Sensitivity Assessment of Nitrous oxide Greenhouse Gas Emissions in Agricultural Lands of Khuzestan Province with Linear and Non-linear Models

Document Type : Original Article

Authors

1 Department of Climatology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran

2 Department of Land Evaluation, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

10.22034/eiap.2023.179285

Abstract

The aim of this study is to estimate emission of nitrous oxide gas in rice, wheat and sugarcane fields of Khuzestan using four models: DAYCENT, DNDC, YLRM and IPCC_EF. For this purpose, nitrous oxide gas precipitation was first measured. Then, using models was estimated nitrous oxide gas expansion rate. To evaluate and compare accuracy of models, statistical characteristics were used, coefficient of determination, maximum error, root mean squares error, modeling efficiency and remaining coefficient of residual mass. Release of nitrous oxide in rice cultivation in four models was estimated to be between 0.17 and 0.171. Rate of nitrous oxide emission from wheat cultivation was between 0.5-0.049 and from Shushtar station sugarcane cultivation was between -0.0371 and from Abadan station sugarcane cultivation was between 0.03-0.85. In linear regression model of rice cultivation (1.17), in IPCC_EF model, wheat cultivation (0.5) and sugarcane (3) obtained the highest amount of nitrous oxide gas per ton per hectare per year. According to results of statistical indicators for four models DAYCENT, DNDC, YLRM and IPCC_EF to estimate nitrous oxide gas were determined, respectively, the coefficient of determination (0.86, 0.94, 0.99 and 0.82), root mean squares error (0.03, 0.01, 0.85 and 0.26) and modeling efficiency (0.55, 0.94, -4.87 and -30.63).Compared to observed values, DAYCENT model for corn, DNDC model for rice, linear regression model for sugarcane cultivation of Abadan station showed good performance. Based on results of coefficient of determination, YLRM and DNDC models received the highest accuracy based on modeling efficiency of DNDC model.

