Arbuscular mycorrhizal fungi (AMF) are beneficial microbes of soil and plant root microbiomes that significantly improve soil health, plant nutrition and productivity. However, the agricultural use of AMF, i.e. by field application, suffers from varying and unpredictable inoculation success. To close this gap, we will first describe the relationship between AMF communities and soil properties with the aim to model AMF communities from soil properties. We will then assess inocula establishment and its effects on plant growth as a function of biological, physical and chemical soil properties. The second goal is the development of an algorithm that recognizes promising combinations of AMF inocula and field conditions to successfully enhance plant yield.
To secure food production while reducing the environmental impacts of agrochemicals, we need to find alternative solutions to mineral fertilizers. Soil and plant root microbiomes comprise beneficial microbes that promote soil fertility and plant productivity. The arbuscular mycorrhizal fungi (AMF) are such beneficial microbiome members and they are well known to contribute to plant nutrition by providing phosphorus (up to 90 % of plant P originates from AMF). In recent work, we show that field inoculation with AMF can enhance plant yield; however, the inoculation success was highly varying between field sites. While AMF reliably improve plant performance in sterilized soils or under laboratory conditions, inoculation success into microbe-rich soils or agricultural fields remains unpredictable and highly context-dependent. We hypothesize that AMF inoculations fail in some soils because the inocula face competition with the indigenous microbiota or because they are not adapted to the soil environment.
In this project, we explore whether soil microbiome diagnostics can be used to specifically predict under which conditions AMF field inoculation will be successful to enhance plant yield. Conceptually similar to ‘personalized medicine’, we pre-diagnose the soil (chemical measurements and microbiome profiling) and we choose the AMF inoculant that fits the local soil conditions best. We plan largescale field inoculations of maize using different AMF species and AMF consortia and testing their impact on plant yield and their potential to replace P fertilizer inputs. We will monitor the establishment of the inocula in the roots and evaluate, under which conditions inoculation affected plant yields. We will then model the establishment of the inocula and their effects on plant growth as a function of the previous soil diagnostics.
Our goal is to develop an app that predicts AMF communities from biological-physical-chemical soil properties and recommends AMF species for successful inoculation for a given field. Our vision is that soil microbiome diagnostics becomes a tool for ‘smart farming’ through which the targeted application of microbials becomes a reliable and sustainable agronomic alternative to the usage of mineral fertilizers.