Integrating <i>Fusarium oxysporum</i> f. sp. <i>strigae</i> into cereal cropping systems in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Striga hermonthica (Del.) Benth. (witchweed) poses the greatest biological constraint to food production in sub-Saharan Africa (SSA). Control options for Striga are currently largely ineffective or unavailable to farmers, and other management possibilities are urgently needed. Biological control obviates some of the problems of several of the other techniques and provides a management option that is durable and environmentally responsive. The efficacy of S. hermonthica control using different formulations of three isolates of Fusarium oxysporum Schlecht. emend. Synder & Hans f. sp. strigae was tested on Striga-resistant and Striga-susceptible varieties of sorghum and maize under African field conditions for the first time. RESULTS: Isolates PSM197 and Foxy 2 were effective in witchweed repression, especially when applied as pesta granules. Isolate M12-4A was less effective under the field conditions investigated. Application of the fungi was generally more beneficial in maize than in sorghum for the varieties tested. Application of the biocontrol agent caused significant decreases in the number of flowering Striga plants, and hence deposition of seeds with impact of enhancing future crop yield. CONCLUSIONS: Synergistic effects between the Striga-resistant maize line and Fusarium oxysporum f. sp strigae led to over 90% reduction in Striga emergence. These results will further encourage the distribution of the isolates tested or selection of country-specific relatives as viable and environmentally safe biocontrol agents to be used against Striga. Pesta was the most effective formulation, while seed coating may be more cost effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it