Drivers of farm commercialization in Nigeria and Tanzania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract While total factor productivity (TFP) difference between the subsistence and commercial farm types is negligible, a large number of subsistence‐based farms remain outside the market economy, and national policies have emphasized the need to bring them into the fold of commercial agriculture. Improving market access may help induce greater farm commercialization and thus greater investment in agriculture. However, there is little empirical evidence on farm‐level factors that stimulate agricultural commercialization in SSA. Using a nationally representative panel data from the Living Standards Measurement Study‐Integrated Surveys on Agriculture, this article estimates the likelihood of being a commercial versus a subsistence farmer and the likelihood of transitioning from one farm type to another based on observable characteristics in Nigeria and Tanzania. The analysis demonstrates that although a substantial proportion of farms have no market participation in a given year, there are rich transition dynamics over time. The results from the probit regression show that resource endowments (land, labor, chemical use) and farm characteristics (multicropping system; irrigation; crop types such as fruits, vegetables, and cash crops; and animal traction use) do matter for market participation and the transitioning of subsistence farms into a market economy. These variables are positively correlated with farm commercialization and increase the likelihood of market participation. Overall, policies aimed at improving farmers' access to resources and promoting sustainable smallholder agriculture could be instrumental in raising productivity in agriculture and enhancing marketable agricultural output.
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.
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 it