|Main authors:||Giulia Bongiorno, Else Bünemann, Ron de Goede, Lijbert Brussaard, Paul Mäder|
|iSQAPERiS editor:||Jane Brandt|
|Source document:||Bongiorno G., Bünemann E., de Goede R., Brussaard L., Mäder P. (2018) Screening of novel soil quality indicators. iSQAPER Project Deliverable 3.4, 66 pp|
|1. Assessment of soil quality|
|2. Experimental sites|
|3. Sampling procedure and handling|
|4. Novel soil quality indicators and their validation|
Agricultural soils sustain a wide range of functions: water cycling, soil aggregation, humification and decomposition, population regulation, habitat provision, and nutrient cycling (Brussaard, 2012; Kibblewhite et al., 2008). Soils are multifunctional in that they can provide multiple functions and ecosystem services (ES) simultaneously. However, soil degradation, which is a common problem in agricultural soil, can hamper the multi-functionality of the soil (Vitousek et al., 1997). Every year 12 Mha of agricultural land are degraded and/or lost (Rickson et al., 2015). This occurs through human-induced or natural soil threats: loss of organic matter, erosion, contamination, landslides, sealing, salinization, and compaction (Glæsner et al., 2014). In this context, the assessment of the impact of agricultural management on soil quality has become increasingly important in the last decades. Soil quality is defined as the capacity of a soil to perform different functions (Karlen, 1997). Soil quality can be measured indirectly, through soil chemical, physical and biological parameters that together determine soil function and the provision of ecosystem services (defined as “soil quality indicators”). Soil quality indicators should be easily measurable, inexpensive, reproducible, highly sensitive towards soil management and threats, and well correlated with soil functions (Larson, 1994).
Modern techniques developed in the last decades, especially in the field of biology, could improve soil quality assessment. These techniques have the potential to explore more efficiently the association between soil parameters and soil functions with the final aim to derive informative and cost-effective indicators. Novel techniques include fractionation techniques to assess the quality of organic carbon, molecular identification of faunal and microbial communities, and soil disease suppressiveness bioassays. Biological and biochemical parameters have a primary role in soil quality assessments because soil organisms are associated with the provision of multiple ecosystem services and they are in general found to be more quickly influenced by agricultural management than most chemical or physical parameters (Bünemann et al., 2018). Before these novel soil quality indicator can be implemented in soil quality assessment, their actual/practical suitability and their potential benefit and limitations need to be assessed. In this section of iSQAPERiS we screen and evaluate a range of newly developed indicators of soil quality in a selection of agricultural long-term field experiments in Europe.
Ten European long-term field experiments (LTEs) with either arable or permanent crops and a minimum duration of 5 years were selected for the study (Figure 1), covering five different pedoclimatic zones: Dfb and Dfc (continental climate with cold winters and warm summer without a dry season, or with cold winters and temperate summers without a dry season, respectively), Cfb and Csb (temperate climate with warm summer with or without dry season, respectively) and Bsk (arid cold steppe climate) (Köppen, 1918). Also, we covered different soil types (Vertic Cambisol, Haplic Luvisol, Fluvisol, Gleyic Podzol, Eutric Gleysol, and Eutric Cambisol (WRB, 2014).
Each site had unique management characteristics, but the main agricultural practices studied can be simplified as tillage (T) and organic matter addition (OM) (Figure 1). The comparison of farming systems (organic or integrated versus conventional) studied in three LTEs (CH3, ES4 and NL2) was allocated to the factor OM addition, even though the treatments differed in other aspects as well (e.g. pesticides input). The contrast in tillage was categorised in conventional tillage (ploughing to 20-25 cm depth, CT) versus reduced tillage (tillage to 0-10 cm, RT). The level in OM input was categorised as low organic matter input (LOW, no organic matter additions or only mineral fertilization) versus high organic matter input (HIGH, organic matter addition or organic matter addition plus mineral fertilizer). At some sites, both treatment factors (i.e. tillage and organic matter addition) were implemented and at other sites only one of them (Figure 1). The layout of the experiments followed different designs, including complete randomized block and split plot design, and per treatment 3 or 4 replicates were present. Most LTEs had arable crop rotations, but two LTEs in drier climates were on grapes as permanent crops.
In total, 167 soil samples were collected in spring 2016 before any major soil management was applied to the fields. Each sample comprised 20 soil cores, randomly collected in the central area of the plot to avoid border effects. In the trials with tillage as management factor, samples were taken from two depths: 0-10 cm and 10-20 cm. In the trials with organic matter input as the only management factor, samples were taken from the 0-20 cm layer. Soils of the 20 cores were bulked to one composite sample and thoroughly mixed for homogenisation.
Fresh soil samples were sent to Wageningen University (The Netherlands), Research Institute of Organic Agriculture (Frick, (Switzerland), University of Trier (Germany) and University Miguel Hernandez (Alicante, Spain), and air-dried samples were sent to University of Ljubljana (Slovenia) shortly after collection. Upon arrival, fresh samples were sieved at 5 mm and stored at 3˚C. The samples were used for measuring chemical, physical and biological parameters. All the analyses were performed within 6 months after sampling. A part of the samples was subsequently air-dried for POXC analysis.
