict soil greenhouse gas emissions.
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Forster, Daniel, Jia Deng, Matthew Tom Harrison, and Narasinha Shurpali. "Simulating Soil-Plant-Climate Interactions and Greenhouse Gas Exchange in Boreal Grasslands Using the DNDC Model." Land 11, no. 11 (November 1, 2022): 1947. http://dx.doi.org/10.3390/land11111947.
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With global warming, arable land in boreal regions is tending to expand into high latitude regions in the northern hemisphere. This entails certain risks; such that inappropriate management could result in previously stable carbon sinks becoming sources. Agroecological models are an important tool for assessing the sustainability of long-term management, yet applications of such models in boreal zones are scarce. We collated eddy-covariance, soil climate and biomass data to evaluate the simulation of GHG emissions from grassland in eastern Finland using the process-based model DNDC. We simulated gross primary production (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco) with fair performance. Soil climate, soil temperature and soil moisture at 5 cm were excellent, and soil moisture at 20 cm was good. However, the model overestimated NEE and Reco following crop termination and tillage events. These results indicate that DNDC can satisfactorily simulate GHG fluxes in a boreal grassland setting, but further work is needed, particularly in simulated second biomass cuts, the (>20 cm) soil layers and model response to management transitions between crop types, cultivation, and land use change.
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Cui, F., X. Zheng, C. Liu, K. Wang, Z. Zhou, and J. Deng. "Assessing biogeochemical effects and best management practice for a wheat–maize cropping system using the DNDC model." Biogeosciences 11, no. 1 (January 7, 2014): 91–107. http://dx.doi.org/10.5194/bg-11-91-2014.
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Abstract. Contemporary agriculture is shifting from a single-goal to a multi-goal strategy, which in turn requires choosing best management practice (BMP) based on an assessment of the biogeochemical effects of management alternatives. The bottleneck is the capacity of predicting the simultaneous effects of different management practice scenarios on multiple goals and choosing BMP among scenarios. The denitrification–decomposition (DNDC) model may provide an opportunity to solve this problem. We validated the DNDC model (version 95) using the observations of soil moisture and temperature, crop yields, aboveground biomass and fluxes of net ecosystem exchange of carbon dioxide, methane, nitrous oxide (N2O), nitric oxide (NO) and ammonia (NH3) from a wheat–maize cropping site in northern China. The model performed well for these variables. Then we used this model to simulate the effects of management practices on the goal variables of crop yields, NO emission, nitrate leaching, NH3 volatilization and net emission of greenhouse gases in the ecosystem (NEGE). Results showed that no-till and straw-incorporated practices had beneficial effects on crop yields and NEGE. Use of nitrification inhibitors decreased nitrate leaching and N2O and NO emissions, but they significantly increased NH3 volatilization. Irrigation based on crop demand significantly increased crop yield and decreased nitrate leaching and NH3 volatilization. Crop yields were hardly decreased if nitrogen dose was reduced by 15% or irrigation water amount was reduced by 25%. Two methods were used to identify BMP and resulted in the same BMP, which adopted the current crop cultivar, field operation schedules and full straw incorporation and applied nitrogen and irrigation water at 15 and 25% lower rates, respectively, than the current use. Our study indicates that the DNDC model can be used as a tool to assess biogeochemical effects of management alternatives and identify BMP.
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Cui, F., X. H. Zheng, C. Y. Liu, K. Wang, Z. X. Zhou, and J. Deng. "Assessing biogeochemical effects and best management practice for a wheat–maize cropping system using the DNDC model." Biogeosciences Discussions 10, no. 5 (May 22, 2013): 8561–609. http://dx.doi.org/10.5194/bgd-10-8561-2013.
