JOURNAL ARTICLE

A practical guide to the discovery of biomolecules with biostimulant activity.

  • Published In: Journal of Experimental Botany, 2024, v. 75, n. 13. P. 3797 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Jing; Lardon, Robin; Mangelinckx, Sven; Geelen, Danny 3 of 3

Abstract

This article focuses on the discovery, development, and commercialization of biomolecule-based plant biostimulants as sustainable agricultural tools that enhance crop growth, nutrient use efficiency, abiotic stress tolerance, and soil health restoration. It outlines a practical, stepwise roadmap starting from biomolecule mining in natural extracts, through extraction, bioactivity identification, formulation, and effectiveness assessment, to regulatory approval and market introduction. The review highlights key biomolecule classes such as flavonoids, terpenoids, carbohydrates, proteins, and nucleic acids, detailing their modes of action and the challenges in isolating active compounds from complex mixtures. It also discusses advances in eco-friendly extraction methods, bioassays, biosensors for in vivo plant signaling monitoring, and the importance of standardized formulations and regulatory compliance across global regions. The article emphasizes that tailored biostimulants with well-characterized bioactive components and validated efficacy are essential for integrating these products into sustainable farming practices.

Additional Information

  • Source:Journal of Experimental Botany. 2024/07, Vol. 75, Issue 13, p3797
  • Document Type:Article
  • Subject Area:Agriculture and Agribusiness
  • Publication Date:2024
  • ISSN:0022-0957
  • DOI:10.1093/jxb/erae156
  • Accession Number:178481102
  • Copyright Statement:Copyright of Journal of Experimental Botany is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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