Digital soil twins as a new technological way of genetic and applied soil science

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The concept of creating dynamic virtual images (digital twins) of soils as a component of the biosphere and a fundamental basis for agricultural production is substantiated. The development of this area is relevant in connection with the strengthening of technological sovereignty and structural adaptation of the Russian economy to modern unprecedented challenges. The current state and role of creating digital twins of soils in the conceptual basis of the digital transformation of agriculture are considered. The development of a standard for the formal description of applied problems and data for digital twins of soils made it possible to create a methodology for constructing a data structure and architecture of digital twins of soils of agricultural landscapes based on standards for integrating soil data and mathematical models.

About the authors

A. L. Ivanov

Dokuchaev Soil Science Institute

Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

A. G. Bolotov

Dokuchaev Soil Science Institute

Author for correspondence.
Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

D. N. Kozlov

Dokuchaev Soil Science Institute

Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

N. A. Vasilyeva

Dokuchaev Soil Science Institute

Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

A. V. Vladimirov

Dokuchaev Soil Science Institute

Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

T. A. Vasiliev

Dokuchaev Soil Science Institute

Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

L. O. Khorosheva

Dokuchaev Soil Science Institute

Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

Yu. A. Dukhanin

Dokuchaev Soil Science Institute

Email: bolotov@esoil.ru
Russian Federation, Moscow, 119017

References

  1. Бондаренко Н.Ф., Жуковский Е.Е., Мушкин И.Г. и др. Моделирование продуктивности агроэкосистем. Л.: Гидрометеоиздат, 1982. 264 с.
  2. Горохова И.Н., Хитров Н.Б. Распознавание каменистых, песчаных и карбонатных с поверхности почв на юге Приволжской возвышенности (Волгоградская область) по космическим снимкам // Почвоведение. 2023. № 11. С. 1340–1356. https://doi.org/10.31857/S0032180X23600609
  3. Докучаев В.В. Сочинения. М.: Изд-во АН СССР, 1951. Т. 6. С. 399.
  4. Жидкин А.П., Рухович Д.И., Мальцев К.А., Королева П.В. Варьирование оценок эрозии почв при использовании разных карт пахотных угодий Белгородской области // Почвоведение. 2024. № 4. С. 621–632. https://doi.org/10.31857/S0032180X24040075
  5. Иванов А.Л. Болотов А.Г., Десяткин Р.В. и др. Землепользование России в условиях изменения глобального климата и беспрецедентных социально-экономических вызовов: состояние почвенного (земельного) покрова, тенденции изменения, деградация, методология учета, прогнозы. М.: МБА, 2023. 100 с.
  6. Кирюшин В.И. Методология комплексной оценки сельскохозяйственных земель // Почвоведение. 2020. № 7. С. 871–879. https://doi.org/10.31857/S0032180X20070060
  7. Кирюшин В.И. Технологическая модернизация земледелия России: предпосылки и условия // Земледелие. 2015. № 6. С. 6–10.
  8. Кирюшин В.И. Управление плодородием почв и продуктивностью агроценозов в адаптивно-ландшафтных системах земледелия // Почвоведение. 2019. № 9. С. 1130–1139. https://doi.org/10.1134/S0032180X19070062
  9. Кирюшин В.И., Дубачинская Н.Н., Юрова А.Ю. Комплексная оценка сельскохозяйственных земель на примере Южного Урала // Почвоведение. 2021. № 11. С. 1363–1375. https://doi.org/10.31857/S0032180X21110083
  10. Кирюшин В.И., Иванов А.Л. Агроэкологическая оценка земель, проектирование адаптивно-ландшафтных систем земледелия и агротехнологий. М.: Российский НИИ информации и технико-экономических исследований по инженерно-техническому обеспечению агропромышленного комплекса, 2005. 784 с.
