Manganese promoted wheat straw decomposition by regulating microbial communities and enzyme activities

Harnessing the potential of exogenous microbial agents: a comprehensive review on enhancing lignocellulose degradation in agricultural waste composting

Composting converts organic agricultural wastes into value-added products, yet the presence of significant non-biodegradable lignocelluloses hinders its efficiency. The introduction of various exogenous microbial agents has been shown to effectively addresses this challenge. In this context, basing on the microbial enzymatic mechanism for lignocellulose degradation, this paper synthesizes the latest research advancements and practical applications of exogenous microbial agents in agricultural waste composting. Given that the effectiveness of lignocellulose degradation is highly dependent on the waste's inherent characteristics, it is crucial to carefully consider the composition of fungi and bacteria, the dosage of microbial agents, and the composting process operation, tailored to the specific type of agricultural waste. Moreover, the combination of additives with exogenous microbial agents can further enhance the degradation of lignocelluloses and the humification of organic matters. Furthermore, insights into the future research and application trends of exogenous microbial agents in agricultural waste composting was prospected.

The positive effect of the enzyme inducer (MnSO4) on the formation of humic substance in rice straw composting by stimulating key microorganisms

This study investigated the impact of adding enzyme inducer (MnSO4) on humic substance (HS) formation during straw composting. The results demonstrated that both enzyme inducer treatment group (Mn) and functional microorganism treatment group (F) led to an increase in the content of HS compared to the treatment group without enzyme inducer and functional microorganism (CK). Interestingly, the enzyme inducer exhibited a higher promoting effect on HS (57.80 % ~ 58.58 %) than functional microbial (46.54 %). This was because enzyme inducer stimulated the growth of key microorganisms and changed the interaction relationship between microorganisms. The structural equation model suggested that the enzyme inducer promoted the utilization of amino acids by the fungus and facilitated the conversion of precursors to humic substance components. These findings provided a direction for improving the quality of composting products from agricultural straw waste. It also provided theoretical support for adding MnSO4 to compost.

Predicting soil fungal communities from chemical and physical properties

Introduction

Biogeography describes spatial patterns of diversity and explains why organisms occur in given conditions. While it is well established that the diversity of soil microbes is largely controlled by edaphic environmental variables, microbiome community prediction from soil properties has received less attention. In this study, we specifically investigated whether it is possible to predict the composition of soil fungal communities based on physicochemical soil data using multivariate ordination.

Materials and Methods

We sampled soil from 59 arable fields in Switzerland and assembled paired data of physicochemical soil properties as well as profiles of soil fungal communities. Fungal communities were characterized using long‐read sequencing of the entire ribosomal internal transcribed spacer. We used redundancy analysis to combine the physical and chemical soil measurements with the fungal community data.

Results

We identified a reduced set of 10 soil properties that explained fungal community composition. Soil properties with the strongest impact on the fungal community included pH, potassium and sand content. Finally, we evaluated the model for its suitability for prediction using leave‐one‐out validation. The prediction of community composition was successful for most soils, and only 3/59 soils could not be well predicted (Pearson correlation coefficients between observed and predicted communities of <0.5). Further, we successfully validated our prediction approach with a publicly available data set. With both data sets, prediction was less successful for soils characterized by very unique properties or diverging fungal communities, while it was successful for soils with similar characteristics and microbiome.

Conclusions

Reliable prediction of microbial communities from chemical soil properties could bypass the complex and laborious sequencing‐based generation of microbiota data, thereby making soil microbiome information available for agricultural purposes such as pathogen monitoring, field inoculation or yield projections.

Dynamic variation of bacterial community assemblage and functional profiles during rice straw degradation

Bacteria is one of the most important drivers of straw degradation. However, the changes in bacterial community assemblage and straw-decomposing profiles during straw decomposition are not well understood. Based on cultivation-dependent and independent technologies, this study revealed that the "common species" greatly contributed to the dynamic variation of bacterial community during straw decomposition. Twenty-three functional strains involved in straw decomposition were isolated, but only seven were detected in the high-throughput sequencing data. The straw decomposers, including the isolated strains and the agents determined by functional prediction, constituted only 0.024% (on average) of the total bacterial community. The ecological network showed that most of the identified decomposers were self-existent without associations with other species. These results showed that during straw composition, community assembly might be greatly determined by the majority, but straw decomposition functions might be largely determined by the minority and emphasized the importance of the rare species in community-specific functions.