JOURNAL ARTICLE
Ecology and diversity of arbuscular mycorrhizal fungi (AMF) in rice (Oryza sativa L.) in South India: an ecological analysis of factors influencing AMF in rice fields.
Published In: Journal of Applied Microbiology, 2024, v. 135, n. 10. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: John, Sayona Anna; Ray, Joseph George 3 of 3
Abstract
This study investigates the diversity, mean spore density (MSD), and root colonization of arbuscular mycorrhizal fungi (AMF) in relation to agroclimatic zones, rice varieties, and soil types across paddy fields in South India. Sixteen AMF species from seven genera were identified in 29 rice varieties spanning three agroclimatic zones and six soil orders, with Acaulospora being the most dominant genus. The research found that 72% of chemically fertilized fields lacked AMF spores, and root colonization was generally low, especially in high-yield (HY) rice varieties compared to traditional ones; organic and upland fields showed significantly higher AMF presence. Soil parameters such as pH, total organic carbon, and nutrient availability (nitrogen, phosphorus, potassium) were correlated with AMF diversity and colonization, highlighting complex interactions among environmental, soil, and crop factors. The findings emphasize the potential of region- and variety-specific AMF strains as biological tools to enhance sustainable rice cultivation and reduce chemical fertilizer dependence.
Additional Information
- Source:Journal of Applied Microbiology. 2024/10, Vol. 135, Issue 10, p1
- Document Type:Article
- Subject Area:Agriculture and Agribusiness
- Publication Date:2024
- ISSN:1364-5072
- DOI:10.1093/jambio/lxae256
- Accession Number:181041956
- Copyright Statement:Copyright of Journal of Applied Microbiology 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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