Soil Microbial Activity and Chemical Properties in Relation to the Topographic Position of Chernozem Arable Lands

The need to automate and simplify the spatial and temporal monitoring of economically important soil characteristics, in particular the carbon content of intensively used lands, dictates the continued search for relatively simple ways of their remote evaluation. We discovered medium-strength significant relations of LS-factor (Slope Length and Steepness factor; erosion potential of the relief), and soil characteristics as a result of a statistical analysis of field observations, laboratory experiments, and digital elevation models obtained from space remote sensing data. Surface data were obtained from model transects located in different relief positions of long-term arable chernozems (Kursk Oblast, Russia). These relationships are expressed in decreased content of the key nutrients and compounds (SOC, nitrogen, and water), as well as in reduced presence and altered activity of soil microbiota. We assume that the main reason for this is water erosion and less water availability on steeper slopes. Based on our results, we believe that LS-factor calculated on the basis of satellite remote sensing data is applicable for evaluation of erosion hazard, as well as for prediction of carbon content and other related significant physical, chemical, and biological indicators of the state of perennial arable haplic chernozems for a large spatial scale. At the same time, we found that the spectral characteristics of the soil surface obtained from remote sensing data are less applicable for these purposes. This is due to the dependence of the obtained satellite data on the survey conditions (weather, soil tillage techniques, vegetation cover characteristics) and the limitations imposed by the insufficient resolution of the available satellite images.

1. Ananyeva N.D., Susyan E.A., Chernova O.V., Wirth S. Microbial respiration activities of soils from different climatic regions of European Russia. Eur. J. Soil Biol., 2008, vol. 44, no. 2, pp. 147–157. https://doi.org/10.1016/j.ejsobi.2007.05.002

2. Anderson J., Domsch K.H. A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem., 1978, vol. 10, pp. 215–221. https://doi.org/10.1016/0038-0717(78)90099-8

3. Angelopoulou T., Tziolas N., Balafoutis A., Zalidis G., Bochtis D. Remote sensing techniques for soil organic carbon estimation: A review. Remote Sens., 2019, vol. 11, 676. https://doi.org/0.3390/rs11060676

4. Baumgardner M.F., Kristof S., Johannsen C.J., Zachary A. Effects of organic matter on the multispectral properties of soils. Indiana Acad. Sci., 1970, vol. 79, pp. 413–422.

5. Buryak Zh.A. Improving approaches to assessing the erosion hazard of agricultural landscapes using GIS technologies. Nauchn. Vedomosti BelGU. Ser.: Estestvennye Nauki, 2014, vol. 23 (194), pp. 140–146. (In Russ.).

6. Chen F., Kissel D.E., West L.T., Adkins W. Field-scale mapping of surface soil organic carbon using remotely sensed imagery. Soil Sci. Soc. Am. J., 2000, vol. 64, pp. 746–753. https://doi.org/10.2136/sssaj2000.642746x

7. COM 2006/231. Communication from the Commission to the Council, the European Parliament, the European Economic and Social Committee and the Committee of the Regions. Thematic Strategy for Soil Protection. Brussels: Commission of the European Communities, 2006.

8. Conant R.T., Ogle S.M., Paul E.A., Paustian K. Measuring and monitoring soil organic carbon stocks in agricultural lands for climate mitigation. Front. Ecol. Environ., 2011, vol. 9, pp. 169–173. https://doi.org/10.1890/090153

9. Croft H., Kuhn N.J., Anderson K. On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems. Catena, 2012, vol. 94, pp. 64–74. https://doi.org/10.1016/j.catena.2012.01.001

10. Crowther T., Todd-Brown K., Rowe C. et al. Quantifying global soil carbon losses in response to warming. Nature, 2016, vol. 540, pp. 104–108. https://doi.org/10.1038/nature20150

11. Friedlingstein P., O’Sullivan M., Jones M.W. et al. Global carbon budget 2020. Earth Syst. Sci. Data, 2020, vol. 12, no. 4, pp. 3269–3340. https://doi.org/10.5194/essd-12-3269-2020

12. Gomez C., Viscarra Rossel R.A., McBratney A.B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, 2008, vol. 146, nos. 3–4, pp. 403–411. https://doi.org/ 10.1016/j.geoderma.2008.06.011

13. Hamzehpour N., Shafizadeh-Moghadam H., Valavi R. Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture. Catena, 2019, vol. 182, 104141. https://doi.org/10.1016/j.catena.2019.104141

14. Irons J.R., Weismiller R.A., Petersen G.W. Soil reflectance. In Theory and Applications of Optical Remote Sensing. Asrar G., Ed. N.Y.: J. Wiley & Sons, Inc., 1989, pp. 66–106.

15. IUSS Working Group WRB. World Reference Base for Soil Resources 2014. Update 2015. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps. World Soil Resources Reports, no. 106. Rome: FAO, 2015. 192 p.

16. Ivashchenko K.V. Abundance and respiratory activity of the soil microbial community during anthropogenic transformation of terrestrial ecosystems. Cand. Sci. (Biol.) Dissertation. Pushchino, 2017. 205 p.

