Unraveling Ecophysiological Mechanisms in Potatoes under Different Irrigation Methods: A Preliminary Field Evaluation

Round 1

Reviewer 1 Report

Comments

 

SUMMARY

 

The paper addresses the research area related to "Water Use and Irrigation" of the MDPI Agronomy journal. I believe that the target journal is an appropriate forum for this article. The paper aims to (i) analyze the effect of water restriction treatments on physiological traits related to photosynthetic recovery and tuber yield, and (ii) characterize the photosynthetic performance of two irrigation methods under different water stress levels, based on gs_max thresholds in Lima–Peru.

 

BROAD COMMENT

 

The Introduction section is well written with recent references. The materials and methods section is well explained and detailed. The results are well presented and discussed in depth. However, just one season (July 5^th to October 10^th, 2017) was analyzed. The main weakness of this paper is that the results are for just one season. The minimum requirement for field trials of this nature is two seasons. Therefore, not enough information is provided to establish a strong conclusion to support findings of ecophysiological mechanisms in potato under different irrigation methods. With one season, the results are inconclusive – major limitation.

 

 

Author Response

Comment # 1 – Reviewer 1

BROAD COMMENT

The Introduction section is well written with recent references. The Materials and Methods section are well explained and detailed. The results are well presented and discussed in depth. However, just one season (July 5^th to October 10^th, 2017) was analyzed. The main weakness of this paper is that the results are for just one season. The minimum requirement for field trials of this nature is two seasons. Therefore, not enough information is provided to establish a strong conclusion to support findings of ecophysiological mechanisms in potato under different irrigation methods. With one season, the results are inconclusive – major limitation.

Our Response

We understand your concern; however, it is important to consider the novel aspects that this study is showing to the scientific community. It had been remarked the usefulness of key physiological based indicators (like maximum light-saturated stomatal conductance – g_{s_max}, please see our reply to the 2^nd comment to Reviewer # 2) for irrigation improvement, and the necessity to test them under real crop conditions for further scaling out (Flexas et al., 2004). Notwithstanding, there are few studies looking to see how g_{s_max} operates under different irrigation methods and what are the photosynthetic consequences of different value of g_{s_max} thresholds. In this study, we carried out an exhausting monitoring of net photosynthesis (30 days), and because this trait depends on fluctuant environmental conditions, we included also carbon isotope discrimination, an integrative trait (external and internal factors) considered important for understanding potato plant performance under different water conditions (Ramirez et al. 2015). This study complements other experiments carried out with the same potato variety (UNICA) and in the same experimental station, but in other crop seasons: October 2016 – January 2017 (Rinza et al., 2019), and October 2017 – January 2018 (Cucho-Padin et al., 2020), which were recently published in Potato Research, and Sensors MDPI journals, respectively. This aspect has been remarked in the Introduction section as follows [Page 2, Lines 75-79]:

 “In this study, two irrigation methods (DI and FI) with water applied at different timings, depending on g_{s_max} average values (0.05 and 0.15 mol H₂O m^-2 s^-1), were tested and compared against controls, where photosynthetic traits were monitored in a potato variety. This study is the second on a series of experiments on the subject, conducted on the same field and with the same potato variety [34,35]. The main difference here is that we wanted to ascertain whether the physiological indicators and thresholds defined for drip irrigation could be extrapolated to furrow irrigation.”

The term “preliminary” used in the tittle [Page 1] has been added in the Conclusion section as follows [Page 14, Line 352]:

“This preliminary study cautions the use...”

We thus recommend reinforcing our findings with more trials, using these traits in other environments and agronomic seasons, finishing the Conclusions section as follows [Page 14, Line 359]:

“… comparing this irrigation with “hi-tech” methods under other environments and agronomic seasons.”

 

Flexas, J.; Bota, J.; Cifre, J.; Mariano Escalona, J.; Galmés, J.; Gulías, J.; Lefi, E.; Martinez-Cañellas, S.F.; Moreno, M.T.; Ribas-Carbó. M.; Riera, D.; Sampol, B.; Medrano, H. Understanding down‐regulation of photosynthesis under water stress: future prospects and searching for physiological tools for irrigation management. {\em Ann. of appl. Biol. 2004, 144, 273-283. https://doi.org/10.1111/j.1744-7348.2004.tb00343.x

Ramírez, D.A.; Rolando, J.L.; Yactayo, W.; Monneveux, P.; Mares, V.; Quiroz, R. Improving potato drought tolerance through the induction of long-term water stress memory. Plant Sci. 2015, 238, 26–32. https://doi.org/10.1016/j.plantsci.2015.05.016

[36].-Rinza, J.; Ramírez, D.A.; García, J.; de Mendiburu, F.; Yactayo, W.; Barreda, C.; Velasquez, T.; Mejía, A.; Quiroz, R. Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions. Potato Res. 2019, 62, 109-122. https://doi.org/10.1007/s11540-018-9400-5

[37].-Cucho-Padin, G.; Rinza, J.; Ninanya, J.; Loayza, H.; Quiroz, R.; Ramírez, D.A. Development of an open-source thermal image processing software for improving irrigation management in potato crops (Solanum tuberosum L.). Sensors, 2020, 20, 472.  https://doi.org/10.3390/s20020472

 

Comment # 2 – Reviewer 2

Why gs_max is defined as maximum conductance? Is it not more a measure of actual conductance? What is maximum referred to?

