the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Oct 2024
Modelling the nutritional implications of ozone on wheat protein and amino acids
Abstract. Ozone (O3) pollution reduces wheat yields as well as the protein and micronutrient yield of the crop. O3 concentrations are particularly high in India, and are set to increase, threatening wheat yields and quality in a country already facing challenges to food security. This study aims to improve the existing DO3SE-CropN model to simulate the effects of O3 on Indian wheat quality by incorporating antioxidant processes to simulate protein, and the concentrations of nutritionally relevant amino acids. As a result, the improved model can now capture the decrease in protein concentration that occurs in Indian wheat exposed to elevated O3. The structure of the modelling framework is transferrable to other abiotic stressors and easily integrable into other crop models, provided they simulate leaf and stem N, demonstrating the flexibility and usefulness of the framework developed in this study. Further, the modelling results can be used to simulate the FAO recommended metric for measuring protein quality, the DIAAS, setting up a foundation for nutrition-based risk assessments of O3 effects on crops. The resulting model was able to capture grain protein, lysine and methionine concentrations reasonably well. As a proportion of dry matter, the simulated percentages ranged from 0.26 % to 0.38 % for lysine, and from 0.13 % to 0.22 % for methionine, while the observed values were 0.16 % to 0.38 % and 0.14 % to 0.22 %, respectively. For grain and leaf protein simulations, the interdependence between parameters reduced the accuracy of their respective relative protein loss under O3 exposure. Additionally, the decrease in lysine and methionine concentrations under O3 exposure was underestimated by ~10 percentage points for methionine for both cultivars, and by 37 and 19 percentage points for lysine for HUW234 and HD3118 respectively. This underestimation occurs despite simulations of relative yield loss being fairly accurate (average deviation of 2.5 percentage points excluding outliers). To provide further mechanistic understanding of O3 effects on wheat grain quality, future experiments should measure nitrogen (N) and protein concentrations in leaves and stems, along with the proportion of N associated with antioxidants, which will aid in informing future model development. Additionally, exploring how grain protein relates to amino acid concentrations under O3 will enhance the model’s accuracy in predicting protein quality and provide more reliable estimates of the influence of O3 on wheat quality. This study builds on the work of Cook et al. (2024) and supports the second phase of the tropospheric O3 assessment report (TOAR) by investigating the impacts of tropospheric O3 on Indian wheat and the potential of this to exacerbate existing malnutrition in India.
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RC1: 'Comment on egusphere-2024-2968', Anonymous Referee #1, 28 Oct 2024
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In this manuscript, Cook et al. present their study on “Modelling the nutritional implications of ozone on wheat protein and amino acids” to improve the DO3SE-CropN model. Through simulations of nutrition-based ozone risk assessment in Indian wheat, the authors develop a flexible framework for crop models by incorporating the antioxidant responses, as well as leaf and stem nitrogen dynamics under ozone exposure. This study provides valuable data and is aligned with the journal's scope. The manuscript is well-written; however, some corrections are required to improve the quality.
I recommend publication after a relatively minor revision as follows:
Line 40: Update “Mills et al. 2018b” to “Mills et al. 2018a” for the first citation of Mills et al. in 2018. The subsequent citation with the same author and year should be referenced as “Mills et al. 2018b.”
Lines 40-42: Expand briefly on the TOAR-I report’s findings regarding current ozone trends.
Lines 42-44: Make this sentence clear by breaking it into two sentences.
Lines 72-73: Provide possible reasons for the high O3 concentration in this region for better context.
Lines 76-77: Rephrase this sentence, as comparing present-day conditions to seasonal changes may not appropriately represent O3 concentration variations.
Line 125: Introduce the existing DO3SE-Crop model here, summarizing essential inputs and its application in a few sentences for reader comprehension.
Line 156: “recovery from O3 damage overnight” Please clarify the phrase.
Line 172: Specify which antioxidants are considered for model integration, as “antioxidants” is a generalized term.
Line 347: “Error! Reference source not found. shows the results of the DIAAS calculation.” Correct the sentence “Table 1 shows the results of the DIAAS calculation”
Line 503: “(Error! Reference source not found., Fig.’s 5b and 5d).” Correct the sentence.