  11. Козловский Ф.И. Теория и методы изучения почвенного покрова. М.: ГЕОС, 2003. 536 с.
  12. Полевой А.Н. Прикладное моделирование и прогнозирование продуктивности посевов. Л.: Гидрометеоиздат, 1988. 319 с.
  13. Полуэктов Р.А. Динамические модели агроэкосистем. СПб.: Гидрометеоиздат, 1993. 310 с.
  14. Романенков В.А., Мешалкина Ю.Л., Горбачева А.Ю., Кренке А.Н., Петров И.К., Голозубов О.М., Рухович Д.И. Карты потенциала секвестрации почвенного углерода в пахотных почвах России // Почвоведение. 2024. № 5. С. 677–692. https://doi.org/10.31857/S0032180X24050037
  15. Савин И.Ю., Терехов А.Г., Амиргалиев Е.Н., Сагатдинова Г.Н. Спутниковый мониторинг засоления орошаемых почв Южного Казахстана // Почвоведение. 2023. № 10. С. 1259–1268. https://doi.org/10.31857/S0032180X23600543
  16. Сиротенко О.Д. Математическое моделирование водно-теплового режима и продуктивности агроэкосистем. Л.: Гидрометеоиздат, 1981. 163 с.
  17. Смирнова М.А., Козлов Д.Н. Почвенные свойства как индикаторы параметров водного режима почв (обзор) // Почвоведение. 2023. № 3. С. 353–369. https://doi.org/10.31857/S0032180X22601037
  18. Чинилин А.В., Виндекер Г.В., Савин И.Ю. VIS-NIR спектроскопия для целей оценки содержания органического углерода почв (метаанализ) // Почвоведение. 2023. № 11. С. 1357–1370. https://doi.org/10.31857/S0032180X23600695
  19. Шеин Е.В., Рыжова И.М. Математическое моделирование в почвоведении. М.: ИП Маракушев А.Б., 2016. 377 с.
  20. Шимшек У., Шеин Е.В., Микаилсой Ф., Болотов А.Г., Эрдель Э. Подпочвенное уплотнение: интенсивность проявления в тяжелосуглинистых аллювиальных карбонатных почвах района Ыгдыр (Восточная Турция) // Почвоведение. 2019. № 3. С. 330–334. https://doi.org/10.1134/S0032180X19030109
  21. Arrouays D., McBratney A., Minasny B., Hempel J., Heuvelink G., MacMillan R., Hartemink A., Lagacherie P., McKenzie N. The GlobalSoilMap project specifications. // GlobalSoilMap: Basis of the Global Spatial Soil Information System, CRC Press, London, 2014. P. 9–12. ISBN 9781138001190
  22. Ashton K. The “Internet of Things” Thing // RFiD J. 2009. V. 22(7). P. 97–114.
  23. Biggs A., Holz G., McKenzie D., Doyle R., Cattle S. Pedology: A vanishing skill in Australia // Soil Sci. Policy J. 2018. V. 1. P. 24–35.
  24. Bolotov A.G., Shein E.V., Makarychev S.V. Water retention capacity of soils in the Altai region // Eurasian Soil Science. 2019. V. 52. № 2. P. 187–192. https://doi.org/10.1134/S1064229319020030
  25. Canedo A. Industrial IoT lifecycle via digital twins // Proc. Elev. IEEE/ACM/IFIP Int. Conf. Hardware/Software Codesign Syst. Synth. CODES ’16, N.Y.: ACM Press, 2016. P. 29. https://doi.org/10.1145/2968456.2974007
  26. Chergui N., Kechadi M.T., McDonnell M. The Impact of Data Analytics in Digital Agriculture: A Review // Proc. 2020 Int. Multi-Conf. on: Organization of Knowledge and Advanced Technologies (OCTA). Tunis, Tunisia, 6–8 February 2020. P. 1–13. https://doi.org/10.1109/OCTA49274.2020.9151851
  27. Delgado J.A., Short Jr. N.M., Roberts D.P., Vandenberg B. Big data analysis for sustainable agriculture on a geospatial cloud framework // Front. Sustain. Food Syst. 2019. V. 3. P. 54. https://doi.org/10.3389/fsufs.2019.00054
  28. Finke P.A. Modeling the genesis of luvisols as a function of topographic position in loess parent material // Quat. Int. 2012. V. 266. P. 3–17. https://doi.org/10.1016/j.quaint.2011.10.016
  29. Finke P.A., Hutson J.L. Modelling soil genesis in calcareous loess // Geoderma. 2008. V. 145. P. 462–479.https://www.researchgate.net/publication/313179796_Modeling_soil_genesis_in_calcareous_loess
  30. Fountas S., Sørensen C.G., Vatsanidou A., Liakos V., Tsiropoulos Z., Cavalaris C., Canavari M., Gemtos T., Blackmore S. Farm management information systems: current situation and future perspectives // Comput. Electron. Agric. 2013. V. 115. P. 40–50. https://doi.org/10.1016/j.compag.2015.05.011
