Microalgae bioinputs as disruptive technology for a sustainable agriculture: A systematic and bibliometric review

Abstract

By 2050, global population growth is expected to double the demand for food, consequently increasing the need for fertilizers, which will intensify the pressure on natural resources and exacerbate environmental impacts. Recent studies have highlighted the potential of microalgal biomass cultivated in wastewater as bioinputs in agriculture. However, these studies remain mostly at laboratory or pilot project scales, requiring significant advances to enable their large-scale application. This study presents a systematic and bibliometric review to evaluate the state of the art of microalgae-based bioinputs, focusing on their production methods, environmental impacts, and techno-economic feasibility. The novelty of this work lies in its integrated approach, which not only consolidates current knowledge on bioinput technologies but also identifies critical gaps and intervention points to advance the large-scale adoption of microalgae-based bioinputs in agriculture. The review highlighted diverse methods for processing microalgal biomass, underscoring its significant potential as a soil conditioner, biostimulant, biopesticide, and biofungicide. Although these applications hold considerable promise, key challenges-such as optimizing cultivation in wastewater and minimizing energy consumption-must be effectively addressed to fully realize this potential. Environmental analyses reveal the potential of microalgae to support nutrient recycling, reduce greenhouse gas emissions, and enhance soil health. Techno-economic evaluations underline the need for scalable and cost-effective processes, highlighting the importance of integrated approaches that combine environmental and economic assessments. This comprehensive review provides a roadmap for future research, emphasizing the importance of addressing technical, environmental, and economic barriers to achieve sustainable and scalable production of microalgae-based bioinputs.