Entrepreneurship through Agriculture In Nigeria
Bibliographic record
Abstract
Poverty is one of the supreme challenges in Nigeria. This paper explores entrepreneurship in agriculture as a strategy for a drastic reduction in unemployment and poverty in Nigeria. Agriculture creates employment opportunities to 70% -75% of the Nigerian working population and contributes about 20.9% of Nigeria’s total gross domestic product. Yet, young educated and ambitious Nigerians do not show much interest in agriculture. Currently, Nigerian farmers are elderly, toiling away with outdated techniques and tools. Not only are these old farmers unlikely to use latest technologies that guarantee rewards in agriculture and afford a modern lifestyle. The youth believe that career in agriculture would “condemn” them to a “backwards”, “dirty” lifestyle associated with the elderly “uneducated” farmers currently performing physical arduous backbreaking farm work. Meanwhile, the educated and ambitious youth struggle almost hopelessly to find employment in the few highly esteemed sectors, such as the civil service, banking, engineering, medicine and law. This paper persuades youths to take up a career in the agricultural sector through entrepreneurship activities; the paper tells stories of successful educated young entrepreneurs in agriculture. Some young successful educated and ambitious agri-preneurs are identified and their stories are told. These agri-preneurs are potential role models (i.e., people whose achievements in agricultural entrepreneurship the youths can emulate/imitate). The paper advises youths to start small with simple straightforward projects capable of producing cash rewards in the short-term and to look out for the several government and UN grants opportunities that encourage agropreneurship. Before launching their enterprises, aspiring agri-preneurs are counselled to avail themselves of training and apprentice opportunities from successful agri-preneurs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".