17. Karelin D.V., Goryachkin S.V., Kudikov A.V., Lopes de Gerenu V.O., Lunin V.N., Dolgikh A.V., Lyuri D.I. Changes in carbon pool and CO2 emission in the course of postagrogenic succession on gray soils (Luvic Phaeozems) in European Russia. Eurasian Soil Sci., 2017, vol. 50, no. 5, pp. 559–572. https://doi.org/10.1134/S1064229317050076

18. Ladoni M., Bahrami H.A., Alavipanah S.K., Norouzi A.A. Estimating soil organic carbon from soil reflectance: A review. Precis. Agric., 2010, vol. 11, pp. 82–99. https://doi.org/10.1007/s11119-009-9123-3

19. Lamichhane S., Kumar L., Wilson B. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review. Geoderma, 2019, vol. 352, pp. 395–413. https://doi.org/10.1016/j.geoderma.2019.05.031

20. Land Degradation Neutrality: Implications and Opportunities for Conservation. Technical Brief. Nairobi: IUCN, 2015, 2nd ed. 19 p.

21. Lu D., Li G., Valladares G.S., Batistella M. Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land Degrad. Dev., 2004, vol. 15, pp. 499–512. https://doi.org/0.1002/ldr.634

22. Lyuri D.I., Goryachkin S.V., Karavaeva N.A., Denisenko E.A., Nefedova T.G Dinamika sel’skokhozyaistvennykh zemel’ Rossii v XX veke i postagrogennoe vosstanovlenie rastitel’nosti i pochv [Dynamics of Agricultural Lands in Russia in the Twentieth Century and Postagrogenic Restoration of Vegetation and Soils]. Moscow: GEOS Publ., 2010. 412 p.

23. Mamontov V.G., Kogut B.M., Rodionova L.P., Rizhkov O.V. Influence of agricultural using on the structural condition of typical Chernozem and content of organic carbon in aggregates of different sizes and quality. Izv. Timiryazev. SKh. Akad., 2016, vol. 6, pp. 22–31. (In Russ.).

24. Mondal A., Khare D., Kundu S., Mondal S., Mukherjee S., Mukhopadhyay A. Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data. Egypt. J. Remote Sens. Space Sci. 2017, vol. 20, pp. 61–70. https://doi.org/10.1016/j.ejrs.2016.06.004

25. Moore I.D., Grayson R.B., Ladson A.R. Digital terrain modeling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process., 1991, vol. 5, pp. 3–30. https://doi.org/10.1002/HYP.3360050103

26. Plotnikova O.O. The role of shallow-water flow transport capacity in transformation of some properties of surface horizons of the eroded typical Сhernozem (Kursk region). Cand. Sci. (Biol.) Dissertation. Moscow: Moscow State Univ., 2020. 224 p.

27. Scharlemann J.P.W., Tanner E.V.J., Hiederer R., Kapos V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag., 2014, vol. 5, no. 1, pp. 81–91. https://doi.org/10.4155/cmt.13.77

28. Schillaci C., Acutis M., Lombardo L., Lipani A., Fantappiè M., Märker M., Saia S. Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling. Sci. Total Environ., 2017, vols. 601–602, pp. 821–832.

29. Schulze D.G., Nagel J.L., Van Scoyoc G.E., Henderson T.L., Baumgardner M.F., Scott D.E. Significance of organic matter in determining soil colors. In Soil Color. Bigham J.M., Ciolkosz E.J., Eds. The Soil Science Society of America, 1993, pp. 71–90. https://doi.org/10.2136/sssaspecpub31.c5

30. Schwanghart W., Jarmer Т. Linking spatial patterns of soil organic carbon to topography – A case study from south-eastern Spain. Geomorphology, 2011, vol. 126, nos. 1–2, pp. 252–263. https://doi.org/10.1016/j.geomorph.2010.11.008

31. Singh J.S., Gupta V.K. Soil microbial biomass: A key soil driver in management of ecosystem functioning. Sci. Total Environ., 2018, vol. 634, pp. 497–500.

32. Suleymanov A., Gabbasova I., Suleymanov R., Abakumov E., Polyakov V., Liebelt P. Mapping soil organic carbon under erosion processes using remote sensing. Hungarian Geogr. Bull., 2021, vol. 70, no. 1, pp. 49–64. https://doi.org/10.15201/hungeobull.70.1.4

33. Sushko S.V. Carbon dioxide emission and microbial respiration of soils of different ecosystems of the subtaiga and forest-steppe (Moscow and Kursk regions). Cand. Sci. (Biol.) Dissertation. Moscow: Moscow State Univ., 2019. 137 p.

34. Takata Y., Funakawa S., Akshalov K., Ishida N., Kosaki T. Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information. Soil Sci. Plant Nutr., 2007, vol. 53, pp. 289–299. https://doi.org/10.1111/j.1747-0765.2007.00142.x

35. Vrieling A. Satellite remote sensing for water erosion assessment: A review. Catena, 2006, vol. 65, pp. 2–18. https://doi.org/10.1016/j.catena.2005.10.005

36. Wang K., Qi Y., Guo W., Zhang J., Chang Q. Retrieval and mapping of soil organic carbon using Sentinel-2A spectral images from bare cropland in autumn. Remote Sens., 2021, vol. 13, 1072. https://doi.org/10.3390/rs13061072

37. Wei Jb., Xiao Dn., Zhang Xy., Li Xy. Topography and land use effects on the spatial variation of soil organic carbon: A case study in a typical small watershed of the black soil region in northeast China. Eurasian Soil Sci., 2008, vol. 41, pp. 39–47.

38. https://doi.org/10.1134/S1064229308010055