Harvesting rainwater for food security: farmers’ perceptions of the Boardwalk Cisterns Program in Alagoas, Brazil
Bibliographic record
Abstract
Introduction: Boardwalk Cisterns is a government program that aims to attend a population with precarious access to water in Brazil. Developed as part of the Zero Hunger strategy, it intends to increase food security and contribute to the realization of the human right to food. The objective of this study was to assess farmers’ perceptions of the Boardwalk Cisterns program and its impact on the food security situation of beneficiary households. Methods: Data were collected through a descriptive cross-sectional quali-quantitative survey. Questionnaires were applied to farming families selected from two municipalities in the state of Alagoas (Northeast Brazil), containing questions on sociodemographic characteristics, production methods, and food security status. The Free Evocation Technique for social representation, analyzed through the software Ensemble of Programs Permettant L'analyze des Évocations, was used to ascertain farmers’ perceptions of the program. Results: The study showed a positive perception of the interviewees in relation to the boardwalk cisterns. The program brought some improvement in the agricultural production conditions and, consequently, in the food security situation of the households. Corn, beans and cassava were the most common crops, with almost half of this production (48.3%) destined for consumption by the families themselves. Farmers also reported having more water, allowing them to plant more fruit trees, as well as medicinal and ornamental plants. Nevertheless, 79.1% of the households interviewed were still in a situation of food insecurity, of which 28.1% were classified as mild food insecurity, 26% as moderate food insecurity, and 25% as severe food insecurity. Conclusion: Farmers were correct in their perception that the Boardwalk Cisterns program improved their food security situation. The program resulted in greater access to water, and greater production and consumption of food. However, by itself, the Boardwalk Cisterns program was not enough to raise beneficiary families above their food-insecure status. The program alleviated the problem of food insecurity, but other complementary government interventions are needed to guarantee the food security of families living in extreme poverty.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".