Application of areal interpolation methods when determining zones of potential fertility of soils of agricultural lands

https://doi.org/10.35595/2414-9179-2021-4-27-120-134

View or download the article (Rus)

About the Authors

Nikolay V. Klebanovich

Byelorussian State University, Faculty of Geography and Geoinformatics,
Leningradskaja, 14, 220004, Minsk, Belarus;
E-mail: N_Klebanovich@inbox.ru

Arkady L. Kindeev

Byelorussian State University, Faculty of Geography and Geoinformatics,
Leningradskaja, 14, 220004, Minsk, Belarus;
E-mail: akindeev@tut.by

Vitalina S. Kizeeva

Byelorussian State University, Faculty of Geography and Geoinformatics,
Leningradskaja, 14, 220004, Minsk, Belarus;
E-mail: tatya.novikova.85@mail.ru

Abstract

The article presents one of the possible options for improving the methodology for identifying zones of potential soil fertility. The necessity of using areal interpolation as the only method of geostatistical analysis that takes into account the area of input objects is proved. To check the data for a Gaussian normal distribution, it is necessary to use several verification methods, since when evaluating only statistical parameters, significant (in the case of phosphorus, abnormal) deviations were found, however, when evaluating histograms and quartile-quartile plots, it is necessary to bring the data to a normal distribution was relevant only for humus and phosphorus. The main advantages and disadvantages of the areal interpolation method are shown. With a significant deviation from the normal distribution, in the absence of built-in functions for automated reduction of data to the Gaussian distribution, one of the few ways can be the logarithm of the data. After zoning, it is necessary to perform a reverse translation to the original values for a representative visualization of the results. As a result of the selection of theoretical semivariograms-deconvolutions, the degrees of spatial dependence and optimal distances for the studied properties are determined. It is clear that the lag of acidity and potassium content is 1000 m and 1050 m, respectively. For phosphorus, it is 1300 m. For the humus content, the lag is much lower—440 m. The maximum autocorrelation distance is typical for potassium and humus—2330 and 1528 m; the minimum for phosphorus is 637. The reliability of the cartograms of agrochemical properties is confirmed by the calculated root-mean-square errors. The deviations of pH values are in the range of up to 0.15 units. The highest mean square error of interpolation is observed in weakly acidic soils. The error in the interpolated values of humus from the initial data is inherent in anthropogenically transformed soils. The root-mean-square error of phosphorus values can be estimated as insignificant. The largest errors in K2O—in isolated cases, they reach 120 mg/ha in the central and eastern parts of the region. The resulting map of potential soil fertility was used to determine the relationship with the granulometric composition of soils. A low level is observed on sandy and sandy loam soils, a high level—on loams. Also, the productivity is affected by the relief of the territory—in the dissected areas, productivity is lower than on the plains.

Keywords

GIS, geostatistics, variation, areal interpolation, soil productivity.

References

  1. Bogdevich I.M. Large-scale agrochemical and radiological examination of soils of agricultural lands of the Republic of Belarus: methodological instructions. Minsk: Institute of Soil Science and Agrochemistry, 2012. 48 p. (in Russian)
  2. Chernysh A.F. Design of anti-erosion complexes and the use of erosion-hazardous lands in different landscape zones of Belarus. Minsk, Institute of Soil Science and Agrochemistry of the National Academy of Sciences of Belarus, 2005. 52 p. (in Russian).
  3. Chervan A.N. Tsyribko V.B., Ustinova A.M. Data of agrophysical properties of soils in the formation of soil-protective systems of agriculture using GIS-technologies on the example of the Braslavsky district of the Vitebsk region. Minsk, Institute of Soil Science and Agrochemistry of the National Academy of Sciences of Belarus, 2016. P. 25–36 (in Russian).
  4. Cressie N.A.C. Statistics for Spatial Data. Revised ed. John Wiley & Sons. New York, 1993. 900 p.
  5. Gotway C.A, Young L.J. A geostatistical approach to linking geographically aggregated data from different sources. Journal of Computational and Graphical Statistics, 2007. V. 16. P. 115–135.
  6. Klebanovich N.V. To develop a geoinformation base of spatial information and analytical data, reflecting the resistance of various types of land in agricultural landscapes to anthropogenic impact: research report (conclusion). Minsk. Institute of Soil Science and Agricultural Chemistry, 2019 (in Russian).
  7. Krivoruchko K., Gribov A., Krause E. Multivariate Areal Interpolation for Continuous and Count Data. Procedia Environmental Sciences 3, 2011. P. 14–19.
  8. Kutsaeva O.A. Creation of management zones for the differentiated application of mineral fertilizers using geostatistical tools. Bulletin of the Belarusian State Agricultural Academy, 2020. No. 2. P. 176–181 (in Russian).
  9. Lark R.M. Estimating variograms of soil properties by the method-ofmoments and maximum likelihood. European Journal of Soil Science, 2000. V. 51. P. 717–728.
  10. Liu X., Shahid R., Patel A.B., Terrence McDonald T., Bertazzon S., Waters N, Judy E. Seidel J.E., Marshall D.A. Geospatial patterns of comorbidity prevalence among people with osteoarthritis in Alberta Canada. BMC Part of Springer Nature. 2020. 16 p. Web resource: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-020-09599-0 (accessed 05.04.2021)
  11. Oliver V.A., Kerry R., Frogbrook Z.L. Sampling in Precision Agriculture. Geostatistical Applications for Precision Agriculture, Springer Science + Business Media B.V., 2010. P. 35–64.
  12. Steeves E.A. Martins P.A., Gittelsohn J. Changing the Food Environment for Obesity Prevention: Key Gaps and Future Directions. Curr Obes Rep., 2014. V. 3 (4). P. 451–458.
  13. Thoughts T.N. Using geostatistical tools to assess the spatial distribution of acid-soluble copper in soil. Bulletin of the Belarusian State Agricultural Academy, 2020. No. 2. P. 170–176.
  14. Wadoux A.M.J.-C., Marchant B.P. Lark R.M. Efficient sampling for geostatistical surveys. The European Journal of Soil Science, 2019. 9 p.

For citation: Klebanovich N.V., Kindeev A.L., Kizeeva V.S. Application of areal interpolation methods when determining zones of potential fertility of soils of agricultural lands InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2021. V. 27. Part 4. P. 120–134. DOI: 10.35595/2414-9179-2021-4-27-120-134 (In Russian)