Mitigation effect of dry spells in Sahelian rainfed agriculture: Case study of supplemental irrigation in Burkina Faso
Bibliographic record
Abstract
This study aims to isolate the supplemental irrigation (SI) scenario from permeable rainwater harvesting basins (RWHBs) best suitable to mitigate the long dry spells (DSs) in Burkinabe Sahel (BS). The water flow in the soil was studied on corn crop during 2013 and 2014 depending on the available water in the monitored RWHB. The experimental design was a block Fisher with four treatments (one under rainfed regime and three under supplemental irrigation). Measurements of the soil water content revealed periods of corn water sufficiency in plots under SI. Average corn yields were respectively 4500 and 4600 kg ha-1 for 2013 and 2014 on plots under SI against 3700 and 3800 kg ha-1 for those in rainfed regime. The average contribution of the SI in increasing corn yield was respectively 24 and 26% in 2013 and 2014 for three supplemental irrigations (SIs), against 19 and 17% for two SIs. With these SIs, the water balance in the RWHB gave respectively at the end of 2013 and 2014, an available water of 60 and 81 mm. The suitable strategy of the SI to mitigate DSs effect in BS was applying two SIs with a dose at least 40 mm around the mid-season. Key words: Supplemental irrigation, rainwater harvesting, dry spell mitigation, sustainable development, corn, Sahel.
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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 | 0.001 |
| 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".