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Tom 80 Nr 1 (2025)

Artykuły

Predicting soil compaction from soil electrical conductivity based on scanning methods. A review

DOI: https://doi.org/10.24326/as.2025.5463
Przesłane: 12 grudnia 2024
Opublikowane: 19.05.2025

Abstrakt

Soil compaction is a crucial agricultural issue impacting plant growth, water infiltration, and soil health. Because of its sensitivity to soil variables such as texture, moisture content, and salinity, soil electrical conductivity (ECa) has emerged as a promising indirect predictor of soil compaction. This review summarizes selected studies on the relationship between soil compaction and apparent electrical conductivity and examines various prediction approaches. It also considers the potential applications and limitations of using ECa to estimate soil compaction, including methods based on machine learning. Future advancements in technology, modeling, and data integration will be key to fully realizing the potential of ECa in soil compaction management.

Bibliografia

  1. Abbaspour-Gilandeh A., Khalilian F.H., 2011. Use of soil EC data for zoning the production field by artificial neural network for applying the precision tillage. J. Agr. Machinery Sci. 7(1), 27–31.
  2. Abdulraheem M., Zhang W., Li S., Moshayedi A., Farooque A., Hu J., 2023. Advancement of remote sensing for soil measurements and applications: A comprehensive review. Sustainability 15(21), 15444. https://doi.org/10.3390/su152115444
  3. Adamchuk V., 2015. Soil technology comparison. Grid Sampling vs. Electrical Conductivity vs. SoilOptix®. Ontario Ministry of Agriculture, Food and Rural Affairs, https://soiloptix.com/wp-content/uploads/2021/11/SoilOptix-Technology-Comparison-Study.pdf [access: 20.11.2024]
  4. Adhikari K., Carre F., Toth G., Montanarella L., 2009. Site Specific Land Management: General Concepts and Applications. EUR 23978 EN. European Commission, JRC53692.
  5. Bache B., Chesworth W., Chesworth W., Gessa C., Lewis D.T., 2008. Bulk density. In: W. Ches-worth (ed.), Encyclopedia of soil science. Encyclopedia of Earth Sciences Series. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-3995-9_80
  6. Barbosa R., Overstreet C., 2022. What is soil electrical conductivity? LSU AgCenter Pub.
  7. 3185, https://www.lsuagcenter.com/nr/rdonlyres/e57e82a0-3b99-4dee-99b5-cf2ad7c43aef/77101/pub3185whatissoilelectricalconductivityhighres.pdf [access: 4.15.2024].
  8. Ben-Dor E., Chabrillat S., Demattê J., Taylor G., Hill J., Whiting M., Sommer S., 2009. Using Imaging Spectroscopy to study soil properties. Remote Sens. Environ. 113, 38–55. https://doi.org/10.1016/j.rse.2008.09.019
  9. Brogi C., Huisman J., Herbst M., Weihermüller L., Klosterhalfen A., Montzka C., Reichenau T.G., Vereecken H., 2020. Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics-based quantitative soil information. Vadose Zone J. 19, e20009. https://doi.org/10.1002/uar2.20009
  10. Corwin D., Lesch S., 2003. Application of soil electrical conductivity to precision agriculture: Theo-ry, principles, and guidelines. Agron. J. 95, 455–471.
  11. Corwin D., Lesch S., 2005. Apparent soil electrical conductivity measurements in agriculture. Comp. Electron. Agric. 46, 11–43. https://doi.org/10.1016/j.compag.2004.10.005
  12. De Feudis C., Ferré C., Comolli R., 2025. Practical insights for ECa-based soil mapping: Case studies in croplands and vineyards. Smart Agric. Technol. 10, 100697. https://doi.org/10.1016/j.atech.2024.100697.
