[en] Breeding climate-robust crops is one of the needed pathways for adaptation to the changing climate. To speed up the breeding process, it is important to understand how plants react to extreme weather events such as drought or waterlogging in their production environment, i.e. under field conditions in real soils. Whereas a number of techniques exist for aboveground field phenotyping, simultaneous non-invasive belowground phenotyping remains difficult. In this paper, we present the first data set of the new HYDRAS (HYdrology, Drones and RAinout Shelters) open-access field-phenotyping infrastructure, bringing electrical resistivity tomography, alongside drone imagery and environmental monitoring, to a technological readiness level closer to what breeders and researchers need. This paper investigates whether electrical resistivity tomography (ERT) provides sufficient precision and accuracy to distinguish between belowground plant traits of different genotypes of the same crop species. The proof-of-concept experiment was conducted in 2023, with three distinct soybean genotypes known for their contrasting reactions to drought stress. We illustrate how this new infrastructure addresses the issues of depth resolution, automated data processing, and phenotyping indicator extraction. The work shows that electrical resistivity tomography is ready to complement drone-based field-phenotyping techniques to accomplish whole-plant high-throughput field phenotyping.
Disciplines :
Agriculture & agronomy
Author, co-author :
Blanchy, Guillaume ; Université de Liège - ULiège > Urban and Environmental Engineering
Deroo, Waldo; ILVO - Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
De Swaef, Tom; ILVO - Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Lootens, Peter; ILVO - Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Quataert, Paul ; ILVO - Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Roldán-Ruíz, Isabel; ILVO - Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Versteeg, Roelof; Subsurface Insights
Garré, Sarah ; ILVO - Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Language :
English
Title :
Closing the phenotyping gap with non-invasive belowground field phenotyping
Abdalla, M., Ahmed, M. A., Cai, G., Wankmüller, F., Schwartz, N., Litig, O., Javaux, M., and Carminati, A.: Stomatal closure during water deficit is controlled by below-ground hydraulics, Ann. Botany, 129, 161-170, https://doi.org/10.1093/aob/mcab141, 2021.
Amato, M., Bitella, G., Rossi, R., Gómez, J. A., Lovelli, S., and Gomes, J. J. F.: Multi-electrode 3D resistivity imaging of alfalfa root zone, Eur. J. Agro., 31, 213-222, https://doi.org/10.1016/j.eja.2009.08.005, 2009.
Araus, J. L. and Cairns, J. E.: Field high-throughput phenotyping: the new crop breeding frontier, Trend. Plant Sci., 19, 52-61, https://doi.org/10.1016/j.tplants.2013.09.008, 2014.
Atkinson, J. A., Pound, M. P., Bennett, M. J., and Wells, D. M.: Uncovering the hidden half of plants using new advances in root phenotyping, Curr. Op. Biotechnol., 55, 1-8, https://doi.org/10.1016/j.copbio.2018.06.002, 2019.
Binley, A.: 11.08 - Tools and Techniques: Electrical Methods, pp. 233-259, Elsevier, Oxford, https://doi.org/10.1016/B978-0-444-53802-4.00192-5, 2015.
Binley, A. and Slater, L.: Resistivity and Induced Polarization: Theory and Applications to the Near-Surface Earth, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781108685955, 2020.
Blanchy, G. and Garré, S.: "Closing the Phenotyping Gap with Non-Invasive Belowground Field Phenotyping", January, Zenodo [code], https://zenodo.org/records/14673647 (last access: 21 January 2025), 2025.
Blanchy, G., Saneiyan, S., Boyd, J., McLachlan, P., and Binley, A.: ResIPy, an intuitive open source software for complex geoelectrical inversion/modeling, Comput. Geosci., 137, 104423, https://doi.org/10.1016/j.cageo.2020.104423, 2020a.
Blanchy, G., Watts, C. W., Ashton, R. W., Webster, C. P., Hawkesford, M. J., Whalley, W. R., and Binley, A.: Accounting for heterogeneity in the _ _ relationship: Application to wheat phenotyping using EMI, Vadose Zone J., 19, e20037, https://doi.org/10.1002/vzj2.20037, 2020b.
