Dynamic and Volatility of World Agricultural Market Prices: Impacts on Importations and Food Security in WAEMU
Bibliographic record
Abstract
Over the last decade, the use of foodstuffs such as corn, wheat and soybean in biofuels production has been growing sharply in the United States, Canada and Europe. This growth has increased total demand for agricultural commodities and stimulated agricultural prices. However, corn, rice, wheat and soybean are the most important sources of calorific energy for West African Economic and Monetary Union (WAEMU) member states’ population, and WAEMU countries are highly dependent on the imports of these products. Consequently, rising prices can have an important impact on imports and severe consequences on food security in these developing countries. This paper aims to investigate: (i) the short-term and long-term relationships between the prices of corn, rice, wheat, soybean and oil and their volatilities, and (ii) the effects of these agricultural commodities prices shocks on the imports of each WAEMU member states. The Autoregressive Distributed Lag (ARDL) model, the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) model and the Granger causality test are used in this investigation. The results show that imports of agricultural commodities in WAEMU countries are highly and significantly sensitive to price changes in international market. In short term as well as in long term, there is a significant relationship between the prices of these products. We find a positive relationship in general between prices volatilities, and negative effects of price volatility on imports. Thus, distortions in world agricultural markets threaten considerably food security in WAEMU countries, especially access to food for vulnerable and low-income populations. Policy makers must adopt viable strategies to increase agricultural production and limit their dependence on imports.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".