  13. Deng X., Gu H., Yang L., Lyu H., Cheng Y., Pan L., Fu Z., Cui L., Zhang L., 2020. A method of electrical conductivity compensation in a low-cost soil moisture sensing measurement based on capacitance. Measurement 150, 107052. https://doi.org/10.1016/j.measurement.2019.107052
  14. Ding J., Yang S., Shi Q., Wei Y., Wang F., 2020. Using apparent electrical conductivity as an indi-cator for investigating potential spatial variation of soil salinity across seven oases along Tarim River in Southern Xinjiang, China. Remote Sens. 12(16), 2601. https://doi.org/10.3390/rs12162601
  15. Dexter A.R., 2004. Soil physical quality: Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma 120, 201–214. https://doi.org/10.1016/j.geoderma.2003.09.004.
  16. Duiker S.W., 2005. Effects of soil compaction. College of Agricultural Sciences. Agricultural Re-search and Cooperative Extension. https://extension.psu.edu/effects-of-soil-compaction [access: 19.10.2024].
  17. Ebrahimzadeh G., Mahabadi N., Bayat H., MatinFar H., 2023. Estimating pre-compression stress in agricultural soils: Integrating spectral indices and soil properties through machine learning. Comp. Electron. Agric. 215. https://doi.org/10.1016/j.compag.2023.108393
  18. Friedman S., 2005. Soil properties influencing apparent electrical conductivity: a review. Comp. Electron. Agric. 46, 45–70. https://doi.org/10.1016/j.compag.2004.11.001
  19. Galambosová J., Macák M., Rataj V., Barát M., Misiewicz P., 2020. Determining trafficked areas using soil electrical conductivity – a pilot study. Acta Technol. Agric. 23, 1–6. https://doi.org/10.2478/ata-2020-0001
  20. Ghazali M.F., Wikantika K., Harto A.B., Kondoh A., 2020. Generating soil salinity, soil moisture, soil pH from satellite imagery and its analysis. Inf. Process. Agric. 7(2), 294–306. https://doi.org/https://doi.org/10.1016/j.inpa.2019.08.003
  21. Grisso R.D., Alley M.M., Holshouser D.L., Thomason W.E., 2005. Precision farming tools. Soil electrical conductivity. Virginia Cooperative Extension 442–508, https://www.researchgate.net/publication/285309866_Precision_Farming_Tools_Soil_Electrical_Conductivity_Virginia_Cooperative_Extension [access: 19.10.2024].
  22. Hara P., Piekutowska M., Niedbała G., 2023. Prediction of pea (Pisum sativum L.) seeds yield using artificial neural networks. Agriculture 13(3), 661. https://doi.org/10.3390/agriculture13030661
  23. Heil K., Schmidhalter U., 2017. The Application of EM38: Determination of soil parameters, selec-tion of soil sampling points and use in agriculture and archaeology. Sensors 17(11), 2540. https://doi.org/10.3390/s17112540
  24. Hemmat A., Adamchuk V., 2008. Sensor systems for measuring soil compaction: Review and analysis. Comp. Electron. Agric. 63, 89–103. https://doi.org/10.1016/j.compag.2008.03.001
  25. Hoorman J., de Moraes Sá J.C., Reeder R., 2009. The biology of soil compaction. Agriculture and Natural Resources. https://ohioline.osu.edu/factsheet/SAG-10 [access: 4.15.2024].
  26. Hu W., Drewry J., Beare M., Eger A., Muller K., 2021. Compaction induced soil structural degrada-tion affects productivity and environmental outcomes: A review and New Zealand case study. Geoderma 395, 115035. https://doi.org/10.1016/j.geoderma.2021.115035
  27. Jeschke M., Lutt N., 2018. Soil compaction in agricultural production. Pioneer, https://www.pioneer.com/us/agronomy/soil-compaction-ag-production.html [access: 4.15.2024].
  28. Korsaeth A., 2005. Soil apparent electrical conductivity (ECa) as a means of monitoring changes in soil inorganic N on heterogeneous morainic soils in SE Norway during two growing seasons. Nutr. Cycl. Agroecosyst. 72, 213–227. https://doi.org/10.1007/s10705-005-1668-6
