An Alternative Strategy for Mitigating the Effect of Rainfall Variability in Burkinabe Sahel
Bibliographic record
Abstract
This study was carried out in the Burkinabe Sahel aimed at studying contribution of the practice of supplemental irrigation (SI) via excavated rainwater harvesting basin (RWHB) for mitigating effect of rainfall variability on agricultural production and impact of these RWHB on the dynamics of the water table. This study was conducted during two growing seasons (2013 and 2014) and used a test plot cultivated in corn and fitted out with measuring instruments to analyze water transfer in the soil-plant-atmosphere system on the one hand and the atmosphere-RWHB-water table system on the other hand. Four treatments—one under rainfall regime (T0) and three under SI (T1, T2, and T3)—were used in the experimental design to assess the contribution of the RWHB in improving corn yield. These SI were applied during the mid-season of corn (flowering, pollination, and grain filling). Water flow beneath a partially waterproofed RWHB was assessed using HYDRUS- 2D/3D program. Results showed that water stored in the RWHB allowed applying up to three SI, and increased corn yield up to 24% and 26% respectively in 2013 and 2014. However, SI targeting flowering and grain filling were the best scenarios to mitigate effect of dry spell in rainfed agriculture. Water flow under RWHB during the simulation period showed that dynamic of the saturated front depended on the magnitude of the water depth in the RWHB and the hydrodynamic characteristics of the underlying layers. Deep drainage was observed around 25th day after sowing (DAS) in 2013 and 45th DAS in 2014 according to water profile. This caused the decrease of the infiltration rate in the RWHB that was associated with a significant rise of 4% of the water table level ten days later in 2014. Recharge rate was estimated at 0.5 mm·d-1 during the mid-season and the late season of corn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".