Determinants of Fertilizer Use in Arable Crop Production among Small Holder Farmers in Osun State, Nigeria
Bibliographic record
Abstract
Over the last three decades food insecurity is increasing in the nations of the world, different strategies were embarked upon to prevent the explosion of this problem one of which is the innovation of improved input materials for agriculture. The use of fertilizer is a result of improved technologies put in place; and since it was introduced farmers have different perception about it, hence difference in level of use of fertilizer among farmers which is due to different factors. The study evaluated the factors influencing the use of fertilizer in arable crop production among smallholder farmers in Osun State, Nigeria. Multistage sampling technique was adopted to obtained information from 120 respondents using purposive and random selection. Data collected were analyzed using descriptive statistics and logistic regression analysis. Results showed that total farm output in naira, level of education, farm size, number of farmland owned and total cost of crop inputs were important factors influencing farmers’ use of fertilizer in arable crop production while gender, age, family size, price of fertilizer and income from other farm enterprise owned by farmers were not significant and can constitute the constraints to the use of fertilizer. Based on these, it was recommended that government and other policy makers therefore need to increase farmers’ knowledge and skills through formal and informal educational institutions such as extension services, public awareness programs to enhance the use of fertilizer among arable crop farmers, resulting in increase in agricultural productivity and food security 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 | 0.000 |
| 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) | 0.001 | 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".