Novel soil quality indicators for the evaluation of agricultural management practices: a biological perspective

Developments in soil biology and methods to characterize soil organic carbon have the potential to deliver novel soil quality indicators that can help to identify soil management practices that sustain soil productivity and environmental resilience. This thesis aimed at investigating the suitability of a range of soil biological and biochemical parameters as novel soil quality indicators for agricultural management. The soil parameters, selected through a literature review, comprised different labile organic carbon fractions (hydrophilic dissolved organic carbon (Hy-DOC), dissolved organic carbon (DOC), permanganate oxidizable carbon (POXC), hot water extractable carbon (HWEC) and particulate organic matter carbon (POMC), ordered here from the smallest to the largest proportion of the total organic carbon), soil disease suppressiveness measured with a Pythium-Cress bioassay, nematode communities characterized with amplicon sequencing and qPCR, and microbial community level physiological profiling (CLPP) measured with MicroRespTM. We tested the sensitivity of the novel indicators to tillage and organic matter addition in 10 European long-term field experiments, and assessed their relationship with already existing soil quality indicators linked to soil functioning. Lastly, the results of these experimental chapters are interpreted relative to each other and to the broader body of literature on soil quality assessments. Moreover, pros and cons of the novel indicators are discussed, and possibilities and needs for future research are outlined. Reduced tillage increased carbon availability, disease suppressiveness, nematode richness and diversity, the stability and maturity of the food web, and microbial activity and functional diversity. Organic matter addition had a weaker role in sustaining soil quality, possibly due to the different compositions of the organic matter inputs in the long-term field experiments that were sampled. Random forest analysis showed that POXC was the indicator that discriminates soil management most, and structural equation modelling showed its central role in nutrient cycling, carbon sequestration, biodiversity conservation, erosion control and disease regulation/suppression. The novel indicators proposed here have great potential to improve existing soil quality assessment schemes, but their usefulness is still to be validated and optimized.