the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Drought impact on productivity: Data informed process-based field-scale modeling of a pre-Alpine grassland region
Abstract. Grasslands are the basis for milk and meat production in alpine and pre-alpine regions, where climate warming is occurring twice as fast as in global average. Warmer and drier conditions have been found to lead to versatile effects on grassland productivity and yields depending on pedo-climatic conditions. Experimentally, it has been discovered that higher and cooler elevations benefit from warming in the absence of drought, whereas lower elevations are more vulnerable to yield losses under climate change. These findings are based on sites covering only a few discrete climatic, soil, and management conditions. This limitation is overcome in the present study, where we compiled a highly detailed field-scale dataset including cutting dates (2018–2020) from remote sensing, informing regional grassland management routines of the biogeochemical model LandscapeDNDC which was applied in the pre-Alpine Ammer region (530 m a.s.l to 2200 m a.s.l., 4600 km2) in southern Germany. The strongest predictor of yields was the management intensity with an average yield increase of 1.2 t ha-1 a-1 per additional cut and associated manure application. At the regional scale for 3- and 5-cut fields, yields decrease on average with increasing elevation by up to 0.4 t ha-1 a-1 per 100 m. We found a mean regional yield decrease of 4 % in the drought year 2018 compared to the year 2020 with average climatic conditions. In addition, due to support of mineralization, soil organic carbon had a positive effect on yields, especially in drier years. Yield increases of 0.09 to 0.22 t ha-1 a-1 per % increase in soil organic carbon were observed. Our results illustrate the complex interactions between management, soil, and climate factors influencing grassland yields, including differences in their importance in drought and non-drought years.
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RC1: 'Comment on egusphere-2024-2864', Anonymous Referee #1, 24 Nov 2024
The present study describes simulations of grassland yields in the pre-Alpine region of Southern Germany under different management conditions and one drought year. The study uses the established model LDNDC together with a wealth of local and regional data and concludes that management, in particular number of cuts impacts grass yield and that drought reduces biomass production.
I found the paper to be very narrow in focus and to rely heavily on knowledge of how LDNDC works and the particulars of grassland management in Bavaria. The title and aim sound very promising and the drought impact on grass yield is critical in multiple regions of the world. However, the paper goes on to discuss aspects like number of cuts in detail, with only a fraction of the analysis focusing on drought effects. I would be interested to see a more in depth analysis of the variation in drought impact and whether management has any impact on how affected by drought yields are. It would also be useful to include a discussion of the generality and portability of this study, especially given that the study area must be one of the regions with the highest data availability in the world.
The underlying research questions around drought and the model simulations in themselves are a good fit for Biogeosciences but in the current form, the paper would find a better form in a more agricultural or even more regional journal.
Specific comments
Abstract the text seems to mainly discuss the effects of biomass harvest, with passing mention of drought effect, contrasting to the title
Section 31, figure 2 It would be good to have an estimate of whether the model performs well compared to observations in the dry year, to lend confidence to the further discussions
Section 3.4 It would help to have some sort of measure of whether differences are significant
Figure captions – please extend the text to be more descriptive of that is the figures
Section 3.5 I do not fully understand the logic behind a corelative analysis of model simulations. Surely since LDNDC is a process-based model, internal drivers can be illustrated based on model output variables and process understanding
Data availability – I believe that the Copernicus policy requires all data and code to be open
Minor comments
L50 As well as translocation experiments, there are also an increasing number of rainfall exclusion experiments which do not suffer from any of the caveats discussed here
L103 this paragraph detailing previous uses of LDNDC belongs more in the introduction than the model description
L117 a 2 year spinup seems extremely short for e.g. soil C and N stocks, what was the reasoning behind this?
L160 what is the spatial resolution for the regional simulation?
L161 is there a reference for this data?
L181 a brief description of how cutting dates were derived would be helpful
L242 why hexagons?
L319 why do MAT and MAP need to be aggregated by field area? Especially the temperature
Figure 6 is this averaged over all fields or averaged over all cuts?
Citation: https://doi.org/10.5194/egusphere-2024-2864-RC1 -
RC2: 'Comment on egusphere-2024-2864', Anonymous Referee #2, 11 Dec 2024
General comments
The title of the study sets a high expectation for drought impact on grassland productivity in the pre-Alpine region of Southern Germany. This was also expressed in the stated main objective, which aims to link the modeled yield to the environmental parameters. Overall, the study seems to be lacking and does not meet the title and objective. Focus was mostly given to the model evaluation, rather than equally explaining the soil, climate, and management factors.
The study also needs to further justify the use of LandscapeDNDC. Are there existing similar and related models that can provide the same outputs? Similarly, the study can further give highlights to the Sentinel-2 extracted cutting dates. How is this better than using SAR-based, or SAR-and-MSI extraction methods for cutting dates? The ability to generate the grassland management information for a large-scale study is indeed significant.
Specific comments
In relation again to the title, the study could have quantified the drought events with indices such as SPIE or SPI. For instance, the opportunity to provide more information about drought can allow for a better visualization of the impacts in 2018. How does the study define drought? Is it simple related to temperature? Drought is a continuous phenomenon that ignores a defined border of years. Certain drought events may start in year 1, and end in year 2. Instead of annual assessment, would a seasonal assessment provide more realistic results? Such is the argument with the increased spatial resolution. Simply identifying 2018 as a drought year limits the degree of comparison with 2019, and 2020.
The study has the potential to show the influence of various factors and the importance of incorporating grassland management when determining drought impact. It should maximize the available data and add information. For example, the time of cuts can also be determined. The harvest of biomass in grasslands is related to the optimum growth of the vegetation, the time of harvest may reflect adaptive practices (management) by farmers.
The environmental factors and results can be summarized in table forms. These can show what were all the considered factors, sources, and resolution.
Technical corrections and minor comments
These are some observed writing concerns.
Line 37 missing sentence or phrase. Or the need to remove of parenthesis for in-text citations that are part of the sentence.
Line 54 Missing year of cited study by De Boeck et al.
Line 55 Consistency with the use of space between values and units of measurement. Some lack space, others have space.
Line 93 For the n=28202; how high is the spatial resolution as compared to other European scale studies?
Line 175 The table tile on top of the table.
Line 238 missing word or phrase
Line 330 Missing figure number
Line 346 In-text citation before the presentation of the figure
Consistency, for some parts the corresponding letters were written before the data (Line 317); while for others these were written after (Line 343)
Line 320 Why were hexagons used? What are the unit of the other input values? For instance, Sentinel 2 pixels are in squares.
Line 374 The decreasing trend in mean yield in 2018 may also be related to the optimal vegetation growth. The timing of cuts might be relevant.
Line 436 It is believed that the basic statistical assumptions were tested. Maybe results can be provided as supplementary material or appendix.
Line 440 For the multiple correlation, why was Principal Component Analysis (PCA) or its equivalent not utilized? It is better to show how all parameters were related.
A number of results were not included in the paper, maybe these can be included as supplementary materials or in the appendix.
Line 478 I commend the possible inclusion of plant functional traits.
Line 580 Review the sentence, “Another reasons” – very minor
Line 620 Use of “a” and “an” – very minor
Citation: https://doi.org/10.5194/egusphere-2024-2864-RC2
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