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Abstract. Contemporary agriculture is shifting from a single-goal to a multi-goal strategy, which in turn requires choosing best management practice (BMP) based on assessment of the biogeochemical effects of management alternatives. The bottleneck is the capacity of predicting the simultaneous effects of different management practice scenarios on multiple goals and choosing BMP among scenarios. The denitrification-decomposition (DNDC) model may provide an opportunity to solve this problem. We validated the DNDC model (version 95) using the observations of soil moisture and temperature, crop yields, aboveground biomass and fluxes of net ecosystem exchange of carbon dioxide, methane, nitrous oxide (N2O), nitric oxide (NO) and ammonia (NH3) from a wheat-maize cropping site in northern China. The model performed well for these variables. Then we used this model to simulate the effects of management practices on the goal variables of crop yields, NO emission, nitrate leaching, NH3 volatilization and net emission of greenhouse gases in the ecosystem (NEGE). Results showed that no-till and straw-incorporated practices had beneficial effects on crop yields and NEGE. Use of nitrification inhibitors decreased nitrate leaching and N2O and NO emissions, but they significantly increased NH3 volatilization. Irrigation based on crop demand significantly increased crop yield and decreased nitrate leaching and NH3 volatilization. Crop yields were hardly decreased if nitrogen dose was reduced by 15% or irrigation water amount was reduced by 25%. Two methods were used to identify BMP and resulted in the same BMP, which adopted the current crop cultivar, field operation schedules and full straw incorporation and applied nitrogen and irrigation water at 15% and 25% lower rates, respectively, than the current use. Our study indicates that the DNDC model can be used as a tool to assess biogeochemical effects of management alternatives and identify BMP.
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Rahmat, Arif, Chusnul Arif, and Yudi Chadirin. "Estimasi Gas Rumah Kaca pada Berbagai Macam Pengelolaan Air Menggunakan Model Denitrifikasi-Dekomposisi (DNDC)." Jurnal Irigasi 13, no. 1 (January 27, 2019): 11. http://dx.doi.org/10.31028/ji.v13.i1.11-20.
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Peningkatan kelangkaan sumber daya air menstimulasi pengembangan berbagai metode untuk menjaga air pada lahan padi. Beberapa penelitian telah dilakukan secara berkelanjutan dalam mengamati efektivitas berbagai rejim air dalam menjaga air, mengurangi fluks gas rumah kaca (GRK), dan mempertahankan hasil panen padi. Pengelolaan irigasi merupakan faktor penting dalam mengendalikan emisi metana (CH4) dan dinitrogen oksida (N2O) di lahan sawah. Penelitian ini bertujuan untuk mengevaluasi Model Denitrifikasi-Dekomposisi (DNDC) dalam mengestimasi emisi gas rumah kaca dari berbagai macam pengelolaan rejim air. Penelitian dilakukan dari Januari hingga Mei 2018. Metode SRI digunakan dalam percobaan plot dengan perlakuan tiga rejim air yang berbeda: rejim tergenang (RT), rejim basah (RB), dan rejim kering (RK). Model DNDC dibuat untuk memprediksi emisi CH4 dan N2O dalam ekosistem pertanian. Model ini telah digunakan dan dievaluasi di tanah subtropis, tetapi model ini masih perlu dievaluasi kemampuannya untuk tanah di iklim tropis seperti Indonesia. Emisi yang dihasilkan menunjukkan pola berbeda antara model simulasi dan model observasi. Nilai R2 dari simulasi emisi CH4 dan N2O dengan fluks aktual masing-masing adalah 0,123 dan -0,237. Temuan dari penelitian menunjukkan bahwa model simulasi memerlukan pengembangan untuk mampu memperkirakan emisi CH4 dan N2O pada kondisi lingkungan Indonesia.
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Grosz, Balázs, Reinhard Well, Rene Dechow, Jan Reent Köster, Mohammad Ibrahim Khalil, Simone Merl, Andreas Rode, Bianca Ziehmer, Amanda Matson, and Hongxing He. "Evaluation of denitrification and decomposition from three biogeochemical models using laboratory measurements of N<sub>2</sub>, N<sub>2</sub>O and CO<sub>2</sub>." Biogeosciences 18, no. 20 (October 21, 2021): 5681–97. http://dx.doi.org/10.5194/bg-18-5681-2021.
