4.7 Article

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

期刊

JOURNAL OF HAZARDOUS MATERIALS
卷 424, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.jhazmat.2021.126631

关键词

Land-use history; Heavy metals; Chronosequence; Machine learning; Predictive model

资金

  1. Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano, Italy
  2. Department of Innovation, Research and University of the Autonomous Province of Bozen/Bolzano

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The study found that intensive agricultural management significantly affects soil chemical properties, especially heavy metal concentrations in areas such as apple orchards and vineyards. By combining soil analysis, historical management information, and machine learning models, it is possible to accurately predict land-use history and understand management legacies.
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.

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