Impact of shifting to full-sun coffee plantations on soil bacterial diversity in the Kafa Forest Biosphere Reserve, Ethiopia

• Shade intensity significantly affects soil bacterial diversity in coffee agroforestry systems • Higher shade levels are associated with increased soil bacterial diversity • Conversion to full-sun coffee plantations may reduce soil microbial diversity • Shade intensity should be considered a key factor in future soil microbial research Soil bacteria constitute one of the most diverse and functionally important components of soil ecosystems, playing a pivotal role in nutrient cycling, organic matter decomposition, and overall ecosystem functioning. However, the impacts of forest transformation and coffee agroforestry management—particularly shade intensity—on soil bacterial diversity remain poorly understood. This study investigated the effects of shade intensity on soil bacterial diversity within coffee agroforestry systems in the Kafa Forest Biosphere Reserve, southwest Ethiopia. Three shade management regimes were established: full sun (no shade), medium shade (40–55% canopy cover), and high shade (65–85% canopy cover), across 45 plots (20 m × 20 m). Soil samples were collected from the top 15 cm and bacterial isolates were characterized using Bergey’s Manual of Systematic Bacteriology and ABIS 7 software. A total of 68 bacterial isolates belonging to 16 genera were identified, with Pseudomonas, Bacillus, Aeromonas, and Corynebacterium being the most dominant genera across all shade levels. Bacterial diversity and community structure varied significantly with shade intensity. High-shade coffee systems exhibited the highest Shannon–Wiener diversity index and evenness, followed by medium-shade systems, while full-sun coffee plantations showed the lowest diversity (p < 0.05). These findings indicate that increasing shade intensity enhances habitat heterogeneity and creates favorable microclimatic conditions that support diverse soil bacterial communities. Overall, the results demonstrate that shade management is a critical determinant of soil bacterial diversity in coffee agroforestry systems. Maintaining high shade levels can promote soil microbial biodiversity and contribute to improved ecosystem health and sustainability of coffee-based landscapes. This study provides empirical evidence supporting shade-based coffee agroforestry as a biodiversity-friendly land-use strategy in tropical forest regions.

Authors

Institutions

Publication Details

Journal
Total Environment Microbiology
Published
2026-01-19
DOI
https://doi.org/10.1016/j.temicr.2026.100060
Primary Topic
Coffee research and impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impact of shifting to full-sun coffee plantations on soil bacterial diversity in the Kafa Forest Biosphere Reserve, Ethiopia

A. Zena Teshale, Yericho Berhanu, Tekalign Mengesha
Total Environment Microbiology
Coffee research and impacts
article

Impact of shifting to full-sun coffee plantations on soil bacterial diversity in the Kafa Forest Biosphere Reserve, Ethiopia

A. Zena Teshale, Yericho Berhanu, Tekalign Mengesha
article en

Abstract

• Shade intensity significantly affects soil bacterial diversity in coffee agroforestry systems • Higher shade levels are associated with increased soil bacterial diversity • Conversion to full-sun coffee plantations may reduce soil microbial diversity • Shade intensity should be considered a key factor in future soil microbial research Soil bacteria constitute one of the most diverse and functionally important components of soil ecosystems, playing a pivotal role in nutrient cycling, organic matter decomposition, and overall ecosystem functioning. However, the impacts of forest transformation and coffee agroforestry management—particularly shade intensity—on soil bacterial diversity remain poorly understood. This study investigated the effects of shade intensity on soil bacterial diversity within coffee agroforestry systems in the Kafa Forest Biosphere Reserve, southwest Ethiopia. Three shade management regimes were established: full sun (no shade), medium shade (40–55% canopy cover), and high shade (65–85% canopy cover), across 45 plots (20 m × 20 m). Soil samples were collected from the top 15 cm and bacterial isolates were characterized using Bergey’s Manual of Systematic Bacteriology and ABIS 7 software. A total of 68 bacterial isolates belonging to 16 genera were identified, with Pseudomonas, Bacillus, Aeromonas, and Corynebacterium being the most dominant genera across all shade levels. Bacterial diversity and community structure varied significantly with shade intensity. High-shade coffee systems exhibited the highest Shannon–Wiener diversity index and evenness, followed by medium-shade systems, while full-sun coffee plantations showed the lowest diversity (p < 0.05). These findings indicate that increasing shade intensity enhances habitat heterogeneity and creates favorable microclimatic conditions that support diverse soil bacterial communities. Overall, the results demonstrate that shade management is a critical determinant of soil bacterial diversity in coffee agroforestry systems. Maintaining high shade levels can promote soil microbial biodiversity and contribute to improved ecosystem health and sustainability of coffee-based landscapes. This study provides empirical evidence supporting shade-based coffee agroforestry as a biodiversity-friendly land-use strategy in tropical forest regions.

Total Environment MicrobiologyVol. 2(1)
Asossa University (ET)
Life in Land
Openalex Percentile: Top 82%
Coffee research and impacts
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.