ArticleFrontiers in plant science2023
A multi-attribute approach to evaluating the impact of biostimulants on crop performance.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Taxon-Specific Physiological and Rhizosphere Responses of Deciduous Ornamental Shrubs to Humic- and Fulvic Acid-Based Biostimulant Treatment.Plants (Basel, Switzerland) · 2026Article
- Beyond the Single Isolate: Leveraging Plant-Associated Microbial Communities for Crop Resilience.Microorganisms · 2026Review
- Foliar Nanoparticulate Sulphur and Amino Acids Modulate Wheat Yield Components and Seed Quality Across Contrasting Environments.Plants (Basel, Switzerland) · 2025Article
- AI-integrated metabolomics maps functional divergence of microbial consortia in field-grown maize.Plant cell reports · 2025Article
- Metagenomic Analysis to Assess the Impact of Plant Growth-Promoting Rhizobacteria on Peanut (Journal of agricultural and food chemistry · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An ever-growing collection of commercial biostimulants is becoming available in a wide variety of forms and compositions to improve crop performance. Given the intricate nature of deciphering the underlying mechanisms of commercial products, which typically comprise various biological components, it is crucial for research in this area to have robust tools to demonstrate their effectiveness in field trials. Here, we took a multi-attribute approach to evaluating the impact of biostimulants on crop performance. First, we assessed the impact of a biostimulant on the soil and rhizosphere microbiomes associated to crops in eight reference farms, including corn (3 farms), soybean (2), cotton (2) and sugarcane (1), in different biomes and production contexts in Brazil and Paraguay. Second, we modeled a set of integrated indicators to measure crop responses to biostimulant application, including five analytical themes as follows: i) crop development and production (9 indicators), ii) soil chemistry (9), iii) soil physics (5), iv) soil biology (6) and v) plant health (10). Amplicon 16S rRNA and ITS sequencing revealed that the use of the biostimulant consistently changes the structure of bacterial and fungal communities associated with the production system for all evaluated crops. In the rhizosphere samples, the most responsive bacterial taxa to biostimulant application were
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Registered trials
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