Determinants of Smallholder Farmers’ Participation in Zambian Dairy Sector’s Interlocked Contractual Arrangements
Bibliographic record
Abstract
Linking smallholder farmers to modern value chains through contract agriculture (CA) is one of the rural development strategies being promoted to address the challenge of smallholders’ integration in markets. However, the conditions under which CA enhances smallholders’ prospects for inclusion in modern value chains is still debatable. This paper examines the determinants of smallholders’ participation in Zambian dairy markets through interlocked contractual arrangements (ICAs). A multi-stage sampling design was used to select 266 households from milk shed areas from three districts in Lusaka and Central provinces of Zambia. A double-hurdle model was estimated from data collected through semi-structured questionnaires, key informant interviews and focus group discussions. Key determinants of smallholders’ participation in ICAs included ownership of improved breed animals, MCC milk price, access to dairy marketing information, income from other sources and landholding size. While most of these factors also affected the proportion of milk sold, the following were also important: household head education level, cattle rearing culture, extent of supplier’s dependency on buyer and trust in the exchange relationship. To enhance smallholders’ market participation, there is need to facilitate their access to extension services, infrastructure (breeding centres, MCCs and water) and affordable stock feed, and to offer them an effective milk price that is higher than the spot market price. Promotion efforts should target smallholders that are literate, from a cattle rearing culture, and particularly encourage youth and women participation. There is also need for building trust in the exchange relationship and judicious use of power by processors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".