  31. Freebairn D.M., Ghahramani A., Robinson J.B., McClymont D.J. A tool for monitoring soil water using modelling, on-farm data, and mobile technology // Environ. Model Softw. 2018. V. 104. P. 55–63. https://doi.org/10.1016/j.envsoft.2018.03.010
  32. Gámez Díaz R., Yu Q., Ding Y., Laamarti F., El Saddik A. Digital Twin Coaching for Physical Activities: A Survey // Sensors. 2020. V. 20. P. 5936. https://doi.org/10.3390/s20205936
  33. Glushakova A.M., Kachalkin A.V., Umarova A.B., Kokoreva A.A., Bolotov A.G., Dunaeva E.A., Maksimova I.A. Yeast Complexes in Urban Soils of Some Southern Cities of Russia (Krasnodar, Maykop, Simferopol, and Sochi) // Microbiology (Russian Federation). 2020. V. 89. P. 603–608. https://doi.org/10.1134/S0026261720050094
  34. Gorokhova I.N., Khitrov N.B., Tarnopolsky L.A. Identification of surface-carbonate soils and soils with variegated underlying rocks in the South of Volga upland on satellite images // Eurasian Soil Science. 2024. V. 57. P. 1297–1307. https://doi.org/10.1134/S1064229324600763
  35. Grieves M. Virtually Perfect: Driving Innovative and Lean Products through Product Lifecycle Management. Space Coast Press: Merritt Island, 2011. 370 p.
  36. Grieves M., Vickers J. Digital twin: manufacturing excellence through virtual factory replication // Transdisciplinary Perspectives on Complex Systems. Springer, 2017. P. 85–113. https://doi.org/10.1007/978-3-319-38756-7_4
  37. Harms B., Brough D., Philip S., Bartley R., Clifford D., Thomas M., Willis R., Gregory L. Digital soil assessment for regional agricultural land evaluation // Global Food Secur. 2015. V. 5. P. 25–36. https://doi.org/10.1016/j.gfs.2015.04.001
  38. Hengl T., Mendes de Jesus J. SoilInfo App: Global soil information on your palm // EGU General Assembly Conference Abstracts. European Geosciences Union. 2015. P. 2372.
  39. Ibanez J.J. Future of soil science // The Future of Soil Science (International Union of Soil Sciences), IUSS, Waginingen, 2006. P. 60–62. ISBN 90-71556+16-6
  40. Jian J., Du X., Stewart R. D. A database for global soil health assessment // Scientific Data. 2020. V. 7. P. 1–8. https://doi.org/10.1038/s41597-020-0356-3
  41. Jo S.K., Park D.H., Park H., Kim S.H. Smart livestock farms using digital twin: Feasibility study // Proc. 2018 Int. Conf. on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, 17–19 October 2018. P. 1461–1463. https://doi.org/10.1109/ICTC.2018.8539516
  42. Johnson D.L., Keller E.A., Rockwell T.K. Dynamic pedogenesis: new views on some key soil concepts, and a model for interpreting Quaternary soils // Quat. Res. 1990. V. 33. P. 306–319. https://doi.org/10.1016/0033-5894(90)90058-S
  43. Johnson D.L., Watson‐Stegner D. Evolution model of pedogenesis // Soil Sci. 1987. V. 143. P. 349–366.
  44. Khitrov N.B., Kravchenko E.I., Rukhovich D.I., Koroleva P.V. Erosion – accumulative soil cover patterns of dry-steppe agrolandscape, Rostov region // Eurasian Soil Science. 2024. V. 57. P. 1409–1432. https://doi.org/10.1134/S1064229324601045
  45. Kidd D., Webb M., Malone B., Minasny B., McBratney A. Digital soil assessment of agricultural suitability, versatility and capital in Tasmania // Australia. Geoderma Reg. 2015. V. 6. P. 7–21. https://doi.org/10.1016/j.geodrs.2015.08.005
  46. Kidd D., Malone B., McBratney A., Minasny B., Webb M. Operational sampling challenges to digital soil mapping in Tasmania, Australia // Geoderma Reg. 2015. V. 4. P. 1–10. https://doi.org/10.1016/j.geodrs.2014.11.002
  47. Kitchenham B., Charters S. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report. Version 2.3. EBSE: Keele, 2007. 57 p. https://www.researchgate.net/publication/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering
