Evaluation of microbial diversification mechanisms in legume-based mixed cropping systems with different legume species and types of fertilizer management
Kimura, A.; Uchida, Y.
Show abstract
Biodiversity loss is becoming a global concern due to its negative impact on services associated with the ecosystem. For agricultural soil to maintain these multi-services, the conservation of soil microbial diversity is of utmost importance. Mixed cropping systems involve the utilisation of multiple crop species on the field as well as the diversification of aboveground plants, although several contradicting results have been reported regarding their impacts on soil microbial diversity. Therefore, for the evaluation of the impact of different leguminous species used in mixed cropping systems as well as types of fertilizer on the diversity of soil microbes, a pot study was performed under maize/legume mixed cropping systems with one of three legumes, including cowpea (Vigna unguiculate (L.) Walp.), velvet bean (Mucuna pruriens (L.) DC.), and common bean (Phaseolus vulgaris L.) as well as one of three types of fertilizer treatments, namely chemical fertilizer (CF), carbonised chicken manure (CM), or the lack of fertilizer (Ctr). 16S rRNA analyses were conducted using the soils sampled from each pot for soil bacterial diversity assessment. Concerning the results, a decrease in the microbial diversity after CM application was shown by the soil with velvet bean + maize (MM) when compared to the Ctr treatment, while an increase in the microbial diversity was shown by the soil with common bean + maize (PM) under the same condition. In case of the CM application, the abundance of treatment-unique bacteria increased in the PM treatment, although their decrease was observed for the MM treatment. In contrast, the abundance of dominant microbes, including Thaumarchaeota was significantly lower in PM but higher in MM when the CM was applied. Legume species-dependent factors, including nutrient absorption and root exudate composition might be important concerning soil bacterial diversities. For the conservation of soil microbial diversity with mixed cropping, the interaction effect of legume species and fertilizer type should be considered in future studies.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Characterization of the habitat- and season-independent increase in fungal biomass induced by the invasive giant goldenrod and its impact on the fungivorous nematode community 93%
- Disproportionate CH4 sink strength from an endemic, sub-alpine Australian soil microbial community 93%
- Potential PGPR properties of cellulolytic, nitrogen-fixing, and phosphate-solubilizing bacteria of a rehabilitated tropical forest soil 93%
Similar papers in this journal
- Soil prokaryotes associated with decreasing pathogen density during anaerobic soil disinfestation 98%
- A meta-analysis of the effect of organic and mineral fertilizers on soil microbial diversity 95%
- Do inoculated microbial consortia perform better than single strains in living soil? A meta-analysis 95%
Similar papers in this journal
- Rhizospheric bacteria from the Atacama Desert hyper-arid core: cultured community dynamics and plant growth promotion 97%
- Concurrent stimulation of diflufenican biodegradation and changes in the active microbiome in gravel revealed by Total RNA 96%
- Controlled irrigation suppresses methane emissions by reshaping the rhizosphere microbiomes in rice 96%
Similar papers in this journal
- Assessing the efficiency and the side effects of atrazine-degrading biocomposites amended to atrazine-contaminated soil 96%
- Plant phenology influences rhizosphere microbial community and is accelerated by serpentine microorganisms in Plantago erecta 95%
- Insights into the desert living skin microbiome: geography, soil depth, and crust type affect biocrust microbial communities and networks in Mojave Desert, USA 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.