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Abstract. Biogeochemical models are essential for the prediction and management of nitrogen (N) cycling in agroecosystems, but the accuracy of the denitrification and decomposition sub-modules is critical. Current models were developed before suitable soil N2 flux data were available, which may have led to inaccuracies in how denitrification was described. New measurement techniques, using gas chromatography and isotope-ratio mass spectrometry (IRMS), have enabled the collection of more robust N2, N2O and CO2 data. We incubated two arable soils – a silt-loam and a sand soil – for 34 and 58 d, respectively, with small field-relevant changes made to control factors during this period. For the silt-loam soil, seven treatments varying in moisture, bulk density and NO3- contents were included, with temperature changing during the incubation. The sandy soil was incubated with and without incorporation of litter (ryegrass), with temperature, water content and NO3- content changing during the incubation. The denitrification and decomposition sub-modules of DeNi, Coup and DNDC were tested using the data. No systematic calibration of the model parameters was conducted since our intention was to evaluate the general model structure or “default” model runs. Measured fluxes generally responded as expected to control factors. We assessed the direction of modeled responses to control factors using three categories: no response, a response in the same direction as measurements or a response in the opposite direction to measurements. DNDC responses were 14 %, 52 % and 34 %, respectively. Coup responses were 47 %, 19 % and 34 %, respectively. DeNi responses were 0 %, 67 % and 33 %, respectively. The magnitudes of the modeled fluxes were underestimated by Coup and DNDC and overestimated by DeNi for the sandy soil, while there was no general trend for the silt-loam soil. None of the models was able to determine litter-induced decomposition correctly. To conclude, the currently used sub-modules are not able to consistently simulate the denitrification and decomposition processes. For better model evaluation and development, we need to design better experiments, take more frequent measurements, use new or updated measurement techniques, address model complexity, add missing processes to the models, calibrate denitrifier microbial dynamics, and evaluate the anaerobic soil volume concept.
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Bierer, Andrew M., April B. Leytem, Robert S. Dungan, Amber D. Moore, and David L. Bjorneberg. "Soil Organic Carbon Dynamics in Semi-Arid Irrigated Cropping Systems." Agronomy 11, no. 3 (March 5, 2021): 484. http://dx.doi.org/10.3390/agronomy11030484.
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Insufficient characterization of soil organic carbon (SOC) dynamics in semi-arid climates contributes uncertainty to SOC sequestration estimates. This study estimated changes in SOC (0–30 cm depth) due to variations in manure management, tillage regime, winter cover crop, and crop rotation in southern Idaho (USA). Empirical data were used to drive the Denitrification Decomposition (DNDC) model in a “default” and calibrated capacity and forecast SOC levels until 2050. Empirical data indicates: (i) no effect (p = 0.51) of winter triticale on SOC after 3 years; (ii) SOC accumulation (0.6 ± 0.5 Mg ha–1 year–1) under a rotation of corn-barley-alfalfax3 and no change (p = 0.905) in a rotation of wheat-potato-barley-sugarbeet; (iii) manure applied annually at rate 1X is not significantly different (p = 0.75) from biennial application at rate 2X; and (iv) no significant effect of manure application timing (p = 0.41, fall vs. spring). The DNDC model simulated empirical SOC and biomass C measurements adequately in a default capacity, yet specific issues were encountered. By 2050, model forecasting suggested: (i) triticale cover resulted in SOC accrual (0.05–0.27 Mg ha–1 year–1); (ii) when manure is applied, conventional tillage regimes are favored; and (iii) manure applied treatments accrue SOC suggesting a quadratic relationship (all R2 > 0.85 and all p < 0.0001), yet saturation behavior was not realized when extending the simulation to 2100. It is possible that under very large C inputs that C sequestration is favored by DNDC which may influence “NetZero” C initiatives.
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Jiang, Zewei, Shihong Yang, Jie Ding, Xiao Sun, Xi Chen, Xiaoyin Liu, and Junzeng Xu. "Modeling Climate Change Effects on Rice Yield and Soil Carbon under Variable Water and Nutrient Management." Sustainability 13, no. 2 (January 8, 2021): 568. http://dx.doi.org/10.3390/su13020568.
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Soil organic carbon (SOC) conservation in agricultural soils is vital for sustainable agricultural production and climate change mitigation. To project changes of SOC and rice yield under different water and carbon management in future climates, based on a two-year (2015 and 2016) field test in Kunshan, China, the Denitrification Decomposition (DNDC) model was modified and validated and the soil moisture module of DNDC was improved to realize the simulation under conditions of water-saving irrigation. Four climate models under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5), which were integrated from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), were ensembled by the Bayesian Model Averaging (BMA) method. The results showed that the modified DNDC model can effectively simulate changes in SOC, dissolved organic carbon (DOC), and rice yield under different irrigation and fertilizer management systems. The normalized root mean squared errors of the SOC and DOC were 3.45–17.59% and 8.79–13.93%, respectively. The model efficiency coefficients of SOC and DOC were close to 1. The climate scenarios had a great impact on rice yield, whereas the impact on SOC was less than that of agricultural management measures on SOC. The average rice yields of all the RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios in the 2090s decreased by 18.41%, 38.59%, 65.11%, and 65.62%, respectively, compared with those in the 2020s. The long-term effect of irrigation on the SOC content of paddy fields was minimal. The SOC of the paddy fields treated with conventional fertilizer decreased initially and then remained unchanged, while the other treatments increased obviously with time. The rice yields of all the treatments decreased with time. Compared with traditional management, controlled irrigation with straw returning clearly increased the SOC and rice yields of paddy fields. Thus, this water and carbon management system is recommended for paddy fields.