Our Response

We would like to clarify the idea about the use of maximum light-saturated stomatal conductance (g_{s_max}) as a physiological indicator for determining irrigation schedules. Medrano et al. (2002) and Flexas et al. (2004,2006) proposed an objective way to establish water status in plants using a standardized parameter based on a physiological threshold that leads to photosynthetic impairment if surpassed. Photosynthesis performs satisfactorily at g_{s_max} values higher than 0.15 mol H₂O m^-2 s^-1, so it is important to prevent crops from falling below that range (indicated in Page 12 lines 270-272), and a reduction of this variable to ≤ 0.05 mol H₂O m^-2 s^-1 could affect photosynthesis [Page 13, lines 320-321]. 

Moreover, Medrano et al. (2002) and Flexas et al. (2004,2006) proposed the use of mid-morning or maximum light-saturated (measured at light saturating conditions for this potato variety, stated at Page 5, lines 158-159) stomatal conductance (g_{s_max}) because stomatal conductance per-se shows high variability along the day. Actual stomatal conductance in potato has been measured using steady-state diffusion porometers (van Loon, 1981; Wilcox and Ashley, 1982; Tourneux et al., 2003; Rud et al., 2014) and LI-6200 photosynthesis portable systems (Liu et al. 2005, 2006, 2009) which are not able to fix a light saturation. This novel perspective of stomatal conductance has been tested in potato (Ramírez et al., 2016, Rinza et al., 2019), and as it has been referred to in Page 12, lines 243-244, g_{s_max} > 0.3 mol H₂O m^-2 s^-1 is recommended as watering timing threshold in this crop to guarantee no significant yield reduction under drip irrigation.

Page 5, lines 158-159:

“The parameters set in the equipment were: 1,500 μmol m^-2 s^-1 PAR, which corresponded to UNICA´s light saturation point for the study area and season, …"

Medrano, H.; Escalona, J.M.; Bota, J.; Gulías, J.; Flexas, J. Regulation of Photosynthesis of C3 Plants in Response to Progressive Drought: Stomatal Conductance as a Reference Parameter. Ann. Bot. 2002, 89, 895–905. https://doi.org/10.1093/aob/mcf079

Flexas, J.; Bota, J.; Cifre, J.; Mariano Escalona, J.; Galmés, J.; Gulías, J.; Lefi, E.-K.; Florinda Martínez-Cañellas, S.; Teresa Moreno, M.; Ribas-Carbó, M. Understanding down-regulation of photosynthesis under water stress: future prospects and searching for physiological tools for irrigation management. Ann. Appl Biol. 2004, 144, 273–283. https://doi.org/10.1111/j.1744-7348.2004.tb00343.x

Flexas, J.; Bota, J.; Galmés, J.; Medrano, H.; Ribas-Carbó, M. Keeping a positive carbonbalance under adverse conditions: responses of photosynthesis and respiration to water stress. Physiol. Plant. 2006, 127, 343–352. https://doi.org/10.1111/j.1399-3054.2006.00621.x

van Loon, C.D. The effect of water stress on potato growth, development, and yield. Am. Potato J. 1981, 58, 51-69. https://doi.org/10.1007/BF02855380

Wilcox, D.A.; Ashley, R.A. The potential use of plant physiological responses to water stress as an indication of varietal sensitivity to drought in four potato (Solanum tuberosum L.) varieties. Am. Potato J. 1982, 59, 533–545. https://doi.org/10.1007/BF02852602

Tourneux, C.; Devaux, A.; Camacho, M.R.; Mamani, P.; Ledent, J-F. Effect of water shortage on six potato genotypes in the highlands of Bolivia (II): water relations, physiological parameters. Agronomie. 2003, 23, 181-190. https://doi.org/10.1051/agro:2002080

Rud, R.; Cohen, Y.; Alchanatis, V.; Levi, A.; Brikman, R.; Shenderey, C.; Heuer, B. Markovitch, T.; Dar, Z.; Rosen, C. Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status. Precis. Agric. 2014, 15, 273–289. https://doi.org/10.1007/s11119-014-9351-z

Liu, F.; Jensen, C.R.; Shahanzari, A.; Andersen, M.N.; Jacobsen, S.E. ABA regulated stomatal control and photosynthetic water use efficiency of potato (Solanum tuberosum L.) during progressive soil drying. Plant Sci. 2005, 168, 831-836. https://doi.org/10.1016/j.plantsci.2004.10.016

