2008
DOI: 10.3354/cr00726
|View full text |Cite
|
Sign up to set email alerts
|

Drought stress patterns in Italy using agro-climatic indicators

Abstract: This paper examines the drought patterns in Italy for the period from 1961 to 2006. The condition of drought was considered from an agricultural perspective, using the variability in the annual yield of maize as an indication of climate patterns. A procedure is presented where weather datasets, a drought stress index (MCDI: Mediterranean crop drought index) and geo-spatial analysis are used to assess the spatial distribution and the general evolution of drought. The regional perspective presented characterizes… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
12
0

Year Published

2010
2010
2020
2020

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 26 publications
(12 citation statements)
references
References 45 publications
0
12
0
Order By: Relevance
“…Vasiliades and Loukas 2009), indices related to soil moisture (Vidal et al 2012) and groundwater indices (Mendicino et al 2008), have also been used. Agricultural drought indices are also prevalent in the Mediterranean literature (Diodato and Bellocchi 2008).…”
Section: Drought Studiesmentioning
confidence: 99%
“…Vasiliades and Loukas 2009), indices related to soil moisture (Vidal et al 2012) and groundwater indices (Mendicino et al 2008), have also been used. Agricultural drought indices are also prevalent in the Mediterranean literature (Diodato and Bellocchi 2008).…”
Section: Drought Studiesmentioning
confidence: 99%
“…In the short term, the consequence of drought periods or trends is a decline in crop yield (Diodato and Bellocchi, 2008) while a long term consequence is a decrease in water resources. The over-exploitation of agricultural lands in semi-arid regions calls for accurate assessment of the moisture status of the soil.…”
mentioning
confidence: 99%
“…Structural analysis (variography) assesses as to whether and to which extent an attribute is structured over the territory. The ability to identify the true spatial variability of a dataset depends, to a great extent, on ancillary knowledge of the underlying measured phenomenon (Diodato and Bellocchi 2008). Data may often be available for more than one attribute per sampled location.…”
Section: The Model For Ordinary Kriging and Cokrigingmentioning
confidence: 99%