Family Planning, Irrigation, and Agricultural Cooperatives for Sustainable Food Security in Kenya
Bibliographic record
Abstract
Rapid population growth causes smallholders to practice unsustainable forms of land intensification to meet increasing food demand. Food insecurity is exacerbated by unreliable rainfall. We revisit family planning, smallholder irrigation, and agricultural cooperatives as potential sustainable solutions. We use primary data from Kakamega Central and Navakholo in Kenya. Results from respondents indicate 83% had no family planning information, while 82% had no access to irrigation. The main reasons are poverty, illiteracy, misconceptions, gender inequality, and constraints in accessing credit, lack of investment in water resources, and lack of family planning. Lack of access to agricultural extension services limits the adoption of sustainable farming practices. Cooperatives’ principles and values make them suitable pathways to reach the poorest and facilitate members’ access to productive resources. Cooperatives can be used to train members in sustainable agricultural practices and educate members on family planning issues. Descriptive statistics and econometric regression results suggest that cooperatives have contributed to better yields, incomes, nutritional status, and reduced poverty. However, they are constrained by a lack of capital, credit, infrastructure, markets, training and technology, delayed payment, and low prices. Governments and development agencies should support the establishment and development of cooperatives with capacity building, market infrastructure, finance, and education in cooperative principles and values.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".