Effects of Government Water Supply on the Smallholder Farmers’ Sustainable Nutrition in Togo
Bibliographic record
Abstract
Water shortage is a global problem. It is predominantly visible in the agricultural sector and in farming communities. Togo is not an exception in this regard because some rural agricultural communities do not have access to water but rely on distance conveyance. Government is under constitutional obligation to supply water in rural areas to boost crop production off rain seasons especially. Can Government Water subsidy improve smallholder farmers’ nutrition? This study, therefore, aims at investigating the impact of Government Water Supply (GWS) on the household of Kara agricultural region in Togo. A two-stage sampling procedure was employed to collect panel data during 2016-2017 and 2017-2018 cropping seasons. Different from previous studies, robust fixed effects regression is used to model the effect of government water subsidy. The core findings reveal that water subsidy improves farm household’s nutrition. The results also indicate that subsidized water influences available per capita calories per day, household’s months of good nutrition, and the probability of being well nourished from own production of cereals and legumes but has statistically insignificant effects on household annual consumption expenditure. The results provide several valuable insights from the policy point of view. A water supply subsidy program has a higher and better influence on the maximum good nutrition, bringing up the question of whether targeting households in the lowest food crops production percentiles give value for money to achieve the goal of sustainable nutrition.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".