A Survey of Mechanization Problems of the Small Scale (Peasant) Farmers in the Middle Belt of Nigeria
Bibliographic record
Abstract
Food shortage problem is increasing every day among the developing nations. So many farmers are on the land on small scale basis and their production has not been enough. Their farm sizes have not increased over the years due to absence of the relevant mechanization machinery. This study was made to evaluate the level of solutions of the problems of small farm mechanization, which is the only viable means of food production in the developing nations such as Nigeria. From the studies, the various levels of mechanization tools in the various farm operations are as follows: land clearing 21.54%, tillage 24.62%, planting 3.85%, spraying 86.15%, fertilization 2.13%, weeding 3.08%, harvesting 40%, crop processing 7.69% and crop storage 0.00%. Most of the farm sizes (93.85%) range from 1-5 hectares. The mechanization process being emphasized in the country is still beyond the scope of the small scale farmers who produce the bulk of the food. It is recommended among other things that government should set up agricultural machinery industries which should developed or purchase and hired out to small scale farmers at subsidized rates to increase the level of mechanization of certain farm operations in the middle belt states of the country.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".