Distribution of Common Maize Diseases and Molecular Characterization of Maize Streak Virus in Kenya
Bibliographic record
Abstract
Maize is an important food crop in Kenya and is susceptible to a wide range of diseases. A survey was conducted in 2012 in different agro-ecological zones (AEZ) of Kiambu, Embu and Nakuru counties to determine the distribution of northern leaf blight (NLB), common rust (CR), maize streak disease (MSD), gray leaf spot (GLS), head smut (HS) and common smut (CS). Data collected included prevalence, incidence and severity of each of the diseases. Maize leaf samples infected with MSD were also collected for molecular characterization of Maize streak virus (MSV). Northern leaf blight was reported in all counties surveyed with 100% disease prevalence. Kiambu had the highest incidence (100%) of CR whereas Embu had the highest prevalence (45%) of MSD. The incidences of GLS and HS were very low with averages of below 2.5%. The highest incidence of GLS was in Kiambu (5%). High altitude areas had higher incidences of NLB and GLS while CS and MSD were widespread in the three counties. Comparison of 797 nucleotides from the open reading frame (ORF) C2/C1 of MSV with other sequences from the GenBank showed sequence similarities of 99 to 100% with MSV-A strain. The study revealed that the major foliar diseases of maize are widespread in Kenya and therefore there is need to institute measures to manage these diseases and reduce associated losses. Also, the high percent sequence similarities of MSV indicate low variability which is good for breeders since developed resistant varieties can be adopted over a wider region.
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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".