Closing the phenotyping gap with non-invasive belowground field phenotyping

Blanchy, Guillaume; Deroo, Waldo; De Swaef, Tom; Lootens, Peter; Quataert, Paul; Roldán-Ruíz, Isabel; Garré, Sarah

doi:https://doi.org/10.5194/egusphere-2024-2082

Preprints

https://doi.org/10.5194/egusphere-2024-2082

Preprints

05 Aug 2024

| 05 Aug 2024

Closing the phenotyping gap with non-invasive belowground field phenotyping

Guillaume Blanchy, Waldo Deroo, Tom De Swaef, Peter Lootens, Paul Quataert, Isabel Roldán-Ruíz, and Sarah Garré

Abstract. 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 above-ground field phenotyping, simultaneous non-invasive belowground phenotyping remains difficult. In this paper, we present the first dataset of the new HYDRAS open access field phenotyping infrastructure, bringing electrical resistivity tomography, alongside drone imagery and environmental monitoring, to a technology 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.

Received: 05 Jul 2024 – Discussion started: 05 Aug 2024

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Guillaume Blanchy, Waldo Deroo, Tom De Swaef, Peter Lootens, Paul Quataert, Isabel Roldán-Ruíz, and Sarah Garré

Status: closed

RC1:
'Comment on egusphere-2024-2082', Luca Peruzzo, 09 Sep 2024

Review of

Closing the phenotyping gap with non-invasive belowground field phenotyping

Dear Editor, Dear Authors,

Thank you for the opportunity to review this study. I commend the authors for the very interesting and extensive work. I found this study innovative, both for the facility design and the data processing.
The manuscript is well written and the figures are of good quality.
I recommend minor revision.

L57 Consider adding a couple of references for readers that are less familiar with this.
For example, Extreme sensitivity of crosshole electrical resistivity tomography measurements to geometric errors by Wilkinson et al, 2008; and Mitigation of installation-related effects for small-scale borehole-to-surface ERT by Ochs et al, 2022.

L66 Consider adding An overview of multimethod imaging approaches in environmental geophysics by Wagner and Uhlemann, as a general reference here.

L89 “rain-out shelters” is then spelled rainout shelters. I think both may be right, just for consistency.

L97 “en” should be “and”?

L106 how these petrophysical relationships were derived, should be clarified later

L112 “Dryer soil, where roots are extracting water, has a larger resistivity (lower electrical conductivity) than wetter soil around the root zone.” I agree, I would add that this is because the root-induced water dynamics dominates other effects, e.g., static and direct contribution of the root biomass vs RWU. You mentioned above these effects, I think connecting them here would clarify this assumption to the general audience.

L123 Missing reference / link to figure 1

L140 The ERT setup is nice, well designed, and robust, also thanks to the many electrodes and reciprocal measurements. I find the one-channel system to be the only limiting aspect here, for the obvious trade-off between time and number of measurements (and thus spatial resolution, temporal resolution, and data quality stability). However, pushing new and cheaper instruments for long-term monitoring is also central here, and thus in line with the paper goal. Consider discussing these points.

L142-145 I agree on the need to verify the impact of the plant-ert alignment, nice side investigation. The supposed causes are reasonable in my opinion; with systematically you also mean independently from their geographical orientation (i.e., N-S vs E-W)?
Was there some extra space - gap (bare soil or grass) between the different varieties, or only the standard row spacing?

Fig3 b and c, Control and pit are very different; what are the implications for the underlying relationships etc.?
Fig3b, the drought VWC seems to respond more slowly on surface than at depth in summer and fall, check if this is ok.

L188 – L193 Yes, I agree that the quadrupole geometry (and associated geometric factor) is a key aspect here. Ideal – perfect placing is impossible at this spacing, and local changes (e.g., small electrode position and contact) over the monitoring period may have surprising effects too, on weak quadrupole geometries. As commented above, there are some references on this, being your geometry equivalent to the XH and IH conditions in borehole measurements, right? I like the numerical testing, adding some references would provide a bit of background.

