Predicting microbiome composition and successful inoculation with arbuscular mycorrhizal fungi

Summary

To reduce the environmental impact of mineral fertilizers including loss of biodiversity and eutrophication of rivers and lakes, it is necessary to find alternative solutions. One such solution is the use of arbuscular mycorrhizal fungi (AMF), which form a symbiotic relationship with most plant species and contribute to plant growth by providing essential nutrients like phosphorus. However, successful inoculation with AMF can vary depending on soil fertility and the soil microbial complexity. To address this issue, we suggest developing “microbiome diagnostics” to better predict the success of AMF inoculation.
The proposed approach involves two steps. The first step is to predict the soil microbiome based on the soil properties, and the second step is to predict the success of AMF inoculation success based on soil properties and the soil microbiome. We carried out on-farm experiments in 54 fields. We analyzed the soil physical-chemical properties and sequenced the fungal communities at the start of the experiment. We then combined the data using redundancy analysis to create a predictive model for soil microbiome. Using leave-one-out validation, we evaluated the model for its suitability for prediction.
In the next step, we developed a model to predict inoculation success. We determined that the abundance of pathogenic fungi present in the soil during the spring season was the most reliable predictor. Additionally, we sequenced the fungal communities in maize roots at harvest time and found that AMF inoculation resulted in a decline in the abundance of pathogenic fungi.
Ultimately, the two approaches must be to be merged to eliminate the need for expensive and time-consuming sequencing. Improving the predictability of AMF inoculation success can lead in a more a more sustainable agriculture and to more efficient use of biofertilizers.