Tree growth and macrofauna colonization in Technosols constructed from recycled urban wastes - Institut Agro Dijon
Journal Articles Ecological Engineering Year : 2020

Tree growth and macrofauna colonization in Technosols constructed from recycled urban wastes

Abstract

Urban greening is a growing societal demand but consumes large amounts of soil. This massive transfer of soil, typically imported from peri-urban and rural areas, raises questions about the environmental sustainability of such projects. It has been suggested that artificial soils made with urban wastes, also called constructed Technosols, might be a sustainable alternative. In this article, we examined during three years, different mixtures of excavated deep horizons of soil, crushed concrete and green waste compost, in order to (i) identify the most suitable mixture for growing trees; (ii) identify tolerant tree species among six different species; and (iii) assess macrofaunal colonization, a major driver of soil fertility, from the surrounding macrofaunal pool. The mixture of excavated deep horizons and green waste compost led to the highest tree mortality. The best tree survival and growth, and quickest soil macrofaunal colonization were obtained with a mixture of 20% of excavated deep horizons, 10% of green waste compost and 70% of crushed concrete (v/v). The survival rate of species Acer campestre and Prunus avium was 100% but only 58% for Carpinus betulus. Our results show the construction of Technosols with urban wastes is a promising alternative for planting trees and hosting soil biodiversity within cities.
Fichier principal
Vignette du fichier
ECOENG_105886_session_report.pdf (1.03 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-02887656 , version 1 (10-11-2020)

Identifiers

Cite

Charlotte Pruvost, Jérôme Mathieu, Naoise Nunan, Agnès Gigon, Anne Pando, et al.. Tree growth and macrofauna colonization in Technosols constructed from recycled urban wastes. Ecological Engineering, 2020, 153, pp.105886. ⟨10.1016/j.ecoleng.2020.105886⟩. ⟨hal-02887656⟩
212 View
322 Download

Altmetric

Share

More