Liu, F.; Shahnazari, A.; Andersen, M.N.; Jacobsen, S.E.; Jensen, C.R. Effects of deficit irrigation (DI) and partial root drying (PRD) on gas exchange, biomass partitioning, and water use efficiency in potato. Sci. Hortic. 2006, 109 (2), 113–117. https://doi.org/10.1016/j.scienta.2006.04.004

Liu, F.; Andersen, M.N.; Jensen, C.R. Capability of the “Ball-Berry” model for predicting stomatal conductance and water use efficiency of potato leaves under different irrigation regimes. Sci. Hortic. 2009, 122, 346–354. https://doi.org/10.1016/j.scienta.2009.05.026

Ramírez, D.A.; Yactayo, W.; Rens, L.R.; Rolando, J.L.; Palacios, S.; de Mendiburu, F.; Mares, V.; Barreda, C.; Loayza, H.; Monneveux, P.; Zotarelli, L.; Khan, A.; Quiroz, R. Defining biological thresholds associated to plant water status for monitoring water restriction effects: Stomatal conductance and photosynthesis recovery as key indicators in potato. Agr. Water Manage. 2016, 117, 369-378. https://doi.org/10.1016/j.agwat.2016.08.028

Rinza, J.; Ramírez, D.A.; García, J.; de Mendiburu, F.; Yactayo, W.; Barreda, C.; Velasquez, T.; Mejía, A.; Quiroz, R. Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions. Potato Res. 2019, 62, 109-122. https://doi.org/10.1007/s11540-018-9400-5

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

Dear authors, I think your paper is interesting and presents novel approaches that can be useful for increasing the irrigation water use efficiency of potato.

You present stimulating insights on potato physiological thresholds used to schedule irrigation and on the development of water stress memory. However, there are some points I would like you to clarify to better address the Agronomy audience. For this reason, I’ve suggested a major revision.

Here are my comments/questions:

Main observation:

It is not completely clear to me how the photosynthesis recovery is related to water stress. In my understanding of this paper, the Prec is the ratio between actual to maximum (potential – no water stress) photosynthesis. In this case, any value lower than 100% should be an indicator of reduced/suboptimal photosynthesis (and therefore biomass accumulation) due to water stress. At the same time, reduction in stomatal conductance (Figure 2) should be an indicator of stomatal closure and therefore reduced transpiration, that is again a response to water stress. Considering this, I have some related questions:

• Why did you use the term recovery when you are referring to the rate of photosynthesis? The value doesn’t necessarily mean that the plant is recovering from a stress (e.g. the rate may be in its decreasing phase).
• Why gs_max is defined as maximum conductance? Is it not more a measure of actual conductance? What is maximum referred to?
• In line 10-12, line 265-270, line 272-281 and line 334-337 you talk about the memory of water stress and you state that FI improved it, giving better performances than DI. Since you applied more water with FI than with DI the stress relief is greater (Prec after FI goes back to higher values respect to DI). With DI less water is applied, and the severity of water stress remains higher (lower Prec). Why do you affirm that this is due to the memory of stress, and not only because the stress remains stronger in DI? The lower Prec remains, the greater water stress should be: this seems to be dependent on the amount of water applied, rather than on the method.

Please try to answer these questions explaining further the meaning of the selected indexes and their relationships with plant processes, transpiration and biomass accumulation. In absence of the more common soil moisture and ET deficit data it may be difficult for the reader to understand these indexes in relation to the severity of water stress.

Minor observations:

Line 79: 6 mm of annual precipitation is an extremely low value. Is the value correct?

Line 129: how did you estimate field capacity?

Line 134: was irrigation applied at 70% of field capacity or at 70% of plant available water?

Line 161: Please include C or something similar in ∆leaf abbreviations (including figures) to let the reader easily remember that you are talking about carbon.

Line 170: it is stated that the RCBDs were analyzed first, and then the results were combined to compare FI and DI after performing the Bartlett’s test. The analysis can be hard to understand for the reader, as the two irrigation methods are tested in spatially segregated main plots and the subplots with different irrigation amounts are nested within these main plots. Please report the complete anova table (e.g. as supplementary material). Could the results be influenced by the spatial segregation (e.g. by different soil characteristics/topography with an east-west gradient?

Figure 1: you used Duncan test for multiple comparisons. However, the Duncan test should be used when comparing few averages only (2-3). With a multiple comparison including 6 treatments, the probability of making a type 1 error should not be overlooked. Please use a more conservative method, like Tukey, Bonferroni or Sidak.

Figure 3: the caption is referred to drip irrigation, but in the graph “FI” is reported

Table S1: n.s. is wrongly attributed to T2-FI.

Table S2: Please insert measures of variability (e.g. standard deviation).

 

Best regards,

 

Author Response

Comment # 1 – Reviewer 2

Dear authors, I think your paper is interesting and presents novel approaches that can be useful for increasing the irrigation water use efficiency of potato.

