A probit Analysis of Determinants of Adoption of Improved Sorghum Technologies Among Farmers in Tanzania
Bibliographic record
Abstract
The adoption of improved sorghum technologies by smallholder farmers is still low in Tanzania. Many farmers fail to acquire quality inputs due to different underlying reasons. This article analyzes factors underlining the adoption of improved technologies among sorghum farmers in Tanzania and evaluates profitability of grain production. A total of 212 individual farmers were interviewed through structured questionnaires from nine districts. Data were analyzed using descriptive statistics, probit regression model and gross margin analysis. Results show that 39.2%, 26.5%, 16.9%, 8.4% and 7.2% of sorghum farmers adopted seeds of improved varieties, insecticides, inorganic fertilizers, threshing machine and optimum seed rate, respectively. Probit estimates indicated that age, sex, number of years in school, group membership, farm size, availability of free seeds, seed accessibility, grain market accessibility and grain market price were the significant factors influencing adoption of these technologies. Adopters obtained higher profitability (822,288 Tshs/ha) than non-adopters (374,363 Tshs/ha) of improved varieties. Further policy actions are needed to improve determinants of adoption, breeding technologies and accessibility of agricultural inputs to ensure benefits to farmers and the sorghum sub-sector.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".