the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 May 2024
Can adaptations of crop and soil management prevent yield losses during water scarcity? – A modelling study
Abstract. With climate change, the increasingly limited availability of irrigation water resources poses a major threat to agricultural production systems world-wide. This study explores climate adaptation options in soil and crop management to reduce yield losses due to water scarcity and irrigation restrictions during the 2022 summer drought. The focus is on potato production in the Broye catchment in Switzerland, which is representative of many mid-sized lowland catchments in Central Europe facing reduced irrigation water availability. We employed the field-scale agro-hydrological model SWAP in a distributed manner to simulate regional irrigation demand, yields and deficits under drought stress. Results suggest that irrigation bans and drought in 2022 led to a 16.4 % reduction in potato yield due to a 59 % deficit in irrigation water. Our findings suggest that adding 1 % soil organic carbon (SOC) down to a depth of 60 cm could have reduced the yield loss to only 7 %. Planting earlier maturing potato varieties in less favorable pedoclimatic conditions further improves irrigation water productivity (IWP) and reduces irrigation water demand by 26 %. In this case, however, there is a trade-off in yield, the reduction of which can only be reduced to -14.8 %. Overall, our findings highlight the great value of soil organic carbon for preventing productivity losses during droughts at the example of a recently experienced drought year. Furthermore, we show that irrigation water use efficiency can be optimized by location-specific combinations of adaptation choices. In the face of future droughts exacerbated by climate change, the measures studied here represent a valuable adaptation to mitigate yield losses and reduce dependence on irrigation.
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RC1: 'Comment on egusphere-2024-1201', Anonymous Referee #1, 16 Jun 2024
General comments:
The authors conducted an interesting modelling based scenario anlaysis for soybean grown in Broye catchment in Switzerland to investigate the potentials of preventing yield lossess facing irrigation supply deficits with increasing soil organic carbon or/and earlier maturing under specific (extreme) drought condition for 2022. The topic is of significance for food security under extreme climates, for sure. However, I feel some text is repetitive across methods, results and discussion sections. Please refer to my following suggestions.
Specific comments:
-In the INTRODUCTION, at least four times mentioning about "shortcomings" in previous studies (Lines 69, 83,104, 112), makes me unclear with the certain paragraph (Line 114) totally sumarize the overall innovative aspects of the study. Please reconsider whehter all the pointed innovative points valid and the flow of the text to clarify the logic. Given that in the discussion, you mentioned also some other studies also did similar analysis
- For section 3, why not combine the 3.2 and 3.4? Data can be in the last part. Actually, three major steps, which are model set-up, model calibration and validation, scenario settings, followed by the data sources.
-In the method, it should be clear which grids you tested. What is the meaning of Figure 1? I feel just figure 4 is enough to show the real modeling objectives.
-Table 1 is confusing, what is the differences between the first two scenarios ( like 1 vs. 2; 5 vs. 6) and last two scenarios ( like 3vs. 4; 7vs. 8)in each sub group?
-For the first limitations you pointed, why not to do some sensitivity analysis to show the robustness of the study facing different levels of drought?
-Lines 521-523, I think it would be nice if at least some examples of certain types of management adaptations towards higher SOC can be listed.
-In section 5.2 and 5.3, please delete some repetative content from results.
-Can different models generate different results?
Technical corrections:
-Line 159, typo.
Citation: https://doi.org/10.5194/egusphere-2024-1201-RC1 -
AC1: 'Reply on RC1', Malve Heinz, 28 Jun 2024
Reply to comments by anonymous referee #1
Dear Reviewer,
We thank you for taking the time to read our manuscript and providing valuable feedback. In the following, we will address each comment and explain how we plan to adapt the manuscript:
Comment 1:
In the INTRODUCTION, at least four times mentioning about "shortcomings" in previous studies (Lines 69, 83,104, 112), makes me unclear with the certain paragraph (Line 114) totally sumarize the overall innovative aspects of the study. Please reconsider whehter all the pointed innovative points valid and the flow of the text to clarify the logic. Given that in the discussion, you mentioned also some other studies also did similar analysis
Thank you for the remark, we will make sure that the introduction reads well and that we discuss all relevant studies already in the introduction. We checked the four research gaps / “shortcomings” mentioned.
- The research gap in l. 69 (“Quantification of irrigation demand and supply deficits on a larger scale”), is being addressed in our objectives (l. 114-115) and results (l. 435-436).
- In l. 104 we specify the research gap of accounting for water supply deficits with water restrictions, which we specifically address in our study (l. 240-242).
