Dynamics of agricultural land systems in western Mediterranean areas: a clustering approach based on the self-organizing map

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Ressource 1Download: 2023, IJoA_Rabelo et al..pdf (1359.64 [Ko])
State: Public
Version: Final published version
License: CC BY-NC 4.0
Serval ID
serval:BIB_25159F601F60
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Dynamics of agricultural land systems in western Mediterranean areas: a clustering approach based on the self-organizing map
Journal
Italian Journal of Agronomy
Author(s)
Rabelo Marya Cristina, Tonini Marj, Silvestri Nicola
ISSN
2039-6805
1125-4718
Publication state
Published
Issued date
18/10/2023
Peer-reviewed
Oui
Volume
18
Number
3
Language
english
Abstract
In the present study, we implemented an unsupervised learning procedure, a self-organizing map (SOM), for characterizing the main agricultural land systems (ALS) in western Mediterranean areas. Input data derived from national agricultural censuses of two periods (2000 and 2010) at the municipality level. The SOM allowed us to aggregate the items into clusters based on the proximity between the associated input variables. The main clusters were then mapped back to the geographical space and interpreted in terms of ASL typologies. The main ALS from the census 2000 included one permanent grassland system with extensive farming; two arable land systems, corresponding to winter and summer crops; and two permanent cropland systems, relatable to intensively cultivated or marginal areas. The ALS from the census 2010 included only one arable land system with a non-intensive use of irrigation; two permanent cropland systems similar to those found in 2000; one more extensive permanent grassland system; and a mixed system characterized by permanent grassland and arable land. In summary, the main trends emerging from the transitions between the two censuses periods were: i) a reduction in agricultural land use; ii) an increase in utilized agricultural and irrigated area; iii) a contraction in arable land and permanent grassland. Using a data-driven approach such as SOM allowed us to discover hidden patterns in the input census data. Therefore, the prevalent agricultural typologies characterising the ALS in the two analysed periods resulted to be shaped by the reality of the surveyed area solely, with regard to its agronomic assessment.
Keywords
Unsupervised learning, agricultural censuses, land-use, land-cover, change analysis, clustering
Web of science
Open Access
Yes
Create date
07/11/2023 17:28
Last modification date
10/02/2024 7:18
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