AGRICULTURAL INPUT AND ECONOMIC GROWTH IN NIGERIA: AN EMPIRICAL ANALYSIS
Bibliographic record
Abstract
Previous studies have established a substantial connection between agricultural and economic growth because of agricultural growth’s ability to attract foreign investment, enhance employment rates, alleviate hunger, and promote economic development. This study has made a unique contribution by examining the link between agricultural input and economic growth in Nigeria while considering factors such as foreign direct investment (FDI), gross national income (GNI), and inflation rate. The secondary data used in this study were collected from the World Bank development indicator from 1990 to 2022. The unit root test was applied, and the results show that the series are integrated of order 1, which eliminated the presence of unit roots that could cause erroneous findings. The Johansen co-integration shows a long-term association between agricultural input and economic growth in Nigeria. Meanwhile, the fitted OLS regression model indicates that agricultural input, foreign direct investment (FDI), and gross national income (GNI) have a positive impact on Nigeria’s economic growth, whereas the inflation rate hinders the nation’s economic growth. Thus, in response to the increasing inflation rate, it is imperative for the government to allocate significant investments toward the agricultural sector. This will lead to an increase in agricultural output, thereby promoting economic growth
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".