Comparative analysis of high and low yielding sugarcane rhizospheric microbiome using high throughput sequencing

Abstract

Sugarcane (Saccharum officinarum L.) is a vital agro-industrial crop that plays a pivotal role in the economy of many tropical and subtropical regions, including India. As one of the world s largest producers of sugarcane, India significantly contributes to global sugar production, with Uttar Pradesh standing out as the leading state in terms of sugarcane cultivation. Sugarcane serves as a primary source of sugar and has gained immense importance as a renewable bioenergy crop. The production of ethanol and other industrial products from sugarcane biomass underscores its potential to support sustainable energy goals, thereby positioning the crop at the forefront of agro-industrial innovation. Despite its economic importance, sugarcane productivity exhibits substantial variability across different regions and even within individual fields. Some fields consistently demonstrate high yields, while others, despite being subjected to similar agronomic practices, exhibit comparatively poor performance. This variability in yield potential has been a subject of intense research, as identifying the underlying factors can significantly impact agricultural management and productivity enhancement. Among the various factors influencing sugarcane yield, the rhizospheric microbiome has recently emerged as a critical determinant of plant health and growth. The rhizosphere is a dynamic and complex region of soil that is directly influenced by root exudates and associated microbial communities. These microbiotas perform essential functions, including nutrient cycling, pathogen suppression, and growth promotion, thereby directly impacting plant productivity. Understanding the composition and functional roles of rhizosphere-associated microbes can unveil novel strategies to boost sugarcane yields through microbiome management. In this study, we investigate the composition and diversity of rhizosphere microbiota associated with sugarcane using 16S rRNA high-throughput sequencing. This powerful molecular technique allows for the compreh