Distinct soil microbial diversity under long-term organic and conventional farming

Abstract

Low-input agricultural systems aim at reducing the use of synthetic fertilizers and pesticides in order to improve sustainable production and ecosystem health. Despite the integral role of the soil microbiome in agricultural production, we still have a limited understanding of the complex response of microbial diversity to organic and conventional farming. Here we report on the structural response of the soil microbiome to more than two decades of different agricultural management in a long-term field experiment using a high-throughput pyrosequencing approach of bacterial and fungal ribosomal markers. Organic farming increased richness, decreased evenness, reduced dispersion and shifted the structure of the soil microbiota when compared with conventionally managed soils under exclusively mineral fertilization. This effect was largely attributed to the use and quality of organic fertilizers, as differences became smaller when conventionally managed soils under an integrated fertilization scheme were examined. The impact of the plant protection regime, characterized by moderate and targeted application of pesticides, was of subordinate importance. Systems not receiving manure harboured a dispersed and functionally versatile community characterized by presumably oligotrophic organisms adapted to nutrient-limited environments. Systems receiving organic fertilizer were characterized by specific microbial guilds known to be involved in degradation of complex organic compounds such as manure and compost. The throughput and resolution of the sequencing approach permitted to detect specific structural shifts at the level of individual microbial taxa that harbours a novel potential for managing the soil environment by means of promoting beneficial and suppressing detrimental organisms.

Figure 1

Management effects on bacterial and fungal community structures. (a) PCO ordinations of Bray–Curtis similarities calculated based on relative OTU abundances showing major differences induced by farmyard manure application, that is, FYM (brown symbols) versus NoFYM (purple symbols), and year of sampling, that is, 2000 (triangles) versus 2007 (diamonds). The variance explained by each PCO axis is given in parentheses. Joint biplots show the correlation between richness or evenness and the ordinations scores on each PCO axis. Correlation coefficient r and level of significance (***P<0.001 and ^ns P>0.05) are provided. (b) CAP ordinations of bacterial and fungal communities maximizing discrimination among the different farming systems, that is, NOFERT (blue circles), CONMIN (pink triangles), BIODYN (green circles), BIOORG (dark green squares) and CONFYM (red triangles). These symbols (same symbol reflects same crop protection strategy) and colours (different farming systems) are used throughout the article where applicable. The canonical correlation (δ²) of each CAP axis, indicating the association strength between the multivariate data cloud and the hypothesis of differences between farming systems, is given in parentheses. The third axes (not shown) further separate BIOORG and BIODYN with δ²=0.52 and 0.55 for bacteria and fungi, respectively. The CAP reclassification rates (in percent) for each farming system are given in parentheses next to each cluster. The reclassification rate of the CAP model provides a quantitative estimate of the degree of discrimination among the systems achieved by the canonical axes. The traceQ_m'HQ_m statistic (sum of canonical eigenvalues) given in the plots tests the null hypothesis of no significant differences in multivariate location among farming systems and represents an overall test of rejecting the null hypothesis.

Figure 2

Management effects on soil chemistry measured biannually between 2000 and 2008. PCO ordinations of Euclidean distances calculated based on z-transformed soil chemical parameters, that is, pH, C_org, N_tot, P, K and Mg. Joint biplots show the correlation between the soil chemical parameters and the ordinations scores on each PCO axis.

Figure 3

Taxonomic dendrograms of the detected bacterial and fungal communities showing the OTU distribution (excluding OTUs with <0.001% relative abundance) across the different taxonomic branches (colour coded by phylum). Nodes correspond to OTUs and node sizes correspond to their relative abundances (square root) in the data set. Edges (that is, lines connecting the nodes) represent the taxonomic path from the root, that is, bacteria or fungi (marked by yellow asterisks), to OTU level, and OTUs were placed at the level of the lowest possible assignment. The most abundant phyla are labelled including the total OTU number and relative abundance in parentheses. Red nodes correspond to OTUs that significantly (q<0.05) differed among farming systems, whereas white nodes represent insensitive OTUs. Supplementary Figure 2 shows the same taxonomic dendrograms with only the significant OTUs colour coded according to the system association information.

Figure 4

Bipartite association network showing positive associations between the farming systems and the 628 significantly (q<0.05) associated OTUs. Node sizes represent relative abundance (square root) of the OTUs in the data sets. Edges represent the association patterns of individual OTUs with the farming systems. The edge-weighted spring-embedded algorithm pulled together OTUs with similar associations and systems with similar structure. OTUs associated with only one farming system are symbol and colour coded according to Figure 1. Diamond-shaped nodes represent OTUs associated with multiple farming systems. White nodes represent multisystem cross-combinations not falling into the same category with respect to either FYM application (FYM or no FYM) or farming regime (conventional or organic). Clusters are labelled as discussed in the text and marked in the Supplementary Data 2. Number of OTUs and relative abundances are provided for each cluster.

Figure 5

Co-correlation networks calculated for the significantly (q<0.05) associated OTUs of the 10 most populated phyla (coded with different colours). Nodes correspond to OTUs and node sizes correspond to their relative abundances (square root) in the data set. Edges represent significant (q<0.01) negative (blue) or positive (red) Spearman's correlations between pairs of OTUs. The edge-weighted spring-embedded algorithm pulled together OTUs that were strongly co-correlated. Dense co-correlation networks indicate that all or most OTUs in this cluster showed either a similar (= positive correlations) or contrasting (=negative correlations) response. Network density (d) calculated for each network represents the number of significant co-correlations divided by all possible co-correlations, that is, higher density represents more uniform response. Symbol coding indicates the association with the different farming systems as provided in Figure 4. Clusters are labelled with the approximate association information with respect to the management regime (that is, farming systems or system combinations such as FYM or NoFYM). For closer inspection, the same network but OTUs colour coded with the system association information is provided in Supplementary Figure 3.