Incidence, Severity, and Prevalence of Sorghum Diseases in the Major Production Regions in Niger
Bibliographic record
Abstract
Sorghum ranks second to pearl millet as the most important cereal in Niger and is used primarily for food, feed, and other uses. During the 2022 growing season, 96 fields from the five major sorghum production regions of Dosso, Maradi, Tahoua, Tillabéri, and Zinder were surveyed for foliar and panicle diseases. In each field, 40 plants were assessed using a W-shaped pattern to cover the whole field. A total of 19 diseases, including leaf blight, anthracnose, long smut, zonate leaf spot, bacterial leaf stripe, oval leaf spot, and rough leaf spot were documented. Leaf blight (100%) was detected in all the fields surveyed. In the regions of Dosso and Zinder, anthracnose was found in all the surveyed fields while oval leaf spot was detected in all surveyed fields in Maradi and Zinder. The highest mean incidence of leaf blight (95%) was recorded in the regions of Tahoua and Zinder while oval leaf spot (80%), anthracnose (56%), and gray leaf spot (25%) were highest in Maradi region. The highest mean severities of leaf blight (30%), long smut (29%), grain mold (18%), and anthracnose (13%) were recorded in Maradi region. The severity of head smut was 100% in all the regions where the disease was observed. Fields with incidence of 90% and above identified during the survey are considered as ‘hot spots’ for disease resistance screening. This work is significant because the information generated by the study can be utilized by sorghum workers, students, funding agencies and government officials to prioritize research projects.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".