Multivariate analysis reveals spatial variability of soil geochemical signals in the area of a medieval manorial farm
•Relicts of medieval landscape were identified using LiDAR survey.•Compositional analysis of geochemical data enabled to determine anthropogenic activities.•Medieval impact in a marginal landscape was connected to agricultural and industrial activities.•Past human activities can best be understood b...
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Veröffentlicht in: | Catena (Giessen) 2023-01, Vol.220, p.106726, Article 106726 |
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Sprache: | eng |
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Zusammenfassung: | •Relicts of medieval landscape were identified using LiDAR survey.•Compositional analysis of geochemical data enabled to determine anthropogenic activities.•Medieval impact in a marginal landscape was connected to agricultural and industrial activities.•Past human activities can best be understood by linking archaeology and the soil sciences.
This study presents the results of a multi-element and multivariate geochemical analysis of a deserted medieval settlement and its agricultural landscape, including a monastic (Cistercian) manorial farm, situated in the Czech Republic. We used LiDAR survey and historic maps to detect relict landscape features and identify past changes in land use-patterns. Comparing archaeological evidence and soil chemistry, we found that geochemical signals - derived from samples collected in the courtyard of the farm and in the surrounding area and processed by PCA on log-transformed and isometrically log-transformed data - responded to different agricultural activities, which could be linked to the farm and - more broadly - to current and past land-uses. The results generally illuminate the environmental impact of monastic settlement in a marginal landscape, with regard to different economic activities, and the study also demonstrates that this type of analysis allows tracing anthropogenic and natural phenomena over a wider area, beyond the limits of the more narrowly defined archaeological site. |
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ISSN: | 0341-8162 1872-6887 |
DOI: | 10.1016/j.catena.2022.106726 |