L196 “The reciprocal errors were not used for filtering, but as a weight during the inversion. We fitted a power-law error model for each survey on a transect using the binned reciprocal errors (Koestel et al., 2008).” Wouldn’t this keep clear outliers, with an error based on the general dataset fitting?
In my opinion, it makes sense to directly remove clear outliers as soon as possible (say rec. err > 50 %?), because the general fitted error would not be representative for these outliers, but also to avoid affecting the fitting procedure. I think this was also done in the cited work too.
I see that you considered stacking, geometry, neg. apparent resistivity, and contact resistances; these would likely capture most of the clear outliers, limiting the effect of the above choice. Overall, I think the quality assessment is quite extensive and innovative considering the scale of the monitoring.

L232 italic font for the parameters a and b, as done for the successive c and d.

Fig8 Consider adding the x label to subplot c; it is just the same as b, right?

Fig9 I agree that time differences can nicely be observed already in the pseudo-sections, also very good from a data management point of view. Having some extra points would help (as discussed above on the ERT acquisition). Are these all the quadrupoles or just some selected ones?

L265 This closing remark is needed. Maybe mentioning some aspects already at the beginning of the section would further highlight the contrast between “whether the electrical resistivity measurements are sensitive enough to detect subtle differences in water depletion patterns and strategies between contrasting genotypes of the same crop species.” and the successive goals that rely on the ERT inversion. Guiding a bit more the general reader.
Consider also giving a couple of simple/practical examples on what it means “apparent resistivities are depth-weighted integrative measurement”. For example, strong evaporation on the surface would lead to larger apparent resistivities in the deeper parts too, which could be confused for RWU; or rain/irrigation would affect the deeper parts too.

L281 capitalize “august”

Fig12 and L281 I don’t see the difference at the beginning of August, do you mean September in the DROUGHT treatment? The CONTROL time series look very similar to me, also relative to the pseudo section. Consider better highlighting the differences.

L287 “August onwards” more end of July? Considering the rain event between July 15 and August 1. For example, the drying area increases significantly during this period but stabilizes in August, correct?

L296 consider referencing Table1 when discussing DA and DD.

Fig14 Were the sigmoids fitted to the actual conductivity profiles or to their ratio with respect to the background (i.e., EC or deltaEC)? Considering figures 11 and 12 it seems that the sigmoid may be more suitable to describe the conductivity itself. For example, is the described sigmoid suitable for the drier-wetter-drier profiles of deltaEC shown by the CONTROL after August in figure 12? Please clarify this point. That said, fitting the actual VWC changes or the distribution of the water potential will be the necessary successive step, and the sigmoid should be suitable for this. Currently, both EC and deltaEC profiles would fail as proxies (and possibly deviate from a sigmoid) in the presence of significant texture stratification or anyway variable pedophysical relationships, right?

L324 “but we did not yet assess how this affects the precision of our derived indicators.” I agree, this could be interesting.

Best regards,
Luca Peruzzo
UNIPD

Citation: https://doi.org/10.5194/egusphere-2024-2082-RC1
- AC3:
  'Reply on RC1', Sarah Garré, 18 Sep 2024
  
  We will provide the requested additional references, additional information and correct highlighted language errors where needed. We thank the reviewer for highlighting some interesting points of discussion that could merit a bit more thoughts/information. We will further develop this as requested.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2082-AC3
  - EC1: 'Reply on AC3', Yuxin Wu, 11 Oct 2024
    
    Please provide a detailed response to the reviewer's comments
    
    Citation: https://doi.org/10.5194/egusphere-2024-2082-EC1
RC2:
'Comment on egusphere-2024-2082', Anonymous Referee #2, 11 Sep 2024

