Irrigation Requirements and Yields of Maize Crop Under Future Climate in Some Cities of Northern Cameroon
Bibliographic record
Abstract
Using meteorological data obtained from Regional Model (REMO), maize yields from the years 2020 to 2099 were simulated by AquaCrop Model in Maroua, Garoua and Kaélé. These future yields are almost nil for the three cities. In view to determine the minimum quantities of water needed to improve them, the hypothesis of no water moisture stress was considered. For the four periods of 20 consecutive years (2020-2039, 2040-2059, 2060-2079 and 2080-2099), average yields of 5.21, 5.11, 4.97, and 4.73 ton/ha are obtained in Garoua, 5.05, 4.97, 4.64, and 3.87 ton/ha in Maroua and 4.91, 4.82, 4.51 and 3.69 t/ha in Kaélé. The average quantities of water irrigation (also obtained with AquaCrop) for the same periods are 13, 19, 46 and 78 mm for Garoua; 34, 48, 84 and 147 mm for Maroua and 57, 68, 111 and 171 mm for Kaélé. The yields by considering these irrigation water quantities are improved and the following values are obtained for the four periods indicated above: 5.20, 5.10, 4.99 and 4.82 ton/ha for Garoua; 5.10, 5.00, 4.78 and 4.35 ton/ha for Maroua and 4.99, 4.91, 4.75 and 4.50 ton/ha for Kaélé.
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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 | 0.001 |
| 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".