You present stimulating insights on potato physiological thresholds used to schedule irrigation and on the development of water stress memory. However, there are some points I would like you to clarify to better address the Agronomy audience. For this reason, I’ve suggested a major revision.

Here are my comments/questions:

Main observation:

It is not completely clear to me how the photosynthesis recovery is related to water stress. In my understanding of this paper, the Prec is the ratio between actual to maximum (potential – no water stress) photosynthesis. In this case, any value lower than 100% should be an indicator of reduced/suboptimal photosynthesis (and therefore biomass accumulation) due to water stress. At the same time, reduction in stomatal conductance (Figure 2) should be an indicator of stomatal closure and therefore reduced transpiration that is again a response to water stress. Considering this, I have some related questions:

Why did you use the term recovery when you are referring to the rate of photosynthesis? The value doesn’t necessarily mean that the plant is recovering from a stress (e.g. the rate may be in its decreasing phase).

Our Response

Thanks for your comment, it gives us the possibility to explain a little bit more about this indicator. Percentage of photosynthesis reduction is a trait defined and used by Resco et al. (2008) to normalize (in relation to the maximum value of net photosynthesis rate, measured by the LI-6400XT) photosynthetic responses for comparing different plant species performances under changed water status before and after water pulses. Considering that the photosynthetic recovery after water pulses could be assessed analyzing the combination of intensity (complete or incomplete) and timing (slow, fast) of recovery (Yi et al. 2016), Ramírez et al. (2016) used percentage of photosynthesis reduction in potato obtaining a close relationship of this indicator with tuber yield reduction. Because, strictus sensus this trait is not exclusively used to indicate when plant is recovering after a stress situation (before irrigation), in this new version, we have changed its definition by “the percentage of maximum assimilation rate (PMA)”, which is similarly calculated by Resco et al. (2009) and Ramírez et al. (2016). This modification was made in the text [page 5, lines 160-164], as well as the corresponding abbreviature throughout the text.

Page 5, lines 160-164:

“The percentage of maximum assimilation rate (PMA) was estimated from current A_n and the maximum value obtained in a subplot throughout the whole experiment (A_max), following Resco et al.’s [46] procedure. The value of A_n = 30.6 μmol CO₂ m⁻² s⁻¹, obtained from a DI subplot during first days of monitoring, was the highest value recorded and thus considered as A_max.”

Resco, V.; Ignace, D.D.; Sun, W.; Huxman, T.E.; Weltzin, J.F.; Williams, D.G. Chlorophyll fluorescence, predawn water potential and photosynthesis in precipitation pulse-driven ecosystems - implications for ecological studies. Funct. Ecol. 2008, 22, 479-483. https://doi.org/10.1111/j.1365-2435.2008.01396.x

Yi, X.P.; Zhang, Y.L.; Yao, H.S.; Luo, H.H.; Gou, L.; Chow, W.S.; Zhang, W.F. Rapid recovery of photosynthetic rate following soil water deficit and rewatering in cotton plants (Gossypium herbaceum L.) is related to the stability of the photosystems. J. Plant Physiol. 2016, 194, 23-34. https://doi.org/10.1016/j.jplph.2016.01.016

Ramírez, D.A.; Yactayo, W.; Rens, L.R.; Rolando, J.L.; Palacios, S.; de Mendiburu, F.; Mares, V.; Barreda, C.; Loayza, H.; Monneveux, P.; Zotarelli, L.; Khan, A.; Quiroz, R. Defining biological thresholds associated to plant water status for monitoring water restriction effects: Stomatal conductance and photosynthesis recovery as key indicators in potato. Agr. Water Manage. 2016, 117, 369-378. https://doi.org/10.1016/j.agwat.2016.08.028

-The following references has been added in the Reference section:

[46] Resco, V.; Ewers, B.E.; Sun, W.; Huxman, T.E.; Weltzin, J.F.; Williams, D.G. Drought-induced hydraulic limitations constrain leaf gas exchange recovery after precipitation pulses in the C-3 woody legume, Prosopis velutina. New Phytol. 2009, 181, 672–682.  https://doi.org/10.1111/j.1469-8137.2008.02687.x

 

Comment # 2 – Reviewer 2

Why gs_max is defined as maximum conductance? Is it not more a measure of actual conductance? What is maximum referred to?

Our Response

We would like to clarify the idea about the use of maximum light-saturated stomatal conductance (g_{s_max}) as a physiological indicator for determining irrigation schedules. Medrano et al. (2002) and Flexas et al. (2004,2006) proposed an objective way to establish water status in plants using a standardized parameter based on a physiological threshold that leads to photosynthetic impairment if surpassed. Photosynthesis performs satisfactorily at g_{s_max} values higher than 0.15 mol H₂O m^-2 s^-1, so it is important to prevent crops from falling below that range (indicated in Page 12 lines 270-272), and a reduction of this variable to ≤ 0.05 mol H₂O m^-2 s^-1 could affect photosynthesis [Page 13, lines 320-321]. 