I have now reviewed the manuscript entitled “Closing the phenotyping gap with non-invasive belowground field phenotyping” by Blanchy et al. This manuscript deals with the use electrical resistivity tomography (ERT) for below-ground phenotyping. Although there has been previous work in this work, this study is the first to convincingly show a full processing pipeline from raw measurements to meaningful indicators that successfully showed differences in water uptake patterns between different genotypes of soybean. Thus, I found this study an important contribution and a significant advance compared to the current state-of-the-art. I do have a list of specific and rather minor comments that I have provided below. Addressing these comments should only constitute a moderate revision. After this, I would be happy to see this published.
Line 30. Up to here, the text was very well written. However, I think you can do better on the introduction of the ERT method. I was missing some keywords here, such as geophysical, imaging, and perhaps inversion. Perhaps you can improve.
Line 33. A key challenge remains that ERT is not only sensitive to soil moisture. I would suggest to emphasize this early on.
Line 35. The sentence starting with Wasson et al. distracted me from the line of argument. Consider rewriting by just citing Wasson et al. for the following statement.
Line 72. I would also mention the limited descriptions of the used inversion approaches, and the determination of the regularization strength. I always thought that there a range of subjective decisions during inversion that are not sufficiently communicated.
Line 73. For the mentioned initiatives, it would be good to add references to a report, website or publication.
Line 108. Should this be near the soil surface? If not, consider rewriting…
Line 112-115. This description of how ERT provides information on root water uptake can be improved.
Figure 2. Does panel b show the electrode numbering? May be good to emphasize in the caption.
Line 134. I would like to have some more information about what it means to have electrode groups of 32. Does this mean that voltage and current measurements must be within the same group of 32, or can all electrode combinations be measured?
Line 139. Perhaps it would nice to also include the geometric factor in the table? I would also find it interesting to reflect a bit more on the selected data acquisition procedure. Would it be possible to have cross-line injections? They may have a better signal-to-noise ratio than some of the other electrode configurations used.
Line 143. Perhaps to imprecise? I am not sure whether you are interested in the measurements. Is this not about the indicators and what we can achieve with this type of field phenotyping?
Line 143-148. Can this not be squeezed into an appropriate section of the results?
Line 150. The surface is an area. This should be inserted in the soil…
Line 158. Does this mean that you are able to assess whether there were differences in ET0 due to the shelter? May be interesting to report this too.
Figure 3. I would say that this overview figure is better placed at the start of the presentation of the results.
Line 180. How are the contact resistances measured or estimated? Is this an additional measurement? Please clarify.
Line 186. Negative apparent resistivities were not only due to negative geometric factors? In cross-borehole studies, negative values are possible, so perhaps this is also the case for your set-up. It is a new approach, so some more reflections would perhaps be nice here.
Line 199. Given the plea in the introduction, please make sure that all data processing are at least mentioned here and briefly described. Details can then be taken from the JUPYTER notebook.
Line 195-199. I miss some general statements about the data quality here. Or will this topic come back later? How do your results compare to previous work? I believe most studies reported a linear relationship between reciprocal error and measured resistance? A temporal presentation of the development of the error model parameters would also be nice here – I would hope that they are rather stable in time.
Line 211. How was convergence determined? Was it possible to achieve a normalized error of 1 without accounting for additional modelling errors?
Line 220. Integrate equation in the text.
Line 224. I find this a little bit short. Some more reflection on this important calibration seems justified to me. The scatter is considerable. How does this affect the presented results?
Line 231. I think the equation should also be provided in the text (or only in the text).
Figure 9. I realize that a lot depends on the visualization here, but the coverage of the different electrode configurations does not seem high in this one. Perhaps the cumulative sensitivity or resolution should additionally be presented to provide some evidence that profiles can adequately be obtained?
Line 255-260. Would be good to discuss whether the observed differences match the expectations for the genotypes.
Figure 10. I am confused about the selection of dates here. Do I understand correctly that all these measurements are made shortly after sowing? Can we really attribute the observed differences to plants in this case? How quick will the root system develop after sowing at 21.06?
Line 275. Reference to figure missing near the start of the paragraph.
Line 315. Not sure it was not designed for that. Perhaps state that you took it considerably further by successfully deriving indicators suggesting significant differences between genotypes.
Line 334. Instead of inverting for the electrode locations, you could perhaps just optimize the shift? This would significantly reduce the degrees of freedom. I also think that you did not really provide proof that the deeper cable is helping with resolution. I can buy it, but perhaps there is scope to address this too in the manuscript. I did not find it excessively long at the moment…

Citation: https://doi.org/10.5194/egusphere-2024-2082-RC2
- AC1: 'Reply on RC2', Sarah Garré, 18 Sep 2024
  
  Thanks for the constructive feedback. We will carefully revise the suggested passages and figures and provide a document in which the proposed changes are highlighted for each comment. We will pay special attention to including additional information on steps in the inversion process and other data processing as requested in several comments. It is indeed better to not only leave this to the notebook, but make it clear enough in the text/tables/figures of the paper itself where possible.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2082-AC1
- AC2:
  'Reply on RC2', Sarah Garré, 18 Sep 2024
  
  Thanks for the constructive feedback. We will carefully revise the suggested passages and figures and provide a document in which the proposed changes are highlighted for each comment. We will pay special attention to including additional information on steps in the inversion process and other data processing as requested in several comments. It is indeed better to not only leave this to the notebook, but make it clear enough in the text/tables/figures of the paper itself where possible.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2082-AC2
  - EC2: 'Reply on AC2', Yuxin Wu, 11 Oct 2024
    
