Crops' exposure, sensitivity and adaptive capacity to drought occurrence
<p>In the context of sustainable agricultural management, drought monitoring plays a crucial role in assessing the vulnerability of agriculture to drought occurrence. Drought events are very frequent in the Iberian Peninsula (and in Portugal in particular), and an increase in frequency of thes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-12-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://www.nat-hazards-earth-syst-sci.net/19/2727/2019/nhess-19-2727-2019.pdf |
| Summary: | <p>In the context of sustainable agricultural management,
drought monitoring plays a crucial role in assessing the vulnerability of
agriculture to drought occurrence. Drought events are very frequent in the
Iberian Peninsula (and in Portugal in particular), and an increase in
frequency of these extreme events are expected in a very near future.
Therefore, the quantitative assessment of the natural-ecosystem
vulnerability to drought is still very challenging, mainly due to the
difficulties of having a common definition of vulnerability. Consequently,
several methods have been proposed to assess agricultural vulnerability. In
this work, a principal component analysis (PCA) was performed based on the
components which characterize the exposure, sensitivity and adaptive
capacity of the agricultural system to drought events with the aim of
generating maps of vulnerability of agriculture to drought in Portugal.
Several datasets were used to describe these components, namely drought
indicators, vegetation indices and soil characterization variables. A
comparison between the PCA-based method and a variance method using the same
indicators was performed. Results show that both methods identify Minho and
Alentejo as regions of low and extreme vulnerability, respectively. The
results are very similar between the two methods, with small differences in
certain vulnerability classes. However, the PCA method has some advantages
over the variance method, namely the ability to identify the sign of the
indicators, not having to use the indicator–component subjective
relationship, and not needing to calculate weights. Furthermore, the PCA method is fully
statistical and presents results according to prior knowledge of the
region and the data used.</p> |
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| ISSN: | 1561-8633 1684-9981 |