- The same holds for l. 112, where we point to the impact of water availability on crop yield, which is mentioned as a shortcoming in l. 99.
For these three points we do not discuss results of similar studies in the discussion. Where we place our results in the context of other publications, we primarily discuss the effects of management, which in the studies cited were examined exclusively on a field scale. The “shortcoming” we mention in l. 83 (including the water source in the simulation) is indeed not a subject of this study and will therefore be excluded in the revised version of the manuscript.
Comment 2:
For section 3, why not combine the 3.2 and 3.4? Data can be in the last part. Actually, three major steps, which are model set-up, model calibration and validation, scenario settings, followed by the data sources.
Thank you for the helpful suggestion, which we will realize in the revised version of the manuscript.
Comment 3:
In the method, it should be clear which grids you tested. What is the meaning of Figure 1? I feel just figure 4 is enough to show the real modeling objectives.
That is a valid point, we will merge Figure 1 and 4 and revise it to make the actual modeling perimeter clearer.
Comment 4:
Table 1 is confusing, what is the differences between the first two scenarios ( like 1 vs. 2; 5 vs. 6) and last two scenarios ( like 3vs. 4; 7vs. 8)in each sub group?
Thank you for pointing that out; there is indeed an error in Table 1, which we will correct in the revised manuscript.
Comment 5:
For the first limitations you pointed, why not to do some sensitivity analysis to show the robustness of the study facing different levels of drought?
We realize, that starting the discussion section with this first limitation is somewhat misleading as it shifts the focus from the actual objective of the study. It is not within the scope of this study to investigate the impacts under different drought patterns and levels. The focus of the study lies on the potentials of adaption measures to decrease yield deficits and reduce irrigation demands for a case study in 2022. We will re-formulate this in the revised version for clarification.
Comment 6:
Lines 521-523, I think it would be nice if at least some examples of certain types of management adaptations towards higher SOC can be listed.
We agree with the reviewer. Although we do mention management practices that can increase SOC content in the Introduction (l. 53-55; cover cropping, compost/manure application), this paragraph should rather be placed in section 5.4 (Practical limitations to managing SOC stocks) in the discussion. We will implement this in the revised version of the manuscript.
Comment 7:
In section 5.2 and 5.3, please delete some repetative content from results.
We will address this suggestion in the revised manuscript.
Comment 8:
Can different models generate different results?
Yes, if another model represents soil hydraulic processes differently, also the resulting water retention and irrigation demand can differ from the results we obtained with SWAP. The advantage of SWAP is its very detailed representation of soil water flow combined with a comprehensive representation of dynamic crop growth in the WOFOST module. We will make this clear in the revised version.
Comment 9:
Line 159, typo.
Thank you for pointing that out; we will correct it in the revised version of the manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-1201-AC1
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AC1: 'Reply on RC1', Malve Heinz, 28 Jun 2024
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RC2: 'Comment on egusphere-2024-1201', Anonymous Referee #2, 05 Sep 2024
The study was conducted in a water catchment in Switzerland, specifically the Broye catchment. The SWAP model was used to simulate irrigation demand and yield losses in the dry year 2022 on especially early maturing potato varieties. The impact of a 1% increase in SOC was also assessed to reduce yield losses. The study feels well thought through and well developed. The figures included were very nicely done.
My more specific comments are attached below:
I think you should provide some measures on how dry year 2022 was. Was e.g. the whole year dry, or only the summer? And in comparison to long-term averages.
It is difficult to sometimes remember what the different scenarios meant in the text, it is good to remind the reader or give them more meaningful names.
Line 44. “It must, of course, be noted that different varieties also lead to different properties and uses.” This is true for potatoes as you explain later, but is it also really true for other crops? In that case, motivate it with references.
In section 3.1 you write about that you use weather and soil data and from where it was obtained. It becomes repetition in the methods when you mention it again in section 3.3
Line 297. Why is a 60 cm depth chosen? Is 60 cm a common cultivation depth in the region?
Line 325. Is the computation time on a PC important information?
Line 337. With CO2 do you mean CO2?
Table 2. Write what the abbreviations in the table mean in the table caption.
Line 361. You never mentioned in the Materials and Method section that you performed a PCA. You have to explain that.
Line 471. It is not clear that this sentence is about Porter et al study
Line 545-546. Stagnated crop yields have not been mentioned earlier in the manuscript, I would include it earlier if you want to use it in the conclusion section.
