Farm management and shade tree species influence coffee soil microbiomes in Central and South America

Abstract

Arabica coffee (Coffea arabica) is cultivated throughout the tropics and is essential to local economies. Globally, most Arabica coffee is farmed in high-density monocultures despite evidence that agroforestry systems with shade trees can increase resiliency of coffee productivity and associated livelihoods. Open questions remain, however, on how Arabica coffee farm management affects local soil properties, especially soil microbial communities which are vital to plant productivity. To test if Arabica coffee soil microbiomes respond to farm management across geographies, we collected soil samples at coffee farms in Colombia and El Salvador that differed in management system (sun grown vs. shade grown) and soil samples in Peru that varied in agroforestry systems (native vs. non-native shade tree species). From these soils, we sequenced microbial DNA on an Illumina MiSeq to identify bacterial and fungal community diversity and composition. We found that each country had distinct soil microbial communities, and we describe their microbial profiles. In El Salvador and Colombia, both bacterial and fungal community composition differed between sun and shade systems, while alpha diversity of bacterial communities was only lower in El Salvador sun grown Arabica coffee soils. Within Peru agroforestry systems, bacterial and fungal composition differed between farms with non-native shade trees (Eucalyptus grandis and Pinus tecunumanii) and native shade trees (dominated by Inga spp.), but alpha diversity was not statistically different between agroforestry systems. Across all farms, composite microbial association networks showed similar topology, but distinctions in connectors and hub taxa, showcasing regional differences in potentially key microbial taxa. Our findings indicate that aboveground Arabica coffee farm management impacts belowground microbial biodiversity. A better understanding of these relationships can assist us in predicting how Arabica coffee production will be impacted by farm management changes and is essential for understanding the resilience of coffee farms.