Timed Strategy for Control of Bollworm for Sustainable Sorghum Crop Yield under Varied Regimes of Rainfall, Temperature and Soil Fertility
Bibliographic record
Abstract
Various environmental factors influence yield of sorghum grain, Sorghum bicolor (L) in Sub-Sahara Africa. Various production conditions of rainfall amount, temperature regimes, soil fertility levels and bollworm Helicoverpa armigera density at specific sorghum grain stage were evaluated for effect to sorghum grain yield. High rainfall amount, high temperature and soil fertility levels were positively correlated to sorghum grain yield at three test sites at Ithookwe, Katumani and Kampi of eastern Kenya. The warmest Kampi site achieved the highest seed viability on germination test at 43, 87 and 99% for grain stage of light-green, cream-dough and hard dough, respectively. High H. armigera density was inversely correlated to grain yield. Comparatively, yield loss of < 10% was observed when grain was at early soft dough and > 35% as the grain ripened to early hard dough stage. Thus initial H. armigera damage occurred at late soft dough stage and increased exponentially as the grain ripened to early hard dough stage. The right time to spray against H. armigera was determined as at soft dough stage of sorghum grain to prevent economic damage of the crop. Thus fertility level, rainfall amount and time of bollworm pest attack were deemed worth considerations towards sustainable yield of sorghum.
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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) | 0.001 | 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".