Preview

GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY

Advanced search

Application Of The Denitrification-Decomposition (DNDC) Model To Retrospective Analysis Of The Carbon Cycle Components In Agrolandscapes Of The Central Forest Zone Of European Russia

https://doi.org/10.24057/2071-9388-2018-85

Full Text:

Abstract

The retrospective  dynamics  of major components  of the carbon cycle under land use changes in the Central Forest zone of European Russia was investigated. This area is known as one of the most important agricultural and economical regions of the country. We applied the process-based simulation model DNDC (DeNitrification-DeComposition) recommended  by UNCCC and world widely used.  In  this study the DNDC model was parameterized  for Russian arable soils using official statistical information and data taken from published sources. Three main carbon  variables in agrolandscapes were modelled: soil organic carbon, soil respiration, and net ecosystem exchange over the period of 1990-2017. For the analysis  six administrative regions  were selected:  three with unchanged (permanent)  arable land structure   (Kaluga,  Moscow,  and Yaroslavl),  and other three with changed crop rotation (Kostroma, Smolensk, and Tver). All regions in the study are characterized by homogeneous  soil cover and similar cultivated crops. The results of the modelling were verified using the data from field CO2 fluxes observations in the European part of Russia. In growing  season, the agrolandscapes function as a net carbon  sink and accumulate C from the atmosphere  into plant biomass. The dynamics of organic carbon in soil under growing  crops depends on organic  fertilizers in cultivation  technologies, and if they aren’t inputted, soil loses carbon. During the last 30 years the cumulative rates of net ecosystem exchange and soil respiration had decreased  mostly due to reduction of arable land area. CO2 emission and soil organic carbon  losses are the most important controls of land degradation.  Based on the dynamic  patterns of CO2  fluxes, the regions of the Central  Forest zone could be separated into two groups. The group with central location characterized  by intensive  soil respiration  and high rate of accumulation  of organic carbon  in soil, whereas peripheral group characterized by losses of soil organic carbon and low rates of soil respiration. According  to the modelling, within the period of observations the inter-annual changes of carbon fluxes are mainly controlled by rising air temperature and heat supply, variable precipitation, and increasing concentration  of CO2 in the atmosphere. Among human activity the most important are change of arable land area and decreasing amount of fertilizers. 

About the Authors

Olga E. Sukhoveeva
Russian Academy of Sciences
Russian Federation

Institute of Geography.

Moscow.



Dmitry V. Karelin
Russian Academy of Sciences
Russian Federation

Institute of Geography; Center for Problems of Ecology and Productivity of Forests.

Moscow.



References

1. Balashov E., Buchkina N., Rizhiya E., and Farkas C.S. (2014). Field validation of DNDC and SWAP models for temperature and water content of loamy and sandy loam spodosols. International agrophysics, 28 (2), pp. 133-142.

2. Bolan N.S., Saggar S., Luo J., Bhandral R., and Singh J. (2004). Gaseous emissions of nitrogen from grazed pastures: processes, measurements and modeling, environmental implications, and mitigation. Advances in agronomy, 84, pp. 38-120.

3. Buchkina N.P., Balashov E.V., Rizhiya E.Y., and Li C. (2007). Application of DNDC model for Russian agro-ecosystems. In: Denitrification: a challenge for pure and applied science. Book of abstracts. University of Aberdeen, pp. 17.

4. Chen C., Chen D., Pan J., and Lam S.K. (2013). Application of the denitrification-decomposition model to predict carbon dioxide emissions under alternative straw retention methods. Scientific World Journal, 25, pp. 851-901. DOI: 10.1155/2013/851901.

5. Chen D. and Chen H.W. (2013). Using the Köppen classification to quantify climate variation and change: an example for 1901-2010. Environmental Development, 6, pp. 69-79.