Moreover, Medrano et al. (2002) and Flexas et al. (2004,2006) proposed the use of mid-morning or maximum light-saturated (measured at light saturating conditions for this potato variety, stated at Page 5, lines 158-159) stomatal conductance (g_{s_max}) because stomatal conductance per-se shows high variability along the day. Actual stomatal conductance in potato has been measured using steady-state diffusion porometers (van Loon, 1981; Wilcox and Ashley, 1982; Tourneux et al., 2003; Rud et al., 2014) and LI-6200 photosynthesis portable systems (Liu et al. 2005, 2006, 2009) which are not able to fix a light saturation. This novel perspective of stomatal conductance has been tested in potato (Ramírez et al., 2016, Rinza et al., 2019), and as it has been referred to in Page 12, lines 243-244, g_{s_max} > 0.3 mol H₂O m^-2 s^-1 is recommended as watering timing threshold in this crop to guarantee no significant yield reduction under drip irrigation.

Page 5, lines 158-159:

“The parameters set in the equipment were: 1,500 μmol m^-2 s^-1 PAR, which corresponded to UNICA´s light saturation point for the study area and season, …"

Medrano, H.; Escalona, J.M.; Bota, J.; Gulías, J.; Flexas, J. Regulation of Photosynthesis of C3 Plants in Response to Progressive Drought: Stomatal Conductance as a Reference Parameter. Ann. Bot. 2002, 89, 895–905. https://doi.org/10.1093/aob/mcf079

Flexas, J.; Bota, J.; Cifre, J.; Mariano Escalona, J.; Galmés, J.; Gulías, J.; Lefi, E.-K.; Florinda Martínez-Cañellas, S.; Teresa Moreno, M.; Ribas-Carbó, M. Understanding down-regulation of photosynthesis under water stress: future prospects and searching for physiological tools for irrigation management. Ann. Appl Biol. 2004, 144, 273–283. https://doi.org/10.1111/j.1744-7348.2004.tb00343.x

Flexas, J.; Bota, J.; Galmés, J.; Medrano, H.; Ribas-Carbó, M. Keeping a positive carbonbalance under adverse conditions: responses of photosynthesis and respiration to water stress. Physiol. Plant. 2006, 127, 343–352. https://doi.org/10.1111/j.1399-3054.2006.00621.x

van Loon, C.D. The effect of water stress on potato growth, development, and yield. Am. Potato J. 1981, 58, 51-69. https://doi.org/10.1007/BF02855380

Wilcox, D.A.; Ashley, R.A. The potential use of plant physiological responses to water stress as an indication of varietal sensitivity to drought in four potato (Solanum tuberosum L.) varieties. Am. Potato J. 1982, 59, 533–545. https://doi.org/10.1007/BF02852602

Tourneux, C.; Devaux, A.; Camacho, M.R.; Mamani, P.; Ledent, J-F. Effect of water shortage on six potato genotypes in the highlands of Bolivia (II): water relations, physiological parameters. Agronomie. 2003, 23, 181-190. https://doi.org/10.1051/agro:2002080

Rud, R.; Cohen, Y.; Alchanatis, V.; Levi, A.; Brikman, R.; Shenderey, C.; Heuer, B. Markovitch, T.; Dar, Z.; Rosen, C. Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status. Precis. Agric. 2014, 15, 273–289. https://doi.org/10.1007/s11119-014-9351-z

Liu, F.; Jensen, C.R.; Shahanzari, A.; Andersen, M.N.; Jacobsen, S.E. ABA regulated stomatal control and photosynthetic water use efficiency of potato (Solanum tuberosum L.) during progressive soil drying. Plant Sci. 2005, 168, 831-836. https://doi.org/10.1016/j.plantsci.2004.10.016

Liu, F.; Shahnazari, A.; Andersen, M.N.; Jacobsen, S.E.; Jensen, C.R. Effects of deficit irrigation (DI) and partial root drying (PRD) on gas exchange, biomass partitioning, and water use efficiency in potato. Sci. Hortic. 2006, 109 (2), 113–117. https://doi.org/10.1016/j.scienta.2006.04.004

Liu, F.; Andersen, M.N.; Jensen, C.R. Capability of the “Ball-Berry” model for predicting stomatal conductance and water use efficiency of potato leaves under different irrigation regimes. Sci. Hortic. 2009, 122, 346–354. https://doi.org/10.1016/j.scienta.2009.05.026

Ramírez, D.A.; Yactayo, W.; Rens, L.R.; Rolando, J.L.; Palacios, S.; de Mendiburu, F.; Mares, V.; Barreda, C.; Loayza, H.; Monneveux, P.; Zotarelli, L.; Khan, A.; Quiroz, R. Defining biological thresholds associated to plant water status for monitoring water restriction effects: Stomatal conductance and photosynthesis recovery as key indicators in potato. Agr. Water Manage. 2016, 117, 369-378. https://doi.org/10.1016/j.agwat.2016.08.028

