Benefits of farmer managed natural regeneration to food security in semi-arid Ghana
Bibliographic record
Abstract
Abstract Promoting Farmer Managed Natural Regeneration (FMNR) aims to increase the productive capacities of farmer households. Under FMNR, farmers select and manage natural regeneration on farmlands and keep them under production. While FMNR contributes to the wealth of farming communities, its contribution to household food security has rarely been researched. We, therefore, used a mixed-methods approach to address the research gap by measuring FMNR’s contribution to food security among farmer households in the Talensi district of Ghana. We adopted the Household Dietary Diversity Score (HDDS) and Food Consumption Score (FCS) to estimate food security status among 243 FMNR farmer households and 243 non-FMNR farmer households. Also, we performed a Chi-square test of independence to compare the frequency of each food group (present vs not present) between FMNR adopters and non-FMNR adopters to establish the relationship between adopting FMNR and consuming the FCS and HDDS food groups. Our results reveal that FMNR farmer households are more food secure than non-FMNR farmer households. The HHDS of the FMNR farmer households was 9.6, which is higher than the target value of 9.1. Conversely, the HHDS of the non-FMNR farmer households was 4.3, which is lower than the target value of 9.1. Up to 86% and 37% of the FMNR farmer households and non-FMNR farmer households fell within acceptable FCS; 15% and 17% of FMNR farmer households and non-FMNR farmer households fell within borderline FCS. While none of the FMNR farmer households fell within poor FCS, 46% of non-FMNR farmer households fell within poor FCS. Adopting FMNR is significantly related to consuming all food groups promoted and benefiting from FMNR practices. The paper recommends enabling farmers in semi-arid environments to practice and invest in FMNR for long-term returns to food security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".