    Please provide a detailed response to the reviewer's comments
    
    Citation: https://doi.org/10.5194/egusphere-2024-2082-EC2

Status: closed

RC1:
'Comment on egusphere-2024-2082', Luca Peruzzo, 09 Sep 2024

Review of

Closing the phenotyping gap with non-invasive belowground field phenotyping

Dear Editor, Dear Authors,

Thank you for the opportunity to review this study. I commend the authors for the very interesting and extensive work. I found this study innovative, both for the facility design and the data processing.
The manuscript is well written and the figures are of good quality.
I recommend minor revision.

L57 Consider adding a couple of references for readers that are less familiar with this.
For example, Extreme sensitivity of crosshole electrical resistivity tomography measurements to geometric errors by Wilkinson et al, 2008; and Mitigation of installation-related effects for small-scale borehole-to-surface ERT by Ochs et al, 2022.

L66 Consider adding An overview of multimethod imaging approaches in environmental geophysics by Wagner and Uhlemann, as a general reference here.

L89 “rain-out shelters” is then spelled rainout shelters. I think both may be right, just for consistency.

L97 “en” should be “and”?

L106 how these petrophysical relationships were derived, should be clarified later

L112 “Dryer soil, where roots are extracting water, has a larger resistivity (lower electrical conductivity) than wetter soil around the root zone.” I agree, I would add that this is because the root-induced water dynamics dominates other effects, e.g., static and direct contribution of the root biomass vs RWU. You mentioned above these effects, I think connecting them here would clarify this assumption to the general audience.

L123 Missing reference / link to figure 1

L140 The ERT setup is nice, well designed, and robust, also thanks to the many electrodes and reciprocal measurements. I find the one-channel system to be the only limiting aspect here, for the obvious trade-off between time and number of measurements (and thus spatial resolution, temporal resolution, and data quality stability). However, pushing new and cheaper instruments for long-term monitoring is also central here, and thus in line with the paper goal. Consider discussing these points.

L142-145 I agree on the need to verify the impact of the plant-ert alignment, nice side investigation. The supposed causes are reasonable in my opinion; with systematically you also mean independently from their geographical orientation (i.e., N-S vs E-W)?
Was there some extra space - gap (bare soil or grass) between the different varieties, or only the standard row spacing?

Fig3 b and c, Control and pit are very different; what are the implications for the underlying relationships etc.?
Fig3b, the drought VWC seems to respond more slowly on surface than at depth in summer and fall, check if this is ok.

L188 – L193 Yes, I agree that the quadrupole geometry (and associated geometric factor) is a key aspect here. Ideal – perfect placing is impossible at this spacing, and local changes (e.g., small electrode position and contact) over the monitoring period may have surprising effects too, on weak quadrupole geometries. As commented above, there are some references on this, being your geometry equivalent to the XH and IH conditions in borehole measurements, right? I like the numerical testing, adding some references would provide a bit of background.

L196 “The reciprocal errors were not used for filtering, but as a weight during the inversion. We fitted a power-law error model for each survey on a transect using the binned reciprocal errors (Koestel et al., 2008).” Wouldn’t this keep clear outliers, with an error based on the general dataset fitting?
In my opinion, it makes sense to directly remove clear outliers as soon as possible (say rec. err > 50 %?), because the general fitted error would not be representative for these outliers, but also to avoid affecting the fitting procedure. I think this was also done in the cited work too.
I see that you considered stacking, geometry, neg. apparent resistivity, and contact resistances; these would likely capture most of the clear outliers, limiting the effect of the above choice. Overall, I think the quality assessment is quite extensive and innovative considering the scale of the monitoring.

L232 italic font for the parameters a and b, as done for the successive c and d.

Fig8 Consider adding the x label to subplot c; it is just the same as b, right?

Fig9 I agree that time differences can nicely be observed already in the pseudo-sections, also very good from a data management point of view. Having some extra points would help (as discussed above on the ERT acquisition). Are these all the quadrupoles or just some selected ones?

