Drone imagery and metabarcoding as diagnostic tools of poor growth in potato fields
Metabarcoding targeting nematodes, bacteria, fungi and oomycetes was used in combination with multispectral drone imagery and traditional soil extraction of nematodes to diagnose poor growth in patches of a potato field in Norway. Areas of good and poor growth as identified by Normalized Difference Vegetation Index (NDVI) based on aerial photography were compared and nematodes were identified as the Iikely drivers of poor growth. This was based on the presence of known plant parasitic nematodes in the field and the significant association of low nematode alpha diversity with areas of poor growth while alpha diversity for other organism groups did not vary between patches with good and poor growth. Metabarcoding represented nematodes well compared to traditional soil extraction. The combination of aerial photography and metabarcoding used in this work offers the promising possibility to identify biological drivers of growth differences across organism groups at field scale.