Blanchy, G., Watts, C. W., Richards, J., Bussell, J., Huntenburg, K., Sparkes, D. L., Stalham, M., Hawkesford, M. J., Whalley, W. R., and Binley, A.: Timelapse geophysical assessment of agricultural practices on soil moisture dynamics, Vadose Zone J., 19, e20080, https://doi.org/10.1002/vzj2.20080, 2020c.
Borra-Serrano, I., De Swaef, T., Quataert, P., Aper, J., Saleem, A., Saeys, W., Somers, B., Roldán-Ruiz, I., and Lootens, P.: Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials, Remote Sens., 12, 1644, https://doi.org/10.3390/rs12101644, 2020.
Campbell, R. B., Bower, C. A., and Richards, L. A.: Change of Electrical Conductivity With Temperature and the Relation of Osmotic Pressure to Electrical Conductivity and Ion Concentration for Soil Extracts, Soil Sci. Soc. Am. J., 13, 66-69, https://doi.org/10.2136/sssaj1949.036159950013000c0010x, 1949.
Carminati, A. and Javaux, M.: Soil Rather Than Xylem Vulnerability Controls Stomatal Response to Drought, Trend. Plant Sci., 25, 868-880, https://doi.org/10.1016/j.tplants.2020.04.003, 2020.
Cassiani, G., Ursino, N., Deiana, R., Vignoli, G., Boaga, J., Rossi, M., Perri, M. T., Blaschek, M., Duttmann, R., Meyer, S., Ludwig, R., Soddu, A., Dietrich, P., and Werban, U.: Noninvasive Monitoring of Soil Static Characteristics and Dynamic States: A Case Study Highlighting Vegetation Effects on Agricultural Land, Vadose Zone J., 11, vzj2011.0195, https://doi.org/10.2136/vzj2011.0195, 2012.
Das, A., Schneider, H., Burridge, J., Ascanio, A. K. M., Wojciechowski, T., Topp, C. N., Lynch, J. P., Weitz, J. S., and Bucksch, A.: Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics, Plant Method., 11, 51, https://doi.org/10.1186/s13007-015-0093-3, 2015.
De Swaef, T., Deroo, W., Blanchy, G., Garré, S., Instituut voor Landbouw en Visserijonderzoek, and Lootens, P.: 2023 HYDRAS Proof of concept experiment with soybean genotypes, In SOIL, Zenodo [data set], https://doi.org/10.5281/zenodo.14175354, 2024.
Ehosioke, S., Nguyen, F., Rao, S., Kremer, T., PlacenciaGomez, E., Huisman, J. A., Kemna, A., Javaux, M., and Garré, S.: Sensing the electrical properties of roots: A review, Vadose Zone J., 19, e20082, https://doi.org/10.1002/vzj2.20082, 2020.
Garré, S., Coteur, I., Wongleecharoen, C., Kongkaew, T., Diels, J., and Vanderborght, J.: Noninvasive Monitoring of Soil Water Dynamics in Mixed Cropping Systems: A Case Study in Ratchaburi Province, Thailand, Vadose Zone J., 12, 1-12, https://doi.org/10.2136/vzj2012.0129, 2013.
Garre, S., Deswaef, T., Borra-Serrano, I., Lootens, P., and Blanchy, G.: The potential of electrical imaging for field root zone phenotyping, in: NSG2021 27th European Meeting of Environmental and Engineering Geophysics, Eur. Assoc. Geosci. Eng., 1-5, https://doi.org/10.3997/2214-4609.202120221, 2021.
Koestel, J., Kemna, A., Javaux, M., Binley, A., and Vereecken, H.: Quantitative imaging of solute transport in an unsaturated and undisturbed soil monolith with 3D ERT and TDR, Water Resour. Res., 44, W12411, https://doi.org/10.1029/2007wr006755, 2008.
LaBrecque, D. J. and Yang, X.: Difference Inversion of ERT Data: a Fast Inversion Method for 3-D In Situ Monitoring, J. Environ. Eng. Geophys., 6, 83-89, https://doi.org/10.4133/jeeg6.2.83, 2001.
Langstroff, A., Heuermann, M. C., Stahl, A., and Junker, A.: Opportunities and limits of controlled-environment plant phenotyping for climate response traits, Theor. Appl. Genet., 135, 1-16, https://doi.org/10.1007/s00122-021-03892-1, 2021.
