Economic analysis and food security contribution of supplemental irrigation and farm ponds: evidence from northern Burkina Faso
Bibliographic record
Abstract
Abstract Background Dry spells are serious obstacles to rainfed agriculture in Sahelian countries. Various water harvesting techniques are used by farmers to reduce the impact of climate variability, but are not sufficient in the case of a prolonged drought lasting 2–3 weeks. The farmers believe supplemental irrigation is a good way to adapt rainfed agriculture to dry spells. In this study, we evaluated the food contribution and profitability of supplemental irrigation of rainfed crops comparing various farm ponds that collect runoff water from the surrounding landscape. Methods We analyzed the contribution of supplemental irrigation to food security and compared the profitability of different types of ponds constructed by farmers in northern Burkina Faso. Human cereal requirement was used as indicators to analyze the contribution of supplemental irrigation to food security. The criteria for analyzing the profitability of the selected ponds were gross margin (GM), net present value (NPV), internal rate of return (IRR) and payback period (PBP). Results Our results show that the additional yield of corn obtained with supplemental irrigation makes it possible to meet the monthly cereal needs of at least 17 people and generates an additional GM of FCFA 178,483 (US$ 309.26) compared to no irrigation. The estimate of the NPV, from IRR and PBP showed that the profitability of supplemental irrigation in 15 agricultural seasons varies between the type of ponds constructed. Conclusions Given the up-front cost and the farmers’ lack of resources, the ponds require a subsidy or a credit policy to facilitate the adoption of supplemental irrigation in Sahelian countries. However, the irrigation strategies to optimize agricultural income remain a field of research to be explored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".