6. Chistotin M.V. and Safonov A.F. (2016). Temporal patterns of respiration of an agro-sod-podzolic soil as controlled by organic matter content and meteorological factors. Problemy agrokhimii i ekologii, 3, pp. 52-58. (in Russian)

7. Estimation of emissions from agriculture. (2004). United Nations framework convention on climate change. FCCC/SBSTA/2004/INF.4. GE.04–61454. – Bonn: UNFCCC, 28 May 2004. Available at: http://unfccc.int/resource/docs/2004/sbsta/inf04.pdf. [Accessed 1 Dec. 2018].

8. FAO-Unesc. (1988). Soil Map of the World. Revised Legend. World Resources Report, 60. Rome: FAO.

9. Fedorov B.G. (2017). Russian carbon balance. Moscow: Scientific consultant (in Russian).

10. Frolking S., Li C., Braswell R., and Fuglestvedt J. (2004). Short- and long-term greenhouse gas and radiative forcing impacts of changing water management in Asian rice paddies. Global Change Biology, 10 (7), pp. 1180-1196.

11. Gifford R.M. (1995). Whole plant respiration and photosynthesis of wheat under increased CO2 concentration and temperature: long-term vs. short-term distinctions for modelling. Global Change Biology, 1, pp. 385-396.

12. Giltrap D.L., Li C., and Saggar S. (2010). DNDC: a process-based model of greenhouse gas fluxes from agricultural soils. Agriculture, Ecosystems & Environment, 136 (3-4), pp. 292-300.

13. Guest G., Kröbel R., Grant B., Smith W., Sansoulet J., Pattey E., Desjardins R., Jégo G., Tremblay N., and Tremblay G. (2017). Model comparison of soil processes in eastern Canada using DayCent, DNDC and STICS. Nutrient Cycling in Agroecosystems, 109 (3), pp. 211–232.

14. Karelin D.V., Goryachkin S.V., Kudikov A.V., Lunin V.N., Dolgikh A.V., Lyuri D.I., and Lopes de Gerenu V.O. (2017). Changes in carbon pool and CO2 emission in the course of postagrogenic succession on gray soils (Luvic Phaeozems) in European Russia. Eurasian Soil Science, 50 (5), pp. 559-572. DOI: 10.7868/80032180x17050070

15. Kirschbaum M.U.F. and Mueller R. (2001). Net Ecosystem Exchange. – Australia: Cooperative Research Centre for Greenhouse Accounting.

16. Kolchugina T.P., Vinson T.S., Gaston G.G., Rozhkov V.A., and Schlentner S.F. (1995). Carbon pools, fluxes, and sequestration potential in soil of the Former Soviet Union. In: R. Lal, J. Kimble, E. Levine, B.A. Stewart, ed., Soil Management and greenhouse effect. Boca Raton, London, Tokyo: Lewis Publishers, pp. 25-40.

17. Kosolapov V.M., Trofimov I.A., Trofimova L.S., and Yakovleva E.P. (2015). Agrolandscapes of Central Cernozem area. zoning and management. Moscow: Nauka (in Russian).

18. Kurbatova J. Li C. Varlagin A. xiao x., and Vygodskaya N. (2008). Modeling carbon dynamics in two adjacent spruce forests with different soil conditions in Russia. Biogeosciences, 5, pp. 969-980.

19. Kurganova I.N. (2010). Emission and balance of carbon dioxide in terrestrial ecosystems of Russia: doctor thesis. Pushchino: IFХiBPP RAN. (in Russian)

20. Lal R. (2004). Soil carbon sequestration to mitigate climate change. Geoderma, 123 (1-2), pp. 1-22. DOI: 10.1016/j.geoderma.2004.01.032.

21. Larionova A.A., Kurganova I.N., De Gerenyu V.O.L., zolotareva B.N., Yevdokimov I.V., and Kudeyarov V.N. (2010). Carbon dioxide emission from agrogrey soils under climate change. Eurasian soil science, 43 (2), pp. 168-176. DOI: 10.1134/S1064229310020067 (in Russian)

22. Leip A., Marci G., Koeble R., Kempen M., Britz W., and Li C. (2008). Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen and carbon losses from arable soils in Europe. Biogeosciences, 5, pp. 73-94.

