Infestation of Insects’ Pest on Six Sorghum Sorghum bicolor (L.) Moench Cultivars at INRAN Station and in Farmers’ Fields of Maradi Region, Niger
Bibliographic record
Abstract
Sorghum, Sorghum bicolor (L.) Moench, is a staple food for the Sahelian population. This crop faces several constraints, including insect pests. A study was carried out at INRAN-CERRA Maradi station and in farmers’ fields during 2021, 2022 and 2023 cropping seasons. The aim of this study is to survey the sorghum insect pests and determining their effects on sorghum cultivars. A Fisher block design with three (3) replicates was established on station, and a complete randomized design with two (2) replicates in farmers’ environment. Observations were done on six (6) improved sorghum cultivars: Mota Maradi (MM), Sepon 82, SSD35, Matché Da Koumya (MDK), IRAT 204 and 90SN7 and one local variety Makaho Da Wayo (MDW) in the farming environment to follow the insect dynamics. The study identified (10) species belonging to five insect orders, the most important were Eurystylus oldi Poppius, Stenodiplosis sorghicola Coquillett, Locris ruben Erichson and Poophilus costalis Walker. The dominant species on station were L. ruben and P. costalis, with proportions of 59.06% and 19.13% respectively. The two species were the most recorded in farming environment, at the rate of 41.73% and 29.19% respectively. They were followed by S. sorghicola, with 18.61% of insects’ density in farmers’ field. The densities of S. sorghicola and E. oldi were higher on Sepon 82 variety, while L. ruben and P. costalis were higher on local variety. Sorghum yields varied depending the cultivar and the cropping season, it ranged from 38 to 1834.31kg/ha. The MDK variety obtained the highest yield in 2021 and 2022 while in 2023 MM produced the highest quantity of grain on station and in rural area. The SSD35 was intermediate during the three years at the two experimental sites. The results of this study may help to strengthen integrated management options for sorghum insect’s pest and will enable farmers to choose and grow the higher yielding or less infested varieties.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".