Lärm, L., Bauer, F. M., Hermes, N., van der Kruk, J., Vereecken, H., Vanderborght, J., Nguyen, T. H., Lopez, G., Seidel, S. J., Ewert, F., Schnepf, A., and Klotzsche, A.: Multi-year belowground data of minirhizotron facilities in Selhausen, Sci. Data, 10, 672, https://doi.org/10.1038/s41597-023-02570-9, 2023.
Linde, N., Ginsbourger, D., Irving, J., Nobile, F., and Doucet, A.: On uncertainty quantification in hydrogeology and hydrogeophysics, Adv. Water Resour., 110, 166-181, https://doi.org/10.1016/j.advwatres.2017.10.014, 2017.
Ma, R., McBratney, A., Whelan, B., Minasny, B., and Short, M.: Comparing temperature correction models for soil electrical conductivity measurement, Prec. Agr., 12, 55-66, https://doi.org/10.1007/s11119-009-9156-7, 2010.
McGrail, R., Van Sanford, D., and McNear, D.: Trait-Based Root Phenotyping as a Necessary Tool for Crop Selection and Improvement, Agronomy, 10, 1328, https://doi.org/10.3390/agronomy10091328, 2020.
Michot, D., Benderitter, Y., Dorigny, A., Nicoullaud, B., King, D., and Tabbagh, A.: Spatial and temporal monitoring of soil water content with an irrigated corn crop cover using surface electrical resistivity tomography, Water Resour. Res., 39, 1138, https://doi.org/10.1029/2002wr001581, 2003.
Ochs, J., Klitzsch, N., and Wagner, F. M.: Mitigation of installation-related effects for small-scale boreholeto-surface ERT, J. Appl. Geophys., 197, 104530, https://doi.org/10.1016/j.jappgeo.2022.104530, 2022.
Oldenborger, G. A., Routh, P. S., and Knoll, M. D.: Sensitivity of electrical resistivity tomography data to electrode position errors, Geophys. J. Int., 163, 1-9, https://doi.org/10.1111/j.1365-246X.2005.02714.x, 2005.
Pranga, J., Borra-Serrano, I., Aper, J., De Swaef, T., Ghesquiere, A., Quataert, P., Roldán-Ruiz, I., Janssens, I. A., Ruysschaert, G., and Lootens, P.: Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning, Remote Sens., 13, 3459, https://doi.org/10.3390/rs13173459, 2021.
Rajurkar, A. B., McCoy, S. M., Ruhter, J., Mulcrone, J., Freyfogle, L., and Leakey, A. D. B.: Installation and imaging of thousands of minirhizotrons to phenotype root systems of field-grown plants, Plant Methods, 18, 39, https://doi.org/10.1186/s13007-022-00874-2, 2022.
Seabold, S. and Perktold, J.: statsmodels: Econometric and statistical modeling with python, in: 9th Python in Science Conference, 92-96, https://doi.org/10.25080/Majora-92bf1922-011, 2010.
Shanahan, P. W., Binley, A., Whalley, W. R., and Watts, C. W.: The Use of Electromagnetic Induction to Monitor Changes in Soil Moisture Profiles beneath Different Wheat Genotypes, Soil Sci. Soc. Am. J., 79, 459-466, https://doi.org/10.2136/sssaj2014.09.0360, 2015.
Snowdon, R. J., Wittkop, B., Chen, T.-W., and Stahl, A.: Crop adaptation to climate change as a consequence of long-term breeding, Theor. Appl. Genet., 134, 1613-1623, https://doi.org/10.1007/s00122-020-03729-3, 2020.
Srayeddin, I. and Doussan, C.: Estimation of the spatial variability of root water uptake of maize and sorghum at the field scale by electrical resistivity tomography, Plant Soil, 319, 185-207, https://doi.org/10.1007/s11104-008-9860-5, 2009.
Svane, S. F., Jensen, C. S., and Thorup-Kristensen, K.: Construction of a large-scale semi-field facility to study genotypic differences in deep root growth and resources acquisition, Plant Method., 15, 26, https://doi.org/10.1186/s13007-019-0409-9, 2019.
