Effects of 40 years applications of inorganic and organic fertilization on soil bacterial community in a maize agroecosystem in northeast China

The impacts of long-term fertilization on soil bacterial communities remain unclear due to non-consistent results from different studies, which may be attributed to different soil types and field managements. Therefore, we studied the responses of bacterial communities and soil properties to different inorganic and inorganic-organic fertilizer treatments after 40-year (1980–2019) operation of a maize agroecosystem established on a black soil (typical Mollisols) in Changchun, Jilin Province, China. Ten fertilizer treatments have been set up since 1980, including the application of no fertilizer (CK), nitrogen (N) fertilizer (N), N and phosphate (P) fertilizer (NP), N and potassium (K) fertilizer (NK), P and K fertilizer (PK), NPK fertilizer (NPK), NPK plus straw (NPKS), and NPK plus manure in three combinations (NPKM1, NPKM2, and NPKM3). The results showed that maize yield and soil nutrients significantly increased by fertilizations, especially when organic incorporation. Soil pH significantly decreased by inorganic fertilizers but alleviated when organic incorporation. Fertilizations significantly enhanced bacterial α-diversity due to the increased nutrient availabilities. The bacterial β-diversity was also significantly changed the changed by fertilizations, especially manure-added. The three dominant bacteria were Proteobacteria, Acidobacteria and Actinobacteria, accounting for 50-70 % of the total bacteria. Fertilizers, especially manure-added, significantly increased Proteobacteria and Acidobacteria and decreased Actinobacteria. The other top 7 bacteria taxa, were significantly correlated with soil pH. The Mantel test showed yield, and soil pH and nutrients were all significantly related to the overall bacterial community structure. The soil pH contributed to 14.3 %, and the yield and soil nutrients contributed to 5.6 % and 15.8 % of bacterial community changes based on a partial CCA-based variation partitioning analysis.