The short-term effect of coffee husk biochar application in acidic soils on soil properties, root mycorrhization, pest, and disease management and yields in Robusta coffee and black pepper plantations in Gia Lai province, Vietnam
Bibliographic record
Abstract
Vietnam is the world's leading exporter of Robusta coffee and black pepper. However, the widespread use of intensive cultivation practices has contributed to progressive soil acidification and an increasing incidence of soilborne diseases, threatening the long-term sustainability of these high-value crops. This study evaluated the short-term effects of applying coffee husk-derived biochar (2.5 t ha−1) on soil quality, pathogen suppression, and crop performance in acidic soils of coffee and black pepper farms in Gia Lai province. Over the course of 1 year, key soil physicochemical and biological properties, soilborne pathogen populations, and crop yields were monitored. The overall impact of biochar at this application rate was limited, and most differences between treated and untreated plots were not statistically significant ( p < 0.05). The study found that biochar application could lead to slight and promising improvements in soil conditions, including modest increases in nutrient availability, soil pH, and arbuscular mycorrhizal colonisation of plant roots. Reductions in specific pathogen populations, particularly plant-parasitic nematodes and Phytophthora spp., were also observed. These findings suggest that while biochar holds promise as a sustainable soil amendment, it requires more time than a single season to deliver substantial agronomic benefits. Additionally, future research should explore higher or repeated application rates, assess medium- and long-term effects, and investigate how biochar can be integrated with complementary biological or ecological soil management strategies to enhance its effectiveness in improving soil health and reducing disease pressure in perennial cropping systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".