the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Dec 2024
Monitoring agricultural and economic drought: the Australian Agricultural Drought Indicators (AADI)
Abstract. Drought events can have significant agricultural and economic impacts, and in many parts of the world their intensity appears to be increasing with climate change. However, drought measurement remains a highly contested space, with a multitude of indicators across both research and operational settings. This article presents a new drought monitoring and forecasting system: the Australian Agricultural Drought Indicators (AADI). Rather than use common meteorological indicators, AADI attempts to estimate specific agricultural and economic drought impacts. An integrated bio-physical and economic modelling system is developed, which translates gridded climate observations and forecasts into outcome-based indicators of crop yields, pasture growth and farm business profits. These indicators are validated against a range of ground-truth data drawn from survey and administrative sources. Results confirm the benefits of the outcome-based approach with the AADI showing higher correlation with both agricultural (crop yield, livestock fertility) and economic outcomes (farm profits, regional incomes) compared with rainfall measures. The novel farm profit indicator also shows promise as a predictor of drought induced financial stress and flow-on socio-economic impacts.
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RC1: 'Comment on egusphere-2024-3731', Anonymous Referee #1, 12 Apr 2025
This article describes the Australian Agricultural Drought Indicators (AADI), a system for integrating several different types of data and modeling to predict outcomes of drought. Meteorological data is one component, along with models predicting crop and pasture yield. Of particular interest is the outcome-based forecast of farm profits. While a companion article focuses on forecast skill of AADI, this one provides an overview of the approach and how closely the component indicators are associated with key agricultural and economic outcomes. It is validated in contrast with rainfall percentiles.
The overview of AADI seems thorough and reasonably comprehensive, and the presentation of results is straightforward (although the figure numbering needs to be checked).
A key contribution of AADI is that by incorporating commodity prices along with climate data, it can help anticipate farmers’ increased need for assistance. It goes beyond crop and pasture yield predictions to anticipating what kind of experience farmers are having. This approach is highly practical and potentially more relevant than indicators based only on rainfall or combinations of biophysical data.
Although the authors say it is beyond the scope of this paper, the connection they found with broader demographic and socioeconomic outcomes is quite interesting and is well worth pursuing.
On the whole, this article is well-organized and clearly describes what may be a valuable advancement in combining drought and climate data with models of farm productivity and commodity prices to predict farm income, which has direct bearing on farmers’ need for assistance.
Minor:
On line 100, capitalize “Indicators” at the start of the sentence.
On lines 225 and 254, there are references to Figure 9 but I do not see a Figure 9. Should this be Figure 4?
On Tables 2 and 3, I suggest adding “with drought indicators” after “Correlation”
Line 286, there is a reference to Fig. 8, which I do not find in the manuscript.
Please check all Figure numbers.
Citation: https://doi.org/10.5194/egusphere-2024-3731-RC1 -
AC1: 'Reply on RC1', Neal Hughes, 26 May 2025
The comments from RC1 were favourable, no major issues were identified. RC1 noted a number of minor issues that will be addressed including checking all table and figure numbering.
Citation: https://doi.org/10.5194/egusphere-2024-3731-AC1
-
AC1: 'Reply on RC1', Neal Hughes, 26 May 2025
-
RC2: 'Comment on egusphere-2024-3731', Mirela-Adriana Anghelache, 23 May 2025
The authors describe a new system of indicators, Australian Drought Indicators, AADI, which comprises several data in order to monitor and forecast draught. One of the important benefits of this system is that of a predictor of drought induced financial stress and flow-on socio-economic impacts. The study also found some correlations between the indicators and demographic outcomes, linking drought in the case of Australia to lower birth rates, higher mortality and negative net migration Its outcomes apply for more detailed future research of the impacts of drought on socio-economic aspects. The article is well articulated, the method of analysis is clearly presented, the results of simulations and statistical correlations are presented in good images and detailed tables, the written English language is excellent.
Row 100: at the beginning of the phrase Indicators instead of indicators.
Row 144: SIMUlator instead of sIMUlator.
Citation: https://doi.org/10.5194/egusphere-2024-3731-RC2 -
AC2: 'Reply on RC2', Neal Hughes, 26 May 2025
RC2 was also favourable, with no major issues identified. We will address all minor issues raised by RC2.
Citation: https://doi.org/10.5194/egusphere-2024-3731-AC2
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AC2: 'Reply on RC2', Neal Hughes, 26 May 2025
-
AC3: 'Comment on egusphere-2024-3731', Neal Hughes, 26 May 2025
In addition to the comments received (from RC1 and RC2) we are expecting to make some small revisions to the paper based on internal feedback and improvements made to the analysis since the original submission.
The key change involves a slight adjustment to two of the benchmark variables used to compare our drought indicators against. Specifically for the farm profit indicators we have updated the analysis to compare against "farm rate of return (%)" defined as farm profit ($) / capital holdings ($), rather than just using profit ($) as used in the current paper. This is a subtle change and has limited effect on the conclusions. However, it reduces noise associated with changes in farm size (because rate of return is scale invariant) and so gives more accurate results.
For the pasture indicators we have updated the analysis to use fodder demand (i.e., quantity of livestock feed purchased per head of livestock) rather than livestock fertility (net birth rate). Internal discussion and testing have revealed this to be a better indicator of pasture condition within a given year, as livestock fertility is subject to longer time lags. With this benchmark the AADI pasture indicator performance is comparable to the alternative (TSDM) measure.
