Evidence map›Paper›PMID 41491811›Full record

ArticleScientific reports2026

Non-aerated compost extracts: optimizing fermentation for enhanced antifungal effects against phytopathogenic fungi.

Raja Jarboui, Raghad Saad Almazyad, Mona S Azab, Shaima M N Moustafa

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Raja JarbouiDepartment of Biology, College of Science, Jouf University, Sakaka, Saudi Arabia. rajajarboui2000@yahoo.fr.
Raghad Saad AlmazyadDepartment of Biology, College of Science, Jouf University, Sakaka, Saudi Arabia.
Mona S AzabDepartment of Biology, College of Science, Jouf University, Sakaka, Saudi Arabia.
Shaima M N MoustafaFaculty of Science, Botany and Microbiology Department, Luxor University, Luxor, Egypt.

Funding

Deanship of Graduate Studies and Scientific Research at Jouf University DGSSR-2024-0X-02187
6 · The paper itself

Abstract

This study aimed to investigate the suppressive effect of non-aerated compost extracts on phytopathogenic fungi isolated from various diseased plants. Two commercial composts, C1 and C2, collected from two regions in Saudi Arabia and prepared in distinct ways, were used, and their physicochemical properties were characterized. To evaluate the individual and interactive effects of key parameters on their antifungal potential, compost extracts were prepared through a fermentation process, using a full factorial design based on the variation of four parameters at two levels: Temperature (25 and 44 ℃), compost concentration (1/5 and 1/8), glucose concentration (0 and 1 g/l), and fermentation time (3 and 7 days). The obtained extracts were characterized both physicochemically and microbiologically, then tested for their suppressive effect on the mycelial growth of four molecularly identified fungi (Syncephalastrum racemosum (F1), Paramyrothecium roridum (F2), Fusarium oxysporum (F3), and Penicillium italicum (F4)). The effect of the four variables on the inhibition percentage of these fungi showed that most compost extracts inhibited all studied fungi. Therefore, fermentation time significantly affects (p<0.05) the growth inhibition of S. racemosum. The concentration of compost and glucose, as well as fermentation time, significantly affected the growth inhibition of P. italicum (p < 0.05). However, not all studied variables significantly influenced P. roridum and F. oxysporum. The optimal conditions for the highest inhibition rate against S. racemosum were 25 °C, a compost concentration of 1/8, glucose supplementation at 1 g/l, and a fermentation time of 3 days. In contrast, the optimal conditions for P. italicum were 25 °C, a compost concentration of 1/8, without glucose addition, and fermentation times of 3 and 8 days. The Pearson test revealed that fermentation time was the key factor enhancing antifungal efficacy against S. racemosum and P. italicum. At the same time, nitrogen parameters (NTK, NH₄⁺), microbial abundance, and phenolic content synergistically contributed to the suppression of pathogens. Autoclaving and filtration reduced fungal inhibition compared to non-sterile extracts, with autoclaving allowing greater fungal growth than filtration. The microbial composition and biochemical profile of the compost extracts, shaped by fermentation, likely contribute to their antagonistic effects against phytopathogenic fungi, driven by the microbial flora and organic/inorganic compounds.

Indexed as

Antifungal AgentsCompostingFermentationFungiPlant DiseasesFusariumPenicilliumSoilTemperatureAntifungal AgentsSoilBiocontrolCompost extractsFermentation designSuppressive compostsSustainable agriculture

Identifiers

PMID41491811
PMCPMC12830594

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.