1887
Geoelectrical Monitoring
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604
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Abstract

ABSTRACT

Several studies have explored the potential of electrical resistivity tomography to monitor changes in soil moisture associated with the root water uptake of different crops. Such studies usually use a set of limited below‐ground measurements throughout the growth season but are often unable to get a complete picture of the dynamics of the processes. With the development of high‐throughput phenotyping platforms, we now have the capability to collect more frequent above‐ground measurements, such as canopy cover, enabling the comparison with below‐ground data. In this study hourly direct‐current resistivity data were collected under the Field Scanalyzer platform at Rothamsted Research with different winter wheat varieties and nitrogen treatments in 2018 and 2019. Results from both years demonstrate the importance of applying the temperature correction to interpret hourly electrical conductivity data. Crops which received larger amounts of nitrogen showed larger canopy cover and more rapid changes in electrical conductivity, especially during large rainfall events. The varieties showed contrasted heights although this does not appear to have influenced electrical conductivity dynamics. The daily cyclic component of the electrical conductivity signal was extracted by decomposing the time series. A shift in this daily component was observed during the growth season. For crops with appreciable difference in canopy cover, high‐frequency direct‐current resistivity monitoring was able to distinguish the different below‐ground behaviours. The results also highlight how coarse temporal sampling may affect interpretation of resistivity data from crop monitoring studies.

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/content/journals/10.1002/nsg.12107
2020-06-07
2020-09-24
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References