Keywords

Main Subjects

References

Akbarzadeh, M. 2013. Methane and its role in global warming, Transplant Science Journal. 2 (2): 37-41. (In persian)
Bavarsad, J. 2018. The use of nitrogen gas and its effects on body health, Encouraging Magazine. (In Persian)
Bouwman, A.F. 1990. Exchange of greenhouse gases between terrestrial ecosystems and the atmosphere, In: Bouwman AF (Ed) Soils and the Greenhouse Effect, 61-127, John Wiley & Sons, New York.
Dashtaki, S.G.; Homaee, M. & Khodaverdiloo, H. 2010. Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data, Soil Use and Management, 26(1): 68-74.
Del Grosso, S.J.; Parton, W.J.; Mosier, A.R.; Hartman, M.D.; Brenner, J.; Ojima, D.S. & Schimel, D.S. 2001. Simulated interaction of carbon dynamics and nitrogen trace gas fluxes using the DAYCENT model, In: Schaffer, M., Ma, L., Hansen, S. (Eds.), Modeling Carbon and Nitrogen Dynamics for Soil Management, CRC Press, Boca Raton, Florida, pp. 303–332.
Del Grosso, S. J.; Halvorson, A. D. & Parton, W. J. 2008. Testing DAYCENT model simulations of corn yields and nitrous oxide emissions in irrigated tillage systems in Colorado, J. Environ. Qual. 37:1383–1389.
Denman, K.L.; Brasseur, G.; Chidthaisong, A.; Ciais, P.; Cox, P.M.; Dickinson, R.E.; Hauglustaine, D.; Heinze, C.; Holland, E.; Jacob, D.; Lohmann, U.; Ramachandran, S.; Da Silva Dias, P.L.; Wofsy, S.C. & Zhang, X. 2007. Couplings between changes in the climate system and biogeochemistry, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge. 499-588.
Elhami, B.; Karam, A. & Khanali, M. 2016. Optimizing energy consumption and reducing greenhouse gas emissions in the production of blue lentils using data envelopment analysis, Iranian Biosystem Engineering, 47 (4): 701-710. (In Persian)
Ewert, F.; Rounsevell, M.D.A.; Reginster, I.; Metzger, M.G. & Leemans, R. 2005. Future scenarios of European agricultural land use, I. Estimating changes in crop productivity, Agricultura Ecosystem Environmental, 107:101–116.
Fuchs, K.; Merbold, l.; Buchmann, N.; Bellocchi, G.; Bindi, M.; Klumpp, K.; Liebig, M.; Lieffering, M.; Martin, R.; McAuliffe, R.; Newton, P.C.D.; Rees, R.M.; Recous, S.; Smith, P.; Soussana, J.F.; Topp, C. F. E. & Snow, V. 2020. Evaluating the potential of legumes to mitigate N2O emissions from permanent grassland using process-based models, Global biogeochemical cycles, Volume 34, Issue12, e2020GB006561.
Gaillard, R. K.; Jones, C.D.; Ingraham, P.; Collier, S.; Izaurralde, R.C.; Jokela, W.; Osterholz, W.; Salas, W.; Vadas, P. & Ruark, M.D. 2017. Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models, Ecological applications, 28(3):694-708.
Gathany, M. A. & Burke, I.C. 2012. DAYCENT simulations to test the influence of fire regime and fire suppression on trace gas fluxes and nitrogen biogeochemistry of Colorado forests, Forests, 3, 506-527; doi:10.3390/f3030506.
Hartman, M.; Merchant, E.R.; Parton, W.J.; Gutmann, M.P.; Lutz, S. & Williams, S.A. 2011. Impact of historical land-use changes on greenhouse gas exchange in the U.S. Great Plains, 1883–2003, Ecological applications, 21(4):1105–1119.
Hartman, M.D.; Parton, W.J.; Del Grosso, S.J.; Easter, M.; Hendryx, J.; Hilinski, T.; Kelly, R.; Keough, C.A.; Killian, K.; Lutz, S.; Marx, E.; McKeown, R.; Ogle, S.; Ojima, D.S.; Paustian, K. & Swan, A.W.S. 2016. DayCent Ecosystem Model, Colorado State University.
Homaee, M.; Dirksen, C. & Feddes, R. 2002. Simulation of root water uptake: I. Non-uniform transient salinity using different macroscopic reduction functions, Agricultural Water Management, 57(2): 89-109.
IPCC. 1997. IPCC (Revised 1996) Guidelines for National Greenhouse Gas Inventories. Workbook, Intergovernmental Panel on Climate Change, Paris.
IPCC. 2007. Changes in Atmospheric Constituents and in Radiative Forcing, Cambridge University Press, UK and New York USA
Intergovernmental Panel on Climate Change. 2007. Summary for Policymakers, Emissions Scenarios, A Special Report of IPCC working Group3, ISBN: 92-9169-113-5.
Khodaverdiloo, H.; Homaee, M.; Van Genuchten, M.T. & Dashtaki, S.G. 2011. Deriving and validating pedotransfer functions for some calcareous soils, Journal of Hydrology, 399(1): 93-99.
Kottegoda, N.T. & Rosso, R. 2008. Applied statistics for civil and environmental engineers: Wiley-Blackwell.
Lai, L.; Kumar, S.; Folle, S. M. & Owens, V. N. 2017. Predicting soils and environmental impacts associated with switchgrass for bioenergy production: a DAYCENT modeling approach, Global change biology bioenergy, 10 (4): 287-302.
