Biofertilizers based on plant growth promoting rhizobacteria (PGPR) are nowadays gaining increasingly attention as a modern tool for a more sustainable agriculture due to their ability in ameliorating root nutrient acquisition. For many years, most research was focused on the screening and characterization of PGPR functioning as nitrogen (N) or phosphorus (P) biofertilizers. However, with the increasing demand for food using far fewer chemical inputs, new investigations have been carried out to explore the potential use of such bacteria also as potassium (K), sulfur (S), zinc (Zn), or iron (Fe) biofertilizers. In this review, we update the use of PGPR as biofertilizers for a smarter and more sustainable crop production and deliberate the prospects of using microbiome engineering-based methods as potential tools to shed new light on the improvement of plant mineral nutrition. The current era of omics revolution has enabled the design of synthetic microbial communities (named SynComs), which are emerging as a promising tool that can allow the formulation of biofertilizers based on PGPR strains displaying multifarious and synergistic traits, thus leading to an increasingly efficient root acquisition of more than a single essential nutrient at the same time. Additionally, host-mediated microbiome engineering (HMME) leverages advanced omics techniques to reintroduce alleles coding for beneficial compounds, reinforcing positive plant-microbiome interactions and creating plants capable of producing their own biofertilizers. We also discusses the current use of PGPR-based biofertilizers and point out possible avenues of research for the future development of more efficient biofertilizers for a smarter and more precise crop fertilization. Furthermore, concerns have been raised about the effectiveness of PGPR-based biofertilizers in real field conditions, as their success in controlled experiments often contrasts with inconsistent field results. This discrepancy highlights the need for standardized protocols to ensure consistent application and reliable outcomes.
Read full abstract