Combined these changes require us to update the numbers in Tables 2, and Table 3 (last three columns only). Figure 4 and Figure B (c and d only). All other tables and figures in the paper would be unchanged. These updates require limited changes to the text as the conclusions are much the same, accept for the discussion of the TSDM vs AADI pasture indicators in section 3.1 (paragraph 4).
Citation: https://doi.org/10.5194/egusphere-2024-3731-AC3
Status: closed
-
RC1: 'Comment on egusphere-2024-3731', Anonymous Referee #1, 12 Apr 2025
This article describes the Australian Agricultural Drought Indicators (AADI), a system for integrating several different types of data and modeling to predict outcomes of drought. Meteorological data is one component, along with models predicting crop and pasture yield. Of particular interest is the outcome-based forecast of farm profits. While a companion article focuses on forecast skill of AADI, this one provides an overview of the approach and how closely the component indicators are associated with key agricultural and economic outcomes. It is validated in contrast with rainfall percentiles.
The overview of AADI seems thorough and reasonably comprehensive, and the presentation of results is straightforward (although the figure numbering needs to be checked).
A key contribution of AADI is that by incorporating commodity prices along with climate data, it can help anticipate farmers’ increased need for assistance. It goes beyond crop and pasture yield predictions to anticipating what kind of experience farmers are having. This approach is highly practical and potentially more relevant than indicators based only on rainfall or combinations of biophysical data.
Although the authors say it is beyond the scope of this paper, the connection they found with broader demographic and socioeconomic outcomes is quite interesting and is well worth pursuing.
On the whole, this article is well-organized and clearly describes what may be a valuable advancement in combining drought and climate data with models of farm productivity and commodity prices to predict farm income, which has direct bearing on farmers’ need for assistance.
Minor:
On line 100, capitalize “Indicators” at the start of the sentence.
On lines 225 and 254, there are references to Figure 9 but I do not see a Figure 9. Should this be Figure 4?
On Tables 2 and 3, I suggest adding “with drought indicators” after “Correlation”
Line 286, there is a reference to Fig. 8, which I do not find in the manuscript.
Please check all Figure numbers.
Citation: https://doi.org/10.5194/egusphere-2024-3731-RC1 -
AC1: 'Reply on RC1', Neal Hughes, 26 May 2025
The comments from RC1 were favourable, no major issues were identified. RC1 noted a number of minor issues that will be addressed including checking all table and figure numbering.
Citation: https://doi.org/10.5194/egusphere-2024-3731-AC1
-
AC1: 'Reply on RC1', Neal Hughes, 26 May 2025
-
RC2: 'Comment on egusphere-2024-3731', Mirela-Adriana Anghelache, 23 May 2025
The authors describe a new system of indicators, Australian Drought Indicators, AADI, which comprises several data in order to monitor and forecast draught. One of the important benefits of this system is that of a predictor of drought induced financial stress and flow-on socio-economic impacts. The study also found some correlations between the indicators and demographic outcomes, linking drought in the case of Australia to lower birth rates, higher mortality and negative net migration Its outcomes apply for more detailed future research of the impacts of drought on socio-economic aspects. The article is well articulated, the method of analysis is clearly presented, the results of simulations and statistical correlations are presented in good images and detailed tables, the written English language is excellent.
Row 100: at the beginning of the phrase Indicators instead of indicators.
Row 144: SIMUlator instead of sIMUlator.
Citation: https://doi.org/10.5194/egusphere-2024-3731-RC2 -
AC2: 'Reply on RC2', Neal Hughes, 26 May 2025
RC2 was also favourable, with no major issues identified. We will address all minor issues raised by RC2.
Citation: https://doi.org/10.5194/egusphere-2024-3731-AC2
-
AC2: 'Reply on RC2', Neal Hughes, 26 May 2025
-
AC3: 'Comment on egusphere-2024-3731', Neal Hughes, 26 May 2025
In addition to the comments received (from RC1 and RC2) we are expecting to make some small revisions to the paper based on internal feedback and improvements made to the analysis since the original submission.
The key change involves a slight adjustment to two of the benchmark variables used to compare our drought indicators against. Specifically for the farm profit indicators we have updated the analysis to compare against "farm rate of return (%)" defined as farm profit ($) / capital holdings ($), rather than just using profit ($) as used in the current paper. This is a subtle change and has limited effect on the conclusions. However, it reduces noise associated with changes in farm size (because rate of return is scale invariant) and so gives more accurate results.
For the pasture indicators we have updated the analysis to use fodder demand (i.e., quantity of livestock feed purchased per head of livestock) rather than livestock fertility (net birth rate). Internal discussion and testing have revealed this to be a better indicator of pasture condition within a given year, as livestock fertility is subject to longer time lags. With this benchmark the AADI pasture indicator performance is comparable to the alternative (TSDM) measure.
Combined these changes require us to update the numbers in Tables 2, and Table 3 (last three columns only). Figure 4 and Figure B (c and d only). All other tables and figures in the paper would be unchanged. These updates require limited changes to the text as the conclusions are much the same, accept for the discussion of the TSDM vs AADI pasture indicators in section 3.1 (paragraph 4).
Citation: https://doi.org/10.5194/egusphere-2024-3731-AC3
Data sets
Australian Agricultural Drought Indicators (AADI) regional indicator evaluation data Neal Hughes https://data.mendeley.com/preview/8yhcr28wbk?a=8c0940ac-bad6-425e-b7c0-c8644f4289c5
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