Soil-Agroecological Types of Lands of the Petrovsky District in the Tambov Region of Russia
Abstract
This article describes methodological approaches and results for digital mapping of water-migrational and erosional-accumulative soil cover structures for the forest-steppe of Tambov Plain. Such maps form the basis for applied maps such as for agroecological studies, forestry, landscape planning, etc. In this study, soil-landscape relationships were simulated as one of the subsystems of structure-functional organization. Linear discriminant analysis, random forest and the supported vector machine were used as simulation methods. The training sample consisted of 256 soil points. The Digital Elevation Model (DEM) had a spatial resolution of 25×25 meters. The simulation was provided for interfluves and valleys separately. A number of factors that describe soil cover type formation within interfluves and valleys were determined. It was established that within interfluves, determinant covariates are linked with moisture regime, whereas factors of lateral transfer and accumulation are most significant within valleys. The hierarchical nature of structure-functional organization was determined. The comparison of the results of the three simulation methods showed that the supported vector machine had the best accuracy values. However, verification by soil maps had the best correlations with the results of the linear discriminant analysis. In addition, soil-agroecological types of lands and their detailed descriptions for the key area were proposed on the basis of the simulation results of the soil combinations.
Keywords: soil-agroecological types, soil cover structures, landscape-adaptive agriculture, digital soil mapping
References
[1] Dokuchaev, V. V. (1892). Our Steppes Before and Now. Saint-Petersburg: Spb.
[2] Glinka, K. D. (1935). The Soil Science. Leningrad: Sel’kolhozgiz. 6th ed. (in Russian)
[3] Fridland, V. M. (1976). Pattern of the Soil Cover. Moscow: My’sl’. (in Russian).
[4] McBratney, A. B., Mendonca Santos, M. L. and Minasny, B. (2003). On Digital Soil Mapping. Geoderma, vol. 117, pp. 3-52.
[5] Kozlov D. N., Levchenko E. A. and Lozbenev, N. I. (2018). Soil Combinations as an Object Of DSM: A Case Study in the Chernozems area of the Russian Plain. In GlobalSoilMap - Digital Soil Mapping from Country to Globe. London: CRC Press, pp. 81-88.
[6] Minasny, B. and McBratney, A. B. (2016). Digital Soil Mapping: A Brief History and Some Lessons. Geoderma, vol. 264, pp. 301-311.
[7] Gerasimov, I. P. (1975). Experience in Genetic Diagnostics of Soils in the USSR based on Elementary Soil Processes. Pochvovedenie, vol. 5, pp. 3-10. (in Russian)
[8] Kozlov, D. N. and Sorokina, N. P. (2012). Tradition and Innovation in Large-Scale Soil Mapping. Digital Soil Mapping: Theoretical and Experimental Research, vol.1, issue 1, pp. 35-57. (in Russian).
[9] Sorokina, N. P. and Kozlov, D. N. (2009). Experience in Digital Mapping of Soil Cover Patterns. Eurasian Soil Science, vol. 42, issue 2, pp. 182-193.
[10] Conrad, O., et al. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development, vol. 8, pp. 1991-2007.
[11] Mitasova, H., et al. (2004). Path Sampling Method for Modeling Overland Water Flow, Sediment Transport and Short Term Terrain Evolution in Open Source GIS. Proceedings of the XV International Conference on Computational Methods in Water Resources (CMWR XV). June 13-17 2004, Chapel Hill, NC, USA: Elsevier, vol. 55, pp. 1479-1490
[12] Thaxton, C. S., et al. (2004). Simulations of Distributed Watershed Erosion, Deposition, and Terrain Evolution using a Path Sampling Monte Carlo Method. ASAE/CSAE Annual International Meeting Sponsored by ASAE/CSAE Fairmont Chateau Laurier, the Westin, Government Centre Ottawa, Ontario, Canada. 1-4 August 2004.
[13] Webster, R. and Burrough, P. A. (1974). Multiple Discriminant Analysis in Soil Survey. European Journal of Soil Science, vol. 25, issue 1, pp. 120-134.
[14] Breiman, L. (2001). Random Forests. Machine Learning, vol. 45, pp. 5-32.
[15] Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer-Verlag New York, USA.
[16] Kiriushin, V. I. (2013). The Theory of Adaptive Landscape Agriculture and Planning of Agricultural Landscapes. Moscow: KolosS.