Copper and zinc as a window to past agricultural land-use

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • G. Genova - , Free University of Bozen-Bolzano, EURAC Research (Author)
  • S. Della Chiesa - , Chair of Geoinformatics (Author)
  • T. Mimmo - , Free University of Bozen-Bolzano (Author)
  • L. Borruso - , Free University of Bozen-Bolzano (Author)
  • S. Cesco - , Free University of Bozen-Bolzano (Author)
  • E. Tasser - , EURAC Research (Author)
  • A. Matteazzi - , Laimburg Research Centre (Author)
  • G. Niedrist - , EURAC Research (Author)

Abstract

Intensive agricultural management significantly affects soil chemical properties. Such impacts, depending on the intensity of agronomic practices, might persist for several decades. We tested how current soil properties, especially heavy metal concentrations, reflect the land-use history over a 24,000 ha area dominated by intensive apple orchards and viticulture (South Tyrol, ITA). We combined georeferenced soil analyses with land-use maps from 1850 to 2010 in a space-for-time approach to detect the accumulation rates of copper and zinc and understand how present-day soil heavy metal concentrations reflect land-use history. Soils under vineyards since the 1850s showed the highest available copper concentration (median of 314.0 mg kg-1, accumulation rate between 19.4 and 41.3 mg kg-1·10 y-1). Zinc reached the highest concentration in the same land-use type (median of 32.5 mg kg-1, accumulation rate between 1.8 and 4.4 mg kg-1·10 y-1). Using a random forest approach on 44,132 soil samples, we extrapolated land-use history on the permanent crop area of the region, reaching an accuracy of 0.72. This suggests that combining current soil analysis, historical management information, and machine learning models provides a valuable tool to predict land-use history and understand management legacies.

Details

Original languageEnglish
Article number126631
JournalJournal of hazardous materials
Volume424
Publication statusPublished - 15 Feb 2022
Peer-reviewedYes

External IDs

PubMed 34334215

Keywords

Sustainable Development Goals

Keywords

  • Chronosequence, Heavy metals, Land-use history, Machine learning, Predictive model