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Jiang, Zewei, Shihong Yang, Jie Ding, Xiao Sun, Xi Chen, Xiaoyin Liu, and Junzeng Xu. "Modeling Climate Change Effects on Rice Yield and Soil Carbon Under Variable Water and Nutrient Management." Sustainability 13, no. 2 (January 8, 2021): 568. http://dx.doi.org/10.3390/su13020568.
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Soil organic carbon (SOC) conservation in agricultural soils is vital for sustainable agricultural production and climate change mitigation. To project changes of SOC and rice yield under different water and carbon management in future climates, based on a two-year (2015 and 2016) field test in Kunshan, China, the Denitrification Decomposition (DNDC) model was modified and validated and the soil moisture module of DNDC was improved to realize the simulation under conditions of water-saving irrigation. Four climate models under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5), which were integrated from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), were ensembled by the Bayesian Model Averaging (BMA) method. The results showed that the modified DNDC model can effectively simulate changes in SOC, dissolved organic carbon (DOC), and rice yield under different irrigation and fertilizer management systems. The normalized root mean squared errors of the SOC and DOC were 3.45–17.59% and 8.79–13.93%, respectively. The model efficiency coefficients of SOC and DOC were close to 1. The climate scenarios had a great impact on rice yield, whereas the impact on SOC was less than that of agricultural management measures on SOC. The average rice yields of all the RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios in the 2090s decreased by 18.41%, 38.59%, 65.11%, and 65.62%, respectively, compared with those in the 2020s. The long-term effect of irrigation on the SOC content of paddy fields was minimal. The SOC of the paddy fields treated with conventional fertilizer decreased initially and then remained unchanged, while the other treatments increased obviously with time. The rice yields of all the treatments decreased with time. Compared with traditional management, controlled irrigation with straw returning clearly increased the SOC and rice yields of paddy fields. Thus, this water and carbon management system is recommended for paddy fields.
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Oo, Aung Zaw, Shigeto Sudo, Tamon Fumoto, Kazuyuki Inubushi, Keisuke Ono, Akinori Yamamoto, Sonoko D. Bellingrath-Kimura, et al. "Field Validation of the DNDC-Rice Model for Methane and Nitrous Oxide Emissions from Double-Cropping Paddy Rice under Different Irrigation Practices in Tamil Nadu, India." Agriculture 10, no. 8 (August 13, 2020): 355. http://dx.doi.org/10.3390/agriculture10080355.
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Two-year field experiments were conducted at Tamil Nadu Rice Research Institute, Aduthurai, Tamil Nadu, India, to evaluate the effect of continuous flooding (CF) and alternate wetting and drying (AWD) irrigation strategies on rice grain yield and greenhouse gas emissions from double-cropping paddy rice. Field observation results showed that AWD irrigation was found to reduce the total seasonal methane (CH4) emission by 22.3% to 56.2% compared with CF while maintaining rice yield. By using the observed two-year field data, validation of the DNDC-Rice model was conducted for CF and AWD practices. The model overestimated rice grain yield by 24% and 29% in CF and AWD, respectively, averaged over the rice-growing seasons compared to observed values. The simulated seasonal CH4 emissions for CF were 6.4% lower and 4.2% higher than observed values and for AWD were 9.3% and 12.7% lower in the summer and monsoon season, respectively. The relative deviation of simulated seasonal nitrous oxide (N2O) emissions from observed emissions in CF were 27% and −35% and in AWD were 267% and 234% in the summer and monsoon season, respectively. Although the DNDC-Rice model reasonably estimated the total CH4 emission in CF and reproduced the mitigation effect of AWD treatment on CH4 emissions well, the model did not adequately predict the total N2O emission under water-saving irrigation. In terms of global warming potential (GWP), nevertheless there was a good agreement between the simulated and observed values for both CF and AWD irrigations due to smaller contributions of N2O to the GWP compared with that of CH4. This study showed that the DNDC-Rice model could be used for the estimation of CH4 emissions, the primary source of GWP from double-cropping paddy rice under different water management conditions in the tropical regions.
