|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|
Note: The following publication is based on the material contained in this section of iSQAPERiS
- Giulia Bongiorno, Else K.Bünemann, Lijbert Brussaard, Paul Mäder, Chidinma U. Oguejiofor, Ron G.M. de Goede. 2020. Soil management intensity shifts microbial catabolic profiles across a range of European long-term field experiments. Applied Soil Ecology, 154, 103596. https://doi.org/10.1016/j.apsoil.2020.103596
Additional analyses have been performed in a selection of LTEs by two students from Wageningen University who wrote their master thesis in the framework of the iSQAPER project.
1. Tillage and organic matter addition on labile carbon fractions and functional diversity
Master thesis by Chidinma Uba Oguejifoor
Here we focused on the effect of tillage and organic matter addition (farming system in particular) on different labile carbon fractions (POXC and HWEC) and functional diversity measured with the MicroResp™ system. The results of the labile carbon measures have been integrated in »Labile carbon fractions as soil quality indicators. For the functional diversity measure we focused on the samples from the upper layer of the trials CH1, CH3, ES4, and HU4. In a later stage, all the other LTEs have been analysed with the same method, but the results are not presented here (the data still have to be processed). The principle of MicroResp™ is to add to a small volume of soil a range of substrates with different complexity (see Table 14 for the list of substrates used for our samples). Afterwards, the decomposition of these substrates by the microbial community is measured and the capacity to degrade multiple substrates is quantified as microbial diversity. For details about the methodology we refer to Campbell et al. (2003).
Table 14. Characteristics of the substrates used in the Microresp™ system indicating the carbon (C) and nitrogen (N) atoms for each substrate (from Chidinma Oguejifor Master thesis).
We analysed CH1, CH3, ES4 and HU4 together with redundancy analysis (RDA) and MANOVA, and we found a significant interaction between tillage and organic matter addition (farming system in this case) (MANOVA result for the interaction between tillage and fertilization: p=0.006) (Figure 10). From Figure 10 we understand that plots with more conventional management (CT and MIN) have a higher decomposition of organic acids (alpha-ketoglutaric and oxalic), while plots with more sustainable management (RT and ORG) have higher respiration of alanine. The sum of the respiration of the different substrates (multiple substrate induced respiration, MSIR) was higher in reduced tillage than conventional tillage and in organic system than conventional systems, but the differences were significant only in HU4 and ES4 (results not shown). In HU4 also the Shannon diversity index (H) was higher in RT compared to CT. MSIR and H resulted to be correlated with POXC (rs=0.86, p<0.01; r=0.52, p<0.001) and HWEC (rs=0.83, p<0.001; rs=0.40, p<0.05). Analysis of the MicroResp™ results of the remaining trials will be used to validate the preliminary findings.
2. Effect of tillage and organic matter input on the enzymatic activities and microbial community composition assessed with phospholipid fatty acid (PLFA) signatures
Master thesis by Julia Miloczki
Here we focused on the effect of tillage and organic matter input (farming system) on the enzymatic activities and microbial community composition assessed with phospholipid fatty acid (PLFA) signatures. For this analysis we focused on the trials CH1, CH3, NL2, ES4 and HU4. The enzymatic activities of the enzymes reported in Table 15 were assessed with a fluorometric assay (Marx et al., 2001).
Table 15. Selected enzymes, corresponding source and product (from Julia Miloczki Master thesis).
We found that enzymatic activities were very variable, expressed by their high standard error, and there was not a consistent pattern n across the LTEs (results not shown). For the PLFA analysis we assessed the PLFAs (Frostegård and Bååth, 1996) shown in Table 16 .
Table 16. Fatty acid biomarkers used in this study, their designated microbial group and reference (from Julia Miloczki Master thesis).
In summary, we found that the absolute abundance of total microbial biomass and main microbial groups (bacteria and fungi) assessed with PLFA were affected by the agricultural management, but not the relative abundance (Figure 11). However, analysis of the entire PLFA profiles expressed in relative abundances revealed that tillage and organic matter affected the total microbial community assemblage (Figure 12) (MANOVA results for tillage p=0.023 and for fertilization p=0.022). We found that the relative abundance of gram+ had a similar correlation as fungi with physical, chemical and biological parameters, and that they were correlated with P and N enzymes (ρ=0.60, P<0.01; ρ=0.46, P<0.05) (data not shown). The PLFA methodology was very laborious and did not assess in detailed the entire microbial community, and it is dependent on the database available for the analysis. However, it resulted to be rather sensitive to soil management. For this reason it might be interesting to explore more in detail the microbial community obtaining more detailed information on taxa and possible functional characteristics.
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