Modeling the Dynamic Relationship between Food Crop Output Volatility and Its Determinants in Nigeria
Bibliographic record
Abstract
The study modeled the short run and long run food crop output volatility equations in Nigeria. Time series data derived from the FAO data base for Nigeria and publications of the CBN covering the period 1961 to 2010 was used in the study. Unit root test conducted on the specified time series shows that all series were integrated of order one at 1% probability level. The GARCH (1, 1) model was used to generate the food crop output volatility for the selected food crops (i.e. rice, maize, sorghum, cassava and yam). The short-run and long-run elastic cities of food crop output volatility with respect to specify explanatory variables were determined using the techniques of co-integration and error correction model estimation based on the OLS estimation. The empirical results revealed that inflation rate, per capita real GDP, loan guaranteed by ACGSF in the food crop sub sector, harvested area of land for food crop and liberalization policy era had mixed influence on food crop output volatility both in the short and long run periods in Nigeria. The result also showed that harvested area of land for the selected food crop was the most important factor that affects food crop volatility in the country. In addition, food crop volatility show an average declines pattern in the liberalization policy period. The study however advocated for appropriate short and long term policy packages that should addressed appropriately the identified significant macroeconomic shifters of food crop output volatility in the country. Also attention should be directed towards improving the quality of land allocated to food crop sub sector. Furthermore, agricultural policies in the liberalization policy package should be design in the short term basis and use as a means for altering food crop output in Nigeria.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".