the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Aug 2024
Measuring extremes-driven direct biophysical impacts in agricultural drought damages
Abstract. Assessing the economic implications of droughts has become increasingly important due to their substantial impacts on agriculture. Existing empirical analyses for drought damages are often conducted on a national scale without spatially distributed data, which might bias estimates. Furthermore, the cumulative effects of multiple weather extremes, such as heat or preceded frost co-occurring with drought, are often overlooked. Measuring the direct biophysical impacts of such extremes on agriculture is essential for more precise risk assessment. This study presents a comprehensive economic impact assessment framework to measure the cumulative damages of droughts and other hydro-meteorological extremes on agriculture, focusing on eight major field crops in Germany. By utilizing a statistical yield model, we isolate the effects of multiple extremes on crop yields from other influencing factors (such as pests & diseases, farm management) and analyze their contribution to farm revenue losses during droughts at the district level from 2016–2022. Our findings indicate that the average annual direct biophysical damage caused by extremes under drought conditions during this period amounts to € 781 million across Germany. The study also reveals that biophysical impacts of extremes alone account for 60 % of reported revenue damages during widespread drought years. For maize, direct biophysical damage explains up to 97 % (2018) of revenue losses. Additionally, comparison of national-level damage estimates using aggregated and spatially disaggregated data shows that the aggregated data matches overall results, but diverges for maize and wheat, highlighting the importance of spatially distributed damage assessment. In this paper, we provide detailed estimates of extremes-driven direct biophysical damages at the district level, offering a high-resolution understanding of the spatial and temporal variability of these impacts. Assessing the extent of revenue losses resulting from these extremes alone can provide valuable insights for the development of effective drought mitigation programs and guide policy planning at local and national levels to enhance the resilience of the agricultural sector against future climate extremes.
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Status: open (until 17 Nov 2024)
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RC1: 'Comment on egusphere-2024-2585', Thomas Slijper, 10 Sep 2024
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This paper explores the economic impacts of multiple climate extremes, focusing on droughts, by estimating revenue changes. The economic damage is defined as the difference between expected and actual revenues. Using a counterfactual that compares expected revenues to realised revenues under drought conditions, the economic impact of droughts is estimated. The topic is timely and relevant to the journal, but I would like to offer a few suggestions that I believe are important to take on board.
One potential concern is the definition of economic impact as the difference between realized and expected revenues. This approach means that a significant portion of the estimated economic impact depends on how expected revenues are defined. You base the counterfactual (expected revenues) on past non-drought revenues within the same region. I am uncertain if this is the best approach, and we might have taken different directions here. To address this, a clear justification for your counterfactual is needed, likely supported by robustness checks to show how results might change with different counterfactuals. Additionally, I would like to discuss (i) your definition of economic impacts and (ii) whether droughts and climate extremes are best measured dichotomously or continuously. These three points form the basis of my general comments. I have also provided a few minor suggestions and textual edits below.
General comments
- My main suggestion is to reconsider your counterfactual and clarify what it is actually measuring. How do you accurately estimate the expected revenues? What is the counterfactual representing? Defining a reliable counterfactual is critical because the economic impacts in your paper are defined as the difference between observed and expected revenues. Currently, you define expected revenues as the average revenues over the past five non-drought years. However, I am uncertain about whether this counterfactual is consistently measuring the same expectations across regions, especially since no other observable factors are considered. As noted in lines 156-173, the counterfactual seems somewhat arbitrarily defined.
For example, consider two regions where neither has experienced a "normal" year during the reference period. Region 1 has had consecutive slightly wet years, while region 2 has had five consecutive slightly dry years (though not extreme). Consequently, your expected revenues for region 1 are based on slightly wet conditions, while for region 2, they reflect slightly dry conditions. As a result, the estimated economic impact of droughts is now being benchmarked against two different baselines, which could affect the accuracy of your estimates.
Then, a second objective of the paper is to investigate the economic impacts of the interplay of droughts and extreme weather events. I do not yet see how this is reflected in your current counterfactual, as those extreme weather events are not considered when you define your counterfactual. The implications of this are that the expected revenues do not consider any past exposure to other extreme weather events, making me wonder how accurate your economic impact estimates are.
One way forward to convince me that your counterfactual is measuring what it intends to measure is to include robustness checks, with different counterfactual definitions (e.g., using shorter or longer reference periods, or incorporating multiple extreme weather events). Alternatively, you could consider defining your counterfactual based on matching or regression-based approaches, which allows you to account for observable characteristics such as the severity of drought (using continuous measures like soil moisture index), crop types, or land area. It would also be useful to indicate how much of the estimated economic impact is driven by the occurrence of droughts versus changes in the expected revenues themselves (i.e. how do your results change when defining different counterfactuals?) - Are you truly estimating the economic impact of droughts? Your analysis focuses on changes in revenues, but it does not account for changes in costs (e.g. inputs, intermediates etc.). I could live with damage but feel like you are not estimating economic impacts.
- Are droughts something to be measured dichotomously? Same for the extreme weather events. There seems to be a slight mismatch between the research gap you identify and your approach in practice. For example, in lines 43-45, you describe the research gap as focusing on the variability and intensity of droughts. This suggests a continuous definition, where drought ranges from slightly dry to extremely dry conditions. However, if I understand correctly, in your paper droughts are defined dichotomously—either present or absent. The same issue arises in lines 58-59. Is the research gap you have identified (regarding the variability of droughts and extreme weather) truly being addressed by your current approach?
Specific suggestions
- I have read your introduction but couldn’t identify the aim of the paper. I could be wrong here but my feeling is that lines 66-70 intend to do this. It is a little vague and would help me if you make this more concrete. I am looking for a sentence like “The aim of this paper is to….” or “This paper addresses the question….”
- Lines 83-85: Perhaps you could consider adding some studies on farm-level economic damage to be complete. There is a lot of ongoing work here on adaptation literature but also on estimating drought damage on the farm level.
- Figure 1: I printed your manuscript in black and white and could not see any colour differences. Consider changing the colours or thinking of some other way to underline what is a biophysical process and what is an economic process.
Citation: https://doi.org/10.5194/egusphere-2024-2585-RC1 - My main suggestion is to reconsider your counterfactual and clarify what it is actually measuring. How do you accurately estimate the expected revenues? What is the counterfactual representing? Defining a reliable counterfactual is critical because the economic impacts in your paper are defined as the difference between observed and expected revenues. Currently, you define expected revenues as the average revenues over the past five non-drought years. However, I am uncertain about whether this counterfactual is consistently measuring the same expectations across regions, especially since no other observable factors are considered. As noted in lines 156-173, the counterfactual seems somewhat arbitrarily defined.
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RC2: 'Comment on egusphere-2024-2585', Anonymous Referee #2, 07 Nov 2024
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2585/egusphere-2024-2585-RC2-supplement.pdf
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