Impact of Energy Price Variability on Global Fertilizer Price: Application of Alternative Volatility Models
Bibliographic record
Abstract
This study evaluates the effects of volatility in crude oil and natural gas prices on fertilizer price variations. Specifically, the study looks at the mean and volatility effects of oil and natural gas prices on both mean and volatility changes in fertilizer prices. Both symmetric models [GARCH (1, 1)] and asymmetric models [GJR (1, 1)] were used to model volatility in fertilizer prices and to evaluate the effects of the volatility over different time periods using Bai-Perron structural break tests. The results show that changes in oil and natural gas prices increased fertilizer prices after the crisis period, during June 2007 to June 2008. Both the ARCH and GARCH had significant effects on fertilizer prices, suggesting that the volatility effects of oil and natural gas prices on fertilizer prices were also significant. Furthermore, the maximum impact of higher energy prices depends on triple superphosphate and diammonium phosphate (DAP) leading to higher production costs and consequent increase in total farm expenditures for crop producers. These higher production costs invariably have a negative effect on farm profitability, thus reducing the investment levels in the farm sector.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".