Assessment of Intervention Strategies for Addressing Agricultural Production Shocks in Tanzania: The Case of Rufiji, Mbarali and Sumbawanga Districts
Bibliographic record
Abstract
This study sought to gain in-depth understanding into smallholder farmers’ perceptions of intervention strategies for addressing agricultural production shocks in Tanzania. It involved identification of local policy and intervention strategies that can be used to address agricultural production shocks and build resilience among smallholder farmers in Tanzania. The study employed mixed research methodology, using primary data collected from six villages in Sumbawanga, Mbarali and Rufiji districts in Tanzania. Overall findings reveal that smallholder farmers have good knowledge of possible strategies for addressing agricultural production shocks. The farmers recommended local policy and intervention strategies for supporting them such as facilitation of access to credit and subsidies, reinforcing and strengthening informal social networks, supporting income diversification activities and introduction of crop insurance system. Other intervention strategies include introduction of participatory village land use plans, promoting information access and training to smallholder farmers and enhancing access to small-scale irrigation technologies. Based on these findings, the study recommends that policy makers and researchers should concentrate on understanding farmers’ perceptions in view of using local knowledge in the design and implementation of intervention strategies. The strength of farmers’ perceptions is that it is the outcome of farmers’ actual experience, and it is based on understanding of the local context. .The paper concludes that unless the strategies are fully implemented, agricultural production shocks will continue to affect smallholder farmers in rural Tanzania.
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 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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".