Mineral vs. Organic Amendments: Microbial Community Structure, Activity and Abundance of Agriculturally Relevant Microbes Are Driven by Long-Term Fertilization Strategies

Abstract

Soil management is fundamental to all agricultural systems and fertilization practices have contributed substantially to the impressive increases in food production. Despite the pivotal role of soil microorganisms in agro-ecosystems, we still have a limited understanding of the complex response of the soil microbiota to organic and mineral fertilization in the very long-term. Here, we report the effects of different fertilization regimes (mineral, organic and combined mineral and organic fertilization), carried out for more than a century, on the structure and activity of the soil microbiome. Organic matter content, nutrient concentrations, and microbial biomass carbon were significantly increased by mineral, and even more strongly by organic fertilization. Pyrosequencing revealed significant differences between the structures of bacterial and fungal soil communities associated to each fertilization regime. Organic fertilization increased bacterial diversity, and stimulated microbial groups (Firmicutes, Proteobacteria, and Zygomycota) that are known to prefer nutrient-rich environments, and that are involved in the degradation of complex organic compounds. In contrast, soils not receiving manure harbored distinct microbial communities enriched in oligotrophic organisms adapted to nutrient-limited environments, as Acidobacteria. The fertilization regime also affected the relative abundances of plant beneficial and detrimental microbial taxa, which may influence productivity and stability of the agroecosystem. As expected, the activity of microbial exoenzymes involved in carbon, nitrogen, and phosphorous mineralization were enhanced by both types of fertilization. However, in contrast to comparable studies, the highest chitinase and phosphatase activities were observed in the solely mineral fertilized soil. Interestingly, these two enzymes showed also a particular high biomass-specific activities and a strong negative relation with soil pH. As many soil parameters are known to change slowly, the particularity of unchanged fertilization treatments since 1902 allows a profound assessment of linkages between management and abiotic as well as biotic soil parameters. Our study revealed that pH and TOC were the majors, while nitrogen and phosphorous pools were minors, drivers for structure and activity of the soil microbial community. Due to the long-term treatments studied, our findings likely represent permanent and stable, rather than transient, responses of soil microbial communities to fertilization.

Keywords: 454 pyrosequencing; long-term fertilization; microbial activity; microbial biomass; soil microbial communities; soil nutrients.

FIGURE 1

Enzymatic activities measured in the four soils studied. Different letters indicate significant differences based on Tukey’s HSD test p < 0.05. Error bars represent one standard error of the mean (n = 5).

FIGURE 2

Box plots of the observed richness and Shannon’s diversity index of (A) bacterial and (B) fungal community in the four soils studied. Different letters indicate significant differences based on Tukey’s HSD test p < 0.05.

FIGURE 3

Relative abundances of the major bacterial phyla (A) and genera (B) in the four soils studied. Different letters indicate significant differences based on Tukey’s HSD test p < 0.05. Error bars represent standard deviation (n = 5).

FIGURE 4

Non-metric multidimensional scaling (NMDS) ordination of (A) bacterial and (B) fungal community in the four soils studied. Bonferroni-corrected p-values were applied in all cases when more than two groups were compared with ANOSIM. (R = degree of separation between test groups ranging from -1 to 1; R = 0, not different; R = 1, completely different; p-values were based on 9999 permutations).

FIGURE 5

Relative abundances of the major fungal phyla (A) and genera (B) in the four soils studied. Different letters indicate significant differences based on Tukey’s HSD test p < 0.05. Error bars represent standard deviation (n = 5).