Li, C.; Farahbakhshazad, N.; Jaynes, D.B.; Dinnes, D.L.; Salas, W. & McLaughlin, D. 2006. Modeling nitrate leaching with a biogeochemical model modified based on observations in a row-crop field in Iowa, Ecol. Model, 196 (1), 116–130.
Liu, X.; Xu, W.; Duan, L.; Du, E.; Pan, Y. & Lu, X. 2017. Atmospheric nitrogen emission, deposition, and air quality impacts in China: an overview, Curr. Pollut. Rep. 3 (2), 65–77
Lu, C. & Tian, H. 2013. Net greenhouse gas balance in response to nitrogen enrichment: perspectives from a coupled biogeochemical model, Glob. Chang. Biol, 19, 571–588.
Moradi‑Majd , N.; Fallah‑Ghalhari , GH. A. & Chatrenor, M. 2022. Prediction of greenhouse gases and global warming potential in agricultural lands of Khuzestan province using DAYCENT model, Iran water and soil research, 51(9):2259-2273. DOI: 10.22059/ijswr.2020.301647.668590
Moradi‑Majd , N.; Fallah‑Ghalhari , GH. A. & Chatrenor, M. 2022. Estimation of greenhouse gas emission flux from agricultural lands of Khuzestan province in Iran, Environ Monit Assess (2022) 194:811, https://doi.org/10.1007/s10661-022-10497-8.
Mousavi, S. M.; Falahtkar, S. & Farajzadeh, M. 2017. Changes in the concentration of carbon dioxide and methane greenhouse gases in relation to environmental variables in Iran, Applied Ecology, 6 (4): 65-78. (In Persian)
Muharri, A. 2003. The role of domestic animals in N2O gas production as one of the greenhouse gases, 3rd regional conference on climate change, Isfahan, Meteorological organization, Isfahan university. (In Persian)
Olivas, P.; Oberbauer, S.F.; Schedlbauer, J. & Gregory, S. 2015. Ecosystem resistance in the face of climate change: a case study from the freshwater marshes of the Florida everglades, Ecosphere, 6(4):1-23.
Prather, M.J. 1998. Time scales in atmospheric chemistry: coupled perturbations to N2O, NOx, and O3, Science 279, 1339–1341.
Rajabi, M. H.; Soltani, A.; Zeinali, E. & Soltani, E. 2012. Evaluation of greenhouse gas emissions and global warming potential due to wheat production in Gorgan, Electronic Journal of Crop Production, 5 (3): 23-44. (In Persian)
Reay, D.S.; Davidson, E.A.; Smith, K.A.; Smith, P.; Melillo, J.M.; Dentener, F. & Crutzen, P.J. 2012. Global agriculture and nitrous oxide emissions, Nature climate change, 2, 410-416.
Statistical Center of Iran. 2018. Country Plan and Budget Organization.
Signor, D. & Cerri, C.E.P. 2013. Nitrous oxide emissions in agricultural soils: a review. Pesq. Agropec. Trop., Goiânia, 43(3): 322-338.
Smith, K.A.; Mosier, A.R.; Crutzen, P.J. & Winiwarter, W. 2012. The role of N2O derived from crop-based biofuels, and from Agriculture in general in earth’s climate, Philosophical transactions of the royal society, 367: 1169–1174.
Tian, H.; Xu, X.; Lu, C.; Liu, M.; Ren, W.; Chen, G.; Melillo, J. & Liu, J. 2011. Net exchanges of CO2, CH4, and N2O between China's terrestrial ecosystems and the atmosphere and their contributions to global climate warming, J. Geophys. Res, https://doi.org/10.1029/2010JG001393.
Tian, H.; Lu, C.; Melillo, J.; Ren, W.; Huang, Y.; Xu, X.; Liu, M.; Zhang, C.; Chen, G.; Pan, S.; Liu, J. & Reilly, J. 2012. Food benefit and climate warming potential of nitrogen fertilizer use in China, Environ. Res. Lett. 7. https://doi.org/10.1088/1748-9326/7/4/044020.
Wang, C.; Amon, B. & Mehdi, K. S. B. 2021. Factors that influence nitrous oxide emissions from agricultural soils as well as their representation in simulation models:a review, Agronomy 2021, 11(4), 770; https://doi.org/10.3390/agronomy11040770.
Watson, R.T.; Zinyowera, M. C.; Moss, R. H. & Dokken, D.J. 1996. Climate change 1995, impacts, adaptation and mitigation of climate change. Scientific technical report analysis, In: Watson, R.T., Zinyowera, M.C., Moss, R.H. (Eds.), Contribution of Working Group II to the Second Assessment Report of the Intergovernmental Panel on Climate Change, p. 880 (Cambridge, New York).
Yue, Q.; Ledo, A.; Cheng, K.; Albanito, F.; Lebender, U.; Sapkota, T.B.; Brentrup, F.; Stirling, C.M.; Smith, P.; Sun, J.; Pan, G. & Hillier, J. 2018. Re-assessing nitrous oxide emissions from croplands across mainland China, Agric. Ecosyst. Environ. 268, 70–78.
Yue, Q.; Cheng, K.; Ogle, S.; Hillier, J.; Smith, P.; Abdalla, M.; Ledo, A.; Sun, J. & Pan, G. 2019. Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland, Sci Total Environ., 20(652):1279-1289.
Environmental Researches
Volume 14, Issue 27
Recerches
September 2023
Pages 43-58

Files

Share

How to cite

Statistics