Rinza, J.; Ramírez, D.A.; García, J.; de Mendiburu, F.; Yactayo, W.; Barreda, C.; Velasquez, T.; Mejía, A.; Quiroz, R. Infrared Radiometry as a Tool for Early Water Deficit Detection: Insights into Its Use for Establishing Irrigation Calendars for Potatoes Under Humid Conditions. Potato Res. 2019, 62, 109-122. https://doi.org/10.1007/s11540-018-9400-5

 

Comment # 3 – Reviewer 2

In line 10-12, line 265-270, line 272-281 and line 334-337 you talk about the memory of water stress and you state that FI improved it, giving better performances than DI. Since you applied more water with FI than with DI the stress relief is greater (Prec after FI goes back to higher values respect to DI). With DI less water is applied, and the severity of water stress remains higher (lower Prec). Why do you affirm that this is due to the memory of stress, and not only because the stress remains stronger in DI? The lower Prec remains, the greater water stress should be: this seems to be dependent on the amount of water applied, rather than on the method.

Our Response

Thank you for the comments. The term “water stress memory”, might sound a little bit shocking, but it has been used in plant physiological literature, including in potato research (Ramirez et al. 2015). The main idea is to induce an acclimation response by “training” the plants (Vincent et al. 2019) during some key moments of the growing season. Plants can improve their response to water restriction in the same (short-term water memory, see Yactayo et al., 2013), or inclusive in the next growing season (long-term water memory, see Ramirez et al., 2015). One of the challenges, is to determine the most appropriate conditions or treatment which promote the induction of water stress memory, and this was our intention in this study. Each irrigation method has a different peculiarity (efficiency in the water delivery focalized in the root zone, less water losses, among others), and impose a different water context to the plants, that is the case of the application of a demanding g_{s_max} threshold (0.15 mol H₂O m^-2 s^-1, T2) which reduced significant tuber yield under drip irrigation (DI) but not under Furrow Irrigation (FI). Based on our physiological indicators (mainly carbon isotope discrimination), we hypothesized (addressed in the Discussion section, Page 13, Lines 287-289) that “memory stress” induction could be an important mechanism that helps plants under FI to respond in a better way than DI under a demanding timing irrigation threshold (T2).

We don’t believe that the better responses in plants under T2-FI were due to receiving more water volume than plants under T2-DI. Regarding the control responses, T1-DI plants yielded relatively more than T1-FI (+17.5%) receiving significantly less irrigation water (-36.5% or 1421 m³ ha^-1). It is possible that the water irrigation threshold applied affected more drip than furrow irrigation method. In this latter, applying a frequent, short time and with a moderate volume of irrigation (after well irrigating the first and most important phenological phases for guaranteeing tuber induction) could have induced a memory response and a senescence delay (reflected on higher P_rec values), but as we remarked in the conclusions more specific studies should be carried out in the future.

Ramírez, D.A.; Rolando, J.L.; Yactayo, W.; Monneveux, P.; Mares, V.; Quiroz, R. Improving potato drought tolerance through the induction of long-term water stress memory. Plant Sci. 2015, 238, 26–32. https://doi.org/10.1016/j.plantsci.2015.05.016

Vincent, C., Rowland, D., Schaffer, B., Bassil, E., Racette, K., Zurweller, B. Primed acclimation: a physiological process offers a strategy for more resilient and irrigation-efficient crop production. Plant Sci. 2019, p. 110240. https://doi.org/10.1016/j.plantsci.2019.110240

Yactayo, W.; Ramírez, D.A.; Gutierrez, R.; Mares, V.; Posadas, A.; Quiroz, R. Effect of partial root-zone drying irrigation timing on potato tuber yield and water use efficiency. Agr. Water Manage. 2013, 123, 65-70. https://doi.org/10.1016/j.agwat.2013.03.009

 

Comment # 4 – Reviewer 2

Please try to answer these questions explaining further the meaning of the selected indexes and their relationships with plant processes, transpiration and biomass accumulation. In absence of the more common soil moisture and ET deficit data it may be difficult for the reader to understand these indexes in relation to the severity of water stress.

Our Response

Please see our responses to your 1^st - 3^rd comments/questions. The meaning of the discussed selected indexes and their relationship with plant processes and yield were referred to the introduction as follows:

Percentage of maximum assimilation rate [Page 2, lines 47-48]:

“The photosynthesis or carbon assimilation rate (A_n), one of the key determinants for plant productivity and survival [29], has been suggested as appropriate but uncommon measure of plant stress [30]. In potato, the percentage of maximum A_n has shown a close relationship with tuber yield reduction under different soil water conditions [31].”