  29. Kumar P., Kumar P., Shukla A., 2021. Spatial modeling of some selected soil nutrients using geosta-tistical approach for Jhandutta Block (Bilaspur District), Himachal Pradesh, India. Agric. Res. 10, 262–273. https://doi.org/10.1007/s40003-020-00494-z
  30. Lund E.D., 2008. Soil electrical conductivity. In: C.D. Logsdon S., Moore D., Tsegaye T. (ed.), Soil science step‐by‐step field analysis. Soil Science Society of America, Inc., 137–146.
  31. Maharjan G.R., Prescher A., Nendel C., Ewert F., Mboh C.M., Gaiser T., Sabine S.J., 2018.
  32. Approaches to model the impact of tillage implements on soil physical and nutrient
  33. properties in different agro-ecosystem models. Soil Till. Res. 180, 210–221. https://doi.org/10.1016/j.still.2018.03.009.
  34. McKenzie R.H., 2010. Agricultural soil compaction: causes and management. Agrifacts. https://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/agdex13331/$file/510-1.pdf [ac-cessed on 4.15.2024].
  35. McNeill J.D., 1980. Electromagnetic terrain conductivity at low induction numbers. Technical Note TN-6 G. Limited, Ontario, Canada.
  36. McNeill J.D., 1992. Rapid, accurate mapping of soil-salinity by electromagnetic ground conductivity meters. In: Advances in measurement of soil physical properties: bringing theory into practice. 209–229. Wiley. https://doi.org/10.2136/sssaspecpub30
  37. Naderi-Boldaji M., Alimardani R., Hemmat A., Sharifi A., Keyhani A., Tekeste M.Z., Keller T., 2013. 3D finite element simulation of a single-tip horizontal penetrometer-soil interaction. Part I: Development of the model and evaluation of the model parameters. Soil Till. Res. 134, 153–162. https://doi.org/10.1016/j.still.2013.08.002
  38. Nawaz M.F., Bourrie G., Trolard F., 2013. Soil compaction impact and modelling. A review. Agron. Sustain. Dev. 33(2), 291–309. https://doi.org/10.1007/s13593-011-0071-8
  39. Niedbała G., 2019. Application of artificial neural networks for multi-criteria yield prediction of winter rapeseed. Sustainability 11(2), 533. https://doi.org/10.3390/su11020533
  40. Nogués J., Robinson D., Herrero J., 2006. Incorporating electromagnetic induction methods into regional soil salinity survey of irrigation districts. Soil Sci. Soc. Am. J. 70, 2075–2085. https://doi.org/10.2136/sssaj2005.0405
  41. Othaman N.N.C., Isa M.N.M., Hussin R., Ismail R.C., Naziri S.Z.M., Murad S.A.Z., Harun A., Ahmad M.I., 2021. Development of soil electrical conductivity (EC) sensing system in paddy field. J. Phys.: Conf. Ser. 1755, 012005. https://doi.org/10.1088/1742-6596/1755/1/012005
  42. Parent E., Parent S., Parent L., 2021. Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling. Plos One 17(8), e0233242. https://doi.org/10.1371/journal.pone.0233242
  43. Pathirana S., Lambot S., Krishnapillai M., Cheema M., Smeaton C., Galagedara L., 2024. Integrated ground-penetrating radar and electromagnetic induction offer a non-destructive approach to pre-dict soil bulk density in boreal podzolic soil. Geoderma 450, 117028. https://doi.org/10.1016/j.geoderma.2024.117028
  44. Pentoś K., Mbah J.T., Pieczarka K., Niedbała G., Wojciechowski T., 2022. Evaluation of multiple linear regression and machine learning approaches to predict soil compaction and shear stress based on electrical parameters. Appl. Sci. 12(17). https://doi.org/10.3390/app12178791
  45. Pentoś K., Pieczarka K., Lejman K., 2020. Application of soft computing techniques for the analysis of tractive properties of a low-power agricultural tractor under various soil conditions. Com-plexity 2020, 7607545. https://doi.org/10.1155/2020/7607545