L265 This closing remark is needed. Maybe mentioning some aspects already at the beginning of the section would further highlight the contrast between “whether the electrical resistivity measurements are sensitive enough to detect subtle differences in water depletion patterns and strategies between contrasting genotypes of the same crop species.” and the successive goals that rely on the ERT inversion. Guiding a bit more the general reader.
Consider also giving a couple of simple/practical examples on what it means “apparent resistivities are depth-weighted integrative measurement”. For example, strong evaporation on the surface would lead to larger apparent resistivities in the deeper parts too, which could be confused for RWU; or rain/irrigation would affect the deeper parts too.

L281 capitalize “august”

Fig12 and L281 I don’t see the difference at the beginning of August, do you mean September in the DROUGHT treatment? The CONTROL time series look very similar to me, also relative to the pseudo section. Consider better highlighting the differences.

L287 “August onwards” more end of July? Considering the rain event between July 15 and August 1. For example, the drying area increases significantly during this period but stabilizes in August, correct?

L296 consider referencing Table1 when discussing DA and DD.

Fig14 Were the sigmoids fitted to the actual conductivity profiles or to their ratio with respect to the background (i.e., EC or deltaEC)? Considering figures 11 and 12 it seems that the sigmoid may be more suitable to describe the conductivity itself. For example, is the described sigmoid suitable for the drier-wetter-drier profiles of deltaEC shown by the CONTROL after August in figure 12? Please clarify this point. That said, fitting the actual VWC changes or the distribution of the water potential will be the necessary successive step, and the sigmoid should be suitable for this. Currently, both EC and deltaEC profiles would fail as proxies (and possibly deviate from a sigmoid) in the presence of significant texture stratification or anyway variable pedophysical relationships, right?

L324 “but we did not yet assess how this affects the precision of our derived indicators.” I agree, this could be interesting.

Best regards,
Luca Peruzzo
UNIPD

Citation: https://doi.org/10.5194/egusphere-2024-2082-RC1
- AC3:
  'Reply on RC1', Sarah Garré, 18 Sep 2024
  
  We will provide the requested additional references, additional information and correct highlighted language errors where needed. We thank the reviewer for highlighting some interesting points of discussion that could merit a bit more thoughts/information. We will further develop this as requested.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2082-AC3
  - EC1: 'Reply on AC3', Yuxin Wu, 11 Oct 2024
    
    Please provide a detailed response to the reviewer's comments
    
    Citation: https://doi.org/10.5194/egusphere-2024-2082-EC1
RC2:
'Comment on egusphere-2024-2082', Anonymous Referee #2, 11 Sep 2024