Maximum stomatal conductance at light saturated [Page 2, Line 52]:

“...the maximum light-saturated stomatal conductance (g_{s_max}) is a useful parameter for comparing photosynthetic performance responses between different species and experimental conditions [33]. Recently, g_{s_max} has been regarded as one of the most pertinent physiological traits, closely related to tuber yield, to define water status for watering purposes under drip irrigation in potato [31,34,35].”

[31].-Ramírez, D.A.; Yactayo, W.; Rens, L.R.; Rolando, J.L.; Palacios, S.; de Mendiburu, F.; Mares, V.; Barreda, C.; Loayza, H.; Monneveux, P.; Zotarelli, L.; Khan, A.; Quiroz, R. Defining biological thresholds associated to plant water status for monitoring water restriction effects: Stomatal conductance and photosynthesis recovery as key indicators in potato. Agr. Water Manage. 2016, 117, 369-378. https://doi.org/10.1016/j.agwat.2016.08.028

 

Comment # 5 – Reviewer 2

Minor observations:

Line 79: 6 mm of annual precipitation is an extremely low value. Is the value correct?

Our Response

Yes, the value is correct. It corresponds to the average of cumulative precipitation per year from 2014-2017, registered at CIP Meteorological Station. Our study was carried out in the central Peruvian coastal (Lima), which belongs to the Peruvian-Chilean desert (8-28°S). In this zone, the precipitation is extremely low due to the presence of the semi-permanent Southeast Pacific Subtropical Anticyclone. Notwithstanding, the precipitation in Lima is higher than other zones closer to the Anticyclone, as Arica (annual mean 0.6 mm), Iquique (annual mean 1.9 mm) or Tocopilla (annual mean 3.8 mm) (Walter et al. 1986).

Walter, H.; Breckle, S.W. The Peruvian-Chilean Desert. In: Ecological Systems of the Geobiosphere. Springer, Berlin, Heidelberg. 1986. pp. 262-273. https://doi.org/10.1007/978-3-662-06812-0_26

 

Comment # 6 – Reviewer 2

Line 129: how did you estimate field capacity?

Our Response

The procedure to estimate field capacity, has been added in the Materials and Methods Section as follows [Page 4, Lines 128-136]:

 

“Within the experimental field, two areas of 1.2×1.2 m² (with edges 0.2 m high) were flooded and covered with plastic to avoid evaporation. After 6 days, when drainage became negligible due to percolation [44], a pit (0.8×0.7×0.5 m³) was dug in each area. Later, soil samples were collected at 0, 0.10, 0.25 and 0.40 m depth levels, using cylinders of known volume (V_t in cm³). The samples were weighed fresh (S_f in g) and after oven dried (S_d in g) at 105 °C during 72h for determining volumetric soil moisture content [45] at field capacity (θ_fc) as follows:

è_fc (%) = (S_f – S_d) / V_t *100

The è_fc corresponding to 0, 0.10, 0.25 and 0.40 m depth levels were 34.5 ± 2.1, 32.7 ± 1.2, 31.1 ± 1.2 and 28.6 ± 0.2% respectively.”

 

The following references have been added in the Reference section:

[44].-Twarakavi, N.K.; Sakai, M.; Šimůnek, J. An objective analysis of the dynamic nature of field capacity. Water Resour. Res. 2009, 45, W10410. https://doi.org/10.1029/2009WR007944

[45].-Topp, G.C.; Parkin, G.W., Ferré, T.P.; Carter, M.R.; Gregorich, E.G. Sample moisture content. In: Soil sampling and methods of analysis. 2^nd edn. Eds MR Carter, EG Gregorich, NW, US, 2008, pp, 41-44. https://doi.org/10.1201/9781420005271

 

Comment # 7 – Reviewer 2

Line 134: was irrigation applied at 70% of field capacity or at 70% of plant available water?

Our Response

We referred to 70% of field capacity. Control subplots (T1) were watered once they reached 70% of volumetric soil moisture content at field capacity (θ_fc). This detail was clarified in response to your comment n°6, and the reference changed by a more recent one, for avoiding misunderstandings [Page 4, Line 147].

“Control subplots (T1) were watered once they reached 70% of θ_fc [35].”

[35].-Cucho-Padin, G.; Rinza, J.; Ninanya, J.; Loayza, H.; Quiroz, R.; Ramírez, D.A. Development of an open-source thermal image processing software for improving irrigation management in potato crops (Solanum tuberosum L.). Sensors, 2020, 20, 472.  https://doi.org/10.3390/s20020472

 

Comment # 8 – Reviewer 2

Line 161: Please include C or something similar in ∆leaf abbreviations (including figures) to let the reader easily remember that you are talking about carbon.

Our Response

Following your suggestion, we have modified the abbreviature of carbon isotope discrimination to ΔC_leaf.. This modification has been done throughout the text, starting from Page 5, lines 174-176, and in Page 10 (Figure 6).    