Line 522: Replace “O3” with “O3”
Line 634: Reference is incomplete, please address the missing information.Citation: https://doi.org/10.5194/egusphere-2024-2968-RC1 -
RC2: 'Comment on egusphere-2024-2968', Anonymous Referee #2, 06 Nov 2024
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This manuscript presents the development of the DO3SE-CropN model, which simulates reductions in protein and amino acids in wheat subjected to ozone (O₃) stress. The authors effectively incorporate antioxidant processes, thereby increasing the model's applicability for predicting O₃-induced quality losses in crops. Including crucial amino acids, such as lysine and methionine, is essential for assessing the effects on protein quality. Further clarification regarding the specific enhancements to the original model framework and how these modifications rectify limitations in earlier versions would enhance the manuscript.
1. The authors illustrate the model's ability to predict protein quality, successfully simulating lysine and methionine concentrations in wheat grain. However, the underestimation of reductions in amino acid content induced by O₃, particularly for lysine, is significant. The manuscript should discuss potential reasons for these discrepancies, such as limitations in the underlying assumptions of antioxidant pathways.
2. The study examines the critical issue of O₃ pollution and its impact on food security in India, highlighting the significance of this research given global nutrition challenges. The study could be improved by addressing potential regional variations in O₃ sensitivity within the context of the model's application to Indian wheat and exploring how this framework may be adapted for other significant wheat-producing regions experiencing comparable environmental stressors.
3. The suggestion to combine nitrogen and protein assessments from leaves and stems, along with a deeper exploration of nitrogen allocation to antioxidants, is noteworthy. These efforts are expected to enhance model precision. It would be beneficial for the authors to delineate the types of experimental data required to refine these aspects and to articulate specific hypotheses concerning the influence of antioxidant allocation on grain protein quality under O₃ stress.
4. While the model accurately predicts yield loss, the discrepancies in amino acid concentration predictions indicate a need for further calibration. Additional validation steps, such as utilizing independent datasets or conducting field trials, may improve the credibility and generalisability of the model outputs.
5. The authors emphasize the model's adaptability in simulating responses to various abiotic stressors. To enhance the manuscript, it would be beneficial to include examples of specific stressors, such as drought and heat, to which this framework could be adapted and discuss any preliminary adaptations made to expand its applicability.
Citation: https://doi.org/10.5194/egusphere-2024-2968-RC2 -
RC3: 'Comment on egusphere-2024-2968', Anonymous Referee #3, 13 Nov 2024
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General comments:
This study extends the DO3SE-CropN model to incorporate an O3-induced modification in nitrogen remobilization from the leaves and stems to the grains. The goal of the new mechanism is to improve the performance of the DO3SE-CropN model in predicting the O3-driven reductions in wheat grain yield, protein, lysine and methionine concentrations in India. The study is novel, interesting and well-written. The model shows acceptable skill in predicting some variables (e.g. grain yield in single years as well as protein concentrations), whilst other outputs need further improvement. The authors report the limitations of the study clearly and concisely. I favour the publication of the manuscript following the modifications below:
Specific comments:
1) You use the concept of relative yield loss (RY loss) (e.g. Fig. 3b and Fig. S8) in a way that is not clear to me. The only definition that I was able to find was in the legend of Fig. S8: ‘RY loss was calculated comparative to preindustrial O3 concentrations of 10 ppb (CLRTAP, 2017).’ You should add some text to the main manuscript to define what is RY loss in the context of this study. Moreover, if RY loss is not measured but estimated, why is the x-axis of Fig 3b defined as ‘observed’? Also, why is it important for DO3SE-CropN to estimate correctly a trait like RY loss which was not observed here?
2) Fig 7: I suggest to add the cumulative O3 concentrations for the years 2017 and 2018 to the plot. If not possible, you can make a new plot with the cumulative O3 for both years (it could be added to the supplementary material if you prefer). It would be interesting to show to time-series comparison of accumulated O3 concentrations between the two years.
Technical corrections:
1) Fig 6-9: How did you calculate the differences in Temperature, O3, PPFD and net photosynthetic rate? Did you subtract the values of the year 2018 from the year 2017 or the opposite? In other words, which is your reference year? Please add this information to the figures’ legends. The same is true for Figures S1-S7.
Citation: https://doi.org/10.5194/egusphere-2024-2968-RC3 -
CC1: 'Comment on egusphere-2024-2968', Owen Cooper, 16 Nov 2024
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This comment can be found in the attached pdf.
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