Effect of Adoption of Improved Soybean Variety on Productivity of Farm Households in Benue State, Nigeria
Bibliographic record
Abstract
The study examines the effect of adoption of improved soybean variety on Farmer’s productivity in Benue State, Nigeria. A multi-stage sampling technique was employed to select 267 respondents for the study. Data were analyzed using descriptive statistics, gross margin analysis and the Heckman selection model. Result shows that most household heads were male with a mean age of 36 10 years and a mean household size of 5 3 members. The awareness characteristics revealed that the TGX 1987-10F soybean variety was the technology adopted in the study area. Majority of the farmers were aware (95.2%) of the improved soybean variety and mostly got their information from extension agents. The economic profitability of the production shows that the average cost incurred on labour was ₦177,091.29 per hectare and constituted the highest proportion (61%) of the total cost of production of the crop. The gross margin and the net revenue per hectare were ₦22,018.44k and ₦311,779.59k. The Heckman model results revealed that the probability of adoption of the improved soybean variety is influenced by the age of the farmer, gender, dependency ratio and early maturity of the variety while the second stage revealed that the household size, farm experience, membership of cooperative, education and access to credit by these farmers influence the effect of adoption on yield. It is therefore recommended that government should improve the education of rural farmers through adult education since education positively affects adoption of improved soybean and the yield of these farmers, set up and implement rural welfare schemes to make credit facilities readily available to farmers and strengthen extension service.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".