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Wang, Zhen, Xiuying Zhang, Lei Liu, Shanqian Wang, Limin Zhao, Xiaodi Wu, Wuting Zhang, and Xianjin Huang. "Estimates of methane emissions from Chinese rice fields using the DNDC model." Agricultural and Forest Meteorology 303 (June 2021): 108368. http://dx.doi.org/10.1016/j.agrformet.2021.108368.
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Giltrap, Donna L., Changsheng Li, and Surinder Saggar. "DNDC: A process-based model of greenhouse gas fluxes from agricultural soils." Agriculture, Ecosystems & Environment 136, no. 3-4 (March 15, 2010): 292–300. http://dx.doi.org/10.1016/j.agee.2009.06.014.
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Congreves, K. A., B. B. Grant, B. Dutta, W. N. Smith, M. H. Chantigny, P. Rochette, and R. L. Desjardins. "Predicting ammonia volatilization after field application of swine slurry: DNDC model development." Agriculture, Ecosystems & Environment 219 (March 2016): 179–89. http://dx.doi.org/10.1016/j.agee.2015.10.028.
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Lee, Kyoungsook, Kwangsik Yoon, Dongho Choi, Jaewoon Jung, Woojung Choi, and Sangsun Lim. "Evaluation of Soil Organic Carbon of Upland Soil According to Fertilization and Agricultural Management Using DNDC Model." Journal of Environmental Impact Assessment 24, no. 1 (February 28, 2015): 1–15. http://dx.doi.org/10.14249/eia.2015.24.1.1.
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Munawaroh, Umi, Komariah Komariah, Dwi Priyo Ariyanto, Muhamad Khoiru Zaki, and Keigo Noda. "Estimates of methane and nitrous oxide emission from a rice field in Central Java, Indonesia, based on the DeNitrification DeComposition model." SAINS TANAH - Journal of Soil Science and Agroclimatology 19, no. 1 (January 28, 2022): 1. http://dx.doi.org/10.20961/stjssa.v19i1.56928.
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<p>Indonesia is the world’s third largest rice producer, with most rice being cultivated (estimated 3.1 million ha) in Central Java. However, one of the environmental challenges in producing rice is greenhouse gas (GHG) emissions from rice fields. Therefore, understanding the GHG emissions (methane and nitrous oxide) from the rice farming system is important for better management practices. The objective of this study is to estimate the GHG emissions supported by a satellite database, namely, the DeNitrification DeComposition (DNDC) model, at three regencies at Central Java, Indonesia, Cilacap, Karanganyar, and Pati, as well as the factors determining the emissions. The DNDC model was obtained from <a href="https://www.dndc.sr.unh.edu/">https://www.dndc.sr.unh.edu</a>, which consists of three main submodels that worked together in simulating N<sub>2</sub>O and N<sub>2</sub> emissions: (1) the soil-climate/thermal-hydraulic flux submodel, (2) the decomposition submodel, and (3) the denitrification submodel. The results showed that the N<sub>2</sub>O emissions from rice farming in Karanganyar, Cilacap, and Pati were 19.0, 18.8, and 12.8 kg N ha<sup>−1</sup> yr<sup>−1</sup>, respectively, while they were 213.7, 270.6, and 360.6 kg C ha<sup>−1</sup> yr<sup>−1</sup> for CH<sub>4</sub> emissions, respectively. Consecutive dry or high precipitation, which resulted in cumulative depleted or elevated soil moisture, respectively, along with warmer temperature likely promoted higher methane and nitrous oxide. Experimental fields for validating the model in accordance with various agricultural practices are suggested for further study. Overall, the DNDC model has successfully estimated the CH<sub>4</sub> and N<sub>2</sub>O emissions in Central Java when incorporated with various secondary climatic and land management big data resources.</p>
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Zhang, Xiao Long, Xue Yang, Kai Di Tian, Bing Shen, and Quan Quan. "Analysis of Observed and DNDC Modeled Soil Respiration of Winter Wheat in Guanzhong Plain." Advanced Materials Research 1073-1076 (December 2014): 1216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.1073-1076.1216.