“Leaf composite samples per subplot (12 medium and apical leaflets from target plants) were collected only the day before (-1) and 4 days after rewatering (+4) to calculate carbon isotope discrimination in leaves (∆C_leaf) following Ramírez et al.’s [39] procedure.”

Comment # 9 – Reviewer 2

Line 170: it is stated that the RCBDs were analyzed first, and then the results were combined to compare FI and DI after performing the Bartlett’s test. The analysis can be hard to understand for the reader, as the two irrigation methods are tested in spatially segregated main plots and the subplots with different irrigation amounts are nested within these main plots. Please report the complete anova table (e.g. as supplementary material). Could the results be influenced by the spatial segregation (e.g. by different soil characteristics/topography with an east-west gradient?

Our response

We appreciate your suggestion. The ANOVA table has been added as Supplementary Material (Table S4). This new table was referenced in the main text (manuscript) as follows [Page 6, Line 205]:

“There was no DTY difference between irrigation methods (Table S4).”

Regarding the influence of spatial segregation on the results, it is negligible since the size of the experimental field is small (31.5 x 62 m²) and it could be considered as homogeneous. Also, prior to establishing the treatments, a spatial analysis of soil organic matter (an important component of the physical and chemical properties of soil) was carried out in the same grid of the experimental field with 66 sampling spatial points. The contour plot of the soil organic matter in all the field is shown in the next figure (Figure R1):

Figure R1. Contour plot of organic matter (OM, %) in the experimental field.

As it could be appreciated, the influence of spatial segregation is negligible since the variability of the soil organic matter was low (ranged from 1 to 1.7%, see Figure R2), and all the values belonged to the same level of classification (very low or < 2%) (Munshower 2018). Furthermore, the field's slope assessed in different parts of the experimental area was very low (~0.2°) corresponding to the category of “flat land” (< 2°) according to the United States Department of Agriculture classification (Arifianti and Agustin 2017). Therefore, a negligible effect of the spatial segregation in the experiment seems warranted.

Munshower, F. F. Practical handbook of disturbed land revegetation. 2018. CRC Press.

Arifianti Y.; Agustin F. An Assessment of the Effective Geofactors of Landslide Susceptibility: Case Study Cibeber, Cianjur, Indonesia. In: Yamagishi H.; Bhandary N. (eds) GIS Landslide. 2017. Springer, pp. 183-195. https://doi.org/10.1007/978-4-431-54391-6_10

Comment # 10 – Reviewer 2

Figure 1: you used Duncan test for multiple comparisons. However, the Duncan test should be used when comparing few averages only (2-3). With a multiple comparison including 6 treatments, the probability of making a type 1 error should not be overlooked. Please use a more conservative method, like Tukey, Bonferroni or Sidak.

Our response

We agree with your comment. The Duncan test for multiple comparisons has been changed by the Tukey test. The letters which indicate significant differences among treatments in Figure 1 were modified according to the test's results [Page 6]. The figure's caption was changed as follows [Page 6]:

“... Different letters mean significant differences (p< 0.05) by the Tukey test ...”

Thus also, the following sentences related to the Duncan test has been changed follows:

Page 5, Line 192:

“... well as combining both methods) and then compared with Tukey multiple range test.”

Page 6, Line 208:

“... This finding was further confirmed with the Tukey multiple range test ...”

Comment # 11 – Reviewer 2

Figure 3: the caption is referred to drip irrigation, but in the graph “FI” is reported

Our Response

We apologize for that mistake. The correction (DI instead of FI) was made in Figure 3 [Page 7].

 

 Comment # 12 – Reviewer 2

Table S1: n.s. is wrongly attributed to T2-FI.

Our Response

We must apologize for that typing error. The correction, “ * ” (significant difference at p < 0.5) instead of  “n.s.” (Non-significant difference), was made on Table S1 [Supplementary Materials, Page 2].

Comment # 13 – Reviewer 2

Table S2: Please insert measures of variability (e.g. standard deviation).

Our response

Thank you for your suggestion. The standard errors of respective means were added for each variable in Table S2, and the table's footnote has been changed as follows [Supplementary Materials, Page 2]:

“TT at MCC and 50% of MCC was estimated by nonlinear regression by fitting temporal data of CC to a Beta function, according to Ramírez et al.’s [1] procedure. CC measurements were made according to CIP’s Protocol for Designing and Conducting Potato Field [2], by coupling digital photography and the software Image Canopy [4] for processing image.”

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

I have undertaken a review of the manuscript (revised) as well as the attached author responses to the initial review where I recommended rejection. However, I am satisfied with the revisions made by the authors as they have addressed most, if not all, of my initial comments and also the explanation they gave about the number of trials. Therefore, I do believe that the manuscript has been significantly improved

Reviewer 2 Report

Dear authors,

I have reviewed the last version of your paper. I find your explanations scientifically sound and very interesting. You addressed all my previous doubts and accurately modified the paper to accommodate my requests.

I think your paper should be published in this journal.

Best regards