Effects of Land-Use Intensification on Distribution and Diversity of Fusarium Species in Machakos County, Kenya
Bibliographic record
Abstract
Land-use intensification has a significant influence on occurrence of soil microorganisms. The effect of this phenomenon on Fusarium species is poorly characterized. One hundred soil samples were obtained from 3 replicated land- use types (LUT) in Mwala and Kauti irrigation regions in Machakos County. These included two intensive land-uses under irrigation and rain-fed agriculture and undisturbed lands. Mwala irrigated lands were divided into four blocks based on history of cultivation. Using soil dilution plate technique, 1,546 isolates of Fusarium were recovered and identified into twelve species namely; F. oxysporum, F. solani, F. nygamai, F. equiseti, F. chlamydosporum, F. beomiforme, F. verticillioides, F. proliferatum, F. acuminatum, F. compactum, F. semitectum, and F. merismoides. Fusarium oxysporum was the most abundant and diverse Fusarium species. Fusarium semitectum, F. compactum and F .merismoides had the least distribution being isolated from only one LUT. Fusarium beomiforme and F. acuminatum were recovered from irrigated farmlands only while F. verticillioides, F. proliferatum and F. acuminatum were restricted to disturbed lands only. The difference in abundance of Fusarium between the three LUTs was significant (P = 0.047) with irrigated lands having the highest abundance. Mwala block A had the highest abundance, richness and diversity of Fusarium. Lands with a higher intensity of disturbance had a higher abundance and richness of Fusarium than the less undisturbed lands. This may have severe implication on crop production as most species of Fusarium isolated are pathogenic. Sustainable ways of controlling these potential crop pathogens should be sought.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".