Integrating biological indicators in a Soil Monitoring Network (SMN) to improve soil quality diagnosis – a study case in Southern Belgium (Wallonia)

Inken Krüger,Caroline Chartin,Bas Van Wesemael,Sandrine Malchair,Monique Carnol

doi:10.25518/1780-4507.13482

Abstract

Description of the subject. Soil organisms and their activities are essential for soil ecosystem functioning and they can thus be used as pertinent indicators of soil quality. Recent efforts have been undertaken to include biological indicators of soil quality into regional/national monitoring networks. Objectives. The aim of this study was to provide a first dataset of six biological indicators and two eco-physiological quotients for two landscape units in Wallonia. These spatial units are characterized by homogeneous climate conditions, soil type, land-use and management (here, grasslands in the Ardennes, and croplands in the Loam Region). Method. Respiration potential, microbial biomass carbon and nitrogen, net nitrogen mineralization, metabolic potential of soil bacteria and earthworm abundance were measured at a total of 60 sites in two different landscape units (LSU). Variability within each LSU was studied. Data was synthesized through calculation of a comprehensive score and presentation as radar plots. Results. All selected biological indicators were significantly higher under grassland than under cropland soils, highlighting the biological indicators’ power of discrimination between main land use types. Variability within LSU depended on the biological indicator and was generally higher in grassland than in cropland soils. Each site could unambiguously be assigned to its landscape unit based on its calculated comprehensive score. Radar plots allowed an assessment of the distribution of values within a landscape unit at a glance. Conclusions. The pilot-study defined the first baseline values for agricultural soils in Wallonia and laid the foundation for a monitoring network of biological soil quality.

Highlights

Soil quality has been defined as “the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation” (Karlen et al, 1997)
Both tools might be adapted to specific questions, by including or excluding different biological indicators or attributing a different weight to individual biological indicators. Both tools are useful for communicating results to stakeholders through the creation of maps and understandable graphics of soil quality. This pilot-study sets the foundation for the establishment of a monitoring network of biological soil quality in Wallonia
This first dataset for six biological indicators, respiration potential, microbial biomass (Cmic and microbial biomass nitrogen normal operating range (NOR) (Nmic)), net nitrogen mineralization, metabolic potential of soil bacteria and earthworm abundance, demonstrates their power of discrimination between two main landscape units” (LSU)

Summary

Introduction

Soil quality has been defined as “the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation” (Karlen et al, 1997). For an early detection of soil degradation at landscape or regional scale and to counteract the decline of soil functions, Soil Monitoring Networks (SMN) are required (Arrouays et al, 2012). Soil characteristics and functions are measured with physical, chemical and biological indicators. Physical and chemical indicators, such as soil pH, particle size distribution, element contents, and bulk density, have been recorded in monitoring networks for decades. Soil Organic Carbon (SOC) is widely accepted as the most important chemical indicator of soil quality. An increase in SOC stocks is considered to be a potential mechanism for climate change mitigation (Smith et al, 2008). At least a decade is required to detect changes in SOC stocks (Smith, 2004; Saby et al, 2008; Schrumpf et al, 2011)

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Conclusion