Optimization of crop rotations based on geostatistical analysis of soil acidity and earth remote sensing data, taking into account financial costs

The article presents the possibilities of detailed analysis of the spatial distribution of soil acidity to reduce liming costs and optimise land use in five working areas of the experimental farm “Gutko S.”. Based on variogram analysis, the patterns of acidity distribution in key areas are determined. Regression analysis showed a significant and high polynomial dependence between the NDVI index and soil acidity (the correlation ratio is 0.60–0.75 at sites No. 2–4) and a significant direct linear relationship at site No. 1. Geostatistical analysis revealed an average spatial dependence (residual variance of 29.9%) at site No. 3. Based on the strong relationship between the average values of the NDVI index for the summer months over 3 years (9 images) and soil acidity, it was proposed to use NDVI as a predictor for optimising the sampling grid using stochastic modelling. It was found that the relationship with NDVI is more pronounced in areas where the relief is less fragmented. Based on calculations of costs for liming, the advantage of detailed acidity accounting over the classical methods of agrochemical survey used in the Republic of Belarus was substantiated. The profit per rotation was about 1,200 US dollars from an area of 184.5 ha. Based on the results of the analysis of acidity distribution, the NDVI index and field history, a more detailed scheme of elementary plots with crop rotations that take into account soil acidity was proposed. The limitations of the available agricultural equipment prevented a more detailed division of the plots.

1. Agropromyshlennyi kompleks Rossii v 2019 godu (Agro-industrial complex of Russia in 2019), Moscow: FGBNU “Rosinformagrotekh”, 2020, 564 p.

2. Bakina L.G., Chugunova M.V., Zaitseva T.B., Nebol'sina Z.P., Vliyanie izvestkovaniya na kompleks pochvennykh mikroorganizmov i gumusovoe sostoyanie dernovo-podzolistoi pochvy v mnogoletnem opyte (The effect of liming on the complex of soil microorganisms and the humus state of sod-podzolic soil in a long-term experiment), Pochvovedenie, 2014, No. 2, pp. 225–234.

3. Bekbaeva A.M., Mazhrenova Sh.K., Ermekov F.K., Tokbergenov I.T., Kurishbaev A.K., Klassifikatsiya soderzhaniya fosfora metodami mashinnogo obucheniya po dannym distantsionnogo zondirovaniya Zemli (Classification of phosphorus content by machine learning methods based on Earth remote sensing data), The Scientific Heritage, 2020, No. 56–3, pp. 29–36.

4. Bogdevich I.M., Itogi i perspektivy optimizatsii agrokhimicheskikh pokazatelei plodorodiya pakhotnykh pochv Belarusi (Results and prospects of optimization of agrochemical fertility indicators of arable soils in Belarus), Izvestiya Natsional'noi akademii nauk Belarusi. Seriya agrarnykh nauk, 2023, Vol. 61, No. 1, pp. 22–33.

5. Vorobei M.V., Kindeev A.L., Geostatisticheskii analiz vnutripol'noi neodnorodnosti pochvennoi kislotnosti dlya provedeniya rabot po izvestkovaniyu (Geostatistical analysis of in-field heterogeneity of soil acidity for liming), Pochvovedenie i agrokhimiya, 2024, No. 1(72), pp. 27–34.

6. Global'noe pochvennoe partnerstvo (Global soil partnership.), URL: https://www.fao.org/global-soil-partnership/resources/highlights/detail/ru/c/1470631/.

7. GOST 26484-85. Metod opredeleniya obmennoi kislotnosti (Method for determining exchangeable acidity).

8. Ivanov A.L., Stolbovoy V.S., Grebennikov A.M., Ogleznev A.K., Petrosyan R.D., Shilov P.M., Ranking of acidic soils by priority of liming in the Russian Federation, Dokuchaev Soil Bulletin, 2020, Vol. 103, pp. 168–187, DOI: https://doi.org/10.19047/0136-1694-2020-103-168-187.