  48. Kolupaeva V., Kokoreva A., Belik A., Bolotov A., Glinushkin A. Modelling Water and Pesticide Transport in Soil with MACRO 5.2: Calibration with Lysimetric Data // Agriculture (Switzerland). 2022. V. 12. P. 505. https://doi.org/10.3390/agriculture12040505
  49. Laamarti F., Badawi H.F., Ding Y., Arafsha F., Hafidh B., El Saddik A. An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities // IEEE Access2020. V. 8. P. 105950–105961. https://doi.org/10.1109/ACCESS. 2020.2999871
  50. Lakeman-Fraser P., Gosling L., Moffat A.J., West S.E., Fradera R., Davies L., Ayamba M.A., Van Der Wal R. To have your citizen science cake and eat it? Delivering research and outreach through Open Air Laboratories (OPAL) // BMC Ecology. 2016. V. 16. P. 57–70. https://doi.org/10.1186/s12898-016-0065-0
  51. Lawes R., Mata G., Chen Y., Chai J., Donohue R., Butler R., Factor D., Pitman T., Sharman C., Hochman Z. Revolutionising Big Data to help Grain Growers Answer on-farm Questions // GRDC Update Papers. 26 Feb 2018. https://grdc.com.au/resources-and-publications/grdc-update-papers/tab-content/grdc-update-papers/2018/02/revolutionising-big-data-to-help-grain-growers
  52. Lee E.A., Seshia S.A. Introduction to Embedded Systems, a Cyber-Physical Systems Approach. Version 2.2. MIT Press, 2017. 564 p.
  53. Madni A., Madni C., Lucero S. Leveraging Digital Twin Technology in Model-Based Systems Engineering // Systems. 2019. V. 7. P. 7. https://doi.org/10.3390/systems7010007
  54. McDermott P.L., Wikle C.K. Deep echo state networks with uncertainty quantification for spatio-temporal forecasting // Environmetrics. 2019. V. 30. P. e2553. https://doi.org/10.1002/env.2553
  55. Minasny B., McBratney A.B. A rudimentary mechanistic model for soil formation and landscape development: II. A two‐dimensional model incorporating chemical weathering // Geoderma. 2001. V. 103. P. 161–179. https://doi.org/10.1016/S0016-7061(01)00075-1
  56. Minasny B., McBratney A.B. Digital soil mapping: A brief history and some lessons // Geoderma. 2016. V. 264. P. 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017
  57. Monteiro J., Barata J., Veloso M., Veloso L., Nunes J. Towards sustainable digital twins for vertical farming // Proc. 2018 Thirteenth Int. Conf. on Digital Information Management (ICDIM). Berlin, Germany. 24–26 September 2018; P. 234–239. https://doi.org/10.1109/ICDIM.2018.8847169
  58. Padarian J., McBratney A.B. A new model for intra-and inter-institutional soil data sharing // Soil. 2020. V. 6. P. 89–94. https://doi.org/10.5194/soil-6-89-2020
  59. Phillips J.D. Progressive and regressive pedogenesis and complex soil evolution // Quaternary Research. 1993. V. 40. P. 169–176. https://doi.org/10.1006/qres.1993.1069
  60. Pylianidis С., Sjoukje O., Athanasiadis I.N. Introducing digital twins to agriculture // Computers and Electronics in Agriculture. 2021. V. 184. P. 105942. https://doi.org/10.1016/j.compag.2020.105942
  61. Rossiter D., Past G. Present & future of information technology in pedometrics // Geoderma. 2018. V. 324. P. 131–137. https://doi.org/10.1016/j.geoderma.2018.03.009
  62. Sarrazin P., Blake D., Feldman S., Chipera S., Vaniman D., Bish D. Field deployment of a portable X-ray diffraction/X-ray flourescence instrument on Mars analog terrain // Powder Diffract. 2005. V. 20. P. 128–133. https://doi.org/10.1154/1.1913719
  63. Searle R., McBratney A., Grundy M., Kidd D. et al. Digital soil mapping and assessment for Australia and beyond: A propitious future // Geoderma Reg. 2021. V. 24. Article No e00359, ISSN 2352-0094. https://doi.org/10.1016/j.geodrs.2021.e00359
  64. Semeraro C., Lezoche M., Panetto H., Dassisti M. Digital twin paradigm: A systematic literature review // Computers in Industry. 2021. V. 130. P. 103469. https://doi.org/10.1016/j.compind.2021.103469
