the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
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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
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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