The Tea Industry and a Review of Its Price Modelling in Major Tea Producing Countries
Bibliographic record
Abstract
The global production and consumption of tea has been steadily increasing over the past decades. The tea industry has become a significant contributor to the economies of producing countries such as Kenya, Sri Lanka, India and China. Apart from its economic importance, the environmental and social importance of tea production has been recognised in the literature. However the industry is confronted by a number of challenges. These challenges include resource constraints, competition for land, unavailability of adequate labour, and climate change, as is noted in this article. All of the major tea producing countries have identified climate change as being a major challenge. Therefore, identification of the appropriate methods for modelling tea prices by incorporating a group of interacting time series variables such as price, production and weather variables to explain the dynamic relationships among these time series is important for producers. This article reviews and examines the approaches used to model tea price. In particular, various time series techniques are reviewed. The analysis clearly shows that quite a number of studies have been done on tea pricing. We found that VAR techniques have the ability to model the non-structural relationship of tea price alongside other time series variables which are endogenous and exogenous in nature. This paper also contributes to the existing literature by summarising the research undertaken on tea pricing to date.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".