  65. Shein Y.V., Bolotov A.G., Dembovetskii A.V. Soil Hydrology of Agricultural Landscapes: Quantitative Description, Research Methods, and Availability of Soil Water // Eurasian Soil Science. 2021. V. 54. P. 1367–1374. https://doi.org/10.1134/S1064229321090076
  66. Shein E.V., Bolotov A.G., Dembovetskiy A.V., Kharkhardinov N.A., Vernyuk Yu.I. Using the SWAT model to characterise the water regime of soils in agrolandscapes // Dokuchaev Soil Bulletin. 2023. V. 117. P. 5–22. https://doi.org/10.19047/0136-1694-2023-117-5-22
  67. Shelley W. mySoil app for smartphones // British Geological Survey. 2012. https://www.bgs.ac.uk/technologies/apps/mysoil-app/
  68. Shonk J. L., Gaultney L.D., Schulze D.G., Van Scoyoc G.E. Spectroscopic sensing of soil organic matter content // Transactions of the American Society of Agricultural and Biological Engineers. 1991. V. 34. P. 1978–1984. https://doi.org/10.13031/2013.31826
  69. Sidorov I.A., Gudkov A.G., Chizhikov S.V., Bolotov A.G., Khokhlov N.F., Porokhov I.O. The Own Radiothermal Radiation of the Earth’s Surface Receiving Process Modeling // Radioelektronika, Nanosistemy, Informacionnye Tehnologii. 2022. V. 14. P. 359–372. https://doi.org/10.17725/rensit.2022.14.359
  70. Silva L., Rodrfguez-Sedano F., Baptista P., Coelho J.H. The Digital Twin Paradigm Applied to Soil Quality Assessment: A Systematic Literature Review // Sensors. 2023. V. 23. P. 1007. https://doi.org/10.3390/s23021007
  71. Skobelev P.O., Mayorov I.V., Simonova E.V., Goryanin O.I., Zhilyaev A.A., Tabachinskiy A.S., Yalovenko V.V. Development of models and methods for creating a digital twin of plant within the cyber-physical system for precision farming management // J. Phys. Conf. Ser. 2020. V. 1703. P. 012022. https://doi.org/10.1088/1742-6596/1703/1/012022
  72. Skobelev P., Tabachinskiy A., Simonova E., Lee T.R., Zhilyaev A., Laryukhin, V. Digital twin of rice as a decision-making service for precise farming, based on environmental datasets from the fields // Proc. 2021 Int. Conf. on Information Technology and Nanotechnology (ITNT), Samara, Russia, 20-24 September 2021. P. 1–8. https://doi.org/10.1109/ITNT52450.2021.9649038
  73. Soderstrom M., Sohlenius G., Rodhe L., Piikki K. Adaptation of regional digital soil mapping for precision agriculture // Precis. Agric. 2016. V. 17. P. 588–607. https://doi.org/10.1007/s11119-016-9439-8
  74. Sreedevi T.R., Kumar M.B.S. Digital twin in smart farming: a categorical literature review and exploring possibilities in hydroponics // 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA). Cochin, India, 2020. P. 120–124. https://doi.org/10.1109/ACCTHPA49271.2020.9213235
  75. Stenberg В., Viscarra Rossel R.A., Mouazen A.M., Wetterlind, J. Visible and near infrared spectroscopy in soil science // Adv. Agronomy. 2010. V. 107. P. 163–215. https://doi.org/10.1016/S0065-2113(10)07005-7
  76. Stockmann U., Padarian J., McBratney A., Minasny В., De Brogniez D., Montanarella L., Hong S.Y., Rawlins В.G., Field D.J. Global soil organic carbon assessment // Global Food Security. 2015. V. 6. P. 9–16. https://doi.org/10.1016/j.gfs.2015.07.001
  77. Stockmann U., Jones E.J., Odeh I.O.A., McBratney A.B. Pedometric treatment of soil attributes // Pedometrics. 2018. P. 115–153. https://doi.org/10.1007/978-3-319-63439-5_5
  78. Sung Y.M., Kim T. Smart Farm Realization Based on Digital Twin // ICIC Express Lett. 2022. V. 13. P. 421–427. https://doi.org/10.24507/icicelb.13.04.421
  79. Targulian V.O., Krasilnikov P.V. Soil system and pedogenic processes: Self-organization, time scales, and environmental significance // Catena. 2007. V. 713. P. 373–381. https://doi.org/10.1016/j.catena.2007.03.007
  80. Tate R.L. Soil microbiology. Wiley-Blackwell, 2020. 592 p. ISBN: 978-0-470-31110-3.
