Determinants of Milk Market Participation and Volume of Sales to Milk Collection Centres of the Smallholder Dairy Value Chain in Zimbabwe
Bibliographic record
Abstract
At the attainment of Zimbabwe’s independence, government of Zimbabwe established the smallholder dairy development programme to encourage smallholder farmers to participate in formal milk markets. Although now more than three decades since the government established this programme, smallholder contribution to the national formal market remains low at 5%. This study was undertaken to determine factors affecting milk market participation and volume of sales to milk collection centres of the smallholder dairy value chain. Four smallholder dairy schemes were purposively selected on the basis of whether the scheme participated in the semi-formal or formal dairy value chain. A total of 185 farmers were then selected through simple random sampling and interviewed using a pretested structured questionnaire. Data were analysed using descriptive statistics and Heckman two-stage selection econometric models. Results show that resources (represented by dairy cows, household size), knowledge (educational level, access to information and extension), experience (household head age) and agro-ecological region significantly determined farmers’ participation in milk markets. The study also shows the determinants of milk sales volumes to be resources (number of dairy cows and landholding size); market access (distance to milk collection centre); ambition of the farmer (age); and natural climatic conditions (agro-ecological region). Government policy interventions therefore need to be targeted at increasing the number of dairy cows, taking into account landholding and market access, targeting educated, young farmers located in agro-ecological regions I and II, providing them with adequate, appropriate information and extension packages in order to enhance milk market participation and volume of sales.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".