Factors Influencing Household Food Security Among Irrigation Smallholders in North West Nigeria
Bibliographic record
Abstract
Motivation: Food insecurity remains a continuing global challenge in most parts of the developing world, including Nigeria. While considerable resources have been devoted to tackling the menace owing to its negative impacts on health and well-being, progress is rather slow and uneven across regions and countries. Indeed, much is yet to be explored on the conditions that result in food insecurity. The paper investigates household food security and the socio-economic factors that determine it among smallholders in the Middle Rima Valley Irrigation Project, Sokoto State, Nigeria.Novelty: The combination of both the Household Dietary Diversity Score (HDDS) and Food Consumption Score (FCS) to measure food security and the regression of these variables against socio-economic characteristics of smallholder households represent an innovative way of assessing the effect of socio-economic characteristics on food security status of smallholder households which has scarcely been done in literature.Methodology and Methods: Quantitative data were successfully collected from 306 randomly selected households using a well-structured questionnaire. The HDDS and FCS were used to assess household food security level, and multivariate regression was used to examine factors associated with food security.Data and Empirical Analysis: Results indicate that at least 45% of the households were food insecure. The relationship between HDDS and FCS was found to be moderate, but positive and statistically significant. This thus validates the food insecurity phenomenon in the study area. Food insecurity is influenced by household income, education, training, farming experience, livestock ownership, and farm size. The latter however emerged as the most significant factor influencing food security of smallholder households.Policy Considerations: Targeted interventions are recommended in the areas of social protection initiatives, human capacity development through farmer training, and market access. The latter is imperative as smallholders may lack the motivation to increase production in an era of inadequate access to market. Furthermore, there is also the need to improve infrastructure to enhance access to market, encourage sustainable intensification and provide access to credit facilities for increased cultivation.
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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 | 0.000 |
| 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) | 0.002 | 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".