Household Requirements Versus Profit Optimization: The Win-Win Solution Strategies Among Small-Holder Farmers in South Western Nigeria
Bibliographic record
Abstract
Satisfaction of household requirements and profit maximization has been the main goals of subsistence small-holder farmers in the rural communities of Nigeria. This necessitated the practice of multiple/mixed cropping of arable crops. On the contrary, a part of the National food security agenda of the Federal government is the raising of maize sole to meet the need of local consumers as well as export market. The need to chat a middle course for meeting the requirements of all the parties concerned calls for this study. Data used were obtained from well organized and supervised rural farmers in south-western Nigeria and analyzed by using budgeting and linear programming. Results obtained from the use of Linear programming techniques have demonstrated that mixed cropping involving maize/cocoyam, maize/cassava/yam and cassava/yam are more rewarding in terms of profits and satisfaction of subsistence goals on small-holder farms in south western Nigeria. This contrasts diametrically with sole cropping of maize which has been the priority of the agricultural extension departments of the ministries of agriculture in tune with government programme. The use of parametric programming suggests that increase in farm size, introduction of labour-enhancing technologies and improved production techniques would raise returns from sole cropping of maize to a competitive level and thus resolve the empirical conflict.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".