9. Kindeev A.L., Perspektivnye napravleniya geostatisticheskogo analiza i stokhasticheskogo modelirovaniya s uchetom ekonomicheskikh izderzhek pri tochnom zemledelii (Promising directions of geostatistical analysis and stochastic modeling taking into account economic costs in precision farming), Vestnik Moskovskogo universiteta. Seriya 17: Pochvovedenie, 2022, No. 2, pp. 59–70.

10. Krasil'nikov P.V., Targul'yan V.O., Na puti k “novoi geografii pochv”: vyzovy i resheniya (obzor) (Towards a “New Soil Geography”: Challenges and Solutions (review)), Pochvovedenie, 2019, No. 2, pp. 131–139.

11. Lapa V.V., Pirogovskaya G.V., Bogdevich I.M., Instruktsiya po izvestkovaniyu kislykh pochv sel'skokhozyaistvennykh zemel' (Instructions for liming acidic soils of agricultural lands), Minsk: In-t pochvovedeniya i agrokhimii, 2019, 32 p.

12. Malyshevskii V.A., Fedulov Yu.P., Ostrovskii N.V., Lebedovskii I.A., Raschet soderzhaniya gumusa s ispol'zovaniem dannykh distantsionnogo zondirovaniya zemli (Calculation of humus content using Earth remote sensing data), Politematicheskii setevoi elektronnyi nauchnyi zhurnal Kubanskogo gosudarstvennogo agrarnogo universiteta, 2013, No. 92, pp. 859–883.

13. Nurlygayanov R.B., Giniyatova F.F., Zainagabdinov A.F., Khaernasov I.I., Izvestkovanie kislykh pochv: proshloe i nastoyashchee (Liming of acidic soils: past and present), Vestnik Bashkirskogo gosudarstvennogo agrarnogo universiteta, 2021, No. 1, pp. 34–41.

14. Okorkov V.V., K teorii khimicheskoi melioratsii kislykh pochv (On the theory of chemical melioration of acidic soils), Agrokhimiya, 2019, No. 9, pp. 3–17, DOI: https://doi.org/10.1134/S0002188119090096.

15. Osnovnye kontseptsii geostatisticheskikh imitatsii (Basic concepts of geostatistical simulations), URL: https://desktop.arcgis.com/ru/arcmap/latest/extensions/geostatistical-analyst/key-concepts-of-geostatistical-simulation.htm.

16. Okhrana okruzhayushchei sredy v Rossii (Environmental Protection in Russia), 2020, Moscow: Rosstat, 2020, 113 p.

17. Pustyl'nik E.I., Statisticheskie metody analiza i obrabotki nablyudenii (Statistical methods of analysis and processing of observations), Moscow, 1968, 289 p.

18. Privalov F.I., Pavlovskii V.K., Grakun V.V., Lapa V.V., Soroka S.V., Vakhonin N.K, Shimanskii L.P., Organizatsionno-tekhnologicheskie normativy vozdelyvaniya zernovykh, zernobobovykh, krupyanykh kul'tur, tekhnicheskikh i kormovykh rastenii. RUP “Nauchno-prakticheskii tsentr NAN Belarusi po zemledeliyu” (Organizational and technological standards for the cultivation of grain, legumes, cereal crops, industrial and forage plants. RUE “Scientific and Practical Center of the NAS of Belarus for Agriculture”), Minsk: IVTs Minfina, 2022, 530 p.

19. Samsonova V.P., Blagoveshchenskii Yu.N., Meshalkina Yu.L., Ispol'zovanie empiricheskogo Baiesovskogo kriginga dlya vyyavleniya neodnorodnostei raspredeleniya organicheskogo ugleroda na sel'khozugod'yakh (Using empirical Bayesian kriging to identify heterogeneities in the distribution of organic carbon on farmland), Pochvovedenie, 2017, No. 3, pp. 321–328.

