the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Sep 2023
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
Abstract. Elevated surface ozone (O3) concentrations can negatively impact growth and development of crop production by reducing photosynthesis and accelerating leaf senescence. Under unabated climate change, future global O3 concentrations are expected to increase in many regions, adding additional challenges to global agricultural production. Presently, few global process-based crop models consider the effects of O3 stress on crop growth. Here, we incorporated the effects of O3 stress on photosynthesis and leaf senescence into the Decision Support System for Agrotechnology Transfer (DSSAT) crop models for maize, rice, soybean, and wheat. The advanced models reproduced the reported yield declines from observed O3-dose field experiments and O3 exposure responses reported in the literature (O3 relative yield loss RMSE < 10 % across all calibrated models). Simulated crop yields decreased as daily O3 concentrations increased above 25 ppb, with average yield losses of 0.16 % to 0.82 % (maize), 0.05 % to 0.63 % (rice), 0.36 % to 0.96 % (soybean), and 0.26 % to 1.23 % (wheat) per ppb O3 increase, depending on the cultivar O3 sensitivity. Increased water deficit stress and elevated CO2 lessen the negative impact of elevated O3 on crop yield, but potential yield gains from CO2 concentration increases may be counteracted by higher O3 concentrations in the future, a potentially important constraint to global change projections for the latest process-based crop models. The improved DSSAT models with O3 representation simulate the effects of O3 stress on crop growth and yield in interaction with other growth factors and can be run in the pDSSAT global gridded modeling framework for future studies on O3 impacts under climate change and air pollution scenarios across agroecosystems globally.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1540', Anonymous Referee #1, 31 Oct 2023
The authors tended to enable DSSAT is able to simulate 1) how O3 affects photosynthesis and leaf senescence, and 2) how O3 effect interacts with CO2 and water stress by associated changes in stomatal conductivity for crops. My comments are as follows:
(1) All stress factors the authors newly incorporated into model are all for the effects of photosynthesis (like FO3) and lead senescence (like SLFO3). But I noticed that the model parameterizations are based on only relative yields (Fig. S1-S3). It is important to have photosynthesis and leaf observations as you tend to simulate their changes to validate model, rather than fitting yields by giving different combinations of parameters. Otherwise, it is very possible you have a “right” yield simulation but wrong parameters, which will give users a lot of trouble when they tend to project model to some unknown conditions.
(2) In line 260, “until the best fit was found for the phenology, growth, and relative yield loss for each cultivar across all O3 treatments.” Where are the “growth” observations? I can only see relative yield data.
(3) For their experiment, I noticed some odd observations in Fig. S2. In rice and soybean, I found yields of some cultivars increase with higher O3, which seems impossible for the new models except unrealistic parameters were fed into (like change some parameters from negative to positive to make it increase rather than decrease. But this is inconsistent with the theory the models were built). Please explain the odd observations in the main text.
(4) I appreciate for the modelers consider the interactions between O3, CO2 and water stress by stomatal conductivity, which I am really interested into. I wish the authors could add more observation points in Fig 4 and 5 to ensure the model can simulate the key interactions quantitively.
Citation: https://doi.org/10.5194/egusphere-2023-1540-RC1 -
RC2: 'Comment on egusphere-2023-1540', Anonymous Referee #2, 15 Dec 2023
This is an impressive compilation of data for many crops from multiple sites to calibrate the data intensive crop models. This was an enormous amount of work. Overall, this is a good paper that highlights the impacts of ozone exposure on crop yields along with other stressors and should be of interest to a wide audience.
The work focuses on the M7 (7-hour daily mean) ozone metric to alter daily photosynthesis and accelerate leaf senescence. The authors claim that their approach is more representative than a generic annual damage function. It would have been nice to see a comparison of a simpler damage function approach with the simulation models presented in this paper to compare the results of the two approaches. The use of the models outlined in this paper may be difficult to apply because of the need for large amounts of detailed data for each site. Using weighted seasonal metrics like AOT40, W126 or SUM06 to modify yield might produce robust results as well. However, I realize the M7 metric for yield loss is the most readily available in the literature.
The models are well thought-out and carefully constructed. However, it is hard to know how the models will work in uncalibrated situations. The only predictive modeling seems to have been done on the evaluation year, 2010, at the SoyFACE study in Illinois calibrated with the 2009 SoyFACE study. More attention to investigating why the simulations overestimated biomass and yield in 2010 could have been presented. For example, even though the rainfall at that site was similar between years, 2010 appears to have more of the rainfall at the beginning of the season compared to rainfall patterns in 2009 (Betzelberger et al., 2012). And the mass of individual seeds were smaller at lower ozone exposures in 2010.