  46. Peralta G., Alvarez C., Taboada M., 2021. Soil compaction alleviation by deep non-inversion tillage and crop yield responses in no-tilled soils of the Pampas region of Argentina. A meta-analysis. Soil Till. Res. 211, 105022. https://doi.org/10.1016/j.still.2021.105022
  47. Piekutowska M., Niedbała G., Piskier T., Lenartowicz T., Pilarski K., Wojciechowski T., Pilarska A.A., Czechowska-Kosacka A., 2021. The application of multiple linear regression and artifi-cial neural network models for yield prediction of very early potato cultivars before harvest. Agronomy 11(5), 885. https://doi.org/10.3390/agronomy11050885
  48. Peterson B.M., Mathur S., Osmer P.S., Vestergaard M., 2003. Quasars. In: Encyclopedia of physi-cal science and technology (third ed.), Academic Press, 465–480. https://doi.org/10.1016/B0-12-227410-5/00629-3
  49. Sanches G. M., Faria H. M., Otto R., Neto A. S., Corá, J. E., 2025. using soil apparent electrical conductivity (eca) to assess responsiveness of nitrogen rates and yield in Brazilian sugarcane fields. Agronomy 15(3), 606. https://doi.org/10.3390/agronomy15030606
  50. Silva S., de Barros N., de Novais R., Comerford N., 2018. Eucalyptus growth and phosphorus nutritional efficiency as affected by soil compaction and phosphorus fertilization. Commun. Soil Sci. Plant Anal. 49, 2700–2714. https://doi.org/10.1080/00103624.2018.1538372
  51. Siqueira G.M., Dafonte J.D., Bueno Lema J., Valcárcel Armesto M., Silva Ê., 2014. using soil apparent electrical conductivity to optimize sampling of soil penetration resistance and to im-prove the estimations of spatial patterns of soil compaction. Sci. World J. 1, 269480. https://doi.org/10.1155/2014/269480
  52. Su A., Adamchuk V., 2023. Temporal and operation-induced instability of apparent soil electrical conductivity measurements. Front. Soil Sci. 3. https://doi.org/10.3389/fsoil.2023.1137731
  53. Sudduth K., Kitchen N., Wiebold W., Batchelor W., Bollero G., Bullock D., Clay D., Palm H.L., Pierce F.J., Schuler R.T., Thelen K.D., 2005. Relating apparent electrical conductivity to soil properties across the north-central USA. Comp. Electron. Agric. 46, 263–283.
  54. Tekin A., Yalçin H., 2019. Design and development of a front-mounted on-the-go soil strength profile sensor. Turkish J. Agric. Forest. 43, 151–163. https://doi.org/10.3906/tar-1803-64
  55. Trinks I., Pregesbauer M., 2016. Efficient mapping of agricultural soils using a novel electromagnet-ic measurement system. Conference: EGU. At: X1.169, vol. 18.
  56. van den Akker J.J.H., ten Damme L., Lamandé M., Keller T., 2023. Compaction. In: Encyclopedia of soils in the environment (second ed.). Academic Press, 85–99. https://doi.org/10.1016/B978-0-12-822974-3.00225-1
  57. Vaz C.M.P., 2003. Use of a combined penetrometer-TDR moisture probe for soil compaction stud-ies. College on Soil Physics, https://www.osti.gov/etdeweb/servlets/purl/20946976 [access: 19.10.2024]
  58. Vrindts E., Mouazen A.M., Reyniers M., Maertens K., Maleki M.R., Ramon H., De Baerdemaeker J., 2005. Management zones based on correlation between soil compaction, yield and crop data. Biosyst. Eng. 92(4), 419–428. https://doi.org/10.1016/j.biosystemseng.2005.08.010
  59. Yue L.K., Wang Y., Wang L., Yao S.H., Cong C., Ren L.D., Zhang B., 2021. Impacts of soil com-paction and historical soybean variety growth on soil macropore structure. Soil Till. Res. 214, 105166. https://doi.org/10.1016/j.still.2021.105166

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