20. Savin I.Yu., Vindeker G.V., Nekotorye osobennosti ispol'zovaniya opticheskikh svoistv poverkhnosti pochv dlya opredeleniya ikh vlazhnosti (Some features of using optical properties of soil surfaces to determine soil moisture content), Pochvovedenie, 2021, No. 7, pp. 806–814.

21. Savin I. Yu., Tanov E. R., Kharzinov S. The use of NDVI profiles for estimating the quality of arable lands (exemplified by the Baksan rgion in Kabardino-Balkaria), Dokuchaev Soil Bulletin, 2015, Vol. 77, pp. 51-65, DOI: https://doi.org/10.19047/0136-1694-2015-77-51-65.

22. Savin I.Yu., Terekhov A.G., Mukhamediev R.I., Soil line concept and its use for soil mapping and monitoring (overview), Dokuchaev Soil Bulletin, 2025, Vol. 122, pp. 174–193, DOI: https://doi.org/10.19047/0136-1694-2025-122-174-193.

23. Khutuev A.M., Zanilov A.Kh., Tutukova D.A., Savin I.Yu., NDVI of crops as a remote indicator of arable soils quality, Dokuchaev Soil Bulletin, 2024, Vol. 121, pp. 70–85, DOI: https://doi.org/10.19047/0136-1694-2024-121-70-85.

24. Shil'nikov I.A., Sychev V.G., Zelenov N.A., Akanova N.I., Fedotova L.S., Izvestkovanie kak faktor urozhainosti i pochvennogo plodorodiya (Liming as a factor of crop yield and soil fertility), Moscow: VNIIA, 2008, 340 p.

25. Yakushev V.V., Informatsionno-tekhnologicheskie osnovy pretsizionnogo proizvodstva rastenievodcheskoi produktsii: Dis. ... dokt. s.-kh. nauk, 06.01.03 (Information and technological foundations of precision production of plant products, Dr. of Agri. Sci. thesis, 06.01.03), Sankt-Peterburg, 2013, 367 p.

26. Heuvelink G.B.M., Webster R.A., Modelling soil variation: past, present, and future, Geoderma, 2001, Vol. 100, Iss. 3/4, pp. 269–301.

27. Hofman D.J. Siegfried C.K., How many sampling points are needed to estimate the mean nitrate-N content of agricultural fields? A geostatistical simulation approach with uncertain variograms, Geoderma, 2021, Vol. 385, Art. 114816.

28. McBratney A.B., Gruijter J., Bryce A., Pedometrics timeline, Geoderma, 2019, Vol. 338, pp. 568–575.

29. McBratney A.B., Minasny B., Stockmann U., Pedometrics, Australia: The University of Sydney, 2018, 713 p.

30. Oliver V.A., Kerry R., Frogbrook Z.L., Sampling in Precision Agriculture, In: M.A. Oliver (Ed.) Geostatistical Applications for Precision Agriculture, Dordrecht, 2010, pp. 35–64.

31. Raj E.F.I., Appadurai M., Athiappan K., Precision farming in modern agriculture, In: Smart agriculture automation using advanced technologies: Data analytics and machine learning, cloud architecture, automation and IoT, Singapore: Springer Singapore, 2022, pp. 61–87.

32. The State of the World’s Land and Water Resources for Food and Agriculture – Systems at breaking point. Synthesis report, Rome, 2021, 82 p., DOI: https://doi.org/10.4060/cb7654en.

33. Uwiragiye Y., Ngaba M.J.Y., Yang M., Elrys A.S., Chen Z., Zhou J., Spatially Explicit Soil Acidification under Optimized Fertilizer Use in Sub-Saharan Africa, Agronomy, 2023, Vol. 13, pp. 632.

34. Xiao S., Ou M., Geng Y., Zhou T., Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK), Soil Use and Management, 2023, Vol. 39(2), pp. 900–916.