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The application of the DeNitrification-DeComposition (DNDC) model in soil respiration of winter wheat at the Ecological Experimental Station of Fuping County, China is researched for the year 2013-2014. The applied results indicate that DNDC is available to research soil respiration in cropland agroecosystems of Guanzhong Plain, China. Also the cumulative and seasonal variation emissions of soil respiration and components (root respiration, soil heterotrophic respiration) are estimated. Based on the simulated results, it can be seen that a significant variation appears in winter wheat growing season, where a downward trend starts from planting season to wintering season, and a steady low level at about 8.3 kg C·hm-2·d-1 keeps until the overwintering, then a significant upward to harvest, where the top point is almost 101.84 kg C·hm-2·d-1, with the total amount is 8342.35 kg C·hm-2. The seasonal amount of root respiration is 5345.47 kg C·hm-2, occupies 61.1% of soil respiration emissions.
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Zhang, L., D. Yu, X. Shi, L. Zhao, W. Ding, H. Wang, J. Pan, and C. Li. "Quantifying methane emissions from rice fields in Tai-Lake region, China by coupling detailed soil database with biogeochemical model." Biogeosciences Discussions 5, no. 6 (December 11, 2008): 4867–96. http://dx.doi.org/10.5194/bgd-5-4867-2008.
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Abstract. China's paddy rice accounts for about 22% of the world's rice fields, therefore it is crucial to accurately estimate the CH4 emissions at regional scale to gauge their contribution to global greenhouse gas effect. This paper reports an application of a biogeochemical model, DeNitrification and DeComposition or DNDC, for quantifying CH4 emissions from rice fields in Tai-Lake region of China by linking DNDC to a 1:50 000 soil database, which was derived from 1107 paddy soil profiles in the Second National Soil Survey of China in the 1980s–1990s. The modeled results estimate that the 2.34 M ha of paddy rice fields in Tai-Lake region emitted about CH4 of 5.67 Tg C for the period of 1982–2000, with the average CH4 flux ranged from 114 to 138 kg C ha−1y−1. The highest emission rate (659.24 kg C ha−1 y−1) occurred in the subgroup of "gleyed paddy soils", while the lowest (90.72 kg C ha−1y−1) were associated with the subgroup "degleyed paddy soils". The subgroup "hydromorphic paddy soils" accounted for about 52.82% of the total area of paddy soils, the largest of areas of all the soil subgroups, with the CH4 flux rate of 106.47 kg C ha−1y−1. On a sub-regional basis, the annual average CH4 flux in the Tai-Lake plain soil region and alluvial plain soil region was higher than that in low mountainous and hilly soil region and polder soil region. The model simulation was conducted with two databases using polygon or county as the basic unit. The county-based database contained soil information coarser than the polygon system built based on the 1:50 000 soil database. The modeled results with the two databases found similar spatial patterns CH4 emissions in Tai-Lake region. However, discrepancies exist between the results from the two methods, the relative deviation is −42.10% for the entire region, and the relative deviation ranged from −19.53% to 97.30% for most counties, which indicates that the more precise soil database was necessary to better simulate CH4 emissions from rice fields in Tai-Lake region using the DNDC model.
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Barneze, Arlete S., Mohamed Abdalla, Jeanette Whitaker, Niall P. McNamara, and Nicholas J. Ostle. "Predicted Soil Greenhouse Gas Emissions from Climate × Management Interactions in Temperate Grassland." Agronomy 12, no. 12 (December 2, 2022): 3055. http://dx.doi.org/10.3390/agronomy12123055.
