Community resource management areas and household food security in northern Ghana: Insights from a socio-ecological systems perspective
Bibliographic record
Abstract
Food insecurity remains a pressing challenge in rural Ghana, particularly in the semi-arid northern regions where sociodemographic, socio-economic, and environmental factors heighten household risks. In response, Community Resource Management Areas (CREMAs) have been introduced as decentralized governance structures to promote sustainable natural resource management, biodiversity, and improve livelihoods. However, the extent to which CREMAs influence household food security remains underexplored. Grounded in the Socio-Ecological Systems (SES) framework, this study has two main objectives: (1) to determine variations in food security between households located within CREMAs and those outside CREMAs (non-CREMA households), and (2) to analyze the socio-demographic and socio-economic factors that explain such variations. Cross-sectional data were collected from 517 smallholder farmer households across four community contexts, Wechiau, Dorimo, Zukpiri, and Chakali, in northern Ghana. Using ordered logistic regression analysis, the results show that households within CREMAs experience lower levels of severe food insecurity compared to non-CREMA households. Food security outcomes varied across zones, influenced by factors such as age, education, gender, household size, wealth, home gardening, livestock rearing, access to credit, and remittances, with context-specific effects. These findings underscore the vital role of CREMAs in enhancing household food security by promoting improved resource governance and sustainable practices. A dual approach is recommended to address food insecurity in northern Ghana. This entails scaling up CREMAs and reinforcing community resource management, while simultaneously strengthening governance, broadening financial and livelihood opportunities, and providing targeted support to vulnerable households. • The SES framework explored how systems and context affect food security in northern Ghana. • CREMA households face less severe food insecurity than non-CREMA communities. • Food security is shaped by age, gender, education, gardening, livestock, credit, and remittances. • Scaling-up CREMAs and supporting smallholder farmers are recommended to combat food insecurity.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".