The novel soil quality indicators were selected after an extensive review of the literature carried out in the first six months of the PhD project of Giulia Bongiorno. This literature study contributed also to the literature review on soil quality from Bünemann et al. (2018). We decided to screen different promising soil parameters with the potential of becoming novel soil quality indicators. Soil organic matter is one of the most important soil quality indicators, however its changes and its functional characteristics are cumbersome to assess. Labile organic carbon fractions give an indication of soil organic matter quality and have been shown to respond rapidly to soil management, and to be linked to microbial characteristics, making it an interesting potential soil quality indicators. Another important aspect of soil quality is the capacity of the soil to suppress pathogens. However, there is no consensus yet about which soil parameters could be used to indicate soil suppressiveness. Our experimental design offered the opportunity to test which soil chemical, physical and chemical parameters are more important for the explanation of soil suppressiveness. Novel molecular tools are becoming cheap and fast methods to determine quantity of taxonomic or functional genes (qPCR), and community composition (sequencing). Faunal and microbial community structure studied with detailed molecular methods also offer a promising tool for the assessment of groups of organisms or taxa which can be used to indicate soil management impacts on soil functions. In addition, because of the important role of microorganisms in soil functions, non- molecular microbial parameters which look into taxonomy (PLFA) and/ or function (community level physiological profiling, enzyme activity) can have the potential to be included in soil quality assessments.
The novel soil quality indicators that were measured in the ten European long term field experiments were as follows:
- Labile organic carbon fractions: hydrophilic dissolved organic carbon (Hy), dissolved organic carbon (DOC), permanganate oxidizable carbon (POXC), hot water extractable carbon (HWEC), and particulate organic matter carbon (POM-C) (»Labile carbon fractions as soil quality indicators);
- Soil general disease suppressiveness (measured as growth reduction upon pathogen addition) to Pythium ultimum (»Soil suppressiveness to Pythium ultimum and its relation with soil parameters);
- Nematode communities assessed with molecular methods (Illumina sequencing) (»Effect of tillage and organic matter additions on nematodes community assessed with 18S squencing);
- Community level physiological profiling with MicroResp®, enzymatic activities and microbial community composition with phospholipid fatty acids (PLFA) (these parameters have been included in the framework of two Master thesis projects) (»Community level physiological profiling with MicroResp®);
Details about each indicator in terms of methods, results and discussion are presented in each article. Various traditional chemical, physical and biological soil parameters were also measured in the samples as described in Table 1 and used to validate the novel soil quality indicators and link them to soil functions.
Table 1. Chemical, physical and biological parameters linked with soil functions as measured and used to validate the novel soil quality indicators.
Parameters assessed in the field (field assessments) were done by the LTEs’ owners.
|Parameters||Methodology||Unit||Laboratory of analysis|
|Total organic carbon (TOC)||SIST ISO 10694: Soil quality - Determination of organic and total carbon after dry combustion ("elementary analysis")||%||University of Ljubljana (SI)|
|Total nitrogen (TON)||SIST ISO 13878:1999: Soil quality - Determination of total nitrogen content by dry combustion ("elementary analysis")||%||University of Ljubljana (SI)|
|pH||CaCl2 determination- SIST ISO 10390:2006: Soil quality - Determination of pH||mol/l||University of Ljubljana (SI)|
|Cation exchange capacity (CEC)||ISO 13536:1995 - Soil quality - Determination of the potential cation exchange capacity and exchangeable cations using barium chloride solution buffered at pH = 8,1||mmol 100 g-1 soil||University of Ljubljana (SI)|
|Plant available phosphorus (P2O5)||ÖNORM L 1087 - modification: ammonium lactate extraction||mg kg-1 soil||University of Ljubljana (SI)|
|Available phosphorus (P-Olsen)||SIST ISO 11263-1996||University of Ljubljana (SI)|
|Plant available potassium (K2O)||ÖNORM L 1087 - modification: ammonium lactate extraction||mg kg-1 soil||University of Ljubljana (SI)|
|Exchangeable magnesium, calcium, sodium, and potassium (Mg2+, Ca2+, Na+, K+)||Ammonium acetate extraction; Soil survey laboratory methods manual, 1992||mg kg-1 soil||University of Ljubljana (SI)|
|Water stable aggregates (WSA)||Wet sieving method modified as in Kandeler (1996)||mg kg-1 soil||FiBL (CH)|
|Bulk density (BD)||Volumetric assessment with ring||g cm-3||Field assessment|
|Silt, Clay and Sand||SIST ISO 11277:2011: Soil quality - Determination of particle size distribution in mineral soil material - Method by sieving and sedimentation||%||University of Ljubljana (SI)|
|Penetration resistance||Pressure needed to insert penetrometer in the soil||Mpa||Field assessment|
|Microbial biomass carbon (MBC)||Fumigation extraction method||mg kg-1 soil||Trier University (DE)|
|Microbial biomass nitrogen (MBN)||Fumigation extraction method||mg kg-1 soil||Trier University (DE)|
|Soil respiration||Incubation of soil at 25ºC for 72 h in thermostat bath||μg h-1 g -1 soil||University Miguel Hernandez (ES)|
|Earthworms abundance and biomass||Hand sorting from 30*30*30 cm3 monolith||Number and fresh weight (g)||Field assessment|
|Decomposition||Tea bag incubation (tea bag index) (Keuskamp et al., 2013)||g mass loss||Field assessment|
Note: For full references to papers quoted in this article see