Reconstructing Italy’s rural landscape before the Great Acceleration: A geospatial baseline from the Catasto Agrario (1929)
Résumé fourni par la source
This paper presents a geospatial dataset detailing the rural landscape of Italy in the 1920s, based on the Catasto Agrario 1929 survey. The dataset integrates data from the survey into a Geographic Information System (GIS), providing insights into land use and land cover (LULC), demographic characteristics, livestock distribution, crop yields, and precipitation patterns. Historical data have been digitised using Optical Character Recognition (OCR) and organised into a vector format, capturing the administrative boundaries of Italy's provinces as they were in the 1920s. By documenting Italy's rural landscape just before the onset of the Great Acceleration (ca. mid-20th century CE) the dataset offers a critical historical baseline for analysing long-term socio-environmental transformations. The research aims to facilitate future studies on the environmental impacts of Italy's rural transitions, offering an open-access resource that enables comparisons between past and present landscapes. It highlights the role of traditional agricultural practices, such as agroforestry, which were widespread before the shift towards modern monoculture systems. This dataset holds potential for applications in environmental sciences, historical geography, and heritage studies, providing a foundation for exploring sustainable agricultural practices and the enduring effects of rural depopulation and land-use change.
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Contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Reconstructing Italy’s rural landscape before the Great Acceleration: A geospatial baseline from the Catasto Agrario (1929)
- Date Crossref
- 27/06/2025
- Éditeur
- F1000 Research Ltd
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.
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