On the issue of semivariograms constructing automation
Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 16 (2020) no. 2, pp. 177-185
Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

Most precision agriculture (PA) problems are based on assessing the variability of agroecological parameters (differentiated fertilizer application, allocation of heterogeneous zones of the agricultural field, etc.). In this connection, the study of the spatial structure of such data using geostatistical methods seems to be a relevant and promising direction. To validate the semivariogram model, determine its parameters, analyze anisotropy, and carry out other stages of the experiment, a large number of numerical calculations are required. Manually all these calculations are extremely difficult to perform, therefore, automation of these processes is necessary. Most existing programs exclude certain actions that may be important in solving PA problems (there is no possibility of studying anisotropy or spatial trend, the number of theoretical variogram models, etc., is limited), and the use of programming languages (R, Python, etc.) requires deep expertise. Therefore, there was a need to automate the solution of a certain range of problems by geostatistical methods. For the implementation of the module under consideration, it seems optimal to use the R programming language, which has a number of significant advantages: open source code and free access, a large number of supported and regularly updated packages, wide graphical capabilities, cross-platform, etc. General suggestions for automating the construction of semivariograms are presented and further use in solving a certain range of PA tasks.
Keywords: variogram analysis, precision agriculture, geostatistics, programming language R.
Mots-clés : automation
@article{VSPUI_2020_16_2_a8,
     author = {V. P. Yakushev and V. M. Bure and O. A. Mitrofanova and E. P. Mitrofanov},
     title = {On the issue of semivariograms constructing automation},
     journal = {Vestnik Sankt-Peterburgskogo universiteta. Prikladna\^a matematika, informatika, processy upravleni\^a},
     pages = {177--185},
     year = {2020},
     volume = {16},
     number = {2},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VSPUI_2020_16_2_a8/}
}
TY  - JOUR
AU  - V. P. Yakushev
AU  - V. M. Bure
AU  - O. A. Mitrofanova
AU  - E. P. Mitrofanov
TI  - On the issue of semivariograms constructing automation
JO  - Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ
PY  - 2020
SP  - 177
EP  - 185
VL  - 16
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/VSPUI_2020_16_2_a8/
LA  - ru
ID  - VSPUI_2020_16_2_a8
ER  - 
%0 Journal Article
%A V. P. Yakushev
%A V. M. Bure
%A O. A. Mitrofanova
%A E. P. Mitrofanov
%T On the issue of semivariograms constructing automation
%J Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ
%D 2020
%P 177-185
%V 16
%N 2
%U http://geodesic.mathdoc.fr/item/VSPUI_2020_16_2_a8/
%G ru
%F VSPUI_2020_16_2_a8
V. P. Yakushev; V. M. Bure; O. A. Mitrofanova; E. P. Mitrofanov. On the issue of semivariograms constructing automation. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 16 (2020) no. 2, pp. 177-185. http://geodesic.mathdoc.fr/item/VSPUI_2020_16_2_a8/

[1] V. V. Iakushev, Precision agriculture: theory and practice, Agrophysical Research Institute Publ, Saint Petersburg, 2016, 364 pp. (In Russian)

[2] N. Chen, X. Zhang, C. Wang, “Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring”, Comput. Electron. Agric., 111 (2015), 78–91 | DOI

[3] M. E. Hohn, Geostatistics, petroleum geology, 2nd ed., Springer, Science+Business Media, Dordrecht, The Netherlands, 1999, 235 pp.

[4] J. L. Gomez, F. T. Pastoriza, E. G. Alvarez, P. E. Oller, “Comparison between geostatistical interpolation and numerical weather model predictions for meteorological conditions mapping”, Infrastructures, 5:15 (2020) | DOI

[5] J. Paz-Ferreiro, E. V. Vazquez, S. R. Vieira, “Geostatistical analysis of a geochemical dataset”, Bragantia, 69 (2009), 121–129 | DOI

[6] A. E. Olthoff, C. Gomez, J. G. Alday, C. Martinez-Ruiz, “Mapping forest vegetation patterns in an Atlantic-Mediterranean transitional area by integration of ordination and geostatistical techniques”, Journal of Plant Ecology, 11:1 (2018), 114–122

[7] V. P. Iakushev, E. E. Zhukovskii, A. F. Petrushin, V. V. Iakushev, A variogram analysis of the spatial heterogeneity of agricultural fields for precision agriculture, Toolkit, Agrophysical Research Institute Publ., Saint Petersburg, 2010, 52 pp. (In Russian)

[8] C. A. Cambardella, T. B. Moorman, J. M. Novak, T. B. Parkin, D. L. Karlen, R. F. Turko et al., “Field-scale variability of soil properties in central Iowa soils”, Soil Science Society of America Journal, 58 (1994), 1501–1511 | DOI

[9] Q. X. Jiang, Q. Fu, Z. L. Wang, “Research on precision irrigation in Western Semiarid Area of Heilonngjiang province in China based on GIS”, Computer and Computing Technologies in Agriculture, 1 (2008), 359–370

[10] M. M. Moustafa, A. Yomota, “Use of a covariance variogram to investigate influence of subsurface drainage on spatial variability of soil-water properties”, Agricultural Water Management, 37 (1998), 1–19 | DOI

[11] V. P. Iakushev, V. M. Bure, O. A. Mitrofanova, E. P. Mitrofanov, “The use of geostatistical methods to analyze the transition feasibility to the differential application of agrochemicals technologies”, Vestnik of Saint Peterburg University. Applied Mathematics. Computer Science. Control Processes, 16:1 (2020), 31–40 (In Russian)

[12] V. V. Dem'ianov, E. A. Savel'eva, Geostatistics: theory and practice, Nuclear safety institute of the Russian Academy of Sciences, Nauka Publ., M., 2010, 327 pp. (In Russian)

[13] Z. Li, X. Zhang, K. C. Clarke, G. Liu, R. Zhu, “An automatic variogram modeling method with high reliability fitness and estimates”, Computers and Geosciences, 120 (2018), 48–59 | DOI