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Grassland management practices and their interactions with climatic variables have significant impacts on soil greenhouse gas (GHG) emissions. Mathematical models can be used to simulate the impacts of management and potential changes in climate beyond the temporal extent of short-term field experiments. In this study, field measurements of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) emissions from grassland soils were used to test and validate the DNDC (DeNitrification-DeComposition) model. The model was then applied to predict changes in GHG emissions due to interactions between climate warming and grassland management in a 30-year simulation. Sensitivity analysis showed that the DNDC model was susceptible to changes in temperature, rainfall, soil carbon and N-fertiliser rate for predicting N2O and CO2 emissions, but not for net CH4 emissions. Validation of the model suggests that N2O emissions were well described by N-fertilised treatments (relative variation of 2%), while non-fertilised treatments showed higher variations between measured and simulated values (relative variation of 26%). CO2 emissions (plant and soil respiration) were well described by the model prior to hay meadow cutting but afterwards measured emissions were higher than those simulated. Emissions of CH4 were on average negative and largely negligible for both simulated and measured values. Long-term scenario projections suggest that net GHG emissions would increase over time under all treatments and interactions. Overall, this study confirms that GHG emissions from intensively managed, fertilised grasslands are at greater risk of being amplified through climate warming, and represent a greater risk of climate feedbacks.
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Horák, Ján, and Irina Mukhina. "Measured and Modeled (DNDC) Nitrous Oxide Emissions (N2O) under Different Crop Management Practices in the Nitra Region, Slovakia." Acta Horticulturae et Regiotecturae 19, no. 2 (November 1, 2016): 54–57. http://dx.doi.org/10.1515/ahr-2016-0012.
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Abstract An important method of investigating N2O emissions from cropland is model simulation. The measured data of N2O emissions under conventional tillage (CT) and reduced tillage (RT) with (N1) and without (N0) N fertilizer application were used to test the DNDC model during the year 2012 (April-December) in Slovakia. There was found a good agreement with seasonal N2O emissions only for CTN0 treatment, but in case of other treatments DNDC overestimated the emissions. The relative deviation between observed and simulated total seasonal N2O emissions (kg N ha−1) from four treatments were 46%, 164%, 346% and 321% for CTN0, CTN1, RTN0 and RTN1, respectively. Also, some discrepancies were found between observed and simulated emissions when evaluating the daily N2O emissions, especially when looking at the magnitude of N2O emissions peaks. The correlation between observed and simulated daily N2O emissions (N = 38) in case of conventional tillage was quite high and significant with r = 0.48 (P <0.01), r = 0.45 (P <0.01) for CTN0 and CTN1 treatment, respectively. On the other hand, there was found poor correlation in reduced tillage treatment with r = 0.22 (P >0.01) and r = 0.39 (P >0.01), for RTN0, RTN1, respectively.
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Tonitto, Christina, Mark B. David, Laurie E. Drinkwater, and Changsheng Li. "Application of the DNDC model to tile-drained Illinois agroecosystems: model calibration, validation, and uncertainty analysis." Nutrient Cycling in Agroecosystems 78, no. 1 (January 30, 2007): 51–63. http://dx.doi.org/10.1007/s10705-006-9076-0.
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Wang, Y., G. J. Sun, F. Zhang, J. Qi, Z. D. Feng, and C. Y. Zhao. "Modeling impacts of farming management practices on greenhouse gas emissions in the oasis region of China." Biogeosciences Discussions 8, no. 2 (March 22, 2011): 3121–53. http://dx.doi.org/10.5194/bgd-8-3121-2011.
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Abstract. Agricultural ecosystems are major sources of greenhouse gas (GHG) emissions, specifically nitrous oxide (N2O) and carbon dioxide (CO2). An important method of researching GHG emissions in agricultural ecosystems is model simulation. Field measurements quantifying N2O and CO2 fluxes were taken in a summer maize ecosystem in Zhangye City, Gansu Province, in northwestern China in 2010. Observed N2O and CO2 fluxes were used for validating flux predictions by a DeNitrification-DeComposition (DNDC) model. Then the validated DNDC model was used for sensitivity tests on three variables under consideration: climatic factors, soil properties, and agricultural management. Results indicate that: (1) the factors that N2O emissions are most sensitive to nitrogen fertilizer application rate, manure amendment and residue return rate; (2) CO2 emission increases with increasing manure amendment, residue return rate and initial soil organic carbon (SOC); and (3) net global warming potential (GWP) increases with increasing N fertilizer application rate and decreases as manure amendment, residue return rate and precipitation increase. Simulation of the long-term impact on SOC, N2O and net GWP emissions over 100 yr of management led to the conclusion that increasing residue return rate is a more efficient method of mitigating GHG emission than increasing fertilizer N application rate in the study area.
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Wang, Y., G. J. Sun, F. Zhang, J. Qi, and C. Y. Zhao. "Modeling impacts of farming management practices on greenhouse gas emissions in the oasis region of China." Biogeosciences 8, no. 8 (August 30, 2011): 2377–90. http://dx.doi.org/10.5194/bg-8-2377-2011.