I am not an expert in the modeling field or the uses of this model. I would have liked to have seen more discussion the implications of this model. How can it be used in the near-term? Do users of the model have to have access to detailed site data or can modeled parameters be used to drive the model?
Citation: https://doi.org/10.5194/egusphere-2023-1540-RC2 - AC1: 'Response to reviewer comments', Jose Guarin, 12 Jan 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1540', Anonymous Referee #1, 31 Oct 2023
The authors tended to enable DSSAT is able to simulate 1) how O3 affects photosynthesis and leaf senescence, and 2) how O3 effect interacts with CO2 and water stress by associated changes in stomatal conductivity for crops. My comments are as follows:
(1) All stress factors the authors newly incorporated into model are all for the effects of photosynthesis (like FO3) and lead senescence (like SLFO3). But I noticed that the model parameterizations are based on only relative yields (Fig. S1-S3). It is important to have photosynthesis and leaf observations as you tend to simulate their changes to validate model, rather than fitting yields by giving different combinations of parameters. Otherwise, it is very possible you have a “right” yield simulation but wrong parameters, which will give users a lot of trouble when they tend to project model to some unknown conditions.
(2) In line 260, “until the best fit was found for the phenology, growth, and relative yield loss for each cultivar across all O3 treatments.” Where are the “growth” observations? I can only see relative yield data.
(3) For their experiment, I noticed some odd observations in Fig. S2. In rice and soybean, I found yields of some cultivars increase with higher O3, which seems impossible for the new models except unrealistic parameters were fed into (like change some parameters from negative to positive to make it increase rather than decrease. But this is inconsistent with the theory the models were built). Please explain the odd observations in the main text.
(4) I appreciate for the modelers consider the interactions between O3, CO2 and water stress by stomatal conductivity, which I am really interested into. I wish the authors could add more observation points in Fig 4 and 5 to ensure the model can simulate the key interactions quantitively.
Citation: https://doi.org/10.5194/egusphere-2023-1540-RC1 -
RC2: 'Comment on egusphere-2023-1540', Anonymous Referee #2, 15 Dec 2023
This is an impressive compilation of data for many crops from multiple sites to calibrate the data intensive crop models. This was an enormous amount of work. Overall, this is a good paper that highlights the impacts of ozone exposure on crop yields along with other stressors and should be of interest to a wide audience.
The work focuses on the M7 (7-hour daily mean) ozone metric to alter daily photosynthesis and accelerate leaf senescence. The authors claim that their approach is more representative than a generic annual damage function. It would have been nice to see a comparison of a simpler damage function approach with the simulation models presented in this paper to compare the results of the two approaches. The use of the models outlined in this paper may be difficult to apply because of the need for large amounts of detailed data for each site. Using weighted seasonal metrics like AOT40, W126 or SUM06 to modify yield might produce robust results as well. However, I realize the M7 metric for yield loss is the most readily available in the literature.
The models are well thought-out and carefully constructed. However, it is hard to know how the models will work in uncalibrated situations. The only predictive modeling seems to have been done on the evaluation year, 2010, at the SoyFACE study in Illinois calibrated with the 2009 SoyFACE study. More attention to investigating why the simulations overestimated biomass and yield in 2010 could have been presented. For example, even though the rainfall at that site was similar between years, 2010 appears to have more of the rainfall at the beginning of the season compared to rainfall patterns in 2009 (Betzelberger et al., 2012). And the mass of individual seeds were smaller at lower ozone exposures in 2010.
I am not an expert in the modeling field or the uses of this model. I would have liked to have seen more discussion the implications of this model. How can it be used in the near-term? Do users of the model have to have access to detailed site data or can modeled parameters be used to drive the model?
Citation: https://doi.org/10.5194/egusphere-2023-1540-RC2 - AC1: 'Response to reviewer comments', Jose Guarin, 12 Jan 2024
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Cited
Jonas Jägermeyr
Elizabeth A. Ainsworth
Fabio A. A. Oliveira
Senthold Asseng
Kenneth Boote
Joshua Elliott
Lisa Emberson
Ian Foster
Gerrit Hoogenboom
David Kelly
Alex C. Ruane
Katrina Sharps
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
(1324 KB) - BibTeX
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- Final revised paper