Propuesta metodológica para el aislamiento de hongos a partir de raíces almacenadoras de Manihot esculenta Crantz con síntomas de pudrición

Cassava (Manihot esculenta Crantz) is a vital staple food for millions of people worldwide,primarily valued for its edible storage roots. However, these roots are vulnerable to Cassava Root Rot Disease (CRRD), often caused by fungal pathogens such as Fusarium, Lasiodiplodia, and Phytophthora. This disease poses a significant threat to food security in tropical and subtropical regions. Accurate microbial identification is essential for understanding CRRD pathology. However, conventional methods for isolating microorganisms from diseased roots—particularly fungi—have limitations. For instance, incomplete surface sterilization can lead to the isolation of non-axenic fungal colonies. To address this issue, we propose a modified methodology that includes a surface particle-removal step and sterility controls to validate its effectiveness. Our method significantly improved the isolation of axenic fungal colonies from CRRD-affected roots compared to traditional techniques across three different culture media. Out of 60 samples per method, the proposed method yielded a statistically higher proportion of axenic fungal colonies (88.3%) than the traditional approach (30%). Although the proposed method facilitates microbial isolation, further validation is needed to assess its reliability across various pathogens and environmental conditions

Publication Details

Journal
Dialnet (Universidad de la Rioja)
Published
2025-01-01
Primary Topic
Research in Social Sciences
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Propuesta metodológica para el aislamiento de hongos a partir de raíces almacenadoras de Manihot esculenta Crantz con síntomas de pudrición

Dialnet (Universidad de la Rioja)
Research in Social Sciences
article

Propuesta metodológica para el aislamiento de hongos a partir de raíces almacenadoras de Manihot esculenta Crantz con síntomas de pudrición

article en

Abstract

Cassava (Manihot esculenta Crantz) is a vital staple food for millions of people worldwide,primarily valued for its edible storage roots. However, these roots are vulnerable to Cassava Root Rot Disease (CRRD), often caused by fungal pathogens such as Fusarium, Lasiodiplodia, and Phytophthora. This disease poses a significant threat to food security in tropical and subtropical regions. Accurate microbial identification is essential for understanding CRRD pathology. However, conventional methods for isolating microorganisms from diseased roots—particularly fungi—have limitations. For instance, incomplete surface sterilization can lead to the isolation of non-axenic fungal colonies. To address this issue, we propose a modified methodology that includes a surface particle-removal step and sterility controls to validate its effectiveness. Our method significantly improved the isolation of axenic fungal colonies from CRRD-affected roots compared to traditional techniques across three different culture media. Out of 60 samples per method, the proposed method yielded a statistically higher proportion of axenic fungal colonies (88.3%) than the traditional approach (30%). Although the proposed method facilitates microbial isolation, further validation is needed to assess its reliability across various pathogens and environmental conditions

Dialnet (Universidad de la Rioja)
Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación
Zero hunger
Openalex Percentile: Top 37%
Research in Social Sciences
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.