Local microbial yield-associating signatures largely extend to global differences in plant growth.

Rapid advancements in high-throughput DNA sequencing have opened new avenues for applying microbiome-based machine learning to predict and model determinants that enhance agricultural productivity and sustainability in agroecosystems. Although early attempts have been made to predict crop yield or measures of soil health through the soil microbiome, it is unclear if microbial patterns associated with plant growth or crop yield on a local scale can be generalized to predict differences in plant growth on a continental or global scale. Herein, we measured the soil bacterial microbiome on a single maize field in Germany with high spatial sampling resolution and correlated the community composition with corresponding volume flow-based high-resolution yield measurements.