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Abstract. Agricultural ecosystems are major sources of greenhouse gas (GHG) emissions, specifically nitrous oxide (N2O) and carbon dioxide (CO2). An important method of investigating GHG emissions in agricultural ecosystems is model simulation. Field measurements quantifying N2O and CO2 fluxes were taken in a summer maize ecosystem in Zhangye City, Gansu Province, in northwestern China in 2010. Observed N2O and CO2 fluxes were used for validating flux predictions by a DeNitrification-DeComposition (DNDC) model. Then sensitivity tests on the validated DNDC model were carried out on three variables: climatic factors, soil properties and agricultural management. Results indicated that: (1) the factors that N2O emissions were sensitive to included nitrogen fertilizer application rate, manure amendment and residue return rate; (2) CO2 emission increased with increasing manure amendment, residue return rate and initial soil organic carbon (SOC); and (3) net global warming potential (GWP) increased with increasing N fertilizer application rate and decreased with manure amendment, residue return rate and precipitation increase. Simulation of the long-term impact on SOC, N2O and net GWP emissions over 100 yr of management led to the conclusion that increasing residue return rate is a more efficient method of mitigating GHG emission than increasing fertilizer N application rate in the study area.
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Giltrap, Donna L., Surinder Saggar, Jagrati Singh, Mike Harvey, Andrew McMillan, and Johannes Laubach. "Field-scale verification of nitrous oxide emission reduction with DCD in dairy-grazed pasture using measurements and modelling." Soil Research 49, no. 8 (2011): 696. http://dx.doi.org/10.1071/sr11090.
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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%.
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Pathak, H., C. Li, and R. Wassmann. "Greenhouse gas emissions from Indian rice fields: calibration and upscaling using the DNDC model." Biogeosciences Discussions 2, no. 1 (January 20, 2005): 77–102. http://dx.doi.org/10.5194/bgd-2-77-2005.
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Abstract. Crop growth simulation models provide a means to quantify the effects of climate, soil and management on crop growth and biogeochemical processes in soil. The Denitrification and Decomposition (DNDC) model was evaluated for its ability to simulate methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) emissions from Indian rice fields with various management practices. The model was calibrated and validated for field experiments in New Delhi, India. The observed yield, N uptake and greenhouse gas (GHG) emissions were in good agreement with the values predicted by the model. The model was then applied for estimation of GHG emissions from rice fields in India using a newly compiled soil/climate/land use database. Continuous flooding of rice fields (42.25 million ha) resulted in annual net emissions of 1.07–1.10, 0.038–0.048 and 21.16–60.96 Tg of CH4-C, N2O-N and CO2-C, respectively, with a cumulated global warming potential (GWP) of 130.93–272.83 Tg CO2 equivalent. Intermittent flooding of rice fields reduced annual net emissions to 0.12–0.13 Tg CH4-C and 16.66–48.80 Tg CO2-C while N2O emission increased to 0.056–0.060 Tg N2O-N. The GWP, however, reduced to 91.73–211.80 Tg CO2 equivalent. The study suggests that the model can be applied for studying the GHG related issues in rice cropping systems of India.
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WANG, Deying, Yanmin YAO, Haiqing SI, and Pengqin TANG. "Using DNDC model to simulate and predict changes in black soil organic carbon." Chinese Journal of Eco-Agriculture 22, no. 3 (June 28, 2014): 277–83. http://dx.doi.org/10.3724/sp.j.1011.2014.30983.
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Telpande, B., T. Bhattacharyya, D. M. Wankhede, P. Jha, P. Tiwary, P. Chandran, and S. K. Ray. "Simulating soil organic carbon in high clay soils in India: DNDC model experience." Climate Change and Environmental Sustainability 1, no. 2 (2013): 118. http://dx.doi.org/10.5958/j.2320-642x.1.2.011.
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Levy, P. E., D. C. Mobbs, S. K. Jones, R. Milne, C. Campbell, and M. A. Sutton. "Simulation of fluxes of greenhouse gases from European grasslands using the DNDC model." Agriculture, Ecosystems & Environment 121, no. 1-2 (June 2007): 186–92. http://dx.doi.org/10.1016/j.agee.2006.12.019.
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