23. Li C., Frolking S., and Frolking T.A. (1992). A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. Journal of geophysical research, 97 (D9), pp. 9759-9776.

24. Li C., Frolking S., xiao x., Moore III B., Boles S., Qiu J., Huang Y., Salas W., and Sass R. (2005). Modeling impacts of farming management alternatives on CO2, CH4, and N2O emissions: A case study for water management of rice agriculture of China. Global Biogeochemical Cycles, 19 (3), GB3010. DOI: 10.1029/2004GB002341.

25. Li С. (2008). Modeling soil organic carbon sequestration potential with modeling approach. In: Simulation of Soil Organic Carbon Storage and Changes in Agricultural Cropland in China and Its Impact on Food Security. China Meteorological Press.

26. Lukin S.M. (2015). Carbon dioxide emission in potato agrocenosis on sod-podzolic sandy soils. Vladimirskii zemledelets, 3-4 (74), pp. 22-23. (in Russian)

27. Pathak H., Li C., and Wassmann R. (2005). Greenhouse gas emissions from India rice fields: calibration and upscaling using the DNDC model. Biogeosciences, 2 (2), pp. 113-123.

28. Report of the thirty-eighth meeting оf the small-scale working group (2012). Bonn: CDM SSC WG.

29. Rosenstock T.S., Rufino M.C., Butterbach-Bahl K., Wollenberg E., and Richards M. (2016). Methods for measuring greenhouse gas balances and evaluating mitigation options in smallholder agriculture. USA: Springer.

30. Sanz M.J., de Vente J., Chotte J.-L., Bernoux M., Kust G., Ruiz I., Almagro M., Alloza J.-A., Vallejo R., Castillo V., Hebel A., and Akhtar-Schuster M. (2017). Sustainable Land Management contribution to successful land-based climate change adaptation and mitigation. In: A Report of the Science-Policy Interface. Bonn: UNCCD.

31. Sapronov D.V. (2008). Long-term dynamics of CO2 emission from grey forest and sod-podzolic soils: PhD thesis. Pushchino: IFHiBPP RAN. (in Russian)

32. Semenov V.M., Ivannikova L.A., Kuznetsova T.V., Semenova N.A., and Tulina A.S. (2008). Mineralization of organic matter and the carbon sequestration capacity of zonal soils. Eurasian soil science, 41 (7), pp. 717-730. DOI: 10.1134/S1064229308070065 (in Russian)

33. Sukhoveeva O.E. (2016). Changes of climatic conditions and agroclimatic recourses in Central Non-Cernozem zone. Proceedings of Voronezh State University. Series: Geography. Geoecology, 4, pp. 41-49. (in Russian)

34. Sukhoveeva O.E. (2018). Evaluation of spatiotemporal variability of CO2 fluxes in agrolandscapes of European Russia on the base of simulation modelling: PhD thesis. Moscow: Institute of Geography RAS. (in Russian)

35. UNCCD. (2015). Science policy brief, 1.

36. Unified state register of soil recourses of Russia. Versa 1.0. (2014). Moscow: Soils institute. WMO. (2017). Greenhouse Gas Bulletin, 13.

37. World reference base for soil resources. (2006). IUSS Working Group. World Soil Resources Reports, 103. Rome: FAO.

38. Yadav D. and Wang J. (2017). Modelling carbon dioxide emissions from agricultural soils in Canada. Environmental Pollution, 230, pp. 1040-1049. DOI: 10.1016/j.envpol.2017.07.066.


For citation:


Sukhoveeva O.E., Karelin D.V. Application Of The Denitrification-Decomposition (DNDC) Model To Retrospective Analysis Of The Carbon Cycle Components In Agrolandscapes Of The Central Forest Zone Of European Russia. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2019;12(2):213-226. https://doi.org/10.24057/2071-9388-2018-85

Views: 115


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)