The Preliminary Study of Climate Change Impact on Rice Production and Economic in Thailand
Bibliographic record
Abstract
Climate change affects crop production in two ways: changes in GDP and population and changes in climatevariables, especially temperature and precipitation. This study aims to investigate preliminary effects of climatechange impacts on Thailand’s rice production, consumption, and export capacity by integrated EPIC model andthe world and Thai rice market models. Therefore, the Biophysical process model (EPIC model) and Economicprocesses model are employed as the research methodology of this study. Main findings of the comparisonshowed both rice production and export in the base year (2007) are likely to expand until 2027, and there will bea sufficient amount of rice surplus for export, which is nearly the same level as that of domestic consumption inA2 scenario. In 2017, the amount of rice production will be only slightly higher than the domestic demand,leaving a small rice surplus of up to 2 million tons for export, compared to 14 million tons in 2016. However, inB2 scenario, the rice production capacity will be much lower than the domestic demand, meeting only half of itin 2017. From 2017 to 2019, the rice production capacity will undergo a constant fall and no longer meet themarket demand as a result; it is estimated that there will be a shortage of approximately 0.038 to 0.218 ton. It istherefore important to note that if B2 scenario became reality in 2017, the rice production capacity of Thailandwould nearly fail to meet the minimum level of domestic demand. However, we assure that Thailand still haveland where can be converted to rice production with multiple cropping through irrigation investment, whilecomprehensive technical adaptation and mitigation to enhance farmer benefits are required.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".