  1. Araus, J.L. and Cairns, J.E. (2014) Field high‐throughput phenotyping: the new crop breeding frontier. Trends in Plant Science, 19(1), 52–61.
    [Google Scholar]
  2. Atkinson, J.A., Pound, M.P., Bennett, M.J. and Wells, D.M. (2019) Uncovering the hidden half of plants using new advances in root phenotyping. Current Opinion in Biotechnology, 55, 1–8.
    [Google Scholar]
  3. Binley, A. (2015) 11.08 ‐ tools and techniques: electrical methods. In: Schubert, G. (Ed.) Treatise on Geophysics, 2nd edition. Oxford: Elsevier, pp. 233–259.
    [Google Scholar]
  4. Binley, A., Hubbard, S.S., Huisman, J.A., Revil, A., Robinson, D.A., Singha, K. and Slater, L.D. (2015) The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales: the emergence of hydrogeophysics. Water Resources Research, 51(6), 3837–3866.
    [Google Scholar]
  5. Blanchy, G., Saneiyan, S., Boyd, J., McLachlan, P. and Binley, A. (2020) ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling. Computers & Geosciences, 137, 104423.
    [Google Scholar]
  6. Cassiani, G., Boaga, J., Vanella, D., Perri, M.T. and Consoli, S. (2015) Monitoring and modelling of soil–plant interactions: the joint use of ERT, sap flow and eddy covariance data to characterize the volume of an orange tree root zone. Hydrology and Earth System Sciences, 19(5), 2213–2225.
    [Google Scholar]
  7. Consoli, S., Stagno, F., Vanella, D., Boaga, J., Cassiani, G. and Roccuzzo, G. (2017) Partial root‐zone drying irrigation in orange orchards: effects on water use and crop production characteristics. European Journal of Agronomy, 82, 190–202.
    [Google Scholar]
  8. Corwin, D.L. and Lesch, S.M. (2005) Characterizing soil spatial variability with apparent soil electrical conductivity. Computers and Electronics in Agriculture, 46(1–3), 103–133.
    [Google Scholar]
  9. Coussement, T., Maloteau, S., Pardon, P., Artru, S., Ridley, S., Javaux, M. and Garré, S. (2018) A tree‐bordered field as a surrogate for agroforestry in temperate regions: where does the water go?Agricultural Water Management, 210, 198–207.
    [Google Scholar]
  10. Furbank, R.T. and Tester, M. (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16(12), 635–644.
    [Google Scholar]
  11. Hayashi, M. (2004) Temperature‐electrical conductivity relation of water for environmental monitoring and geophysical data inversion. Environmental Monitoring and Assessment, 96(1–3), 119–128.
    [Google Scholar]
  12. Hayley, K., Bentley, L.R., Gharibi, M. and Nightingale, M. (2007) Low temperature dependence of electrical resistivity: implications for near surface geophysical monitoring. Geophysical Research Letters, 34(18). https://doi.org/10.1029/2007GL031124.
    [Google Scholar]
  13. Hodgkinson, L., Dodd, I.C., Binley, A., Ashton, R.W., White, R.P., Watts, C.W. and Whalley, W.R. (2017) Root growth in field‐grown winter wheat: some effects of soil conditions, season and genotype. European Journal of Agronomy, 91, 74–83.
    [Google Scholar]
  14. Horton, J.L. and Hart, S.C. (1998) Hydraulic lift: a potentially important ecosystem process. Trends in Ecology & Evolution, 13(6), 232–235.
    [Google Scholar]
  15. Jayawickreme, D.H., Dam, R.L.V and Hyndman, D.W. (2008) Subsurface imaging of vegetation, climate, and root‐zone moisture interactions. Geophysical Research Letters, 35(18). https://doi.org/10.1029/2008GL034690.
    [Google Scholar]
  16. LaBrecque, D.J. and Yang, X. (2001) Difference inversion of ERT Data: a fast inversion method for 3‐D in situ monitoring. Journal of Environmental & Engineering Geophysics, 6(2), 83–89.
    [Google Scholar]
  17. Lesparre, N., Robert, T., Nguyen, F., Boyle, A. and Hermans, T. (2019) 4D electrical resistivity tomography (ERT) for aquifer thermal energy storage monitoring. Geothermics, 77, 368–382.
    [Google Scholar]
  18. Ma, R., McBratney, A., Whelan, B., Minasny, B. and Short, M. (2011) Comparing temperature correction models for soil electrical conductivity measurement. Precision Agriculture, 12(1), 55–66.
    [Google Scholar]
  19. Mares, R., Barnard, H.R., Mao, D., Revil, A. and Singha, K. (2016) Examining diel patterns of soil and xylem moisture using electrical resistivity imaging. Journal of Hydrology, 536, 327–338.
    [Google Scholar]
  20. Michot, D., Benderitter, Y., Dorigny, A., Nicoullaud, B., King, D. and Tabbagh, A. (2003) Spatial and temporal monitoring of soil water content with an irrigated corn crop cover using surface electrical resistivity tomography: soil water study using electrical resistivity. Water Resources Research, 39(5). https://doi.org/10.1029/2002WR001581.
    [Google Scholar]
  21. Prasanna, B.M., Araus, J.L., Crossa, J., Cairns, J.E., Palacios, N., Das, B. and Magorokosho, C. (2013) High‐throughput and precision phenotyping for cereal breeding programs. In: Gupta, P. K. and Varshney, R. K. (Eds.) Cereal Genomics II. Dordrecht: Springer, pp. 341–374.
    [Google Scholar]
  22. Rücker, C. and Günther, T. (2011) The simulation of finite ERT electrodes using the complete electrode model. Geophysics, 76(4), F227–F238.
    [Google Scholar]
  23. Sadeghi‐Tehran, P., Virlet, N., Sabermanesh, K. and Hawkesford, M.J. (2017) Multi‐feature machine learning model for automatic segmentation of green fractional vegetation cover for high‐throughput field phenotyping. Plant Methods, 13(1), 103.
    [Google Scholar]
  24. Seabold, S. and Perktold, J. (2010) Statsmodels: econometric and statistical modeling with Python. 92 Proceedings of the 9th Python in Science Conference (SCIPY 2010).
  25. Senapati, N. and Semenov, M.A. (2020) Large genetic yield potential and genetic yield gap estimated for wheat in Europe. Global Food Security, 24, 100340.
    [Google Scholar]
  26. Sheets, K.R. and Hendrickx, J.M.H. (1995) Noninvasive soil water content measurement using electromagnetic induction. Water Resources Research, 31(10), 2401–2409.
    [Google Scholar]
  27. Srayeddin, I. and Doussan, C. (2009) Estimation of the spatial variability of root water uptake of maize and sorghum at the field scale by electrical resistivity tomography. Plant and Soil, 319(1–2), 185–207.
    [Google Scholar]
  28. Vanella, D., Cassiani, G., Busato, L., Boaga, J., Barbagallo, S., Binley, A. and Consoli, A. (2018) Use of small scale electrical resistivity tomography to identify soil‐root interactions during deficit irrigation. Journal of Hydrology, 556, 310–324.
    [Google Scholar]
  29. Verhoef, A., Fernández‐Gálvez, J., Diaz‐Espejo, A., Main, B.E. and El‐Bishti, M. (2006) The diurnal course of soil moisture as measured by various dielectric sensors: effects of soil temperature and the implications for evaporation estimates. Journal of Hydrology, 321(1), 147–162.
    [Google Scholar]
  30. Virlet, N., Sabermanesh, K., Sadeghi‐Tehran, P. and Hawkesford, M.J. (2017) Field scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Functional Plant Biology, 44(1), 143.
    [Google Scholar]
  31. Werban, U., al Hagrey, S.A. and Rabbel, W. (2008) Monitoring of root‐zone water content in the laboratory by 2D geoelectrical tomography. Journal of Plant Nutrition and Soil Science, 171(6), 927–935.
    [Google Scholar]
  32. Whalley, W.R., Binley, A., Watts, C.W., Shanahan, P., Dodd, I.C., Ober, E.S., Ashton, R.W., Webster, C.P., White, R.P. and Hawkesford, M.J. (2017) Methods to estimate changes in soil water for phenotyping root activity in the field. Plant and Soil, 415(1–2), 407–422.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): Electrical resistivity tomography , Hydrogeophysics and Near‐surface
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