Current Situation of Egyptian Cotton: Econometrics Study Using ARDL Model
Bibliographic record
Abstract
The Egyptian cotton crop have experienced challenges in recent years from a drop in the quantity produced and exported, to a decrease in cultivated areas, this have affected the production quantity and value of exports. This study aims to bridge the research gap by exploring the nexus between cultivated area of cotton in Egypt, Relative profitability (cotton-clover/rice-clover), export quantity of cotton, the export prices of Egyptian cotton and the export prices of American cotton (Pima). In order to clarify the relationship between the variables studied and the cultivated area of cotton, the research use time series data from 1980 to 2016, using the Autoregressive Distributed Lag (ARDL) bound test to the find the co-integration between the variables after checking the stationarity in chosen variables with different unit root tests e.g. Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP). The results show, significant factors that influence the cultivated area of cotton include Relative profitability (cotton-clover/rice-clover), export quantity of cotton in long run term. Which underscores the need for government support in agriculture, in particular, cotton crop support. The increasing trend of cotton cost with declining revenue and decreasing in exports quantity is the main cause of decreased cultivated area of Egyptian cotton. Research recommends that support should be given to cotton farmers, in the form of agricultural equipment or training in good agricultural practices or set a price for cotton guaranteeing a decent profit margin for the farmers. The government (policy makers) should improve the productivity of cotton with the purpose of reducing the total costs and increasing the degree of competitiveness of the Egyptian cotton. Some effective policy measures may include but not limited to, farmer training programs and providing better extension services that will led to the capacity development of farmers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".