Soil Maize Cultivar-related Challenges on Striga hermonthica Infested Fields in Western Kenya
Bibliographic record
Abstract
Maize production in Western Kenya is constrained by Striga hermonthica and declining soil fertility. Integrated Striga Management (ISM) packages have been proposed. An ISM field experiment assessed combination of 4 maize varieties with 5 levels of soil fertility amendments. Imazapyr Resistant (IR) maize and local yellow seed Shipindi had highest germination percentages of 90% and 81% respectively, compared to commercial white seed Duma and local white seed Rachar. Duma had significantly large plants in terms of leave size and plant height; and taking least time to silking and tasseling while producing heaviest cobs and grains per plant. Synthetic fertilizer (DAP+CAN) was associated with the least germination percentage, but produced the largest plants with many leaves, took the shortest time to silking, and produced highest cob weight and grain weight, with very low S. hermonthica infestations regardless of the maize varieties. Cattle manure (CM) and water hyacinth compost containing cattle manure culture (HCM) and Effective Microbes™ (HEM) had the highest S. hermonthica population per unit area. Maize grown with water hyacinth compost containing Effective Microbes™ (HEM) positively influenced cob weight than those receiving cattle manure (CM) and the controls; while being associated with the highest numerical increase in grain yield/area. Alternative soil fertility interventions based on these observations are therefore proposed.
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.
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 | 0.001 | 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".