Trachsel, S., Kaeppler, S. M., Brown, K. M., and Lynch, J. P.: Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field, Plant Soil, 341, 75-87, https://doi.org/10.1007/s11104-010-0623-8, 2010.
Tso, C.-H. M., Iglesias, M., Wilkinson, P., Kuras, O., Chambers, J., and Binley, A.: Efficient multiscale imaging of subsurface resistivity with uncertainty quantification using ensemble Kalman inversion, Geophys. J. Int., 225, 887-905, https://doi.org/10.1093/gji/ggab013, 2021.
Uhlemann, S., Wilkinson, P. B., Maurer, H., Wagner, F. M., Johnson, T. C., and Chambers, J. E.: Optimized survey design for electrical resistivity tomography: combined optimization of measurement configuration and electrode placement, Geophys. J. Int., 214, 108-121, https://doi.org/10.1093/gji/ggy128, 2018.
Vamerali, T., Bandiera, M., and Mosca, G.: Minirhizotrons in Modern Root Studies, 341-361 pp., Springer Berlin Heidelberg, https://doi.org/10.1007/978-3-642-22067-8_17, 2011.
Voss-Fels, K. P., Stahl, A., and Hickey, L. T.: Q-A: modern crop breeding for future food security, BMC Biology, 17, 18, https://doi.org/10.1186/s12915-019-0638-4, 2019.
Wagner, F. M. and Uhlemann, S.: Chapter One - An overview of multimethod imaging approaches in environmental geophysics, in: Inversion of Geophysical Data, edited by Schmelzbach, C., Vol. 62, Adv. Geophys., 1-72 pp., Elsevier, https://doi.org/10.1016/bs.agph.2021.06.001, 2021.
Weigand, M. and Kemna, A.: Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems, Biogeosciences, 14, 921-939, https://doi.org/10.5194/bg-14-921-2017, 2017.
Weigand, M., Zimmermann, E., Michels, V., Huisman, J. A., and Kemna, A.: Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT), Geosci. Instrum. Method. Data Syst., 11, 413-433, https://doi.org/10.5194/gi-11-413-2022, 2022.
Whalley, W., Binley, A., Watts, C., Shanahan, P., Dodd, I., Ober, E., Ashton, R., Webster, C., White, R., and Hawkesford, M. J.: Methods to estimate changes in soil water for phenotyping root activity in the field, Plant Soil, 415, 407-422, https://doi.org/10.1007/s11104-016-3161-1, 2017.
Wilkinson, P. B., Chambers, J. E., Lelliott, M., Wealthall, G. P., and Ogilvy, R. D.: Extreme sensitivity of crosshole electrical resistivity tomography measurements to geometric errors, Geophys. J. Int., 173, 49-62, https://doi.org/10.1111/j.1365-246X.2008.03725.x, 2008.
Wilkinson, P. B., Chambers, J. E., Meldrum, P. I., Gunn, D. A., Ogilvy, R. D., and Kuras, O.: Predicting the movements of permanently installed electrodes on an active landslide using time-lapse geoelectrical resistivity data only, Geophys. J. Int., 183, 543-556, https://doi.org/10.1111/j.1365-246X.2010.04760.x, 2010.
Wilkinson, P. B., Uhlemann, S., Chambers, J. E., Meldrum, P. I., and Loke, M. H.: Development and testing of displacement inversion to track electrode movements on 3-D electrical resistivity tomography monitoring grids, Geophys. J. Int., 200, 1566-1581, https://doi.org/10.1093/gji/ggu483, 2015.
Wohner, C., Peterseil, J., and Klug, H.: Designing and implementing a data model for describing environmental monitoring and research sites, Ecol. Inform., 70, 101708, https://doi.org/10.1016/j.ecoinf.2022.101708, 2022.
Ye, H., Song, L., Schapaugh, W. T., Ali, M. L., Sinclair, T. R., Riar, M. K., Mutava, R. N., Li, Y., Vuong, T., Valliyodan, B., Pizolato Neto, A., Klepadlo, M., Song, Q., Shannon, J. G., Chen, P., and Nguyen, H. T.: The importance of slow canopy wilting in drought tolerance in soybean, J. Experiment. Botany, 71, 642-652, https://doi.org/10.1093/jxb/erz150, 2019.