Impact on household food security of promoting sustainable agriculture among farming households in Borno State, Nigeria
Bibliographic record
Abstract
This paper evaluates the impact of the project Promoting Sustainable Agriculture in Borno (PROSAB) on the livelihoods of farming households in Borno State, Nigeria. Specifically, the paper identifies and provides information on farmers adoption of improved crop varieties introduced by PROSAB, measures their adoption rates and food security levels, and analyses the factors that affect the households food security status. The study used mainly primary data collected from a sample of 693 farming households in the study area. The analysis of data was carried out using descriptive statistics, Cost of Calorie calculations, and Logit regression techniques. The study results suggest that PROSAB has made a significant contribution towards improving the food security of households. In project intervention communities, food insecurity has been reduced from 58% in 2004 to 30% in 2015, indicating a 28% improvement in food security over the 11-year period. The paper clearly demonstrates how the adoption of crop technologies and crop management practices with linkages to markets has significantly contributed in improving households food security. The paper recommends increased promotion of improved crop technologies, trainings delivered to farmers on such technologies, and policies that enhance farmers access to inputs, credit, and output markets. Acknowledgement : The authors acknowledge the following; i) Canadian International Development Agency (CIDA) for funding the research (2004-2009); ii) financial assistance from the funded N2Africa project; iii) the contribution of IITA as an institution for providing a conducive environment and various Scientists from IITA that were involved in delivering research for development technologies and farmers training in the PROSAB project area, which had significantly increased crop yields, farmers income and enhanced household food security and iv) the contribution of Scientists from the University of Maiduguri and Borno State Agricultural Development Programmes who are Collaborators in implementing the PROSAB project.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".