  81. Tekinerdogan B., Verdouw C. Systems architecture design pattern catalog for developing digital twins // Sensors. 2020. V. 20. P. 5103. https://doi.org/10.3390/s20185103
  82. Thomas M., Clifford D., Bartley R., Philip S., Brough D., Gregory L., Willis R., Glover M. Putting regional digital soil mapping into practice in tropical Northern Australia // Geoderma. 2015. V. 241–242. P. 145–157. https://doi.org/10.1016/j.geoderma.2014.11.01
  83. Trienekens J.H., Van der Vorst J.G.A.J., Verdouw C.N. Global food supply chains // Encyclopedia of Agriculture and Food System. Academic Press, 2014. P. 499–517. https://doi.org/10.1016/B978-0-444-52512-3.00118-2
  84. Van Der Horn E., Mahadevan S. Digital Twin: Generalization, characterization and implementation // Decis. Support Syst. 2021. V. 145. P. 113524. https://doi.org/10.1016/j.dss.2021.113524
  85. Verdouw C., Kruize J.W. Digital twins in farm management: Illustrations from the FIWARE accelerators SmartAgriFood and Fractals // 7th Asian-Australasian Conf. Precis. Agric, 2017. P. 1–5. https://doi.org/10.5281/zenodo.893662
  86. Verdouw C., Tekinerdogan B., Beulens A., Wolfert S. Digital twins in smart farming // Agric. Syst. 2021. V. 189. P. 103046. https://doi.org/10.1016/j.agsy.2020.103046
  87. Viscarra Rossel R.A., Behrens T., Ben-Dor E. et al. A global spectral library to characterize the world’s soil // Earth-Science Reviews. 2016. V. 155. P. 198–230. https://doi.org/10.1016/j.earscirev.2016.01.012
  88. Viscarra Rossel R.V., McKenzie N., Grundy M. Using Proximal Soil Sensors for Digital Soil Mapping // Digital Soil Mapping. Progress in Soil Science. 2010. V. 2. P. 79–82. https://doi.org/10.1007/978-90-481-8863-5_7
  89. Wadoux A.M.J.-C., Minasny В., McBratney A.В. Machine learning for digital soil mapping: Applications, challenges and suggested solutions // Earth-Science Reviews. 2020. V. 210. P. 103359. https://doi.org/10.1016/j.earscirev.2020.103359
  90. Wadoux A.M.J‐C., McBratney A.B. Digital soil science and beyond // Soil Sci. Soc. Am. J. 2021. V. 85.5. P. 1313–1331. https://doi.org/10.1002/saj2.20296
  91. Wadoux A.M. J.-C., Román-Dobarco M., McBratney A.B. Perspectives on data-driven soil research // Eur. J. Soil Sci. 2021. V. 72. P. 1675–1689. https://doi.org/10.1111/ejss.13071
  92. Weindorf D.C., Bakr N., Zhu Y. Advances in portable X-ray fluorescence (PXRF) for environmental, pedological, and agronomic applications // Adv. Agron. 2014. V. 128. P. 1–45. https://doi.org/10.1016/B978-0-12-802139-2.00001-9
  93. Yang Y., Viscarra Rossel R.A., Li S., Bissett A., Lee J., Shi Z., Behrens T., Court L. Soil bacterial abundance and diversity better explained and predicted with spectro-transfer functions // Soil Biol. Biochem. 2019. V. 129. P. 29–38. https://doi.org/10.1016/j.soilbio.2018.11.005
  94. Yin C., McKay A. Introduction to modeling and simulation techniques // The 8th Int. Symp. on Comput. Intell. and Ind. Appl. (ISCIIA2018) The 12th China-Japan Int. Work. on Inf. Technol. Control Appl. (ITCA2018) Binjiang International Hotel, Tengzhou, Shandong, China, Nov. 2-6, 2018. P. 1–6. https://www.researchgate.net/publication/332962311_Introduction_to_Modeling_and_Simulation_Techniques.
  95. Yu D., Zha Y., Shi L., Bolotov A., Tso C.-H.M. Spatiotemporal sampling strategy for characterization of hydraulic properties in heterogeneous soils // Stochastic Environ. Res. Risk Assessm. 2021. V. 35. P. 737–757. https://doi.org/10.1007/s00477-020-01882-1

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Domain model of the architecture of soil CD.

Download (294KB)
Indexing metadata
3. Fig. 2. Diagram of the initialization sequences of soil CD.

Download (366KB)
Indexing metadata
4. Fig. 3. Scheme for constructing a general ontology using data description schemes. Adding new data.

Download (279KB)
Indexing metadata

Copyright (c) 2025 Russian Academy of Sciences