I have now reviewed the manuscript entitled “Closing the phenotyping gap with non-invasive belowground field phenotyping” by Blanchy et al. This manuscript deals with the use electrical resistivity tomography (ERT) for below-ground phenotyping. Although there has been previous work in this work, this study is the first to convincingly show a full processing pipeline from raw measurements to meaningful indicators that successfully showed differences in water uptake patterns between different genotypes of soybean. Thus, I found this study an important contribution and a significant advance compared to the current state-of-the-art. I do have a list of specific and rather minor comments that I have provided below. Addressing these comments should only constitute a moderate revision. After this, I would be happy to see this published.
Line 30. Up to here, the text was very well written. However, I think you can do better on the introduction of the ERT method. I was missing some keywords here, such as geophysical, imaging, and perhaps inversion. Perhaps you can improve.
Line 33. A key challenge remains that ERT is not only sensitive to soil moisture. I would suggest to emphasize this early on.
Line 35. The sentence starting with Wasson et al. distracted me from the line of argument. Consider rewriting by just citing Wasson et al. for the following statement.
Line 72. I would also mention the limited descriptions of the used inversion approaches, and the determination of the regularization strength. I always thought that there a range of subjective decisions during inversion that are not sufficiently communicated.
Line 73. For the mentioned initiatives, it would be good to add references to a report, website or publication.
Line 108. Should this be near the soil surface? If not, consider rewriting…
Line 112-115. This description of how ERT provides information on root water uptake can be improved.
Figure 2. Does panel b show the electrode numbering? May be good to emphasize in the caption.
Line 134. I would like to have some more information about what it means to have electrode groups of 32. Does this mean that voltage and current measurements must be within the same group of 32, or can all electrode combinations be measured?
Line 139. Perhaps it would nice to also include the geometric factor in the table? I would also find it interesting to reflect a bit more on the selected data acquisition procedure. Would it be possible to have cross-line injections? They may have a better signal-to-noise ratio than some of the other electrode configurations used.
Line 143. Perhaps to imprecise? I am not sure whether you are interested in the measurements. Is this not about the indicators and what we can achieve with this type of field phenotyping?
Line 143-148. Can this not be squeezed into an appropriate section of the results?
Line 150. The surface is an area. This should be inserted in the soil…
Line 158. Does this mean that you are able to assess whether there were differences in ET0 due to the shelter? May be interesting to report this too.
Figure 3. I would say that this overview figure is better placed at the start of the presentation of the results.
Line 180. How are the contact resistances measured or estimated? Is this an additional measurement? Please clarify.
Line 186. Negative apparent resistivities were not only due to negative geometric factors? In cross-borehole studies, negative values are possible, so perhaps this is also the case for your set-up. It is a new approach, so some more reflections would perhaps be nice here.
Line 199. Given the plea in the introduction, please make sure that all data processing are at least mentioned here and briefly described. Details can then be taken from the JUPYTER notebook.
Line 195-199. I miss some general statements about the data quality here. Or will this topic come back later? How do your results compare to previous work? I believe most studies reported a linear relationship between reciprocal error and measured resistance? A temporal presentation of the development of the error model parameters would also be nice here – I would hope that they are rather stable in time.
Line 211. How was convergence determined? Was it possible to achieve a normalized error of 1 without accounting for additional modelling errors?
Line 220. Integrate equation in the text.
Line 224. I find this a little bit short. Some more reflection on this important calibration seems justified to me. The scatter is considerable. How does this affect the presented results?
Line 231. I think the equation should also be provided in the text (or only in the text).
Figure 9. I realize that a lot depends on the visualization here, but the coverage of the different electrode configurations does not seem high in this one. Perhaps the cumulative sensitivity or resolution should additionally be presented to provide some evidence that profiles can adequately be obtained?
Line 255-260. Would be good to discuss whether the observed differences match the expectations for the genotypes.
Figure 10. I am confused about the selection of dates here. Do I understand correctly that all these measurements are made shortly after sowing? Can we really attribute the observed differences to plants in this case? How quick will the root system develop after sowing at 21.06?
Line 275. Reference to figure missing near the start of the paragraph.
Line 315. Not sure it was not designed for that. Perhaps state that you took it considerably further by successfully deriving indicators suggesting significant differences between genotypes.
Line 334. Instead of inverting for the electrode locations, you could perhaps just optimize the shift? This would significantly reduce the degrees of freedom. I also think that you did not really provide proof that the deeper cable is helping with resolution. I can buy it, but perhaps there is scope to address this too in the manuscript. I did not find it excessively long at the moment…

Citation: https://doi.org/10.5194/egusphere-2024-2082-RC2
- AC1: 'Reply on RC2', Sarah Garré, 18 Sep 2024
  
  Thanks for the constructive feedback. We will carefully revise the suggested passages and figures and provide a document in which the proposed changes are highlighted for each comment. We will pay special attention to including additional information on steps in the inversion process and other data processing as requested in several comments. It is indeed better to not only leave this to the notebook, but make it clear enough in the text/tables/figures of the paper itself where possible.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2082-AC1
- AC2:
  'Reply on RC2', Sarah Garré, 18 Sep 2024
  
  Thanks for the constructive feedback. We will carefully revise the suggested passages and figures and provide a document in which the proposed changes are highlighted for each comment. We will pay special attention to including additional information on steps in the inversion process and other data processing as requested in several comments. It is indeed better to not only leave this to the notebook, but make it clear enough in the text/tables/figures of the paper itself where possible.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2082-AC2
  - EC2: 'Reply on AC2', Yuxin Wu, 11 Oct 2024
    
    Please provide a detailed response to the reviewer's comments
    
    Citation: https://doi.org/10.5194/egusphere-2024-2082-EC2

Guillaume Blanchy, Waldo Deroo, Tom De Swaef, Peter Lootens, Paul Quataert, Isabel Roldán-Ruíz, and Sarah Garré

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Short summary

This work uses automated electrical resistivity tomography (ERT) for belowground field phenotyping alongside conventional field breeding techniques, thereby closing the phenotyping gap. We show that ERT is not only capable of measuring differences between crops, but also has sufficient precision to capture the differences between genotypes of the same crop. We automatically derive indicators, which can be translated to static and dynamic plant traits, directly useful for breeders.


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