Citation: https://doi.org/10.5194/egusphere-2024-1201-RC2 -
AC2: 'Reply on RC2', Malve Heinz, 13 Sep 2024
Reply to comments by anonymous referee #2
Dear Reviewer,
We thank you for taking the time to read our manuscript and providing valuable feedback. In the following, we will address each comment and explain how we plan to adapt the manuscript:
Comment 1:
I think you should provide some measures on how dry year 2022 was. Was e.g. the whole year dry, or only the summer? And in comparison to long-term averages.
Thank you for the remark, in l. 133-134 we mention the long-term average and the 2022 annual precipitation sum for the whole catchment area as well as the for measuring station in Payerne:
“The mean daily temperature in the catchment is 9°C, and the mean annual precipitation is 1158 mm (1991-2020; MeteoSwiss (2021b, a)). In 2022, the mean temperature was 10.9 °C, and the annual precipitation was 1003 mm (-13.4% compared to the long-term mean). For the meteorological station 135 in Payerne, located within the main agricultural zone, the mean temperature was 11.4°C and annual precipitation 816mm (-29.5% compared to the long-term mean, MeteoSwiss, 2024).”
We added a sentence for clarification where we also addressed the seasonality: “In 2022, precipitation was below the long-term monthly means already in spring, but especially in July and August (MeteoSwiss, 2024).”
Comment 2:
It is difficult to sometimes remember what the different scenarios meant in the text, it is good to remind the reader or give them more meaningful names.
Thank you for the helpful suggestion, we will insert reminders at the beginning of new chapters when we revise the manuscript.
Comment 3:
Line 44. “It must, of course, be noted that different varieties also lead to different properties and uses.” This is true for potatoes as you explain later, but is it also really true for other crops? In that case, motivate it with references.
That is a valid point, this statement is meant to be directed at potatoes, we clarified it as such: “It should be noted that potatoes, for example, have very different uses depending on their maturity class. Late-maturing potatoes give higher yields and are most suitable for processing (crisps and fries), while earlier varieties are mainly used as table potatoes.”
Comment 4:
In section 3.1 you write about that you use weather and soil data and from where it was obtained. It becomes repetition in the methods when you mention it again in section 3.3
Thank you for pointing that out; we will remove the repeated text in the revised manuscript.
Comment 5:
Line 297. Why is a 60 cm depth chosen? Is 60 cm a common cultivation depth in the region?
For clarification of this point, we revised the text in ll. as follows:
In line with Turek et al. (2023), we test a hypothetical scenario of soil carbon accumulation, where we assume that continued cover cropping and organic amendments could lead to an increase of SOC by 1% down to a depth of 60 cm. This scenario is thought to represent a maximum possible scenario of SOC accumulation given observed differences in SOC contents by depth depending on management (Diacono and Montemurro, 2010; Hirte et al., 2018; Lianhai, 2022; Skadell et al., 2023). Although most studies point to the concentration of SOC in the topsoil
Comment 6:
Line 325. Is the computation time on a PC important information?
We thought it might be interesting to have an idea about the runtime in terms of understanding practical limitations in the number of simulations.
Comment 7:
Line 337. With CO2 do you mean CO2?
Indeed, thank you for pointing out that error, we will correct it.
Comment 8:
Table 2. Write what the abbreviations in the table mean in the table caption.
I am not sure what is meant here; the parameters are defined within the table, and the abbreviation DVS is defined in the table caption. To define the parameters (=abbreviations?) within the caption would decrease the readability. But we will restructure the table for clarity.
Comment 9:
Line 361. You never mentioned in the Materials and Method section that you performed a PCA. You have to explain that.
That is a valid point, we will add the following sentence in the methods section 3.6.2 : Regional Application: “To assess if and how spatial variability of climate and soil properties may explain spatial patterns in yield and irrigation water variability, we conduct a principal component analysis (PCA from FactoMineR, Husson et al. 2010, predictor variables scaled to unit variance before analysis).”
Comment 10:
Line 471. It is not clear that this sentence is about Porter et al study
We agree and edit the sentence for clarification = “Porter et al. (1999) evaluated the effect of enhancing SOC levels on potato yield and irrigation demand. During field trials in Maine in 1993-1995, they observed that organic matter content was increased by +1.2% due to cover cropping and organic amendments. While enhancing SOC alone did not make up for a lack of irrigation, potato yields could be improved significantly (Porter et al. 1999).”
Comment 11:
Line 545-546. Stagnated crop yields have not been mentioned earlier in the manuscript, I would include it earlier if you want to use it in the conclusion section.
Thank you for the comment, we agree and have eliminated this part of the sentence.
Citation: https://doi.org/10.5194/egusphere-2024-1201-AC2
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AC2: 'Reply on RC2', Malve Heinz, 13 Sep 2024
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